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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 1,243 records · Page 69

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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↗

DOE OSTI2020

ASSESSING FUEL PROPERTY EFFECTS ON CAVITATION AND EROSION PROPENSITY USING A COMPUTATIONAL FUEL SCREENING TOOL

To advance compression ignition combustion strategies, researchers have evaluated fuel property effects and their impact on achieving higher efficiencies and lower emissions levels relative to current capabilities. Within the Department of Energy's Co-Optima initiative, there has been a recent focus on understanding the influence of fuel properties on fuel injection performance. To help identify candidate fuels that can meet desired injector performance metrics, a computational fuel screening tool is under development that can link fuel properties with the tendency of a given fuel to cavitate and lead to cavitation-induced erosion. In the initial development of this tool, five liquid fuel properties were selected to represent candidate fuels, namely density, viscosity, vapor pressure, surface tension, and heat of vaporization. A design of experiments methodology was employed to generate a set of pseudo fuel cases, which can represent the main effects and interactions among the fuel properties and be related to cavitation erosion predictions. Large eddy simulations were performed using a mixture modeling approach to predict the cavitation and erosion propensity of these pseudo-fuels in terms of the mean fuel vapor mole fraction and stored impact energy from repeated cloud collapse events. The low order regression models generated from this study revealed the importance of liquid fuel density on cavitation formation, whereas liquid viscosity was found to have a strong negative correlation with erosion severity. The surrogate models were then used in the fuel screening tool to rank the cavitation and erosion tendency of four candidate single-component fuels: methyl decanoate, isooctane, ethanol and n-dodecane. While the fuel screening tool was not able to quantitatively predict the cavitation and erosion response metrics, the tool was able to accurately rank the relative cavitation and erosion propensity of the four fuels. Overall, ethanol and iso-octane were observed to produce the highest levels of cavitation and erosion, respectively.

cavitation-induced erosion↗

DOE OSTI2020

Cyber Security Analysis for Nuclear Reactor Control Systems (Final Technical Report)

This project investigated the cyber-security impacts of moving from an all analog, point-to-point, instrumentation and control (I&C) system to a digital I&C system based on Modbus and a shared communication medium. A formalism called a hybrid attack graph was expanded to support the nuclear research reactor system. The hybrid attack graph allows one to check a system for vulnerabilities, in this case cyber-security vulnerabilities, and to document the attack vectors (scenarios) causing those vulnerabilities. In parallel, a simulation of the system was developed to model both the physical reactor parameters and operations, as well as the network interconnects and communications. This simulation platform was modeled on the nuclear research reactor located at Washington State University. The simulation platform provided a sandbox to evaluate and quantify the impact of identified and proposed vulnerabilities in the system and to determine the effectiveness of countermeasures at stopping these attacks. The simulation and hybrid attack graph tools were integrated to provide a streamlined process of generating attack scenarios, playing those scenarios out in the simulation, and then analyzing the results to correlate system state to states in the hybrid attack graph. This process was used to (1) quantify the impact of attack scenarios and (2) to determine if the system moved through the hybrid attack graph as anticipated. The hybrid attack graph tool was extended and customized to produce a tool to automatically identify critical assets (CAs) and critical digital assets (CDAs) as defined by NRC Regulatory Guide 5.71. This tool was verified using the nuclear research reactor at Washington State University. Finally, a series of educational modules covering the findings of the different aspects of this research have been created.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO 2 . Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal–Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. In conclusion, a strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Real-Time Coupling of Geographically Distributed Research Infrastructures: Taxonomy, Overview, and Real-World Smart Grid Applications

