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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Research goals for minimizing the cost of CO 2 capture when using steam methane reforming for hydrogen production

This paper presents a techno-economic assessment of adding state-of-the-art solvent-based CO 2 capture technologies to greenfield steam methane reforming (SMR)-based H 2 production plants and quantifies the impacts of improvements in CO 2 capture technology. Current conventional capture technologies are reviewed, and future technologies in intermediate and long-term scenarios are analyzed. The results show that adding significantly more efficient solvent-based capture technologies leads to an equivalent rate of natural gas consumption as that of a conventional SMR plant without capture, despite capturing most of the CO 2 and producing the same amount of H 2 . Overall, improvements in reboiler duty and reductions in capital costs can significantly reduce the cost of H 2 production and cost of capture. Particularly, the reboiler duty of pre-combustion capture and the capital cost of post-combustion capture have the greatest impact. Based on the results, research goals are suggested. Solvent development is recommended—particularly pre-combustion solvents—for reducing the reboiler duties, and process schemes to reduce the capital costs. Costlier but more efficient solvents can be considered. A sensitivity analysis using natural gas price shows that technological improvements can reduce the impacts of high natural gas prices. The degree of economic feasibility of CO 2 capture increases with improvements to the capture technology.

08 HYDROGEN↗
DOE OSTI2021

Minimizing development costs for efficient many-core visualization using MCD3

Scientific visualization software increasingly needs to support many-core architectures. However, development time is a significant challenge due to the breadth and diversity of both visualization algorithms and architectures. With this work, we introduce a development environment for visualization algorithms on many-core devices that extends the traditional data-parallel primitive (DPP) approach with several existing constructs and an important new construct: meta-DPPs. We refer to our approach as MCD 3 — Meta-DPPs, Convenience routines, Data management, DPPs, and Devices. The twin goals of MCD 3 are to reduce developer time and to deliver efficient performance on many-core architectures, and our evaluation considers both of these goals. For development time, we study 57 algorithms implemented in the VTK-m software library and determine that MCD 3 leads to significant savings. For efficient performance, we survey ten studies looking at individual algorithms and determine that the MCD 3 hardware-agnostic approach leads to performance comparable to hardware-specific approaches: sometimes better, sometimes worse, and better in the aggregate. In total, we find that MCD 3 is an effective approach for scientific visualization libraries to support many-core architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Economic Risk-Informed Maintenance Planning and Asset Management (Final Report)

The proposed work will provide a holistic framework for cost-minimizing risk-informed maintenance planning, including inspection, in light water reactors (LWRs). Specifically, we develop a two-tier framework that (a) coarsely minimizes the total maintenance cost during the remaining normal operating cycle of the plant prior to the next scheduled outage (long-term), subject to safety requirements, and (b) uses the outputs of the first model to develop a secondary optimization model to finely schedule maintenance activities to maximize the financial impact of these activities in the next week (short-term).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Optimal Design and Operation of a Solvent-Sorbent Hybrid Capture Process for Minimizing the Cost of High Capture

High capture can cause a significant increase in energy penalty if the appropriate technology/technologies are not utilized. The optimal technology for bulk capture is not necessarily the optimal technology for polishing capture (i.e., high extent of capture from the flue gas with very low partial pressure of CO2). This work investigates the use of MEA for bulk capture with the polishing capture being accomplished by a functionalized metal organic framework (MOF).

