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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗
DOE OSTI2024

Design and Optimization of Processes for Recovering Rare Earth Elements from End-of-Life Hard Disk Drives

In this conference paper, we propose a superstructure-based approach to finding the optimal pathways for recovering rare earth elements in their commercialized rare earth oxide form from end-of-life HDDs. The proposed superstructure was modeled as a MILP optimization problem, selecting the net present value as the objective function. Whenever possible, costing data taken from the literature was used to inform this mode. However, due to the novelty of this research area data were often not available thus requiring the generation of flowsheets that were implemented in Aspen Plus. To establish the base case optimal result, projections for the number of EOL HDDs in the U.S. available for recycling and estimates of the projected rare earth oxide prices over the lifetime of the plant were used to inform the model. The model was then expanded to include the recycling of EOL HDDs generated prior to the beginning of plant production (period ranging from 2006 through 2024).

Laliwala, Chris↗
DOE OSTI2024

Recycling Rare Earth Elements from End-of-Life Electric and Hybrid Electric Vehicle Motors

In this paper, we propose a superstructure-based approach to finding the optimal pathways for recovering rare earth elements in their commercialized rare earth oxide form from end-of-life EV and HEV motors. The proposed superstructure was modeled as a MILP optimization problem, selecting the net present value as the objective function. Whenever possible, costing data taken from the literature was used to inform this mode. However, due to the novelty of this research area data were often not available thus requiring the generation of flowsheets that were implemented in Aspen Plus.

Laliwala, Chris↗
DOE OSTI2025

Long-Range Metal–Sorbent Interactions Determine CO 2 Capture and Conversion in Dual-Function Materials

Carbon capture and utilization involve multiple energy- and cost-intensive steps. Dual-function materials (DFMs) can reduce these demands by coupling CO 2 adsorption and conversion into a single material with two functionalities: a sorbent phase and a metal for catalytic CO 2 conversion. The role of metal catalysts in the conversion process seems salient from previous work, but the underlying mechanisms remain elusive and deserve deeper investigation to achieve maximum utilization of the two phases. Here, for this work, preformed colloidal Ru nanoparticles were deposited onto a “NaOx”/Al 2 O 3 sorbent to prepare prototypical DFMs with controlled phases for CO 2 capture and hydrogenation to CH 4 . Ru addition was found to double the high-temperature CO 2 adsorption capacity by activating the “NaOx”/Al 2 O 3 sorbent phase during a reductive pretreatment step. Most importantly, low Ru loadings were sufficient to ensure maximum CO 2 adsorption and conversion. This was attributed to the key role of the metal–sorbent interactions, wherein Ru was required to hydrogenate strongly bound CO 2 on the “NaO x ”/Al 2 O 3 sorbent to CH 4 via the H 2 activated on Ru. This interaction facilitated rate-determining carbonate migration and subsequent hydrogenation at the metal–sorbent interface. Overall, Ru controlled the CO 2 hydrogenation reaction rate, while the “NaO x ”/Al 2 O 3 sorbent dictated the CO 2 uptake capacity. By controlling metal–sorbent interactions at the molecular level, we demonstrate the critical role of the two phases and their synergy, facilitating the design of DFMs with maximum CO 2 capture and conversion efficiency.

carbon capture↗
DOE OSTI2022

Comparing machine learning and interpolation methods for loop-level calculations

The need to approximate functions is ubiquitous in science, either due to empirical constraints or high computational cost of accessing the function. In high-energy physics, the precise computation of the scattering cross-section of a process requires the evaluation of computationally intensive integrals. A wide variety of methods in machine learning have been used to tackle this problem, but often the motivation of using one method over another is lacking. Comparing these methods is typically highly dependent on the problem at hand, so we specify to the case where we can evaluate the function a large number of times, after which quick and accurate evaluation can take place. We consider four interpolation and three machine learning techniques and compare their performance on three toy functions, the four-point scalar Passarino-Veltman D_0 D 0 function, and the two-loop self-energy master integral M. We find that in low dimensions (d = 3), traditional interpolation techniques like the Radial Basis Function perform very well, but in higher dimensions (d=5, 6, 9) we find that multi-layer perceptrons (a.k.a neural networks) do not suffer as much from the curse of dimensionality and provide the fastest and most accurate predictions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Efficient lattice QCD computation of radiative-leptonic-decay form factors at multiple positive and negative photon virtualities

