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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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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification

In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low-dimensional (latent) descriptors that efficiently parameterize the response of the high-dimensional computational model. To this end, we employ Principal Geodesic Analysis on the Grassmann manifold of the response to identify a set of disjoint principal geodesic submanifolds, of possibly different dimension, that captures the variation in the data. Since operations on the Grassmann require the data to be concentrated, we propose an adaptive algorithm based on Riemannian K-means and the minimization of the sample Fréchet variance on the Grassmann manifold to identify “local” principal geodesic submanifolds that represent different system behavior across the parameter space. Polynomial chaos expansion is then used to construct a mapping between the random input parameters and the projection of the response on these local principal geodesic submanifolds. Here, the method is demonstrated on four test cases, a toy-example that involves points on a hypersphere, a Lotka-Volterra dynamical system, a continuous-flow stirred-tank chemical reactor system, and a two-dimensional Rayleigh-Bénard convection problem.

42 ENGINEERING↗
DOE OSTI2026

Surrogate modeling of Monte Carlo radiation transport with convolutional neural networks for shielding optimization

Here, we present a machine learning (ML)-based surrogate model using convolutional neural networks (CNN) designed to emulate the attenuation of neutron fields as they pass through various shielding materials. This model can compute the outgoing neutron flux almost instantaneously and achieves reasonable accuracy compared to traditional Monte Carlo (MC)-based codes, which are computationally intensive. This emulator alleviates the complexity of neutron radiation transport through shielding materials by reducing the dimensionality and enables shielding optimization for a known radiation environment. This optimization process, which would have taken an unrealistic timeline due to several complex radiation transport simulations, can now be achieved in minutes, thus increasing computational capabilities in radiation shielding assessment. We demonstrate the applications of this emulator in computing effective dose rates and optimizing shielding solutions for a heavy-ion accelerator facility, such as the Facility for Rare Isotope Beams, where secondary neutrons produced via beam interactions dominate the radiation environment.

accelerator shielding↗
DOE OSTI2024

Dispersal of high-burnup fuel fragment surrogate particles during and after loss-of-coolant accident tests

The issue of fuel fragmentation, relocation, and dispersal is critical in the licensing and use of high-burnup (>62 GWd/MTU) nuclear fuel in light water reactors (LWRs). In this work, two test series are reported that examine the fragment dispersal during a burst event and an additional dispersal following the burst due to vibrations in the rod, such as those induced by accident recovery systems. To examine dispersal during the balloon and burst portion of a Loss-of-Coolant Accident event, HfO 2 fragments and yttria-stabilized zirconia pellets were filled into an as-fabricated cladding tube, which was then pressurized and subjected to loss-of-coolant accident testing in steam. Results from this test were found to be highly non-prototypic, and dispersal was both significantly more violent and significantly greater in magnitude than identified for actual fuel tests. These findings were attributed to the conservative (more dispersive) nature of the particles chosen for dispersal and to the details of the test conditions used that led to particularly wide bursts. Further, the second set of tests examined dispersal following the burst when vibrations were induced in the rod, primarily via recovery activities such as Emergency Core Cooling System actuation leading to rapid water addition. Post-burst dispersal testing was performed by inducing sinusoidal oscillations with 2–25 nm peak-to-peak amplitude and 2–5 Hz frequencies in pre-burst rods that had been refilled with HfO 2 fragments or high-burnup fragment surrogate mixture of HfO 2 fragments and yttria-stabilized zirconia sands. Testing revealed that rods with large burst openings (7 mm wide in this work) led to unmitigated dispersal from above the burst zone but that smaller bursts (5 mm wide), although still much larger than the mean fragment size of 3 mm, led to effectively no dispersal because of interparticle locking. Additionally, mixture and moisture were found to impact the amount of dispersal: mixtures increased dispersal, and moisture drastically reduced it. The implications of these findings on likely dispersal from actual fuel are discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Chemical kinetic interactions of NO with a multi-component gasoline surrogate: Experiments and modeling

