Publications

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A multiple input multiple output (MIMO) power line communication (PLC) model for industrial facilities was developed that uses the physics of a bottom-up model but can be calibrated like top-down models. The PLC model considers 4-conductor cables (three-phase conductors and a ground conductor) and has several load types, including motor loads. The model is calibrated to data using mean field variational inference with a sensitivity analysis to reduce the parameter space. The results show that the inference method can accurately identify many of the model parameters, and the model is accurate even when the network is modified.

The Reynolds-averaged Navier–Stokes (RANS) equations remain a workhorse technology for simulating compressible fluid flows of practical interest. Due to model-form errors, however, RANS models can yield erroneous predictions that preclude their use on mission-critical problems. This work presents a data-driven turbulence modeling strategy aimed at improving RANS models for compressible fluid flows. The strategy outlined has three core aspects: (1) prediction for the discrepancy in the Reynolds stress tensor and turbulent heat flux via machine learning (ML), (2) estimating uncertainties in ML model outputs via out-of-distribution detection, and (3) multi-step training strategies to improve feature-response consistency. Results are presented across a range of cases publicly available on NASA’s turbulence modeling resource involving wall-bounded flows, jet flows, and hypersonic boundary layer flows with cold walls. We find that one ML turbulence model is able to provide consistent improvements for numerous quantities-of-interest across all cases.

Computational simulations of high-speed flow play an important role in the design of hypersonic vehicles, for which experimental data are scarce; however, high-fidelity simulations of hypersonic flow are computationally expensive. Reduced order models (ROMs) have the potential to make many-query problems, such as design optimization and uncertainty quantification, tractable for this domain. Residual minimization-based ROMs, which formulate the projection onto a reduced basis as an optimization problem, are one promising candidate for model reduction of large-scale fluid problems. This work analyzes whether specific choices of norms and objective functions can improve the performance of ROMs of hypersonic flow. Specifically, we investigate the use of dimensionally consistent inner products and modifications designed for convective problems, including ℓ1 minimization and constrained optimization statements to enforce conservation laws. Particular attention is paid to accuracy for problems with strong shocks, which are common in hypersonic flow and challenging for projection-based ROMs. We demonstrate that these modifications can improve the predictability and efficiency of a ROM, though the impact of such formulations depends on the quantity of interest and problem considered.

Computational simulations of high-speed flow play an important role in the design of hypersonic vehicles, for which experimental data are scarce; however, high-fidelity simulations of hypersonic flow are computationally expensive. Reduced order models (ROMs) have the potential to make many-query problems, such as design optimization and uncertainty quantification, tractable for this domain. Residual minimization-based ROMs, which formulate the projection onto a reduced basis as an optimization problem, are one promising candidate for model reduction of large-scale fluid problems. This work analyzes whether specific choices of norms and objective functions can improve the performance of ROMs of hypersonic flow. Specifically, we investigate the use of dimensionally consistent inner products and modifications designed for convective problems, including ℓ1 minimization and constrained optimization statements to enforce conservation laws. Particular attention is paid to accuracy for problems with strong shocks, which are common in hypersonic flow and challenging for projection-based ROMs. We demonstrate that these modifications can improve the predictability and efficiency of a ROM, though the impact of such formulations depends on the quantity of interest and problem considered.

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This paper presents an investigation into sampling strategies for reducing the computational expense of creating error models for steady hypersonic flow surrogate models. The error model describes the quantity of interest error between a reduced-order model prediction and a full-order model solution. The sampling strategies are separated into three categories: distinct training sets, single training set, and augmented single training set for the reduced-order model and the error model. Using a distinct training set, three sampling strategies are investigated: latin hypercube sampling, latin hypercube sampling with a maximin criterion, and a D-Optimal design. It was found that using a D-Optimal design was the most effective at producing an accurate error model with the fewest number of training points. When using a single training set, the leave-one-out cross validation approach was used on the D-Optimal design training set. This produced an error model with an R2 value of greater than 0.8, but it had some outliers due to high nonlinearities in the space. Augmenting the training points of the error model helped improve its accuracy. Using a D-Optimal design with distinct training sets cut the computational cost of creating the error model by 15% and using the LOOCV approach with the D-Optimal design cut the cost by 64%.

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Bayesian inference is used to calibrate a bottom-up home PLC network model with unknown loads and wires at frequencies up to 30 MHz. A network topology with over 50 parameters is calibrated using global sensitivity analysis and transitional Markov Chain Monte Carlo (TMCMC). The sensitivity-informed Bayesian inference computes Sobol indices for each network parameter and applies TMCMC to calibrate the most sensitive parameters for a given network topology. A greedy random search with TMCMC is used to refine the discrete random variables of the network. This results in a model that can accurately compute the transfer function despite noisy training data and a high dimensional parameter space. The model is able to infer some parameters of the network used to produce the training data, and accurately computes the transfer function under extrapolative scenarios.

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