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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Global particle buildup simulations with gas puff scan: application to WEST discharge

This paper deals with the distribution of sources, transport, and exhaust of particles in a tokamak. Knowledge and understanding of all the physical phenomena involved in the global particle buildup are necessary to study and predict density regimes and subsequently to develop optimized scenarios for tokamak operation in order to control heat and particle exhaust. Neutral particles and their interactions with plasma are central in this perspective. This paper discusses the impact of varying the intensity of particle fueling in 2D transport simulations of a WEST discharge. Simulations are performed with an updated version of SOLEDGE-HDG that allows a more realistic transport of neutrals using a self-consistent diffusive model based on charge exchange and ionization processes. New code capabilities allow the entire WEST poloidal cross section to be simulated in a realistic configuration for both geometry and the range of control parameters. A gas puff scan illustrates the main features of the sheath-limited, high-recycling, and detached regimes, such as the buildup of the temperature gradient and the pressure drop in the scrape-off layer (SOL), the target temperature falling to 1 eV, and the ionization source moving away from the targets, as well as the particle flux rollover. A crude estimate of wall erosion is also provided, showing the respective role of each plasma wall component in each of these regimes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Catalytic resonance theory for parametric uncertainty of programmable catalysis

Microkinetic models are useful tools for screening catalytic materials; however, errors in their input parameters can lead to significant uncertainty in model predictions of catalyst performance. Here, in this work, we investigate the impact of linear scaling and Brønsted-Evans-Polanyi relation parametric uncertainty on microkinetic predictions of programmable-catalyst performance. Two case studies are considered: a generic A-to-B prototype reaction and the oxygen evolution reaction (OER). The results show that error-unaware models can accurately predict trends and, for the prototype reaction, values of optimal waveform parameters. The specific model parameters driving output uncertainty are identified via variance-based global sensitivity analysis. However, predictions of dynamic rate enhancement can decrease when uncertainty is propagated into the models. In both cases, we identify operating conditions where the programmable catalyst achieves a rate enhancement of at least one order of magnitude despite parametric uncertainty in the model, supporting programmable catalysis as a viable strategy for exceeding the Sabatier limit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unlocking the potential of biogas systems for energy production and climate solutions in rural communities

On-site conversion of organic waste into biogas to satisfy consumer energy demand has the potential to realize energy equality and mitigate climate change reliably. However, existing methods ignore either real-time full supply or methane escape when supply and demand are mismatched. Here, we show an improved design of community biogas production and distribution system to overcome these and achieve full co-benefits in developing economies. We take five existing systems as empirical examples. Mechanisms of synergistic adjusting out-of-step biogas flow rates on both the plant-side and user-side are defined to obtain consumption-to-production ratios of close to 1, such that biogas demand of rural inhabitants can be met. Furthermore, carbon mitigation and its viability under universal prevailing climates are illustrated. Coupled with manure management optimization, Chinese national deployment of the proposed system would contribute a 3.77% reduction towards meeting its global 1.5 °C target. Additionally, fulfilling others’ energy demands has considerable decarbonization potential.

09 BIOMASS FUELS↗
DOE OSTI2024

Automated Operational Forecasting of Monsoon Low Pressure Systems

Monsoon low pressure systems (LPSs) are the dominant rain-bearing weather system of South Asia, often producing extreme precipitation and hydrological disasters in a region inhabited by nearly two billion people. Despite the importance of these storms, no operational system has automatically identified and tracked LPS in real time in numerical weather prediction model output; many commonly used vortex-tracking algorithms are ill suited for monsoon LPS because of the weak winds and cold cores of these systems. Here, we describe a new system that uses optimized algorithms to identify monsoon LPS in short- to medium-range forecasts from the U.S. Global Ensemble Forecast System (GEFS) and a version of the deterministic Global Forecast System (GFS) adapted and used operationally by the Indian Institute of Tropical Meteorology (IITM). We also assess the historical performance of these models in forecasting South Asian monsoon LPS, comparing this with the performance of the Integrated Forecasting System of the ECMWF. We assess the accuracy of model predictions of LPS genesis, position, intensity, and precipitation rates for forecast lead times of 1–5 days, yielding quantitative information on model biases to guide operational forecasters and disaster managers. The system we introduce here could be extended to other low-latitude regions affected by dynamically weak, heavily precipitating atmospheric vortices that are often not included in tropical cyclone inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Refining water and carbon fluxes modeling in terrestrial ecosystems via plant hydraulics integration

