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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Relationships Between the Bulk-Skin Sea Surface Temperature Difference, Wind, and Net Air-Sea Heat Flux

The primary purpose of this project was to evaluate and improve models for the bulk-skin temperature difference to the point where they could accurately and reliably apply under a wide variety of environmental conditions. To accomplish this goal, work was conducted in three primary areas. These included production of an archive of available data sets containing measurements of the skin and bulk temperatures and associated environmental conditions, evaluation of existing skin layer models using the compiled data archive, and additional theoretical work on the development of an improved model using the data collected under diverse environmental conditions. In this work we set the basis for a new physical model of renewal type, and propose a parameterization for the temperature difference across the cool skin of the ocean in which the effects of thermal buoyancy, wind stress, and microscale breaking are all integrated by means of the appropriate renewal time scales. Ideally, we seek to obtain a model that will accurately apply under a wide variety of environmental conditions. A summary of the work in each of these areas is included in this report. A large amount of work was accomplished under the support of this grant. The grant supported the graduate studies of Sandra Castro and the preparation of her thesis which will be completed later this year. This work led to poster presentations at the 1999 American Geophysical Union Fall Meeting and 2000 IGARSS meeting. Additional work will be presented in a talk at this year's American Meteorological Society Air-Sea Interaction Meeting this May. The grant also supported Sandra Castro during a two week experiment aboard the R/P Flip (led by Dr. Andrew Jessup of the Applied Physics Laboratory) to help obtain additional shared data sets and to provide Sandra with a fundamental understanding of the physical processes needed in the models. In a related area, the funding also partially supported Dr. William Emery and Daniel Baldwin in the preparation of their publication "Accuracy of in situ sea surface temperatures used to calibrate infrared satellite measurements". The remainder of this report is drawn from these publications and presentations.

Emery, William J.↗
DOE OSTI2019

Preface to the Focus Section on Machine Learning in Seismology

Machine learning (ML) is a collection of algorithms and statistical models that enable computers to extract relevant patterns and information from large data sets. Unlike physical modeling approaches, in which scientists develop theories based on physical laws to compare with real–world observations, ML approaches learn directly from data without explicitly reasoning about the underlying physical mechanisms. ML algorithms are often categorized into supervised and unsupervised learning (see fig. 2 in Kong et al., 2018). Supervised learning algorithms build a model from existing labeled input data with the goal of predicting the labels of new data.

58 GEOSCIENCES↗
DOE OSTI2019

Demonstration of Machine Learning-Based Model-Independent Stabilization of Source Properties in Synchrotron Light Sources

Synchrotron light sources, arguably among the most powerful tools of modern scientific discovery, are presently undergoing a major transformation to provide orders of magnitude higher brightness and transverse coherence enabling the most demanding experiments. In these experiments, overall source stability will soon be limited by achievable levels of electron beam size stability, presently on the order of several microns, which is still 1–2 orders of magnitude larger than already demonstrated stability of source position and current. Until now source size stabilization has been achieved through corrections based on a combination of static predetermined physics models and lengthy calibration measurements, periodically repeated to counteract drift in the accelerator and instrumentation. In this paper, we now demonstrate for the first time how the application of machine learning allows for a physics- and model-independent stabilization of source size relying only on previously existing instrumentation. Such feed-forward correction based on a neural network that can be continuously online retrained achieves source size stability as low as 0.2 μm (0.4%) rms, which results in overall source stability approaching the subpercent noise floor of the most sensitive experiments.

43 PARTICLE ACCELERATORS↗
DOE OSTI2011

Strategic Plan for Nuclear Energy – Knowledge Base for Advanced Modeling and Simulation (NE-KAMS)

