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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Graph Neural Networks for Surrogate Modeling of Offshore Floating Platforms

Floating offshore wind turbines (FOWTs) present an significant opportunity to increase renewable energy generation. However, significant challenges remain before FOWTs can be widely commercialized and deployed. In particular, hydrodynamic loading on the platforms can stress the overall structure, damage the mooring systems, and impact power generation. Studying these loads is difficult and often relies on computationally expensive models or experiments. In this work, we explore the use of graph neural networks (GNNs) to construct flexible, data-driven surrogates for hydrodynamic loads on platforms. We leverage the natural graph-like structure of offshore wind platform designs to enable the GNN model to learn to approximate the loads for different wave conditions and structural designs. We demonstrate potential uses for the surrogate by performing parameter sweeps and ridge analysis on the trained model to identify the impacts of different wave and structural features on the loads.

floating offshore wind turbines↗
DOE OSTI2019

Surrogate-Based Robust Design for a Non-Smooth Radiation Source Detection Problem

In this paper, we develop and numerically illustrate a robust sensor network design to optimally detect a radiation source in an urban environment. This problem exhibits several challenges: penalty functionals are non-smooth due to the presence of buildings, radiation transport models are often computationally expensive, sensor locations are not limited to a discrete number of points, and source intensity and location responses, based on a fixed number of sensors, are not unique. We consider a radiation source located in a prototypical 250 m × 180 m urban setting. To address the non-smooth properties of the model and computationally expensive simulation codes, we employ a verified surrogate model based on radial basis functions. Using this surrogate, we formulate and solve a robust design problem that is optimal in an average sense for detecting source location and intensity with minimized uncertainty.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A Rapid Sintering Method for Cerium Nitride Pellet: A Uranium Mononitride Surrogate

Uranium mononitride (UN) is a candidate fuel material for light water reactors with higher uranium (U) loading and thermal conductivity than uranium dioxide (UO 2 ). However, the sintering of UN pellets is challenging as the UN powder particles oxidize rapidly at high temperatures unless the oxygen concentration is extremely low. Oxidation during sintering either reduces the relative density of the sintered UN pellet or disintegrates the sintered UN pellet to powder. To address this problem, the present work developed a rapid sintering method for producing highly densified UN surrogate pellets with minimal oxidation. Cerium nitride (CeN) is used as a surrogate for UN to reduce radiation hazards. With the custom-developed fast-heating system, the sintering process was completed within 150 s. The sintering atmosphere was flowing nitrogen (N 2 ). The sintered CeN pellet density was 95% of the theoretical density (TD) or higher. The microstructure was uniform with a 10–25 µm grain size as demonstrated by scanning electron microscopy (SEM) and contained trivial levels of oxides as demonstrated by X-ray diffraction (XRD). The resultant pellets indicate that the rapid sintering method is a promising method to make UN fuel pellets with equivalent or higher density to pellets made by conventional sintering methods, while also being more efficient in time and costs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow

In many areas of constrained optimization, representing all possible constraints that give rise to an accurate feasible region can be difficult and computationally prohibitive for online use. Satisfying feasibility constraints becomes more challenging in high-dimensional, non-convex regimes which are common in engineering applications. A prominent example that is explored in the manuscript is the security-constrained optimal power flow (SCOPF) problem, which minimizes power generation costs, while enforcing system feasibility under contingency failures in the transmission network. In its full form, this problem has been modeled as a nonlinear two-stage stochastic programming problem. In this work, we propose a hybrid structure that incorporates and takes advantage of both a high-fidelity physical model and fast machine learning surrogates. Neural network (NN) models have been shown to classify highly non-linear functions and can be trained offline but require large training sets. In this work, we present how model-guided sampling can efficiently create datasets that are highly informative to a NN classifier for non-convex functions. We show how the resultant NN surrogates can be integrated into a non-linear program as smooth, continuous functions to simultaneously optimize the objective function and enforce feasibility using existing non-linear solvers. Overall, this allows us to optimize instances of the SCOPF problem with an order of magnitude CPU improvement over existing methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Heterogeneous oxidation of amorphous organic aerosol surrogates by O 3 , NO 3 , and OH at typical tropospheric temperatures