Plant hydraulics substantially affects terrestrial water and carbon cycles by modulating water transport and carbon assimilation. Despite improved drought simulations in certain ecosystems through their integration into land surface models (LSMs), the broader application of plant hydraulics in diverse ecosystems and hydroclimates is still underexplored. Here, in this study, we implemented the recently developed Noah-Multiparameterization Land Surface Model (Noah-MP LSM) equipped with a plant hydraulics scheme (Noah-MP-PHS) across 40 FLUXNET sites globally. Employing the Shuffled Complex Evolution-University of Arizona (SCE-UA) auto-calibration algorithm, we optimized key plant hydraulics parameters for these sites spanning eight vegetation types in both arid and humid climates. Noah-MP-PHS significantly improves the simulation of evapotranspiration (ET) and gross primary production (GPP) by better representing atmospheric and soil water stress compared to traditional soil hydraulic schemes (SHSs, such as Noah and CLM). The augmented Noah-MP-PHS models reduce surface flux overestimation and underestimation, exhibiting an average increase of 0.14 and 0.15 in Kling-Gupta Efficiency (KGE) compared to Noah and CLM, respectively. The explicit consideration of plant capacitance in PHS reveals substantial deep-layer and nocturnal root water uptake especially under dry conditions. We employed eXplainable Machine learning (XML) to quantify the model’s relative sensitivity to newly introduced leaf-, stem and root-related parameters in PHS. The sensitivity analysis reveals a rise in root parameter importance and a decline in leaf and stem parameters as conditions shift from humid to arid. These findings indicate that as aridity states vary, the most influential parameters affecting surface fluxes variation may change in parameter calibration for PHS applications. Our findings underscore the importance of incorporating plant hydraulics into LSMs to enhance simulations of terrestrial water and carbon dynamics. These findings are crucial for understanding ecosystem responses to global climate changes and guide the broader application of PHS at larger scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Optimal Twirling Depth for Classical Shadows in the Presence of Noise

The classical shadows protocol is an efficient strategy for estimating properties of an unknown state p using a small number of state copies and measurements. In its original form, it involves twirling the state with unitaries from some ensemble and measuring the twirled state in a fixed basis. It was recently shown that for computing local properties, optimal sample complexity (copies of the state required) is remarkably achieved for unitaries drawn from shallow depth circuits composed of local entangling gates, as opposed to purely local (zero depth) or global twirling (infinite depth) ensembles. Here, we consider the sample complexity as a function of the depth of the circuit, in the presence of noise. We find that this noise has important implications for determining the optimal twirling ensemble. Under fairly general conditions, we (i) show that any single-site noise can be accounted for using a depolarizing noise channel with an appropriate damping parameter f, (ii) compute thresholds f th at which optimal twirling reduces to local twirling for Pauli operators, (iii) nth order Renyi entropies (n ≥2), and (iv) provide a meaningful upper bound t max on the optimal circuit depth for any finite noise strength f, which applies to observables and entanglement entropy measurements. In conclusion, these thresholds strongly constrain the search for optimal strategies to implement shadow tomography and are easily tailored to the experimental system at hand.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Scale-Bridging Optimization Framework for Desalination Integrated Produced Water Networks

In this work, we develop a Pyomo-based non-linear optimization strategy that includes rigorous MVR models. The detailed desalination unit is integrated into the multiperiod produced water network problem using the trust region filter (TRF) method. TRF decomposes the integrated problem into a master problem consisting of the network variables and a simplified surrogate model for the detailed desalination unit. The surrogate is updated using zero and first-order corrections from the optimal solution of the detailed models at every iteration. This framework allows us to co-optimize the design of the desalination units and operating policy for the multiperiod network. A common design is ensured across all periods using global capacity constraints. We validate the solution obtained using the TRF method by solving the full integrated problem for small network instances and show our results on real case studies on produced water networks from the Permian and Appalachian basins. In this work, we describe our TRF formulation, give details on our implementation in Pyomo, and analyze the results obtained by solving the optimization problem using IPOPT. We also present a discussion on the computational efficiency and scaling using the TRF approach against a full-scale integration of the rigorous models within the water network.

