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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Modular, Multifunction, Multiport and Medium Voltage Utility Scale SiC PV Inverter (Final Report)

This project is to develop and demonstrate a Modular, Multi-function, Multiport and Medium Voltage utility scale SiC solar inverter (M4 Inverter). The M4 Inverter is a next generation utility scale PV + storage inverter that can substantially reduce the levelized cost of energy (LCOE) while providing grid friendly ancillary services. Conventional utility scale solar inverters includes three parts, a megawatt rated solar inverter, a low frequency transformer (LFT) and a medium voltage switch gear. The proposed M4 Inverter directly converts the DC output of solar panels to medium voltage AC, eliminating the bulky and costly LFT. The M4 Inverter also has a DC port to interface with an additional energy storage device. This plus its bidirectional power flow capability, allows multiple functions in addition to PV maximum power tracking be achieved. These additional functionalities include but not limited to reactive power support, peak shaving, fast frequency regulation and synthetic inertia. Predicted LCOE reductions with these added functionalities, improved efficiency and longer lifetime time are more than 30% compared with the 2017 utility scale solar LCOE. A 1MVA SiC M4 Inverter was developed and tested at high power level. The project has also advanced the state of the art in controller hardware in the loop simulation capability. Power electronic innovations introduced in the M4 Inverter includes 1700V SiC MOSFET application in 1500V PV system, soft switching three-port power converters and modular converter architecture.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Differentiating densities on smooth manifolds

Lebesgue integration of derivatives of strongly-oscillatory functions is a recurring challenge in computational science and engineering. Integration by parts is an effective remedy for huge computational costs associated with Monte Carlo integration schemes. In case of Lebesgue integrals over a smooth manifold, however, integration by parts gives rise to a derivative of the density implied by charts describing the domain manifold. This paper focuses on the computation of that derivative, which we call the density gradient function, on general smooth manifolds. We analytically derive formulas for the density gradient and present examples of manifolds determined by popular differential equation-driven systems. Furthermore, we highlight the significance of the density gradient by demonstrating a numerical example of Monte Carlo integration involving oscillatory integrands.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Generalized Nuclear Code of Accounts for Cost Estimation Standardization

This is a joint INL-EPRI study. Link to corresponding page at the Electric Power Research Institute (EPRI): https://www.epri.com/research/products/000000003002028937 Recent Advanced Reactor (AR) designs typically offer features and attributes that depart from traditional water-cooled reactors in terms of fuel forms, coolants, structural materials, size, safety margins, and other important design and operational aspects. These departures, relative to the industry experience, will likely present a challenge for potential owner-operators when evaluating one or more AR designs for commercial deployment on a like-for-like basis. This is further exacerbated with new classes of reactors gaining prominence (e.g., microreactors) and new emerging applications (e.g., floating barge reactors or space propulsion reactors). The differences in design-specific technologies are compounded by the differences in approach to cost estimation. The lack of consistency in build up toward cost estimates creates a challenge when comparing competing concepts and evaluating their associated cost drivers. The outcome of a 2019 Scoping Study conducted by the Electric Power Research Institute (EPRI) indicated the need for a cost modeling guide (CMG), which would be underpinned by a technology-neutral and inclusive Generalized Nuclear Code of Accounts (GN-COA). A code of accounts (COA) is a tool by which costs are identified in even more specific categories, providing clarity on what specific costs are included in an estimate. A parallel independent effort was meanwhile taking place at Idaho National Laboratory via funding by the Systems Analysis Integration (SA&I) campaign of the Office of Nuclear Energy under the U.S. Department of Energy (U.S. DOE-NE) to also update the COA structure to enable more flexibility and encompass a wider variety of reactor technologies. After being made aware of these synergistic efforts, the two parties decided to combine efforts and develop a joint new standard format for nuclear cost estimation that builds on previous structures. This document describes the development of this joint GN-COA and provides guidance on the implementation of the tool, which is provided as the associated GN-COA Excel document. The main attributes of this novel COA format are that all items are functionally defined (in order to be technology and application agnostic) and grouped into logical arrangements that facilitate the tabulation of costs for reactor constructions under consideration. The intent is for the GN-COA to form a standard that is endorsed by future vendors and customers.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Bounding uncertainty in functional data: A case study

Functional data are fast becoming a preeminent source of information across a wide range of industries. A particularly challenging aspect of functional data is bounding uncertainty. In this unique case study, we present our attempts at creating bounding functions for selected applications at Sandia National Laboratories (SNL). The first attempt involved a simple extension of functional principal component analysis (fPCA) to incorporate covariates. Though this method was straightforward, the extension was plagued by poor coverage accuracy for the bounding curve. This led to a second attempt utilizing elastic methodology which yielded more accurate coverage at the cost of more complexity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

