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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 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Purification of Lithium-Ion Battery Black Mass through Tailored Alkaline Corrosion

Obtaining high-purity material outputs is crucial to the viability of novel process aimed at direct recycling of lithium-ion batteries. Metallic impurities in recycled cathodes have been shown to inhibit performance, thereby threatening mainstream acceptance of recycled battery products. Thus, shredded black mass (BM) must be purified to remove metallic contaminants, and specifically Al and Cu originating from the electrode current collectors. We herein explore a process to ionize solid copper and aluminum to ionic form based on tailored alkaline chemistry, without incurring damage to the target cathode material (Li(NixMnyCo1-x-y)O2; NMC). Al and Cu corrosion may be enhanced through the addition of chloride salt, elevated temperatures, and the use of ultrasonication - all of which disrupt the formation of passivating films on the metallic surface, and thereby increase corrosion rate. We demonstrate optimized parameters for Al and Cu corrosion both from a kinetic and overall process cost perspective. Further, we analyze the impact of these conditions on the structural (XRD, SEM), chemical (EDS, ICP), and electrochemical (impedance, cycling, dQ/dV) properties of NMC, and suggest that the present purification method does not significantly disrupt NMC performance. Finally, we present preliminary results from a promising bench-scale demonstration of this purification process applied to a simulated black mass.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
DOE OSTI2026

Improving ideal MHD equilibrium accuracy with physics-informed neural networks

We present a novel approach to compute three-dimensional magnetohydrodynamic equilibria with isotropic pressure profiles and nested surfaces by parametrizing Fourier modes with artificial neural networks (NNs). The full nonlinear global force residual of single equilibria across the volume in real space is then minimized with first order optimizers and compared to equilibria computed by conventional solvers. Already, we observe competitive computational cost to arrive at the same minimum residuals computable with existing codes. With increased computational cost, lower minima of the residual are computable with the NNs than with any other tested solver, establishing a new lower bound for the force residual. We use minimally complex NNs, and we expect significant improvements for solving not only single equilibria with NNs, but also for creating NN models valid over continuous distributions of equilibria.

ideal magnetohydrodynamics↗
DOE OSTI2023

Enhanced magnetostriction through dilute Ce doping of Fe-Ga

Doping of magnetostrictive galfenol (Fe 82 Ga 18 , in at.%) with rare-earth elements significantly enhances magnetostriction, with the largest gains achieved in textured melt-spun ribbons. Here, it is demonstrated that even extremely dilute Ce, as little as 65 ppm, can double the magnetostrictive response of galfenol when coupled with an appropriate heat treatment. This improvement is correlated with a compression of the host lattice, both of which reach their maximum extent at the calculated solubility limit of Ce in in body-centered cubic (bcc) galfenol, ~ 50 ppm. Beyond this point, excess Ce segregates into CeGa 2 , which forms an interdendritic network throughout the sample at high Ce levels and cannot be resolutionized through heat treatments. These findings point to the importance of solubility limits (i.e., equilibrium thermodynamics) in determining appropriate doping levels or heat treatment couples to optimize magnetostrictive performance, confirming that overdoping is actively detrimental to both material properties and cost.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A refined assessment of nuclear data target accuracy requirements for ALFRED

The interest in target accuracy assessment has been recently renewed within the OECD/NEA Working Party on International Nuclear Data Evaluation Cooperation with an update exercise, thanks to the invaluable initiative of Massimo Salvatores. This work, moving from evaluations on the main core integral parameters of the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED), and the identification of the associated target accuracies needed for improving its design, approaches the retrieval of target accuracy requirements for nuclear data, as the inverse problem of uncertainty quantification. The preliminary results obtained in a previous work are here refined by explicitly taking into account the total contribution due to correlations among variables in the optimization constraint, while its various components are evaluated individually. Moreover, three different sets of cost parameters are considered to introduce a bias in the solution in order to take in due account the relative difficulties in the execution of new differential experiments as needed for achieving the proposed target accuracies. By this, the obtained results are deemed usefully informative, and proposed for the update of a High Priority List which identifies the isotopes-reaction couples of most interest for future refinement experiments, especially when integrated with further analogous information collected on other advanced nuclear systems. Among the cross-sections whose refinement contributes the most to the reduction of the multiplication factor, the fission of Pu{sup 239} was found, which needs to be fixed below 1% in the range of interest for ALFRED (2 keV-4 MeV). Another contribution for the amelioration of the multiplication factor calculation accuracy, is due to the capture channel of Pu{sup 239}, for which the initial high uncertainty (around 15%) can be significantly reduced resorting to easily practicable experiments. The same occurs for the inelastic of Pb{sup 207}, for which the initial uncertainty value of around 50% can be drastically reduced up to target values between 4% and 7%.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Development of Prognostic Models Using Plant Asset Data