Naik, Sakshi↗
DOE OSTI2024

Performance Results on CPU/GPU Exascale Architectures for OMEGA: The Ocean Model for E3SM Global Applications

The US Department of Energy (DOE) conducts climate simulations on some of the world’s largest supercomputers. These exascale machines use heterogeneous architectures with both CPUs and GPUs, and scientific codes must adapt to make full use of this computing power. Los Alamos National Lab is developing Omega: The Ocean Model for E3SM Global Applications, which is specifically designed for modern exascale computers. It uses external libraries that have been optimized for a variety of architectures to run on different supercomputers. Omega is an unstructured-mesh ocean model based on TRiSK numerical methods. It will be the new ocean component of the DOE’s Energy Exascale Earth System Model (E3SM). The algorithms in Omega follow those of the current ocean component, MPAS-Ocean, but it will be written in C++ rather than Fortran to take advantage of the Kokkos performance portability library. Omega spatial operators are written as Kokkos kernels to run efficiently on both CPUs and GPUs. Work on Omega began in 2023 with a new C++ framework for unstructured mesh partitioning, halo exchanges, parallel IO, and Kokkos interfaces. The current version, Omega-0, is being developed to solve the shallow water equations and at present includes all of the tendency terms but not time stepping. Here we share the results of Omega-0 verification and performance testing. Verification includes unit tests implemented with CTest as well as convergence tests in Polaris, an in-house python package with a large suite of test problems. Performance tests compare simulations conducted on CPUs versus GPUs and across different architectures: tests are run on Frontier, which has AMD “Optimized 3rd Gen EPYC” CPUs and AMD MI250X GPUs, as well as Perlmutter, which is composed of AMD EPYC 7763 CPUs and NVIDIA A100 GPUs.

58 GEOSCIENCES↗
DOE OSTI2024

Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Repository of HydroSMADE: Hydropower Site-level Monthly Availability Data Ensemble for 1950-2100 at Existing and Potential Global Sites

This repository presents HydroSMADE—Hydropower Site-level Monthly Availability Data Ensemble, a new open dataset that provides monthly hydropower availability for 1,593 existing and 124,333 potential sites worldwide over the period 1950–2100. The dataset is generated by using a global hydrologic model (Xanthos) with explicit representation of hydropower operation. Specifically, HydroSMADE distinguishes between storage and diversion sites, applies optimized operating rules, and incorporates site-specific characteristics such as generation capacity, maximum turbine flow, and reservoir storage. Driven by bias-corrected meteorological inputs, the data is provided for 30 alternative future scenarios. The scenarios consist of the full factorial combination of three standard CMIP6 atmospheric forcing pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5) and ten CMIP6 General Circulation Models (GCMs): GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, EC-Earth3, CanESM5, MIROC6, CNRM-ESM2-1, UKESM1-0-LL, and CNRM-CM6-1. The repository contains a total of 122 files: a text file (readme.txt) containing a brief description of the included data, a CSV file containing site attributes, and the remaining 120 files (in CSV) containing site-level monthly hydropower availability. Example Jupyter Notebooks to explore the HydroSMADE dataset are available on GitHub at https://github.com/kamal0013/HydroSMADE More details on the methods and technical validation of HydroSMADE are available in the following paper by the same authors: Chowdhury, A. K., Abeshu, G. W., Zhao, M., Wild, T. B., Hassan, N., Ying, Z., Kim, G. J., Matthew, B., Jonathan, L., & Li, H.-Y. (Submitted). Hydropower Site-level Monthly Availability Data Ensemble for 1950-2100 at Existing and Potential Global Sites.

Existing and Potential Sites↗
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 OSTI2025

Operational optimization for multi-functional charging station with electric and hydrogen-powered vehicles

The rapid adoption of electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs), combined with global efforts to reduce carbon emissions, has accelerated the development of EV charging and hydrogen refueling stations. In response to this demand, this paper introduces the concept of Multi-Functional Charging Station (MFCS) that integrates power generation, EV charging, battery swapping, and hydrogen refueling. A comprehensive operational model is developed for the MFCS that couples electricity and hydrogen conversion and storage technologies to enhance infrastructure utilization and improve overall system efficiency. The model also considers multiple revenue streams, including participation in energy and ancillary markets. To validate the effectiveness of the proposed model and evaluate its performance, a series of numerical experiments are conducted with different charger numbers, different electricity purchase limits, and different charger allocations. Numerical results demonstrate that shared charger configurations can lead to 8.11 % improvement in operational profit by improving resource utilization and reducing the number of depleted batteries at the end of operations compared to allocated charger setups. By varying the number of chargers, sensitivity analysis identifies diminishing marginal returns beyond about 45 chargers, suggesting it as an optimal sizing point under current settings. The integration of electricity and hydrogen conversion is also explored under scenarios with limited external electricity purchases. In conclusion, these findings indicate that optimizing charger allocation and energy management can significantly enhance station productivity and profitability, ultimately supporting the broader adoption of electrified and hydrogen-based transportation solutions.

