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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Quantum dynamics on a three-sheeted six-dimensional ab initio potential-energy surface of the phosphine cation: Simulation of the photoelectron spectrum and the ultrafast radiationless decay dynamics

A diabatic three-sheeted six-dimensional potential-energy surface has been constructed for the ground state and the lowest excited state of the PH{sub 3}{sup +} cation. Coupling terms of Jahn-Teller and pseudo-Jahn-Teller origin up to eighth order had to be included to describe the pronounced anharmonicity of the surface due to multiple conical intersections. The parameters of the diabatic Hamiltonian have been optimized by fitting the eigenvalues of the potential-energy matrix to ab initio data calculated at the CASSCF/MRCI level employing the correlation-consistent triple-ζ basis. The theoretical photoelectron spectrum of phosphine and the non-adiabatic nuclear dynamics of the phosphine cation have been computed by propagating nuclear wave packets with the multiconfiguration time-dependent Hartree method. The theoretical photoelectron bands obtained by Fourier transformation of the autocorrelation function agree well with the experimental results. It is shown that the ultrafast non-radiative decay dynamics of the first excited state of PH{sub 3}{sup +} is dominated by the exceptionally strong Jahn-Teller coupling of the asymmetric bending vibrational mode together with a hyperline of conical intersections with the electronic ground state induced by the umbrella mode. Time-dependent population probabilities have been computed for the three adiabatic electronic states. The non-adiabatic Jahn-Teller dynamics within the excited state takes place within ≈5 fs. Almost 80% of the excited-state population decay to the ground state within about 10 fs. The wave packets become highly complex and delocalized after 20 fs and no further significant transfer of electronic population seems to occur up to 100 fs propagation time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Open-Datasets for WEC Simulation

SAND2025-11468O The Open-Datasets for WEC Simulation is a tool that uses datasets and simulation configuration files to conduct OpenFOAM wave energy converter (WEC) simulations. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Chartrand, Chris [Sandia National Lab. (SNL-CA), L↗
DOE OSTI2026

Dark Energy Survey Year 3 results: Simulation-based 𝑤CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design

Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. Here, this work presents the first simulation-based inference (SBI) pipeline that combines weak lensing and galaxy clustering maps in a realistic Dark Energy Survey Year 3 (DES Y3) configuration and serves as preparation for a forthcoming analysis of the survey data. We develop a scalable forward model based on the CosmoGridV1 suite of N-body simulations to generate over one million self-consistent mock realizations of DES Y3 at the map level. Leveraging this large dataset, we train deep graph convolutional neural networks on the full survey footprint in spherical geometry to learn low-dimensional features that approximately maximize mutual information with target parameters. These learned compressions enable neural density estimation of the implicit likelihood via normalizing flows in a ten-dimensional parameter space spanning cosmological 𝑤CDM, intrinsic alignment, and linear galaxy bias parameters, while marginalizing over baryonic, photometric redshift, and shear bias nuisances. To ensure robustness, we extensively validate our inference pipeline using synthetic observations derived from both systematic contaminations in our forward model and independent Buzzard galaxy catalogs. Our forecasts yield significant improvements in cosmological parameter constraints, achieving 2−3× higher figures of merit in the 𝛺 𝑚 − 𝑆 8 plane relative to our implementation of baseline two-point statistics and effectively breaking parameter degeneracies through probe combination. These results demonstrate the potential of SBI analyses powered by deep learning for upcoming Stage-IV wide-field imaging surveys.