Novel concepts enabling a resilient future power system and their subsequent experimental evaluation are experiencing a steadily growing challenge: large scale complexity and questionable scalability. The requirements on a research infrastructure (RI) to cope with the trends of such a dynamic system therefore grow in size, diversity and costs, making the feasibility of rigorous advancements questionable by a single RI. Analysis of large scale system complexity has been made possible by the real-time coupling of geographically separated RIs undertaking geographically distributed simulations (GDS), the concept of which brings the equipment, models and expertise of independent RIs, in combination, to optimally address the challenge. This article presents the outputs of IEEE PES Task Force on Interfacing Techniques for Simulation Tools towards standardization of GDS as a concept. First, the taxonomy for setups utilized for GDS is established followed by a comprehensive overview of the advancements in real-time couplings reported in literature. The overview encompasses fundamental technological design considerations for GDS. The article further presents four application oriented case studies (real-world implementations) where GDS setups have been utilized, demonstrating their practicality and potential in enabling the analysis of future complex power systems.

distributed laboratories↗

DOE OSTI2021

densegranFoam

densegranFoam is a dense granular material flow solver developed using the OpenFoam Computational Fluid Dynamics (CFD) tool. It is developed using Euler-Euler framework. The transport model is implemented using the shear rate dependant constitutive model. The primary focus of the solver is to simulate shear rate dependency on the dense granular flow bulk behavior.

Ahsan, Syed↗

DOE OSTI2021

A spatial evaluation of Arctic sea ice and regional limitations in CMIP6 historical simulations

The Arctic sea ice response to a warming climate is assessed in a subset of models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), using several metrics in comparison with satellite observations and results from the Pan-Arctic Ice Ocean Modeling and Assimilation System and the Regional Arctic System Model. Our study examines the historical representation of sea ice extent, volume, and thickness using spatial analysis metrics, such as the integrated ice-edge error, Brier score, and Spatial Probability Score. We find that the CMIP6 multi-model mean captures the mean annual cycle and 1979-2014 sea ice trends remarkably well. However, individual models experience a wide range of uncertainty in the spatial distribution of sea ice when compared against satellite measurements and reanalysis data. Our metrics expose common and individual regional model biases, which sea ice temporal analyses alone do not capture. We identify large ice edge and ice thickness errors in Arctic sub-regions, implying possible model specific limitations in or lack of representation of some key physical processes. We postulate that many of them could be related to the oceanic forcing, especially in the marginal and shelf seas, where seasonal sea ice changes are not adequately simulated. We therefore conclude that an individual model’s ability to represent the observed/reanalysis spatial distribution still remains a challenge. We propose the spatial analysis metrics as useful tools to diagnose model limitations, narrow down possible processes affecting them, and guide future model improvements critical to the representation and projections of Arctic climate change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Power system model parameter conditioning tool

A power system model parameter conditioning tool including a server control processor in communication with phasor measurement unit monitored data records of multiple disturbance events, a model calibration unit providing event screening, power system model simulation, and simultaneous tuning of model parameters. The model calibration performing a simulation using default model parameters, the processor comparing the simulation results to the monitored data. If the prediction is within threshold, then terminating conditioning; else performing parameter identifiability analysis to determine differing effects of various model parameters on power system model accuracy, selecting a parameter set causing a degradation in power system model prediction, and updating the default model parameters corresponding to members of the parameter set with values selected to reduce the degradation. A method and a non-transitory computer readable medium are also disclosed.

Baone, Chaitanya Ashok↗

DOE OSTI2023

Simulating Quantum Systems with NWQ-Sim on HPC

NWQ-Sim is a cutting-edge quantum system simulation environment designed to run on classical multi-node, multi-CPU/GPU heterogeneous HPC systems. In this work, we provide a brief overview of NWQ-Sim and its implementation in simulating quantum circuit applications, such as the transverse field Ising model. We also demonstrate how NWQ-Sim can be used to examine the effects of errors that occur on real quantum devices, using a combined device noise model. Moreover, NWQ-Sim is particularly well-suited for implementing variational quantum algorithms where circuits are dynamically generated. Therefore, we also illustrate this with the variational quantum eigensolver (VQE) for the Ising model. In both cases, NWQ-Sim's performance is comparable to or better than alternative simulators. We conclude that NWQ-Sim is a useful and flexible tool for simulating quantum circuits and algorithms, with performance advantages and noise-aware simulation capabilities.

