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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Experimental Study on Surrogate Nuclear Fuel Rods under Reversed Cyclic Bending

The mechanical behavior of spent nuclear fuel (SNF) rods under reversed cyclic bending or bending fatigue must be understood to evaluate their vibration integrity in a transportation environment. This is especially important for high-burnup fuels (>45 GWd/MTU), which have the potential for increased structural damage. It has been demonstrated that the bending fatigue of SNF rods can be effectively studied using surrogate rods. In this investigation, surrogate rods made of stainless steel (SS) 304 cladding and aluminum oxide pellets were tested under load or moment control at a variety of amplitude levels at 5 Hz using the Cyclic Integrated Reversible-Bending Fatigue Tester developed at Oak Ridge National Laboratory. The behavior of the rods was further characterized using flexural rigidity and hysteresis data, and fractography was performed on the failed rods. The proposed surrogate rods captured many of the characteristics of deformation and failure mode observed in SNF, including the linear-to-nonlinear deformation transition and large residual curvature in static tests, PPI and PCMI failure mechanisms, and large variation in the initial structural condition. Rod degradation was measured and characterized by measuring the flexural rigidity; the degradation of the rigidity depended on both the moment amplitude applied and the initial structural condition of the rods. It was also shown that a cracking initiation site can be located on the internal surface or the external surface of cladding. Finally, fatigue damage to the bending rods can be described in terms of flexural rigidity, and the fatigue life of rods can be predicted once damage model parameters are properly evaluated. The developed experimental approach, test protocol, and analysis method can be used to study the vibration integrity of SNF rods in the future.

cladding failure↗
DOE OSTI2024

Complete development of LAROMance-type surrogate models for Zircaloy, as well as initial integration and testing in MOOSE, which addresses industry needs for licensing advanced LWR fuels

This milestone report details the progress in the constitutive surrogate modeling framework for Zircaloy’s mechanical behavior under extreme conditions, within the NEAMS program throughout the U.S. fiscal year 2024 (FY24). The presented work expands on the spectral based mechanical solver and the mechanistic material model for Zircaloy, which were introduced in the NEAMS program during FY22 and detailed in the previous fiscal year (FY23). As part of the NEAMS program, the current effort described in this report focused on the development of a novel viscplastic (creep) surrogate for the mechanistic, polycrystal-based model. To this end, the work was subdivided into two interdependent components related to the necessary improvements to the LApx constitutive model and algorithm and, to the development and use of a surrogate model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

A Novel Method for Controlling Crud Deposition in Nuclear Reactors Using Optimization Algorithms and Deep Neural Network Based Surrogate Models

This work presents the use of a high-fidelity neural network surrogate model within a Modular Optimization Framework for treatment of crud deposition as a constraint within light-water reactor core loading pattern optimization. The neural network was utilized for the treatment of crud constraints within the context of an advanced genetic algorithm applied to the core design problem. This proof-of-concept study shows that loading pattern optimization aided by a neural network surrogate model can optimize the manner in which crud distributes within a nuclear reactor without impacting operational parameters such as enrichment or cycle length. Several analysis methods were investigated. Analysis found that the surrogate model and genetic algorithm successfully minimized the deviation from a uniform crud distribution against a population of solutions from a reference optimization in which the crud distribution was not optimized. Strong evidence is presented that shows boron deposition in crud can be optimized through the loading pattern. This proof-of-concept study shows that the methods employed provide a powerful tool for mitigating the effects of crud deposition in nuclear reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2021

Optimal Experimental Design With Fast Neural Network Surrogate Models

Designing optimal experiments minimizes the uncertainty of results and maximizes the efficient use of resources. Herein, machine learning surrogate models and the approximate coordinate exchange (ACE) algorithm are used to determine optimum experimental designs over large or arbitrarily restrictive design spaces. Optimal experimental design is particularly salient in materials science where experiments are expensive and material properties must often be inferred indirectly. The proposed framework is demonstrated by finding optimal experiments with which the hidden constituent properties of composite materials can be most efficiently inferred from observable experimental outcomes. The optimum experimental design is given by an information-theoretic criteria, which maximizes the conditional mutual information between the hidden properties and the expected experimental outcomes. To perform tractable optimization a neural network is trained as a surrogate model to mimic a physics based simulation, which can calculate the expected experimental outcome based on a candidate experimental design and sampled constituent properties. The ACE algorithm is used to optimize over large design spaces with many tests and controlled parameters where an exhaustive search would be intractable even with the surrogate model. Using this approach, optimal experimental designs that are consistent with those produced by heuristic knowledge and established best practices are found; then optimal designs in larger design spaces where heuristic knowledge is unavailable are examined.