Kasturi, Pooja↗
DOE OSTI2024

A Scalable Approach to Minimize Charging Costs for Electric Bus Fleets

Incorporating battery electric buses into bus fleets faces three primary challenges: a BEB’s extended refuel time, the cost of charging, both by the consumer and the power provider, and large compute demands for planning methods. When BEBs charge, the additional demands on the grid may exceed hardware limitations, so power providers divide a consumer’s energy needs into separate meters even though doing so is expensive for both power providers and consumers. Prior work has developed a number of strategies for computing charge schedules for bus fleets; however, prior work has not worked to reduce costs by aggregating meters. Additionally, because many works use mixed integer linear programs, their compute needs make planning for commercial-sized bus fleets intractable. This work presents a multi-program approach to computing charge plans for electric bus fleets. The proposed method solves a series of subproblems where the solution to the charge problem becomes more refined with each problem, moving closer to the optimal schedule. The results demonstrate how runtimes are reduced by using intermediate subproblems to refine the bus charge solution so that the proposed method can be applied to large bus fleets of 100+ buses. Not only will we demonstrate that runtimes scale linearly with the number of buses but we will also show how the proposed method scales to large bus fleets of over 100 buses while managing the monthly cost of energy.

Mortensen, Daniel (ORCID:0000000276494452)↗
DOE OSTI2025

A High-Efficiency Delayed Update Algorithm for Evaluating Slater Determinants in Quantum Monte Carlo

For quantum Monte Carlo simulations of molecular systems or supercells with thousands of electrons, matrix operations related to Slater determinants lead the computational cost. McDaniel et al. [J. Chem. Phys. 2017, 147, 174107] proposed a delayed update algorithm to increase computational efficiency by using matrix–matrix multiplication when updating the inverse matrices of Slater determinants. However, preparing intermediate matrices for applying the Sherman–Morrison–Woodbury formula remained a bottleneck. Here, in this work, we introduce an improved algorithm for CPUs and GPUs that (1) reduces this bottleneck by iteratively updating the intermediate matrices and (2) is efficient at any acceptance ratio, with no cost for rejected moves on CPUs and minimal cost on GPUs. We show the full scheme of integrating the delayed update algorithm into a single-electron move. The high efficiency of our algorithm is demonstrated on CPUs and GPUs for a 512 atom/6144 valence electron calculation, with 12× and 2× overall speed-up compared to traditional rank-1 update schemes in diffusion quantum Monte Carlo, respectively.

Luo, Ye [Argonne National Laboratory (ANL), Argonn↗
DOE OSTI2020

Optimal assignment for the single-household shared autonomous vehicle problem

Autonomous vehicles have the potential to transform the way people are transported. While driverless technology may mean fewer vehicles are required to transport people to and from their daily activities, such changes may result in increased congestion or total miles traveled. In this study, we solve the single-household shared autonomous vehicle problem to identify cost-optimal routings of vehicles throughout the day. Such a tool will be useful for consumers seeking to minimize cost and for regulators seeking to understand and predict how people may behave in different scenarios. Here, we provide a thorough literature review and construct a mixed-integer linear program to minimize the daily travel cost of a household attending a given set of activities. Since solution time is a determinant for applicability of such a model, we present the model in a component-wise fashion. This approach allows us to understand which features most affect the problem complexity and solution time. We note that modeling carpooling is the feature that most increases time to find an optimal solution, and we therefore propose a novel modeling technique for carpooling two people. We illustrate the performance of our model by comparing it with other models from the literature and note that our model can solve significantly larger problem instances and in a time that is short enough to facilitate real-time scheduling. We also highlight the utility of our model for regulators, who can use it to analyze quickly produced optimal routes under different cost/tax scenarios.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Integral and Separate Effects Test Facilities To Support Water Cooled Small Modular Reactors: A Review

