Thesis Book by izkara

RE-IMAGINE EARTH MATERIALS

Yeliz Karadayi

TABLE OF CONTENTS

ADVISORS: Jeremy Ficca Dana Cupkova Frank Melendez

ACKNOWLEDGEMENTS: 4 chapter 1: THE PACKARD PLANT - 7 chapter 2: RESEARCH ON EARTH MATERIAL - 13 chapter 3: INFORMED SPECULATION - 35 chapter 4: EXPERIMENT - 43 chapter 5: OPTIMIZE - 99 chapter 6: CONSTRUCT - 151 chapter 7: CONCLUDING STATEMENTS - 171 BIBLIOGRAPHY: 174

ACKNOWLEDGEMENTS DISCOVERIES

I could not have done this project without the help and support of my advisors and peers. I would first like to thank Dana Cupkova for always supporting and encouraging me to take the path I was most inspired by, as well as helping tie all my interests into one cohesive project. Thanks to Jeremy Ficca who encouraged my shift toward the computational aspect of my project, and to Frank Melendez who encouraged my exploration of casting. Beyond my three advisors I would specifically like to thank Steve Lee, who gave me a lot to think about and had me step back and evaluate myself. Also thanks to Kai Gutschow who encouraged me to pursue thesis and guided me toward an understanding of what it takes to make a proper thesis. Special thanks to those in attendance of my final review, Mary Lou for keeping the reviews under control, and the Miller Gallery for giving us the opportunity to present in a more professional space. Lastly, a big thanks to my peers: Alex Fischer for keeping me in check and making sure I stay focused; Jacob Russo for showing support and excitement for my project and helping me figure out my next steps. Thanks to all my other thesis peers who got involved in group discussions, sat in at my reviews, and helped me gather my thoughts.

THE PACKARD PLANT

THEORY move away from auto industry focus on community

past (1903)

present clean up and revitalize the plant

Open Source Ecology focuses its concern upon the performance of machinery necessary for basic living needs. These products are designed to be long lasting, cheap, and highly functional. This thesis will focus on the re-implementation of brick in the same manner.

1911

The Packard Plant sits derelict in Detroit, but has many visitors due to the haunting beauty of the abandoned scape. The erie quality of this abandoned factory is commonly used by visiting photographers to depict Detroit to the world as a city in ruins. If this building is some sort of iconic representation of Detroit, it can then be used to represent the revival of Detroit through its own revival. This building is something that should not be forgotten and crumbled, but actually celebrated.. Re-purposing part of the plant into an experimental public workshop where people could bring salvaged materials and re-purpose them would allow the community to participate in cleaning up Detroit as well as benefit from cheaper materials, and the ability to experiment with design processes. There could be public shop spaces as well as allocated areas for businesses or firms to move into, and even a public space where visitors can continue to take pictures of Detroit- up and coming. This building could begin to eat its own used materials to rebuild itself with the designs of its inhabitants. In essence, this plant could become a public work space where people can learn about the experimental work being done with brick, which will be implemented in the building as it is worked on, making the building a kind of living thing that eats and outputs earth material products.

ANALYSIS program blocking

traffic

solar february

may

august

november

10 am

rail shop open

12 pm

3 pm N

Packard Plant site analysis

RESEARCH ON EARTH MATERIAL

INGREDIENTS

silicon dioxide + aluminum oxide + feldspar =

clay + sand + silt + gravel

calcite + dolomite

stone + sand

coconut + sisal + bamboo + straw

+ alumina + calcite + silica

IMPLEMENTATIONS (final product) • • • •

must not have plant life plastic state has binding force dried state has compressive/tensile strength higher porosity = vapor diffusion = frost resistance

• • • • • •

shrinks while drying swells to direct water contact balances air humidity well stores heat preserves timber continuously recyclable

• • • • •

can erode/calcify -paint with casein to remedy reduces fungus/purifies water reduces water penetration lime-casein glue increases bind makes mix thinner

• • • • •

makes mix leaner (coarser particles) reduces cracking/shrinking expanded/foamed: reduces density increases compressive strength makes mix thinner

• • • • • • •

makes mix leaner increases absorbancy must be <3% of total mix reduces cracking/shrinking increases tensile/compressive strength reduces water sensitivity softens acoustic properties

• • • •

stabilizes wet mixture increases compressive strength fast-drying chemical drying process: unnatural

clay

loam

limestone

aggregate

organic aggregate

properties • • • •

of prominently earthen materials only used in compressive structures preserves timber often should avoid water contact

cement

cement ingredients

SPECIFIC PROCESSES (notes)

CINVA ram

mud brick • • • •

adobe/mud brick: wet earth manually thrown into form-work soil/earth block: compacted in a press green brick: produced by extruder, light-weight

overturning of facade gable or non-loadbearing wall due to out-of-plane forces

out-of-plane cracking and rocking of walls

out-of-plane mechanism of facade gable or non-load-bearing wall

wall cracking or discontinuity

in-plane mechanism of facade gable or non-load-bearing wall

detaching of wall from horizontal elements

cracking of arch openings

corner cracking, damage or collapse

cracking of barrel vaults

requires cement in mix, but requires less water so that bricks dry almost immediately

unbaked brick absorbs 50x more moisture than baked reduces fungus unbaked mud is frost resistant also known as adobe

automatic block press

cracking or collapse of towers

out-of-plane cracking or breaking at mid-wall

if wetted, modules can stick without mortar

in-plane cracking of load-bearing

instability due to erosion and weathering

damage typology of adobe walls

mud brick notes

SPECIFIC PROCESSES (notes)

direct loam • • • •

loam extruder

not optimal for cold sculptural qualities manual labor-intensive mud brick-like qualities

wattle and daub why or casein powder added to mixture makes this wall type stronger and more water resistant 15% clay optimizes for minimal crackage

wood structure lost form-work structures the spray technique. wrapping the structure in straw decreases shrinkage

extruded loam can be implemented more sculpturally

fabric form-work can make for interesting shapes when loam is poured (BUILDING WITH EARTH)

can be made by wrapping light weight loam in elastic cotton hose

direct loam notes

all unlabeled images from BUILDING WITH EARTH

SPECIFIC PROCESSES (notes)

rammed earth • • • • • •

too heavy and expensive to be used in form-works like concrete, but can still be done

protects from pests low shrinkage, high strength balances air humidity stores heat; bad insulator preserves timber continuously recyclable

horizontally packed rammed earth walls cause horizontal cracking, which allows for water penetration

ram

dries faster than masonry or concrete requires less manual labor than masonry

lime mortar dries for a month, maintaining elasticity during. this allows for layering of rammed earth without uneven shrinkage

needs little to no surface treatment popular in California [climate)

fine soil % 100

sieve graded soils

silt [m]

clay

fine

to reduce joint cracks, a mechanism has been devised to slide the containing framework up the wall as it dries, reaching up to a single story successfully without cracking or shrinking.

sand [s]

medium

coarse

fine

gravel [g]

medium

coarse

fine

medium

coarse

stone

one side of the form-work can be replaced with a thermal wall so that less form-work is needed [thus less cost] and insulation is also added

90 80 70 60 50 40 30 20 10 0 0.001

0.002

0.0063

0.02

0.063

0.2

0.63

6.3

100

fig. 3.1 - stages of plastering in pipes for wall heating

rammed earth notes

all unlabeled images from BUILDING WITH EARTH

SPECIFIC PROCESSES (notes)

common brick • • • • •

can be structural cheap not frost resistant remains sturdy tends to be imperfect

manufacturing

should be kept away from moisture during production

high clay content required to achieve sufficient strength after firing

rounded corners: optimized acoustic behavior, less chance of breaking corners, hollow interior absorbs sound

common brick notes

press

all unlabeled images from BUILDING WITH EARTH

SPECIFIC PROCESSES (notes)

prefabricated slabs • • • • •

non-load bearing made less dense often lined with timber allows for dry construction does not shrink

fig. 6.2 - dry plaster tiles

requires sub-construction and reinforce-

pre-designed slabs can be used as wall heating treatments

fig. 6.1 - plaster-boarding panels light-weight mineral loam blocks: high loam content and expanded clay 24

prefabricated slab notes

fig. 6.3 - heated panels all unlabeled images from BUILDING WITH EARTH

SPECIFIC PROCESSES (notes)

robot applications â&#x20AC;˘ â&#x20AC;˘

allows for patterning can be systematic

free standing, self supporting bricks rotated to allow controlled light

f robot on a track laying bricks

milled concrete per

forming capabilities

subtractive process

robot applications notes

all unlabeled images from GRAMAZZIO KOHLER projects

SPECIFIC PROCESSES (notes)

fabric casting • • • •

allows for patterning can be systematic reusable fabric mold wide range of shapes

supports can be variable. fabric thrown over and poured into

fabric moulds

fabric mould

fabric casting notes

on-site fabric mould

all unlabeled images from MARK WEST projects

SPECIFIC PROCESSES (notes)

3d printing • • •

textured shapes allows for complexity in the design size limited to robot

black firing clay is lumpy and dries too quickly

interactive virtual lathe: 3d prints desired geometry

Belgian Design Studio Unfold

porcelain prints might break apart if too dry and might not support itself if too wet

Belgian Design Studio Unfold

blended buff stoneware clay print very easily due to plasticity

Ronald Rael

can be modular

3d printing notes

all unlabeled images from hyrel3d projects

INTERVIEW

what i gathered:

I have gotten in contact with the Brick Industry Association via email and was able to get some more information on the qualities of implementing used brick in new mixtures:

Can brick be crushed up and re-mixed with other materials? If so, what has been done?

clay

fire clay

crush fired clay

aggregate

Old brick is most often recycled crushed into other products such as landscaping and concrete aggregates, or even finer material such as baseball infields and tennis courts. It is typically not remixed with raw material for new brick because the reclaimed brick impacts the ability to keep colors consistent.

Does it effect the performance of the final product or anything other than color inconsistency?

add to raw mix

fire again

final brick

It would affect performance in a way similar to when clays are blended to produce a desired color. However, we can adjust the proportions of each and firing if necessary in order to produce the characteristics and required properties that we want in the final product. Because the primary criterion used to select brick is color, color consistency is typically the limiting factor determining how much recycled material we can incorporate into face brick. Incorporating some previously fired material (although not recycled from previous use) into the raw material for new brick is fairly common and actually helps improve properties of the final product. For building brick or where color consistency is otherwise not important much higher recycled percentages can be achieved.

Brick Industry Association

INFORMED SPECULATION

SPECULATION questions asked:

what other ingredients can i mix this with? what effects do they create for me? have they been done before? if not, why not? if so, how did that go? can i improve the performative qualities? can i improve the value? what are the qualities of clay after it has been fired? can it be re-used in the same way? can it be turned back into its raw clay form?

brick condition

application

properties

lime mortar fired clay + mud + aggregate infilled with some kind of fiber

sculpture

garden

1mm crushed pieces [powder]

floor

earth container stacked to form planters stacked or cut into to house animal or plant life

Gramazio and Kohler-type stacking used as test material for new wall system proposals cleaned and re-used for residential purposes stacked and then cut into

Gramazio and Kohler-type stacking cleaned and re-used for residential purposes cut into or stacked irregularly to allow for plant or animal life

cleaned and reused on floor

puzzled together to create forms that are derived by the irregularity of the broken pieces could be build upon with other materials

gabien-type wall, could allow for plant growth or animal life within it kept in moisture for extensive period of time, softened, and used in its original state

gabien-type wall, could allow for plant growth within it used as test material for new wall system proposals puzzled back together to form wall with cracks in it to allow for light/view [not load bearing]

gabien-type wall, could allow for plant growth within it cut into or stacked irregularly to allow for plant or animal life

irregular floor patterns

kept together with added clay-what will the effects of firing already baked clay mixed with unbaked baked clay be? poured into different formworks fabric formwork wood formwork

aggregate for plant growth added to mixture that could be poured into formwork to house plant/animal life water filtration system

aggregate in mixtures for formwork sculpted walls? performance? added to mixture for modular components that build performative wall what if mixed in glass? effects of light?

aggregate in mixtures for formwork sculpted walls? performance? added to mixture for modular components that build performative wall what if mixed in glass? effects of light? can it be load bearing?

can you create different densities and ‘draw’ with this on the floor? water collection floor panels

kept together with addhesives: what is the compressive/tensile strength? poured into different formworks fabric formwork wood formwork

3d printed into various forms that can house plant/animal life compressed into a block and then subtractively sculpted to create forms?

formed into panels and then cut into to form new shapes cast into fabric/wood formwork and tested for performative qualities