Suh, In-Saeng↗

DOE OSTI2022

Generalizability analysis of tool condition monitoring ensemble machine learning models

Tool condition monitoring (TCM) is an essential research area for the optimization and automation of metal machining processes, and could help manufacturers reduce costs, production time, machine downtime, energy use, and part scrappage. However, TCM systems developed in prior studies have struggled to reach the high level of generalizability which is necessary for industrial applications. This study addresses TCM system generalizability to new machining conditions, how variations in machining and environmental conditions may be used to improve model generalizability, and ensemble machine learning techniques for TCM. Further, milling tool life experiments were conducted using various machining conditions, and the processes' sound, spindle power, and axial load signals were collected. Different machine learning models were evaluated for the prediction of tool wear levels, including four individual models and five ensemble models. Changes in cutting speed were found to display a large effect on model performance, while the chip load showed some effect, and the feed rate had little effect. A simulated noise data augmentation technique for model improvement is applied within TCM for the first time, and resulted in increased model generalizability and reduced overfitting. Across several performance metrics the extremely randomized trees ensemble machine learning model generally performed the best for this application, achieving a leave-one-group-out cross validation accuracy score of 92.4 %, a 10-fold cross validation score of 98.9 %, and an averaged accuracy across 11 generalizability tests of 87.3 %.

42 ENGINEERING↗

DOE OSTI2021

A hierarchical evaluation framework for assessing climate simulations relevant to the energy-water-land nexus (Final Report)

The overarching goal was to construct a hierarchy of new and well-tested metrics and analysis tools that support both fundamental and use-inspired research. Motivating this goal was a convergence of needs from climate scientists and stakeholders alike for a systematic, robust framework of model evaluation and diagnosis to provide scientific insights, inform model development, support best practices for the use of climate model outputs, and facilitate communication of climate information in the evolving landscapes of multi-model, multi-resolution, and large ensemble simulations that generate terabytes of data for any single climate run. Steps toward attaining these goals benefited from expertise and established capability of the project team, which included leadership of the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Downscaling Experiment (CORDEX), development of hierarchical model evaluation approaches, and successful research in the analysis and diagnosis of climate model skill, as well as the understanding and modeling of regional climate processes in North America. As part of the overarching goal, the project worked to disseminate a suite of methodologies, algorithms, and software components that the wider community can employ to advance climate science and applications. With rigorous demonstration, the evaluation framework and the mix of standard and high risk / high reward approaches helped form the basis for future development of a computationally enabled user-friendly system for community use.

17 WIND ENERGY↗

DOE OSTI2022

Hydropower flexibility valuation tool for flow requirement evaluation

Timing of generation is becoming more and more valuable. This creates greater potential tension between environmental and power system objectives since both systems require their own flow patterns. Identifying win–win outcomes in this context requires being able to discuss the value of flexibility across stakeholder groups. This research proposes a two-stage optimization method to understand hydropower flexibility to meet both environmental and power system requirements. The tool simulates the two-settlement market process in the U.S. by maximizing revenues from both the day-ahead and real-time markets, subject to plant operational limits, regulatory flow and ramping requirements, and uncertainties associated with water availability and market prices. The model is formulated as linear programming problems and solved using IBM ILOG CPLEX optimizer. By examining a range of flow requirements, ramping constraints, and storage capacities, the proposed tool shows how to make more informed decisions to weigh the cost of specific flow requirements in the context of the overall license requirements. Results from the case study show that revenue is more sensitive to the ramping constraints than the minimum flow constraints. We also demonstrate that removing flow constraints in a dry month increases monthly revenue by up to 118%, as opposed to only 1% in a wet month. In addition, our results suggest that using a learning-based water flow forecast results in an increase of monthly revenue up to 6.4% compared with persistence forecast.