NE-KAMS knowledge base will assist computational analysts, physics model developers, experimentalists, nuclear reactor designers, and federal regulators by: (1) Establishing accepted standards, requirements and best practices for V&V and UQ of computational models and simulations, (2) Establishing accepted standards and procedures for qualifying and classifying experimental and numerical benchmark data, (3) Providing readily accessible databases for nuclear energy related experimental and numerical benchmark data that can be used in V&V assessments and computational methods development, (4) Providing a searchable knowledge base of information, documents and data on V&V and UQ, and (5) Providing web-enabled applications, tools and utilities for V&V and UQ activities, data assessment and processing, and information and data searches. From its inception, NE-KAMS will directly support nuclear energy research, development and demonstration programs within the U.S. Department of Energy (DOE), including the Consortium for Advanced Simulation of Light Water Reactors (CASL), the Nuclear Energy Advanced Modeling and Simulation (NEAMS), the Light Water Reactor Sustainability (LWRS), the Small Modular Reactors (SMR), and the Next Generation Nuclear Power Plant (NGNP) programs. These programs all involve computational modeling and simulation (M&S) of nuclear reactor systems, components and processes, and it is envisioned that NE-KAMS will help to coordinate and facilitate collaboration and sharing of resources and expertise for V&V and UQ across these programs. In addition, from the outset, NE-KAMS will support the use of computational M&S in the nuclear industry by developing guidelines and recommended practices aimed at quantifying the uncertainty and assessing the applicability of existing analysis models and methods. The NE-KAMS effort will initially focus on supporting the use of computational fluid dynamics (CFD) and thermal hydraulics (T/H) analysis for M&S of nuclear reactor systems, components and processes, and will later expand to include materials, fuel system performance and other areas of M&S as time and funding allow.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2011

Testing cloud microphysics parameterizations in NCAR CAM5 with ISDAC and M-PACE observations

Arctic clouds simulated by the National Center for Atmospheric Research (NCAR) Community Atmospheric Model version 5 (CAM5) are evaluated with observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Indirect and Semi-Direct Aerosol Campaign (ISDAC) and Mixed-Phase Arctic Cloud Experiment (M-PACE), which were conducted at its North Slope of Alaska site in April 2008 and October 2004, respectively. Model forecasts for the Arctic spring and fall seasons performed under the Cloud-Associated Parameterizations Testbed framework generally reproduce the spatial distributions of cloud fraction for single-layer boundary-layer mixed-phase stratocumulus and multilayer or deep frontal clouds. However, for low-level stratocumulus, the model significantly underestimates the observed cloud liquid water content in both seasons. As a result, CAM5 significantly underestimates the surface downward longwave radiative fluxes by 20-40 W m{sup -2}. Introducing a new ice nucleation parameterization slightly improves the model performance for low-level mixed-phase clouds by increasing cloud liquid water content through the reduction of the conversion rate from cloud liquid to ice by the Wegener-Bergeron-Findeisen process. The CAM5 single-column model testing shows that changing the instantaneous freezing temperature of rain to form snow from -5 C to -40 C causes a large increase in modeled cloud liquid water content through the slowing down of cloud liquid and rain-related processes (e.g., autoconversion of cloud liquid to rain). The underestimation of aerosol concentrations in CAM5 in the Arctic also plays an important role in the low bias of cloud liquid water in the single-layer mixed-phase clouds. In addition, numerical issues related to the coupling of model physics and time stepping in CAM5 are responsible for the model biases and will be explored in future studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture

Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process–guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence

In this work, we propose a new Gaussian process regression (GPR)-based multifidelity method: physics-informed CoKriging (CoPhIK). In CoKriging-based multifidelity methods, the quantities of interest are modeled as linear combinations of multiple parameterized stationary Gaussian processes (GPs), and the hyperparameters of these GPs are estimated from data via optimization. In CoPhIK, we construct a GP representing low-fidelity data using physics-informed Kriging (PhIK), and model the discrepancy between low- and highfidelity data using a parameterized GP with hyperparameters identified via optimization. Our approach reduces the cost of optimization for inferring hyperparameters by incorporating partial physical knowledge. We prove that the physical constraints in the form of deterministic linear operators are satisfied up to an error bound. Furthermore, we combine CoPhIK with a greedy active learning algorithm for guiding the selection of additional observation locations. The eciency and accuracy of CoPhIK are demonstrated for reconstructing the partially observed modified Branin function, reconstructing the sparsely observed state of a steady state heat transport problem, and learning a conservative tracer distribution from sparse tracer concentration measurements.

