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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Improving quantum-to-classical data decoding using optimized quantum wavelet transform
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Simulating stellar merger using HPX/Kokkos on A64FX on Supercomputer Fugaku
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OpenMP offload toward the exascale using Intel® GPU Max 1550: evaluation of STREAmS compressible solver
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Characterization and Implementation of A Dynamic Neutron Imaging System at the PULSTAR Reactor
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Attack graph-based stochastic modeling approach for enabling cybersecure semiconductor wafer fabrication
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ADAM: A web platform for graph-based modeling and optimization of supply chains
Modeling and optimization are essential tasks that arise in the analysis and design of supply chains (SCs). SC models are essential for understanding emergent behavior such as transactions between participants, inherent value of products exchanged, as well as impact of externalities (e.g., policy and climate) and of constraints. Unfortunately, most users of SC models have limited expertise in mathematical optimization, and this hinders the adoption of advanced decision-making tools. Here, in this work, we present ADAM, a web platform that enables the modeling and optimization of SCs. ADAM facilitates modeling by leveraging intuitive and compact graph-based abstractions that allow the user to express dependencies between locations, products, and participants. ADAM model objects serve as repositories of experimental, technology, and socio-economic data; moreover, the graph abstractions facilitate the organization and exchange of models and provides a natural framework for education and outreach. Here, we discuss the graph abstractions and software design principles behind ADAM, its key functional features and workflows, and application examples.
Recent advances and challenges in optimization models for expansion planning of power systems and reliability optimization
Optimization models for expansion planning of power systems aim to determine capacities, investment timing, and location of power systems to satisfy the power demands while minimizing the total cost. The models have become complex in recent years to reflect both regulations on conventional energy sources and the increasing penetration of renewable energy sources (RES). This paper reviews the basic concepts and optimization models for expansion planning of power systems. We first explain the definition and features of generation expansion planning (GEP), transmission expansion planning (TEP), and generation and transmission expansion planning (GTEP). To address the computational challenges of large-scale expansion planning problems, we review several simplifications including temporal and spatial aggregation, and decomposition methods. This paper also addresses power system reliability defined as the probability of satisfying the load demand while withstanding failures of components. Finally, the goal of this paper is to provide a research overview, discuss trends in expansion planning of power systems, and suggest directions for future research.
An MINLP formulation to identify thermodynamically-efficient distillation configurations
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Optimal nonlinear dynamic sparse model selection and Bayesian parameter estimation for nonlinear systems
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Maximum-likelihood estimators in physics-informed neural networks for high-dimensional inverse problems
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