Typical tropospheric temperatures render possible phase states of amorphous organic aerosol (OA) particles of solid, semisolid, and liquid. This will affect the multiphase oxidation kinetics involving the organic condensed-phase and gaseous oxidants and radicals. To quantify this effect, we determined the reactive uptake coefficients (γ) of O 3 , NO 3 , and OH by substrate films composed of single and binary OA surrogate species under dry conditions for temperatures from 213 to 313 K. A temperature-controlled coated-wall flow reactor coupled to a chemical ionization mass spectrometer was applied to determine γ with consideration of gas diffusion transport limitation and gas flow entrance effects, which can impact heterogeneous reaction kinetics. The phase state of the organic substrates was probed via the poke-flow technique, allowing the estimation of the substrates' glass transition temperatures. γ values for O 3 and OH uptake to a canola oil substrate, NO 3 uptake to a levoglucosan and a levoglucosan / xylitol substrate, and OH uptake to a glucose and glucose / 1,2,6-hexanetriol substrate have been determined as a function of temperature. We observed the greatest changes in γ with temperature for substrates that experienced the largest changes in viscosity as a result of a solid-to-liquid phase transition. Organic substrates that maintain a semisolid or solid phase state and as such a relatively higher viscosity do not display large variations in heterogeneous reactivity. From 213 to 293 K, γ values of O 3 with canola oil, of NO 3 with a levoglucosan / xylitol mixture, and of OH with a glucose / 1,2,6-hexanetriol mixture and canola oil, increase by about a factor of 34, 3, 2, and 5, respectively, due to a solid-to-liquid phase transition of the substrate. These results demonstrate that the surface and bulk lifetime of the OA surrogate species can significantly increase due to the slowed heterogeneous kinetics when OA species are solid or highly viscous in the middle and upper troposphere. This experimental study will further our understanding of the chemical evolution of OA particles with subsequent important consequences for source apportionment, air quality, and climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Building a machine learning surrogate model for wildfire activities within a global Earth system model

Abstract. Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90 % higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2=0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

58 GEOSCIENCES↗
DOE OSTI2025

Transfer learning of neural surrogates on multifidelity groundwater simulations

Multifidelity data used in the paper published in Advances in Water Resources 206 (2025) 105140, https://doi.org/10.1016/j.advwatres.2025.105140 The code used to process the data is openly available on GitHub at https://github.com/Model-Reduction-and-UQ-Group/Transfer_Learning_K_reconstruction Computationally inexpensive surrogates of process-based models, such as deep neural networks, enable ensemble-based computations used in risk assessment, data assimilation, etc. However, generation of large datasets required to train a neural network can be as expensive as the ensemble simulations themselves. We ameliorate this challenge by using data from multifidelity (MF) groundwater simulations and transfer learning (TL) to reduce data generation costs while maintaining model accuracy. As a computational example, we train a deep convolutional neural network (CNN) to reconstruct permeability fields from saturation maps derived from a multiphase flow model. Starting with very low- and low-fidelity data generated on increasingly coarse meshes, we pretrain the CNN, followed by output-layer training and fine-tuning using only a limited number of high-fidelity samples. We demonstrate the surrogate’s robustness when interpreting low-quality inputs—such as interpolated maps or data affected by noise—which has strong implications for the applicability in practical hydrogeological scenarios. This multilevel MF-TL strategy achieves a favorable trade-off between computational efficiency and predictive accuracy, significantly outperforming high-fidelity-only approaches under the same computational budget.

Chiofalo, Alessia [University of Bologna] (ORCID:0↗
NASA NTRS1987

Surrogate measures: A proposed alternative in human factors assessment of operational measures of performance

Surrogate measures are proposed as an alternative to direct assessment of operational performance for purposes of screening agents who may have to work under unusual stresses or in exotic environments. Such measures are particularly proposed when the surrogate can be empirically validated against the operational criterion. The focus is on cognitive (or throughput) performances in humans as opposed to sensory (input) or motor (output) measures, but the methods should be applicable for development of batteries which will tap input/output functions. A menu of performance tasks is under development for implementation on a battery-operated portable microcomputer, with 21 tests currently available. The tasks are reliable and become stable in minimum amounts of time; appear sensitive to some agents; comprise constructs related to actual job tasks; and are easily administered in most environments. Implications for human factors engineering studies in environmental stress are discussed.

Kennedy, Robert S.↗
NASA NTRS2016

Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process

Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases [1]. Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission [2]. Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an advanced design methods (ADM) based approach. This approach applies the concepts of design of experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development effort. In order to fit a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of ground rules and assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated "what if" questions.

