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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An asynchronous parallel high-throughput model calibration framework for crystal plasticity finite element constitutive models

Crystal plasticity finite element model (CPFEM) is a powerful numerical simulation in the integrated computational materials engineering toolboxes that relates microstructures to homogenized materials properties and establishes the structure–property linkages in computational materials science. However, to establish the predictive capability, one needs to calibrate the underlying constitutive model, verify the solution and validate the model prediction against experimental data. Bayesian optimization (BO) has stood out as a gradient-free efficient global optimization algorithm that is capable of calibrating constitutive models for CPFEM. Here in this paper, we apply a recently developed asynchronous parallel constrained BO algorithm to calibrate phenomenological constitutive models for stainless steel 304 L, Tantalum, and Cantor high-entropy alloy.

304L stainless steel↗
DOE OSTI2016

Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model

We evaluate the sensitivity of simulated turbine-height winds to 26 parameters applied in a planetary boundary layer (PBL) scheme and a surface layer scheme of the Weather Research and Forecasting (WRF) model over an area of complex terrain during the Columbia Basin Wind Energy Study. An efficient sampling algorithm and a generalized linear model are used to explore the multiple-dimensional parameter space and quantify the parametric sensitivity of modeled turbine-height winds. The results indicate that most of the variability in the ensemble simulations is contributed by parameters related to the dissipation of the turbulence kinetic energy (TKE), Prandtl number, turbulence length scales, surface roughness, and the von Kármán constant. The relative contributions of individual parameters are found to be dependent on both the terrain slope and atmospheric stability. The parameter associated with the TKE dissipation rate is found to be the most important one, and a larger dissipation rate can produce larger hub-height winds. A larger Prandtl number results in weaker nighttime winds. Increasing surface roughness reduces the frequencies of both extremely weak and strong winds, implying a reduction in the variability of the wind speed. All of the above parameters can significantly affect the vertical profiles of wind speed, the altitude of the low-level jet and the magnitude of the wind shear strength. The wind direction is found to be modulated by the same subset of influential parameters. Remainder of abstract is in attachment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Probabilistic Model-Based Diagnostic Framework for Nuclear Engineering Systems

A fault diagnostic framework was investigated in this study for applications in thermal–hydraulic systems of nuclear power plants. The proposed framework consists of quantitative model-based diagnosis, statistical change detection and probabilistic reasoning. The use of physics-based diagnostic models provides high detection sensitivity and allows noise and measurement uncertainty to be incorporated robustly. Performance-related parametric models for each component are constructed based on first principles. Numerical model residuals are generated using the concept of analytical redundancy. Statistical change detection methods are employed to detect non-zero residuals in the presence of uncertainty. The diagnosis task is performed using Bayesian inference to detect and localize possible faults. Application to a single-phase heat exchanger for demonstration showed that the proposed probabilistic framework can provide improved results in comparison with traditional approaches while remaining less sensitive to false alarms in the presence of measurement and modeling uncertainty.

Bayesian network↗
DOE OSTI2024

Forward and inverse predictive transient models of TREAT using surrogate reactivity models

In this work, we present two novel approaches for predicting the power evolution and control rod height of the Transient Reactor Test Facility (TREAT) to support experiment modeling; specifically, transient analysis of the NASA-sponsored Sirius series of experiments. These approaches utilize steady-state Monte Carlo model, point kinetics model, and surrogate models to predict power evolution and control rod axial position during transient experiments. Both approaches were tested and validated against several Sirius experiments that were performed in the TREAT facility at different power levels. The validation test results show very good agreement with the experimental data, and the models were able to accurately predict the power evolution and the axial control rod position with an average error within 3.0%. Finally, this indicates that these approaches will help the reactor engineering team of the TREAT facility in preparing and predicting the power and temperature of the experiment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Impacts of building geometry modeling methods on the simulation results of urban building energy models