13 HYDRO ENERGY↗

DOE OSTI2024

Wind farm structural response and wake dynamics for an evolving stable boundary layer: computational and experimental comparisons

Abstract. The wind turbine design process requires performing thousands of simulations for a wide range of inflow and control conditions, which necessitates computationally efficient yet time-accurate models, especially when considering wind farm settings. To this end, FAST.Farm is a dynamic-wake-meandering-based mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. This work is an extension of a study that addressed constructing a diurnal cycle evolution based on experimental data (Quon, 2024). Here, this inflow is used to validate the turbine structural and wake-meandering response between experimental data, FAST.Farm simulation results, and high-fidelity large-eddy simulation results from the coupled Simulator fOr Wind Farm Applications (SOWFA)–OpenFAST tool. The validation occurs within the nocturnal stable boundary layer when corresponding meteorological and turbine data are available. To this end, we compared the load results from FAST.Farm and SOWFA–OpenFAST to multi-turbine measurements from a subset of a full-scale wind farm. Computational predictions of blade-root and tower-base bending loads are compared to 10 min statistics of strain gauge measurements during 3.5 h of the evolving stable boundary layer, generally with good agreement. This time period coincided with an active wake-steering campaign of an upstream turbine, resulting in time-varying yaw positions of all turbines. Wake meandering was also compared between the computational solutions, generally with excellent agreement. Simulations were based on a high-fidelity precursor constructed from inflow measurements and using state-of-the-art mesoscale-to-microscale coupling.

17 WIND ENERGY↗

DOE OSTI2024

Development of a coarse-grained molecular dynamics model for poly(dimethyl- co -diphenyl)siloxane

Polydimethylsiloxane is an important polymeric material with a wide range of applications. However, environmental effects like low temperature can induce crystallization in this material with resulting changes in its structural and dynamic properties. The incorporation of phenyl-siloxane components, e.g., as in a poly(dimethyl-co-diphenyl)siloxane random copolymer, is known to suppress such crystallization. Molecular dynamics (MD) simulations can be a powerful tool to understand such effects in atomistic detail. Unfortunately, all-atomistic molecular dynamics (AAMD) is limited in both spatial dimensions and simulation times it can probe. Here, to overcome such constraints and to extend to more useful length- and time-scales, we systematically develop a coarse-grained molecular dynamics (CGMD) model for the poly(dimethyl-co-diphenyl)siloxane system with bonded and non-bonded interactions determined from all-atomistic simulations by the iterative Boltzmann inversion (IBI) method. Additionally, we propose a lever rule that can be useful to generate non-bonded potentials for such systems without reference to the all-atomistic ground truth. Our model captures the structural and dynamic properties of the copolymer material with quantitative accuracy and is useful to study long-time dynamics of highly-entangled systems, sequence-dependent properties, phase behaviour, etc.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Self-Consistent Relativistic Electron Scattering using the Sherlock Scattering Model for X-ray Diagnostics

We present on a new, self-consistent, arbitrary-temperature Romberg integration scheme for modeling electron scattering in materials in a LANL Lagrangian Shock Hydro (LSH) code. Electron beam-target interactions are fundamental to a wide range of scientific and technological applications. When high-energy electron beams hit their target, they may scatter, deposit energy, or ionize the source. These processes govern the behavior and outcomes in nanotechnology manufacturing, electron microscopy, and modern X-ray diagnostics. Simulating these interactions is essential for interpreting experimental results, predicting material responses, and designing efficient tools and experiments. At Los Alamos, this is done using a LSH code, which is a multi-dimension, multi-material, massively parallel, multi-physics code used to simulate applications from asteroid impacts to electron beam interactions. By effectively and efficiently modeling the way that electrons scatter from the beam we can bolster these simulations and more accurately predict experimental outcomes. The model currently implemented in the LSH of interest is based on work by Papp and does not self-consistently preserve momentum in the slightly relativistic regime; here we adopt a model proposed by Braams and Karney and implement a Romberg integration scheme to compute the diffusion tensor. In this paper we will provide background on the Braams-Karney diffusion tensor as well as the Romberg integration scheme we employed to numerically solve for it. We will show that our integration scheme is accurate in solving for the set of scalar potentials used to re-express the diffusion tensor in differential form, and in solving for the diffusion coefficients in the larger LSH code. By using this diffusion tensor rather than the existing Papp one, and numerically integrating it with a Romberg method, we produce much more accurate, self-consistent results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