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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning – especially deep learning – to molecular simulation. Furthermore, these techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Understanding Discrete Fracture Networks Through Spectral Graph Theory

Discrete Fracture Network models (DFNs) are used to simulate fluid flow and particle transport through fracture networks in low permeability rock. Understanding these processes are essential in many subsurface applications, such as environmental restoration of contaminated fractured media, CO 2 sequestration, detection of low-level nuclear tests, and hydrocarbon extraction. Compared with other models, DFNs allow for incorporation of a wider range of network characteristics but have substantially greater computation cost. These networks can be represented with graphs, allowing the use of graph theory tools to study the networks. I used Python to simulate flow and transport on a range of DFNs and analyzed these networks using methods from network analysis and spectral graph theory. My purpose was to find ways to gain insight about flow and transport on DFNs using these graph representations, bypassing the computationally intensive meshing typically required. My work is still in progress, but I have discovered several interesting trends and patterns that I believe could be useful towards my goal. If I am able to bring these results to fruition, they will aid subsurface geologists in extracting flow and transport information about fracture networks more efficiently.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

ezAlign: A Tool for Converting Coarse-Grained Molecular Dynamics Structures to Atomistic Resolution for Multiscale Modeling

Soft condensed matter is challenging to study due to the vast time and length scales that are necessary to accurately represent complex systems and capture their underlying physics. Multiscale simulations are necessary to study processes that have disparate time and/or length scales, which abound throughout biology and other complex systems. Herein we present ezAlign, an open-source software for converting coarse-grained molecular dynamics structures to atomistic representation, allowing multiscale modeling of biomolecular systems. The ezAlign v1.1 software package is publicly available for download at github.com/LLNL/ezAlign. Its underlying methodology is based on a simple alignment of an atomistic template molecule, followed by position-restraint energy minimization, which forces the atomistic molecule to adopt a conformation consistent with the coarse-grained molecule. The molecules are then combined, solvated, minimized, and equilibrated with position restraints. Validation of the process was conducted on a pure POPC membrane and compared with other popular methods to construct atomistic membranes. Additional examples, including surfactant self-assembly, membrane proteins, and more complex bacterial and human plasma membrane models, are also presented. By providing these examples, parameter files, code, and an easy-to-follow recipe to add new molecules, this work will aid future multiscale modeling efforts.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Port of New York and New Jersey Drayage Electrification Analysis

