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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Improving Accuracy of Structural Dynamic Modification with Augmented Residual Vectors

It is often important to perform sensitivity analysis to determine how a structural model will be impacted by design changes. Often, the structural analysts will manually make changes to the finite element model (FEM) to determine the effects. But when dealing with a large FEM with millions of degrees of freedom these manual changes can be cumbersome and calculation of the effects can computationally expensive. Therefore, it is desirable to determine the effects of model changes through approximation methods. One common technique is to determine the analytical sensitivity of the FEM model with respect to the given change. These analytical sensitivities are valid when small changes are made to the structural model, but invalid if large changes need to be assessed. Another approach is to use Structural Dynamic Modification (SDM) to create a surrogate model to analyze model changes. SDM is a widely-used sensitivity method and is used in applications of model updating, uncertainty quantification, and model design studies. SMD is valid for moderate (10-20 percent) changes in the structural model, but model approximations are often needed for large parameter changes (greater than 20 percent). Structural Dynamic Modification can be improved by using residual vectors to augment the surrogate model formulation from SDM. Adding the residual modes increases the fidelity of the surrogate model while keeping the computational cost low. This paper discusses the application and limitations of the augmented residual modes method to two structures: the Integrated Spacecraft and Payload Element (ISPE) of the Space Launch System (SLS) and the full SLS as it is configured during its Integrated Modal Test (IMT).

Stewart, Eric C.↗
DOE OSTI2023

A flexible event reconstruction based on machine learning and likelihood principles

Event reconstruction is a central step in many particle physics experiments, turning detector observables into parameter estimates; for example estimating the energy of an interaction given the sensor readout of a detector. A corresponding likelihood function is often intractable, and approximations need to be constructed. Here, in our work, we first show how the full likelihood for a many-sensor detector can be broken apart into smaller terms, and secondly how we can train neural networks to approximate all terms solely based on forward simulation. Our technique results in a fast, flexible, and close-to-optimal surrogate model proportional to the likelihood and can be used in conjunction with standard inference techniques allowing for a consistent treatment of uncertainties. We illustrate our technique for parameter inference in neutrino telescopes based on maximum likelihood and Bayesian posterior sampling. Given its great flexibility, we also showcase our method for geometry optimization enabling to learn optimal detector designs. Lastly, we apply our method to realistic simulation of a ton-scale water-based liquid scintillator detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Data-Efficient Methods for Determining Flory–Huggins χ Parameters in Multicomponent Polymer Formulations

Polymer formulations are essential in diverse applications including personal care products, coatings, paints, adhesives, and plastic materials. Designing these formulations requires navigating large, complex design spaces, where phase and self-assembly behavior critically impact performance. The Flory–Huggins χ parameter, which quantifies segmental miscibility, is widely used to parametrize the excess free energy of mixing in formulation models. In this work, we introduce two data-efficient, top-down methods for estimating χ parameters using the Random Phase Approximation (RPA): (i) Boundary Nonlinear Regression (Boundary-NLR), which fits theoretical spinodal boundaries to experimental phase boundaries, and (ii) Surrogate Model Inverse Parameter Estimation (SMIPE), which uses a Gaussian Process Classifier to fit sparse phase maps via a surrogate model. Both methods allow rapid parametrization of polymer field-theoretic models without the need for additional experiments. We evaluate these approaches on data sets involving polymer–solvent–nonsolvent ternary mixtures and block copolymer–solvent systems, demonstrating their robustness to experimental noise and their relevance for real-world formulation design.

copolymers↗
DOE OSTI2022

Uncertainty Aware Deep Learning for Particle Accelerators

Standard deep learning models for classification and regression applications are ideal for capturing complex system dynamics. However, their predictions can be arbitrarily inaccurate when the input samples are not similar to the training data. Implementation of distance aware uncertainty estimation can be used to detect these scenarios and provide a level of confidence associated with their predictions. In this paper, we present results from using Deep Gaussian Process Approximation (DGPA) methods for errant beam prediction at Spallation Neutron Source (SNS) accelerator (classification) and we provide an uncertainty aware surrogate model for the Fermi National Accelerator Lab (FNAL) Booster Accelerator Complex (regression).

