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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Scale-bridging Optimization Framework for Desalination Integrated Produced Water Networks

Produced water (PW) management poses a major challenge to U.S. oil and gas development. Due to freshwater scarcity and PW injection curtailments, desalination may become a necessity in some parts of the country. Integrating rigorous desalination process models within a produced water network for co-optimization of desalination design and network operation can be challenging to scale-up due to computational complexity. In this work we use the trust region filter framework to decompose the integrated optimization problem into an optimization master problem consisting of the network model and simplified surrogates for the desalination units and an optimization sub problem for the rigorous desalination unit model. The approach is demonstrated on a multiperiod produced water network from the PARETO library with thermal desalination units.

Naik, Sakshi↗
DOE OSTI2023

Atomistic simulations and machine learning of solute grain boundary segregation in Mg alloys at finite temperatures

Understanding solute segregation thermodynamics is the first step in investigating grain boundary (GB) properties, such as strong yttrium (Y) effects on grain growth and texture evolution in micro-scale polycrystalline magnesium (Mg) alloys. To estimate the average GB segregation behavior in low-solute-concentration Mg alloys (e.g., 2 at.% Y), a state-of-the-art spectral approach is applied based on a per-site segregation energy spectrum for Y solute atoms at zero K obtained from molecular statistics (MS) simulations of ~10 4 GB sites in Mg symmetric tilt GBs (STGBs). Although selected MS simulation results are consistent with verification by density functional theory (DFT) calculations, estimates of average segregation tendency based on the zero-K energy spectrum deviate from experimental observations. To resolve this problem, thermodynamic integration (TI) methods based on molecular dynamics (MD) simulations are used to determine the per-site segregation free energies of Y at representative GB sites, which show contributions beyond harmonic approximations can be important for certain GB sites at high temperatures. A surrogate model of per-site segregation free energy is constructed from a small set of TI data points using stacking cross-validation regressors and physics-informed descriptors. This model is applied to predict the Y segregation free energy spectra for thousands of GB sites in Mg STGBs with uncertainty quantification. Finally, the average segregation tendency predicted by the spectral approach based on the free energy spectra agrees well (within the uncertainty range) with experimental observations for micro-scale polycrystalline Mg alloys at typical thermomechanical processing temperatures (500 ~ 800 K), where thermodynamic equilibrium states are likely to be achieved due to fast diffusion.

Atomistic simulations↗
DOE OSTI2024

Physics-coupled data-driven design of high-temperature alloys

We present a materials design loop, which streamlines physics-coupled machine learning (ML) surrogate models to discover new alloy chemistries with improved properties. The efficacy is demonstrated by discovering a high-temperature alumina-forming austenitic (AFA) stainless steel with enhanced creep, followed by experimental validation. The ML models have been trained using a well-curated, highly consistent experimental dataset augmented with synthetic microstructural features from a computational thermodynamic approach. We have populated a large number of hypothetical AFA alloys to explore the high-dimensional composition space and have predicted their creep properties by providing the same synthetic input features obtained from the trained ML models. Uncertainties from the ML training were taken as thresholds for truncating predicted results to identify alloys with improved or deteriorated creep. Individual elemental compositions have been determined via probability density distribution analysis from the group of alloys at the top and bottom of the predicted creep values for further virtual and experimental validations. In conclusion, we anticipate that this workflow can be applied to screen desired conditions, such as chemistry and processing parameters, in high-dimensional space through physics-guided data analytics.

Alloy design↗
DOE OSTI2025

Part distortion monitoring in additive manufacturing using machining

In additive manufacturing, accumulation of residual stresses can result in severe part distortion from the desired preform shape. Current methods for in-situ part distortion monitoring in additive manufacturing typically require expensive sensors, or capital equipment, and require time-consuming post-processing to understand the shape deviation. This paper presents an in-situ method, in the context of hybrid manufacturing, for part distortion detection using machining of additively manufactured parts. As a surrogate, three test artifacts were used to represent different distorted geometries. The tool axis positions from the machine tool controller and the cutting power were monitored during a facing operation. Cutting power data was used to detect the tool entry and exit in the workpiece using a novel approach with power standard deviation metric. The workpiece geometry and distorted configuration was subsequently predicted for positional and rotational deviations to within 2 mm accuracy using synchronized tool position data with cutting power. The proposed method can be used in a hybrid (additive and subtractive) machine tool to periodically check part distortion in the additive build. The method is applicable for any additive process and is low-cost and computationally inexpensive.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Process-structure-property relationships for droplet-on-demand liquid-metal-jetted parts