In previous work [D. Giusti, Methods for high-precision determinations of radiative-leptonic decay form factors using lattice QCD, Phys. Rev. D 107, 074507 (2023)], we showed that form factors for radiative leptonic decays of pseudoscalar mesons can be determined efficiently and with high precision from lattice QCD using the “three-dimensional (3D) method,” in which three-point functions are computed for all values of the current insertion time and the time integral is performed at the data-analysis stage. Here, we demonstrate another benefit of the 3D method: the form factors can be extracted for any number of nonzero photon virtualities from the same three-point functions at no extra cost. We present results for the $D_s → ℓνγ*$ vector form factor as a function of photon energy and photon virtuality, for both positive and negative virtuality, for a single ensemble with 340 MeV pion mass and 0.11 fm lattice spacing. In our analysis, we separately consider the two different time orderings and the different quark flavors in the electromagnetic current. We discuss in detail the behavior of the unwanted exponentials contributing to the three-point functions, as well as the choice of fit models and fit ranges used to remove them for various values of the virtuality. While positive photon virtuality is relevant for decays to multiple charged leptons, negative photon virtuality suppresses soft contributions and is of interest in QCD-factorization studies of the form factors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

PANNA: Properties from Artificial Neural Network Architectures

We report prediction of material properties from first principles is often a computationally expensive task. Recently, artificial neural networks and other machine learning approaches have been successfully employed to obtain accurate models at a low computational cost by leveraging existing example data. Here, we present a software package “Properties from Artificial Neural Network Architectures” (PANNA) that provides a comprehensive toolkit for creating neural network models for atomistic systems following the Behler–Parrinello topology. Besides the core routines for neural network training, it includes data parser, descriptor builder for Behler–Parrinello class of symmetry functions and force-field generator suitable for integration within molecular dynamics packages. PANNA offers a variety of activation and cost functions, regularization methods, as well as the possibility of using fully-connected networks with custom size for each atomic species. PANNA benefits from the optimization and hardware-flexibility of the underlying TensorFlow engine which allows it to be used on multiple CPU/GPU/TPU systems, making it possible to develop and optimize neural network models based on large datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Estimating the cost and energy demand of producing lithium manganese oxide for Li-ion batteries

Lithium Manganese Oxide (LMO) is one of the important cathode active materials used in lithium ion batteries of several electric vehicles. In this paper, the production of LMO cathode material for use in lithium-ion batteries is studied. Spreadsheet-based process models have been set up to estimate and analyze the factors affecting the cost of manufacturing, the energy demand, and the environmental impact. Two processes based on the solid-state synthesis method and a sol-gel method have been explored. Results show that the solid-state process is more cost-effective because of its lower cost of raw materials. The production cost for a solid-state process is $7 kg -1 and requires 6 kWh·kg -1 of energy. The pack level cost of electric vehicle battery using LMO as a primary active material is studied as a function of LMO production cost and other parameters. The potential for reducing the cost of automotive batteries to $100 per kWh is explored in terms of LMO price and plant production volume (economy of scale), using Argonne’s BatPaC spreadsheet tool.

25 ENERGY STORAGE↗
DOE OSTI2022

Material Architectures to Integrate Multiple Reactor Functions for Fission Batteries

It is necessary for Advanced Reactors to have highly advanced engineered material systems that can combine multiple functions to save weight and cost. Metamaterial structures can be used to achieve this function. Material properties that could feasibly be combined were identified and explored in the literature. They were then proposed for future studies gauging the effectiveness of these proposals.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Total Cost of Vehicle Ownership - Development of Analysis Webtool and Visualization

This report has been created to give an in-depth walkthrough of the functionality of the Total Cost of Operation webtool. The webtool was created to give users the ability to calculate the total cost of owning a vehicle over from the year they bought the vehicle to the end of the vehicles lifetime. This tool was developed using a combination of front-end and back-end technologies. To create the front-end HTML, CSS, and JavaScript were utilized. On the backend PHP is used as a scripting language with a database powered by MySQL. Through a combination of these technologies, a fully featured well developed webtool was created allowing users to view a cost breakdown of vehicle ownership over the lifetime of that vehicle.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Improving Predictions of Spin-Crossover Complex Properties through DFT Calculations with a Local Hybrid Functional

