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a multidisciplinary team comprised of fellow postdoctoral appointees, experimentalists, and staff scientists with computational fluid dynamics (CFD) and reduced-order modeling (ROM) expertise, with
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staff scientists with computational fluid dynamics (CFD) and artificial intelligence/machine learning (AI/ML) expertise, with the goal to enhance predictive capability and scalability of multi-scale and
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cycle systems. The particular focus of this position includes the integration of the physical modeling of advanced reactor fuels and materials, treatment of multiphase/multicomponent fluid dynamics, and
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multidisciplinary team comprised of fellow postdoctoral appointees and staff scientists with computational fluid dynamics (CFD) and artificial intelligence/machine learning (AI/ML) expertise, with the goal to enhance
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scalability of multi-scale and multi-physics simulation codes. Develop turbulent combustion modeling approaches for predictive computational fluid dynamics (CFD) simulations of combustion dynamics and emissions
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computational scientists to enhance the predictive capability for next-generation engine modeling code. Perform high-fidelity Computational Fluid Dynamics (CFD) simulations of turbulent combustion flows in gas
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multidisciplinary team involving computational fluid dynamics experts, gas turbine modelers and experimentalists to enhance the predictive capability of gas turbine modeling codes for stationary power generation and
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-physics simulation codes. The candidate will conduct multi-physics and multi-scale computational fluid dynamics (CFD) simulations for innovative internal combustion engines (ICEs) fueled with low/zero
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of molecular reactions occurring at the surface of various materials. In addition, computational fluid dynamics (CFD) simulations combined with microkinetic modeling will be carried out to study the heat