Here this work reports an experimental and modeling study on the chemical kinetic interactions of NO with a multi-component gasoline surrogate, namely PACE-20, using a twin-piston rapid compression machine at a stochiometric fuel loading with 20% EGR (exhaust gas recirculation) by mass, pressures of 20 and 40 bar, and temperatures from 700 to 930 K. Five NO concentrations are investigated, namely 0, 20, 50, 70 and 150 ppm, where NO addition effects are characterized through changes in PACE-20 ignition reactivity and heat release characteristics. Experiments indicate that within the low-temperature regime, NO promotes low-temperature heat release rate and main ignition reactivity at low addition levels, with saturation or even inhibiting effects observed at >50 ppm NO addition, while within the NTC/intermediate-temperature regime, adding NO only promotes reactivity. A recently updated, detailed chemical kinetic model with chemistry specific to NOx/hydrocarbons interaction incorporated is used to simulate the experiments, and reasonable agreement is obtained. In-depth sensitivity and rate of production analyses are further performed. The results indicate that NO interacts with PACE-20 via two types of interaction: (a) direct interactions between NO and PACE-20 derivatives, primarily through NO+HO 2 ↔NO 2 +OH and RO 2 +NO↔RO+NO 2 , and (b) indirect interactions between PACE-20 derivatives and NO 2 produced from the direct interactions, primarily through R+NO 2 ↔RO+NO. The observed NO inhibiting effect at low temperatures and 150 ppm NO addition is attributed to the lack of HO 2 radicals to sustain NO consumption via NO+HO 2 ↔NO 2 +OH, and the take-up of inhibiting pathways via RO 2 +NO↔RO+NO 2 . The results also indicate that even with the presence of multiple fuel components, NOx/hydrocarbons interactions are highly selective, and are mainly initiated by the interactions between NO and RO 2 radicals from cyclopentane and ethanol, as well as between NO 2 and R radicals from toluene, 1,2,4-trimethylbenzene and 1-hexene. Further studies on these interactive reactions are therefore highly recommended.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Turbulence-parameter estimation for current-energy converters using surrogate model optimization

Surrogate models maximize information utility by building predictive models in place of computational or experimentally expensive model runs. Marine hydrokinetic current energy converters require large-domain simulations to estimate array efficiencies and environmental impacts. Meso-scale models typically represent turbines as actuator discs that act as momentum sinks and sources of turbulence and its dissipation. An OpenFOAM model was developed where actuator disc k-ε turbulence was characterized using an approach developed for flows through vegetative canopies. Turbine-wake data from laboratory flume experiments collected at two influent turbulence intensities were used to calibrate parameters in the turbulence-source terms in the k-ε equations. Additionally, parameter influences on longitudinal wake profiles were estimated using Gaussian process regression with subsequent optimization minimizing the objective function within 3.1% of those obtained using the full model representation, but for 74% of the computational cost (far fewer model runs). This framework facilitates more efficient parameterization of the turbulence-source equations using turbine-wake data.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Density functional theory-based surrogate kinetic models for heterogeneous reactions of hydrocarbon intermediates on silicon carbide

The increasing demand for high-performance materials in advanced technologies highlights the importance of achieving a fundamental understanding and potential control of silicon carbide (SiC) deposition processes. However, existing models often lack sufficient theoretical detail, relying heavily on empirical data and offering limited predictive capability. In particular, the complex surface chemistry governing SiC growth remains poorly understood. This study addresses these challenges by employing density functional theory (DFT) to investigate key heterogeneous reactions involving hydrocarbon intermediates on SiC surfaces, including dehydrogenation, hydrogenation, and carbon deposition. Transition state searches were conducted to identify reaction pathways and energy barriers. While first-principles calculations offer high accuracy, they are computationally intensive. To extend the utility of these first-principles results, vibrational analyses were performed using phonon-based statistical thermochemistry to compute temperature-dependent reaction rates which were used to develop Arrhenius-type surrogate kinetic models. Furthermore, the resulting framework provides a more rigorous, physically grounded basis for integrating atomistic insights into continuum-scale modeling, ultimately enabling improved prediction and optimization of SiC film growth in high-performance material systems.

Density Functional Theory↗
DOE OSTI2020

ROM-Based Surrogate Systems Modeling of EBR-II

We report the System Analysis Module (SAM), developed and maintained by Argonne National Laboratory, is designed to provide whole-plant transient safety analysis capabilities for a number of advanced non-light water reactors, including sodium-cooled fast reactor (SFR), lead-cooled fast reactor (LFR), and molten salt reactor (MSR)/fluoride-salt-cooled high-temperature reactor (FHR) designs. SAM is primarily constructed as a systems-level analysis tool, with the potential to incorporate reduced order models from three-dimensional computational fluid dynamics (CFD) simulations to improve characterization of complex, multidimensional physics. It is recognized that the computational expense associated with CFD can be intractable for various engineering analyses, such as uncertainty quantification, inference, and design optimization. This paper explores the reducibility of a SAM model using recent advances in randomized linear algebra techniques, which attempt to find recurring patterns in the various realizations generated by a model after randomly perturbing all its input parameters. The reduction is described in terms of fewer degrees of freedom (DOFs), referred to as the active DOFs, for the model variables such as input model parameters and model responses. The results indicate that there is significant room for additional reduction that may be leveraged for additional computational gains when employing SAM for engineering-intensive analyses that require repeated model executions. Different from physics-based reduction approaches, the proposed approach allows one to estimate upper bounds on the reduction errors, which are rigorously developed in this work. Finally, different methods for surrogate model construction, such as regression and neural network-based training, are employed to correlate the input and output active DOFs, which are related back to the original variables using matrix-based linear transformations.

