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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
DOE OSTI2021

A Hybrid Finite-Volume, Discontinuous Galerkin Discretization for the Radiative Transport Equation

In this report we propose a hybrid spatial discretization for the radiative transport equation that combines a second-order discontinuous Galerkin (DG) method and a second-order finite-volume (FV) method. The strategy relies on a simple operator splitting that has been used previously to combine different angular discretizations. Unlike standard FV methods with upwind fluxes, the hybrid approach is able to accurately simulate problems in scattering dominated regimes. However, it requires less memory and yields a faster computational time than a uniform DG discretization. In addition, the underlying splitting allows naturally for hybridization in both space and angle. Numerical results are given to demonstrate the efficiency of the hybrid approach in the context of discrete ordinate angular discretizations and Cartesian spatial grids.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Higher order multipoint flux mixed finite element methods on quadrilaterals and hexahedra

We develop higher order multipoint flux mixed finite element (MFMFE) methods for solving elliptic problems on quadrilateral and hexahedral grids that reduce to cell-based pressure systems. The methods are based on a new family of mixed finite elements, which are enhanced Raviart–Thomas spaces with bubbles that are curls of specially chosen polynomials. The velocity degrees of freedom of the new spaces can be associated with the points of tensor-product Gauss–Lobatto quadrature rules, which allows for local velocity elimination and leads to a symmetric and positive definite cell-based system for the pressures. We prove optimal [Formula: see text]th order convergence for the velocity and pressure in their natural norms, as well as [Formula: see text]st order superconvergence for the pressure at the Gauss points. Moreover, local postprocessing gives a pressure that is superconvergent of order [Formula: see text] in the full [Formula: see text]-norm. Numerical results illustrating the validity of our theoretical results are included.

Mathematics↗
DOE OSTI2022

AutoBEM-DynamicArchetypes

Automatic Building Energy Modeling (AutoBEM, https://bit.ly/AutoBEM) has been used to create an OpenStudio and EnergyPlus building energy model of 122.9 million U.S. buildings (https://bit.ly/ModelAmerica). Simulating and analyzing a model of every building for large areas (e.g. cities) is often not feasible. This dynamic archetyping capability uses a representative building and calculates a floor-space multiplier that allows millions of buildings to be represented by less than 100 buildings. This script (WRF_Archetypes_Parallel.py) calculates these building archetypes for each of the grid cells from a Weather Research and Forecasting (WRF) model in a parallel fashion. The script works by looping through each of the grid cells in the shapefile in parallel, spatially joining the building metadata table to each grid cell, aggregating relevant archetypes and calculating necessary statistics related to area and number of buildings in each cell. The output is a table (.csv) in which each row is an archetype building with properties about that building as well as statistics that relate that building to the total cell (such as an area multiplier). The following inputs are required: WRF zone shapefile (.shp) (wrf-grids-origin_Vegas_Select_100.geojson) The projection of the shapefile ("EPSG:XXXX") Input table containing building metadata for area corresponding to shapefile (.csv) (https://zenodo.org/record/4552901#.YZQEotDMJPY - ClarkCounty2.csv) The number of cores that will be parallelized (integer) The output file name for the archetype table (.csv) Sample command line inputs: python3 ~/WRF_Archetypes_Parallel.py -i ~/wrf-grids-origin_Vegas_Select_100.geojson -c ~/ClarkCounty2.csv -o ~/OutputArchetypes.csv -j 72 -e EPSG:4326 Using Geopandas Version 0.9.0

Bass, Brett (0000000240988434)↗
DOE OSTI2026

Data Center Market Report

The data center market is poised to explode in the coming decade due to undeniable drivers such as continued adoption of generative AI, increased data storage needs, and enterprise integration of AI in numerous industries [1] [2] [3]. Scalable power and increased computational capacity are at the forefront of considerations for hyperscalers, the major cloud service providers in this space. Lawrence Livermore National Laboratory is uniquely poised to help with informed decision making for data center market leaders during this phase of explosive expansion. National grid modeling expertise and cutting edge innovations in computer cooling systems place LLNL in an enviable position for creating economic impact in the data center industry by leveraging its expertise in these areas which can help the data center market keep up with growing demand.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

