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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Agreement of Analytical and Simulation-Based Estimates of the Required Land Depth in Climate Models

Previous analytical and simulation-based analyses suggest that deeper land surface models are needed to realistically simulate the terrestrial thermal state in climate models, with implications for land-atmosphere interactions. Analytical approaches mainly focused on the subsurface propagation of harmonics such as the annual temperature signal, and a direct comparison with climate-change model output has been elusive. Here, this study addresses the propagation of a harmonic pulse fitted to represent the timescale and amplitude of anthropogenic warming. Its comparison to land model simulations with stepwise increased bottom boundary depth leads to an agreement between the simulation-based and analytical frameworks for long-term climate trends. Any depth increase gradually decreases the relative error in the subsurface thermodynamics, and a minimum depth of 170 m is recommended to simulate the ground climate adequately. The approach provides an accurate estimate of the required land-model depth for climate-change simulations and assesses the relative bias in insufficiently deep land models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine learning force field model for kinetic Monte Carlo simulations of itinerant Ising magnets

Here, we present a scalable machine learning (ML) framework for large-scale kinetic Monte Carlo (kMC) simulations of itinerant electron Ising systems. As the effective interactions between Ising spins in such itinerant magnets are mediated by conducting electrons, the calculation of energy change due to a local spin update requires solving an electronic structure problem. Such repeated electronic structure calculations could be overwhelmingly prohibitive for large systems. Assuming the locality principle, a convolutional neural network (CNN) model is developed to directly predict the effective local field and the corresponding energy change associated with a given spin update based on Ising configuration in a finite neighborhood. As the kernel size of the CNN is fixed at a constant, the model can be directly scalable to kMC simulations of large lattices. Our approach is reminiscent of the ML force field models widely used in first-principles molecular dynamics simulations. Applying our ML framework to a square-lattice double-exchange Ising model, we uncover unusual coarsening of ferromagnetic domains at low temperatures. Our work highlights the potential of ML methods for large-scale modeling of similar itinerant systems with discrete dynamical variables.

machine learning↗
DOE OSTI2025

CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

Nweye, Kingsley↗
DOE OSTI2019

An efficient correlation for heat and mass transfer effectiveness in tumble-type clothes dryer drums

A heat and mass transfer effectiveness definition relevant to clothes dryers is developed in this work. A correlation is presented for determining the effectiveness of heat and mass transfer in a horizontal-axis, tumble-type clothes dryer drum with axial airflow. The correlation is a function of four measurable quantities: air mass flow rate, cloth mass, air mass in the drum, and the fall time experienced by a cloth falling the full height (i.e. diameter) of the drum. The Buckingham Pi Theorem is applied to the problem in order to derive dimensionless terms upon which the effectiveness depends. Empirical data from several dryers are used to derive an empirical correlation as a function of the dimensionless variables. Three cloth types were investigated, and a separate empirical correlation is proposed for each. Accordingly, the drum effectiveness concept and the correlation presented provide a new, more accurate, computationally efficient, and readily implemented framework for modeling and simulating clothes dryers. It is relevant to conventional gas and electric clothes drying appliances, vapor compression heat pump dryers, and thermoelectric heat pump clothes dryers. Along with appropriate empirical inputs, the framework is extensible to any thermal-evaporative cloth drying systems, including radial-flow horizontal axis tumble drying drums.

42 ENGINEERING↗
DOE OSTI2026

High-Voltage DC MULTI-terminal SIMulation (HVDC MULTISIM): Technical Program Summary

Expansion of the power grid in the USA is essential to fulfilling the rising energy demand of the country. In particular, the transmission grid, a network of electrical energy corridors that enable the flow of large amounts of energy from the point of generation to the point of consumption, needs significant expansion to cater to this growth of electrification. The current infrastructure is also dated, and any new transmission corridor should be based on a vision of building a modern infrastructure that is futureproof. High voltage DC (HVDC) transmission technology falls in this category: it is the most economical way to build large transmission lines and relies on sophisticated electronics and flexible controls, rather than just passive components like switchgear and transformers. This project is aimed at establishing the technical and economic feasibility of a network of HVDC lines that are interconnected to form a multi-terminal DC network. HVDC transmission is not a new technology and United States has a number of such lines; however, these are point to point transmissions and do not form a DC grid. Modeling and analyzing DC grids is challenging since there are no established modeling tools and the traditional ways of modeling AC grids fall short of providing the fidelity required the fast dynamics of the DC grid. This project aims to build a software-in-the-loop simulator for a multi-terminal HVDC grid, called MULTISIM and, establish new control and protection algorithms to operate such a connected DC system. NLR, a key partner, established the core framework of the MULTISIM simulator and validated its functionality. The GE Vernova team, created a full-scale model of a four-terminal HVDC network using PSCAD (Power Systems Computer Aided Design) software and established the baseline performance of the system during normal and fault operation. The project was started on 10/12024 with a kick-off meeting held on 12/11/2024 and ran through two quarters till termination. All the tasks, milestones and deliverables during this period were met. Several interim reports detailing the various tasks were submitted. The following sections provide details on the completed tasks till the project pause and termination after Q2.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Dynamical simulation via quantum machine learning with provable generalization