Charging station↗
DOE OSTI2025

Environmental and socio-economic Pareto-front trade-off analysis of U.S. PET packaging material in a circular economy

Various recycling technologies are emerging to implement circular economy in plasticssupply chain systems. However, the environmental and socio-economic trade-offs of in circular economy are not well understood at a systems level. Particularly, quantifying these trade-offs as a function of end-of-life (EOL) management decisions, including transition of recycling technologies, systems level metrics such as circularity, recycled content, and the need for fossil-derived plastics are not well understood. Here, the present study addressed these research gaps by applying a systems analysis modeling approach that utilizes material flow analysis, life cycle assessment, socioeconomic data, and system optimization techniques for polyethylene terephthalate (PET) packaging supply chains in the United States. Pareto-front trade-offs between conflicting environmental and socio-economic impacts as well as those between socioeconomic impacts and circularity were explored using the epsilon constraint method. The Pareto-front trade-off analysis revealed the transition of EOL management strategies for PET packaging systems, including changes in selection of recycling technologies, to aid decision making process by quantifying studied system metrics. Transitioning from environmentally optimal to socio-economically optimal systems led to increased employment (by 17%), wages (by 26%), and revenues (by 6%) but also led to increased global warming potential (GWP; by 65%), energy consumption (by 59%), and reliance on fossil PET in the system (by 78%). Finally, the results show that there is not a unique set of recycling technologies to achieve a sustainable circular economy of PET packaging system, instead it depends on the decision maker’s objectives and targeted metrics of the system.

54 - ENVIRONMENTAL SCIENCES/GLOBAL CLIMATE CHANGE ↗
DOE OSTI2025

On optimal control of hybrid dynamical systems using complementarity constraints

Optimal control for switch-based dynamical systems is a challenging problem in the process control literature. In this study, we model these systems as hybrid dynamical systems with finite number of unknown switching points and reformulate them using non-smooth and non-convex complementarity constraints as a mathematical program with complementarity constraints (MPCC). We utilize a moving finite element based strategy to discretize the differential equation system to accurately locate the unknown switching points at the finite element boundary and achieve high-order accuracy at intermediate non-collocation points. We propose a globalization approach to solve the discretized MPCC problem using a mixed NLP/MILP-based strategy to converge to a non-spurious first-order optimal solution. The method is tested on three dynamic optimization examples, including a gas–liquid tank model and an optimal control problem with a sliding mode solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Optimized Floating Offshore Wind Turbine Substructure Design Trends for 10–30 MW Turbines in Low-, Medium-, and High-Severity Wave Environments

Floating offshore wind is a promising renewable energy source, as 60% of the wind resources globally are found at depths requiring floating technologies, it minimizes construction at sea, and provides opportunities for industrialization given a lower site dependency. While floating offshore wind has numerous advantages, a current obstacle is its cost in comparison to more established energy sources. One cost-reduction approach for floating wind is increasing turbine capacities, which minimizes the amount of foundations, moorings, cables, and O&M equipment. This work presents trends in mass-optimized VolturnUS hull designs as turbine capacity increases for various wave environments. To do this, a novel rapid hull optimization framework is presented that employs frequency domain modeling, estimations of statistical extreme responses, industry constructability requirements, and genetic algorithm optimization to generate preliminary mass-optimal VolturnUS hull designs for a given turbine design and set of site conditions. Using this framework, mass-optimized VolturnUS hull designs were generated for 10–30 MW turbines for wave environments of varying severities. These design studies show that scaling up turbine capacities increases the mass efficiency of substructure designs, with decreasing returns, throughout the examined turbine capacity range. Additionally, increased wave environment severity is shown to increase the required mass of a given substructure design.

VolturnUS↗
DOE OSTI2025

Sustainable recovery of critical metals from spent lithium-ion batteries through gluconic acid-based bioleaching: Techno-economic analysis, life cycle assessment and process optimization