CHARACTERIZATION AND DEVELOPMENT OF THE ALL–AQUEOUS COPPER THERMALLY REGENERATIVE AMMONIA BATTERY

Low temperature thermal energy is an abundant and untapped energy source for large scale electrical power and energy production around the world. Many different stationary sources such as industrial and thermal power plants produce significant amounts of low temperature heat that goes unused. The primary reason for the energy loss is because the temperature of the heat is often too low for traditional systems to economically generate power from this waste heat. Many different technologies are being researched to produce grid–scale power from low temperature heat to increase grid efficiency, advance decarbonization efforts, and progress electrical power production to a more distributed system. A thermally regenerative battery (TRB) is one promising technology in this space, as it has large power and energy densities relative to competitive technologies and therefore has the potential to disrupt the current power generation mix. TRBs combine two unit operations (a redox flow battery and a distillation column) which are easily scalable and mature, meaning that if a viable TRB chemistry can be identified, its development to pilot and commercial scale can be streamlined. The all–aqueous, copper thermally regenerative ammonia battery (Cuaq–TRAB) was the first TRB that did not use an electrodeposition/dissolution reaction by stabilizing Cu(I) and Cu(II) in the electrolyte, allowing all electroactive species to be stable in an aqueous phase. Through these electrolyte modifications, coulombic efficiency, open circuit potential, and copper solubility all increased, resulting in large gains to power density, energy density, and energy efficiency.

Cross, Nicholas↗
DOE OSTI2025

Microstructure‐Dependent Sodium Storage Mechanisms in Hard Carbon Anodes

Sustainable energy storage is essential to support the transition to renewables and meet the increasing demand for energy. Sodium‐ion batteries (NIBs) are attractive for grid‐scale energy storage due to the abundance and low cost of sodium, sustainability of other battery components, and electrochemical performance. Hard carbon (HC) is a leading anode material for NIBs, but its complex microstructure complicates the understanding of sodium storage mechanisms. Using X‐ray total scattering and density functional theory calculations, this study clarifies how HC's microstructural variations influence sodium storage across the slope (high potential) and plateau (low potential) regions of the potential capacity curve. In the slope region, sodium initially adsorbs at high‐binding energy defect sites and subsequently intercalates between graphene layers, adsorbing at low‐binding energy defect sites, correlating with different slopes observed during initial sodiation. Initial irreversibility arises from sodium trapping at surface defects and solid electrolyte interface formation. In the plateau region, sodium simultaneously intercalates and fills pores, influenced by pore size, interlayer spacing, and defect concentration. HCs with larger pore sizes form larger sodium clusters. In conclusion, the proposed mechanism underscores the role of microstructure engineering in enhancing HC performance and advancing NIBs for grid‐scale energy storage.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Surface Cloud Grid Version 2 (SFCCLDGRID2) Value-Added Product: Description of Update"

This document describes the algorithm used for the Surface Cloud Grid Value-Added Product (VAP). This VAP uses as input the 15-min. output from the Shortwave (SW) Flux Analysis VAP (see Long 2001; Long and Ackerman 2000; Long et al. 1999) from the Atmospheric Radiation Measurement (ARM) Climate Reseach Facility (ACRF) Southern Great Plains (SGP) Central Facility and extended facilities. This network of 21 sites is unevenly spaced over northern Oklahoma into southern Kansas, covering an area from 95.5° to 99.5° west longitude and 34.5° to 38.5° north latitude. For research applications such as single-column modeling, an estimate of the cloud and cloud effects distribution over this entire domain is desirable. The Surface Cloud Grid VAP applies a multi-pass weighted sum analytic approximation technique (Caracena 1987), which uses Gaussian weighting and an imposed scale length, to interpolate to a 0.25° by 0.25° lat/long grid over the SGP domain. The output, like the input, includes solar elevation angles of 10° or greater.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Autocalibration of the E3SM Version 2 Atmosphere Model Using a PCA-Based Surrogate for Spatial Fields

Global Climate Model tuning (calibration) is a tedious and time-consuming process, with high-dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand-picked values of tuning parameters. Many, in both the statistical and climate literature, have proposed alternative calibration methods, but most are impractical or difficult to implement. We present a practical, robust, and rigorous calibration approach on the atmosphere-only model of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can be summarized into two main parts: (a) the training of a surrogate that predicts E3SM output in a fraction of the time compared to running E3SM, and (b) gradient-based parameter optimization. To train the surrogate, we generate a set of designed ensemble runs that span our input parameter space and use polynomial chaos expansions on a reduced output space to fit the E3SM output. We use this surrogate in an optimization scheme to identify values of the input parameters for which our model best matches gridded spatial fields of climate observations. To validate our choice of parameters, we run E3SMv2 with the optimal parameter values and compare prediction results to expertly-tuned simulations across 45 different output fields. This flexible, robust, and automated approach is straightforward to implement, and we demonstrate that the resulting model output matches present day climate observations as well or better than the corresponding output from expert tuned parameter values, while considering high-dimensional output and operating in a fraction of the time.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Assessing the impacts of wakes on floating wind farms with shared anchors

This paper examines wake effects for floating wind farms with shared anchors. Three 20-turbine farms are examined: a grid-formation baseline with no shared anchors, a farm based on 3-line anchors, and a farm based on 6-line anchors, governed by a wind turbine spacing of 8 rotor diameters. The IEA 15 MW reference turbine on the UMaine semisubmersible platform was used with a taut mooring system in deep- water depths representative of U.S. west coast lease areas. A steady-state wake model showed that, when evaluating a sweep of wind headings, the baseline design had the lowest wake losses, with a value of 11.7 %, followed by the 6-line at 12.9% and the 3-line at 13.8%. Dynamic simulations were run in FAST.Farm to analyse the effects of wakes on mean anchor loads for wind headings that showed significant wake losses. The baseline farm showed the largest anchor load reductions due to wake effects (up to 16%), followed by the 3-line farm (up to 8%), and the 6-line farm, which showed relatively consistent load magnitudes on the 6-line anchors across all headings.