3d printed modules panel infill for texture/performance compacted into blocks and then cut into

can you create different densities and ‘draw’ with this on the floor? water collection floor panels

poured into different formworks fabric formwork wood formwork mixed into 3d printable mixture and 3d printed

placed into soil mixture: the lime content in mortar is good for plants, the rest of the material is clay and mudgood for plants! included in mixture to form plant/animal carrying components

added to mixture for modular components that build performative wall fabric formwork wood formwork 3d printed modules cast into sculptural wall panels

added to mixture to be rolled into cotton fabric and create extruded walls foamed or expanded and tested for performative qualities

layered over mix after it’s been poured into formwork- what are the effects? compacted and cut into to create modular floor system that can be easily re-oriented

damaged

1cm crushed pieces

exterior wall

stacked in a similar style to that of Gramazio and Kohler, touch on notion of memory and preservation. could be build upon with other materials

in tact

2.5cm crushed pieces

interior wall

Brick Speculation Matrix

SPECULATION

re-used brick

clay

fire clay

crush fired clay

aggregate

it is feasible to use old bricks for multiple purposes, such as larger aggregates to thin heavy mixes or powderform additive to remedy a lean mixture of cement.

add to raw mix

fire again

final brick

FOCUSING ON FABRIC CASTING

SPECULATION

Mark West

precedents

surface

column

Airi Isoda

concept

hanging concrete light fixture

cast jewelry

informed speculation

Both Mark West and Airi Isoda consider the effects of fabric on concrete. The texture of the surface is different, and the relationship between concrete and fabric allows for very intriguing effects on one another. As West put it in “The Fore Case”, the “final forms are found by the materials themselves through their own negotiations/struggles to reach a certain precise shape.” (Manufacturing the Bespoke 137). This struggle is one I would like to encapsulate in my explorations inspired from this work.

concrete-dipped clothing

EXPERIMENT

FIRST ITERATION GROUP typologies collum

floor

ceiling

misc

a d

laying casts

hanging casts

process

wall

Simple and direct speculations such as what a wall could look like with fabric formwork allow me to consider the possible paths I could explore. The results definitely have a shared sense of “‘time’ about them that entwines their fluid and solid states” (Manufacturing the Besboke 139).

informed speculation

DISCOVERIES

Casting against different materials has an effect on the drying material.

twine

wood

Casting against wood creates a striated texture in response to the grain. The wet rockite also absorbed into the wood slightly such that while drying some of the wood grain got pulled into the drying mixture. This creates a grainy texture with evidence of embedded wood flakes.

Against twine the hypothesized result was not similar to reality. The hope was for the wet rockite to rest up against the string such that it could be peeled off and leave indented textures across the surface. The reality was that the rockite inevitably went straight through the twine and dried around it. This opened up a lot of new questions about the relationship between drying mixtures and strings, including the possibility of a new type of lost form-work.

canvas

Canvas casts, similarly to the wood casts, appear to have a dissolving texture to them as well as traces of the material it was cast into being pulled into the drying mix and embedded into the surface. With canvas, the surface has a particularly dusty feel to it, leading me to take an interest in the difference in how it might react to water. My hypothesis is that this texture along with the wrinkly flowing surface can take advantage of natural erosion and calcification along the surface to create beautiful colors/textures and soften in select areas to allow for plant growth.

informed speculation

nothing

Though nothing already known was observed about drying rockite against no surfaces, it is important to note the differences between the texture in lean [right] and thick [left] mixtures.

SECOND ITERATION GROUP typologies modules connected by string

hyperboloid strings gridded strings 48

free-standing component

modules cast from string

free-standing cast from string

o p

lit on fire

single-direction

wrapped string embedded string

cast in plastic film

process

r layers spaced equidistant

n s

t informed speculation

These iterations focused at first on exploring more potential textures to poured material on different materials. However after the first two runs, which involved plastic bags laid over spaced apart wood [l] and string [m], I began to ask the question of whether the mixture could form around these materials and use them as newly embedded properties when dried.

DISCOVERIES

Casting against different materials has an effect on the drying material.

Interactions between a drying liquid mixture and an unconventional formwork gives a lot of room to explore the variability.

the drip-effect

hard plastic

The mixture poured over string inevitably had a lot of excess mix falling off of the forms. A plastic plate was placed beneath to catch the excess, and the resulting hardened material comes out smooth and shiny. This is not new information, but it can still inform design decisions later on.

tension + compression

plastic bag

An old plastic bag was used as a form-work to cast in simply out of convenience. However upon post-rationalizing the result, this process could be quite beneficial due to the economic factor, using recycled plastic. It also leaved an aesthetically pleasing dimpled surface that is quite smooth, though not as much as that i resulting in hard plastic casts.

string

Pouring a mix over string allows for a field of extreme depth and texture in the resulting surface. The final result is a hardened web of sorts, which has an eerie yet beautiful effect. The particularly challenging aspect of this combination of materials is that does not necessarily contain compressive strength, nor tensile strength, for what it is. This is because the two materials are polar opposites in their properties. The dried mixture is dispersed with gaps that make it weak and unable to resist serious pressure. The object can indeed handle tensile forces, meaning the strings within do not break; however, the dried mixture crumbles in the process, which sort of ruins the whole thing.

informed speculation

Dripping from the inter-webbed strings was the excess liquid mixture falling through the gaps. The viscosity of the mixture plays into the formation of these drips, as well as the number of times the mixture can be dried and poured over again with more. This will essentially create stalactites/stalagmites. If it can be controlled, and even shaped, the drip-effect may have a lot of potential.

Using string as form-work for poured material opens a lot of new opportunities to explore. Of particular interest currently is that of lost form-work. Can string be exploited for its ability to make tension-based surfaces, and then cast in concrete to uphold compressive qualities? Can the process of the mixture drying as it is poured feed into the controlled versus uncontrollable nature of two interacting materials, as described by Mark West?

lost form-work

Based on the two properties listed above, the notion of lost form-work came into play. Lost form work is any type of temporary cast for poured material. More popular lost form-works include wood that gets lit on fire once the poured mixture is dried, or plastics that melt at lower temperature than the poured mixture, (usually that mixture is silicone, but Iâ&#x20AC;&#x2122;m assuming it can be any material in reality). Using materials known for properties that are not inherent to earth material as lost form-works will allow for a new way of thinking about earth as a building material.

WHAT I’VE GATHERED, WHERE I’M GOING. This thesis began with an interest in reforming used materials , particularly brick, such that the end product is of higher value than the orginal object. Knowing this, the most reasonable thing to do first seemed to be to acquire a deep understanding of earth material and the methods used to create a brick. Intensive research allows for a solid basis to work from when beginning the process of up-cycling the material. From this research I learned the inherent properties of earth material and the different ingredients and mixtures that make them, as well the many applications of each different mixture proportion and making process. This opened up a lot of doors, but the most interesting to me in particular was fabric casting. Fabric formwork can be applied on many levels of design and the size is limited only by the amount of fabric and concrete available. I chose to use rockite as a temporary alternative to the used brick concoction I’d initially ben interested in exploring. I believed that after finding my niche of exploration, I could go back to the brick and how it can become a part of my design research. After playing with fabric casting while referencing the research I’d been doing, I considered the idea of using fibers in the mixture. The fibers were taken quite literally by me, so I used string. This concept opens up a whole world of possibilities to explore during the contintuation of my thesis. Unresolved aspects of my thesis currently are the relation of my newfound interest in string casting back to brick. For the sake of convenience and accessibility, I had chosen rockite as my mixture for testing. However, I recognize that rockite is not the same as used brick, and I do need to understand my own formula for casting with. With this in mind, the next step in the thesis is to create my own method, based on the research done, and optimize it to the design process I have chosen to pursue. The parameters of this way of working need to be fine-tuned so that I can begin to apply it to actual architectural designs in the coming semester. 52

I will start by choosing specifc parameters to deal with and optimize.

CHOOSING PARAMETERS single array

double array

uneven array

fixed width

length

fra

string

wi dt

inc

rem

measurements

STRING TYPES

glacĂŠ finish cotton string This string was chosen for its fineness and strength. Being slightly glossed over gave it a waxy feeling, and meant that this string does not absorb the wet rockite but instead allows for it to form over it smoothly and without bubbles or gaps. The problem with this type of string is that it does not hold on to the rockite well enough to keep it stable when dry. It is also very limited in the amount of rockite that will adhere to it, and thus makes for a rather weak structure.

STRING TYPES

six-strand cotton string This string has a softer feel to it as well as loosely twisted strands that one could pull apart. This makes for a better string to cast over. The absorbant quality of this string allows for the rockite to ahere more strongly to it, and the multiple strands also allow for the rockite to grab hold of the string more tightly. This leads to a higher threshold for the amount of rockite that remains stuck to the string, and thus a stronger end result.

PROCESS

wrapping method The easiest method for getting string to evenly span between two rigid frame components is to use a kind of crutch around which to do the wrapping. By gluing sticks to each end of a wooden slab and wrapping it I acheive a stable method for measuring string and ensuring evenness across the frame. Using hot glue fastens the string to the frame and at this point the frame can be pulled off from the crutch.

PROCESS

low-tech high-speed After several runs using the crutch method I came to find the process slow and tedious. I also realized after iterating through that more string = more rockite, and an exact measurement of the string increments was not quite necessary. With this in mind I decided to design a new process using cheap technology. A battery-powered screwdriver can fit a wood 1/4â&#x20AC;?x1/4â&#x20AC;? dowel into its slot in place of an actual driver. From there A frame can be fastened to the protruding dowel such that when the screwdriver is run , the frame rotates. The string can be tied to this and quickly wrapped around the frame.

PROCESS dipping into rockite There are several ways to dip the frame into rockite. The easiest is as seen in the image shown below, where the frame is variable and not rigid. This means that the string can be dipped in a cup and the fram can be used to mix it in and then pulled out and laid or hung to dry. The image on the left shows a rigid frame [fastened in its position] being dipped in a shallow puddle of rockite. The cup method works better because it guarantees full immersion and mixing ability, whereas the latter process might not be as effective.

DISCOVERIES variables

first dip increment = 1/8” fixed width = 4” frame width = 4” string length = 4”

second dip final notes After a second dip in the rockite it has become evident that this particular arrangement will not work, due to the high tension in the string preventing clumping.

DISCOVERIES variables

first dip increment = 1/8” fixed width = 3-1/2” frame width = 4” string length = 4”

second dip final notes This held more rockite but the string was still too taught and thus too fragile to feel stable when held.

DISCOVERIES variables

first dip increment = 1/4” fixed width = 3” frame width = 4” string length = 4”

second dip final notes The patterns formed in catenary during initial dipping were of interest, but too fragile. Upon a second dip the patterns broke apart and proved to still be too fragile to function.

DISCOVERIES variables

first dip increment = 1/4” fixed width = 2” frame width = 4” string length = 4”

second dip final notes The patterns of initial dipping were of interest, but too fragile. Upon a second tip the patterns broke apart and proved to still be too fragile to function.

DISCOVERIES variables

first dip increment = 1/4” fixed width = 3-1/2” frame width = 4” string length = 4”

second dip final notes The patterns of initial dipping were of interest, but too fragile. Upon a second tip the patterns broke apart and proved to still be too fragile to function.

DISCOVERIES variables

first dip increment = 1/8” fixed width = 3” frame width = 4” string length = 4”

second dip final notes This kind of string is more successful at collecting rockit within its strands. However a second dip resulted in an uglier result. Can we get the string to absorb more rockite upon a single dip so that a second is not necessary?

DISCOVERIES variables

first dip increment = 1/8” fixed width = 2” frame width = 4” string length = 4”

final notes Dual wrapping has proven to be a more successful method for dipping, due to the higher quantity of strings. It can be assumed at this point that more string = stronger results. What is the optimal ratio of string-to-rockite?

DISCOVERIES variables

first dip increment = 1/8” fixed width = 2” frame width = 4” string length = 4” & 3“

final notes In an attempt to have the strings cluster in three dimensions rather than two I had two different lengths applied. However the distance proved to be a little too far apart.

DISCOVERIES variables

first dip increment = 1/8” fixed width = 4” slant frame width = 4” string length = 4”

final notes This iteration looks more intently at the results of casting in an array. For each layer of strings, one side of the frame is rotated to show results of variation in fixed frame width. This particular array was hung vertically so that the excess string clumps at the bottom.

top

bottom

DISCOVERIES variables

first dip increment = 1/8” fixed width = 4” slant frame width = 4” string length = 3-1/2” & 4”

final notes By having the lengths vary slightly and hanging the frame after dipping, the strings of greater length begin to clump up along the shorter taught strings. this creates interesting texture. The realization that the strings must really be mixed into the rockite occured at this point.

DISCOVERIES variables

first dip final notes increment = 1/8” fixed width = 24” frame width = 4” string length = 24”

Can this process bescaled up? If so, how does it work? On a first attempt at casting 2’ string it becomes evident that as the length scales up, so must the quantity of string, in order to maintain a proper proportion of strength to size.

DISCOVERIES variables

first dip final notes increment = 1/16” fixed width = 24” frame width = 4” string length = 24”

For this iteration I decided to see what would happen when dipping the string into water before dipping it into rockite. The hypothesis was that with water in the string, it will accept the rockite into the fibers more easily. It does seem to have worked in comparison to the last iteration, in addition to the doubling up of string. I will continue to dip in water from now on.

