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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Durable Low-Cost Pressure Vessels for Bulk Hydrogen Storage

Long-duration energy storage (LDES) in the form of gaseous hydrogen is vital for fossil fuel power plants to reduce frequent shutdowns and rapid start-ups. It's also crucial in renewable energy power plants for grid stabilization. WireTough Cylinders (WTC) has developed a Type II-S pressure vessel that uses steel liners that are hoop-wrapped with multiple layers of high-strength, cost-effective, and readily available steel wires. This design enables the vessel to withstand significantly higher pressures. It addresses hydrogen embrittlement issues and cyclic loading from repeated pressurization and depressurization during service. The WireTough design is cost-efficient, robust, and fatigue-resistant (with a 30-year design life) and is ASME certified for ground storage of hydrogen at a maximum operating pressure (MOP) of 350 bar and a design pressure of 500 bar. The cylinder has a 645 mm outside diameter and a water capacity of 1,715 liters, capable of storing 40.7 KG of hydrogen at MOP. Two prototypes were successfully produced to showcase the manufacturing technologies, with one being certified under ASME Section VIII Division 3 rules. Preliminary design work for a similar Type II-S vessel with a 500 bar MOP was also completed. The 350 bar and 500 bar Type II-S vessels were shown respectively to be 47.5% to 60% more cost-effective and 55% and 62.5% lighter based on per KG of hydrogen stored, when compared with their Type 1 counterparts.

08 HYDROGEN↗
DOE OSTI2023

Data-driven discovery of a formation prediction rule on high-entropy ceramics

The interest in high entropy ceramics (HECs) has increased steadily due to their superior properties. However, the prediction of their formation still poses challenges for the discovery of new systems. Here, we discover a rational rule for designing single-phase high entropy metal diborides (HEBs) using data-driven approach. The machine learning (ML) model is trained on data collected via high-throughput experiments (HTEs). K nearest neighbor (KNN) model shows an experimental validation accuracy of 93.75%. By implementing interpretable ML method, we demonstrate that a mismatch of the bonds between boron and transition metals (δ B-TM ) dominates the formation of HEBs. We propose an empirical rule that HEBs favor forming a single phase when δ B-TM < 3.66; otherwise, multiphase. The rule has a high accuracy of 93.33% for new HEBs predictions. In addition, we contribute 165 high quality HEBs data in total, which can promote the development of materials informatics in HEBs. Furthermore, this data-driven strategy can be expanded to accelerate the search for new HECs, paving a pathway to design novel HECs with superior properties rapidly.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Community Solar Subscription Credit Considerations and Case Study

Together New Orleans (TNO) requested technical assistance through the US Department of Energy's (DOE's) National Community Solar Partnership (NCSP). The National Community Solar Partnership is a coalition of community solar stakeholders working to expand access to affordable community solar to every U.S. household and enable subscribers and their communities to realize meaningful benefits, such as reduced energy burden, increased resilience, community ownership, and equitable workforce development. TNO asked for a subject matter expert from NCSP to review the Entergy New Orleans (ENO) proposed revised Rate Schedule for Community Solar Generating Facilities. TNO requested that the proposed methodology for subscription credits for applicable residential and non-residential rate schedules be reviewed to determine the expected credit rate. The aim behind the analysis is to provide TNO and engaged stakeholders with an informed understanding of the proposed rate schedule before making decisions on the appropriate rate design for community solar (CS) subscriptions. This report is an exploration of CS subscription credit rate calculation considerations using the CS program in New Orleans as a case study. The report provides a framework for modeling CS credit rates in addition to topics that may be helpful to address when undertaking program design or rule making.

14 SOLAR ENERGY↗
DOE OSTI2020

Unexpected crystalline homogeneity from the disordered bond network in La(Cr 0.2 Mn 0.2 Fe 0.2 Co 0.2 Ni 0.2 )O 3 films

