Comparing reproduced cyber experimentation studies across different emulation testbeds.
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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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This report is a brief overview and evaluation of Physics Informed Neural Networks (PINNs). Karniadakis and co-workers, e.g., Karniadakis et al. (2021) assert that the PINNs approach integrates seamlessly both data and mathematical physics models, even in partially understood, uncertain and high-dimensional contexts. They further claim that PINNs are effective and efficient for ill-posed and inverse problems, and when combined with domain decomposition, are scalable to large problems and a tool to discover hidden physics. While they demonstrate the capabilities in specific academic instances, their overarching claims about PINNs seem to be an overstatement, at least at the current time. We have briefly considered a few of the limitations of PINNs in this investigation. It is not clear to us if the PINNs approach can ever be competitive with approaches that use specialized algorithms to achieve high-accuracy solutions of governing equations and other techniques that can combine observational data with such solutions. As an example of the latter, consistent with the principles of Bayesian inference, data assimilation, or more generally data-model fusion, is a process that fuses observational data typically with a computational model that respects certain constraints such as conservation laws. For example, improvements in observational network combined with data assimilation have been key in improving weather predictions over the past four decades Kalnay (2003).
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Abstract not provided.
For heat pipe cooled microreactor development, it is essential to understand the characteristics of heat pipes and how they function under a wide range of operating conditions. Modeling the startup and shutdown of heat pipe cooled microreactors has been a major challenge for the modeling code. Validating these parameters within the code is key for the development and licensing of heat pipe cooled microreactor designs. Idaho National Laboratory (INL) has completed testing on power transients of a heat pipe with a range of operating conditions. The resulting temperature profiles from this testing can be utilized to aid in the validation of the startup and shutdown portions of the heat pipe modeling code.
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For heat-pipe cooled microreactor development, it is essential to understand the characteristics of heat pipes and how they function under a wide range of operating conditions. An important process for heat-pipe cooled microreactor development is the passive heat removal and how the performance may change over long periods of time. Increased experimental data provides additional information to assess the operational lifetime of alkali metal heat pipes. Idaho National Laboratory has completed testing over a long duration of a high-performance sodium filled heat pipe while monitoring axial temperature profile, power supplied by the heaters, and heat removed by a gas-gap calorimeter. The results from this testing can aid in heat pipe validation efforts.
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I am going to present my work on multifidelity timeseries models at MS&T in Pittsburgh. We develop efficient machine learning methodologies to accelerate time-series predictions from a hierarchy of complex physics-based models.
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Within operational technology (OT) systems design, the construction of testing environments for simulation is often a tedious, manual process that slows down safety and security evaluations. This document details the design and functionality of Scan2Sim, a program designed to construct high-fidelity topological schematics for OT systems without significant manual human input. Scan2Sim may take as input a detailed network scan of a system, and produces an instruction set to re-create the original scanned network within a virtualized simulation network. This construction is achieved via heuristic methods of machine template selection, which allows for a fast, performant approach to automated environment construction. The current tool is designed to produce topology schematics compatible with the Minimega, a tool designed by Sandia National Laboratories for repeatable experimentation management.
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Poster for DOE/SC/ASCR PI's meeting
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