Yang, Xiu↗
DOE OSTI2021

HED Hydrodynamics in the Common Modeling Framework

Within the Common Modeling Framework (CMF), the HED Hydrodynamics Project has implemented a common methodology, or "Authority", for modeling high energy density (HED) experiments by extending development of the Eulerian Applications Project code Authority, "EAPA," to include relevant physics models, customization tools and templates, and in situ and post-processing capabilities. Additionally, several models for small-scale HED experiments were successfully added to the CMF and simulations reproduce the results of preexisting, benchmarked input decks. We expect use of the CMF to improve our effectiveness in developing predictive capabilities for experiments ranging from small-scale planar single-interface, single-shock to multi-interface, multi-shock configurations and up through multi-shell inertial confinement fusion (ICF) implosions. Additionally, this will strengthen our ability to design future targeted experiments. Using the CMF infrastructure and Authorities for HED modeling has three key impacts. First, it enables a version-controlled, pedigreed and archived base model for each experiment with tools that allow easy setup, execution and analysis of simulations to assess and/or expose sensitivities to various parameters. Second, having a common base set of inputs and physics definitions helps identify systematic differences between models in the codes and experimental data to support code validation, and build confidence in our predictive capabilities. Finally, designed to work in concert with multiple programs and projects including PEM, IC, OES, V&V and DSW, the CMF supports improved collaboration and integration through shared model definitions, data, simulations and results, enhancing the workflow required to underwrite the knowledge base and capabilities ultimately required for stockpile stewardship

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

High-energy-density-physics measurements in implosions using Bayesian inference

Convergent high-energy-density (HED) experimental platforms are used to study matter under some of the most extreme conditions that can be produced on Earth, comparable to the interior of stars. There are many challenges in using these systems for fundamental measurements currently being addressed by new analysis methods, such as the combination of a reduced physics model and Bayesian inference, allowing a self-consistent inference of physical quantities with a robust error analysis. These methods in combination with simple (as compared to inertial confinement fusion implosions) implosion platforms, which can be modified to show sensitivity to different physical mechanisms of interest, are used to study the physical properties of matter under extreme conditions. This work discusses a subset of implosion targets for studying opacity effects, electron-ion equilibration, and thermal conductivity and, as an example, a system consisting of a thick-shelled, gas-filled laser-direct-drive implosion is used to show how a reduced model and Bayesian inference can help inform experimental design decisions such as diagnostic choice. It is shown that for this system that a combination of neutron and x-ray self-emission diagnostics is critical for constraining the details of the thermodynamic states in the system and that the conductivity exponent in a Spitzer like framework can be constrained to the 30% level in deuterium at gigabar conditions. Furthermore, this process can be applied to many HED systems to make underlying model assumptions explicit and facilitate experimental design and analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Technical Note: Improvements in GEANT4 energy-loss model and the effect on low-energy electron transport in liquid water

Purpose: The GEANT4-DNA physics models are upgraded by a more accurate set of electron cross sections for ionization and excitation in liquid water. The impact of the new developments on low-energy electron transport simulations by the GEANT4 Monte Carlo toolkit is examined for improving its performance in dosimetry applications at the subcellular and nanometer level. Methods: The authors provide an algorithm for an improved implementation of the Emfietzoglou model dielectric response function of liquid water used in the GEANT4-DNA existing model. The algorithm redistributes the imaginary part of the dielectric function to ensure a physically motivated behavior at the binding energies, while retaining all the advantages of the original formulation, e.g., the analytic properties and the fulfillment of the f-sum-rule. In addition, refinements in the exchange and perturbation corrections to the Born approximation used in the GEANT4-DNA existing model are also made. Results: The new ionization and excitation cross sections are significantly different from those of the GEANT4-DNA existing model. In particular, excitations are strongly enhanced relative to ionizations, resulting in higher W-values and less diffusive dose-point-kernels at sub-keV electron energies. Conclusions: An improved energy-loss model for the excitation and ionization of liquid water by low-energy electrons has been implemented in GEANT4-DNA. The suspiciously low W-values and the unphysical long tail in the dose-point-kernel have been corrected owing to a different partitioning of the dielectric function.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping

Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Modeling of Turbulence Effects on Liquid Jet Atomization and Breakup

Recent experimental investigations and physical modeling studies have indicated that turbulence behaviors within a liquid jet have considerable effects on the atomization process. This study aims to model the turbulence effect in the atomization process of a cylindrical liquid jet. Two widely used models, the Kelvin-Helmholtz (KH) instability of Reitz (blob model) and the Taylor-Analogy-Breakup (TAB) secondary droplet breakup by O Rourke et al, are further extended to include turbulence effects. In the primary breakup model, the level of the turbulence effect on the liquid breakup depends on the characteristic scales and the initial flow conditions. For the secondary breakup, an additional turbulence force acted on parent drops is modeled and integrated into the TAB governing equation. The drop size formed from this breakup regime is estimated based on the energy balance before and after the breakup occurrence. This paper describes theoretical development of the current models, called "T-blob" and "T-TAB", for primary and secondary breakup respectivety. Several assessment studies are also presented in this paper.