Zwack, Mathew R.↗
NASA NTRS2017

Near Real-Time Probabilistic Damage Diagnosis Using Surrogate Modeling and High Performance Computing

This work investigates novel approaches to probabilistic damage diagnosis that utilize surrogate modeling and high performance computing (HPC) to achieve substantial computational speedup. Motivated by Digital Twin, a structural health management (SHM) paradigm that integrates vehicle-specific characteristics with continual in-situ damage diagnosis and prognosis, the methods studied herein yield near real-time damage assessments that could enable monitoring of a vehicle's health while it is operating (i.e. online SHM). High-fidelity modeling and uncertainty quantification (UQ), both critical to Digital Twin, are incorporated using finite element method simulations and Bayesian inference, respectively. The crux of the proposed Bayesian diagnosis methods, however, is the reformulation of the numerical sampling algorithms (e.g. Markov chain Monte Carlo) used to generate the resulting probabilistic damage estimates. To this end, three distinct methods are demonstrated for rapid sampling that utilize surrogate modeling and exploit various degrees of parallelism for leveraging HPC. The accuracy and computational efficiency of the methods are compared on the problem of strain-based crack identification in thin plates. While each approach has inherent problem-specific strengths and weaknesses, all approaches are shown to provide accurate probabilistic damage diagnoses and several orders of magnitude computational speedup relative to a baseline Bayesian diagnosis implementation.

Warner, James E.↗
NASA NTRS2019

Calibration of a Structural Finite Element Model for a Representative Inflatable Space Structure Utilizing Probabilistic Methods and Surrogate Models

A structural finite element model representing a novel inflatable airlock concept has been calibrated using full‐scale test data. The concept, denoted as the Non‐Axisymmetric Inflatable Pressure Structure (NAIPS), was developed under NASA’s Minimalistic Advanced Softgoods Hatch (MASH) Program. The current studies extended previous numerical efforts by incorporating the midbody section of the NAIPS to the dome section and calibrating the model with test data using a process that included surrogate models. Brief overviews of the finite element model and calibration process are provided. The completion of the calibration process provided a model that adequately replicated the test data. The successful demonstration of calibration of a finite element model representing an inflatable habitat provides confidence in the ability to use numerical simulations and associated surrogate models to support design and certification of inflatable space habitats.

Lyle, Karen H.↗
DOE OSTI2016

MISO - Mixed Integer Surrogate Optimization

MISO is an optimization framework for solving computationally expensive mixed-integer, black-box, global optimization problems. MISO uses surrogate models to approximate the computationally expensive objective function. Hence, derivative information, which is generally unavailable for black-box simulation objective functions, is not needed. MISO allows the user to choose the initial experimental design strategy, the type of surrogate model, and the sampling strategy.

Mueller, Juliane↗
DOE OSTI2019

Impact of Ethanol additions on Autoignition Characteristics of a Full Boiling Range Gasoline and its Surrogates at Advanced Engine Conditions

Ethanol is widely blended into commercial gasolines, as an oxygenate to mitigate pollutant formation, as well as an octane enhancer to improve knock resistance. Ethanol can also facilitate the use of lower-grade, or less-refined gasoline in modern internal combustion (IC) engines. However, the chemical kinetic interactions of ethanol with petroleum-based gasoline are not well understood, especially towards autoignition propensity over a range of conditions. These interactions are critical for improving knock resistance, as well as towards performance in low temperature combustion schemes, such as gasoline compression ignition. This study investigates the chemical kinetic interactions of ethanol with a full boiling range gasoline, where blending ratios of 0 to 30 vol./vol. are covered. A mid-octane range (Anti Knock Index 91.5) Fuels for Advanced Combustion Engines gasoline is employed, while a variety of multi-component surrogate blends are also studied. Autoignition data are acquired within a rapid compression machine at temperature, pressure, and fuel loading conditions representative of advanced IC engines. Changes in low-temperature behavior, including first-stage ignition times and extents of low temperature heat release, as well as main ignition times are quantified for the neat fuels and the ethanol-blended fuels. A recently-updated, detailed chemical kinetic model for the gasoline surrogates is used to interpret the measurements, and better understand the kinetic pathways.

autoignition↗
DOE OSTI2019

EFFICIENT SURROGATE FOR LOW ENERGY DAMAGE EFFECTS IN POLYATOMIC MATERIALS

We present an efficient surrogate for binary-collision Monte-Carlo (BCMC) calculations in polyatomic materials for low ion energies based on Parkin and Coulter’s net displacement function. Most of BCMC’s execution time is spent on the low energy tail of cascades when recoils’ residual travel distance is small compared to the desired resolution of the damage calculation. We propose to curtail an ions’ random walk if its energy reduces below a threshold Et and locally deposit an estimate of the expected number of defects it creates; the estimate of the number of defects is obtained from Parkin and Coulter’s net displacement functions. We find that results obtained with the BCMC code MyTRIM with and without truncation of the random walks compare well for large energy thresholds Et > 5 keV. For Et = 5 keV larger discrepancies are observed because of large differences between the net displacement function and MyTRIM for very low energy recoils. Future work will investigate and resolve this discrepancy. The energy threshold should be chosen so that ions’ average distance to next nuclear scattering is small compared to the desired resolution. A reduction of execution time of damage cascades by over 90 % is observed for an energy threshold of Et = 5 keV and a 1 MeV Xe-135 primary impinging on UO2. The desired application of this surrogate model are coupled FEM-BCMC calculations with varying compositions where net displacement functions are tabulated by composition. For reducing the number of required net displacement functions, derivatives of the net displacement function with respect to number fraction are computed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