Rajput, Kishansingh↗
DOE OSTI2023

Code Demonstration: Approximation of nearly-periodic symplectic maps via structure-preserving neural networks

In this notebook, we use the symplectic gyroceptron architecture from [Duruisseaux et al., 2022] to learn a surrogate map for the nearly-periodic symplectic flow map associated to a nearly-periodic Hamiltonian system composed of two nonlinearly coupled oscillators, where one of them oscillates significantly faster than the other: $\Big\lbrace$ $ {\dot{q}_1 = p_1 \hspace{5mm} \dot{p}_1 = -q_1 - \epsilon \partial_{q1}U(q_1 , q_2 )} \atop {\dot{q}_2 = \epsilon p_2 \hspace{4mm} \dot{p}_2 = -\epsilon q_2 - \epsilon \partial_{q2}U(q_1 , q_2 )}$. These equations of motion are the Hamilton's equations associated to the Hamiltonian $H_{\epsilon}(q_1,q_2,p_1,p_2)=\frac{1}{2}(q^2_1+p^2_1)+\frac{1}{2}\epsilon(q^2_2+p^2_2)+\epsilon$$U(q_1,q_2)$.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Integrated Off-gas System: A Preconceptual Design of an Integrated Off-Gas Treatment System

The U.S. has a vested interest in the advancement of nuclear energy to achieve aggressive net-zero goals, with reprocessing and recycling of used nuclear fuels (UNF) playing a vital role. It will not be possible to meet U.S. regulatory requirements without robust off-gas treatment, so it is crucial to advance treatment technologies to facilitate the design of future reprocessing facilities. For many years, teams of researchers across the U.S. Department of Energy (DOE) National Laboratory complex have been investigating off-gas treatment technologies for the capture and removal of volatile radionuclides (i.e., 85 Kr, Xe, 14 C, and 129 I) and oxides of nitrogen (NO X ) that are produced from reprocessing. These investigations have been focused on developing individual technologies for the capture of Kr, Xe, iodine, and CO 2 . Capture technologies for each constituent were tested independently from one another by utilizing nonradioactive surrogates to simulate simplified off-gas streams. The tests have been relatively small, laboratory-scale experiments of up to approximately 1 L/minute total gas flow rate. To increase the readiness of these technologies for deployment, an integrated test system with a larger-scale capacity is needed to bridge the gap between promising bench scale and fully scalable UNF reprocessing off-gas treatment. This document contains the goals, design basis, functional requirements, preconceptual design, and cost estimates for an integrated off-gas demonstration system for the capture and removal of NO x , Kr, Xe, CO 2 , and iodine at 10× higher throughput than earlier laboratory studies. The order-of-magnitude cost estimate for this system is approximately $\$$886,000. Next phases include conceptual design, detailed design, fabrication, and commissioning.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗
DOE OSTI2021

Sequential Learning of Active Subspaces

This study shows that in recent years, active subspace methods (ASMs) have become a popular means of performing subspace sensitivity analysis on black-box functions. Naively applied, however, ASMs require gradient evaluations of the target function. In the event of noisy, expensive, or stochastic simulators, evaluating gradients via finite differencing may be infeasible. In such cases, often a surrogate model is employed, on which finite differencing is performed. When the surrogate model is a Gaussian process (GP), we show that the ASM estimator is available in closed form, rendering the finite-difference approximation unnecessary. We use our closed-form solution to develop acquisition functions focused on sequential learning tailored to sensitivity analysis on top of ASMs. We also show that the traditional ASM estimator may be viewed as a method of moments estimator for a certain class of GPs. We demonstrate how uncertainty on GP hyperparameters may be propagated to uncertainty on the sensitivity analysis, allowing model-based confidence intervals on the active subspace. Our methodological developments are illustrated on several examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Calibration of the Diffusivity Predictions of Centipede Using Approximate Bayesian Computation and Applications in Nyx (Engineering Scale) and Xolotl-MARMOT (Meso-Scale) Simulations