N 5 -Scaling Excited-State-Specific Perturbation Theory

In this paper, we show that by working in a basis similar to that of the natural transition orbitals and using a modified zeroth-order Hamiltonian, the cost of a recently introduced perturbative correction to excited-state mean field theory can be reduced from seventh to fifth order in the system size. The (occupied) 2 (virtual) 3 asymptotic scaling matches that of ground-state second-order Møller-Plesset theory but with a significantly higher prefactor because the bottleneck is iterative: it appears in the Krylov-subspace-based solution of the linear equation that yields the first-order wave function. Here, we discuss the details of the modified zeroth-order Hamiltonian we use to reduce the cost and the automatic code generation process we used to derive and verify the cost scaling of the different terms. Overall, we find that our modifications have little impact on the method's accuracy, which remains competitive with singles and doubles equation-of-motion coupled cluster.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Production of Fischer-Tropsch Synfuels at Nuclear Plants

A case study analysis was performed to evaluate nuclear-powered synthetic fuel production in the midwestern United States (U.S.). A Fischer-Tropsch (FT) fuel synthesis plant design was used as the basis for the analysis. The FT plant design was configured to produce a product slate consisting of diesel fuel, jet fuel, and motor gasoline blend stocks from carbon dioxide (CO 2 ) and hydrogen (H 2 ) feedstocks. The CO 2 feedstock for the FT plant was assumed to be sourced from biorefineries in the region around a Midwest light water reactor (LWR) nuclear power plant (NPP). The analysis specifies that power from the LWR is used to produce H 2 via high-temperature steam electrolysis and to operate the FT synfuel production plant. Capital costs were estimated for the FT plant while capital costs for the electrolysis plant were based on previous Idaho National Laboratory (INL) studies. In addition to labor and maintenance costs for the FT and electrolysis plants, operating costs also include the costs for CO 2 feedstock transport. An analysis was performed to determine the cost of transporting CO 2 from the distributed biorefinery sources to the centralized fuel synthesis plant as a function of the synfuel plant capacity and corresponding CO 2 demand. The primary revenue streams are associated with sales of the synthetic fuel products. The synthetic fuel products will likely follow the same market trends as the conventional fuel products. The synfuel price data was thus based on projections made by the U.S. Energy Information Administration (EIA) 2021 Annual Energy Outlook (AEO) for conventional fuel products minus federal and state taxes, as well as marketing and distribution costs. The economic analysis also considered cases that included and excluded revenues from the 2022 Inflation Reduction Act (IRA) clean hydrogen production tax credit (PTC) of $\$ $3.00/kg for the first ten years of operation. The economic analysis calculated the net present value (NPV) for cases involving steady-state synfuel production for comparison with the NPV for a business-as-usual case in which NPP continues to sell only electric power to the grid. A synfuel production “Reference Case” was considered in addition to sensitivity cases in which the plant capacity, electricity price, and synthetic fuel product prices were perturbed. The synfuel production Reference Case considered a scenario in which the electrolysis and synfuel plants utilized a combined electrical load of 1000 megawatt electrical (MWe) from the LWR with the balance of the LWR power output being sold to the electric grid. The economic analysis suggests that the synfuel production Reference Case evaluated in this analysis would lead to considerable economic potential for near-term deployment of a nuclear-based synfuel production plant. Specifically, the economic analysis suggests that the deployment of a 1000 megawatt (MW) nuclear-powered synfuel plant could result in a NPV increase of approximately $\$ $1.7 billion for a case with no clean synfuel price premium relative to conventional petroleum fuels when accounting for the additional revenues from the 2022 IRA clean hydrogen PTCs of $\$ $3/kg. Sensitivity analysis was performed to evaluate the effect of perturbation of selected model input parameters on the NPV for the synfuel production Reference Case. The sensitivity analysis indicates that the plant capacity has the largest impact on the differential NPV, with a smaller synfuel production capacity resulting in a decrease in revenue when a larger fraction of the power from the NPP is sold to the grid and a smaller fraction of the power is used to produce synthetic fuel products. The synfuel product pricing has the next largest impact on the differential NPV, with lower synfuel prices resulting in decreased NPV from decreased synfuel sales revenue while higher synfuel prices result in increased NPV from increased synfuel sales revenue. Electricity pricing has a smaller effect on the NPV than the fuel sales price since, in the Reference Case, most of the energy from the NPP is used for synfuel production and a smaller amount of the system revenues are associated with electrical power sales. However, the electricity price sensitivity does indicate that the Synfuel Integrated Energy System (IES) would have a greater NPV than the business-as-usual case (e.g., grid power sales only) when electricity market prices are low, suggesting that synfuel production could provide a strategy for decreasing the economic risks to NPPs posed by a loss of revenues attributed to falling electricity market prices.