DISCOVERIES variables

first dip increment = 1/16” fixed width = 24” frame width = 4” string length = 24”

final notes By twisting, the rockite can all center around a cylindrical shape as opposed to a flat shape. How much stronger does this make the resulting object? It appears to be much more rigid than its former counterpart.

DISCOVERIES variables

first dip final notes increment = 1/16” fixed width = 4” frame width = 4” string length = 4”

A continuation of the criss-cross sample using the superior thread and higher thread-count. This sample seems to be one of the most durable.

DISCOVERIES variables

first dip final notes increment = 1/16” fixed width = 4” frame width = 4” string length = 4”

This iteration looks at the variability of string increments. Using this will allow for optimization in areas that might need more support. From this you can also see a difference in the texture from the top to the bottom of the catinary, where the bottom has a smoother surface. Could this be taken advantage of?

DISCOVERIES variables

first dip final notes increment = 1/16” fixed width = 2” to 3” frame width = 4” string length = 4”

Here we observe the qualities of variation across a component with variable slack. This iteration begins to open up new understanding of geometry and structure. What variation of this module yields an optimal structure?

OPTIMIZE

WHAT Iâ&#x20AC;&#x2122;VE GATHERED, WHERE Iâ&#x20AC;&#x2122;M GOING.

The theisis is taking a turn toward understanding the behavior of string-casted rockite. The ideas behind this relate back to Detroit as a sort of low-technology means to potentially resolve advanced input of informationl. The existing parameters have helped me to understand the general forms possible, but are currently not specific enough to understand particular behavior, as each iteration is too different from the last to be comparable. To understand what geometries work best I have written up a script in Grasshopper that can take in specific parameters and change them around, each parameter acheiving its own output, which is a simulation of what string hanging from two frames would look like. These models are then put under three different kinds of stress tests that will be described in detail below, as with everything else mentioned here. The stress tests then yield a displacement value in milimeters of how much these geometries would move under the same force. This information is collected and placed into a matrix system which organizes each model iteration based on the parameters. From here the optimal models can be selected and utilized in an applicable manner.

100

I will narrow down specifc parameters to deal with and optimize.

101

PARAMETERS WITHIN MATRIX DISCOVERIES

left frame angle

right frame angle

72˚ 54˚ 36˚

18˚

0˚

108˚

72˚ 126˚

54˚ 144˚

36˚ 162˚

180˚

18˚

0˚

108˚ 126˚ 144˚

162˚

180˚

angle variations Both the left and right frame will range between 0 degrees, meaning pointing all the way left, and 180 degrees, all the way right. The left and right frame angles will be the organizing factor for the x and y axes respectively.