Thomsen, A. [Zurich, ETH] (ORCID:0000000203099021)↗
DOE OSTI2022

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

Building performance simulation has been adopted to support decision making in the building life cycle. An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations. Building energy model calibration is a technique that takes various types of measured performance data (e.g., energy use) and tunes key model parameters to match the simulated results with the actual measurements. This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings. A public building dataset that includes high-level building attributes (e.g., building type, vintage, total floor area, number of stories, zip code) of 111 buildings in San Francisco, California, USA, was used to generate building models in EnergyPlus. Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings' monthly electricity and natural gas consumption. The results showed 57 out of 111 buildings were successfully calibrated against actual buildings, while the remaining buildings showed opportunities for future calibration improvements. Enhancements to the pattern-based model calibration method are identified to expand its use for: (1) central heating, ventilation and air conditioning (HVAC) systems with chillers, (2) space heating and hot water heating with electricity sources, (3) mixed-use building types, and (4) partially occupied buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Understanding Power and Energy Utilization in Large Scale Production Physics Simulation Codes

Power is an often-cited reason for moving to advanced architectures on the path to Exascale computing. This is due to the practical concern of delivering enough power to successfully site and operate these machines, as well as concerns over energy usage while running large simulations. Since accurate power measurements can be difficult to obtain, processor thermal design power (TDP) is a possible surrogate due to its simplicity and availability. However, TDP is not indicative of typical power usage while running simulations. Using commodity and advance technology systems at Lawrence Livermore National Laboratory (LLNL) and Sandia National Laboratory, we performed a series of experiments to measure power and energy usage in running simulation codes. These experiments indicate that large scale LLNL simulation codes are significantly more efficient than a simple processor TDP model might suggest.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Operator learning for energy-efficient building ventilation control with computational fluid dynamics simulation of a real-world classroom

Energy-efficient ventilation control plays an important role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD) simulations provide detailed and physically accurate representations of indoor airflow, their high computational cost limits their use in real-time building control. In this work, we present a neural operator learning framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable building ventilation control with the high-fidelity fluid dynamics models. Our method jointly optimizes the airflow supply rates and vent angles to reduce energy use and adhere to air quality constraints. We train an ensemble of neural operator transformer models to learn the mapping from building control actions to airflow fields using high-resolution CFD data. This learned neural operator is then embedded in an optimization-based control framework for building ventilation control. Experimental results show that our approach achieves significant energy savings compared to maximum airflow rate control, rule-based control, as well as data-driven control methods using spatially averaged CO 2 prediction and deep learning–based reduced-order models, while consistently maintaining safe indoor air quality. These results highlight the practicality and scalability of our method in maintaining energy efficiency and indoor air quality in real-world buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Understanding power and energy utilization in large scale production physics simulation codes

Power is an often-cited reason for the move to advanced architectures on the path to Exascale computing. Here, this is due to practical considerations related to delivering enough power to successfully site and operate these machines, as well as concerns about energy usage while running large simulations. Since obtaining accurate power measurements can be challenging, it may be tempting to use the processor thermal design power (TDP) as a surrogate due to its simplicity and availability. However, TDP is not indicative of typical power usage while running simulations. Using commodity and advanced technology systems at Lawrence Livermore and Sandia National Labs, we performed a series of experiments to measure power and energy usage in running simulation codes. These experiments indicate that large scale Lawrence Livermore simulation codes are significantly more efficient than a simple processor TDP model might suggest.

HPC↗
DOE OSTI2024

Energy-dependent and Energy-integrated Two-moment General-relativistic Neutrino Transport Simulations of a Hypermassive Neutron Star

Abstract We compare two-moment-based energy-dependent and three variants of energy-integrated neutrino transport general-relativistic magnetohydrodynamics simulations of a hypermassive neutron star. To study the impacts due to the choice of the neutrino transport schemes, we perform simulations with the same setups and input neutrino microphysics. We show that the main differences between energy-dependent and energy-integrated neutrino transport are found in the disk and ejecta properties, as well as in the neutrino signals. The properties of the disk surrounding the neutron star and the ejecta in energy-dependent transport are very different from the ones obtained using energy-integrated schemes. Specifically, in the energy-dependent case, the disk is more neutron-rich at early times and becomes geometrically thicker at later times. In addition, the ejecta is more massive and, on average, more neutron-rich in the energy-dependent simulations. Moreover, the average neutrino energies and luminosities are about 30% higher. Energy-dependent neutrino transport is necessary if one wants to better model the neutrino signals and matter outflows from neutron star merger remnants via numerical simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A Simple Non-Planckian Radiation Source for ICF and HED Simulations (Rev.1)