10 SYNTHETIC FUELS↗
DOE OSTI2025

Final Report for FE0032098: Improving the cost-effectiveness of algal CO2 utilization by synergistic integration with power plant and wastewater treatment operations

The overall goal of this project was to demonstrate an engineering-scale open raceway pond algae cultivation system (approximately 180 m²), including the integration of technologies that utilized carbon dioxide (CO₂) from a coal-fired power plant and wastewater-derived nutrient inputs for cost-effective and environmentally friendly biomass production. The key advantages associated with the innovative algae cultivation system and its integration with wastewater treatment functions, as described herein, had been demonstrated in previous bench- and pilot-scale work by the project team partners. This project combined those approaches to maximize practical benefits and available synergies, resulting in a significant reduction in the net cost of producing algal biomass products. The primary target algal species for the project was Spirulina, which served as a high-protein content ingredient for food and animal feed. Spirulina was selected because it had already been approved by the FDA, was in use as a food ingredient, and commanded prices of up to $30/kg. It had a typical protein content of 50–75%, comparable to other high-protein concentrates, featured high digestibility without requiring pretreatment, and offered a high conversion ratio in animal feed applications. In addition, Spirulina had a relatively high content of the blue pigment phycocyanin, which could be extracted as a high-value co-product prior to using the remaining biomass for nutritional purposes.

Schideman, Lance [University of Illinois]↗
DOE OSTI2021

Facile ab initio approach for self-localized polarons from canonical transformations

Electronic states in a crystal can localize due to strong electron-phonon (e-ph) interactions, forming so-called small polarons. Methods to predict the formation and energetics of small polarons are either computationally costly or not geared toward quantitative predictions. Here we show a formalism based on canonical transformations to compute the polaron formation energy and wave function using ab initio e-ph interactions. Comparison of the calculated polaron and band-edge energies allows us to determine whether charge carriers in a material favor a localized small polaron over a delocalized Bloch state. Due to its low computational cost, our approach enables efficient studies of the formation and energetics of small polarons, as we demonstrate by investigating electron and hole polaron formation in alkali halides and metal oxides and peroxides. We outline refinements of our scheme and extensions to compute transport in the polaron hopping regime.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Shorter function summaries for finite state machine-based high consequence systems using logic synthesis and tautologies (Final Report LDRD 24-1302)

Computer programs are often viewed as collections of functions – each function has parameters (inputs) and computes a return value, and each has potential side effects that modify program state (outputs). In this research, a Sandia symbolic execution tool designed to support “human-in-the-loop” analysis was modified to automatically create “function summaries,” and a new tool, “diaboolical,” was created to support enhancing readability of the summary using a novel approach to bit-vector simplification that leverages logic synthesis and tautologies. For this effort, students at Auburn University created several finite state machines (FSMs) to serve as exemplars for high-consequence systems. Function summaries for each of the machines were obtained, and then portions of the summaries were simplified using both diaboolical and the simplification procedure of a popular SMT solver. A comparison of the results shows that diaboolical can often produce smaller function summaries, with expression length improvements over the unsimplified function summaries ranging from 0% to 90% for diaboolical and 0% to 65% for the SMT solver, though diaboolical had a significantly greater cost in time. Diaboolical was evaluated against a collection of “arbitrary” C-code as well as FSM exemplars, and for both datasets it achieved an approximately 10% improvement in expression length compared to simplifications that could be obtained using existing techniques. Function summaries can assist assurance efforts that evaluate existing systems and their executable code. A smaller function summary is likely easier for humans to understand and could thus increase the ability and efficacy of assurance practices centered around the analysis of executable artifacts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Transforming Energy through Computational Excellence. Exascale Computing: Combustion; Deep Learning for Presumed Probability Density Function (PDF) Models

NREL researchers use advanced machine learning techniques to define improved methods using deep learning models to resolve reacting flows in turbulent combustion flows, reducing the computational burden, increasing computational speed, and improving accuracy. These advancements reduce cost and improve fidelity of rapid-turn-around engineering calculations.