machine learning↗
NASA NTRSDate not supplied

Probabilistic Calibration of Expensive Models using Efficiently Trained Surrogates

Calibration of computational models in the presence of uncertainty is often cast as a Bayesian inference problem and solved via sampling methods, e.g., Markov chain Monte Carlo. When the computational model is expensive, this task becomes intractable due to the large number of samples required to accurately estimate the posterior distribution of the calibration parameters. A popular solution to this problem is to use machine learning to develop a faster-to-evaluate, lower-fidelity substitute for the original model to serve as a surrogate while solving the inference problem. Although considered an offline cost, generating training data to construct this surrogate model can still be an expensive task in practice. An active learning algorithm is presented that focuses training on improving surrogate accuracy specifically in and around the bulk of the posterior distribution, as this is where the model is exercised during calibration. Candidate samples are drawn from families of distributions related to an approximation of the posterior. The sample maximizing predictive variance is then selected for evaluation by the original computational model, yielding a label for the training point. Iterating this approach increases efficiency relative to space filling designs (e.g., Latin hypercube sampling) by avoiding low probability points. Practical considerations are discussed, including the benefits of using a sequential Monte Carlo sampling approach, convergence heuristics, and the importance of both exploration and exploitation given that the true posterior is unknown a priori.

uncertainty quantification↗
DOE OSTI2019

A Chemical Functionality Approach Towards the Formulation of a High-Fidelity Surrogate Fuel for FACE Gasoline F

1H and 13C nuclear magnetic resonance spectroscopy are used to determine an accurate, quantitative description of the atom type population of FACE-F, a research-grade gasoline. On this basis, a surrogate palette of seventeen components is proposed. Two surrogate fuels are formulated; a seven-component mixture (FGF-TCD1), and a nine-component mixture (FGF-TCD2). The autoignition behavior of FGF-TCD1 and FGF-TCD2 is investigated. Measurements are compared to tests with FACE-F, and blends of FACE-F with ethanol. Conditions at the transition from low-temperature to NTC chemistry are targeted such that first-stage, main ignition times, and exothermicity can be monitored. From the same manner, perturbed ignition characteristics due to the ethanol addition can be quantified. The results of this study demonstrate that although the atom type populations of both surrogate fuels are consistent with FACE-F and each other, the observed reactivity is different. This is may be attributed to atom type descriptors alone being insufficient to capture the combustion behavior of FACE-F. Interestingly, however, similar degree of reactivity differences in response to ethanol blending is observed.

autoignition↗
DOE OSTI2023

Towards an Era of Low-Temperature Integral Critical Experiments: Surrogate Testing of Low-Temperature TEX Configurations

United States and IAEA regulations for the transportation of radioactive materials require that fissile materials must be subcritical under expected ambient temperatures ranging from -40°C to 38°C. While historical experiments have been conducted at room and elevated temperatures, no criticality integral benchmarks exist for temperatures below room temperature. The Low-Temperature Thermal Epithermal eXperiments (LT-TEX) aim to provide benchmark evaluations down to -40°C. The LT-TEX design incorporates stacked HEU plates interspersed with varying thicknesses of polyethylene disks to provide varying levels of moderation, with five configurations at 20°C having already been benchmarked. A novel vacuum/cryogenic chamber was fabricated to contain the LT-TEX configurations in a thermally isolated environment and cool it to -40°C. Prior to testing with fissile materials, testing with surrogate fuel plates was performed to characterize the thermal properties of the system, ensure no condensation forms on the stack, and to test that the uranium plates would not warp, or fracture during the thermal cycling. Surrogate testing has characterized the cryogenic chamber and verified its viability to perform the benchmark experiments, allowing for the next steps in the LT-TEX experimental campaign. The results of the surrogate testing are discussed and compared to heat transfer calculations performed as part of the experiment design process, to establish confidence in the thermal behavior of the system.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Harnessing on-machine metrology data for prints with a surrogate model for laser powder directed energy deposition