Fission gas evolution and release in UO 2 nuclear fuel are important fuel performance metrics and occur in several distinct stages: 1) nucleation, growth and resolution of intra-granular bubbles, 2) diffusion to grain boundaries and 3) nucleation and growth of bubbles at grain boundaries, which eventually form a connected network (percolation) enabling release of gas from grain boundaries through connections to triple junctions, grain edges or free surfaces. The NE-SciDAC project is developing several computational tools to model this problem, which are connected in a hierarchical multi-scale framework. The information transfer in the multi-scale framework is a critical step that, in addition to best-estimates, should include uncertainty quantification. Despite taking a first-principles multi-scale approach, there is a need to perform parameter calibration to ensure consistency with available experimental data. In the present study, uncertainty quantification (UQ) and parameter calibration is demonstrated for one of the lower length scale codes in the multi-scale framework (Centipede) and then the results, including instances of the propagated uncertainties, are used in other codes within the framework, specifically Nyx and Xolotl-MARMOT. We calibrated the model parameters in Centipede, a computer code used to predict diffusivities of uranium (U) and xenon (Xe) in the context of the simulation of fission gas in uranium oxide (UO 2 ) nuclear fuel. The Centipede code depends on 183 parameters, all of which are subject to uncertainty. The three data sets used in our calibration effort are taken from the literature. This data is available as a set of measurements, including measurement errors. Our goal is to calibrate a statistical model that predicts both the value of the measurement and the uncertainty associated with the measurement. We perform a Bayesian calibration of the model parameters using a dedicated approximate Bayesian computation (ABC) likelihood function. To avoid excessive computational costs, we replace the expensive Centipede simulation code by a higher-order surrogate model, constructed using only the 9 most important parameters. These important parameters are identified by a preliminary global sensitivity analysis (GSA) study. Among the important parameters are T0 (the temperature at which UO 2 is perfectly stoichiometric) and Hf_pO2 (the temperature dependence of the oxygen (O) partial pressure) that should be considered as operating conditions to be estimated along with the other parameters. We consider two different cases: one where we define one set of these operating conditions for all data sets, and one where we define distinct operating condition parameters for each data set. The Xe diffusivities predicted by the latter case show distinct features that could not be observed in the former. Next, we use the diffusivity predictions by Centipede as input to Nyx, a reduced order fuel performance code focused on gas behavior alone, in order to estimate quantities associated with inter-granular bubble formation at conditions specified by the experiments. Finally, the diffusivities obtained from the calibrated Centipede runs were used in coupled Xolotl-MARMOT simulations of intra- and inter-granular gas evolution. The results are compared to simulations using the baseline diffusivities from Turnbull et al.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Robust second-order approximation of the compressible Euler equations with an arbitrary equation of state

Here, this paper is concerned with the approximation of the compressible Euler equations supplemented with an arbitrary or tabulated equation of state. The proposed approximation technique is robust, formally second-order accurate in space, invariant-domain preserving, and works for every equation of state, tabulated or analytic, provided the pressure is nonnegative. An entropy surrogate functional that grows across shocks is proposed. The numerical method is verified with novel analytical solutions and then validated with several computational benchmarks seen in the literature including problems with composite waves.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Passive Earth Entry Vehicle Landing Test

Two full-scale passive Earth Entry Vehicles (EEV) with realistic structure, surrogate sample container, and surrogate Thermal Protection System (TPS) were built at NASA Langley Research Center (LaRC) and tested at the Utah Test and Training Range (UTTR). The main test objective was to demonstrate structural integrity and investigate possible impact response deviations of the realistic vehicle as compared to rigid penetrometer responses. With the exception of the surrogate TPS and minor structural differences in the back shell construction, the two test vehicles were identical in geometry and both utilized the Integrated Composite Stiffener Structure (ICoSS) structural concept in the forward shell. The ICoSS concept is a lightweight and highly adaptable composite concept developed at NASA LaRC specifically for entry vehicle TPS carrier structures. The instrumented test vehicles were released from a helicopter approximately 400 m above ground. The drop height was selected such that at least 98% of the vehicles terminal velocity would be achieved. While drop tests of spherical penetrometers and a low fidelity aerodynamic EEV model were conducted at UTTR in 1998 and 2000, this was the first time a passive EEV with flight-like structure, surrogate TPS, and sample container was tested at UTTR for the purpose of complete structural system validation. Test results showed that at a landing vertical speed of approximately 30 m/s, the test vehicle maintained structural integrity and enough rigidity to penetrate the sandy clay surface thus attenuating the landing load, as measured at the vehicle CG, to less than 600 g. This measured deceleration was found to be in family with rigid penetrometer test data from the 1998 and 2000 test campaigns. Design implications of vehicle structure/soil interaction with respect to sample container and sample survivability are briefly discussed.