We present that urban-scale building energy modeling (UBEM)—using building modeling to understand how a group of buildings will perform together—is attracting increasing attention in the energy modeling field. Unlike modeling a single building, which will use detailed information, UBEM generally uses existing building stock data consisting of high-level building information. This study evaluated the impacts of three zoning methods and the use of floor multipliers on the simulated energy use of 940 office and retail buildings in three climate zones using City Building Energy Saver. The first zoning method, OneZone, creates one thermal zone per floor using the target building's footprint. The second zoning method, AutoZone, splits the building's footprint into perimeter and core zones. A novel, pixel-based automatic zoning algorithm is developed for the AutoZone method. The third zoning method, Prototype, uses the U.S. Department of Energy's reference building prototype shapes. Results show that simulated source energy use of buildings with the floor multiplier are marginally higher by up to 2.6% than those modeling each floor explicitly, which take two to three times longer to run. Compared with the AutoZone method, the OneZone method results in decreased thermal loads and less equipment capacities: 15.2% smaller fan capacity, 11.1% smaller cooling capacity, 11.0% smaller heating capacity, 16.9% less heating loads, and 7.5% less cooling loads. Source energy use differences range from -7.6% to 5.1%. When comparing the Prototype method with the AutoZone method, source energy use differences range from -12.1% to 19.0%, and larger ranges of differences are found for the thermal loads and equipment capacities. This study demonstrated that zoning methods have a significant impact on the simulated energy use of UBEM. Finally, one recommendation resulting from this study is to use the AutoZone method with floor multiplier to obtain accurate results while balancing the simulation run time for UBEM.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Stochastic modeling and statistical calibration with model error and scarce data

This paper introduces a procedure to assess the predictive accuracy of stochastic models subject to model error and sparse data. Model error is introduced as uncertainty on the coefficients of appropriate polynomial chaos expansions (PCE). The error associated with finite sample size allows us to conceive of these coefficients as statistics of the data that we describe as random variables whose influence on output quantities of interest is evaluated through the extended polynomial chaos expansion (EPCE). A Bayesian data assimilation scheme is introduced to update these expansions by considering the resulting nested chaos expansion as a hierarchical probabilistic model. Stochastic models of quantities of interest (QoI) are thus constructed and efficiently evaluated. Here, the Metropolis–Hastings Markov chain Monte Carlo procedure is used to sample the posterior. Two illustrative analytical and numerical problems are used to demonstrate the proposed approach.

Bayesian inference↗
DOE OSTI2015

Simplified models for dark matter searches at the LHC

This document a outlines a set of simplified models for dark matter and its interactions with Standard Model particles. It is intended to summarize the main characteristics that these simplified models have when applied to dark matter searches at the LHC, and to provide a number of useful expressions for reference. The list of models includes both s-channel and t-channel scenarios. For s-channel, spin-0 and spin-1 mediations are discussed, and also realizations where the Higgs particle provides a portal between the dark and visible sectors. The guiding principles underpinning the proposed simplified models are spelled out, and some suggestions for implementation are presented.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Numerical simulations of depressurization-induced gas production from gas hydrates using 3-D heterogeneous models of L-Pad, Prudhoe Bay Unit, North Slope Alaska

Gas production potential is estimated using a three-dimensional reservoir model based on gas hydrate deposits located in the Prudhoe Bay region of the Alaska's North Slope. The model incorporates two hydrate-bearing sand units using detailed reservoir geological and structural information obtained from past and recent drilling programs. Geostatistical porosity models, conditioned to log data from 78 wells drilled in the vicinity of the Prudhoe Bay “L-Pad,” were developed, providing 3D heterogeneity in porosity, porosity-dependent hydrate saturation, and intrinsic permeability. The simulations utilize both vertical and inclined wellbores to induce depressurization of the reservoir at a constant bottom-hole pressure. The results show the superior performance of the inclined well design. Average gas production rates during the first five years were ~6.0 × 10 4 ST m 3 /day (~2.1 MMSCF/day) and ~2.7 × 10 4 ST m 3 /day (~1.0 MMSCF/day) for incline and vertical wells, respectively. After 30 years 5.3 × 10 8 - 5.7 × 10 8 ST m 3 (18.6–20.2 BSCF) and 6.2 × 10 8 - 6.4 × 10 8 ST m 3 (22.0–22.7 BSCF) gas were produced using the vertical and inclined well configurations, respectively. The analysis reveals that 2D reservoir models with homogeneous representations for porosity and hydrate saturation significantly underestimate production potential. The heterogeneity implemented in this work provides complex porous network and preferential pathways for mobile phase flow to a producing well. Consequently, no secondary hydrate formation around a wellbore is predicted in contrast to models that utilize uniform porosity and hydrate saturation distributions.

03 NATURAL GAS↗
DOE OSTI2020

Multiscale model predictive control of battery systems for frequency regulation markets using physics-based models

In this work, we propose a multiscale model predictive control (MPC) framework for stationary battery systems that exploits high-fidelity models to trade-off short-term economic incentives provided by energy and frequency regulation (FR) markets and long-term degradation effects. We find that the MPC framework can drastically reduce long-term degradation while properly responding to FR and energy market signals (compared to MPC formulations that use low-fidelity models). Our results also provide evidence that sophisticated battery models can be embedded within closed-loop MPC simulations by using modern nonlinear programming solvers (we provide an efficient and easy-to-use implementation in Julia). We use insights obtained with our simulations to design a low-complexity MPC formulation that matches the behavior obtained with high-fidelity models. This is done by designing a suitable terminal penalty term that implicitly captures long-term degradation. The results suggest that complex degradation behavior can be accounted for in low-complexity MPC formulations by properly designing the cost function. We believe that our proof-of-concept results can be of industrial relevance, as battery vendors are seeking to participate in fast-changing electricity markets while maintaining asset integrity.