The purpose of this paper is to present a simple way to build frequency dependent spectral (FDS) sources for use in inertial confinement fusion and high energy density physics simulations. This process takes an arbitrary temperature history and converts it into a radiation source with both Planckian and non-Planckian components, the latter of which is to describe high energy photon energies emitted by gold hohlraums. This method is then used to generate spectral energy sources for use in inertial confinement fusion (ICF) and high energy density (HED) simulations which are compared to integrated laser simulations along with experimental measurements.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2013

Demonstration of Data Center Energy Use Prediction Software

This report documents a demonstration of a software modeling tool from Romonet that was used to predict energy use and forecast energy use improvements in an operating data center. The demonstration was conducted in a conventional data center with a 15,500 square foot raised floor and an IT equipment load of 332 kilowatts. It was cooled using traditional computer room air handlers and a compressor-based chilled water system. The data center also utilized an uninterruptible power supply system for power conditioning and backup. Electrical energy monitoring was available at a number of locations within the data center. The software modeling tool predicted the energy use of the data center's cooling and electrical power distribution systems, as well as electrical energy use and heat removal for the site. The actual energy used by the computer equipment was recorded from power distribution devices located at each computer equipment row. The model simulated the total energy use in the data center and supporting infrastructure and predicted energy use at energy-consuming points throughout the power distribution system. The initial predicted power levels were compared to actual meter readings and were found to be within approximately 10 percent at a particular measurement point, resulting in a site overall variance of 4.7 percent. Some variances were investigated, and more accurate information was entered into the model. In this case the overall variance was reduced to approximately 1.2 percent. The model was then used to predict energy use for various modification opportunities to the data center in successive iterations. These included increasing the IT equipment load, adding computer room air handler fan speed controls, and adding a water-side economizer. The demonstration showed that the software can be used to simulate data center energy use and create a model that is useful for investigating energy efficiency design changes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

ADVANTG Results for a Proposed NDA System for Detecting Pin Diversion from SNF - Paper 120

This summary describes the results of applying the ADVANTG variance reduction parameter generator to accelerate continuous-energy Monte Carlo simulations of the {sup 252}Cf Interrogation with Prompt Neutron (CIPN) detection system proposed by Hu et al. for detecting the diversion of fuel elements from spent nuclear fuel (SNF) assemblies. In previous work, ADVANTG was applied to a suite of nine radiation detection, special nuclear material (SNM) movement detection, and safeguards test cases. That effort demonstrated that ADVANTG was able to provide large increases in tally figures of merit (FOMs) in nearly all of the cases without introducing bias into the Monte Carlo results. The objective of the work described here is to further evaluate ADVANTG, and also to identify gaps in capability with regard to nuclear safeguards applications, by applying the code to a challenging problem that is of a different nature from the ones previously studied. The CIPN nondestructive assay (NDA) system consists of three fission chambers embedded in a C-shaped block of polyethylene and a polyethylene-moderated {sup 252}Cf source that are placed on opposite sides of a subject SNF assembly. The idea of the detection system is to measure the difference between the background neutron count rate, due to spontaneous fission in the fuel material, and the count rate observed once the source is introduced, which is dominated by multiplication within the assembly. Based on the difference between these count rates, the fissile content of the assembly can be inferred, and this information can be used to determine if some fraction of the original fuel pins have been replaced with depleted uranium substitutes. The authors report that the system can detect the replacement of at least eight pins (3% of total mass). The ADVANTG code generates space- and energy-dependent weight-window bounds and biased source distributions based on approximate transport solutions that are efficiently generated by the Denovo 3-D discrete ordinates package. ADVANTG implements the Consistent Adjoint Driven Importance Sampling (CADIS) method for accelerating individual tallies and the Forward-Weighted CADIS (FW-CADIS) method for obtaining relatively uniform uncertainties across tallies over multiple regions and/or energy bins. The variance reduction parameters are generated in a format directly usable by standard versions of MCNP5 and MCNPX. In this work, the ADVANTG variance reduction parameter generator was applied to accelerate continuous energy MCNPX simulations of the {sup 252}Cf Interrogation with Prompt Neutron detection system for detecting the diversion of fuel elements from SNF assemblies. Variance reduction parameters generated with the existing version of ADVANTG actually slowed down tally convergence in all cases. The cause of the poor performance was found to be the lack of an induced fission source treatment in the fixed-source discrete ordinates calculations employed by ADVANTG. Because fission was treated as capture and not as a source, the importance maps generated by ADVANTG significantly underestimated the importance of thermal neutrons in and around the fuel elements. In order to obtain improved results, we developed an experimental implementation of a fission source outer iteration for the adjoint calculations. We used this prototype capability to generate new variance reduction parameters and found that the mean fission rates agreed well with those predicted by the conventional MCNPX simulations, as was expected. With these new parameters, speedup factors of 59 to 80 were obtained for the case with the background source, and factors of 17 to 24 were obtained for the cases with the interrogating source. These results demonstrate that a fission source treatment is needed to improve the effectiveness of ADVANTG in problems where the effects of induced fission are important. This includes many types of NDA problems that are relevant to safeguards applications. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Status on the Development of a Modeling and Simulation Framework for the Economic Assessment of Nuclear Hybrid Energy