Kellas, Sotiris↗
DOE OSTI2024

Model-form Error Correction using Universal Differential Equations for an Agent-Based Model of Infectious Disease

This report demonstrates universal differential equations (UDEs) as an approach to bridge the gap between ordinary differential equations (ODE) models and agent-based models (ABMs). Using UDE models as surrogates for ABMs allows us to preserve the foundational ODE that represents global disease dynamics while coupling it with a neural network model to approximate functions for the local behaviors of the ABM.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Airborne Release Fractions from Surrogate Nuclear Waste Fires Containing Lanthanide Nitrates and Depleted Uranium Nitrate in 30% Tributyl Phosphate in Kerosene

Airborne contaminants from fires containing nuclear waste represent significant health hazards and shape the design and operation of nuclear facilities. Much of the data used to formulate DOE-HDBK-3010-94, “Airborne Release Fractions/Rates and Respirable Fractions for Nonreactor Nuclear Facilities,” from the U.S. Department of Energy, were taken over 40 years ago. The objectives of this study were to reproduce experiments from Pacific Northwest Laboratories conducted in June 1973 employing current aerosol measurement methods and instrumentation, develop an enhanced understanding of particulate formation and transport from fires containing nuclear waste, and provide modeling and experimental capabilities for updating current standards and practices in nuclear facilities. A special chamber was designed to conduct small fires containing 25 mL of flammable waste containing lutetium nitrate, ytterbium nitrate, or depleted uranium nitrate. Carbon soot aerosols showed aggregates of primary particles ranging from 20 to 60 nm in diameter. In scanning electron microscopy, ~200-nm spheroidal particles were also observed dispersed among the fractal aggregates. The 200-nm spherical particles were composed of metal phosphates. Airborne release fractions (ARFs) were characterized by leaching filter deposits and quantifying metal concentrations with mass spectrometry. The average mass-based ARF for 238 U experiments was 1.0 × 10 -3 with a standard deviation of 7.5 × 10 -4 . For the original experiments, DOE-HDBK-3010-94 states, “Uranium ARFs range from 2 × 10 -4 to 3 × 10 -3 , an uncertainty of approximately an order of magnitude.” Therefore, current measurements were consistent with DOE-HDBK-3010-94 values. ARF values for lutetium and ytterbium were approximately one to two orders of magnitude lower than 238 U. Furthermore, metal nitrate solubility may have varied with elemental composition and temperature, thereby affecting ARF values for uranium surrogates (Yb and Lu). In addition to ARF data, solution boiling temperatures and evaporation rates can also be deduced from experimental data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Skin CO 2 sniffing for wearable metabolic monitoring

CO 2 is a key physiological parameter used to assess hypoventilation and to estimate metabolic rates. However, current CO 2 monitoring relies on bulky breath-analysis systems that are impractical for continuous use in daily life. Here, we present a wearable on-skin gas-sniffing system that quantifies skin-emitted CO 2 and establishes its physiological relevance through direct correlation with exhaled CO 2 and metabolic rate. Participant studies demonstrate strong correlations between CO 2 flow rates from the skin and breath during both rest and exercise, with skin-emitted CO 2 approximately four orders of magnitude lower than exhaled CO 2 . Skin-emitted CO 2 also correlates with metabolic rate, suggesting its potential as a surrogate for breath-based indirect calorimetry. With its wearable form factor and physiological relevance, this skin gas sniffing system enables continuous, noninvasive metabolic monitoring and opens opportunities for studying skin gas exchange.