The National Renewable Energy Laboratory (NREL) evaluated the potential for drayage electrification in the Port of New York and New Jersey (PoNYNJ), with a focus on operators: Harbor Freight Transport (HF), Safeway Trucking (SWT), and International Motor Freight Inc (IMF). This report summarizes the data collection and electrification evaluation of all three drayage operators, includes detailed operational data, and identifies the performance requirements for battery electric tractors (BETs) and corresponding infrastructure operated within the context of PoNYNJ drayage operation. This report also details a methodology to evaluate opportunities, strategies, and challenges associated with future expansions of BETs in meeting PANYNJ emissions goals. The Port Authority has established a goal of achieving Net Zero carbon emissions by 2050 across all facilities, including from tenant and stakeholder sources such as drayage trucks. NREL used real-world performance data collected on the three PoNYNJ drayage operations, along with modeling and analysis tools to compare BET to diesel trucks. From March to July 2021, NREL collected 1Hz vehicle and engine data from 46 drayage trucks at the three operators totaling nearly 121,000 miles of operation, providing enough information to assess vehicle operations for electrification potential. A Future Automotive Systems Technology Simulator (FASTSim) electric truck powertrain model was validated using PoNYNJ data and scenarios were run to evaluate drayage truck electrification requirements over the real-world cycles. The first scenario examined BET viability with minimal changes to existing operations. This assumes the trucks charge when stopped for two hours or longer, have a functional battery size of 375 kWh, and can charge at 270 kilowatts (kW) average which are the specification of the commercially available Freightliner eCascadia. The second scenario looked at what operational, charging infrastructure, and BET technology changes would be needed to fully electrify. Finally, detailed analysis was run on charging rate structure to understand operational costs to the fleets. The studied drayage trucks averaged 5.1 MPG, spent roughly 9% of their energy at idle, and drove an average of 140 miles per day with a maximum daily distance of 573 miles. The FASTSim model results indicate a comparable BET would use 417 kWh of energy per day on average accounting for cargo weight, which is close to the full usable capacity of the eCascadia currently available on the market. Based on the daily average operating data, partial fleet electrification is possible with current technology. However, some specific days of operation would require over 1,600 kWh of energy due to longer distances traveled by the trucks and more intense operation. Trucks used for long distance and intense operation cannot be readily electrified with current technology without operational changes. Full adoption of BETs could reduce CO 2 emissions from these fleets by roughly 75% today, eliminating 76 metric tons of CO 2 (MTCO 2 ) per vehicle each year, which equates to 24,100 MTCO 2 per year for all three operators. Commercially available direct current fast chargers (DCFC) have charge rates up to 350 kW. Based on the average daily modeled energy use for each operator, current industrial rate structures, and the assumption of 350 kW peak charging, full drayage electrification would increase electricity consumption. In addition, peak demand usage would increase with unmanaged charging along with cost of electricity having a direct impact on cost per mile for electric vehicles. The resulting cost per mile for BETs along with comparable cost per mile for conventional diesel trucks are also examined at $\$$4.00 per gallon of diesel. It will be important for PANYNJ and the drayage operators within the PoNYNJ to consider these load impacts to their existing electrical infrastructure and devise operational strategies that avoid coincident charging of vehicles to mitigate demand charges. Despite these electricity cost increases, savings from reductions in diesel consumption will help offset the costs of this increased electricity consumption. However, prices of both electricity and diesel are subject to change based on various factors meaning the realized savings will vary over time. This shows BETs could be cost-competitive on an energy cost per mile basis for all scenarios while diesel is above $\$$3.00/gal. Further, if diesel prices dropped to the 15-year low of $2.33/gal, it would still be cost competitive to operate the EVs with electricity costs of 16.3 ¢/kWh or less.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

OC7 phase I: CFD investigation of viscous forces on rectangular members of semisubmersibles

In Phase I of the Offshore Code Comparison Collaboration 7 (OC7) project, we aim to address key questions related to the modeling of viscous hydrodynamic loads on floating platforms. Here, this paper presents results from a collaborative effort within Phase I of the OC7 project. We investigate viscous forces acting on the rectangular members of semisubmersible platforms using high-fidelity computational fluid dynamics (CFD) simulations. The parametric CFD analyses include two-and-a-half-dimensional (2.5D) simulations of pontoon sections of two different semisubmersible platform designs (the VolturnUS-S and the INO WINDMOOR) and full three-dimensional (3D) structure simulations of the VolturnUS-S platform. We extract both the added mass and drag coefficients that are typically used in engineering-level hydrodynamic models from the 2.5D forced oscillation simulations with various Keulegan-Carpenter numbers and compare them to the coefficients from the 3D forced oscillation simulations. We observe consistent variation of the coefficients along the pontoon legs due to 3D platform and free-surface effects. With the breadth of the simulations and component-level detailed analyses, this work establishes a useful database and several empirical formulas for selecting hydrodynamic coefficients when modeling semisubmersible platforms in engineering tools.

13 HYDRO ENERGY↗
DOE OSTI2023

A Prediction Method for Catchment Efficiency Loss due to Coaxial Nozzle Wear in Powder Fed Directed Energy Deposition Systems

Abstract Powder fed laser directed energy deposition systems show significant promise and their widespread adoption could greatly advance manufacturing technology; however, a significant amount of gas atomized metal powder is wasted during material deposition. As contaminated powder must be collected and disposed of as hazardous waste, this process is often criticized for its low catchment efficiency. This presents potential implementation and scaling issues, as widespread use of this technology would directly increase the amount of contaminated powder in need of safe disposal. A direct contribution to this waste issue was identified in past work as wearing exterior and interior surfaces on a replaceable nozzle component within the additive head of such a system integrated into a hybrid manufacturing machine tool. A computational fluid dynamics simulation is developed specifically for use within the manufacturing process by predicting catchment efficiency losses before a deposition operation is performed. The model is robust enough to calculate a catchment efficiency estimate within a reasonable transition time before deposition and is designed to replace visual estimation of nozzle deterioration by the machine operator. Both of two previously identified wear modes are represented in the simulation. Their effects on powder stream geometry are discussed in detail and the results are compared with in-situ measurements of powder stream geometry and concentrations. The resulting work contributes to the growing understanding of using prediction and prevention methods on catchment efficiency related part defects within these systems.