This study reviews previous experimental facilities and test programs relevant to water-cooled reactor system design and analysis to meet regulatory compliances. This study aims to find the best solution for designing the required experiments, obtaining necessary test data, and verifying the developed computer code/models to support the new reactor design and development while minimizing cost and time while leveraging experiences from previous facilities to minimize. Nuclear reactor licensing requires supportive design, analysis, and experimental results to ensure the safety of the full-scale prototype reactor in regular operation, as well as during postulated accident scenarios. These reactor design analyses are generally performed using system codes and other associated simulation tools that require assessment, verification and validation using an appropriate experimental dataset. Experimental facilities used for reactor system safety analysis and system code assessments are categorized into integral effects test (IET) and separate effects test (SET) facilities. Further, these IET and SET experiments and studies use geometrically scaled systems to reproduce the prototype system behavior at a reasonable cost, albeit with some scaling-related distortions. The design challenge of these model facilities is to identify and minimize scaling distortions while reproducing the most important operational phenomena in steady-state operation and in postulated accident scenarios. Lessons learned from previous experimental facilities, models, and correlations can support the development of new multipurpose, scaled, hybrid, integrated, and modular experimental facilities for advanced light water-cooled small modular reactors (SMRs). Successful operation of these facilities can significantly reduce upfront reactor development and demonstration costs and time to deployment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Whole Algae Hydrothermal Liquefaction and Upgrading: A review of progress and challenges and insight into the future

This report summarizes the research at Pacific Northwest National Laboratory (PNNL) to evaluate the economic viability and environmental impact of using microalgae to produce fuels and other products via hydrothermal liquefaction (HTL). Over the past several years, PNNL has examined key aspects of feedstock cost and availability, formatting and conversion techniques, and the utilization of all HTL products. Investigations of feedstock cost led to opportunities to work with cost-advantaged algal feedstocks that can be provided at minimal cost for HTL processing. Cost-advantaged algae include wastewater-grown algae and harvested algal blooms. Although farm-cultivated algae offer the best possible biomass composition and scalability for HTL processing, the cost of the feedstock is too high to yield an economically competitive biofuel. Processing cost-advantaged feedstocks creates other unique challenges in adapting HTL to upgrade biomass with higher than typical ash content and less preferred composition (low lipid). Despite the challenges, HTL of cost-advantaged algae results in economically competitive pricing scenarios and significant advantages in reducing net emissions below 70% of the petroleum baseline. The utilization of a variety of potential non-fuel products from algal HTL, such as the use of HTL solids as a cement additive, provides a significant reduction in net emissions by offsetting emissions from other carbon-intense products. This report presents an analysis of the research conducted at PNNL to develop an economically and environmentally beneficial process for algae HTL.

09 BIOMASS FUELS↗
DOE OSTI2020

Spent-fuel recycling molecule degradation product standards can be produced with preparative liquid chromatography

Solvent systems used for spent nuclear fuel separation schemes are subject to intense radiation fields from the spent fuel, resulting in a degradation of separation performance over time from both the radiolytic destruction of active compounds (extractants, holdback agents, etc.) and the production of degradation compounds (compounds produced from the destruction of the active compounds). Development of a quantitative understanding of the effects of radiolysis on separations is impeded by a near-complete lack of availability of degradation products from chemical manufacturers. Synthesis of these degradation products through traditional means is both cost and time prohibitive, inhibiting advancement of new fuel separation processes. We hypothesize that comprehensive sets of degradation products can be synthesized using preparative radiolysis, and purified using high performance liquid chromatography in quantities sufficient for unequivocal identification and subsequent use as quantitative analytical studies. Achieving this objective will facilitate development of a much more accurate understanding of radiolysis reactions and their effects on solvent extraction processes, positioning Idaho National Laboratory (INL) in a leadership role in the science of fuel cycle separations. We propose producing these degradation compounds by irradiating concentrated samples of solvent ligands in the INL gamma irradiator, separating the resulting mixture with preparative chromatography, and removing the mobile phase in the separated fractions with a vacuum concentrator. This will yield milligram to gram quantities of purified degradation products, sufficient for fundamental research, at a fraction of the time and cost of synthesis. Facile production of separation molecule degradation products at minimal cost will enable the fundamental research that will accelerate the development of fuel cycles that will ensure the competitiveness of nuclear energy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Technoeconomic Design Optimization for Fast Reactors. Part I: Workflow Development and Case Study for Small LFR District Energy Application