Continued effort to design and build a modeling and simulation framework to assess the economic viability of Nuclear Hybrid Energy Systems (NHES) was undertaken in fiscal year (FY) 2016. The purpose of this report is to document the various tasks associated with the development of such a framework and to provide a status of their progress. Several tasks have been accomplished. First, a synthetic time history generator has been developed in RAVEN, which consists of Fourier series and autoregressive moving average model. The former is used to capture the seasonal trend in historical data, while the latter is to characterize the autocorrelation in residue time series (e.g., measurements with seasonal trends subtracted). As demonstration, both synthetic wind speed and grid demand are generated, showing matching statistics with database. In order to build a design and operations optimizer in RAVEN, a new type of sampler has been developed with highly object-oriented design. In particular, simultaneous perturbation stochastic approximation algorithm is implemented. The optimizer is capable to drive the model to optimize a scalar objective function without constraint in the input space, while the constraints handling is a work in progress and will be implemented to improve the optimization capability. Furthermore, a simplified cash flow model of the performance of an NHES in the electric market has been developed in Python and used as external model in RAVEN to confirm expectations on the analysis capability of RAVEN to provide insight into system economics and to test the capability of RAVEN to identify limit surfaces. Finally, an example calculation is performed that shows the integration and proper data passing in RAVEN of the synthetic time history generator, the cash flow model and the optimizer. It has been shown that the developed Python models external to RAVEN are able to communicate with RAVEN and each other through the newly developed RAVEN capability called “EnsembleModel”.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Italian prototype building models for urban scale building performance simulation

Urban building energy modeling (UBEM) seeks to evaluate strategies to optimize building energy use at urban scale to support a city's building energy goals. Prototype building models are usually developed to represent typical urban building characteristics of a specific use type, construction year, and climate zone, as detailed characteristics of individual buildings at urban scale are difficult to obtain. This study investigated the Italian building stock, developing 46 building prototypes, based on construction year, for residential and office buildings. The study included 16 single-family buildings, 16 multi-family buildings, and 14 office buildings. Building envelope properties and heating, ventilation, and air conditioning system characteristics were defined according to existing building energy codes and standards for climatic zone E, which covers about half the Italian municipalities. Novel contributions of this study include (1) detailed specifications of prototype building energy models for Italian residential and office buildings that can be adopted by UBEM tools, and (2) a dataset in GeoJSON format of Italian urban buildings compiled from diverse data sources and national standards. Finally, the developed prototype building specifications, the building dataset, and the workflow can be applied to create other building prototypes and to support Italian national building energy efficiency and environmental goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

NEAMS FPL M2 Milestone Report: Development of a UO₂ Grain Size Model using Multicale Modeling and Simulation