DeWitte, Lisa↗
DOE OSTI2021

Measuring Saturn's Electron Beam Energy Spectrum using Webb's Wedges

It is very difficult to measure the voltage of the load on the Saturn accelerator. Time-resolved measurements such as vacuum voltmeters and V-dot monitors are impractical at best and completely change the pulsed power behavior at the load at worst. We would like to know the load voltage of the machine so that we could correctly model the radiation transport and tune our x-ray unfold methodology and circuit simulations of the accelerator. Step wedges have been used for decades as a tool to measure the end - point energies of high energy particle beams. Typically, the technique is used for multi-megavolt accelerators, but we have adapted it to Saturn's modest <2 MV end-point energy and modified the standard bremsstrahlung x-ray source to extract the electron beam without changing the physics of the load region. We found clear evidence of high energy electrons >2 MV. We also attempted to unfold an electron energy spectrum using a machine learning algorithm and while these results come with large uncertainties, they qualitatively agree with PIC simulation results.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

AutoBEM for BlocPower

As part of a cooperative research and development agreement (CRADA), the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL) and BlocPower partnered to rectify disparities between simulated and actual energy consumption in buildings using various correction methods. This team extended ORNL’s building energy modeling capabilities and used bias-correction techniques to reduce the difference between simulated energy consumption estimations and ground-truth consumption data of buildings provided by utilities. ORNL is one of three core DOE laboratories developing the energy modeling tools EnergyPlus and OpenStudio.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

A Cybersecurity Testbed for Smart Buildings

Smart buildings are equipped with a plethora of cyber-physical systems, such as Internet of Things (IoT) devices and building automation systems. These devices, especially in commercial buildings, use legacy communications and hardware that were not designed with cybersecurity in mind. With increasing cyber threats in recent years, smart buildings have become an increasing target for attacks, but not enough published data are available from these incidents to study or replicate the scenarios to defend buildings. As part of the U.S. Department of Energy-funded project focusing on developing the Building Intelligence with Layered Defense Using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS) platform, we developed a cybersecurity test bed for smart buildings. This test bed includes a building simulation tool, virtual devices, emulated operational technology networks, and remote hardware-in-the-loop. Using this test bed, we performed different cyberattacks on the smart building model and collected both physical building data, to understand the impacts on the building, and network data, to aid in separating mechanical faults from cyberattacks during the detection. This test bed is a significant tool in protecting smart buildings from cyberattacks because it can aid in both cybersecurity analysis and the evaluation of other cyberattack detection tools by testing the tools in a secure environment without impacting the building operations.

cyber-physical systems↗
DOE OSTI2024

A Cybersecurity Testbed for Smart Buildings

Smart buildings are equipped with a plethora of cyber-physical systems, such as Internet of Things (IoT) devices and building automation systems. These devices, especially in commercial buildings, use legacy communications and hardware that were not designed with cybersecurity in mind. With increasing cyber threats in recent years, smart buildings have become an increasing target for attacks, but not enough published data are available from these incidents to study or replicate the scenarios to defend buildings. As part of the U.S. Department of Energy-funded project focusing on developing the Building Intelligence with Layered Defense Using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS) platform, we developed a cybersecurity test bed for smart buildings. This test bed includes a building simulation tool, virtual devices, emulated operational technology networks, and remote hardware-in-the-loop. Using this test bed, we performed different cyberattacks on the smart building model and collected both physical building data, to understand the impacts on the building, and network data, to aid in separating mechanical faults from cyberattacks during the detection. This test bed is a significant tool in protecting smart buildings from cyberattacks because they can aid in both cybersecurity analysis and the evaluation of cyberattack detection tools by testing the tools in a secure environment without impacting the building operations.

alfalfa↗
DOE OSTI2025

Cluster Dynamics Modeling Needs for the Advanced Materials and Manufacturing Technologies Program