Designing and understanding functional electronic and magnetic properties in perovskite oxides requires controlling and tuning the underlying crystal lattice. In this work, we report the structure, including oxygen and cation positions, of a single-crystal, entropy stabilized perovskite oxide film of La(Cr 0.2 Mn 0.2 Fe 0.2 Co 0.2 Ni 0.2 )O 3 grown on SrTiO3 (001). The parent materials range from orthorhombic (LaCrO 3 , LaMnO 3 , and LaFeO 3 ) to rhombohedral (LaCoO 3 and LaNiO 3 ), and first principles calculations indicate that these structural motifs are nearly degenerate in energy and should be highly distorted site to site. Despite this extraordinary local configurational disorder on the B-site sublattice, we find a structure with unexpected macroscopic crystalline homogeneity with a clear orthorhombic unit cell, whose orientation is demonstrated to be controlled by the strain and crystal structure of the substrate for films grown on (La 0.3 Sr 0.7 )(Al 0.65 Ta 0.35 )O 3 and NdGaO 3 (110). Furthermore, quantification of the atom positions within the unit cell reveals that the orthorhombic distortions are small, close to LaCrO 3, which may be driven by a combination of disorder averaging and the average ionic radii. This is a step toward understanding the rules for designing crystal motifs and tuning functional properties through controlled configurational complexity.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

The pressing need for novel materials that can serve rising demands in solar cell and optoelectronic technologies makes the nexus of halide perovskites, high-throughput computations, and machine learning, very promising. Ever increasing amounts of data on the structure, fundamental properties, and device performance of halide perovskites provide opportunities for learning chemical rules and design principles that make these materials attractive, and applying them across wide chemical spaces. In this work, we show that impurity properties of halide perovskites computed using density functional theory (DFT) can be combined with machine learning (ML) to deliver predictive models and quick identification of optoelectronically active impurity atoms. Our computation lead to the largest reported dataset of the formation energies and charge transition levels of Pb-site impurities in methylammonium lead halide (MAPbX 3 ) perovskites. Descriptors are defined to uniquely represent any impurity atom in any MAPbX 3 compound and mapped to the computed impurity properties using regression techniques such as Gaussian process regression, neural networks, and random forests. We use the best optimized predictive models to make predictions for hundreds of impurities across 9 MAPbX 3 compounds and create lists of dominating impurities, that is, impurities that can shift the equilibrium Fermi level in the perovskite as determined by native point defects. Finally, this accelerated screening powered by computations and machine learning can guide the identification of problematic impurities that may cause undesired recombination of charge carriers, as well as impurities that can be deliberately introduced to tune the perovskite conductivity and resulting photovoltaic absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Methanol Synthesis from CO 2 Hydrogenation over a Potassium-Promoted Cu x O/Cu(111) ( x ≤ 2) Model Surface: Rationalizing the Potential of Potassium in Catalysis

Alkalis have been reported as a promotor in the heterogeneous catalysis, being able to enhance the activity and selectivity of catalysts. The effective utilization of alkalis in catalyst optimization requires the fundamental understanding of the underlying mechanism. In this work, we take a potassium (K)-modified Cu x O/Cu(111) (x ≤ 2) model surface as a case study to rationalize the nature of K during the carbon dioxide hydrogenation using combined density functional theory (DFT) calculation and the kinetic Monte Carlo (KMC) simulation. Our result demonstrates the significant tuning of selectivity from carbon monoxide to methanol on going from Cu(111) to K-modified Cu x O/Cu(111). The deposited K + stabilizes the Cu x O thin film under the reducing condition of carbon dioxide hydrogenation. More importantly, our study reveals that K + acts as an active center for selective tuning in the binding, an accelerator for charge transfer, and a mediator for the electron tunneling. As a result, the K-modified Cu x O/Cu(111) opens a methanediol [H 2 C(OH) 2 ]-mediated formate pathway to facilitate the selective conversion of carbon dioxide to methanol. Our study develops the intrinsic rules of design to tune the catalytic performance using alkali metals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

First-principles studies of substituent effects on squaraine dyes

Squaraine dyes are candidates for DNA-templated excitonic interactions. This work presents substituent effects on the electronic and photophysicalproperties of squaraine dyes and a correlation between empirical Hammettconstant and those properties.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Differentiable Quantum Programming with Unbounded Loops

The emergence of variational quantum applications has led to the development of automatic differentiation techniques in quantum computing. Existing work has formulated differentiable quantum programming with bounded loops, providing a framework for scalable gradient calculation by quantum means for training quantum variational applications. However, promising parameterized quantum applications, e.g., quantum walk and unitary implementation, cannot be trained in the existing framework due to the natural involvement of unbounded loops. To fill in the gap, we provide the first differentiable quantum programming framework with unbounded loops, including a newly designed differentiation rule, code transformation, and their correctness proof. Technically, we introduce a randomized estimator for derivatives to deal with the infinite sum in the differentiation of unbounded loops, whose applicability in classical and probabilistic programming is also discussed. We implement our framework with Python and Q# and demonstrate a reasonable sample efficiency. Through extensive case studies, we showcase an exciting application of our framework in automatically identifying close-to-optimal parameters for several parameterized quantum applications.