Trinh, Huu P.↗
NASA NTRS2003

Assimilation of Precipitation Information Retrieved from Passive Microwave Sensors

Estimates of surface precipitation and the associated vertical latent heating structure can be retrieved from space-borne passive microwave sensors such as the TRMM Microwave Imager (TMI) and Special Sensor Microwave/Imager (SSM/I). Studies have shown that assimilation of microwave rain rates can improve the quality of global analysis and forecast. Numerical weather prediction (NWP) centers are beginning to make operational use of these data. In the next few years, there will be a gradual increase in microwave rain products available from operational and research satellites, culminating to a target constellation of 9 satellites to provide global rain measurements every 3 hours with the proposed Global Precipitation Measurement (GPM) mission to be launched around 2007. Realizing the full potential of these observations in data assimilation will require continued advances in retrieval algorithms, assimilation techniques, and model physics. I present results from two research efforts at NASA GSFC. The first is on the assimilation of tropical precipitation using temperature/moisture tendency corrections within a continuous variational assimilation framework to improve analyses and forecasts produced by the Goddard Earth Observing System (GEOS) global data assimilation system. Results show that assimilation of TMI and SSM/I rain rates improves not only precipitation and moisture, but also the related clouds, radiation energy fluxes, and large-scale circulations in GEOS analyses. The improved analyses also yield better short-range forecasts, quantitative precipitation forecast (QPF) threat scores, and storm track predictions. The second part consists of results from exploratory experiments on variational assimilation of convective and stratiform latent heating profiles within the general framework of model parameter estimation as a way to identify systematic model errors and optimize physical paremeterization schemes in global forecast and climate models.

Hou, Arthur Y.↗
DOE OSTI2015

Recent Advances in Continuum Modeling of Interfacial and Transport Phenomena in Electric Double Layer Capacitors

Here, whis paper reviews recent advances in physical modeling of interfacial and transport phenomena in electric double layer capacitors (EDLCs) under both equilibrium and dynamic cycling. The models are based on continuum theory and account for (i) the Stern layer at the electrode/electrolyte interface, (ii) finite ion sizes, (iii) steric repulsions, (iv) asymmetric electrolytes featuring ions with different valencies, effective diameters, or diffusion coefficients, (v) electric-field-dependent dielectric permittivity of the electrolyte, and/or (vi) porous three-dimensional morphology of the electrodes. Typical characterization methods such as electrochemical impedance spectroscopy, cyclic voltammetry, and galvanostatic cycling were reproduced numerically to identify the dominant physical phenomena and to gain insight into experimental observations. In addition, recent thermal models derived from first principles for EDLCs under constant-current cycling accounting for irreversible Joule heating and reversible heat generation rates due to ion diffusion, steric effects, and changes in entropy are discussed. Scaling analyses of both equilibrium and dynamic models are also presented as a way to identify self-similar and asymptotic behaviors as well as to develop design rules for electrodes and electrolytes of next generation EDLCs. Throughout the document, special effort has been made to compare model predictions with experimental observations and reported data.