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. Our algorithm is thus resource efficient in terms of qubit and data requirements. Furthermore, our preliminary numerics for the XY model exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota. Published by the American Physical Society 2024

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Seasonal Cycle of Idealized Polar Clouds: Large Eddy Simulations Driven by a GCM

The uncertainty in polar cloud feedbacks calls for process understanding of the cloud response to climate warming. As an initial step toward improved process understanding, we investigate the seasonal cycle of polar clouds in the current climate by adopting a novel modeling framework using large eddy simulations (LES), which explicitly resolve cloud dynamics. Resolved horizontal and vertical advection of heat and moisture from an idealized GCM are prescribed as forcing in the LES. The LES are also forced with prescribed sea ice thickness, but surface temperature, atmospheric temperature, and moisture evolve freely without nudging. A semigray radiative transfer scheme without water vapor and cloud feedbacks allows the GCM and LES to achieve closed energy budgets more easily than would be possible with more complex schemes. This enables the mean states in the two models to be consistently compared, without the added complications from interaction with more comprehensive radiation. Furthermore, we show that the LES closely follow the GCM seasonal cycle, and the seasonal cycle of low-level clouds in the LES resembles observations: maximum cloud liquid occurs in late summer and early autumn, and winter clouds are dominated by ice in the upper troposphere. Large-scale advection of moisture provides the main source of water vapor for the liquid-containing clouds in summer, while a temperature advection peak in winter makes the atmosphere relatively dry and reduces cloud condensate. The framework we develop and employ can be used broadly for studying cloud processes and the response of polar clouds to climate warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Coupled Multiphysics Simulations of Heat Pipe Microreactors Using DireWolf

DireWolf is a multiphysics software driver application designed to simulate heat pipe–cooled nuclear microreactors. Developed under the U.S. Department of Energy, Office of Nuclear Energy Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the DireWolf software application’s objective is to provide the nuclear community with a design and safety analysis simulation capability. Based upon the NEAMS program Multiphysics Object-Oriented Simulation Environment (MOOSE) computational framework, DireWolf tightly couples nuclear microreactor physics, reactor physics, radiation transport, nuclear fuel performance, heat pipe thermal hydraulics, power generation, and structural mechanics to resolve the interdependent nonlinearities. DireWolf is capable of simulating both steady and transient normal reactor operation and several postulated failure scenarios. We will present the fundamental physics of heat pipe–cooled nuclear microreactors and the MOOSE-based software employed in DireWolf. Both steady and transient results for coupled reactor physics, radiation transport, and nuclear fuel performance are demonstrated.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Adaptive Computing for Scale-Up Problems

Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocation adaptive to some objective such as optimization or model training. The framework enables the characterization and management of uncertainties associated with predictive models of complex systems when scale-up questions lead to significant model extrapolation. A key advancement of this framework is its integration of multi-fidelity surrogate modeling, uncertainty management, and automated orchestration of various computing and experimentation resources into a single integrated software package. This enables efficient multi-fidelity modeling across multiple computing resources by incorporating real-world constraints such as relative queue times and throughput on individual machines into the multi-fidelity sampling decision. We discuss applications of this framework to problems in the renewable energy space, including biofuels production, material synthesis, perovskite crystal growth, and building electrical loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Assessment of occupant-behavior-based indoor air quality and its impacts on human exposure risk: A case study based on the wildfires in Northern California

he recent wildfires in California, U.S., have caused not only significant losses to human life and property, but also serious environmental and health issues. Ambient air pollution from combustion during the fires could increase indoor exposure risks to toxic gases and particles, further exacerbating respiratory conditions. This work aims at addressing existing knowledge gaps in understanding how indoor air quality is affected by outdoor air pollutants during wildfires -- by taking into account occupant behaviors (e.g., movement, operation of windows and air-conditioning) which strongly influence building performance and occupant comfort. A novel modeling framework was developed to simulate the indoor exposure risks considering the impact of occupant behaviors by integrating building energy and occupant behaviour modeling with computational fluid dynamics simulation. Occupant behaviors were found to exert significant impacts on indoor air flow patterns and pollutant concentrations, based on which, certain behaviors are recommended during wildfires. Further, the actual respiratory injury level under such outdoor conditions was predicted. The modeling framework and the findings enable a deeper understanding of the actual health impacts of wildfires, as well as informing strategies for mitigating occupant health risk during wildfires.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Theoretical Model and Numerical Simulation of Adsorption and Deformation in Flexible Metal–Organic Frameworks