Computer Science↗
DOE OSTI2022

Decision Science for Machine Learning (DeSciML)

The increasing use of machine learning (ML) models to support high-consequence decision making drives a need to increase the rigor of ML-based decision making. Critical problems ranging from climate change to nonproliferation monitoring rely on machine learning for aspects of their analyses. Likewise, future technologies, such as incorporation of data-driven methods into the stockpile surveillance and predictive failure analysis for weapons components, will all rely on decision-making that incorporates the output of machine learning models. In this project, our main focus was the development of decision scientific methods that combine uncertainty estimates for machine learning predictions, with a domain-specific model of error costs. Other focus areas include uncertainty measurement in ML predictions, designing decision rules using multiobjecive optimization, the value of uncertainty reduction, and decision-tailored uncertainty quantification for probability estimates. By laying foundations for rigorous decision making based on the predictions of machine learning models, these approaches are directly relevant to every national security mission that applies, or will apply, machine learning to data, most of which entail some decision context.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Modified 316H constitutive model updated with high temperature stress relaxation test data

This report describes the calibration of a new high temperature constitutive model for 316H stainless steel, suitable for use with the ASME Boiler & Pressure Vessel Section III, Division 5, Class A rules for design by inelastic analysis. The model retains the same mathematical form used by the reference model included in Nonmandatory Appendix Z of the Code, but refits the model to an expanded dataset including all the data used to fit the original model plus seven new stress relaxation tests. The addition of these high temperature stress relaxation tests improves the model's accuracy in predicting relaxation at temperatures greater than 700 ⁰C, without compromising the accuracy of the model versus the original calibration data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modeling Graphite Degradation (Oxidation and Irradiation)

Presentation Outline: Background and motivation Component design (ASME rules) Graphite degradation Oxidation Irradiation MOOSE modeling Oxidation modeling Graphite component stress modeling Oxidized conditions Irradiated conditions Future work

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Graphite Degradation Modeling and Analysis

A graphite component in a nuclear reactor core is subjected to variety of stresses and can experience degradation during normal and off-normal operation. Understanding how a graphite component will behave in service is essential to ensuring core structural stability and safe reactor operation. This report summarizes a graphite modeling tool currently under development at Idaho National Laboratory. The model incorporates several of the anticipated stresses during service and includes the effects of oxidation and irradiation prior to turnaround. This tool is intended to be used to help assess the design of graphite components by utilizing design code rules found in Section III, Division 5 of the American Society of Mechanical Engineering Boiler and Pressure Vessel Code. Specifically, the tool uses the methodologies found within the Full and Simplified assessments from Article HHA-3000 to verify that a graphite component has an acceptably low probability of failure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Curating a comprehensive set of enzymatic reaction rules for efficient novel biosynthetic pathway design

Enzyme substrate promiscuity has significant implications for metabolic engineering. The ability to predict the space of possible enzymatic side reactions is crucial for elucidating underground metabolic networks in microorganisms, as well as harnessing novel biosynthetic capabilities of enzymes to produce desired chemicals. Reaction rule-based cheminformatics platforms have been implemented to computationally enumerate possible promiscuous reactions, relying on existing knowledge of enzymatic transformations to inform novel reactions. However, past versions of curated reaction rules have been limited by a lack of comprehensiveness in representing all possible transformations, as well as the need to prune rules to enhance computational efficiency in pathway expansion. To this end, we curated a set of 1224 most generalized reaction rules, automatically abstracted from atom-mapped MetaCyc reactions and verified to uniquely cover all common enzymatic transformations. Additionally, we developed a framework to systematically identify and correct redundancies and errors in the curation process, resulting in a minimal, yet comprehensive, rule set. These reaction rules were capable of reproducing more than 85% of all reactions in the KEGG and BRENDA databases, for which a large fraction of reactions is not present in MetaCyc. Our rules exceed all previously published rule sets for which reproduction was possible in this coverage analysis, which allows for the exploration of a larger space of known enzymatic transformations. By leveraging the entire knowledge of possible metabolic reactions through generalized enzymatic reaction rules, we are able to better utilize underground metabolic pathways and accelerate novel biosynthetic pathway design to enable bioproduction towards a wider range of new molecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Codes and standards for ceramic composite core materials for High Temperature Reactor applications