42 ENGINEERING↗
DOE OSTI2021

Modeling dissociation of ionic liquids with electrolyte NRTL model

Dissociation of ionic liquids (ILs) is critical to electrochemical process performance by affecting ionic conductivity. Prior studies of thermodynamic modeling either assumed no dissociation or complete dissociation that were not consistent with experimental observations. This work presented an eNRTL model for three aqueous imidazolium-based IL systems: [Emim][EtSO 4 ], [Emim][TFA], and [Emim][TfO]. The model accurately correlates the IL dissociation and VLE data in the entire concentration range from pure IL to infinite dilution aqueous solution. The system non-ideality is dominated by the dissociation chemistry. Dissociation behavior is driven by strong short-range interaction of the molecular IL-water pair relative to the dissociated IL-water pair, consistent with prior studies indicating that molecular IL and water can form an energetically favorable complex. The interaction energy parameters calculated in this work qualitatively agrees with the hydrogen bonding ability of the anions previously reported. This research not only provides a model that well describes the IL dissociation but also demonstrates the physical significance of eNRTL model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Global critical materials markets: An agent-based modeling approach

As part of efforts to position the United States as a leader in clean energy technology production, the U. S. Department of Energy (DOE) issued two Critical Materials Strategy reports, which assessed 16 materials on the basis of their importance to clean energy development and their supply risk ( U.S. Department of Energy (DOE), 2010 and DOE, 2011). To understand the implications for clean energy of disruptions in supplies of critical materials, it is important to understand supply chain dynamics from mining to final product production. As a case study of critical material supply chains, we focus on the supply of two rare earth metals, neodymium (Nd) and dysprosium (Dy), for permanent magnets used in wind turbines, electric vehicles and other applications. We introduce GCMat, a dynamic agent-based model that includes interacting agents at five supply chain stages consisting of mining, metal refining, magnet production, final product production and demand. Agents throughout the supply chain make pricing, production and inventory management decisions. Deposit developers choose which deposits to develop based on market conditions and detailed data on 57 rare earth deposits. Wind turbine and electric vehicle producers choose from a set of possible production technologies that require different amounts of rare earths. We ran the model under a baseline scenario and four alternative scenarios with different demand and production technology inputs. Model results from 2010 to 2013 fit well with historical data. Projections through 2025 show a number of possible future price, demand, and supply trajectories. For each scenario, we highlight reasons for turning points under market conditions, for differences between Nd and Dy markets, and for differences between scenarios. Because GCMat can model causal dynamics and provide fine-grain representation of agents and their decisions, it provides explanations for turning points under market conditions that are not otherwise available from other modeling approaches. Our baseline projections show very different behaviors for Nd and Dy prices. Nd prices continue to drop and remain low even at the end of our simulation period as new capacity comes online and leads to a market in which production capacity outpaces demand. Dy price movements, on the other hand, change directions several times with several key turning points related to inventory behaviors of particular agents in the supply chain and asymmetric supply and demand trends. Scenario analyses show the impact of stronger demand growth for rare earths, and in particular finds that Nd price impacts are significantly delayed as compared to Dy. This is explained by the substantial excess production capacity for Nd in the early simulation years that keeps prices down. Scenarios that explore the impact of reducing the Dy content of magnets show the intricate interdependencies of these two markets as price trends for both rare earths reverse directions – reducing the Dy content of magnets reduces Dy demand, which drives down Dy prices and translates into lower magnet prices. This in turn raises the demand for magnets and therefore the demand for Nd and eventually drives up the Nd price.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Accounting for erroneous model structures in biokinetic process models

In engineering practice, model-based design requires not only a good process-based model, but also a good description of stochastic disturbances and measurement errors to learn credible parameter values from observations. However, typical methods use Gaussian error models, which often cannot describe the complex temporal patterns of residuals. Consequently, this results in overconfidence in the identified parameters and, in turn, optimistic reactor designs. Here, we assess the strengths and weaknesses of a method to statistically describe these patterns with autocorrelated error models. This method produces increased widths of the credible prediction intervals following the inclusion of the bias term, in turn leading to more conservative design choices. However, we also show that the augmented error model is not a universal tool, as its application cannot guarantee the desired reliability of the resulting wastewater reactor design.