The nuclear industry is developing small reactor designs that can target a variety of deployment locations and energy products. Smaller nuclear designs have traditionally struggled to handle the steep trade-offs between size and cost that have historically incentivized large reactors. This motivates computational optimization of small reactors to minimize costs and quantify the trade-off between size and cost. In this paper, the cost/size trade-off for a small fast reactor is derived using a multi-objective genetic algorithm optimization, with steady-state, transient, and cost analysis of the fast reactor being performed. Specifically, the method is demonstrated on a small 10- to 120-MW(thermal) U-Pu-Zr–fueled lead-cooled fast reactor with a 10-year core life for district energy applications, which can have a thermal load compatible with this range. The results reinforced that fast reactor cores at the lower end of this power range suffer cost penalties due to critical mass considerations. It was found that high power density cores with strong reactivity swings and many control rods were favored over designing to minimize reactivity swing. Furthermore, this contrasts with some traditional configurations designed using engineering judgment and demonstrates that optimizers can find nontraditional but realistic solutions, along with demonstrating the value of incorporating cost functions into whole-reactor design optimization.

Fast reactor↗
DOE OSTI2026

Lessons Learned for Transmission Cost Allocation in U.S. Regional Markets

Expanding electric transmission can facilitate generator interconnection and improve grid reliability. Assigning costs for new transmission infrastructure is highly contentious because these costs can have a direct impact on energy prices and ratepayer bills. In this report, we evaluate what factors influence successful transmission cost allocation agreements. Through a review of legal disputes, existing cost allocation practices, and regional case studies, we identify potential strategies to minimize cost allocation disputes for future projects. The report also highlights the processes by which regions can update their cost allocation methods. While we do not consider cost allocation methods currently under development for compliance with FERC Order 1920, the trends and lessons learned identified in this report can inform discussions on effective cost allocation methods to reduce barriers for transmission development.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Optimizing fluvial flood mitigation strategies: A multi-objective approach for cost-effective and socially-aware infrastructure feasibility analysis

Effective levee planning must balance capital cost, risk reduction, and community priorities. These objectives are rarely optimized together. This study presents a feasibility phase, simulationin-the-loop framework that couples terrain-based flood modeling with a socially aware multiobjective optimizer. Flood risk is measured as Expected Annual Exposed Population (EAEP), obtained by integrating exposure over Annual Exceedance Probability (AEP) nodes, mirroring the Hydrologic Engineering Center's Flood Damage Reduction Analysis (HEC-FDA) expected-annual formulation but with people rather than dollars. Exposure per scenario is computed by overlaying binary inundation masks with a population surface at the tract level. Distributional fairness is encoded through a Group Benefit Share (GBS) constraint that requires high-SVI tracts to receive at least a baseline share of annualized benefits. Capital cost is represented by a height-dependent unit-cost model suitable for screening. This study addresses the two-objective problem, minimize cost and expected annual exposure subject to the GBS constraint, using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and leveraging Pareto front for feasibility phase decision making. Implemented with terrain-based flood modeling, GeoFlood, for rapid scenario evaluation, the framework is demonstrated in Southeast Texas. The results reveal clear trade-offs among cost, risk, and social benefits and identify non-dominated levee height configurations that satisfy the benefit-share floor. The contributions are a scalable decision support method that operationalizes expected annual population-based risk, embeds enforceable benefit-sharing guarantees, and uses lightweight simulation to explore large design spaces before higher fidelity design stages.

Flood mitigation↗
DOE OSTI2025

Learning from Arctic Microgrids: Cost and Resiliency Projections for Renewable Energy Expansion with Hydrogen and Battery Storage

Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however, uncertainty remains about optimal grid architectures to minimize cost, including how and when to incorporate long-duration energy storage. This study implements a novel, multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue, which has already successfully deployed solar photovoltaics, wind turbines, and battery storage systems. Using real community load, resource, and generation data, we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation, considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs, but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase, but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue, we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability, sustainability, and security.

