Leveraging Physics-based Surrogates for Efficient Density Estimation of Sparse Observable Data on Low-dimensional Manifolds.
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For multi-scale multi-physics applications e.g., the turbulent combustion code Pele, robust and accurate dimensionality reduction is crucial to solving problems at exascale and beyond. A recently developed technique, Co-Kurtosis based Principal Component Analysis (CoK-PCA) which leverages principal vectors of co-kurtosis, is a promising alternative to traditional PCA for complex chemical systems. To improve the effectiveness of this approach, we employ Artificial Neural Networks for reconstructing thermo-chemical scalars, species production rates, and overall heat release rates corresponding to the full state space. Our focus is on bolstering confidence in this deep learning based non-linear reconstruction through Uncertainty Quantification (UQ) and Sensitivity Analysis (SA). UQ involves quantifying uncertainties in inputs and outputs, while SA identifies influential inputs. One of the noteworthy challenges is the computational expense inherent in both endeavors. To address this, we employ the Monte Carlo methods to effectively quantify and propagate uncertainties in our reduced spaces while managing computational demands. Our research carries profound implications not only for the realm of combustion modeling but also for a broader audience in UQ. By showcasing the reliability and robustness of CoK-PCA in dimensionality reduction and deep learning predictions, we empower researchers and decision-makers to navigate complex combustion systems with greater confidence.
This final report briefly reviews the main technical accomplishments of the INVERT award, summarizes existing or planned publications or transitions from the effort, and lastly reviews T2M strategies resulting from the program. Specifically, under the award, our team studied three main technical areas and performed one preliminary T2M study on the cost-benefit analysis of Inverse Design Methods and one major software release (the Maryland Inverse Design Benchmark Suite).
This report presents the final conclusions, achievements, and other details from the Department of Energy sponsored research. It is structured to follow standard DOE annual reports as submitted through the Portfolio Analysis and Management System (PAMS).
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High-speed aerospace engineering applications rely heavily on computational fluid dynamics (CFD) models for design and analysis. This reliance on CFD models necessitates performing accurate and reliable uncertainty quantification (UQ) of the CFD models, which can be very expensive for hypersonic flows. Additionally, UQ approaches are many-query problems requiring many runs with a wide range of input parameters. One way to enable computationally expensive models to be used in such many-query problems is to employ projection-based reduced-order models (ROMs) in lieu of the (high-fidelity) full-order model (FOM). In particular, the least-squares Petrov–Galerkin (LSPG) ROM (equipped with hyper-reduction) has demonstrated the ability to significantly reduce simulation costs while retaining high levels of accuracy on a range of problems, including subsonic CFD applications. This allows LSPG ROM simulations to replace the FOM simulations in UQ studies, making UQ tractable even for large-scale CFD models. This work presents the first application of LSPG to a hypersonic CFD application, the Hypersonic International Flight Research Experimentation 1 (HIFiRE-1) in a three-dimensional, turbulent Mach 7.1 flow. This paper shows the ability of the ROM to significantly reduce computational costs while maintaining high levels of accuracy in computed quantities of interest.
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In one example, a system for a flow cell for a flow battery, comprising: a first flow field; and a polymeric frame, comprising: a top face; a bottom face, opposite the top face; a first side; a second side, opposite the first side; a first electrolyte inlet located on the top face and the first side of the polymeric frame; a first electrolyte outlet located on the top face and the second side of the polymeric frame; a first electrolyte inlet flow path located within the polymeric frame and coupled to the first electrolyte inlet; and a first electrolyte outlet flow path located within the polymeric frame and coupled to the first electrolyte outlet. In this way, shunt currents may be minimized by increasing the length and/or reducing the cross-sectional area of the electrolyte inlet and electrolyte outlet flow paths.
In one example, a system for a flow cell for a flow battery, comprising: a first flow field; and a polymeric frame, comprising: a top face; a bottom face, opposite the top face; a first side; a second side, opposite the first side; a first electrolyte inlet located on the top face and the first side of the polymeric frame; a first electrolyte outlet located on the top face and the second side of the polymeric frame; a first electrolyte inlet flow path located within the polymeric frame and coupled to the first electrolyte inlet; and a first electrolyte outlet flow path located within the polymeric frame and coupled to the first electrolyte outlet. In this way, shunt currents may be minimized by increasing the length and/or reducing the cross-sectional area of the electrolyte inlet and electrolyte outlet flow paths.
The present disclosure describes techniques for cooling power electronics in automotive applications. The present disclosure utilizes a jet impingement of a dielectric fluid on electrical interconnections to cool power electronics.
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