Liquid metal jetting droplet-on-demand additive manufacturing is an attractive alternative to industry-standard, laser-based metal additive manufacturing techniques that use powder feedstocks and produce large heat affected zones. Liquid metal jetting provides a low feedstock footprint, minimal materials waste, less contamination and inclusions from feedstock, and is compatible with a large range of materials. However, little has been reported on the liquid metal jetting process-structure-property relationships that are crucial to understanding how to consistently print high quality metal parts. We investigate the effects of build plate temperature, infill pattern, and track raster rate on the macro- and microstructure, density, hardness, and tensile strength of metal parts created with our custom droplet-on-demand setup using pure tin feedstock as a surrogate for more relevant structural materials. We found that although the build plate temperature had the largest influence on the structure and properties, the infill pattern is an important factor for optimal structural integrity. It was observed that the track raster rate influenced track-to-track porosity and should be considered in toolpath development. Despite printing in ambient air, we could not identify oxide on the surface or in the bulk of the parts and saw no adverse effects on microstructure or tensile performance. The results and methods of this study will help guide future work in optimizing the liquid metal jetting process to reliably print parts using any liquid metal jetting system for a wide variety of metal feedstocks.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications in aerospace and medical industries, but mechanical anisotropy and microstructural inhomogeneity prohibits its wider adoption. Although successful microstructure prediction models have been developed, a remaining challenge is their limited integration across length/time scales and validation by experimental studies. Here, this work proposes a physics-augmented machine learning surrogate model to unite predictions of LPBF temperature, β phase morphology and texture, and α/α’ formation into a single framework that is calibrated and validated with experiments. First, a phase field (PF) model of the martensitic β→α’ transformation is developed and calibrated using data from in-situ synchrotron cyclic heating/cooling studies quantifying the variation of α phase fraction with time. In parallel, an established finite difference-Monte Carlo (FDMC) model predicts the part-scale temperature profile and β grain formation during solidification. A dataset is developed using LPBF cyclic temperature descriptors from the FDMC model as inputs and corresponding α/α’ phase fraction and width from the PF model as outputs. Five machine learning (ML) regression models are tested and optimized, having mean absolute error in testing ≤ 4 %, and the k-nearest neighbors (KNN) model is selected as the best performing. The KNN model is called at the nodal level during post-processing of the FDMC model to replace and downscale the response of the PF model. The combined agility and accuracy of the hybrid FDMC-ML model enables part-scale microstructure predictions that can be further used for property predictions to accelerate AM process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Micromechanical and fatigue in situ synchrotron characterization of an additively manufactured superalloy with porosity

Additive manufacturing (AM) has the potential to transform component production, but its widespread adoption is constrained by defects, such as pores, that compromise structural integrity. Here, this study investigates the influence of porosity on the micromechanical and fatigue response of AM Inconel 718 (IN718), a widely used aerospace superalloy. One baseline specimen with minimal stochastic porosity and another intentionally seeded with lack of fusion (LOF) pores were examined using high-energy X-ray diffraction microscopy (HEDM) and micro-computed tomography during cyclic loading. Fatigue cracks in the LOF specimen initiated more frequently and at lower cycle counts than in the baseline specimen. The number fraction of fatigue cracks that grew was comparatively higher in the LOF specimen. Grain-level metrics, including stress and diffraction spot widths, were quantified using far-field HEDM. Across the thousands of grains detected in both specimens, the pores in the LOF specimen increased the variability of stress and spot width evolution in the azimuthal direction, the latter serving as a surrogate measure of plastic deformation. However, no correlations emerged between these metrics and grain proximity to fatigue crack initiation sites or pores, underscoring the limitations of grain-averaged metrics for predicting fatigue. Nonetheless, the rich dataset reported here provides a foundation for future modeling efforts.