17 WIND ENERGY↗
DOE OSTI2026

Explicit Monotone Stable Super-Time-stepping Methods for Finite Time Singularities

We explore a novel way to numerically resolve the scaling behavior of finite-time singularities in solutions of nonlinear parabolic PDEs. The Runge–Kutta–Legendre (RKL) and Runge–Kutta–Gegenbauer (RKG) super-time-stepping methods were originally developed for nonlinear complex physics problems with diffusion. These are multistage single step second-order, forward-in-time methods with no implicit solves. The advantage is that the time-step size for stability scales with stage number 𝑠 as $\mathcal{O}$⁡(𝑠 2 ). Many interesting nonlinear PDEs have finite-time singularities, and the presence of diffusion often limits one to using implicit or semi-implicit time-step methods for stability constraints. Finite-time singularities are particularly challenging due to the large range of scales that one desires to resolve, often with adaptive spatial grids and adaptive time steps. Here, in this study, we show two examples of nonlinear PDEs for which the self-similar singularity structure has time and space scales that are resolvable using the RKL and RKG methods, without forcing even smaller time steps. Compared to commonly used implicit numerical methods, we achieve a significantly smaller run time while maintaining comparable accuracy. We also prove numerical monotonicity for both the RKL and RKG methods under their linear stability conditions for the constant coefficient heat equation, in the case of infinite domain and periodic boundary condition, leading to a theoretical guarantee of the superiority of the RKL and RKG methods over traditional super-time-stepping methods, such as the Runge-Kutta-Chebyshev and the orthogonal Runge-Kutta-Chebyshev methods. Code can be found at https://github.com/ZT220501/SRK-Singularity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Low-cost Manufacturing of Semitransparent CdTe PV for Building Integration

Solar has been demonstrated to be a robust renewable energy source, constituting a significant portion of the United States’ renewable energy portfolio. Despite its growth across residential, commercial, and utility sectors over the past decades, it remains a small fraction of the overall energy infrastructure. Challenges persist in fully harnessing solar power to meet the nation's escalating energy demands. Among these challenges lies the hurdle of efficiently distributing large quantities of solar-generated electricity to densely populated regions with the highest energy needs and costs. Traditional utility-scale arrays demand extensive land, a luxury often unavailable in metropolitan areas. Consequently, installations must be situated at a distance, necessitating additional infrastructure for electricity transmission to service areas. While metropolitan landscapes lack sprawling open spaces suitable for conventional utility-scale solar deployment, they offer a different resource: windows. Semitransparent photovoltaic window technology has the potential to not only bolster the grid's energy capacity, but to also provide HVAC and economic advantages to building owners. However, commercial availability of building-integrated photovoltaic windows remains limited. Silicon based photovoltaics currently dominate the solar market but adapting them for use in windows poses a variety of engineering and economic challenges such as relatively low power density, high costs associated with custom manufacturing, and aesthetic considerations. Addressing these challenges, this project explored the use of laser ablation patterning to manufacture cost-effective, high-efficiency, semitransparent Cadmium Telluride photovoltaic modules. Results showcased the potential of this methodology in developing photovoltaic windows and other innovative semitransparent PV applications. The ablation manufacturing technique demonstrated great versatility in achieving different patterns and levels of visible light transmission, and the power loss due to ablation was nearly directly proportional to the amount of material removed. Furthermore, the manufacturing process for Cadmium Telluride modules already has established advantages in material and energy efficiency, and the conversion of a standard submodule to semitransparent essentially requires a single additional process step, ensuring scalability. It is important to note that during this work, Toledo Solar experienced substantial organizational upheaval stemming from a lawsuit with First Solar. An external investigation led the board of directors to remove and replace the previous management team, and several other members of the staff elected to depart as well, including the then acting Principle Investigator on this project. The remaining Toledo Solar team attempted to recover from the disruption and deliver on the remaining tasks, but upon its own review, the Department of Energy ruled the project in default and terminated the contract in December 2023.