25 ENERGY STORAGE↗
DOE OSTI2024

Incorporating Diurnal and Meter-Scale Variations of Ambient CO 2 Concentrations in Development of Direct Air Capture Technologies

To be implemented on climate-relevant scales, direct air capture of CO 2 (DAC) will require large capital-intensive facilities and careful attention to cost minimization. In making decisions among potential sites for DAC facilities, all of the factors that will impact process cost and efficiency should be considered. In this paper we focus on a factor that has previously received little attention in the DAC community, namely variations in atmospheric conditions on hourly time scales and length scales of meters. We present data curated from extensive previous studies of biosphere-atmosphere fluxes with observations of CO 2 concentration, temperature, and relative humidity (RH) with hourly resolution from many sites in North America. These include locations where typical diurnal variations in CO 2 concentration during summer months exceeds 150 ppm. These variations are larger than the seasonal variations that exist between averaged CO 2 concentrations in winter and summer, and they are highly correlated with diurnal variations in temperature and RH. Diurnal variations are dependent on the height above ground at which CO 2 concentrations are measured, with smaller variations existing at heights of 10 m or more than at ground level. We illustrate the potential implications of these short-term variations for the operation and optimization of a DAC process with process-level calculations for a specific adsorption-based process using amine-rich adsorbents.

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

Electrolyte and Cutoff Potential Effects on Cycle Life of Li4Ti5O12/LiNi0.9Mn0.1O2 Batteries for Behind-the-Meter Storage Applications

Behind-the-Meter Storage (BTMS) is a stationary battery energy storage system that is connected to the electrical distribution system on the customer's side of the utility's service meter. BTMS systems are used to store electrical energy from the grid as well as inconstant, renewable energy, such as local solar and wind generation. A successful BTMS system will allow the customer to pair their energy generation and storage to optimize electrical consumption from the grid, improving reliability and minimizing cost. For BTMS applications, batteries must be designed and optimized with different set of criteria from other leading segments of the Li-ion battery market, like transportation, due the system being stationary and proximal to the residential or commercial building it's benefitting. BTMS applications prioritize safety, cost (low/no-critical materials), reliability (20-year calendar life), and durability (10,000 cycle life), while having the ability to (minimally) compromise energy density and rate capability. Lithium titanate (Li4Ti5O12-, LTO) is a promising anode candidate for BTMS applications due to its high safety and capacity retention, while maintaining a reasonable 160 mAhg-1 reversable capacity and composition of relatively abundant materials. (1) Specifically, LTO has a high working voltage which helps to prevent Li dendrite formation, improving safety. Furthermore, LTO also has negligible lithiation-based volume change, leading to less mechanical pulverization, or loss of active material, upon cycling. For the cathode, materials with little or no Co are of high interest due to the high cost and low abundance of Co. LiMn2O4 (LMO) has been paired with LTO for BTMS applications in the past due to its safety, low cost (abundancy), and reasonably high operating voltage. (2-4) However, the low capacity of LMO limits energy density and specific energy. While not the highest priority for BTMS applications, increasing energy density will enable deployment in space constrained BTMS applications and decrease total cost. LiNi0.9Mn0.1O2 (LN-MO) is a recently developed material with promise due to its high operating voltage and relatively low price. (5) However, Ni-rich layered oxides, including LNMO, tend to struggle with capacity retention during high-voltage cycling due to mechanical pulverization, irreversible phase transitions, and unstable solid-electrolyte interphase. The study presented here focuses on building an understanding of how electrolyte solvent and varied cutoff potentials will impact the cycle life of LTO/LN-MO cells. Specifically, a comparison is provided between ethylene carbonate (EC), ethyl methyl carbonate (EMC), fluoroethylene carbonate (FEC), and Gen2 electrolyte solvents with 1M Lithium hexafluorophosphate (LiPF6) salt, cycling to two upper termination potentials, 2.6V and 2.7V. Electrochemical testing and diagnostics (e.g., differential capacity analysis, area specific impedance, constant voltage hold, and rate capability) and post-mortem characterization will be used to understand the aging behavior and failure mechanisms of the 8 cell combinations (four electrolytes and two voltage cutoffs). Cells with FEC electrolyte showed a lower initial capacity compared to cells with Gen2, EMC, and EC cycling at both voltages; however, the cells with FEC showed consistent trends in capacity retention with 2.6V and 2.7V termination potentials, while the cells with the other electrolytes showed much higher rates of capacity loss when cycling to the higher voltage. These results indicate that FEC may play a role in improving durability of high-voltage, Ni-rich electrode systems for use in high-cycle applications, such as BTMS.