Recycling spent lithium-ion batteries (LIB) could potentially bridge the ever increasing supply and demand gap for critical metals and simultaneously facilitate the management of hazardous battery waste. This study investigated the optimization of gluconic acid-based bioleaching technology through design of experiments (DOE), combined with techno-economic analysis (TEA), and life cycle assessment (LCA) with the aim of maximizing the net present value (NPV) and minimizing global warming impacts of the process. Biolixiviant containing predominantly gluconic acid produced by the genetically engineered (ΔpstS, P 112 :mgdh) Gluconobacter oxydans B58 through fermentation using non-recyclable paper as a growth substrate was used for the LIB leaching. At optimal bioleaching conditions of gluconic acid (160 mM), leaching time (2.5 h), reducing agent FeSO 4 to metal, i.e., cobalt (Co), nickel (Ni) and manganese (Mn), mole ratio (0.88), temperature (55 °C) and pulp density (2.5 %), the leaching efficiency was 87 % 72 %, 94 %, and 88 % for Co, Ni, Mn and lithium (Li), respectively. TEA analysis confirmed that bioleaching plant with an annual black mass processing capacity of 10,000 metric tons and plant life of 30 years would be economically viable with an NPV and profit margin of $136 million and 11 %, respectively. The predicted carbon footprint of gluconic acid-based bioleaching for recovering 1 kg of Co (13.2 kg of CO 2 eq.) is lower compared to that of most state-of-the-art leaching technologies. Moreover, gluconic acid-based bioleaching effectively recovered target metals when tested for different black mass chemistries.

Bioleaching↗
DOE OSTI2025

Characterization of Kariya ( Hildegardia barteri (Mast.) Kosterm) Seed Oil Fatty Acid Methyl Ester Prepared from Basic Catalytic Transesterification

The rising global energy demand, alongside concerns regarding environmental deterioration due to the use of fossil fuels, has spurred extensive investigation into renewable energy alternatives. Biomass-derived biodiesel, especially from lesser-known oil sources, emerges as a promising option. This research focuses on analyzing the fatty acid methyl esters (FAMEs) derived from Kariya (Hildegardia barteri (Mast.) Kosterm) seed oil through basic catalytic transesterification using gas chromatography–flame ionization detector (GC–FID) analysis, assessing its potential as a biodiesel feedstock. Oil extraction from Kariya seeds was carried out using three solvents (n-hexane, ethanol, and a 1:1 blend of hexane and ethanol), followed by transesterification with methanol. Gas chromatography–mass spectrometry (GC–MS) and GC–FID analyses were utilized to identify and quantify FAMEs in the resulting biodiesel. The results revealed various FAMEs, including methyl myristate, methyl palmitate, methyl stearate, methyl oleate, methyl linoleate, and methyl linolenate. Significant differences in FAME composition were observed among the samples, with hexane–ethanol Kariya oil biodiesel (HE-KOB) showing the highest FAME content (76.1%). This combination of solvents exhibited synergistic effects on the composition of HE-KOB, suggesting potential optimization strategies for biodiesel production. Fourier transform infrared spectroscopy (FTIR) provided additional insights into the molecular composition of the biodiesel samples, confirming their biodiesel nature through the identified functional groups such as methyl, methylene, hydrocarbon, ester, aldehyde, and alkene. Thermogravimetric analysis (TGA) for thermal decomposition also gave an insight into FAME composition and its contribution to the degree of conversion of biodiesel to energy. These findings highlight the feasibility of utilizing Kariya seed oil as a biodiesel feedstock, emphasizing the importance of solvent selection and transesterification conditions in optimizing FAME yield and composition. This research contributes to the exploration of underutilized oil sources for sustainable biodiesel production, aligning with the global shift towards cleaner and renewable energy sources.

09 BIOMASS FUELS↗
DOE OSTI2024

Simple and Scalable Process for Nanocellulose Production from Residues and Waste (CRADA Final Report)

This project successfully developed an innovative, cost-effective, and scalable method for converting low-cost agricultural waste feedstocks into nanocellulose—a sustainable material with wide-ranging industrial applications. It addressed the critical challenges of high production costs and limited global supply, which have hindered the widespread adoption of nanocellulose in industry. Through this work, significant advancements were made in optimizing resource use, improving process efficiency, and reducing costs. Notable achievements included a 50% reduction in water usage, 30% lower chemical consumption, and 20% energy savings, all while maintaining high-quality product standards. The technical feasibility of the process was further validated at a 10L scale in collaboration with the Advanced Biofuels and Bioproducts Development Unit (ABPDU), demonstrating scalability and replicability. Key challenges in reducing nanocellulose production costs were addressed by utilizing low-cost biomass feedstocks and implementing low-temperature conversion reactions, leading to lower capital and operating expenses. The project also mitigated financial risk by generating critical data on the feasibility, adaptability, and scalability of the conversion technology, paving the way for its commercial implementation. Public benefits include advancing the circular bioeconomy, reducing environmental impact, fostering job creation, and enabling a shift toward biobased materials as sustainable alternatives to fossil-based products. These outcomes align closely with national goals to reduce greenhouse gas emissions and promote sustainable technological innovation.

09 BIOMASS FUELS↗