combustion↗
DOE OSTI2022

Ultrafast Early Warning of Heart Attacks through Plasmon‐Enhanced Raman Spectroscopy using Collapsible Nanofingers and Machine Learning

Abstract As the leading cause of death, heart attacks result in millions of deaths annually, with no end in sight. Early intervention is the only strategy for rescuing lives threatened by heart disease. However, the detection time of the fastest heart‐attack detection system is >15 min, which is too long considering the rapid passage of life. In this study, a machine learning (ML)‐driven system with a simple process, low‐cost, short detection time (only 10 s), and high precision is developed. By utilizing a functionalized nanofinger structure, even a trace amount of biomarker leaked before a heart attack can be captured. Additionally, enhanced Raman profiles are constructed for predictive analytics. Five ML models are developed to harness the useful characteristics of each Raman spectrum and provide early warnings of heart attacks with >98% accuracy. Through the strategic combination of nanofingers and ML algorithms, the proposed warning system accurately provides alerts on silent heart‐attack attempts seconds ahead of actual attacks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Design of robust and versatile hydrocarbon-based single-ion-conducting polymer electrolytes

Hydrocarbon-based polymers offer several advantages, including lower environmental impacts, cost effectiveness, and the ability to finely tune properties. Here, we have developed trifluoromethanesulfonimide (TFSI)-functionalized poly(norbornene) (PNB) polymers utilizing a specifically designed oxa-Michael addition of a vinyl TFSI anion to an alcohol. Our results reveal that PNB-TFSI derivatives exhibit superior thermal stability and mechanical robustness compared with Nafion. The optimized PNB-TFSI-H-48 polymer (IEC 1.86 mmol/g) exhibits equivalent performance to Nafion as an anode ionomer in a proton exchange membrane fuel cell. Exchanging the counter ion to Li + enables PNB-TFSI to be used for Li-ion battery applications. Propylene carbonate plasticized PNB-TFSI derivatives achieve an Li-ion conductivity of over 10 −5 S/cm at 30°C. This Li polymer electrolyte exhibits excellent electrochemical stability (5 V vs. Li + /Li) and good cycling in a Li symmetric cell. These results highlight the potential and rational design of PNB-TFSI polymers for next-generation energy storage and conversion technologies.

08 HYDROGEN↗
DOE OSTI2025

Modular Integrated System for Carbon-Neutral Methanol Synthesis Using Direct Air Capture and Carbon-Free Hydrogen Production

This study investigates the development and economic analysis of a modular integrated system for carbon-neutral methanol synthesis, leveraging direct air capture (DAC) and solid oxide electrolysis cells (SOEC) for carbon dioxide and hydrogen production, respectively. The proposed system integrates a novel building-based DAC process, functionalized solid sorbents, and low-energy SOEC technology, aiming to minimize operational and capital costs. A comparison between the base case system (1,000 t methanol/year) and a scaled-up model (14,758 t methanol/year) reveals significant improvements in efficiency and economic feasibility. The scaled-up system achieves a levelized cost of methanol (LCOM) of $740/t, a 7.5% reduction compared to that of conventional DAC-based systems, while utilizing existing building HVAC infrastructure for air handling. Detailed sensitivity analyses were conducted, evaluating the effects of plant capacity and air flow rate on the LCOM, demonstrating the scalability of the building-based DAC system. The cradle-to-gate life cycle analysis shows that the proposed process using renewable-sourced electricity achieves a 38% reduction in greenhouse gas (GHG) emission compared to reported values of green methanol production technologies that use a conventional DAC and a conventional methanol synthesis catalyst. When fossil-sourced electricity is used in the proposed process, it leads to about a 37.5% reduction in GHG emission in comparison to reported values for conventional methanol production technologies using steam methane reforming technology and fossil-sourced electricity.

alcohols↗
DOE OSTI2022

Recent Advances toward Efficient Calculation of Higher Nuclear Derivatives in Quantum Chemistry