High-energy X-ray diffraction microscopy↗
DOE OSTI2022

GW-PINN: A deep learning algorithm for solving groundwater flow equations

Machine learning methods provide new perspective for more convenient and efficient prediction of groundwater flow. In this study, a deep learning method “GW-PINN” without labeled data for solving groundwater flow equations with wells was proposed. GW-PINN takes the physics inform neural network (PINN) as the backbone and uses either the hard or soft constraint in the loss function for training. A locally refined sampling strategy (LRS) is adopted to generate the consistent spatial sampling points for problems with strong hydraulic head change, and then combined with an appropriate temporal sampling scheme to obtain the final spatial-temporal sampling points. A snowball-style two-stage training strategy by dividing the temporal domain into two subdomains is designed to decrease the sampling points. Five cases were designed to test the training performance of GW-PINN under different sampling strategies and two constraints. The predicted results of GW-PINN were compared with MODFLOW and the analytical solution. The results demonstrate that GW-PINN possesses strong ability in capturing the hydraulic head change for both confined and un-confined aquifers. The hard constraint owns more robust learning ability than the soft constraint. The LRS strategy can generate more accurate results with much fewer sampling points than traditional sampling strategies, and the snowball-style two-stage training strategy is significantly efficient for problems with the drastic change of hydraulic head. Additionally, the application of GW-PINN as a surrogate model for parameterized groundwater flow equations is illustrated. This study provides an option tool for efficient groundwater flow simulation, especially for those with local refinements are needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Causality guided machine learning model on wetland CH 4 emissions across global wetlands

Wetland CH 4 emissions are among the most uncertain components of the global CH 4 budget. The complex nature of wetland CH 4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH 4 emissions. In this study, we used the flux measurements of CH 4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH 4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH 4 emissions in all studied wetland types. Ecosystem respiration (CO 2 ) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH 4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH 4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH 4 emissions within earth system land models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Urbanization effects on the spatial patterns of spring vegetation phenology depend on the climatic background

Cities have been considered ideal surrogates for evaluating ecological responses to climate warming. Although research has revealed that the urban heat island effect is not the only determinant that drives the rural-urban difference in spring phenology, limited attempts further explore the effect of complex interactions between urbanization and climates on the start of the season (SOS). Here, we employed the percentage of impervious surface area as an indicator of urbanization levels, incorporating the urban heat island (UHI) effect and climates to decipher the multiple effects on the temporal shift in SOS. Our results suggest that urbanization, the UHI effect, and climates jointly drive the phenological timing in cities. Further, the SOS is highly linearly correlated with urbanization levels and UHI effects (p<0.001) in the zones with warm and humid climates, whereas relatively warmer, cooler, or arid climates break down the linearity. Geographically, the SOS is north-south symmetrical for the rural-urban ecosystem at the national scale, which initiates at the mid-latitudes (around 30 °N, 67.58 DOY ± 2.23 days) first and then towards the low (around 20 °N, 105.25 DOY ± 1.31 days) and high-latitudes (45 °N, 123.01 DOY ± 1.45 days). Chilling accumulation and spring temperature jointly contribute to this phenomenon. As the UHI effect is similar to the projected global warming in the future, understanding the phenological responses to urbanization in various climates is insightful for evaluating the impact of future warming on plants' phenological behavior in the global ecosystem.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A multifidelity method for a nonlocal diffusion model

Nonlocal models feature a finite length scale, referred to as the horizon, such that points separated by a distance smaller than the horizon interact with each other. Such models have proven to be useful in a variety of settings. However, due to the reduced sparsity of discretizations, they are also generally computationally more expensive compared to their local differential equation counterparts. In this work, we introduce a multifidelity Monte Carlo method that combines the high-fidelity nonlocal model of interest with surrogate models that use coarser grids and/or smaller horizons and thus have lower fidelities and lower costs. Using the multifidelity method, the overall computational cost of uncertainty quantification is reduced without compromising accuracy. It is shown for a one-dimensional nonlocal diffusion example that speedups of up to two orders of magnitude can be achieved using the multifidelity method to estimate the expectation of an output of interest.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Constrained Bayesian optimization of criticality experiments

The design of criticality experiments is typically an iterative process that employs a Monte Carlo transport code. The goal is to find a design that optimizes some variable, like the sensitivity of a response to a cross section, while simultaneously ensuring criticality. The high fidelity of the Monte Carlo code is a great asset, but it makes exploring the design space computationally expensive. Herein, we present how a constrained Bayesian optimization algorithm can be used to efficiently design a criticality experiment. It uses Gaussian processes as a surrogate model to probe the design space and to reduce the number of code executions that are needed to find the optimum. Furthermore, we demonstrate constrained Bayesian optimization with a Pu-239/polyethylene solution system and a TEX experiment that is designed for criticality safety validation of a nuclear waste model at the Hanford Site. For both systems, a global optimum was found within 75 Monte Carlo simulations.