We report that metal–organic frameworks (MOFs) have recently attracted considerable attention as new nanoporous materials with applications to separation of fluid mixtures, catalysis, gas capture and storage, and drug delivery. A subclass of MOFs, the MIL-53 (with Al or Cr) family, exhibits a complex structural phase transition, often referred to as the breathing transition, which occurs when gas molecules are adsorbed in its pore space. In this phenomenon, the material morphology oscillates between two distinct phases, usually referred to as the narrow-pore (np) and large-pore (lp) phases. We describe a statistical mechanical model based on the energetics of the system that couples the adsorbates with the host and the ensuing structural deformation of the materials. The Hamiltonian of the system consists of the elastic energy of the MOF, the fluid phase energy, and the interaction energy between the gas and MOFs. Minimizing the total energy with respect to both the gas density and the displacement field in the material yields the governing equations for both. The model is used to study the adsorption of CO 2 and CH 4 in MIL-53(Al). Here, the results demonstrate that all reported experimental features of the phenomenon are produced by the model. In particular, consistent with experimental data, the model predicts that the material experiences contraction in the np phase, whereas it undergoes swelling in the lp phase, and that the two phases coexist in the transition zone. The model is, however, completely general and is applicable to any type of guest-responsive material that undergoes deformation as a result of its interaction with the guest molecules.

36 MATERIALS SCIENCE↗
DOE OSTI2018

The ATLAS Trigger Simulation with Legacy Software

Physics analyses at the LHC require accurate simulations of the detector response and the event selection processes, generally done with the most recent software releases. The trigger response simulation is crucial for determination of overall selection efficiencies and signal sensitivities and should be done with the same software release with which data were recorded. This requires potentially running with software dating many years back, the so-called legacy software, in which algorithms and configuration may differ from their current implementation. Therefore having a strategy for running legacy software in a modern environment becomes essential when data simulated for past years start to present a sizeable fraction of the total. The requirements and possibilities for such a simulation scheme within the ATLAS software framework were examined and a proof-of-concept simulation chain has been successfully implemented. One of the greatest challenges was the choice of a data format which promises long term compatibility with old and new software releases. Over the time periods envisaged, data format incompatibilities are also likely to emerge in databases and other external support services. Software availability may become an issue, when e.g. the support for the underlying operating system might stop. The encountered problems and developed solutions will be presented, and proposals for future development will be discussed. Some ideas reach beyond the retrospective trigger simulation scheme in ATLAS as they also touch more generally aspects of data preservation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Peculiarities of the Self-Action of Inclined Wave Beams Incident on a Discrete System of Optical Fibers

Based on a discrete nonlinear Schrödinger equation (DNSE), we studied analytically and numerically the peculiarities of the self-action of one-dimensional quasi-optic wave beams injected into a spatially inhomogeneous medium consisting of a set of equidistant mutually coupled optical fibers. A variational approach allowing the prediction of the global evolution of localized fields with the initially plane phase front was developed. The self-consistent equations are obtained for the main parameters of such beams (the position of the center of mass, the effective width, and linear and quadratic phase-front corrections) in the aberrationless approximation. The case of radiation incident on a periodic system of nonlinear optical fibers at an angle to the axis oriented along them is analyzed in detail. It is shown that for the radiation power exceeding a critical value, the self-focusing of the wave field is observed, which is accompanied by the shift of the intensity maximum followed by the concentration of the main part of radiation only in one of the structural elements of the array under study. In this case, the beams propagate along paths considerably different from linear and the direction of their propagation changes compared to the initial direction. Asymptotic expressions are found that allow us to estimate the self-focusing length and to determine quite accurately the final position of a point with the maximum field amplitude after radiation trapping a channel. The results of the qualitative study of the possible self-channeling regimes for wave beams in a system of weakly coupled optical fibers in the aberrationless approximation are compared with the results of direct numerical simulations within the DNSE framework.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Framework For Performing Time-dependent Pebble Bed Reactor Simulations

The present work details the creation of a high-fidelity Monte Carlo methodology for analyzing the run-in and subsequent approach to equilibrium for PBRs. The methodology entails a Python module wrapped around Serpent so as to perform neutronics calculations, move pebbles, refuel the core, and discharge pebbles, thereby modeling the explicit behavior of the PBR run-in. The code kugelpy is within the GitHub repository `pyrates`.

Stewart, RyanH. [Idaho National Laboratory (INL), ↗
DOE OSTI2026

Tachyon: Intelligent Multi-Scale Modeling of Distributed Resilient Infrastructure and Workflows for Data Intensive HEP Analyses

The DOE High Energy Physics (HEP) program in Neutrino and Collider science drives data-intensive science and simulation on extreme-scale platforms. Modeling and optimizing the complex distributed components from experimental to leadership computing facilities are essential for HEP workflows to achieve required response times and resilience under various conditions. Tachyon proposes a framework for scalable modeling, simulation, and validation of key performance characteristics for the distributed infrastructure between FNAL and ALCF, along with associated HEP workflows.

Carothers, Chris [Rensselaer Poly.]↗