Inanc Senocak, PhD

609 Benedum Hall | 3700 O’Hara Street | Pittsburgh, PA 15261 P: 412-624-5430

senocak@pitt.edu Associate Professor

High Performance Simulation Laboratory for Energy and Environment

Our research vision is to contribute toward the creation of a sustainable energy economy by providing innovative computational solutions to engineering problems that arise at the intersection of energy and environment. We work toward this vision by developing high performance computing solutions that transcend traditional disciplines. In our research, we integrate fundamentals of thermal and fluid sciences with computational mathematics and supercomputing. We routinely use parallel rendering and data analysis tools to elucidate the fundamental processes underlying the problem and improve our models.

Grid Integration of Wind Power

We apply the large-eddy simulation technique to simulate air flow through wind farms over complex terrain. We deploy the computing power of multiple graphics processing units to predict and forecast variable power generation for potential applications in power scheduling and energy trading.

Engineering Simulation Software Development

With ever-increasing-computing power, engineering simulation software has to evolve along with new computer architectures and programming models. In our research, we apply modern software engineering principles to develop scalable parallel solvers and versatile computational geometry algorithms to tackle a variety of large-scale simulation problems that can easily overwhelm high-end workstations.

Weather-aware Transmission Lines

Congestion in transmission lines is a growing concern and hinders integration of the renewable energy resources into the grid. The common practice of static rating of transmission lines operates on conservative assumptions on local weather conditions to avoid excessive line sag. However, for windy locations, these assumptions create unnecessary bottlenecks limiting transmission capacity. In our research we have developed computational tools to challenge the static rating practice in favor of the dynamic line rating, where we have computed and made use of local wind conditions (i.e., speed and direction) imminent on the transmission lines crossing complex terrain (shown above). We have shown that ampacity can be increased by 40-50% without jeopardizing the structural integrity of transmission lines. By tailoring our computational tools for emerging computing hardware, we enable forecasting of local wind conditions to better schedule transmission. availability. Our research also showed that industry standards to operate transmission lines could benefit from adopting modern numerical methods.