42 ENGINEERING↗
DOE OSTI2021

A two-phase three-field modeling framework for heat pipe application in nuclear reactors

Heat pipes and two-phase thermosyphons are highly efficient heat transfer devices utilizing continuous evaporation and condensation of working fluid for two-phase heat transport in closed systems. Because of the nearly isothermal and fully passive phase-change heat transfer mechanism, heat pipes and thermosyphons have found many applications in nuclear engineering, space technologies, and other energy systems. High-temperature heat pipes are used in nuclear microreactors to remove fission power from the primary system and are coupled with power conversion systems or process heat applications. Modeling of the two-phase flow phenomena inside a heat pipe is essential to its design and safety analysis. In this study, a comprehensive one-dimensional two-phase three-field flow model has been developed for the analysis of heat pipes in normal operation conditions and transients. The conservation or field equations of mass, momentum, and energy were developed for the liquid film, vapor, and droplet. In addition, constitutive models or correlations were reviewed thoroughly and provided for the closure of the three-field equations. Specific constitutive equations regarding interfacial mass and heat transfer at two interfaces, namely film-gas interface and gas-droplet interface, were reviewed for droplet entrainment and deposition rates as well as film and droplet evaporation rates. Furthermore, mechanistic correlations of annular flow film thickness were recommended for the modeling of the thermosyphons without a wick as a critical constitutive correlation. Furthermore, experimental data needs from new experiments using a prototype working fluid or surrogate fluids for the model validation of high-temperature heat pipes in microreactors were recommended for future research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Potential of deep learning methods to enhance satellite-based monitoring of nuclear power plants focusing on remote operation evaluations

The anticipated expansion of the nuclear industry and the deployment of new nuclear reactors (200 + GW of new nuclear capacity by 2050) require the development of monitoring systems that align with safety and security concerns, providing enhanced evaluation capabilities. A remote monitoring system using satellites and deep learning techniques was evaluated for its ability to detect anomalies and capture various features of nuclear reactors independently of the conditions on the ground. Satellite images of current operational and under-construction nuclear power plants were collected from Google Earth Pro as a surrogate database. Subsequently, five datasets were created from the collected images. Transfer learning technique was used for several classification tasks utilizing VGG16, ResNet50V2, Xception, DenseNet121, and MobileNetV2 pre-trained models. In the first task, the capability of the monitoring system to detect abnormal conditions or processes in a nuclear power plant was investigated. In the second task, the ability to capture operational features remotely was examined. As an example, for the purposes of this study, these features included classifying reactors based on type, power range, or onsite condition. Several evaluation metrics were used to compare the performance of the pre-trained models and the overall monitoring system. Here, the evaluation results demonstrated that deep learning techniques and pre-trained models applied to satellite images have the potential to facilitate further and expand capabilities in monitoring systems to assess plant operation details.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

The molten salt tritium transport experiment: A pumped fluoride salt loop for hydrogen isotope experimentation