We conducted a study on the performance of the local hybrid exchange-correlation functional PBE0r for a set of 95 experimentally-characterized iron spin crossover (SCO) complexes. The PBE0r functional is a variant of PBE0 where the exchange correction is restricted to on-site terms formulated within the basis of local orbitals. We determine the free parameters of the PBE0r functional against experimental data and other hybrid functionals. With a Hartree-Fock (HF) exchange factor of 4%, the PBE0r functional accurately reproduces the electronic and free energy trends predicted in prior DFT studies for these 95 complexes using the B3LYP functional. Larger values of HF exchange stabilize high-spin states. The PBE0r-predicted bond lengths tend to exceed the experimental bond lengths, and bond lengths are less sensitive to HF exchange. The predicted SCO transition temperatures T 1/2 from PBE0r correlate moderately with the experimental transition temperatures, showing a slight improvement compared to the previous modB3LYP-predicted T 1/2 . Furthermore, this study suggests the PBE0r functional as computationally cost-effective and offers the possibility of simulating larger complexes with accuracy comparable to other global hybrid functionals, provided the HF exchange parameter is carefully optimized.

25 ENERGY STORAGE↗
DOE OSTI2022

Cost Efficient-by-Design Microreactors: Trade-offs between cost, technical, and regulatory factors

This research evaluates a cost reduction investigation through adoption of a functional containment approach on the microreactor system and structure. Trade-offs between microreactor system designs, fuels and reactor module sizes are evaluated based on performance-based and risk-informed design procedures. Cost savings are evaluated against trade-offs in the reliability of passive heat removal systems, reactivity control, and radioactive material containment. Results can inform the Microreactor Program and reactor designs on “sweet spots” for microreactors that are cost-efficient while meeting required safety limits.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Understand the Costs of an Energy or Water Outage with the Customer Damage Function Calculator

The Customer Damage Function (CDF) Calculator is a free, publicly available web tool that helps users estimate the costs incurred at their site due to an electric grid or water service outage. Developed by the National Renewable Energy Laboratory (NREL) with support from the Federal Energy Management Program (FEMP), the tool is designed to help federal facility owners, building energy managers, and resilience planners understand and quantify the value of resilience to justify investments that would prevent or lessen the impact of a disruption.

CDF Calculator↗
DOE OSTI2024

Considerations for Improving the Technoeconomic Viability of Heat Pipe Microreactors by Employing a Functional Containment Approach

The economic cost of a microreactor is being investigated by leveraging MARVEL cost information in conjunction with some simplified design and analysis activities to present a hypothetical, yet feasible, option for investigation. This bottom-up cost estimate for a hypothetical commercial microreactor will be used to provide guidance to the industry and research communities regarding where priorities should be set to improve the viability of broad microreactor deployment. To support the design and analysis activities required to present a feasible option, some understanding of maximum accidental radionuclide releases and consequences is needed to provide bounding estimates on containment or confinement systems for radionuclide retention. To estimate fission product barrier and retention performance, a gaussian plume dose and dispersion model is employed, with isotopic inventories being propagated between barriers assuming conservative fractional release rates. Demonstrably conservative meteorological assumptions are also assumed. Standard reference release rates are assumed where safety requirements could be easily and economically employed. Results are provided in units of total effective dose equivalent as a function of distance from the release (i.e., the reactor). Initial results indicate that the hypothetical microreactor will likely need to credit the fuel cladding and at least one other barrier, or combination of barriers, to ensure public health and safety as required during a postulated accident. This approach is referred to as “functional containment,” since no single barrier will be relied upon for satisfying regulatory requirements and fundamental safety functions for radionuclide retention. Finally, a set of barrier options are provided so that future economic analyses can select the most viable design path.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