102

103

DISCOVERIES OF EACH MATRIX PARAMETERS string slack variations loose

medium

tight

force

forces to center

forces down

forces up

loose variation

loose variation reversed

104

medium variation

tight variation reversed

105

MATRIX CELL

MATRIX CELL SAMPLE DISCOVERIES

maximum displacement in cm left frame angle degrees right frame angle degrees

layout of each cell in matrixes

visible displacement original frame plaement

high density of string median density of string low density of string

least displacement of all cells in matrix

106

max displacement median displacement min displacement

most displacement of all cells in matrix

107

DISCOVERIES OPTIMIZE

variables

forces to center

0.000796 162.0 28.8

0.001088 172.8 39.6

0.001554 162.0 50.4

0.002066 162.0 57.6

0.003366 169.2 93.6

0.003569 158.4 100.8

0.011117 158.4 140.4

0.010221 165.6 140.4

0.011921 165.6 151.2

0.010541 176.4 162.0

0.00074 140.4 10.8

0.000508 154.8 28.8

0.000477 140.4 61.2

0.000622 140.4 68.4

0.002499 147.6 90.0

0.003677 147.6 97.2

0.007641 147.6 122.4

0.006791 140.4 122.4

0.008241 147.6 126.0

0.009432 147.6 133.2

0.000347 136.8 25.2

0.00032 136.8 28.8

0.000348 140.4 43.2

0.000189 126.0 46.8

0.000142 129.6 54.0

0.000146 129.6 64.8

0.0002 133.2 64.8

0.000978 126.0 86.4

0.002181 129.6 97.2

0.002035 115.2 0.0

0.001343 118.8 7.2

0.001997 104.4 7.2

0.000359 126.0 28.8

0.000259 115.2 46.8

0.000348 108.0 57.6

0.000322 115.2 79.2

0.001428 108.0 93.6

0.009884 115.2 140.4

0.017568 104.4 162.0

0.002253 90.0 10.8

0.001544 97.2 14.4

0.000956 100.8 25.2

0.0007 93.6 50.4

0.000507 100.8 57.6

0.000468 100.8 64.8

0.000905 97.2 86.4

0.012704 100.8 144.0

0.017908 93.6 154.8

0.025915 86.4 169.2

0.004116 79.2 0.0

0.002961 75.6 10.8

0.002951 68.4 14.4

0.001773 72.0 28.8

0.001157 82.8 36.0

0.001159 75.6 82.8

0.002854 82.8 97.2

0.012635 64.8 122.4

0.021086 68.4 151.2

0.027468 79.2 165.6

0.002051 54.0 46.8

0.001568 61.2 54.0

0.001718 54.0 54.0

0.001404 61.2 57.6

0.000818 64.8 72.0

0.002367 61.2 86.4

0.002622 57.6 86.4

0.012024 61.2 118.8

0.014161 64.8 126.0

0.039094 57.6 165.6

0.005999 32.4 7.2

0.005022 32.4 14.4

0.002575 50.4 25.2

0.002286 43.2 79.2

0.004665 28.8 82.8

0.00274 46.8 82.8

0.00496 43.2 90.0

0.02799 39.6 133.2

0.029672 50.4 144.0

0.043096 28.8 144.0

0.007743 18.0 3.6

0.003276 28.8 39.6

0.001879 25.2 54.0

0.001494 14.4 64.8

0.001218 28.8 64.8

0.01422 21.6 100.8

0.031395 18.0 122.4

0.032383 21.6 126.0

0.044204 18.0 136.8

0.062914 28.8 176.4

0.008917 0.0 3.6

0.008711 3.6 3.6

0.006993 10.8 10.8

0.006918 3.6 14.4

0.003714 0.0 68.4

0.004003 7.2 72.0

0.056039 0.0 136.8

0.052529 10.8 140.4

0.064249 0.0 147.6

0.068873 7.2 165.6

results

loose

108

109

DISCOVERIES OPTIMIZE

variables

forces to center medium

110

0.00049 172.8 0

0.000534 158.4 10.8

0.0003 158.4 39.6

0.000761 165.6 61.2

0.001299 172.8 68.4

0.008391 165.6 147.6

0.008029 172.8 154.8

0.005885 176.4 158.4

0.010459 172.8 176.4

0.000433 136.8 10.8

0.000399 158.4 25.2

0.000147 144.0 32.4

0.000345 158.4 36.0

0.000365 158.4 43.2

0.000318 154.8 50.4

0.000267 144.0 72.0

0.000513 158.4 79.2

0.003176 147.6 118.8

0.004308 147.6 158.4

0.000699 126.0 7.2

0.000134 129.6 32.4

7.9E-05 129.6 43.2

6E-05 122.4 64.8

0.00148 129.6 115.2

0.001801 133.2 118.8

0.003203 136.8 147.6

0.003463 136.8 158.4

0.003422 126.0 162.0

0.003784 129.6 172.8

0.000663 122.4 7.2

0.000127 115.2 39.6

0.000107 115.2 43.2

0.000684 122.4 57.6

8E-05 115.2 61.2

6E-05 122.4 64.8

0.000798 122.4 104.4

0.001922 118.8 126.0

0.002661 118.8 140.4

0.003405 118.8 158.4

0.001285 108.0 0

0.000402 100.8 25.2

0.000104 108.0 54.0

8.4E-05 104.4 82.8

0.000988 104.4 108.0

0.003426 108.0 151.2

0.00358 104.4 151.2

0.004185 104.4 162.0

0.00549 100.8 176.4

0.001539 97.2 0

0.001334 82.8 7.2

0.000755 97.2 14.4

0.000386 93.6 28.8

0.000601 75.6 28.8

0.000486 82.8 54.0

0.002165 86.4 118.8

0.00415 79.2 136.8

0.0042 86.4 144.0

0.004744 90.0 154.8

0.000664 72.0 28.8

0.000601 75.6 28.8

0.000573 75.6 32.4

0.000591 72.0 68.4

0.000269 68.4 82.8

0.001357 75.6 104.4

0.003274 72.0 122.4

0.00518 72.0 140.4

0.00591 75.6 151.2

0.006738 75.6 158.4

0.003347 36.0 0

0.000985 57.6 28.8

0.001161 54.0 43.2

0.00053 68.4 72.0

0.000331 61.2 86.4

0.001141 36.0 86.4

0.010368 36.0 133.2

0.008914 57.6 151.2

0.010517 54.0 154.8

0.014813 50.4 165.6

0.003154 18.0 18.0

0.002258 28.8 32.4

0.001979 32.4 43.2

0.001698 28.8 54.0

0.000953 25.2 64.8

0.009082 21.6 115.2

0.013612 32.4 140.4

0.028978 25.2 162.0

0.035089 25.2 169.2

0.039075 21.6 169.2

0.006042 0.0 3.6

0.005925 0.0 7.2

0.000544 0.0 68.4

0.001363 14.4 79.2

0.004045 3.6 86.4

0.010055 3.6 104.4

0.015121 7.2 118.8

0.020257 3.6 126.0

0.028137 3.6 136.8

0.030688 10.8 147.6

results

111

DISCOVERIES OPTIMIZE

variables

forces to center tight

112

0.000407 165.6 3.6

0.000398 176.4 7.2

8.1E-05 165.6 32.4

6.6E-05 162.0 54.0

0.000181 151.2 82.8

0.000213 162.0 82.8

0.000789 169.2 108.0

0.001175 144.0 122.4

0.005788 169.2 144.0

0.007159 172.8 154.8

0.000324 144.0 0.0

5.9E-05 140.4 21.6

4.8E-05 136.8 25.2

3.5E-05 133.2 32.4

3E-05 133.2 36.0

2.6E-05 133.2 39.6

0.001032 140.4 129.6

0.001131 140.4 158.4

0.001075 136.8 162.0

0.001022 136.8 165.6

0.000165 118.8 7.2

0.000162 122.4 7.2

7.2E-05 133.2 18.0

4.1E-05 133.2 28.8

1.8E-05 129.6 50.4

1.7E-05 126.0 54.0

2E-05 115.2 57.6

0.000901 133.2 136.8

0.000789 108.0 172.8

0.00078 111.6 172.8

9.3E-05 108.0 14.4

4.1E-05 100.8 39.6

3.3E-05 108.0 43.2

5.3E-05 108.0 75.6

9.4E-05 97.2 75.6

3.6E-05 108.0 90.0

0.00013 100.8 97.2

0.000136 97.2 97.2

0.000192 100.8 100.8

0.000201 97.2 100.8

8.3E-05 93.6 21.6

0.00019 82.8 54.0

0.000228 79.2 54.0

0.00012 90.0 54.0

0.000133 90.0 57.6

7.6E-05 97.2 57.6

0.000292 79.2 100.8

0.001614 75.6 147.6

0.001267 93.6 158.4

0.000711 90.0 176.4

0.00015 68.4 21.6

0.000441 61.2 54.0

0.000201 72.0 75.6

1.7E-05 61.2 86.4

0.001298 68.4 126.0

0.00176 64.8 136.8

0.002099 61.2 154.8

0.001299 72.0 169.2

0.001458 64.8 169.2

0.001308 61.2 172.8

0.000339 50.4 7.2

0.000285 50.4 21.6

0.000521 54.0 36.0

0.000465 57.6 61.2

0.00051 54.0 61.2

7.1E-05 57.6 82.8

0.00033 54.0 93.6

0.000884 57.6 108.0

0.001341 54.0 115.2

0.00229 57.6 151.2

0.000347 46.8 21.6

0.000708 39.6 25.2

0.000697 46.8 43.2

0.000543 46.8 64.8

0.000858 39.6 97.2

0.000741 43.2 97.2

0.00226 46.8 126.0

0.002178 50.4 129.6

0.002642 46.8 162.0

0.001107 28.8 3.6

0.000724 25.2 10.8

0.000941 21.6 10.8

0.001853 18.0 21.6

0.000782 39.6 28.8

0.001702 14.4 50.4

0.001306 25.2 50.4

0.000586 25.2 86.4

0.008617 14.4 165.6

0.003571 7.2 0.0

0.003216 7.2 10.8

0.001782 14.4 10.8

0.004748 0.0 14.4

0.000827 0.0 64.8

0.006332 3.6 108.0

0.005335 14.4 115.2

0.007855 7.2 118.8

0.012936 7.2 144.0

0.014176 7.2 154.8

results

113

DISCOVERIES OPTIMIZE

variables

forces to center loose variation

114

0.001915 169.2 43.2

0.003038 169.2 57.6

0.003911 172.8 61.2

0.004907 169.2 68.4

0.004265 172.8 97.2

0.005718 169.2 115.2

0.006781 169.2 126.0

0.006421 172.8 129.6

0.007467 176.4 162.0

0.007881 176.4 169.2

0.000167 165.6 0.0

0.000598 158.4 7.2

0.001472 154.8 46.8

0.001958 154.8 54.0

0.002968 158.4 61.2

0.003199 158.4 64.8

0.013186 158.4 154.8

0.012466 162.0 162.0

0.015674 154.8 162.0

0.016097 154.8 165.6

0.001787 126.0 3.6

0.000941 144.0 46.8

0.000944 140.4 54.0

0.003707 154.8 79.2

0.005728 147.6 93.6

0.006317 151.2 100.8

0.008159 147.6 108.0

0.01187 140.4 133.2

0.02605 129.6 172.8

0.014652 140.4 176.4

0.002079 118.8 0.0

0.000258 115.2 39.6

0.000146 122.4 43.2

0.000147 122.4 43.2

0.001531 118.8 75.6

0.001966 111.6 79.2

0.00385 111.6 90.0

0.007429 111.6 104.4

0.028945 108.0 158.4

0.025473 118.8 158.4

0.002801 93.6 7.2

0.002063 93.6 10.8

0.001387 93.6 18.0

0.000801 108.0 21.6

0.000562 97.2 46.8

0.001989 108.0 79.2

0.015641 93.6 122.4

0.028944 108.0 158.4

0.031491 104.4 162.0

0.042314 90.0 176.4

0.002614 86.4 10.8

0.001532 79.2 25.2

0.001256 86.4 25.2

0.000842 86.4 43.2

0.001023 79.2 43.2

0.000826 82.8 57.6

0.003656 79.2 82.8

0.016732 79.2 118.8

0.02867 86.4 144.0

0.047131 79.2 176.4

0.004206 64.8 10.8

0.001526 79.2 25.2

0.001658 72.0 28.8

0.001422 64.8 43.2

0.00181 64.8 68.4

0.002036 72.0 72.0

0.002161 68.4 72.0

0.006167 72.0 90.0

0.026192 68.4 129.6

0.047831 75.6 172.8

0.005583 57.6 7.2

0.002652 46.8 32.4

0.002441 39.6 43.2

0.007077 54.0 86.4

0.023329 36.0 108.0

0.015438 61.2 108.0

0.025639 50.4 118.8

0.035235 54.0 133.2

0.052965 61.2 169.2

0.05548 43.2 176.4

0.008137 36.0 7.2

0.008843 18.0 10.8

0.008426 25.2 10.8

0.006249 32.4 18.0

0.00235 14.4 46.8

0.002358 18.0 61.2

0.004412 18.0 68.4

0.013143 28.8 90.0

0.01447 32.4 93.6

0.054674 28.8 162.0

0.00937 10.8 3.6

0.008725 3.6 7.2

0.001738 0.0 50.4

0.002523 14.4 61.2

0.021954 10.8 97.2

0.024237 0.0 97.2

0.024152 0.0 97.2

0.025192 10.8 100.8

0.048234 14.4 158.4

0.042115 10.8 169.2

results

115

DISCOVERIES OPTIMIZE

variables

forces to center

0.000599 147.6 18.0

0.000248 144.0 43.2

0.000229 158.4 46.8

0.000677 162.0 72.0

0.00235 158.4 79.2

0.004588 147.6 104.4

0.00347 169.2 104.4

0.003801 169.2 111.6

0.009719 154.8 158.4

0.015917 154.8 176.4

0.000462 133.2 10.8

0.000531 144.0 25.2

0.000175 133.2 50.4

0.000194 133.2 64.8

0.000814 140.4 68.4

0.002217 136.8 90.0

0.00704 136.8 136.8

0.007077 136.8 140.4

0.016822 144.0 172.8

0.016935 140.4 172.8

0.001495 126.0 0.0

0.000466 126.0 18.0

0.000181 126.0 32.4

6.3E-05 126.0 54.0

6.2E-05 126.0 54.0

0.000107 129.6 54.0

0.000103 126.0 64.8

0.000408 129.6 72.0

0.005229 129.6 115.2

0.010487 126.0 144.0

0.000698 104.4 14.4

0.000186 118.8 36.0

0.000124 118.8 43.2

0.000282 104.4 46.8

0.000282 104.4 54.0

0.000308 115.2 75.6

0.001335 115.2 90.0

0.002158 118.8 97.2

0.00858 111.6 133.2

0.017871 100.8 162.0

0.0023 97.2 0.0

0.001341 93.6 7.2

0.00105 93.6 14.4

0.00049 93.6 46.8

0.000344 100.8 57.6

0.001229 93.6 86.4

0.002512 93.6 97.2

0.004016 100.8 108.0

0.007128 97.2 122.4

0.01224 93.6 140.4

0.002415 93.6 0.0

0.001364 90.0 10.8

