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At least 163 records · Page 9

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DOE OSTI2026

Impact of in situ nuclear networks and atomic opacities on neutron star merger ejecta dynamics, nucleosynthesis, and kilonovae

Context. Binary neutron star merger (BNSM) ejecta are key sites of rapid neutron capture (r-process) nucleosynthesis and they produce kilonovae powered by the radioactive decay of freshly synthesized nuclei. Modeling their evolution requires multi-physics simulations involving hydrodynamics, nuclear reactions, and radiative processes. The impact of nuclear burning and atomic opacity is poorly understood and often treated with simplified prescriptions. Aims. We systematically investigate different treatments of nuclear heating, particle thermalization, and atomic opacities in radiation-hydrodynamics simulations of BNSM ejecta and kilonova light curves. Methods. Ejecta profiles from long-term numerical-relativity simulations of asymmetric neutron star binaries with a massive neutron star remnant were evolved to ∼30 days using a 2D ray-by-ray approach. We compared simplified heating-rate and thermalization prescriptions with in situ Nuclear reaction Network (NN) calculations that track nuclear energy deposition and include a composition-dependent thermalization scheme. We also contrasted various gray opacity models with a frequency-dependent treatment based on atomic calculations. Results. Coupling NN and hydrodynamics significantly affects nucleosynthesis and kilonova emission. Assuming homologous expansion alters abundance evolution and produces a narrower, less populated second r-process peak and a third peak shifted to higher mass numbers. The back-reaction of nuclear heating affects the temperature evolution enough to delay and redden the early (t∼ hours) kilonova peaks. A constant thermalization efficiency underestimates and reddens the early emission while overestimating the late-time luminosity compared to the composition-dependent treatment. Analytical opacity prescriptions yield a more extended, colder photosphere, resulting in dimmer, redder kilonovae at early times (t≲ hour), while the delayed recession of the photosphere prolongs the red emission at t ≳ 5 days. Conclusions. Coupling hydrodynamics to an in situ NN is crucial for reliable nucleosynthesis and kilonova predictions. Resolving the first several hundred milliseconds of the hydrodynamics is essential for robust nucleosynthesis calculations. Composition-dependent thermalization and frequency-dependent, atomic-physics-based opacities are needed to accurately capture the temperature evolution of the ejecta and the brightness and color evolution of the kilonova. Calibrated analytic nuclear-power fits with simplified thermalization and opacity prescriptions can still reproduce the density and temperature evolution of the ejecta.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2025

Artificial intelligence-based predictive modeling for imaging neutral particle analyzers on the DIII-D tokamak

The Imaging Neutral Particle Analyzer (INPA) at DIII-D is a diagnostic system used to accurately resolve the energy and spatial distributions of fast ions in fusion plasmas. A novel artificial intelligence (AI) technique named INPA-net is based on Reservoir Computing Networks and developed here to predict active and passive signals produced by charge-exchange reactions from injected and edge-cold neutrals, respectively, in magnetically confined fusion plasmas. This model is trained using a set of 21 time domain signals between 0 s to 3.35 s that includes injected beam and thermal plasma information, and 6444 real 2D experimental images of the INPA in 12 plasma discharges at DIII-D. The trained neural network is able to forecast experimental images in real-time. The model achieves an R-squared value of 0.91, which is higher than the 0.83 value achieved by a simple linear regression model. This improvement highlights the model's enhanced predictive accuracy for measured images from the validation set. This AI approach is valuable due to its rapid response times and potential for integration into real-time plasma control systems. A version of this model capable of generating syntehic images would be useful for the real-time monitoring of fast-ion transport. A comprehensive sensitivity study reveals that INPA-net maintains high performance even with variations in the input parameters, indicating the model's robustness and reliability. While developed for the INPA, the underlying architecture is adaptable and may be applied to various 2D imaging diagnostics in fusion research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