electrolyte↗
DOE OSTI2021

Economic solution for low carbon process heat: A horizontal, compact high temperature gas reactor

In 2018, nuclear energy generated 55% of United States’ and one third of the world’s carbon free electricity, making nuclear energy a key tool in efforts to mitigate climate change before 2050. However, the current nuclear technology, light water reactors (LWRs), is limited to 300°C, so it cannot be used to decarbonize industrial process heat which accounts for 12% of US greenhouse gas emissions. High temperature gas reactors (HTGRs) can meet the high temperature demand with carbon free nuclear heat. The estimated cost of HTGRs, such as the Next Generation Nuclear Plant (NGNP), are even higher than state-of-the-art LWRs. In this paper, we expanded our nuclear cost estimating tool to include HTGRs and find that the NGNP overnight capital costs were 32% higher than an advanced LWR per unit capacity. The higher cost will naturally result in larger risk to cost overrun as recently experienced by larger LWRs in western nations. With a design-to-build mindset to minimize cost and construction risk, we introduce the horizontal, compact HTGR (HC-HTGR). The reactor core and steam generator are mounted horizontally on rails and in-line with one another, decreasing the size of the reactor building relative to the power capacity four times when compared to traditional HTGRs. The HC-HTGR reduced overnight civil structure costs by 42%, indirect costs by 38%, and total capital costs by 20% from NGNP. We discussed the required engineering of new systems for the HC-HTGR including vessel supports, the reactor cavity cooling system, and steam generator design. Finally, we estimated the fuel and operations costs of the HC-HTGR, and a survey of low-carbon industrial process heat technology showed the HC-HTGR can deliver a highly competitive levelized cost of heat in the range of $6.13–12.48/GJ.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Distilling Knowledge from Ensembles of Cluster-Constrained-Attention Multiple-Instance Learners for Whole Slide Image Classification

The peculiar nature of whole slide imaging (WSI), digitizing conventional glass slides to obtain multiple high resolution images which capture microscopic details of a patient’s histopathological features, has garnered increased interest from the computer vision research community over the last two decades. Given the unique computational space and time complexity inherent to gigapixel-size whole slide image data, researchers have proposed novel machine learning algorithms to aid in the performance of diagnostic tasks in clinical pathology. One effective algorithm represents a Whole slide image as a bag of smaller image patches, which can be represented as low-dimension image patch embeddings. Weakly supervised deep-learning methods, such as cluster-constrained-attention multiple instance learning (CLAM), have shown promising results when combined with image patch embeddings. While traditional ensemble classifiers yield improved task performance, such methods come with a steep cost in model complexity. Through knowledge distillation, it is possible to retain some performance improvements from an ensemble, while minimizing costs to model complexity. In this work, we implement a weakly supervised ensemble using clustering-constrained-attention multiple-instance learners (CLAM), which uses attention and instance-level clustering to identify task salient regions and feature extraction in whole slides. By applying logit-based and attention-based knowledge distillation, we show it is possible to retain some performance improvements resulting from the ensemble at zero cost to model complexity.

Alamudun, Folami↗