This milestone report aims to identify and assess the cluster dynamics (CD) modeling requirements within the Department of Energy's Office of Nuclear Energy (DOE-NE) Advanced Materials and Manufacturing Technologies (AMMT) program and to communicate these needs to the DOE-NE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. The goal is to ensure NEAMS is well-informed about the CD modeling requirements to support AMMT's mission of accelerating the development, qualification, demonstration, and deployment of advanced structural materials and manufacturing for nuclear energy applications. CD modeling is an essential tool for predicting the degradation of structural materials under irradiation, which is a key component of AMMT's accelerated qualification process. The AMMT program focuses on both additively manufactured and wrought structural alloys, such as laser powder-bed fusion 316H austenitic stainless steel, alloy 709, Haynes 244, and alloy 617. These materials require a generalized CD modeling framework to facilitate rapid model development and computational simulation. A flexible, generalized CD software, similar to the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element framework, would enable modeling of various cluster types, including defect clusters, defect-solute clusters, and multicomponent clusters, incorporating thermodynamics and kinetics parameters. Radiation effects, microstructural feature evolution, and multi-dimensional modeling are critical considerations for the CD model. The usability of the CD code should allow for easy modification and coupling with MOOSE-based simulations. Additionally, the software should adhere to Nuclear Quality Assurance-1 standards, include a testing suite for verification and validation, and be version-controlled within a national laboratory-managed Git repository. Benchmark problems are needed to assess code predictions and performance.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Multiscale CFD simulation of biomass fast pyrolysis with a machine learning derived intra-particle model and detailed pyrolysis kinetics

Coupling particle and reactor scale models is as essential as reactor fluid dynamics and particle motion for accurate Computational Fluid Dynamic (CFD) simulations of biomass fast pyrolysis reactors due to intraparticle heat transfer and chemical reactions controlling conversion time and product distributions. Direct online coupling of a particle model with a reactor model is computationally expensive, while offline coupling is case-dependent. In this research, solutions from a series of particle pyrolysis simulations were regressed with Artificial Neural Network (ANN). Furthermoer, this machine learning-derived model predicted the same temperature and conversion profiles compared with particle resolved simulation while the isothermal approach overpredicted the temperature by 130 K and underpredicted the conversion time by 30 s. The ANN model was then integrated into CFD simulations of fluidized bed biomass fast pyrolysis with varied feedstocks via coupling PyTorch and MFiX. The averaged error of simulation predicted bio-oil yields with four feedstocks is 6.4%. This multi-scale approach provides an efficient tool for the coupled particle and reactor scale simulations of biomass pyrolysis.

09 BIOMASS FUELS↗
DOE OSTI2021

A Study on Computational Cost Reduction of Simulations of Phase-Change Material (PCM) Embedded Heat Exchangers

Thermal storage can be implemented using Phase-Change Materials (PCM), which absorb significant latent heat with a relatively small temperature change. PCM phase-change processes are transient and are driven by thermal diffusion and natural convection – the latter, especially for melting process. Modeling and simulation of PCM heat exchangers (HX’s) is typically computationally intensive due to the relatively complex time-dependent physics. Most of the PCM modeling work in the literature uses high-order modeling tools such as Computational Fluid Dynamics (CFD) and Lattice-Boltzmann Method (LBM). For design purposes, the existing PCM modeling approaches are not practical, limiting researchers in their ability to investigate new ideas and different PCM’s with faster turnarounds. This paper presents a study investigating the reduction of computational cost of PCM embedded HX’s CFD models by evaluating the feasibility of spatial reduction without losing accuracy. The analysis consists of comparing full and partial domain under full melting conditions. The subject of this study is a single straight tube with circular transverse fins in the vertical orientation, using PCM’s with 35oC nominal melting temperature. Different tube and fin dimensions are investigated. Results indicate that the reduced domain reproduces - in half the run time - the same behavior as the full domain since the buoyancy effects are localized and patterned. The outputs from the partial domain simulation were used to build a non-general correlation for the PCM heat transfer characteristics and demonstrated how it can be implemented in a Finite Control Volume Reduced Order Model (ROM). The ROM can accurately reproduce the CFD simulations at 4 to 5 orders of magnitude faster.