ThermoPore: Predicting part porosity based on thermal images using deep learning

Part qualification is often a critical and labor-intensive process in additive manufacturing, particularly in the detection of defects such as porosity, which stands to benefit significantly from advancements in machine learning. We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a R 2 score of 0.57 and our model for porosity localization produced an average Intersection over Union (IoU) score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity “Digital Twins” based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

Deep learning↗

DOE OSTI2020

Evaluation of low-exergy heating and cooling systems and topology optimization for deep energy savings at the urban district level

District energy systems have the potential to achieve deep energy savings by leveraging the density and diversity of loads in urban districts. However, planning and adoption of district thermal energy systems is hindered by the analytical burden and high infrastructure costs. It is hypothesized that network topology optimization would enable wider adoption of advanced (ambient temperature) district thermal energy systems, resulting in energy savings. In this study, energy modeling is used to compare the energy performance of “conventional” and “advanced” district thermal energy systems at the urban district level, and a partial exhaustive search is used to evaluate a heuristic for the topology optimization problem. For the prototypical district considered, advanced district thermal energy systems mated with low-exergy building heating and cooling systems achieved a source energy use intensity that was 49% lower than that of conventional systems. The minimal spanning tree heuristic was demonstrated to be effective for the network topology optimization problem in the context of a prototypical district, and contributes to mitigating the problem’s computational complexity. The work presented in this paper demonstrates the potential of advanced district thermal energy systems to achieve deep energy savings, and advances to addressing barriers to their adoption through topology optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning

The ground state electron density — obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations — contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation, making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data, while comprehensively sampling system configurations using thermalization. Our ML models are less reliant on heuristics, and being based on Bayesian neural networks, enable uncertainty quantification. We show that our models incur significantly lower data generation costs while allowing confident — and when verifiable, accurate — predictions for a wide variety of bulk systems well beyond training, including systems with defects, different alloy compositions, and at multi-million-atom scales. Moreover, such predictions can be carried out using only modest computational resources.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1993

Program Computes Flows Of Fluids And Heat

SINDA'85/FLUINT incorporates lumped-parameter-network and one-dimensional-flow mathematical models. System enables analysis of mutual influences of thermal and flow phenomena. Offers two finite-difference numerical solution techniques: forward-difference explicit approximation and Crank-Nicholson approximation. Enables simulation of nonuniform heating and facilitates mathematical modeling of thin-walled heat exchangers. Ability to model nonequilibrium behavior within two-phase volumes included. Recent changes in program improve modeling of real evaporator pumps and other capillary-assist evaporators. Written in FORTRAN 77.

Cullimore, Brent↗

NASA NTRS1972

Simulation of a steady-state integrated human thermal system.

The mathematical model of an integrated human thermal system is formulated. The system consists of an external thermal regulation device on the human body. The purpose of the device (a network of cooling tubes held in contact with the surface of the skin) is to maintain the human body in a state of thermoneutrality. The device is controlled by varying the inlet coolant temperature and coolant mass flow rate. The differential equations of the model are approximated by a set of algebraic equations which result from the application of the explicit forward finite difference method to the differential equations. The integrated human thermal system is simulated for a variety of combinations of the inlet coolant temperature, coolant mass flow rate, and metabolic rates. Two specific cases are considered: (1) the external thermal regulation device is placed only on the head and (2) the devices are placed on the head and the torso. The results of the simulation indicate that when the human body is exposed to hot environment, thermoneutrality can be attained by localized cooling if the operating variables of the external regulation device(s) are properly controlled.

Hsu, F. T.↗

NASA NTRS1972

Simulation of a steady-state integrated human thermal system.