The recent growth of machine learning and artificial intelligence technologies provides opportunities for leveraging data-driven algorithms to address the problems of diagnostics and prognostics in the nuclear power industry. The use of machine learning and other statistical methods as prognostic models is of particular interest in the nuclear industry to accurately predict future equipment or plant state given a set of measurements. Such predictive capability will enable predictive assessment of component condition and remaining life and allow for condition-based predictive maintenance. The resulting optimization of maintenance scheduling and reduction in unnecessary maintenance activities will lower overall maintenance costs and improve the economics of nuclear power. This report discusses the various aspects of data processing and model development that are likely to influence the performance of prognostic models. Data from a boiling-water reactor was used to evaluate several prognostic models to identify key considerations for developing such models to predict data-driven plant state and equipment degradation condition. Preliminary results indicate the need for data sets that are relevant to the problem at hand and contain signatures that may be correlated to the prediction problem. Assuming such data exist, development of prognostic models using data-driven methods requires an understanding of the various sources of influence on the prediction accuracy (such as the model architecture, data preprocessing approaches, and potentially external factors influencing the equipment or plant system under assessment). Ongoing research is evaluating these factors in greater detail and examining techniques for calculating prediction uncertainty bounds.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Techno-Economic Analysis Using REopt for Community Solar on Multifamily Affordable Housing Properties [Slides]

Multifamily affordable housing (MFAH) providers can identify and prioritize properties in their portfolios for which community solar development is feasible by following portfolio screening steps in a process outlined by NREL. Once MFAH providers have identified the most feasible sites, they can conduct more detailed analyses for a select number of sites to assess how distributed energy resources such as solar plus storage can help them meet their goals. The step is completed using NREL's REopt (https://reopt.nrel.gov/tool), a free techno-economic optimization model that determines DER sizes and dispatch strategies that minimize the life cycle cost of energy at a site. This presentation describes how to perform this step using REopt to help MFAH providers answer questions such as: What size solar PV system will result in the most energy bill savings at this site? What size solar-plus-storage system would be needed to power critical loads through a utility grid outage? What is the financial impact of rate switching, net metering, and/or meter aggregation? What percentage of the site's load can be offset with renewable energy? What are the emissions benefits of this renewable generation?

14 SOLAR ENERGY↗
DOE OSTI2022

Analysis of Photovoltaic Energy: CRADA Number CRD-21-17862 (Cooperative Research and Development Final Report)

For this project, NREL will work with the Participant to model phase change material performance, model photovoltaic thermodynamics performance, and predict photovoltaic module durability based on environmental conditions to help develop optimized photovoltaic roofing designs. This project will develop thermodynamic models of integrated photovoltaic roofing with phase change materials. These models will be used to optimize the materials and design selections based on reducing photovoltaic operating temperatures while balancing cost effective integration of phase change materials.

14 SOLAR ENERGY↗
DOE OSTI2025

Probing Ultrafast Charge Transfer Dynamics in Heterostructured Nanomaterials

The increasing demand for renewable and low‐cost energy has triggered a vast amount of research aimed at developing, characterizing and optimizing materials that can efficiently convert sun light into useful chemical energy in the form of electricity or chemical fuels. So‐ called quantum dot sensitized solar cell (QDSC) materials have gained much attention in recent years based on the advantageous physicochemical properties of semiconducting quantum dots (QDs) as sensitizers, as well as their promising potential to increase the conversion efficiency beyond the Shockley‐Queisser limit of ∼33% by making use of hot‐electron transfer (HET) routes and multiple exciton generation (MEG). By finely tuning the size of the QDs, the band gap can be carefully aligned to the metal oxide (MO) conduction band such that charge transfer from the photoexcited QD is thermodynamically favorable.

14 SOLAR ENERGY↗
DOE OSTI2025

Design and Validation of Mechanical & Autonomous Systems in Harsh Environments [Poster]

The autonomous systems within the Mobile Hot Cell provide advanced capabilities over traditional hot cells, pushing the boundaries of these systems in harsh environments. The degraded state of some radiological devices and the variety of designs present a series of challenges that require innovative tooling and procedural solutions. By leveraging technical knowledge and experience from disposition experts at SwRi and other institutions, initial design concepts were produced using rapid prototyping techniques. These concepts were then validated and optimized in a non-hazardous test bed, resulting in iterative improvements at minimal cost.