42 ENGINEERING↗
DOE OSTI2023

Modeling Global Vegetation Gross Primary Productivity, Transpiration and Hyperspectral Canopy Radiative Transfer Simultaneously Using a Next Generation Land Surface Model—CliMA Land

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity at global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. For example, gross primary productivity (GPP) and transpiration (T) that traditional LSMs simulate are not directly measurable from space, although they can be inferred from spaceborne observations using assumptions that are inconsistent with those LSMs. In comparison, canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we presented an overview of the next generation land model developed within the Climate Modeling Alliance (CliMA), and simulated global GPP, T, and hyperspectral canopy radiative transfer (RT; 400–2,500 nm for reflectance, 640–850 nm for fluorescence) at hourly time step and 1° spatial resolution using CliMA Land. CliMA Land predicts vegetation indices and outgoing radiances, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given sun-sensor geometry. The spatial patterns of modeled GPP, T, SIF, NDVI, EVI, and NIRv correlate significantly with existing data-driven products (mean R 2 = 0.777 for 9 products). CliMA Land would be also useful in high temporal resolution simulations, for example, providing insights into when GPP, SIF, and NIRv diverge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Towards an open model intercomparison platform for integrated assessment models scenarios

The majority of scenarios in the IPCC database are generated by integrated assessment models (IAMs) and come from model intercomparison projects. However, the way in which the current model intercomparison projects are organized is not open to all IAM teams worldwide. Here we propose a transparent and inclusive platform that is open to anyone with an IAM regarding protocols development, scenario submissions and results evaluation. We discuss the challenges of this approach, particularly human resources and financial support. Here, we identify diversity in the level of model capability and quality of model output as possibly critical issues. Despite such challenges, the IAM community and its scientific activities can improve and benefit from the proposed platform, ultimately contributing to better climate policymaking.

IAM↗
DOE OSTI2025

Enhancing Drinking Water Quality Modeling: Leveraging Physics Informed Neural Networks for Learning with Imperfect Reaction Models and Partial Data

Chemical kinetics models, typically formulated as systems of ordinary or partial differential equations, are valuable tools for simulating drinking water quality. However, these models often face inaccuracies due to discrepancies between the laboratory and the real-world conditions, as well as limitations in experimental analytical methods, hindering the accurate representation of the true underlying chemical mechanisms. In this study, we propose a Physics Informed Neural Network (PINN), using the eXtreme Theory of Functional Connections, to improve the prediction of chemical concentrations over time. The PINN method accounts for imperfect chemical models and incorporates partial data to improve predictions. Focusing on reactions describing water disinfection residual and disinfectant byproduct formation, which are crucial for public health and regulatory compliance, we demonstrate that the PINN model is able to accurately predict the concentrations of chemical species across various pH values. Notably, the model extends its accuracy to predict concentrations of chemical species not originally included in its training data. The developed method can be extended to a variety of chemical systems, offering a wide array of potential applications.

13 HYDRO ENERGY↗
DOE OSTI2019

Demonstration of the Advanced Dynamic System Modeling Tool TRANSFORM in a Molten Salt Reactor Application via a Model of the Molten Salt Demonstration Reactor

Liquid-fueled nuclear reactors, particularly molten salt reactors (MSRs), have recently gained significant interest in the advanced reactor community. As with all reactors, modeling and simulation are critical to advanced reactor design and licensing and will be required for MSR deployment. However, there are significant gaps in existing simulation capabilities for MSRs, particularly with the unique challenges of liquid-fueled systems (e.g., fission product transport). Furthermore, advanced reactor designers require near-term tools that are readily modifiable to perform design and analysis, including the ability to extend their analysis beyond the primary system to auxiliary systems. Transient Simulation Framework of Reconfigurable Models (TRANSFORM), a Modelica-based, system modeling library developed at Oak Ridge National Laboratory, is an advanced tool that can help meet some of the near-term needs of the advanced reactor community. Here, this paper describes advanced system modeling criteria and presents TRANSFORM to the advanced reactor community by demonstration of system modeling capabilities and support of advanced analysis workflows, i.e., the Risk Analysis Virtual Environment (RAVEN) framework from Idaho National Laboratory, using the liquid-fueled Molten Salt Demonstration Reactor (MSDR) as a reference design.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations

Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high- delity simulations of radiation transport within an urban domain. The model is constructed to employ a source location (x, y, z) as input and return the estimated count rate for a set of speci ed detector locations. Because MCNP simulations are computationally expensive, we develop e cient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes (GP) and neural networks (NN) that we train and verify using the MCNP simulations. The trained surrogate models provide an e cient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo (MCMC) algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source's location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