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Toward A Gpu-Accelerated Immersed Boundary Method For Wind Forecasting Over Complex Terrain, Rey Deleon, Kyle Felzien, Inanc Senocak
Toward A Gpu-Accelerated Immersed Boundary Method For Wind Forecasting Over Complex Terrain, Rey Deleon, Kyle Felzien, Inanc Senocak
Inanc Senocak
A short-term wind power forecasting capability can be a valuable tool in the renewable energy industry to address load-balancing issues that arise from intermittent wind fields. Although numerical weather prediction models have been used to forecast winds, their applicability to micro-scale atmospheric boundary layer flows and ability to predict wind speeds at turbine hub height with a desired accuracy is not clear. To address this issue, we develop a multi-GPU parallel flow solver to forecast winds over complex terrain at the micro-scale, where computational domain size can range from meters to several kilometers. In the solver, we adopt the immersed …
Stochastic Reconstruction Of Multiple Source Atmospheric Contaminant Dispersion Events, Derek Wade, Inanc Senocak
Stochastic Reconstruction Of Multiple Source Atmospheric Contaminant Dispersion Events, Derek Wade, Inanc Senocak
Inanc Senocak
Reconstruction of intentional or accidental release of contaminants into the atmosphere using concentration measurements from a sensor network constitutes an inverse problem. An added complexity arises when the contaminant is released from multiple sources. Determining the correct number of sources is critical because an incorrect estimation could mislead and delay response efforts. We present a Bayesian inference method coupled with a composite ranking system to reconstruct multiple source contaminant release events. Our approach uses a multi-source data-driven Gaussian plume model as the forward model to predict the concentrations at sensor locations. Bayesian inference with Markov chain Monte Carlo (MCMC) sampling …