0.001607 82.8 10.8

0.000566 90.0 39.6

0.000445 90.0 64.8

0.000659 86.4 75.6

0.001707 90.0 90.0

0.007218 86.4 118.8

0.009202 93.6 129.6

0.012998 82.8 136.8

0.00161 64.8 18.0

0.001099 72.0 25.2

0.000918 79.2 25.2

0.001042 72.0 28.8

0.000992 72.0 39.6

0.002616 79.2 93.6

0.005731 75.6 108.0

0.011792 64.8 122.4

0.029901 68.4 162.0

0.001726 57.6 21.6

0.001528 57.6 28.8

0.001638 54.0 32.4

0.001484 57.6 32.4

0.001373 57.6 43.2

0.001218 61.2 46.8

0.001066 61.2 54.0

0.026457 54.0 144.0

0.036807 50.4 158.4

0.046613 50.4 169.2

0.004241 36.0 10.8

0.002381 39.6 36.0

0.001288 36.0 57.6

0.001259 25.2 61.2

0.0044 28.8 82.8

0.008324 43.2 100.8

0.012733 25.2 100.8

0.035195 25.2 133.2

0.049519 36.0 158.4

0.007968 18.0 7.2

0.00419 10.8 36.0

0.01998 3.6 100.8

0.035195 25.2 133.2

0.050594 7.2 144.0

0.052244 14.4 147.6

0.055273 18.0 162.0

0.048843 7.2 162.0

0.054708 18.0 165.6

0.05577 25.2 172.8

results

medium variation

116

117

DISCOVERIES OPTIMIZE

variables

forces to center

0.000347 165.6 21.6

0.000433 165.6 61.2

0.000644 169.2 72.0

0.001086 169.2 79.2

0.000883 165.6 82.8

0.001605 172.8 90.0

0.004282 172.8 104.4

0.007018 172.8 118.8

0.007732 176.4 122.4

0.007634 172.8 122.4

0.000411 162.0 18.0

0.000318 158.4 32.4

0.000335 154.8 32.4

0.000262 162.0 57.6

0.000273 162.0 57.6

0.001863 151.2 93.6

0.004245 158.4 111.6

0.002497 147.6 122.4

0.003265 151.2 151.2

0.004041 151.2 176.4

0.001392 136.8 0.0

0.00065 133.2 25.2

0.000556 144.0 79.2

0.002263 147.6 115.2

0.001919 136.8 126.0

0.002102 136.8 129.6

0.002103 136.8 129.6

0.003351 140.4 154.8

0.003555 133.2 158.4

0.003645 144.0 162.0

0.001606 118.8 0.0

0.001082 133.2 7.2

0.00116 126.0 14.4

0.001161 126.0 14.4

0.001019 118.8 21.6

0.00028 122.4 79.2

0.000355 129.6 90.0

0.000823 122.4 108.0

0.001597 118.8 122.4

0.003045 129.6 147.6

0.001849 104.4 14.4

0.001137 108.0 25.2

0.000734 90.0 57.6

0.000758 90.0 61.2

0.000519 100.8 68.4

0.000432 100.8 97.2

0.000673 115.2 104.4

0.001553 97.2 115.2

0.002531 111.6 133.2

0.007026 100.8 176.4

0.002671 86.4 7.2

0.001319 86.4 28.8

0.00111 75.6 39.6

0.000981 79.2 43.2

0.001385 68.4 50.4

0.00108 72.0 72.0

0.00078 72.0 79.2

0.002885 82.8 122.4

0.003318 82.8 126.0

0.009755 82.8 176.4

0.0046 68.4 0.0

0.00188 64.8 25.2

0.001758 61.2 46.8

0.001446 68.4 46.8

0.001914 57.6 46.8

0.001318 64.8 68.4

0.001201 61.2 72.0

0.004535 68.4 126.0

0.005927 61.2 129.6

0.015764 57.6 176.4

0.002622 43.2 36.0

0.001957 39.6 61.2

0.001701 43.2 64.8

0.001699 39.6 64.8

0.001681 50.4 97.2

0.003035 54.0 108.0

0.004884 54.0 118.8

0.008207 50.4 133.2

0.010537 50.4 144.0

0.018792 43.2 169.2

0.004602 32.4 7.2

0.003203 36.0 21.6

0.003389 28.8 32.4

0.001589 28.8 64.8

0.000772 32.4 82.8

0.003461 36.0 100.8

0.005733 36.0 111.6

0.009677 28.8 122.4

0.011039 36.0 133.2

0.018537 25.2 147.6

0.003922 25.2 18.0

0.004067 0.0 36.0

0.00383 14.4 36.0

0.003393 14.4 43.2

0.002811 21.6 50.4

0.001368 7.2 61.2

0.003258 7.2 86.4

0.00496 10.8 93.6

0.024545 7.2 144.0

0.036869 10.8 172.8

results

loose variation reversed

118

119

DISCOVERIES OPTIMIZE

variables

forces to center

0.000438 176.4 0.0

0.0003 172.8 7.2

0.000417 169.2 10.8

0.00025 172.8 10.8

0.000348 176.4 14.4

0.000226 162.0 25.2

0.000296 162.0 61.2

0.005268 165.6 118.8

0.005814 165.6 122.4

0.007989 172.8 165.6

0.000253 144.0 28.8

0.000124 140.4 72.0

0.000648 154.8 82.8

0.00431 158.4 115.2

0.003408 154.8 162.0

0.002816 144.0 162.0

0.002936 147.6 172.8

0.003021 151.2 176.4

0.000671 129.6 18.0

0.000408 140.4 21.6

0.000329 140.4 25.2

0.000153 140.4 46.8

0.000135 133.2 61.2

0.000962 140.4 100.8

0.000758 136.8 100.8

0.002282 133.2 144.0

0.002439 126.0 147.6

0.002398 133.2 147.6

0.000157 118.8 72.0

0.000198 111.6 72.0

0.000152 118.8 86.4

0.000201 122.4 90.0

0.000649 100.8 104.4

0.000854 118.8 111.6

0.001518 108.0 122.4

0.001856 111.6 129.6

0.003286 104.4 151.2

0.003929 111.6 176.4

0.001032 97.2 21.6

0.00074 93.6 28.8

0.000577 97.2 32.4

0.000394 93.6 39.6

0.000434 86.4 97.2

0.002171 93.6 126.0

0.002984 82.8 129.6

0.003647 86.4 140.4

0.003018 97.2 140.4

0.003015 97.2 140.4

0.000712 75.6 46.8

0.000651 79.2 50.4

0.000225 79.2 86.4

0.000216 75.6 90.0

0.000757 68.4 97.2

0.002064 75.6 115.2

0.002361 82.8 122.4

0.004399 68.4 133.2

0.004486 82.8 147.6

0.00894 64.8 169.2

0.003627 57.6 0.0

0.002639 50.4 10.8

0.001425 61.2 21.6

0.001201 57.6 32.4

0.001175 61.2 43.2

0.001614 50.4 43.2

0.001621 50.4 46.8

0.001555 50.4 54.0

0.002081 61.2 108.0

0.003791 50.4 115.2

0.001903 43.2 21.6

0.00162 50.4 46.8

0.000362 50.4 86.4

0.000947 43.2 90.0

0.001581 39.6 93.6

0.001622 46.8 97.2

0.00339 43.2 108.0

0.005552 39.6 118.8

0.008135 43.2 136.8

0.013717 43.2 165.6

0.003511 39.6 0.0

0.004531 18.0 3.6

0.002226 36.0 32.4

0.002025 25.2 54.0

0.001499 39.6 61.2

0.000762 21.6 68.4

0.001022 36.0 68.4

0.007766 25.2 118.8

0.011147 28.8 136.8

0.01588 39.6 169.2

0.004785 3.6 7.2

0.00407 10.8 10.8

0.003444 18.0 14.4

0.003529 7.2 32.4

0.000953 14.4 64.8

0.000658 18.0 68.4

0.000849 18.0 79.2

0.026767 3.6 151.2

0.03393 0.0 158.4

0.028815 3.6 162.0

results

medium variation reversed

120

121

DISCOVERIES OPTIMIZE

variables

forces down

0.002476 169.2 28.8

0.002095 162.0 54.0

0.001802 169.2 64.8

0.001354 162.0 115.2

0.001741 162.0 118.8

0.004584 176.4 133.2

0.004054 169.2 133.2

0.004865 158.4 154.8

0.005499 169.2 158.4

0.004937 162.0 169.2

0.002465 158.4 7.2

0.002776 151.2 14.4

0.001511 158.4 25.2

0.001697 158.4 28.8

0.001204 154.8 28.8

0.002009 154.8 54.0

0.001929 158.4 64.8

0.001426 151.2 72.0

0.001808 158.4 122.4

0.004995 158.4 158.4

0.001091 140.4 32.4

0.000986 144.0 50.4

0.00073 140.4 64.8

0.001212 151.2 79.2

0.000683 144.0 82.8

0.000447 136.8 86.4

0.000674 140.4 108.0

0.002975 144.0 140.4

0.004295 140.4 154.8

0.00434 144.0 154.8

0.002944 133.2 14.4

0.002492 122.4 21.6

0.000935 129.6 36.0

0.000202 133.2 46.8

0.000552 133.2 97.2

0.000895 129.6 115.2

0.001588 133.2 126.0

0.001907 133.2 129.6

0.002728 122.4 136.8

0.004282 118.8 154.8

0.002309 111.6 0.0

0.000213 111.6 79.2

0.000283 108.0 86.4

0.000648 115.2 104.4

0.000748 104.4 108.0

0.000718 111.6 108.0

0.000904 111.6 111.6

0.001929 108.0 126.0

0.002159 115.2 129.6

0.002807 115.2 136.8

0.002012 104.4 3.6

0.001239 93.6 21.6

0.000434 93.6 39.6

0.000355 100.8 43.2

0.000169 97.2 75.6

0.000145 86.4 86.4

2.7E-05 90.0 90.0

0.000952 100.8 111.6

0.002614 100.8 133.2

0.00334 93.6 140.4

0.00087 72.0 14.4

0.000424 75.6 21.6

0.000892 68.4 64.8

0.000986 68.4 79.2

0.000406 79.2 90.0

0.000771 72.0 97.2

0.000527 86.4 104.4

0.001193 86.4 115.2

0.001464 86.4 118.8

0.004863 72.0 158.4

0.001486 54.0 36.0

0.001183 57.6 36.0

0.000986 68.4 79.2

0.001506 61.2 86.4

0.001289 64.8 104.4

0.002033 57.6 118.8

0.003823 50.4 140.4

0.004591 57.6 172.8

0.0041 50.4 176.4

0.005377 39.6 0.0

0.00218 46.8 43.2

0.002565 43.2 46.8

0.003798 32.4 68.4

0.00241 50.4 86.4

0.003518 39.6 93.6

0.003877 39.6 115.2

0.0043 36.0 122.4

0.004227 36.0 158.4

0.004172 43.2 169.2

0.005578 18.0 0.0

0.004448 3.6 3.6

0.004652 0.0 39.6

0.005153 18.0 100.8

0.004534 0.0 122.4

0.005113 21.6 122.4

0.003848 7.2 144.0

0.003598 21.6 162.0

0.003023 14.4 169.2

0.002658 10.8 176.4

results

loose

122

123

DISCOVERIES OPTIMIZE

variables

forces down

0.00067 176.4 0.0

0.001463 172.8 14.4

0.00101 165.6 18.0

0.002026 176.4 54.0

0.001936 176.4 72.0

0.000756 162.0 86.4

0.000427 176.4 108.0

0.000386 162.0 118.8

0.001507 165.6 136.8

0.003335 172.8 147.6

0.000446 154.8 25.2

0.000435 147.6 39.6

0.000861 151.2 50.4

0.000466 158.4 100.8

0.000724 162.0 129.6

0.000791 151.2 129.6

0.001348 158.4 144.0

0.002083 151.2 151.2

0.00324 147.6 165.6

0.004132 158.4 176.4

0.002125 118.8 3.6

0.001245 147.6 21.6

0.000333 126.0 39.6

6.8E-05 122.4 64.8

0.000601 147.6 115.2

0.000761 144.0 122.4

0.001385 115.2 129.6

0.003097 115.2 154.8

0.003294 126.0 158.4

0.004057 144.0 172.8

0.001307 115.2 0.0

0.000879 100.8 25.2

0.000153 108.0 46.8

8.9E-05 111.6 54.0

4E-05 115.2 64.8

0.001632 104.4 133.2

0.001855 104.4 136.8

0.003109 108.0 154.8

0.003565 100.8 162.0

0.003604 108.0 176.4

0.001378 97.2 7.2

0.000547 86.4 25.2

0.000645 90.0 25.2

0.000337 86.4 43.2

0.000305 90.0 43.2

2.7E-05 90.0 90.0

0.000479 97.2 108.0

0.001781 90.0 136.8

0.003932 86.4 169.2

0.003926 90.0 169.2

0.000235 82.8 0.0

0.000404 79.2 50.4

0.000399 79.2 54.0

0.000523 72.0 57.6

0.000555 68.4 90.0

0.00035 68.4 104.4

0.001238 68.4 133.2

0.001948 82.8 140.4

0.002206 82.8 144.0

0.004257 79.2 176.4

0.001006 54.0 14.4

0.000611 54.0 25.2

0.000832 54.0 39.6

0.00068 64.8 57.6

0.001064 57.6 75.6

0.00083 57.6 115.2

0.001492 57.6 133.2

0.002021 54.0 140.4

0.002265 57.6 147.6

0.00298 57.6 154.8

0.00185 50.4 0.0

0.000744 54.0 32.4

0.001624 46.8 75.6

0.002065 39.6 86.4

0.001132 50.4 111.6

0.001871 39.6 122.4

0.0031 32.4 136.8

0.003894 36.0 158.4

0.003761 39.6 158.4

0.004456 32.4 162.0

0.002662 21.6 25.2

0.002767 25.2 46.8

0.002985 25.2 64.8

0.002795 28.8 100.8

0.003391 21.6 108.0

0.00342 21.6 111.6

0.003195 25.2 122.4

0.004219 21.6 144.0

0.004366 21.6 147.6

0.004629 21.6 154.8

0.001792 3.6 0.0

0.003701 10.8 32.4

0.003809 0.0 64.8

0.003953 3.6 82.8

0.004113 10.8 100.8

0.004566 3.6 104.4

0.004481 10.8 118.8

0.00508 3.6 122.4

0.004781 14.4 136.8

0.003928 7.2 165.6

results

medium

124

125

DISCOVERIES OPTIMIZE

variables

forces down tight

126

0.001869 176.4 0.0

0.001326 176.4 32.4

0.000933 165.6 61.2

0.000647 165.6 93.6

0.000261 172.8 122.4

0.000256 172.8 126.0

0.000302 172.8 140.4

0.001114 162.0 151.2

0.001864 169.2 165.6

0.004314 169.2 176.4

0.001354 144.0 0.0

0.000707 158.4 64.8

0.000224 140.4 82.8

0.000259 144.0 82.8

0.000486 154.8 108.0

0.001025 147.6 144.0

0.001061 158.4 147.6

0.00181 154.8 158.4

0.002044 158.4 162.0

0.004168 147.6 176.4

0.000172 133.2 39.6

7.3E-05 133.2 50.4

8.8E-05 133.2 54.0

9.7E-05 129.6 72.0

0.000157 136.8 75.6

0.000482 129.6 118.8

0.000608 129.6 126.0

0.000764 136.8 133.2

0.001722 129.6 151.2

0.001653 136.8 151.2