EFFICIENT SURROGATE FOR LOW ENERGY DAMAGE EFFECTS IN POLYATOMIC MATERIALS

We present an efficient surrogate for binary-collision Monte-Carlo (BCMC) calculations in polyatomic materials for low ion energies based on Parkin and Coulter’s net displace- ment function. Most of BCMC’s execution time is spent on the low energy tail of cascades when recoils’ residual travel distance is small compared to the desired resolution of the damage calculation. We propose to curtail an ions’ random walk if its energy reduces below a threshold Et and locally deposit an estimate of the expected number of defects it creates; the estimate of the number of defects is obtained from Parkin and Coulter’s net displacement functions. We find that results obtained with the BCMC code MyTRIM with and without truncation of the random walks compare well for large energy thresholds Et > 5 keV. For Et = 5 keV larger discrepancies are observed because of large differ- ences between the net displacement function and MyTRIM for very low energy recoils. Future work will investigate and resolve this discrepancy. The energy threshold should be chosen so that ions’ average distance to next nuclear scattering is small compared to the desired resolution. A reduction of execution time of damage cascades by over 90 % is observed for an energy threshold of Et = 5 keV and a 1 MeV Xe-135 primary impinging on UO2. The desired application of this surrogate model are coupled FEM-BCMC cal- culations with varying compositions where net displacement functions are tabulated by composition. For reducing the number of required net displacement functions, derivatives of the net displacement function with respect to number fraction are computed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

New directions for surrogate models and differentiable programming for High Energy Physics detector simulation

The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning methods are being explored to replace computationally expensive components. Additionally, differentiable programming has been proposed as a complementary approach, providing controllable and scalable simulation routines. In this document, new and ongoing efforts for surrogate models and differential programming applied to detector simulation are discussed in the context of the 2021 Particle Physics Community Planning Exercise (`Snowmass').

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Forming Complex Nuclear Fuel Shapes in High-Loaded Silicide Surrogates

This work provides proof of the concept that high silicide loading nuclear fuel meat surrogates with complicated geometries can be produced with uniform density through an application of cold isostatic pressing (CIP). Dispersion fuels with high volumetric loading of U_3 Si_2 have challenges in fabrication. Fabrication involves a series of processes including powder compaction, rolling pack assembly, and roll forming. PNNL has undertaken extensive experimental work using MoSi2 and WSi2 as surrogates for U3Si2 to explore the feasibility of reducing or eliminating the issues through the application of CIP in the powder pressing step prior to rolling pack assembly. The composites were prepared at >40 vol% silicide loading, which was representative of 4.8 gU/cm^3 and formed via CIP at 50 KSI (Kilopound per Square Inch) pressure. The CIP mold design was taken through a design process which aimed to reduce defects and increase precision. The application of CIP here provides a broad strategy for producing highly loaded dispersion fuels with complex geometries and uniform density.

Clelland, Dustin T.↗
DOE OSTI2026

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost. This gap is particularly large for FPGA deployment, where cost is dominated by a multi-dimensional budget of lookup tables, DSPs, flip-flops, BRAM, and latency. We present the Surrogate Neural Architecture Codesign Package (SNAC-Pack), an open-source AutoML framework for hardware-aware neural architecture codesign and end-to-end FPGA deployment. SNAC-Pack runs a multi-objective global search with Optuna and NSGA-II, loading trials to a shared SQLite store that enables parallel workers across compute nodes. A hardware surrogate model outputs per-trial resource and latency estimates, avoiding the synthesis cost that would otherwise dominate the search loop. A local search stage then applies quantization-aware training (QAT) together with iterative magnitude pruning in a combined compression loop, after which the final model is synthesized to FPGA firmware via the hls4ml Python library. A YAML configuration and an optional agentic frontend let users run the pipeline on new datasets without modifying the framework. We demonstrate SNAC-Pack on jet classification at the Large Hadron Collider and superconducting qubit readout, discovering compact architectures that match or exceed strong baselines on the task metric while reducing FPGA resource utilization and, in the qubit readout case, reducing the design space exploration process from months of manual fine-tuning to hours of automated search.

Weitz, Jason [UC, San Diego]↗