14 SOLAR ENERGY↗
DOE OSTI2024

Grid Planning for Building Electrification: A Report by the Energy Systems Integration Group’s Grid Planning for Building Electrification Task Force

The increased electrification of buildings across the United States is being driven by technical advancements, cost reductions for some building technologies, consumer preferences, and policy goals for decarbonization. However, the effects of this load growth on the electric distribution system are often only a minor consideration in policymaking and long-term planning studies. The long lead time and useful life of power system equipment means that the decisions taken today are expected to support our society well into the 2060s and beyond. Distribution planning stakeholders can take steps today to establish a grid foundation that captures the new challenges presented by building electrification. This report focuses on building electrification for the residential and commercial sectors, focusing most heavily on space heating, as this end use stands to most strongly impact demand.1 Across all end uses, building electrification could require somewhere between 10% and 70% more electricity generation capacity than exists today depending on technology adoption and energy efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Islanding Detection of Distributed Generation Using Electrical Variables in Space Vector Domain

This paper presents a new approach for islanding detection in distribution systems incorporating synchronous generator-based distributed generations. The proposed method first utilizes the dynamic equations of three electrical variables under islanding situations. The variables are: change of frequency; rate of change of frequency; and rotor angle deviation. Second, transformation of these variables is conducted in the space vector domain, which gives rise to three parameters, namely, semi-major axis, semi-minor axis, and inclination angle. Finally, exploiting these parameters, a decision boundary is established to classify grid events resulting in the formation of an island or not. The proposed method is comprehensively investigated under different operating conditions, which include excess/deficit of reactive/active power imbalance scenarios in the formed island containing different load-types (constant impedance, constant power, and constant current) with varying quality factor. A large variety of islanding and non-islanding events are generated using a practical distribution network simulated in real time on OPAL-RT digital simulation platform under the aforementioned operating conditions. A detailed evaluation of the proposed approach is conducted and it demonstrates the effectiveness of the method.

F-measure↗
DOE OSTI2023

Dimensionality Reduction with Variational Encoders Based on Subsystem Purification

Efficient methods for encoding and compression are likely to pave the way toward the problem of efficient trainability on higher-dimensional Hilbert spaces, overcoming issues of barren plateaus. Here, we propose an alternative approach to variational autoencoders to reduce the dimensionality of states represented in higher dimensional Hilbert spaces. To this end, we build a variational algorithm-based autoencoder circuit that takes as input a dataset and optimizes the parameters of a Parameterized Quantum Circuit (PQC) ansatz to produce an output state that can be represented as a tensor product of two subsystems by minimizing $Tr(ρ^2)$. The output of this circuit is passed through a series of controlled swap gates and measurements to output a state with half the number of qubits while retaining the features of the starting state in the same spirit as any dimension-reduction technique used in classical algorithms. The output obtained is used for supervised learning to guarantee the working of the encoding procedure thus developed. We make use of the Bars and Stripes (BAS) dataset for an 8 × 8 grid to create efficient encoding states and report a classification accuracy of 95% on the same. Thus, the demonstrated example provides proof for the working of the method in reducing states represented in large Hilbert spaces while maintaining the features required for any further machine learning algorithm that follows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A learned closure method applied to phase mixing in a turbulent gradient-driven gyrokinetic system in simple geometry

We present a new method for formulating closures that learn from kinetic simulation data. We apply this method to phase mixing in a simple gyrokinetic turbulent system – temperature-gradient-driven turbulence in an unsheared slab. The closure, called the learned multi-mode (LMM) closure, is constructed by, first, extracting an optimal basis from a nonlinear kinetic simulation using singular value decomposition. Subsequent nonlinear fluid simulations are projected onto this basis and the results are used to formulate the closure. We compare the closure with other closures schemes over a broad range of the relevant two-dimensional parameter space (collisionality and gradient drive). We find that the turbulent kinetic system produces phase-mixing rates much lower than the linear expectations, which the LMM closure is capable of capturing. We also compare radial heat fluxes. A Hammett–Perkins closure, generalized to include collisional effects, is quite successful throughout the parameter space, producing ${\sim }14\,\%$ root-mean-square (r.m.s.) error. The LMM closure is also very effective: when trained at three (two) points (in a 35 point parameter grid), the LMM closure produces $8\,\%$ ( $12\,\%$ ) r.m.s. errors. The LMM procedure can be readily generalized to other closure problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

New Capabilities for Sampling Tools

This report will discuss new capabilities that have been added to the sample.py and uniform_sampler.py codes. The codes have been updated to allow for both log-uniform sampling and categorical variables. The categorical variables do not have to be numeric. The different values for a categorical variable are specified in a limits file by having spaces between them. The difference between sample.py and uniform_sampler.py is that sample.py is for generating random samples and uniform_sampler.py is for generating samples or points on a fixed grid. After sourcing a file to set the environment, execute the codes with the commands: sample.py and uniform sampler.py .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