6.9E-05 126.0 68.4

5.6E-05 118.8 72.0

8.3E-05 118.8 79.2

0.000141 118.8 90.0

0.00056 115.2 122.4

0.001004 122.4 136.8

0.00116 122.4 140.4

0.001336 122.4 144.0

0.001542 118.8 147.6

0.001763 118.8 151.2

0.000526 100.8 25.2

0.000478 111.6 28.8

0.000275 111.6 36.0

0.000182 97.2 39.6

3.2E-05 104.4 72.0

4.8E-05 108.0 79.2

8.8E-05 104.4 90.0

0.000453 100.8 118.8

0.001313 100.8 144.0

0.001945 100.8 154.8

0.000608 93.6 0.0

0.000625 86.4 14.4

0.000157 97.2 43.2

0.000124 90.0 61.2

3.5E-05 90.0 86.4

2.8E-05 90.0 90.0

2.9E-05 82.8 97.2

0.001328 82.8 147.6

0.001478 97.2 147.6

0.001802 86.4 154.8

0.000169 79.2 75.6

0.000251 72.0 79.2

0.000165 75.6 115.2

0.000116 72.0 115.2

0.000401 75.6 126.0

0.000823 72.0 140.4

0.001147 82.8 144.0

0.001883 75.6 158.4

0.002084 72.0 162.0

0.002501 79.2 165.6

0.000344 43.2 7.2

0.000566 54.0 21.6

0.000495 57.6 50.4

0.000435 61.2 57.6

0.000371 64.8 79.2

0.00051 54.0 97.2

0.000813 43.2 100.8

0.000485 46.8 115.2

0.000347 46.8 122.4

0.0003 43.2 129.6

0.001349 32.4 46.8

0.002084 21.6 57.6

0.001856 25.2 64.8

0.001419 32.4 82.8

0.001609 28.8 82.8

0.001743 25.2 93.6

0.001503 28.8 97.2

0.001183 32.4 108.0

0.001644 32.4 169.2

0.001771 14.4 10.8

0.002264 18.0 39.6

0.002903 10.8 68.4

0.003184 7.2 72.0

0.002324 18.0 75.6

0.002309 18.0 79.2

0.002474 14.4 93.6

0.001851 21.6 104.4

0.003443 0.0 118.8

0.002383 14.4 176.4

results

127

DISCOVERIES OPTIMIZE 0.002218 172.8 28.8

0.00186 169.2 46.8

0.000569 172.8 93.6

0.002397 169.2 115.2

0.002486 172.8 115.2

0.00344 169.2 122.4

0.004212 176.4 126.0

0.003196 172.8 133.2

0.005313 176.4 136.8

0.005026 176.4 169.2

variables

forces down

results 0.001432 158.4 14.4

0.001616 165.6 21.6

0.001973 162.0 32.4

0.001826 154.8 32.4

0.002301 162.0 39.6

0.002215 154.8 43.2

0.00096 151.2 86.4

0.000504 158.4 100.8

0.003241 165.6 122.4

0.004791 151.2 172.8

0.002638 140.4 21.6

0.001487 147.6 39.6

0.001466 147.6 39.6

0.001611 151.2 57.6

0.001315 140.4 68.4

0.001056 147.6 90.0

0.000718 144.0 100.8

0.001946 136.8 122.4

0.004048 147.6 136.8

0.005072 147.6 169.2

0.000496 126.0 61.2

0.000629 129.6 61.2

0.000628 129.6 82.8

0.000811 129.6 108.0

0.000802 133.2 108.0

0.001532 136.8 118.8

0.00278 122.4 129.6

0.003205 122.4 133.2

0.004054 133.2 140.4

0.005287 133.2 158.4

0.002453 104.4 14.4

0.000529 111.6 75.6

0.000553 111.6 82.8

0.000756 122.4 104.4

0.000976 104.4 108.0

0.001514 111.6 115.2

0.002106 115.2 122.4

0.005281 118.8 158.4

0.005404 108.0 162.0

0.005319 118.8 165.6

0.001947 104.4 28.8

0.000887 90.0 36.0

0.001458 104.4 36.0

0.000357 93.6 79.2

0.00026 90.0 82.8

2.7E-05 90.0 90.0

0.000406 86.4 97.2

0.00033 93.6 97.2

0.001784 90.0 115.2

0.005514 93.6 165.6

0.000459 79.2 21.6

0.00053 79.2 25.2

0.000654 82.8 32.4

0.000553 86.4 64.8

0.000634 75.6 64.8

0.000794 75.6 82.8

0.000184 86.4 90.0

0.000394 82.8 90.0

0.002689 82.8 122.4

0.005236 79.2 147.6

0.002516 46.8 0.0

0.001179 72.0 14.4

0.00464 46.8 14.4

0.003816 46.8 90.0

0.002794 57.6 97.2

0.003128 57.6 111.6

0.004885 61.2 140.4

0.005337 75.6 154.8

0.004679 50.4 165.6

0.004399 54.0 172.8

0.002732 28.8 0.0

0.005782 28.8 10.8

0.004794 46.8 10.8

0.003756 43.2 36.0

0.004899 39.6 104.4

0.005658 28.8 111.6

0.005793 25.2 111.6

0.005306 36.0 115.2

0.005299 36.0 118.8

0.004076 39.6 165.6

0.005552 14.4 28.8

0.005065 0.0 43.2

0.005673 10.8 61.2

0.004988 3.6 97.2

0.005667 7.2 104.4

0.005745 10.8 108.0

0.00497 18.0 133.2

0.005088 21.6 133.2

0.003324 21.6 165.6

0.002114 0.0 172.8

loose variation

128

129

DISCOVERIES OPTIMIZE

variables

forces down

0.001021 0.001742 158.4 162.0 18.0 0.0

0.001212 0.001437 162.0 176.4 21.6 14.4

0.001376 0.002094 162.0 165.6 25.2 32.4

0.000758 0.002216 158.4 165.6 25.2 46.8

0.001208 0.002049 154.8 176.4 64.8 50.4

0.001215 0.002065 162.0 169.2 82.8 72.0

0.00116 0.000419 169.2 158.4 86.4 104.4

0.000816 0.003192 158.4 165.6 90.0 129.6

0.000522 0.003412 158.4 118.8 136.8

0.002206 0.00434 172.8 165.6 133.2 176.4

0.001955 0.002148 144.0 147.6 10.8 7.2

0.002385 0.002606 133.2 140.4 14.4

0.000277 0.001572 144.0 151.2 39.6 21.6

0.00128 0.001013 154.8 158.4 50.4 32.4

8.9E-05 0.00153 126.0 154.8 61.2 43.2

0.000648 0.001868 151.2 158.4 86.4 43.2

0.000564 0.00185 144.0 158.4 104.4 68.4

0.000815 0.000906 144.0 151.2 118.8

0.002316 0.001089 154.8 144.0 154.8 122.4

0.003528 0.005003 147.6 162.0 172.8

0.00163 0.002616 115.2 126.0 21.6 10.8

0.00026 0.000718 118.8 140.4 86.4 28.8

0.000442 0.000243 108.0 129.6 100.8 68.4

0.000508 0.000539 104.4 136.8 104.4 100.8

0.00076 0.000744 108.0 133.2 111.6 115.2

0.000971 0.001092 126.0 140.4 118.8 122.4

0.001058 0.002452 111.6 133.2 118.8 136.8

0.001857 0.003253 111.6 126.0 133.2 144.0

0.002006 0.003889 122.4 140.4 136.8 151.2

0.002812 0.00541 104.4 126.0 147.6 176.4

0.000262 0.002347 82.8 126.0 3.6 18.0

0.000935 0.000745 90.0 100.8 14.4 36.0

0.000418 0.000602 82.8 104.4 57.6 39.6

2.8E-05 0.000341 90.0 115.2 90.0 86.4

2.8E-05 0.000402 90.0 122.4 90.0

0.00069 0.000417 97.2 108.0 111.6 97.2

0.000946 0.000603 93.6 126.0 118.8 104.4

0.001625 0.000905 104.4 118.8 129.6 115.2

0.001593 0.002941 86.4 122.4 133.2 140.4

0.00284 0.004658 90.0 126.0 151.2 165.6

0.000563 0.000772 75.6 90.0 43.2 28.8

0.000442 0.000314 82.8 97.2 50.4 46.8

0.000564 0.000272 75.6 97.2 50.4 54.0

0.000717 0.000214 68.4 100.8 50.4 64.8

0.000542 0.000159 75.6 100.8 64.8 79.2

0.000376 0.000155 79.2 97.2 79.2 86.4

0.001639 0.001726 79.2 100.8 136.8 122.4

0.002043 0.002365 68.4 97.2 147.6 129.6

0.003413 0.003097 79.2 90.0 165.6 136.8

0.003648 0.00414 79.2 100.8 172.8 147.6

0.000571 0.00039 57.6 90.0 21.6 43.2

0.001141 0.000714 54.0 68.4 50.4 57.6

0.001008 0.000537 61.2 75.6 64.8 61.2

0.001385 2.7E-05 54.0 90.0 72.0 90.0

0.001184 0.000244 57.6 82.8 79.2 93.6

0.000949 0.000406 61.2 79.2 86.4 97.2

0.001803 0.000962 61.2 82.8 147.6 111.6

0.002522 0.00152 57.6 79.2 158.4 118.8

0.003088 0.001785 61.2 82.8 165.6 122.4

0.0036 0.002731 68.4 176.4 129.6

0.001233 0.002746 50.4 43.2 46.8 28.8

0.001106 0.001762 54.0 50.4 46.8 39.6

0.001395 0.000923 50.4 68.4 54.0 68.4

0.001607 0.00101 46.8 68.4 54.0 90.0

0.001899 0.002849 43.2 54.0 57.6 126.0

0.00198 0.002492 43.2 61.2 64.8 126.0

0.00187 0.002812 43.2 64.8 90.0 129.6

0.001575 0.003929 43.2 46.8 104.4 133.2

0.001271 0.004076 46.8 50.4 133.2 136.8

0.003214 0.00515 46.8 43.2 176.4 169.2

0.000948 0.005557 36.0 18.0 7.2

0.001333 0.00416 43.2 39.6 36.0 18.0

0.002384 0.004555 36.0 32.4 57.6 21.6

0.002463 0.004155 36.0 68.4 21.6

0.002465 0.003827 36.0 72.0 25.2

0.002292 0.003156 36.0 39.6 79.2 28.8

0.002035 0.003643 39.6 93.6

0.001711 0.005516 39.6 32.4 108.0 140.4

0.001524 0.005457 36.0 126.0 151.2

0.001918 0.004949 39.6 36.0 136.8 165.6

0.003407 0.00575 21.6 7.2 14.4

0.002172 0.004961 21.6 18.0 18.0 39.6

0.002363 0.004924 21.6 18.0 21.6 43.2

0.003465 0.003964 18.0 28.8 43.2 46.8

0.003099 0.004631 25.2 21.6 54.0 50.4

0.002103 0.004787 32.4 18.0 111.6 64.8

0.003135 0.005233 14.4 21.6 133.2 72.0

0.003438 0.005347 28.8 18.0 144.0 97.2

0.005122 0.005876 21.6 154.8 136.8

0.003212 0.005527 18.0 14.4 172.8 140.4

0.004045 0.003725 3.6 0.0 14.4 3.6

0.003686 0.004591 10.8 3.6 25.2 14.4

0.004119 0.005555 0.0 14.4 54.0 18.0

0.003433 0.005353 10.8 14.4 115.2 28.8

0.003807 0.005378 0.0 3.6 115.2 46.8

0.004102 0.006205 3.6 147.6 75.6

0.004764 0.005954 10.8 3.6 154.8 86.4

0.002457 0.005537 0.0 10.8 172.8 86.4

0.003195 0.005628 14.4 176.4 97.2

0.002786 0.006026 7.2 14.4 176.4 100.8

results

medium variation

130

131

DISCOVERIES OPTIMIZE

variables

forces down

0.002391 176.4 18.0

0.002001 165.6 21.6

0.002374 172.8 50.4

0.00206 169.2 68.4

0.001272 162.0 79.2

0.000354 169.2 100.8

0.001289 169.2 122.4

0.002712 176.4 126.0

0.002875 172.8 140.4

0.003985 176.4 144.0

0.000962 151.2 36.0

0.002037 158.4 39.6

0.001128 147.6 50.4

0.000647 151.2 108.0

0.000828 151.2 118.8

0.001309 147.6 133.2

0.001099 158.4 140.4

0.001642 151.2 140.4

0.003072 151.2 154.8

0.00362 158.4 162.0

0.002923 144.0 3.6

0.002881 129.6 10.8

0.002523 133.2 18.0

0.002263 133.2 21.6

0.000692 140.4 32.4

0.001161 140.4 122.4

0.002777 133.2 144.0

0.003644 140.4 154.8

0.004619 140.4 165.6

0.004819 136.8 169.2

0.002208 111.6 7.2

7.8E-05 126.0 50.4

6.9E-05 126.0 54.0

6.5E-05 115.2 61.2

0.000385 126.0 86.4

0.000714 122.4 104.4

0.001088 108.0 115.2

0.001936 115.2 129.6

0.002348 126.0 140.4

0.003927 118.8 154.8

0.000911 90.0 7.2

0.000924 90.0 21.6

0.00044 90.0 50.4

6E-05 100.8 75.6

0.000228 90.0 75.6

4E-05 100.8 79.2

2.8E-05 90.0 90.0

0.000358 108.0 93.6

0.001232 100.8 118.8

0.002041 93.6 133.2

0.000455 79.2 79.2

0.000251 82.8 86.4

0.000325 86.4 104.4

0.000577 79.2 115.2

0.00097 86.4 118.8

0.000761 79.2 118.8

0.000962 79.2 122.4

0.001074 82.8 122.4

0.001627 86.4 129.6

0.001918 79.2 136.8

0.001206 61.2 10.8

0.000559 68.4 21.6

0.000965 61.2 36.0

0.00097 64.8 46.8

0.001236 61.2 75.6

0.000944 64.8 86.4

0.000712 68.4 90.0

0.000881 64.8 90.0

0.003547 64.8 165.6

0.003951 64.8 176.4

0.001315 50.4 36.0

0.00176 50.4 57.6

0.002072 43.2 57.6

0.001655 54.0 72.0

0.000889 57.6 104.4

0.001713 43.2 108.0

0.00121 61.2 136.8

0.002969 61.2 158.4

0.002899 50.4 158.4

0.003321 54.0 165.6

0.003772 21.6 7.2

0.001162 36.0 18.0

0.003238 25.2 36.0

0.003473 25.2 43.2

0.003884 21.6 50.4

0.002637 39.6 68.4

0.002095 32.4 126.0

0.002698 36.0 136.8

0.00564 21.6 158.4

0.00532 25.2 165.6

0.004638 7.2 46.8

0.003353 0.0 57.6

0.004556 7.2 61.2

0.004538 3.6 68.4

0.003855 10.8 111.6

0.003563 14.4 118.8

0.003417 14.4 129.6

0.004 3.6 133.2

0.005309 14.4 151.2

0.003256 10.8 165.6

results

loose variation reversed

132

133

DISCOVERIES OPTIMIZE

variables

forces down

0.001021 158.4 18.0

0.001212 162.0 21.6

0.001376 162.0 25.2

0.000758 158.4 25.2

0.001208 154.8 64.8

0.001215 162.0 82.8

0.00116 169.2 86.4

0.000816 158.4 90.0

0.000522 158.4 118.8

0.002206 172.8 133.2

0.001955 144.0 10.8

0.002385 133.2 14.4

0.000277 144.0 39.6

0.00128 154.8 50.4

8.9E-05 126.0 61.2

0.000648 151.2 86.4

0.000564 144.0 104.4

0.000815 144.0 118.8