Molten salt reactors (MSRs) and fusion reactors propose to use molten salts as coolants and breeder blanket materials, respectively. Tritium, however, poses safety concerns in both reactor types due to its ability to permeate through reactor materials and potential for environmental release. This manuscript addresses the tritium transport phenomena in molten salts and presents the design and analysis of the Molten Salt Tritium Transport Experiment (MSTTE). MSTTE is a forced-convection fluoride salt loop intended to measure hydrogen isotope permeation through structural materials in a flowing salt system. In the first phase, MSTTE will use FLiNaK salt and deuterium as surrogates for FLiBe and tritium, with future plans to utilize tritium and FLiBe. MSTTE couples a Copenhagen Atomics pumped salt loop with an external test section that introduces hydrogen isotopes into the loop and measures transport phenomena. The Hydrogen Injection System (HIS) controls hydrogen isotope introduction into the molten salt loop. Here, the permeation test section measures the permeation rate through stainless steel tubing in contact with flowing salt. Computational fluid dynamics (CFD) analysis ensures fully developed salt flow in the permeation test section. MSTTE is modeled with MELCOR-TMAP to predict the permeation rate as a function of experimental variables such as source term, salt flow rate, and salt temperature. Results indicate that the source term is the only parameter with a significant effect on the permeation rate. Pressure drop analysis suggests that the loop should operate below 200 LPM to maintain a pressure drop below 200 kPa. Additionally, finite-element analysis assesses thermal stress during loop operation to ensure the experiment's safe design. MSTTE will provide semi-integral data on tritium transport phenomena in molten salts and serve as a testbed for advancing molten salt technology.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Introducing the SLICE Method for estimating pebble-bed reactor inventories at equilibrium operation with SCALE

This paper introduces the SCALE Leap-In method for Cores at Equilibrium (SLICE) for estimating pebble-bed reactor equilibrium core isotopic inventories using capabilities in the SCALE code system, requiring only a small computational cluster and a few days of computation. This method uses an iterative approach that relies on (1) a surrogate spectrum model that captures spatial and time-dependent spectral conditions, (2) a multi-pass model that captures the pebble’s evolving nuclide inventory as a function of location and time in the core, and (3) a full-core model that captures the core’s spatial neutron flux distribution. The SLICE approach is applied to a generic fluoride salt–cooled high-temperature reactor, demonstrating fuel inventory convergence through nuclide concentration inspection across iterations and comparisons for core realizations with varying discretizations. Results agree within ~5% with another state-of-the-art code, with differences attributed to input parameter or modeling assumption variations in the equilibrium generation methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Reduced-order modeling for efficient cross section library development in high-temperature gas reactor pebble-bed depletion analysis

Accurate modeling of running-in and equilibrium conditions in pebble-bed reactors (PBRs) requires precise microscopic multigroup neutron cross sections. In Griffin, deterministic neutronics calculations rely on multivariate interpolation over large cross section libraries, resulting in significant memory usage and performance bottlenecks. This work, together with a companion paper on Griffin integration, explores reduced-order models (ROMs) to replace interpolation with lightweight surrogates. Several ROM techniques are benchmarked, with deep neural networks (DNNs) demonstrating superior memory efficiency, scalability, and predictive accuracy. A total of 295 DNNs were trained to build a comprehensive isotope library, integrated into Griffin through a custom LibTorch interface for depletion analysis. Initial results demonstrate that DNN-based ROMs drastically reduce memory demands while preserving accuracy, enabling finer tabulations and additional state variables without overhead. In conclusion, the framework also supports online cross section generation and real-time DNN updates through transfer learning, improving fidelity by capturing self-shielding and evolving nuclide compositions during burnup.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Vibrational metrics for the evaluation of internal conditions in a scaled nuclear dry storage cask

In many countries, permanent repositories for high burnup spent nuclear fuel assemblies have yet to be established. As a result, much of the spent fuel from nuclear reactors is contained within dry storage casks, with many being beyond their designed service life. The assessment of the internal structural integrity of these casks and the fuel assemblies contained therein is of critical importance for both extended storage and transport to permanent repositories. The large size, structural complexity, and inaccessibility of the interior of the casks make this task challenging. To address these difficulties, a 1:6 scaled model based closely on the design of a Transnuclear (TN-32) dry storage cask was fabricated to facilitate controlled studies of these structures in the laboratory. Vibrational spectroscopy was used to evaluate the state of the cargo and internal structures within the cask utilizing only measurements on the outer surface. Using modes identified through Finite Element modeling corresponding to those previously measured on a full-scale TN-32 cask, we report on the development of amplitude- and phase-based metrics that are sensitive to internal conditions in the lab cask. Steel rod bundles and steel shot were used as surrogates for intact and damaged fuel assemblies, and various internal configurations of these materials were investigated. Finally, the metrics were based on acquired spectra involving the (1, 2) global bending mode and the (2, 1) radial-with-shearing mode. The results show that the metrics are sensitive to the condition of a single assembly and have some ability to determine the locations of damaged and empty slots.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