In this article, we provide an overview of state-of-the-art techniques that are being developed for efficient calculation of second and higher nuclear derivatives of quantum mechanical (QM) energy. Calculations of nuclear Hessians and anharmonic terms incur high costs and memory and scale poorly with system size. Three emerging classes of methods—machine learning (ML), automatic differentiation (AD), and matrix completion (MC)—have demonstrated promise in overcoming these challenges. We illustrate studies that employ unsupervised ML methods to reduce the need for multiple Hessian calculations in dynamics simulations and those that utilize supervised ML to construct approximate potential energy surfaces and estimate Hessians and anharmonic terms at reduced cost. By extension, if electronic structure operations could be written in a manner similar to functions underlying ML methods, rapid differentiation or AD routines can be employed to inexpensively calculate higher arbitrary-order derivatives. While ML approaches are typically black-box, we describe methods such as compressed sensing (CS) and MC, which explicitly leverage problem-specific mathematical properties of higher derivatives such as sparsity and low-rank, to complete higher derivative information using only a small, incomplete sample. The three classes of methods facilitate reliable predictions of observables ranging from infrared spectra to thermal conductivity and constitute a promising way forward in accurately capturing otherwise intractable higher-order responses of QM energy to nuclear perturbations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Machine learned features from density of states for accurate adsorption energy prediction

Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence there is a significant demand for developing descriptors or features, in lieu of DFT, to accurately predict catalytic properties, such as adsorption energies. Here, we demonstrate an approach to predict energies using a convolutional neural network-based machine learning model to automatically obtain key features from the electronic density of states (DOS). The model, DOSnet, is evaluated for a diverse set of adsorbates and surfaces, yielding a mean absolute error on the order of 0.1 eV. In addition, DOSnet can provide physically meaningful predictions and insights by predicting responses to external perturbations to the electronic structure without additional DFT calculations, paving the way for the accelerated discovery of materials and catalysts by exploration of the electronic space.

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

Spread spectrum time domain reflectometry (SSTDR) and frequency domain reflectometry (FDR) cable inspection using machine learning

Cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, justification for continued cable use must shift to a condition-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. The Pacific Northwest National Laboratory (PNNL) Accelerated and Real Time Experimental Nodal Analysis (ARENA) cable motor test bed was used to test the response of a commercial spread spectrum time domain reflectometry (SSTDR) system, a laboratory instrument software-controlled SSTDR, and a vector network analyzer-based frequency domain reflectometry (FDR) system to various cable anomalies. The three instrument systems were able to interrogate cables over a range of frequency bandwidths that can be helpful for human data analysis. Data were subjected to supervised and unsupervised machine learning (ML) analyses to distinguish normal undamaged cable responses from anomalous cable responses. Both supervised and unsupervised ML approaches produced encouraging results with an undamaged/anomalous prediction accuracy from 0.69% to 0.87%. Recommendations for further development and field implementation include increased and more balanced sample sets particularly including more training data.

SSTDR, FDR, Reflectometry, Machine Learning, ARENA↗
DOE OSTI2021

Energy Management Information Systems Technical Resources Report

This report illustrates how energy management information systems (EMIS) can enable federal agencies to improve energy performance, reduce costs, and meet energy goals. The guide discusses how to identify and implement needed EMIS functions and capabilities, covering the entire EMIS life cycle including system design, procurement, implementation, and ongoing maintenance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Nuclear Data for Spallation Neutron Radioisotope Production (Final Technical Report)

Over 50 million nuclear medicine procedures are performed annually, leading to a multi-billion dollar market for radioisotope production. The demand for new medical and research isotopes is growing, and radioisotope supply is insufficient. Most radioisotope production today utilizes charged particle or low-energy neutron irradiation of a target. Isotope production with tens to hundred MeV incident energies is a relatively unexplored option. There is a tremendous opportunity associated with a growing number of suitable domestic and international facilities buttressed by hundred million dollar global investments (e.g., the Los Alamos and Brookhaven Isotope Production Facilities, the European Spallation Source in Lund, and the Korean Multi-purpose Accelerator Complex in Gyeongbuk). In part due to a lack of supporting nuclear data that would make modeling radioisotope yields and purities possible, these facilities do not utilize their high-energy neutron fluxes for isotope production. This project attempted to measure neutron reaction excitation functions relevant to the large-scale production of critical radioisotopes, enabling development of cost-efficient isotope production methods, contributing to the improvement of theoretical models, and enhancing the value of national isotope production facilities. Reactions which form 67 Cu, 32 Si, and alpha-emitting isotopes like 225 Ac were chosen for their consistent prioritization by expert panels, representation of diverse reaction mechanisms, and relative lack of supporting nuclear data. Accurate measurement of these data is presently made only using quasi-monoenergetic neutron beams, which are produced by bombarding thin lithium targets with protons at only a few laboratories in the world. Ultimately, the projects efforts were incompletely accomplished due to a combination of multi-year technical issues at the only laboratory in the world which can make the proposed measurements, iThemba Labs in South Africa, and the onset of a global pandemic, which precluded the significant effort and time required to develop an independent measurement capability in the United States.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