07 - ISOTOPES AND RADIATION SOURCES↗
DOE OSTI2023

Sparsity for Gradient-Based Optimization of Wind Farm Layouts

Optimizing wind farm layouts is an important step in designing an efficient wind farm. Optimizing wind farm layouts is also a difficult task due to computation times increasing with the number of turbines present in the farm. The most computationally expensive part of gradient-based optimization is calculating the gradient. In order to reduce the expense of gradient calculation, we performed a study on the use of sparsity in wind farm layout optimization. This paper presents the findings of the sparsity study and provides a method to use sparsity in wind farm layout optimization. We tested this sparsity method by optimizing multiple farms with sparse methods and compared the results to optimizations of the same farms using traditional methods. By using the sparse method to optimize multiple farms, we found that the objective results were comparable between sparse and traditional methods and that sparse methods were 4 times faster than traditional methods on average. We expect more speedups with improved methodology and larger wind farms. By using sparse methods, it is possible to solve the wind farm layout optimization problem more efficiently, thus allowing for a more thorough study of the wind farm layout design space without excessive computational costs. Further work is required to refine the method and prepare for testing on real-world wind farm layout applications.

gradient↗
DOE OSTI2024

A Two-Stage Stochastic Programming Approach for the Design of Renewable Ammonia Supply Chain Networks

This work considers the incorporation of renewable ammonia manufacturing sites into existing ammonia supply chain networks while accounting for ammonia price uncertainty from existing producers. We propose a two-stage stochastic programming approach to determine the optimal investment decisions such that the ammonia demand is satisfied and the net present cost is minimized. We apply the proposed approach to a case study considering deploying in-state renewable ammonia manufacturing in Minnesota’s supply chain network. We find that accounting for price uncertainty leads to supply chains with more ammonia demand met via renewable production and thus lower costs from importing ammonia from existing producers. These results show that the in-state renewable production of ammonia can act as a hedge against the volatility of the conventional ammonia market.

Mitrai, Ilias (ORCID:0000000289896864)↗
DOE OSTI2020

Policy-Driven Sustainable Saline Drainage Disposal and Forage Production in the Western San Joaquin Valley of California

Environmental policies to address water quality impairments in the San Joaquin River of California have focused on the reduction of salinity and selenium-contaminated subsurface agricultural drainage loads from westside sources. On 31 December 2019, all of the agricultural drainage from a 44,000 ha subarea on the western side of the San Joaquin River basin was curtailed. This policy requires the on-site disposal of all of the agricultural drainage water in perpetuity, except during flooding events, when emergency drainage to the River is sanctioned. The reuse of this saline agricultural drainage water to irrigate forage crops, such as ‘Jose’ tall wheatgrass and alfalfa, in a 2428 ha reuse facility provides an economic return on this pollutant disposal option. Irrigation with brackish water requires careful management to prevent salt accumulation in the crop root zone, which can impact forage yields. The objective of this study was to optimize the sustainability of this reuse facility by maximizing the evaporation potential while achieving cost recovery. This was achieved by assessing the spatial and temporal distribution of the root zone salinity in selected fields of ‘Jose’ tall wheatgrass and alfalfa in the drainage reuse facility, some of which have been irrigated with brackish subsurface drainage water for over fifteen years. Electromagnetic soil surveys using an EM-38 instrument were used to measure the spatial variability of the salinity in the soil profile. The tall wheatgrass fields were irrigated with higher salinity water (1.2–9.3 dS m -1 ) compared to the fields of alfalfa (0.5–6.5 dS m -1 ). Correspondingly, the soil salinity in the tall wheatgrass fields was higher (12.5 dS m -1 –19.3 dS m -1 ) compared to the alfalfa fields (8.97 dS mm -1 –14.4 dS mm -1 ) for the years 2016 and 2017. Better leaching of salts was observed in the fields with a subsurface drainage system installed (13–1 and 13–2). The depth-averaged root zone salinity data sets are being used for the calibration of the transient hydro-salinity computer model CSUID-ID (a one-dimensional version of the Colorado State University Irrigation Drainage Model). This user-friendly decision support tool currently provides a useful framework for the data collection needed to make credible, field-scale salinity budgets. In time, it will provide guidance for appropriate leaching requirements and potential blending decisions for sustainable forage production. This paper shows the tie between environmental drainage policy and the role of local governance in the development of sustainable irrigation practices, and how well-directed collaborative field research can guide future resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Accelerating Optimal Integration of Energy Efficiency Strategies with Industrialized Modular Construction: Preprint