25 ENERGY STORAGE↗
DOE OSTI2020

A review of SWAT applications, performance and future needs for simulation of hydro-climatic extremes

Hydro-climatic extremes, such as droughts and floods, have most likely increased due to climatic change and could lead to severe impacts on socio-economic, structural and environmental sectors. With nearly 4000 publications, the Soil and Water Assessment Tool (SWAT) is clearly one of the most extensively used ecohydrological models worldwide. The model has been widely used for projecting the impacts of future hydro-climatic changes, but application for extreme streamflow conditions is still rarely reported. To date, SWAT application reviews have focused on compilations of SWAT studies for specific or relatively new applications such as eco-hydrological modelling, ecosystem services, sub-daily simulations, and pesticide fate and transport simulations. However, no existing SWAT review studies have focused on simulation of hydro-climatic extremes. Furthermore, this research aims to bridge this gap by compiling and reviewing the findings of studies reporting SWAT hydro-climatic extremes including highlighting the performance and future research needs. A total of 111 articles have been identified since 1999; most of these studies were conducted in the United States and China. These articles can be divided into extreme flow assessments, drought studies, flood studies, drought and flood studies, SWAT coupling with other models, and SWAT improvements. Most of the extreme performance assessment studies reported “satisfactory ”performance, with a particular emphasis on peak flow comparisons. Future research needs regarding this topic include: (1) a unified SWAT extreme performance assessment framework; (2) SWAT improvements that result in improved replication of peak and low flows; (3) reliability assessment of global and satellite products for SWAT extreme simulations; (4) bias correction of CMIP6 and regional climate projections; (5) comparison of SWAT + and SWAT for extreme flow simulations in different types of basins; (6) development of an extreme flow module within an overall SWAT modelling system; and (7) integration of artificial intelligence within SWAT modelling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Neural net modeling of equilibria in NSTX-U

Neural networks (NNs) offer a path towards synthesizing and interpreting data on faster timescales than traditional physics-informed computational models. In this work we develop two NNs relevant to equilibrium and shape control modeling, which are part of a suite of tools being developed for the National Spherical Torus Experiment-Upgrade for fast prediction, optimization, and visualization of plasma scenarios. The networks include Eqnet, a free-boundary equilibrium solver trained on the EFIT01 (Equilibrium FITtting 01) reconstruction algorithm, and Pertnet, which is trained on the Gspert code and predicts the non-rigid plasma response, a nonlinear term that arises in shape control modeling. The NNs are trained with different combinations of inputs and outputs in order to offer flexibility in use cases. In particular, Eqnet can use magnetic diagnostics as inputs and act as an EFIT-like reconstruction algorithm, or, by using pressure and current profile information the NN can act as a forward Grad–Shafranov equilibrium solver. This forward-mode version is envisioned to be implemented in the suite of tools for simulation of plasma scenarios. The reconstruction-mode version gives some performance improvements compared to the online reconstruction code real-time EFIT, especially when vessel eddy currents are significant. Here, we report strong performance for all NNs indicating that the models could reliably be used within closed-loop simulations or other applications. Some limitations are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Uncertainty Assessment of CFD Investigation of the Nonlinear Difference-Frequency Wave Loads on a Semisubmersible FOWT Platform

Current mid-fidelity modeling approaches for floating offshore wind turbines (FOWTs) have been found to underpredict the nonlinear, low-frequency wave excitation and the response of semisubmersible FOWTs. To examine the cause of this underprediction, the OC6 project is using computational fluid dynamics (CFD) tools to investigate the wave loads on the OC5-DeepCwind semisubmersible, with a focus on the nonlinear difference-frequency excitation. This paper focuses on assessing the uncertainty of the CFD predictions from simulations of the semisubmersible in a fixed condition under bichromatic wave loading and on establishing confidence in the results for use in improving mid-fidelity models. The uncertainty for the nonlinear wave excitation is found to be acceptable but larger than that for the wave-frequency excitation, with the spatial discretization error being the dominant contributor. Further, unwanted free waves at the difference frequency have been identified in the CFD solution. A wave-splitting and wave load-correction procedure are presented to remove the contamination from the free waves in the results. A preliminary comparison to second-order potential-flow theory shows that the CFD model predicted significantly higher difference-frequency wave excitations, especially in surge, suggesting that the CFD results can be used to better calibrate the mid-fidelity tools.

17 WIND ENERGY↗