25 ENERGY STORAGE↗
DOE OSTI2016

SHARP User Manual

SHARP is an advanced modeling and simulation toolkit for the analysis of nuclear reactors. It is comprised of several components including physical modeling tools, tools to integrate the physics codes for multi-physics analyses, and a set of tools to couple the codes within the MOAB framework. Physics modules currently include the neutronics code PROTEUS, the thermal-hydraulics code Nek5000, and the structural mechanics code Diablo. This manual focuses on performing multi-physics calculations with the SHARP ToolKit. Manuals for the three individual physics modules are available with the SHARP distribution to help the user to either carry out the primary multi-physics calculation with basic knowledge or perform further advanced development with in-depth knowledge of these codes. This manual provides step-by-step instructions on employing SHARP, including how to download and install the code, how to build the drivers for a test case, how to perform a calculation and how to visualize the results. Since SHARP has some specific library and environment dependencies, it is highly recommended that the user read this manual prior to installing SHARP. Verification tests cases are included to check proper installation of each module. It is suggested that the new user should first follow the step-by-step instructions provided for a test problem in this manual to understand the basic procedure of using SHARP before using SHARP for his/her own analysis. Both reference output and scripts are provided along with the test cases in order to verify correct installation and execution of the SHARP package. At the end of this manual, detailed instructions are provided on how to create a new test case so that user can perform novel multi-physics calculations with SHARP. Frequently asked questions are listed at the end of this manual to help the user to troubleshoot issues.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

An efficient optimization of well placement and control for a geothermal prospect under geological uncertainty

This work applies an efficient optimization technique based on a multivariate adaptive regression spline (MARS) technique to determine the optimal design and engineering of a potential geothermal production operation at a prospect near Superstition Mountain in Southern California, USA. The faster MARS-based statistical model is used as a surrogate for higher-fidelity physical models within the intensive optimization process. Its use allows for the exploration of the impacts of specific engineering design parameters in the context of geologic uncertainty as a means to both understand and maximize profitability of the production operation. The MARS model is initially developed from a training dataset generated by a finite set of computationally complex hydrothermal models applied to the prospect. Its application reveals that the optimal engineering design variables can differ considerably assuming different choices of hydrothermal flow properties, which, in turn, indicates the importance of reducing the uncertainty of key geologic properties. The major uncertainty sources in the natural-system are identified and ranked first by an efficient MARS-enabled total order sensitivity quantification, which is then used to assist evaluating the effect of geological uncertainties on optimized results. At the Southern California prospect, this parameter sensitivity analysis suggests that groundwater circulation through high permeable structures, rather than heat conduction through impermeable granite, is the primary heat transfer method during geothermal extraction. Reservoir histories simulated using optimal parameters with different constraints are analyzed and compared to investigate the longevity and maximum profit of the geothermal resources. The comparison shows that the longevity and profit are very likely to be overestimated by optimizations without appropriate constraints on natural conditions. In addition to geothermal energy production, this optimization approach can also be used to manage other geologic resource operations, such as hydrocarbon production or CO 2 sequestration, under uncertain reservoir conditions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

A quantum algorithm for evolving open quantum dynamics on quantum computing devices

Abstract Designing quantum algorithms for simulating quantum systems has seen enormous progress, yet few studies have been done to develop quantum algorithms for open quantum dynamics despite its importance in modeling the system-environment interaction found in most realistic physical models. In this work we propose and demonstrate a general quantum algorithm to evolve open quantum dynamics on quantum computing devices. The Kraus operators governing the time evolution can be converted into unitary matrices with minimal dilation guaranteed by the Sz.-Nagy theorem. This allows the evolution of the initial state through unitary quantum gates, while using significantly less resource than required by the conventional Stinespring dilation. We demonstrate the algorithm on an amplitude damping channel using the IBM Qiskit quantum simulator and the IBM Q 5 Tenerife quantum device. The proposed algorithm does not require particular models of dynamics or decomposition of the quantum channel, and thus can be easily generalized to other open quantum dynamical models.

42 ENGINEERING↗
DOE OSTI2019

Decision-support and Emissions Prediction Interactive Cities Tool

The Decision-support and Emissions Prediction Interactive Cities Tool (DEPICT) aims at predicting the cross-sectoral (building construction, operation and vehicles) energy consumption and greenhouse gases emissions at campus and urban scale. It leverages capabilities of multiple modeling tools: EnergyPlus for building operation, CalEEMod for building construction and gas vehicle, BEAM for electric vehicles and SAM for building–integrated photovoltaics. It distinguishes from other cross-sectoral model such as CalEEMod or BEST in that it uses physical models, rather than empirical models, for all sectors, which allows for a more accurate and customizable quantification. It is relevant for short-term multi-sectoral decision making by developers and city planners and long-term policy making by municipal and state officials. Information about all the developers

Ravache, Baptiste↗