Kim, Seung-Rok [University of California, Berkeley↗
NASA NTRS2023

Efficient Calibration of Expensive Computational Models

Accounting for uncertainty when calibrating expensive computational models is a common challenge faced by scientists and engineers. Often Bayesian techniques are adopted to estimate a probability density function over the model parameters given noisy empirical data. The methods used to perform this type of probabilistic calibration are computationally prohibitive in that they require a large number of evaluations of the expensive model. In these cases, surrogate modeling -- that is, using a fast-to-evaluate, lower fidelity stand-in for the original computational model -- may be the only option to alleviate this computational burden. However, the upfront cost of generating training data to build a surrogate model can itself be expensive. As such, it is important to be judicious when selecting training points at which the full-fidelity model is evaluated. Here, an active learning approach is proposed that enables efficient selection of training points using approximate samples of the calibrated parameter probability density function. In this way, the training points can be concentrated in regions where the calibration algorithm requires high model accuracy.

active learning↗
DOE OSTI2024

Prediction of performance and turbulence in ITER burning plasmas via nonlinear gyrokinetic profile prediction

Burning plasma performance, transport, and the effect of hydrogen isotope (H, D, D-T fuel mix) on confinement has been predicted for ITER baseline scenario (IBS) conditions using nonlinear gyrokinetic profile predictions. Accelerated by surrogate modeling (Rodriguez-Fernandez et al 2022 Nucl. Fusion 62 076036), high fidelity, nonlinear gyrokinetic simulations performed with the CGYRO code (Candy et al 2016 J. Comput. Phys. 324 73), were used to predict profiles of T i , T e , and n e while including the effects of alpha heating, auxiliary power (NBI + ECH), collisional energy exchange, and radiation losses inside of $r/a$ = 0.9. Predicted profiles and resulting energy confinement are found to produce fusion power and gain that are approximately consistent with mission goals ($P_\textrm{fusion} = 500$ MW at Q = 10) for the baseline scenario and exhibit energy confinement that is within 1σ of the H-mode energy confinement scaling. The power of the surrogate modeling technique is demonstrated through the prediction of alternative ITER scenarios with reduced computational cost. These scenarios include conditions with maximized fusion gain and an investigation of potential resonant magnetic perturbation (RMP) effects on performance with a minimal number of gyrokinetic profile iterations required (3–6). These predictions highlight the stiff ITG nature of the core turbulence predicted in the ITER baseline and demonstrate that $Q \gt$ 17 conditions may be accessible by reducing auxiliary input power while operating in IBS conditions. Prediction of full kinetic profiles allowed for the projection of hydrogen isotope effects around ITER baseline conditions. The gyrokinetic fuel ion species was varied from H, D, and 50/50 D-T and kinetic profiles were predicted. Results indicate that a weak or negligible isotope effect will be observed to arise from core turbulence in IBS conditions. The resulting energy confinement, turbulence, and density peaking, and the implications for ITER operations will be discussed.

gyrokinetics↗
DOE OSTI2024

Computationally efficient and error aware surrogate construction for numerical solutions of subsurface flow through porous media

Limiting the injection rate to restrict the pressure below a threshold at a critical location can be an important goal of simulations that model the subsurface pressure between injection and extraction wells. The pressure is approximated by the solution of Darcy’s partial differential equation for a given permeability field. The subsurface permeability is modeled as a random field since it is known only up to statistical properties. This induces uncertainty in the computed pressure. Solving the partial differential equation for an ensemble of random permeability simulations enables estimating a probability distribution for the pressure at the critical location. These simulations are computationally expensive, and practitioners often need rapid online guidance for real-time pressure management. An ensemble of numerical partial differential equation solutions is used to construct a Gaussian process regression model that can quickly predict the pressure at the critical location as a function of the extraction rate and permeability realization. The Gaussian process surrogate analyzes the ensemble of numerical pressure solutions at the critical location as noisy observations of the true pressure solution, enabling robust inference using the conditional Gaussian process distribution. Our first novel contribution is to identify a sampling methodology for the random environment and matching kernel technology for which fitting the Gaussian process regression model scales as O ( n log n ) instead of the typical O ( n 3 ) rate in the number of samples n used to fit the surrogate. The surrogate model allows almost instantaneous predictions for the pressure at the critical location as a function of the extraction rate and permeability realization. Our second contribution is a novel algorithm to calibrate the uncertainty in the surrogate model to the discrepancy between the true pressure solution of Darcy’s equation and the numerical solution. Finally, although our method is derived for building a surrogate for the solution of Darcy’s equation with a random permeability field, the framework broadly applies to solutions of other partial differential equations with random coefficients.

54 ENVIRONMENTAL SCIENCES↗