0.002316 154.8 154.8

0.003528 147.6 162.0

0.00163 115.2 21.6

0.00026 118.8 86.4

0.000442 108.0 100.8

0.000508 104.4 104.4

0.00076 108.0 111.6

0.000971 126.0 118.8

0.001058 111.6 118.8

0.001857 111.6 133.2

0.002006 122.4 136.8

0.002812 104.4 147.6

0.000262 82.8 3.6

0.000935 90.0 14.4

0.000418 82.8 57.6

2.8E-05 90.0 90.0

0.00069 97.2 111.6

0.000946 93.6 118.8

0.001625 104.4 129.6

0.001593 86.4 133.2

0.00284 90.0 151.2

0.000563 75.6 43.2

0.000442 82.8 50.4

0.000564 75.6 50.4

0.000717 68.4 50.4

0.000542 75.6 64.8

0.000376 79.2 79.2

0.001639 79.2 136.8

0.002043 68.4 147.6

0.003413 79.2 165.6

0.003648 79.2 172.8

0.000571 57.6 21.6

0.001141 54.0 50.4

0.001008 61.2 64.8

0.001385 54.0 72.0

0.001184 57.6 79.2

0.000949 61.2 86.4

0.001803 61.2 147.6

0.002522 57.6 158.4

0.003088 61.2 165.6

0.0036 68.4 176.4

0.001233 50.4 46.8

0.001106 54.0 46.8

0.001395 50.4 54.0

0.001607 46.8 54.0

0.001899 43.2 57.6

0.00198 43.2 64.8

0.00187 43.2 90.0

0.001575 43.2 104.4

0.001271 46.8 133.2

0.003214 46.8 176.4

0.000948 36.0 18.0

0.001333 43.2 36.0

0.002384 36.0 57.6

0.002463 36.0 68.4

0.002465 36.0 72.0

0.002292 36.0 79.2

0.002035 39.6 93.6

0.001711 39.6 108.0

0.001524 36.0 126.0

0.001918 39.6 136.8

0.003407 21.6 7.2

0.002172 21.6 18.0

0.002363 21.6 21.6

0.003465 18.0 43.2

0.003099 25.2 54.0

0.002103 32.4 111.6

0.003135 14.4 133.2

0.003438 28.8 144.0

0.005122 21.6 154.8

0.003212 18.0 172.8

0.004045 3.6 14.4

0.003686 10.8 25.2

0.004119 0.0 54.0

0.003433 10.8 115.2

0.003807 0.0 115.2

0.004102 3.6 147.6

0.004764 10.8 154.8

0.002457 0.0 172.8

0.003195 14.4 176.4

0.002786 7.2 176.4

results

medium variation reversed

134

135

DISCOVERIES OPTIMIZE

variables

forces up

0.001097 151.2 10.8

0.000592 165.6 14.4

0.001063 162.0 14.4

0.000652 169.2 14.4

0.004273 176.4 61.2

0.00562 165.6 72.0

0.006063 169.2 72.0

0.007779 154.8 93.6

0.007525 169.2 104.4

0.005364 172.8 151.2

0.002704 144.0 7.2

0.005025 122.4 21.6

0.003037 144.0 54.0

0.006698 144.0 72.0

0.00943 151.2 79.2

0.001692 126.0 79.2

0.005478 147.6 90.0

0.001524 126.0 104.4

0.001277 122.4 118.8

0.002244 151.2 158.4

0.005765 115.2 25.2

0.004611 122.4 36.0

0.000334 108.0 82.8

0.001109 118.8 104.4

0.001126 118.8 111.6

0.000919 111.6 118.8

0.00108 115.2 126.0

0.001348 122.4 136.8

0.001168 111.6 140.4

0.001456 108.0 176.4

0.007006 108.0 21.6

0.001911 104.4 54.0

8.8E-05 97.2 86.4

0.000416 108.0 86.4

0.000344 97.2 100.8

0.000268 90.0 104.4

0.000401 97.2 104.4

0.00039 93.6 108.0

0.000739 100.8 122.4

0.001041 100.8 144.0

0.008693 86.4 3.6

0.009724 86.4 18.0

0.001577 86.4 54.0

0.001103 75.6 61.2

0.000896 82.8 64.8

0.000739 82.8 68.4

2.8E-05 90.0 90.0

0.000107 79.2 104.4

0.000279 82.8 111.6

0.000469 79.2 126.0

0.00815 72.0 10.8

0.007145 72.0 18.0

0.000716 61.2 75.6

0.000468 61.2 90.0

0.000361 64.8 93.6

0.000305 68.4 93.6

0.000415 61.2 93.6

0.000273 61.2 126.0

0.001047 57.6 172.8

0.00111 61.2 176.4

0.004347 50.4 21.6

0.000477 54.0 97.2

0.000421 57.6 97.2

0.000586 46.8 97.2

0.000432 54.0 100.8

0.000339 50.4 115.2

0.00028 50.4 122.4

0.000255 50.4 126.0

0.000252 50.4 129.6

0.001039 54.0 172.8

0.006019 46.8 10.8

0.001016 36.0 75.6

0.00049 43.2 111.6

0.000579 36.0 115.2

0.000459 43.2 115.2

0.000553 36.0 118.8

0.000317 46.8 126.0

0.000342 43.2 136.8

0.000427 46.8 140.4

0.000637 36.0 162.0

0.002998 28.8 21.6

0.002423 25.2 25.2

0.002258 21.6 25.2

0.001594 36.0 43.2

0.001474 32.4 50.4

0.001486 28.8 50.4

0.001255 21.6 68.4

0.000924 28.8 90.0

0.000876 32.4 90.0

0.001092 32.4 172.8

0.001712 3.6 25.2

0.001963 0.0 25.2

0.001879 0.0 28.8

0.00139 7.2 64.8

0.001285 7.2 75.6

0.000906 14.4 122.4

0.001094 3.6 129.6

0.001038 18.0 165.6

0.001 21.6 165.6

0.001683 0.0 169.2

results

loose

136

137

DISCOVERIES OPTIMIZE

variables

forces up

0.001321 176.4 32.4

0.001683 162.0 46.8

0.003168 165.6 68.4

0.002896 162.0 68.4

0.003963 172.8 79.2

0.005647 162.0 82.8

0.006422 162.0 108.0

0.005222 176.4 111.6

0.002092 162.0 162.0

0.001463 158.4 169.2

0.001588 158.4 0

0.001983 151.2 3.6

0.002627 144.0 64.8

0.003298 154.8 68.4

0.00397 147.6 100.8

0.002078 158.4 154.8

0.001434 140.4 165.6

0.00144 140.4 169.2

0.001463 151.2 176.4

0.001488 147.6 176.4

0.002495 122.4 7.2

0.000664 118.8 57.6

0.002819 129.6 68.4

0.002999 136.8 68.4

0.002705 133.2 75.6

0.001537 129.6 79.2

0.001063 126.0 118.8

0.001637 136.8 118.8

0.001122 126.0 140.4

0.001103 118.8 147.6

0.003038 93.6 0

0.004473 104.4 18.0

0.003124 93.6 39.6

0.000363 111.6 61.2

0.000186 90.0 79.2

0.00012 90.0 82.8

0.000166 97.2 93.6

0.000573 115.2 93.6

0.000745 93.6 140.4

0.000796 93.6 144.0

0.003551 79.2 0

0.001914 79.2 43.2

0.000474 86.4 68.4

0.000304 86.4 75.6

0.000196 79.2 86.4

3E-05 82.8 97.2

0.00061 86.4 136.8

0.000704 90.0 140.4

0.000663 86.4 140.4

0.005947 72.0 10.8

0.006336 61.2 14.4

0.003268 61.2 28.8

0.002856 64.8 32.4

0.000322 64.8 90.0

0.000265 79.2 118.8

0.000653 75.6 147.6

0.000769 75.6 154.8

0.000879 68.4 165.6

0.00098 61.2 176.4

0.00404 43.2 18.0

0.002982 46.8 25.2

0.000685 54.0 72.0

0.000648 57.6 72.0

0.000188 54.0 133.2

0.000169 43.2 136.8

0.000337 57.6 140.4

0.000478 46.8 154.8

0.000593 43.2 162.0

0.000681 54.0 162.0

0.002121 32.4 25.2

0.00144 36.0 36.0

0.001352 39.6 39.6

0.001101 39.6 50.4

0.001137 32.4 50.4

0.000512 32.4 111.6

0.000272 43.2 118.8

0.000326 39.6 118.8

0.00019 39.6 133.2

0.000755 43.2 169.2

0.001303 18.0 39.6

0.001166 18.0 57.6

0.001026 25.2 68.4

0.000966 25.2 75.6

0.000977 21.6 79.2

0.000741 32.4 90.0

0.000769 25.2 97.2

0.000698 25.2 104.4

0.000512 32.4 111.6

0.000557 21.6 126.0

0.001314 3.6 36.0

0.001094 18.0 68.4

0.001166 7.2 72.0

0.001127 10.8 72.0

0.001192 3.6 72.0

0.001111 3.6 90.0

0.001088 0.0 104.4

0.000846 7.2 126.0

0.000829 7.2 129.6

results

medium

138

139

DISCOVERIES OPTIMIZE

variables

forces up

0.001755 162.0 0

0.001774 169.2 3.6

0.001331 176.4 50.4

0.001215 176.4 68.4

0.001554 169.2 68.4

0.004459 165.6 122.4

0.002462 176.4 126.0

0.005704 165.6 136.8

0.004211 169.2 162.0

0.005457 172.8 165.6

0.00158 162.0 7.2

0.001143 147.6 21.6

0.001112 144.0 25.2

0.002603 151.2 82.8

0.001437 162.0 93.6

0.003999 154.8 100.8

0.003211 147.6 115.2

0.004877 162.0 126.0

0.001059 147.6 162.0

0.00118 154.8 165.6

0.000718 136.8 50.4

0.002908 140.4 93.6

0.002726 140.4 97.2

0.002471 140.4 100.8

0.001505 136.8 108.0

0.001139 136.8 122.4

0.000617 126.0 129.6

0.000702 129.6 129.6

0.000794 126.0 154.8

0.001911 115.2 3.6

0.002161 115.2 14.4

0.002222 118.8 28.8

0.002359 115.2 43.2

0.00074 122.4 54.0

0.000542 122.4 90.0

0.0002 104.4 100.8

0.000451 118.8 104.4

0.000288 100.8 118.8

0.000546 115.2 136.8

0.002609 82.8 10.8

0.003071 86.4 14.4

0.003891 82.8 32.4

0.003912 86.4 32.4

0.000379 90.0 61.2

2.9E-05 82.8 97.2

0.000392 82.8 140.4

0.000622 86.4 154.8

0.000773 93.6 162.0

0.002309 75.6 3.6

0.002864 72.0 7.2

0.004422 79.2 25.2

0.000425 75.6 61.2

0.000298 75.6 68.4

8E-05 64.8 104.4

4E-05 64.8 111.6

0.000241 72.0 133.2

0.000355 68.4 144.0

0.000896 75.6 169.2

0.005194 54.0 14.4

0.000877 61.2 46.8

0.000516 64.8 57.6

0.000196 61.2 90.0

0.000224 57.6 90.0

0.000173 61.2 93.6

0.000208 54.0 97.2

6.8E-05 54.0 118.8

9.7E-05 61.2 126.0

0.002511 50.4 28.8

0.002245 46.8 28.8

0.001693 46.8 32.4

0.000767 50.4 46.8

0.00024 50.4 97.2

0.000171 50.4 108.0

7.8E-05 43.2 136.8

0.000279 50.4 147.6

0.000386 46.8 154.8

0.000681 46.8 165.6

0.001784 39.6 28.8

0.000845 32.4 39.6

0.000779 39.6 43.2

0.000711 39.6 46.8

0.000777 25.2 46.8

0.000677 21.6 75.6

0.000422 21.6 129.6

9.7E-05 39.6 133.2

0.000315 25.2 133.2

0.000775 39.6 169.2

0.005106 14.4 7.2

0.001795 7.2 10.8

0.001024 18.0 28.8

0.000826 18.0 46.8

0.000904 7.2 104.4

0.000631 18.0 108.0

0.000579 18.0 118.8

0.000591 14.4 133.2

0.000567 14.4 136.8

results

tight

140

141

DISCOVERIES OPTIMIZE

variables

forces up

0.003171 151.2 7.2

0.00229 169.2 28.8

0.007683 147.6 68.4

0.009494 158.4 97.2

0.007989 165.6 111.6

0.007282 162.0 115.2

0.007281 162.0 115.2

0.006867 172.8 129.6

0.004948 172.8 165.6

0.00245 151.2 172.8

0.002376 136.8 0

0.001287 122.4 64.8

0.006302 147.6 97.2

0.00268 133.2 97.2

0.002057 129.6 104.4

0.001561 126.0 126.0

0.001797 129.6 151.2

0.001959 136.8 176.4

0.008006 111.6 28.8

0.007489 115.2 39.6

0.000101 115.2 64.8

0.000265 108.0 79.2

0.000459 111.6 79.2

0.001449 122.4 108.0

0.00127 115.2 133.2

0.001449 122.4 136.8

0.001399 115.2 144.0

0.001615 115.2 172.8

0.00628 100.8 3.6

0.009006 97.2 10.8

0.007419 100.8 25.2

0.001485 108.0 57.6

0.000709 108.0 97.2

0.000479 100.8 100.8

0.000374 97.2 100.8

0.000673 97.2 118.8

0.001065 97.2 144.0

0.001432 104.4 165.6

0.004011 90.0 0

0.003564 97.2 46.8

0.002268 90.0 50.4

0.000178 90.0 82.8

2.8E-05 90.0 90.0

0.000663 93.6 122.4

0.000659 90.0 126.0

0.000957 86.4 147.6

0.001047 86.4 154.8

0.001387 93.6 176.4

0.004621 79.2 0

0.007946 82.8 3.6

0.008287 68.4 10.8

0.001017 79.2 64.8

0.000871 79.2 68.4

0.000174 79.2 108.0

0.000591 72.0 136.8

0.000921 79.2 151.2

0.000996 75.6 158.4

0.001228 72.0 176.4

0.006046 57.6 3.6

0.001985 57.6 46.8

0.001222 54.0 61.2

0.000966 64.8 68.4

0.000661 64.8 79.2

0.000639 61.2 82.8

0.000285 64.8 100.8

0.000223 68.4 100.8

0.000576 64.8 140.4

0.000826 54.0 158.4

0.006504 39.6 3.6

0.00675 43.2 7.2

0.006234 39.6 10.8

0.006517 50.4 14.4

0.003094 39.6 28.8

0.000611 43.2 54.0

0.000752 43.2 90.0

0.000693 43.2 97.2

0.000576 50.4 97.2

0.000634 46.8 97.2

0.00396 28.8 18.0

0.001397 25.2 64.8

0.00137 21.6 68.4

0.00118 21.6 82.8

0.001045 14.4 108.0

0.000988 18.0 108.0

0.000989 18.0 108.0

0.000633 32.4 122.4

0.000677 25.2 144.0

0.000668 25.2 147.6

0.004235 10.8 7.2

0.003124 14.4 14.4

0.002114 0.0 25.2

0.001583 3.6 28.8

0.002028 7.2 32.4

0.00162 0.0 50.4

0.001633 3.6 50.4

0.0011 10.8 108.0

0.001458 0.0 154.8

0.001874 0.0 172.8

results

loose variation

142

143

DISCOVERIES OPTIMIZE

variables

forces up

0.002097 172.8 32.4

0.002762 169.2 50.4

0.002377 176.4 54.0

0.002753 176.4 57.6

0.005338 162.0 72.0

0.005519 172.8 79.2