This report summarizes development work funded by the Nuclear Energy Advanced Modeling Simulation program's Fuels Product Line (FPL) to develop a mechanistic model for the average grain size in UO₂ fuel. The model is developed using a multiscale modeling and simulation approach involving atomistic simulations, as well as mesoscale simulations using INL's MARMOT code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Warthog Framework Update: Depletion Capabilities

The Nuclear Energy Advanced Modeling and Simulation (NEAMS) program the US Department of Energy (DOE) Office of Nuclear Energy supports two simulation frameworks to provide advanced simulation techniques for analyzing nuclear reactors. Primarily at Idaho National Laboratory (INL), the fuels product line (FPL) is developing the fuel performance code BISON from Hales et al. [2016], which depends on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework developed by Gaston et al. [2009]. Likewise, at Argonne National Laboratory (ANL), in the reactor product line (RPL), PROTEUS is being developed by Lee et al. [2013] and Nek5000 by Fischer et al. [2015] based on the Simulation-based High-efficiency Advanced Reactor Prototyping (SHARP) framework from Tautges et al. [2013]. MOOSE employs a bottom-up approach to multiphysics coupling, where component codes are built upon a common framework. SHARP employs a top-down approach in which legacy component codes are connected after being separately developed. Each approach has benefits and disadvantages, but this work focuses on having the two approaches work in harmony.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Stout Smearing on a Quantum Computer

Smearing of gauge-field configurations in lattice field theory improves the results of lattice simulations by suppressing high energy modes from correlation functions. In quantum simulations, high kinetic energy eigenstates are introduced when the time evolution operator is approximated such as Trotterization. While improved Trotter product formulae exist to reduce the errors, they have diminishing accuracy returns with respect to resource costs. Therefore having an algorithm that has fewer resources than an improved Trotter formula is desirable. In this work I develop a representation agnostic method for quantum smearing and show that it reduces the coupling to high energy modes in the discrete nonabelian gauge theory $D_4$

Gustafson, Erik J.↗
DOE OSTI2020

Method of information entropy for convergence assessment of molecular dynamics simulations

The lack of a reliable method to evaluate the convergence of molecular dynamics simulations has contributed to discrepancies in different areas of molecular dynamics. Here, the method of information entropy is introduced to molecular dynamics for stationarity assessment. The Shannon information entropy formalism is used to monitor the convergence of the atom motion to a steady state in a continuous spatial domain and is also used to assess the stationarity of calculated multidimensional fields such as the temperature field in a discrete spatial domain. It is demonstrated in this work that monitoring the information entropy of the atom position matrix provides a clear indicator of reaching steady state in radiation damage simulations, non-equilibrium molecular dynamics thermal conductivity computations, and simulations of Poiseuille and Couette flow in nanochannels. A main advantage of the present technique is that it is non-local and relies on fundamental quantities available in all molecular dynamics simulations. Unlike monitoring average temperature, the technique is applicable to simulations that conserve total energy such as reverse non-equilibrium molecular dynamics thermal conductivity computations and to simulations where energy dissipates through a boundary as in radiation damage simulations. The method is applied to simulations of iron using the Tersoff/ZBL splined potential, silicon using the Stillinger–Weber potential, and to Lennard–Jones fluid. Its applicability to both solids and fluids shows that the technique has potential for generalization to other areas in molecular dynamics.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Energy-efficient, Large-scale Molecular Dynamics Simulations via Hardware- and Algorithm-level Optimization

This work aims to develop a framework for energy-efficient computing that will enable molecular dynamics (MD) simulations of large-scale phenomena with atomic precision and simultaneously remove computational bottlenecks limiting the speed of MD simulations. We seek to implement such an approach through the development of surrogate models for the interatomic force calculation combined with the use of mixed numerical precision formats. For a model system of neutral atoms (only pairwise interactions), significant force calculation efficiency improvements were achieved, without detrimental effects on atomic structures or average energies, using single precision, by developing a surrogate model (deep neural network), and by quantizing this surrogate model. For a model system of charged atoms, the reciprocal-space calculation of electrostatic interactions was identified as the main bottleneck, and the development of a surrogate model should be pursued to achieve an estimated one-order-of-magnitude additional speedup.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