In this study, we leverage the massive amount of multi-modal on-machine metrology data generated from Laser Powder Directed Energy Deposition (LP-DED) to construct a comprehensive surrogate model of the 3D printing process. By employing Dynamic Mode Decomposition with Control (DMDc), a data-driven technique, we capture the complex physics inherent in this extensive dataset. This physics-based surrogate model emphasizes thermodynamically significant quantities, enabling us to accurately predict key process outcomes. The model ingests 21 process parameters, including laser power, scan rate, and position, while providing outputs such as melt pool temperature, melt pool size, and other essential observables. Furthermore, it incorporates uncertainty quantification to provide bounds on these predictions, enhancing reliability and confidence in the results. We then deploy the surrogate model on a new, unseen part and monitor the printing process as validation of the method. Our experimental results demonstrate that the predictions align with actual measurements with high accuracy, confirming the effectiveness of our approach. Furthermore, this methodology not only facilitates real-time predictions but also operates at process-relevant speeds, establishing a basis for implementing feedback control in LP-DED.

Digital twins↗
DOE OSTI2021

Model-Free Voltage Control of Active Distribution System with PVs Using Surrogate Model-Based Deep Reinforcement Learning

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage control performance, but this is difficult to obtain in practice. This paper proposes a physical-model-free voltage control method based on a surrogate-model-enabled deep reinforcement learning approach. Specifically, a surrogate model is trained in a supervised manner using the recorded limited number of historical data to learn the relationship between the power injections and voltage fluctuations of each node. Then, the deep reinforcement learning algorithm is applied to learn an optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The proposed method can achieve physical-model-free control of unbalanced distribution network and inform real-time decisions to deal with fast voltage fluctuations caused by the rapid variation of PV generation. Simulation results on an unbalance IEEE 123-bus system show that the proposed method can achieve similar performance as that of perfect physical-model-based approaches while being advantageous over other traditional methods.

active distribution network↗
DOE OSTI2018

Multiscale topology optimization using neural network surrogate models

In this study, we are concerned with optimization of macroscale elastic structures that are designed utilizing spatially varying microscale metamaterials. The macroscale optimization is accomplished using gradient-based nonlinear topological optimization. But instead of using density as the optimization decision variable, the decision variables are the multiple parameters that define the local microscale metamaterial. This is accomplished using single layer feedforward Gaussian basis function networks as a surrogate models of the elastic response of the microscale metamaterial. The surrogate models are trained using highly resolved continuum finite element simulations of the microscale metamaterials and hence are significantly more accurate than analytical models e.g. classical beam theory. Because the derivative of the surrogate model is important for sensitivity analysis of the macroscale topology optimization, a neural network training procedure based on the Sobolev norm is described. Since the SIMP method is not appropriate for spatially varying lattices, an alternative method is developed to enable creation of void regions. Lastly, the efficacy of this approach is demonstrated via several examples in which the optimal graded metamaterial outperforms a traditional solid structure.

42 ENGINEERING↗
DOE OSTI2020

Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold

This paper introduces a surrogate modeling scheme based on Grassmannian manifold learning to be used for cost-efficient predictions of high-dimensional stochastic systems. The method exploits subspace-structured features of each solution by projecting it onto a Grassmann manifold. This point-wise linear dimensionality reduction harnesses the structural information to assess the similarity between solutions at different points in the input parameter space. The method utilizes a solution clustering approach in order to identify regions of the parameter space over which solutions are sufficiently similarly such that they can be interpolated on the Grassmannian. In this clustering, the reduced-order solutions are partitioned into disjoint clusters on the Grassmann manifold using the eigen-structure of properly defined Grassmannian kernels and, the Karcher mean of each cluster is estimated. Then, the points in each cluster are projected onto the tangent space with origin at the corresponding Karcher mean using the exponential mapping. For each cluster, a Gaussian process regression model is trained that maps the input parameters of the system to the reduced solution points of the corresponding cluster projected onto the tangent space. Using this Gaussian process model, the full-field solution can be efficiently predicted at any new point in the parameter space. In certain cases, the solution clusters will span disjoint regions of the parameter space. In such cases, for each of the solution clusters we utilize a second, density-based spatial clustering to group their corresponding input parameter points in the Euclidean space. The proposed method is applied to two numerical examples. Here, the first is a nonlinear stochastic ordinary differential equation with uncertain initial conditions where the surrogate is used to predict the time history solution. The second involves modeling of plastic deformation in a model amorphous solid using the Shear Transformation Zone theory of plasticity, where the proposed surrogate is used to predict the full strain field of a material specimen under large shear strains.