The National Renewable Energy Laboratory's (NREL's) Industrialized Construction Innovation team first introduced the Industrialized Construction Assessment Framework to achieve affordable, net-zero energy (NZE) modular multifamily buildings in the 2020 ACEEE paper "Integrating Energy Efficiency Strategies with Industrialized Construction for our Clean Energy Future." Since then, NREL has continued to drive the ambitious plan to accelerate optimal integration of energy efficiency strategies during industrialized construction with little or no additional cost, labor, and production time. This follow-on paper introduces the Energy in Modular (EMOD) buildings method and presents NREL's research efforts over the last two years in collaboration with industry, including affordable housing partners. NREL has developed an idealized NZE modular multifamily building design that incorporates five energy efficiency strategies well suited for industrialized construction in factories: (1) envelope thermal control, (2) envelope infiltration control, (3) mechanical, electrical, and plumbing systems, (4) smart controls, and (5) solar plus storage. This paper highlights results from leveraging design for manufacturing and assembly principles, testing, and validation pilots with factory partners; demonstrating pod prototypes in test stand at NREL; and performing simulations. Overall, these research efforts address barriers to whole-building system integration, such as poor installation quality of thermal and air barriers; lack of unitized systems for space conditioning, energy recovery and ventilation, and water heating; problematic on-site installation, commissioning, and configuration of controls; and lack of cost-effective integration for grid-friendly design and emerging technologies. Conclusively, the paper delineates next steps for future work with NREL's partners toward developing a transformational pathway for our clean energy future.

affordable housing↗
DOE OSTI2022

Optimizing Rate of Penetration and Tripping Decision-Making using Real-Time Bit Wear Monitoring While Drilling Geothermal Wells

Understanding bit wear while drilling is critical to minimizing non-productive time (NPT) and optimizing rate of penetration (ROP). Lengthening drilling runs with damaged bits does not only lower the ROP, but also elevates the risk of inducing severe bit damage, which could potentially lead to time-consuming fishing operations. When drillers believe the bit has worn off substantially, the bit is tripped out to be replaced. On geothermal wells, tripping can take up to 20% of the overall well construction time, and this is generally acknowledged as an opportunity for improvement. Ideally, a bit run should be terminated before the bit is damaged beyond repair. At the same time, premature bit pulls are to be avoided as well. This study aims to leverage bit and tooth wear metrics that can be obtained in real time to characterize bit condition in order to optimize ROP and determine the optimal time to pull the bit. Two metrics were explored in this study: a bit wear metric that incorporated depth-of-cut, and a tooth wear metric developed by Bourgoyne & Young characterizing the state of bit teeth dull. Both metrics were computed using recorded data from 12¼ inches roller cone insert bit runs in five geothermal wells targeting a granodiorite formation in the western United States. Together with the actual dull grades, determined after the bits were pulled to surface, the metric trends were interpreted to characterize the downhole bit condition and identify the point at which the bit should have optimally been tripped out. The insights from studying the actual dull grades and how they relate to the two metrics were used to establish a reliable bit pull criterion. The bit wear metric trend correctly showed a noticeable departure from baseline for bits experiencing major dulling behavior. Additionally, the tooth wear model predicted the cutter dull within two dull grades for most runs, with better performance in predicting the inner teeth dull. Moreover, the combination of the bit wear and tooth wear metrics was effective in revealing the cause of the bit performance impairment. Proactive tracking of these two metrics in real-time can facilitate geothermal drilling ROP optimization and better-informed tripping decision-making, thereby avoiding wasted time and cost.

Ashari, Rahmat↗
DOE OSTI2024

Incorporating A Risk-Informed, Performance-Based Concept into Nuclear Fuel and Materials Development for Advanced Reactors

The NRC’s regulatory concept for advanced reactors under 10 CFR Part 53 promotes the use of an RIPB concept. RIPB is usually referenced as a general, overarching concept. This paper is focused on how to, and if it would be worthy to, incorporate an RIPB concept into nuclear fuel and materials development for advanced reactors. This paper proposes three potential RIPB applications for nuclear fuel and materials development, including RIPB test matrix development, RIPB test matrix reduction, and RIPB design optimization. This paper discusses the projected benefits from incorporating RIPB in reducing development timeline, cost, and regulatory risk.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Graph-based Simulation Framework for Power Resilience Estimation and Enhancement

The increasing frequency of extreme weather events poses significant risks to power distribution systems, leading to widespread outages and severe economic and social consequences. This paper presents a novel simulation framework for assessing and enhancing the resilience of power distribution networks under such conditions. Resilience is estimated through Monte Carlo simulations, which simulate extreme weather scenarios and evaluate the impact on infrastructure fragility. Due to the proprietary nature of power network topology, a distribution network is synthesized using publicly available data. To generate the weather scenarios, an extreme weather generation method is developed. To enhance resilience, renewable resources such as solar panels and energy storage systems (batteries in this study) are incorporated. A customized Genetic Algorithm is proposed to determine the optimal locations and capacities for solar panels and battery installations, maximizing resilience while balancing cost constraints. Experiment results demonstrate that on a large-scale synthetic distribution network with more than 300,000 nodes and 300,000 edges, the proposed framework can efficiently evaluate the resilience, and enhance the resilience through the installations of distributed energy resources (DERs), providing utilities with valuable insights for community-level power system resilience estimation and enhancement.

Wang, Xuesong [Wayne State Univ., Detroit, MI (Uni↗