Hsu, F. T.↗

DOE OSTI2023

Modelling and Simulation of a Thermoelectric Heat Pump with Micro-Channel Heat Transfer

Thermoelectric heat pumps (TEHPs) have advantages of modularity and simple design for heating and cooling in some applications. Models of thermoelectric (TE) heat pumps are widely studied. Nevertheless, most existing modeling work focuses on the TE material or air-sourced TE modules. TEHP modeling, especially the relationship between modules and heat exchangers, has not been comprehensively conducted. This work presents a water-to-water TEHP modeling framework that combines Goldsmid’s approach for TE material performance, Gnielinski’s correlation for convective heat transfer, and thermal balance theory for heat exchange networks. This combined framework provides an accurate theoretical analysis of the water-to-water TEHP system. Subsequently, the framework was used to empirically determine TE material properties (electric resistivity, thermal conductivity, and Seebeck coefficient) that minimize modeling errors versus experimentally observed values from the literature. Finally, an additional set of experimental TEHP data was used to validate the model, all with relative absolute deviations of approximately 10% when predicting heating capacity and 10%–20% when forecasting cooling capacity at a 30 K temperature lift. For future work, people can further develop models of TEHPs with an air-based heat sink on one side and water channels on the other side.

Wan, Hanlong↗

DOE OSTI2020

Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control

District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Analytic model of dislocation density evolution in fcc polycrystals accounting for dislocation generation, storage, and dynamic recovery mechanisms

Here, an analytic model of the evolution of dislocation density in fcc polycrystals is described. The evolution equations approximately account for most known dislocation storage, dynamic recovery, and dislocation generation mechanisms in fcc polycrystals. Specifically, the model incorporates network (forest) and grain boundary storage, mobile-network and mobile–mobile annihilation, screw–screw annihilation via athermal and thermal single cross-slip, generation by double cross-slip (Koehler mechanism, including dipole formation), Frank-Read sources, grain boundary nucleation, and mobile–immobile dislocation nucleation due to shock loading. Single cross-slip is assumed to proceed through the Friedel–Escaig (FE) mechanism; the corresponding activation energy is calculated using a modified FE model. The activation energy for double cross-slip is calculated for the first time by extending the FE model. The exact evolution equations are integro-differential equations, and as such are difficult to implement in a code; hence, the evolution equations are simplified by making several approximations. Preliminary results on copper are presented, including comparisons to experimental data.

36 MATERIALS SCIENCE↗

NASA NTRS2017

Numerical Modeling of Saturated Boiling in a Heated Tube

This paper describes a mathematical formulation and numerical solution of boiling in a heated tube. The mathematical formulation involves a discretization of the tube into a flow network consisting of fluid nodes and branches and a thermal network consisting of solid nodes and conductors. In the fluid network, the mass, momentum and energy conservation equations are solved and in the thermal network, the energy conservation equation of solids is solved. A pressure-based, finite-volume formulation has been used to solve the equations in the fluid network. The system of equations is solved by a hybrid numerical scheme which solves the mass and momentum conservation equations by a simultaneous Newton-Raphson method and the energy conservation equation by a successive substitution method. The fluid network and thermal network are coupled through heat transfer between the solid and fluid nodes which is computed by Chen's correlation of saturated boiling heat transfer. The computer model is developed using the Generalized Fluid System Simulation Program and the numerical predictions are compared with test data.

Majumdar, Alok↗

DOE OSTI2023

Understanding oxidation of Fe-Cr-Al alloys through explainable artificial intelligence

Abstract The oxidation resistance of FeCrAl based on alloying composition and oxidizing conditions is predicted using a combinatorial experimental and artificial intelligence approach. A neural network (NN) classification model was trained on the experimental FeCrAl dataset produced at GE Research. Furthermore, using the SHapley Additive exPlanations (SHAP) explainable artificial intelligence (XAI) tool, we explore how the NN can showcase further material insights that are unavailable directly from a black-box model. We report that high Al and Cr content forms protective oxide layer, while Mo in FeCrAl creates thick unprotective oxide scale that is vulnerable to spallation due to thermal expansion. Graphical abstract

Materials Science↗

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↗

NASA NTRS1988

SINDA - Systems Improved Numerical Differencing Analyzer

Capability to model fluid flow added. SINDA is software system developed to solve physical problems governed by diffusion-type equations mathematically modeled by lumped-parameter representations. Used as general thermal analyzer with resistor-and-capacitor-network representations. Adapted to wide range of problems represented by Fourier, Possion, Laplace, or other differential equations. Written in FORTRAN and Assembler.