0.006087 165.6 79.2

0.007645 154.8 82.8

0.001382 165.6 151.2

0.003515 162.0 151.2

0.001603 126.0 25.2

0.003086 129.6 28.8

0.002021 147.6 46.8

0.00284 129.6 61.2

0.003192 129.6 72.0

0.006118 154.8 72.0

0.001146 122.4 122.4

0.001291 126.0 122.4

0.002793 147.6 129.6

0.001514 133.2 133.2

0.003643 122.4 7.2

0.004029 122.4 21.6

0.005358 115.2 28.8

0.006039 115.2 39.6

0.003877 118.8 46.8

0.005393 115.2 46.8

0.001105 122.4 86.4

0.000839 115.2 104.4

0.001296 122.4 140.4

0.001282 118.8 144.0

0.002041 108.0 10.8

0.008954 100.8 36.0

0.000417 108.0 64.8

0.000106 104.4 79.2

0.000594 108.0 100.8

0.000714 111.6 104.4

0.000986 111.6 126.0

0.001124 111.6 136.8

0.001228 108.0 144.0

0.001352 111.6 151.2

0.00637 90.0 32.4

0.001485 97.2 54.0

0.000203 86.4 82.8

0.000224 100.8 90.0

2.8E-05 90.0 90.0

0.000354 100.8 97.2

0.000246 93.6 100.8

0.000526 97.2 115.2

0.001017 82.8 162.0

0.001055 86.4 162.0

0.006657 82.8 3.6

0.008359 82.8 18.0

0.0068 82.8 25.2

0.0016 75.6 50.4

0.000798 68.4 68.4

0.000597 79.2 72.0

0.000458 82.8 75.6

8.3E-05 79.2 104.4

0.000887 75.6 158.4

0.000908 72.0 162.0

0.003683 50.4 25.2

0.001783 54.0 43.2

0.001209 54.0 54.0

0.00111 50.4 57.6

0.000155 61.2 122.4

0.000177 57.6 126.0

0.000406 50.4 144.0

0.000686 61.2 154.8

0.000716 57.6 158.4

0.000824 54.0 165.6

0.001437 32.4 46.8

0.001003 36.0 50.4

0.000948 50.4 72.0

0.00092 50.4 75.6

0.001076 32.4 75.6

0.000796 36.0 86.4

0.000504 36.0 115.2

0.000273 43.2 129.6

0.000307 39.6 133.2

0.001038 36.0 176.4

0.003626 25.2 14.4

0.002476 25.2 21.6

0.001427 18.0 57.6

0.001229 14.4 75.6

0.001207 18.0 75.6

0.000966 25.2 86.4

0.000934 21.6 93.6

0.000681 18.0 140.4

0.000603 21.6 140.4

0.000749 25.2 165.6

0.002334 7.2 0

0.001924 7.2 21.6

0.001858 3.6 21.6

0.001566 0.0 32.4

0.001456 14.4 57.6

0.001527 0.0 61.2

0.000941 7.2 126.0

0.000858 10.8 133.2

0.000849 10.8 140.4

0.000758 14.4 144.0

results

medium variation

144

145

DISCOVERIES OPTIMIZE

variables

forces up

0.002164 158.4 36.0

0.008037 169.2 82.8

0.009403 158.4 93.6

0.008666 172.8 108.0

0.008365 169.2 111.6

0.008462 172.8 111.6

0.004859 154.8 118.8

0.006324 165.6 126.0

0.003513 158.4 144.0

0.003289 169.2 169.2

0.002278 147.6 0

0.001852 151.2 39.6

0.003198 144.0 54.0

0.007251 154.8 72.0

0.007055 154.8 79.2

0.007466 144.0 82.8

0.003715 140.4 97.2

0.002184 136.8 122.4

0.00411 154.8 126.0

0.001955 144.0 165.6

0.004298 129.6 7.2

0.004563 129.6 14.4

0.004282 133.2 75.6

0.001506 126.0 118.8

0.001621 129.6 126.0

0.002013 136.8 129.6

0.001492 122.4 154.8

0.001518 122.4 158.4

0.00166 129.6 162.0

0.001708 129.6 172.8

0.002549 122.4 0

0.000446 115.2 61.2

0.000791 118.8 75.6

0.000512 111.6 82.8

0.000725 111.6 93.6

0.001137 118.8 97.2

0.001011 111.6 126.0

0.001432 118.8 154.8

0.001439 115.2 162.0

0.00145 111.6 169.2

0.005969 104.4 43.2

0.00313 108.0 50.4

0.000888 104.4 61.2

0.000247 108.0 68.4

0.000116 104.4 79.2

0.000732 108.0 108.0

0.000526 97.2 111.6

0.000769 108.0 111.6

0.00085 93.6 136.8

0.001007 104.4 136.8

0.007562 86.4 7.2

0.004932 86.4 36.0

0.001168 82.8 61.2

0.000967 82.8 64.8

2.8E-05 90.0 90.0

0.000217 90.0 100.8

0.000238 82.8 108.0

0.000334 75.6 118.8

0.000969 86.4 151.2

0.001213 79.2 176.4

0.001001 75.6 64.8

0.000853 72.0 68.4

0.000476 75.6 79.2

0.000115 72.0 104.4

0.000294 72.0 118.8

0.000395 75.6 122.4

0.000456 75.6 126.0

0.000478 72.0 129.6

0.005668 43.2 10.8

0.003666 32.4 18.0

0.004065 61.2 28.8

0.001971 61.2 46.8

0.001719 61.2 50.4

0.001116 43.2 64.8

0.001051 32.4 75.6

0.000517 46.8 104.4

0.000317 46.8 129.6

0.000895 54.0 162.0

0.005091 28.8 7.2

0.004229 18.0 7.2

0.003205 10.8 10.8

0.001591 14.4 46.8

0.001465 18.0 54.0

0.001006 25.2 86.4

0.000719 25.2 126.0

0.001075 10.8 151.2

0.000874 25.2 158.4

0.001307 25.2 176.4

0.002601 7.2 14.4

0.001817 0.0 32.4

0.001759 0.0 36.0

0.001604 7.2 46.8

0.001509 7.2 54.0

0.001425 7.2 61.2

0.001355 0.0 72.0

0.00122 3.6 86.4

0.001489 3.6 165.6

0.001564 7.2 172.8

results

loose variation reversed

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DISCOVERIES OPTIMIZE

variables

forces up

0.00156 172.8 0

0.001145 176.4 3.6

0.002727 165.6 54.0

0.003168 172.8 72.0

0.007205 158.4 97.2

0.006662 158.4 104.4

0.005379 154.8 108.0

0.001907 154.8 162.0

0.002861 165.6 162.0

0.001934 158.4 169.2

0.00268 140.4 10.8

0.001633 154.8 14.4

0.000343 154.8 25.2

0.00598 144.0 79.2

0.003557 151.2 118.8

0.002909 151.2 129.6

0.002253 151.2 144.0

0.001509 136.8 158.4

0.001456 133.2 158.4

0.001679 147.6 172.8

0.003753 118.8 7.2

0.003698 118.8 39.6

0.003749 133.2 72.0

0.000927 122.4 79.2

0.001076 122.4 115.2

0.001317 129.6 126.0

0.001143 115.2 147.6

0.001179 115.2 151.2

0.001456 133.2 158.4

0.001431 126.0 169.2

0.003112 108.0 0

0.005933 104.4 36.0

0.001994 104.4 50.4

0.000335 108.0 86.4

0.00103 115.2 136.8

0.001019 111.6 140.4

0.00093 100.8 144.0

0.001058 108.0 147.6

0.001248 108.0 165.6

0.001221 100.8 169.2

0.006803 90.0 10.8

0.001558 86.4 50.4

0.000671 90.0 64.8

0.000354 100.8 68.4

0.000404 82.8 75.6

0.000192 86.4 82.8

2.8E-05 90.0 90.0

0.000637 79.2 140.4

0.000848 93.6 144.0

0.001173 90.0 172.8

0.007339 79.2 3.6

0.002862 61.2 32.4

0.002557 64.8 36.0

0.002385 72.0 39.6

0.002001 72.0 43.2

0.000931 68.4 61.2

0.000564 75.6 72.0

5.6E-05 75.6 104.4

0.000574 61.2 147.6

0.000695 68.4 151.2

0.005233 46.8 0

0.003545 54.0 25.2

0.002807 50.4 28.8

0.002073 61.2 39.6

0.000849 57.6 64.8

0.000776 57.6 68.4

0.000547 54.0 86.4

0.000184 54.0 118.8

0.000237 50.4 118.8

0.000192 50.4 129.6

0.003582 28.8 14.4

0.001899 36.0 32.4

0.001389 32.4 43.2

0.001316 39.6 46.8

0.000903 28.8 82.8

0.000357 36.0 133.2

0.000367 39.6 144.0

0.000449 43.2 147.6

0.001146 32.4 151.2

0.0008 46.8 165.6

0.005581 25.2 3.6

0.002489 14.4 14.4

0.001533 25.2 36.0

0.001446 18.0 39.6

0.001446 10.8 39.6

0.001402 14.4 43.2

0.00139 21.6 43.2

0.001314 18.0 50.4

0.001176 14.4 68.4

0.001054 18.0 79.2

0.002026 7.2 14.4

0.001266 0.0 68.4

0.00114 3.6 86.4

0.001078 3.6 97.2

0.001085 0.0 104.4

0.001044 0.0 118.8

0.001043 0.0 129.6

0.000973 3.6 129.6

0.001051 0.0 133.2

0.00123 10.8 172.8

results

medium variation reversed

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CONSTRUCT

SELECT =first SELECTfirsttry try optimal forces From each matrix the optimal variation is selected. Now it is time to apply these shapes to a system. For simplicity, a diagrid wall is used as a base reference on which to adhere the system. The population density of string is determined by the max and min displacements calculated, such that red = high density; blue = low density.

152

down

153

CONSTRUCT DISCOVERIES custom pieces The custom pieces are designed to have the string tend toward the convex areas when being wrapped.

diagrid variability

diameter = 1/4” width = 1/2” length = 3’

154

155

CONSTRUCT DISCOVERIES

156

157

CONSTRUCT DISCOVERIES

158

159

CONSTRUCT DISCOVERIES

160

161

SELECT - second try DISCOVERIES optimal forces From each matrix the optimal variation is selected. Now it is time to apply these shapes to a system. For simplicity, a diagrid wall is used as a base reference on which to adhere the system. The population density of string is determined by the max and min displacements calculated, such that red = high density; blue = low density.

162

down

163

CONSTRUCT DISCOVERIES

164

165

CONSTRUCT DISCOVERIES

166

167

CONSTRUCT DISCOVERIES

168

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CONCLUDING STATEMENTS

DISCOVERIES CONCLUSIONS

This project in the end does connect back to Detroit as a proposal for reconstruction in Detroit by means of DIY designs optimized through computational processes. Detroit, currently seen as a decrepit city, deserves the chance to be reincarnated as a growing city in the arts and music movement. Currently Detroit is famous for its derelict buildings, as mentioned earlier in this book, and the pinnacle of ruined architecture there is the Packard Plant. If this factory can be re-implemented as a textile and earth material schop or even art school, the youth can further exploration and show the world something to take pictures of beyond the ruins: rebirth. The resulting wall that was built with my process turned out to be less rigid in the jointery, despite the success of the dipped string in rockite. Future tests would revise the frame upon which the string rests. and can be easier to apply. There is so much left to be done and I would love to pursue this project for its enormous variety of features left to be pursued and understood.

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Detroit deserves a pinnacle for re-incarnation, which can be placed within the Packard Plant, already famous for its existing ruins. Using advanced computation to optimize and organize typologies, the process of creating is left as low-tech so that anybody can participate, although high-tech processes are certainly open to exploration as well. With all of the varieties of experience and applications, the possibilities are endless.

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BIBLIOGRAPHY DISCOVERIES “3D-Printing in Clay.” MAKE. “The Brick Industry Association Home.” The Brick Industry Association Home. “Building Bytes 3D Printed Bricks by Brian Peters at Dutch Design Week.” Dezeen Building Bytes 3D Printed Bricks Br by Brian Peters Comments. “CAST :: Cast Panels.” CAST :: Cast Panels. Falk, Robert H., and Brad Guy. Unbuilding: Salvaging the Architectural Treasures of Unwanted Houses. Newtown, CT: Taunton, 2007. Print. “HYREL 3D.” HYREL 3D. Minke, Gernot, and Gernot Minke. Building with Earth: Design and Technology of a Sustainable Architecture. Basel: Birkhauser-Publishers for Architecture, 2006. Print. Peterson, Chris. Building with Secondhand Stuff: How to Re-claim, Re-vamp, Re-purpose & Re-use Salvaged & Leftover Building Materials. Minneapolis, MN: Creative Pub. International, 2011. Print. “RAEL SAN FRATELLO.” Digital Ceramics “Unfold -fab.” : Clay. “Www.3ders.org.” 3ders.org.

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