42 ENGINEERING↗
DOE OSTI2022

Uncertainty-Proof Hosting Capacity with Surrogate Affine Policy

Physical constraints must be enforced when dis-tributed energy resources, such as PV, are integrated into distribution network. Hosting capacity (HC) is thus introduced to define the maximum renewable that distribution system can accommodate. When the grid is further pushed towards low-carbon, many research efforts are devoted to increasing HC. Security and cost-efficiency become even more important in determining HC. This work proposes an uncertainty-proof HC with surrogate affine policy. Flexible resources are leveraged to increase HC. We propose a novel hybrid two-stage AC model with variable uncertainty set. An iterative algorithm is designed to solve the problem. The proposed model and solution approach are validated in modified 141-node feeders, and HC performance is also analyzed.

hosting capacity↗
DOE OSTI2026

Forensic characterization of surrogate nuclear explosion debris: radiochemical and spectroscopic strategies for method validation

Surrogate nuclear explosion debris (SNED) has emerged as a critical platform for advancing post-detonation nuclear forensic analysis in the absence of readily accessible historic materials. SNED enables controlled investigation and validation of analytical methodologies used to interrogate the chemical, isotopic, radiological, and microstructural signatures preserved in nuclear explosion debris. This review presents an integrated assessment of destructive and non-destructive analytical techniques commonly employed within decision-driven nuclear forensic workflows. Each technique is discussed individually while highlighting how it contributes to different stages of post-detonation analysis. Core methods – including gamma and alpha spectrometry, ICP-MS, TIMS, SIMS, SEM-EDS, XRF, LIBS, vibrational spectroscopy, and X-ray absorption spectroscopy – are critically evaluated with respect to forensic maturity, information content, and matrix limitations. Emphasis is placed on the role of SNED in benchmarking multi-modal workflows and identifying gaps in reproducing heterogeneity, fractionation, and radiation-driven evolution relevant to forensic attribution.

X-ray spectroscopic methods↗
DOE OSTI2025

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation

We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and inverse uncertainty propagation. Traditional surrogate modeling approaches approximate only the deterministic component of physical models, requiring separate noise characterization and computationally expensive sampling methods for inverse problems. Here, in this work, we develop the conditional PR-NF model to directly learn and efficiently generate samples from the conditional probability density functions (PDFs). The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Once trained, our model efficiently generates samples from conditional PDFs for any input within the training domain. Moreover, the pseudo-reversibility feature allows for the use of fully connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional PR-NF model, showing its ability to converge to the target conditional PDF using the Kullback−Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Creep and Creep Fracture Modeling with Surrogate Creep Models and the Extended Finite Element Method

Alloy components in advanced nuclear reactors will be subjected to environmental conditions that could include high temperatures, irradiation, and exposure to corrosive salts. These conditions could lead to the formation of crack-like defects, which could grow over time in a mechanism known as creep crack growth (CCG). Predicting growth rates of these defects is important for assessing the safe operating life of advanced reactors. This project documents progress toward developing and testing next-generation data-driven constitutive models for deformation creep. It also documents the application of the extended finite element method in conjunction with surrogate creep models to predict CCG parameters under a variety of conditions. These important incremental developments contribute to the longer-term objective of developing microstructure-aware constitutive models that can be used for predicting creep deformation and CCG at the component scale with improved accuracy.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Surrogate Fuel Chemistry from ReaxFF Molecular Dynamics

Reactive molecular dynamics with ReaxFF simulations are used to study the chemistry of four component jet fuel mixtures of n-dodecane, iso-octane, n-propylbenzene, and mesitylene. ReaxFF does not require knowledge of the chemistry a priori providing insight into reactions otherwise not seen in established chemical kinetic mechanisms. The results show that a difference in the local chemical environment can change the fuel decomposition pathways across the surrogate mixtures where the same fuel species are used. These simulations highlight the need to consider cross-chemical interactions in chemical kinetic mechanism development and the importance of resonance stabilized polyaromatic hydrocarbon reactions for soot growth and formation. A method for predicting the threshold sooting index from the carbon species formed at the end of the simulations is established. The results of the simulations are consistent with measurements of the threshold sooting index with the predicted values within 20% of the measured data