42 ENGINEERING↗
DOE OSTI2023

Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems

We consider a high-dimensional nonlinear computational model of a dynamical system, parameterized by a vector-valued control parameter, in the presence of uncertainties represented by an uncontrolled parameter modeled by a vector-valued random variable, and possibly with stochastic excitation. The objective is to construct a statistical surrogate model where the input is any deterministic value of the control parameter, and the output is a vector-valued observation of the computational model, which is a random vector whose probability measure is updated using a target dataset. To construct this statistical surrogate model, the stochastic response of the computational model must be built, which is a vector-valued time-discretized stochastic process in high dimension, depending on the control parameter. It is assumed that the computational cost of a single evaluation of the deterministic model is high. For the probabilistic updating, we consider a subset of the components of the observation of the computational model, defined as the “identification observation” of the computational model, for which a small target dataset is available. Therefore, the target dataset is associated with partial observability, corresponding to an incomplete data case. Given a prior probability model of the random control and uncontrolled parameters, a training dataset is constructed, consisting of realizations of the random triplet composed of the stochastic response, the random identification observation, and the random control parameter. Since the computational cost of a single evaluation of the deterministic model is assumed to be large, the training dataset is also of small size. The main challenges in this problem are the high dimensionality, partial observability leading to incomplete data in the target dataset for the identification observation of the computational model (which is not sufficient to identify the computational stochastic responses), and the availability of a small training dataset. To address these challenges, we propose a methodology based on statistical methods for constructing necessary reduced representations, direct probabilistic learning under constraints using probabilistic learning on manifolds (PLoM) constrained by the target dataset, and the use of a weak formulation of the Fourier transform of probability measures. Statistical conditioning is also employed to explore the learned dataset. The constructed predictive statistical surrogate model can be implemented in the context of online computation. Here, we apply this approach to a problem of nonlinear stochastic dynamics in high dimensions within the framework of deformable solids mechanics.

Engineering↗
DOE OSTI2025

Surrogate models for development of unconventional shale reservoirs by an integrated numerical approach of hydraulic fracturing, flow and geomechanics, and machine learning

We develop well-completion surrogate models by taking an integrated workflow of hydraulic fracturing, flow, geomechanics, and machine learning simulation. There are three steps in the proposed workflow. First, history-matching processes are conducted with the field data including pumping and production data for characterization. Second, full-physics simulation is performed with various parameters of the field development (e.g., cluster spacing, clusters per stage, pumping rates and times, amount of proppant, and well spacing) to generate multiple simulation results by changing the parameters of the completion design with well-known hydraulic fracturing, reservoir, geomechanics simulators to calculate fracture geometry, reservoir depressurization, induced stress changes. The workflow is demonstrated over a field in the Southern Midland Basin. Here, we take two completion scenarios: a single well case followed by a multi-well case. Finally, a Long Short-Term Memory (LSTM) machine learning algorithm is employed to create surrogate models that can replicate the full-physics simulation results. Furthermore, results show that the trained models applied in the single well and multi-well cases for a particular geological system can provide good accuracy close to those provided by full-physics simulations. Specifically, the site-specific surrogate models can predict fracture parameters (length, height, and surface area) and cumulative production accurately with computational efficiency, suggesting our proposed workflow can be used as a pragmatic tool for expediting the well completion optimization process.

Geomechanics↗
DOE OSTI2015

Modeling Pancreatic Tumor Motion Using 4-Dimensional Computed Tomography and Surrogate Markers