Damico, Steve J.↗

NASA NTRS1990

SINDA '85/FLUINT, Version 2.2

SINDA, Systems Improved Numerical Differencing Analyzer, software system developed for solving physical problems governed by diffusion-type equations modeled by lumped-parameter representations. Used as general thermal analyzer with resistor-and-capacitor-network representations, adapted to wide range of problems represented by such differential equations as Fourier, Poisson, or LaPlace. Software system updated. FLUINT, FLUid INTegrator, is advanced, one-dimensional fluid-analysis program solving equations of arbitrary fluid-flow networks. Written in FORTRAN and ASSEMBLER.

Cullimore, Brent↗

NASA NTRS2011

NASA Tech Briefs, October 2011

Topics covered include: Laser Truss Sensor for Segmented Telescope Phasing; Qualifications of Bonding Process of Temperature Sensors to Deep-Space Missions; Optical Sensors for Monitoring Gamma and Neutron Radiation; Compliant Tactile Sensors; Cytometer on a Chip; Measuring Input Thresholds on an Existing Board; Scanning and Defocusing Properties of Microstrip Reflectarray Antennas; Cable Tester Box; Programmable Oscillator; Fault-Tolerant, Radiation-Hard DSP; Sub-Shot Noise Power Source for Microelectronics; Asynchronous Message Service Reference Implementation; Zero-Copy Objects System; Delay and Disruption Tolerant Networking MACHETE Model; Contact Graph Routing; Parallel Eclipse Project Checkout; Technique for Configuring an Actively Cooled Thermal Shield in a Flight System; Use of Additives to Improve Performance of Methyl Butyrate-Based Lithium-Ion Electrolytes; Li-Ion Cells Employing Electrolytes with Methyl Propionate and Ethyl Butyrate Co-Solvents; Improved Devices for Collecting Sweat for Chemical Analysis; Tissue Photolithography; Method for Impeding Degradation of Porous Silicon Structures; External Cooling Coupled to Reduced Extremity Pressure Device; A Zero-Gravity Cup for Drinking Beverages in Microgravity; Co-Flow Hollow Cathode Technology; Programmable Aperture with MEMS Microshutter Arrays; Polished Panel Optical Receiver for Simultaneous RF/Optical Telemetry with Large DSN Antennas; Adaptive System Modeling for Spacecraft Simulation; Lidar-Based Navigation Algorithm for Safe Lunar Landing; Tracking Object Existence From an Autonomous Patrol Vehicle; Rad-Hard, Miniaturized, Scalable, High-Voltage Switching Module for Power Applications; and Architecture for a 1-GHz Digital RADAR.

Source record↗

DOE OSTI2021

Modeling heat transport processes in enhanced geothermal systems: Validation study from EGS Collab Experiment 1

Heat recovery from enhanced geothermal systems (EGS) is a complex process involving heat transport in both fracture networks and rock formations. A comprehensive understanding of and the ability to model the underlying heat transport mechanisms is important for the success of EGS but remains challenging in practice due to the generally insufficient characterization of EGS reservoirs. In the present study, we analyze an extensively monitored intermediate-scale EGS field experiment performed in a well-characterized testbed. The high-resolution, high-quality measurements from the field experiment enable the development of a high-fidelity model incorporating a well-constrained fracture network. Based on the field experiment, we investigate the complex heat transport processes in an EGS-relevant environment and validate the capability of a numerical approach in simulating these inherently coupled heat transport processes. A series of numerical simulations were performed to study the effects of different heat transport mechanisms, including thermal convection with fracture flow, thermal conduction in rock formations, and the Joule-Thomson effect. The agreement of thermal responses between field measurements and simulation results indicates that our numerical approach can appropriately model the heat transport processes pertaining to heat recovery from EGS reservoirs.

Wu, Hui↗