Fiber-reinforced ceramic matrix composites are attractive for high-temperature nuclear applications due to excellent thermal and mechanical properties as well as reasonable-to-outstanding radiation resistance. Over the past 20 years, the use of ceramic matrix composite applications expanded to many commercial non-nuclear industries as fabrication and application of the technologies mature. The ASME Boiler Pressure Vessel Code, under Section III Division 5, provides the design and construction rules for High Temperature Reactor components. It published the first rules for ceramic matrix composites to be used for reactor core components. The rules lay out the quality requirements together with the design and materials criteria for the use and application of silicon carbide- and carbon-based matrix material technologies. As with the established graphite rules, the ceramic composite material rules are structured in Subsection HH (from Section III), that addresses the criteria for class SN nonmetallic core components. The code rules rely heavily on the development and publication of standards for composite specification, classification, and testing of mechanical, thermal, and other properties. These test methods are developed in ASTM Committee C28 on Advanced Ceramics, with a current focus on ceramic composite tubes. This article describes the detail of the composites code, the design methodology and similarities to the graphite code, the guidance for the development of specifications for ceramic composites (for nuclear applications) including recent standard developments, and it mentions the next steps to support licensing aspects by validating the code with benchmarking data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Efficient quadrature rules for finite element discretizations of nonlocal equations

In this paper we design efficient quadrature rules for finite element discretizations of nonlocal diffusion problems with compactly supported kernel functions. Two of the main challenges in nonlocal modeling and simulations are the prohibitive computational cost and the nontrivial implementation of discretization schemes, especially in three-dimensional settings. In this work we circumvent both challenges by introducing a parametrized mollifying function that improves the regularity of the integrand, utilizing an adaptive integration technique, and exploiting parallelization. We first showthat the “mollified” solution converges to the exact one as the mollifying parameter vanishes, then we illustrate the consistency and accuracy of the proposed method on several two- and three-dimensional test cases. Furthermore, we demonstrate the good scaling properties of the parallel implementation of the adaptive algorithm and we compare the proposed method with recently developed techniques for efficient finite element assembly.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Efficient quadrature rules for finite element discretizations of nonlocal equations

Here, in this paper, we design efficient quadrature rules for finite element (FE) discretizations of nonlocal diffusion problems with compactly supported kernel functions. Two of the main challenges in nonlocal modeling and simulations are the prohibitive computational cost and the nontrivial implementation of discretization schemes, especially in three-dimensional settings. In this work, we circumvent both challenges by introducing a parametrized mollifying function that improves the regularity of the integrand, utilizing an adaptive integration technique, and exploiting parallelization. We first show that the “mollified” solution converges to the exact one as the mollifying parameter vanishes, then we illustrate the consistency and accuracy of the proposed method on several two- and three-dimensional test cases. Furthermore, we demonstrate the good scaling properties of the parallel implementation of the adaptive algorithm and we compare the proposed method with recently developed techniques for efficient FE assembly.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Impact of Timing in the Design Process on Students’ Application of Design for Additive Manufacturing Heuristics

The goal of this work is to study the way student designers use design for additive manufacturing (DfAM) rules or heuristics. It can be challenging for novice designers to create successful designs for additive manufacturing (AM), due to its recent surge in popularity and lack of formal education or training. A study was carried out to investigate the way novices apply DfAM heuristics when they receive them at different points in the design process. A design problem was presented to students, and three different groups of student participants were given a lecture on DfAM heuristics at three different points in the design process: before the initial design, between the initial design and redesign, and after the redesign. The novelty and quality of each of the resulting designs were evaluated. Results indicate that although the DfAM heuristics lecture had no impact on the overall quality of the designs generated, participants who were given the heuristics lecture after the initial design session produced designs that were better-suited for 3D printing in the second phase of the design activity. However, receiving this additional information appears to prevent students from creatively iterating upon their initial designs, as participants who received heuristic information between the design sessions experienced a decrease in novelty between the two sessions. Additionally, receiving the heuristics lecture increased all students’ perceptions of their ability to perform DfAM-related tasks. Furthermore, these results validate the practicality of design heuristics in lecture form as AM training tools while also emphasizing the importance of iteration in the design process.

computer-aided design↗