Surrogate Fuels↗
NASA NTRSDate not supplied

Coaxial Rotor CFD Validation and ML Surrogate Model Generation

A fixed-pitch speed-controlled coaxial rotor system was tested in the NASA Langley Transonic Dynamics Tunnel in September 2022. The rotors have a diameter of 1.35 meters and an inter-rotor spacing of 25% of the diameter. Though this test was focused on the NASA New Frontiers Dragonfly mission, the resulting dataset is relevant to a wide array of applications of multirotor vehicles, especially those using fixed-pitch variable-speed rotors. Most notably beyond Dragonfly, perhaps, is the application of coaxial rotor pair configurations for eVTOL and Urban Air Mobility (UAM) aircraft. This work provides a thorough CFD validation study quantifying coaxial rotor performance estimation with accuracy on order 5-10% using an efficient hybrid BEMT-URANS flow solver over a wide range of operating conditions. This accuracy was achieved using novel approaches for the construction of both the C81 airfoil performance lookup tables and the BEMT rotor model. These novel approaches are combined with advanced scripting to further accelerate the commercial off-the-shelf CFD solver on GPU accelerated machines. Finally, the development of machine learning surrogate models is presented to produce highly efficient and accurate rotor performance predictions for fixed-pitch variable-speed multirotor aircraft over the complete flight regime including scout, cruise, climb, descent, as well as limiting cases of vortex-ring and windmill-brake states.

Coaxial↗
DOE OSTI2022

Stable Element Doping of Sol-gel Toward Simulating Environmental Matrix in Surrogate Explosive Nuclear Debris

Training nuclear forensic analysis personnel in post-detonation scenarios is of critical importance to nuclear threat response capabilities. Thus, realistic nuclear debris simulants which resemble the size, color, elemental composition, and radionuclide content of actual nuclear fallout from a recent detonation would be valuable for training nuclear first responders in realistic scenarios. As nuclear fallout types vary significantly in each of these parameters based on detonation environment (rural, urban, maritime etc.) and collection location, the ability to tailor each of these parameters accurately in simulated debris would be of immense benefit to the post-detonation analysis community for training both in-field collections and triage and the validation of laboratory level nuclear forensic techniques. Sol-gel synthesis techniques can provide the tunability of size, shape and composition required for producing surrogate nuclear debris of a wide variety. The sol-gel process consists of forming a metal oxide material, often silica, through polymerization of a metal-alkoxy precursor. In this work, we characterize the ability to load the sol-gel particles with secondary elemental components such as iron, aluminum, and calcium toward approximating the elemental composition of debris from various detonation environments and demonstrate the ability to produce particles with controllable size, shape, and color. We also demonstrate quantitative radionuclide encapsulation toward reproducing the radionuclide content of actual fallout from a recent detonation. Finally, we then employ these techniques in producing simulated aerodynamic debris samples with realistic elemental matrix composition and radionuclide content simulating a recent uranium-fueled detonation taking place in a rural desert environment and compare it to historic fallout from the Nevada Nuclear Security Site.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Surrogate Model Based Optimization for Finding Robust Deep Learning Model Architectures

Deep Learning (DL) models are increasingly used throughout the sciences. However, their performance and usefulness depend greatly on their architecture which is defined by hyperparameters such as the number of nodes, layers, the learning rate, etc. Tuning these hyperparameters is time-consuming because evaluating their performance requires a lengthy training step. Stochastic optimizers used in training lead to performance variability and potentially prediction reliability issues. In this talk, we will describe an automated optimization method based on surrogate models and active learning strategies for tuning DL model architectures. We take into account the prediction variability with the goal to identify architectures that make reliable and robust predictions. We demonstrate our developments on an application arising in particle physics.

deep learning↗
DOE OSTI2022

Stable Element Doping of Sol-gel Toward Simulating Environmental Matrix in Surrogate Explosive Nuclear Debris

Training nuclear forensic analysis personnel in post-detonation scenarios is of critical importance to nuclear threat response capabilities. Thus, realistic nuclear debris simulants which resemble the size, color, elemental composition, and radionuclide content of actual nuclear fallout from a recent detonation would be valuable for training nuclear first responders in realistic scenarios. As nuclear fallout types vary significantly in each of these parameters based on detonation environment (rural, urban, maritime etc.) and collection location, the ability to tailor each of these parameters accurately in simulated debris would be of immense benefit to the post-detonation analysis community for training both in-field collections and triage and the validation of laboratory level nuclear forensic techniques. Sol-gel synthesis techniques can provide the tunability of size, shape and composition required for producing surrogate nuclear debris of a wide variety. The sol-gel process consists of forming a metal oxide material, often silica, through polymerization of a metal-alkoxy precursor. In this work, we characterize the ability to load the sol-gel particles with secondary elemental components such as iron, aluminum, and calcium toward approximating the elemental composition of debris from various detonation environments and demonstrate the ability to produce particles with controllable size, shape, and color. We also demonstrate quantitative radionuclide encapsulation toward reproducing the radionuclide content of actual fallout from a recent detonation. Finally, we then employ these techniques in producing simulated aerodynamic debris samples with realistic elemental matrix composition and radionuclide content simulating a recent uranium-fueled detonation taking place in a rural desert environment and compare it to historic fallout from the Nevada Nuclear Security Site.

36 MATERIALS SCIENCE↗