Purpose: To assess intrafractional positional variations of pancreatic tumors using 4-dimensional computed tomography (4D-CT), their impact on gross tumor volume (GTV) coverage, the reliability of biliary stent, fiducial seeds, and the real-time position management (RPM) external marker as tumor surrogates for setup of respiratory gated treatment, and to build a correlative model of tumor motion. Methods and Materials: We analyzed the respiration-correlated 4D-CT images acquired during simulation of 36 patients with either a biliary stent (n=16) or implanted fiducials (n=20) who were treated with RPM respiratory gated intensity modulated radiation therapy for locally advanced pancreatic cancer. Respiratory displacement relative to end-exhalation was measured for the GTV, the biliary stent, or fiducial seeds, and the RPM marker. The results were compared between the full respiratory cycle and the gating interval. Linear mixed model was used to assess the correlation of GTV motion with the potential surrogate markers. Results: The average ± SD GTV excursions were 0.3 ± 0.2 cm in the left-right direction, 0.6 ± 0.3 cm in the anterior-posterior direction, and 1.3 ± 0.7 cm in the superior-inferior direction. Gating around end-exhalation reduced GTV motion by 46% to 60%. D95% was at least the prescribed 56 Gy in 76% of patients. GTV displacement was associated with the RPM marker, the biliary stent, and the fiducial seeds. The correlation was better with fiducial seeds and with biliary stent. Conclusions: Respiratory gating reduced the margin necessary for radiation therapy for pancreatic tumors. GTV motion was well correlated with biliary stent or fiducial seed displacements, validating their use as surrogates for daily assessment of GTV position during treatment. A patient-specific internal target volume based on 4D-CT is recommended both for gated and not-gated treatment; otherwise, our model can be used to predict the degree of GTV motion.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Evaluation of kriging based surrogate models constructed from mesoscale computations of shock interaction with particles

Macro-scale computations of shocked particulate flows require closure laws that model the exchange of momentum/energy between the fluid and particle phases. Closure laws are constructed in this work in the form of surrogate models derived from highly resolved mesoscale computations of shock-particle interactions. The mesoscale computations are performed to calculate the drag force on a cluster of particles for different values of Mach Number and particle volume fraction. Two Kriging-based methods, viz. the Dynamic Kriging Method (DKG) and the Modified Bayesian Kriging Method (MBKG) are evaluated for their ability to construct surrogate models with sparse data; i.e. using the least number of mesoscale simulations. It is shown that if the input data is noise-free, the DKG method converges monotonically; convergence is less robust in the presence of noise. The MBKG method converges monotonically even with noisy input data and is therefore more suitable for surrogate model construction from numerical experiments. This work is the first step towards a full multiscale modeling of interaction of shocked particle laden flows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Bending testing and characterization of surrogate nuclear fuel rods made of Zircaloy-4 cladding and aluminum oxide pellets

We studied behavior of surrogate nuclear fuel rods made of Zircaloy-4 (Zry-4) cladding with alumina pellets under reversed cyclic bending. Tests were performed under load or moment control at 5 Hz, and an empirical correlation was established between rod fatigue life and amplitude of the applied moment. Fatigue response of Zry-4 cladding was further characterized by using flexural rigidity. Degradation of flexural rigidity was shown to depend on the moment applied and the prefatigue condition of specimens. Pellet-to-pellet interface (PPI), pellet-to-cladding interface (PCI), and pellet condition all affect surrogate rod failure. Bonding/debonding of PPI/PCI and pellet fracturing contribute to surrogate rod bending fatigue. Also, the effect of sensor spacing on curvature measurement using three-point deflections was studied; the method based on effective specimen gauge length is effective in sensor spacing correction. Finally, we developed the database and gained understanding in this study such that it will serve as input to analysis of SNF vibration integrity.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Advanced surrogate model for electron-scale turbulence in tokamak pedestals

We derive an advanced surrogate model for predicting turbulent transport at the edge of tokamaks driven by electron temperature gradient (ETG) modes. Our derivation is based on a recently developed sensitivity-driven sparse grid interpolation approach for uncertainty quantification and sensitivity analysis at scale, which informs the set of parameters that define the surrogate model as a scaling law. Our model reveals that ETG-driven electron heat flux is influenced by the safety factor q, electron beta β e and normalized electron Debye length λ D , in addition to well-established parameters such as the electron temperature and density gradients. To assess the trustworthiness of our model's predictions beyond training, we compute prediction intervals using bootstrapping. The surrogate model's predictive power is tested across a wide range of parameter values, including within-distribution testing parameters (to verify our model) as well as out-of-bounds and out-of-distribution testing (to validate the proposed model). Overall, validation efforts show that our model competes well with, or can even outperform, existing scaling laws in predicting ETG-driven transport.

fusion plasma↗