SPECs Early-Stage Decision Model: User Manual

The SPECs Early-Stage Decision (ESD) model is central to the SPECs procurement solutions toolkit. The ESD model is an Excel-based spreadsheet model, which provides information about the economic and strategic value of a proposed battery-storage project or solar-plus-storage (solar-plus) project. The model can be used to explore combinations of storage-related project value streams in order to define a potential project, while educating co-op decision-makers about project benefits and costs. A sensitivity analysis function speeds the development of "what-if" scenarios. A gap analysis function solves for top-priority metrics and supports the inclusion of hard-to-monetize strategic values, such as the value of storage to defer costly system upgrades in light of increasing distributed solar and other distributed energy resources (DERs). Model outputs include the utility data, assumptions, and use-case scenarios that are recommended content for the requests for proposals (RFPs). The model may also provide an initial "sanity check" for RFP responses, supporting further discussions among utility staff, vendors and stakeholders. The ESD is not a "finance-grade" modeling tool, and users are cautioned to be mindful of its limitations, but the model has been reviewed by users, who recommend it as a way to drive faster and better project design and planning, as well as to facilitate better communications with vendors, grid partners, and stakeholders.

14 SOLAR ENERGY↗
DOE OSTI2022

Stochastic evaluation of four-component relativistic second-order many-body perturbation energies: A potentially quadratic-scaling correlation method

A second-order many-body perturbation correction to the relativistic Dirac-Hartree-Fock energy is evaluated stochastically by integrating 13-dimensional products of four-component spinors and Coulomb potentials. The integration in the real space of electron coordinates is carried out by the Monte Carlo (MC) method with the Metropolis sampling, whereas the MC integration in the imaginary-time domain is performed by the inverse-CDF (cumulative distribution function) method. The computational cost to reach a given relative statistical error for spatially compact but heavy molecules is observed to be no worse than cubic and possibly quadratic with the number of electrons or basis functions. This is a vast improvement over the quintic scaling of the conventional, deterministic second-order many-body perturbation method. The algorithm is also easily and efficiently parallelized with demonstrated 92% strong scalability going from 64 to 4096 processors for a fixed job size.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Multifidelity multiobjective optimization for wake-steering strategies

Abstract. Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of perpendicular alignment with the incoming wind, thereby “steering” wakes away from downstream turbines. However, trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present a multifidelity multiobjective optimization approach for exploring the Pareto front of trade-offs between power and loading during wake steering. A large eddy simulation is used as the high-fidelity model, where an actuator line representation is used to model wind turbine blades and a rainflow-counting algorithm is used to compute damage equivalent loads. A coarser simulation with a simpler loads model is employed as a supplementary low-fidelity model. Multifidelity Bayesian optimization is performed to iteratively learn both a surrogate of the low-fidelity model and an additive discrepancy function, which maps the low-fidelity model to the high-fidelity model. Each optimization uses the expected hypervolume improvement acquisition function, weighted by the total cost of a proposed model evaluation in the multifidelity case. The multifidelity approach is able to capture the logit function shape of the Pareto frontier at a computational cost only 30 % that of the single-fidelity approach. Additionally, we provide physical insights into the vortical structures in the wake that contribute to the Pareto front shape.

17 WIND ENERGY↗
DOE OSTI2024

Residential Integrated Heat Pump to Meet All the Home Comfort Needs

This paper will introduce development and field trial of a residential air-source integrated heat pump for cold climates. The heat pump is multi-functional to meet all the home comfort demands, including space cooling, space heating, domestic water heating. The integrated heat pump is an ideal solution to decarbonize northern homes via providing efficient space heating and water heating to replace natural gas. It uses a three-stage compressor and a single set of heat exchangers and valves to deliver all the functions, and thus achieve cost reduction. We developed an innovative system configuration and related controls to solve typical charge unbalance, accelerate charge migration and smoothen mode transition in integrated heat pumps. Laboratory investigations were conducted for individual modes and verified the control functions. Laboratory tests demonstrated that the unit delivered outstanding performance. It achieved 17.0 SEER (seasonal cooling energy efficiency rating) and 11.0 HSPF (heating seasonal performance factor). In the most efficient mode (combined space cooling and water heating mode), the unit reached a total energy efficiency > 30.0 EER and required only 25 minutes to heat a 50-gallon tank of water. One heat pump prototype is going through a field trial since April, 2023 in Syracuse, New York. The one-year field test results are summarized.

Shen, Bo↗