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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1

The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-β-lactamase 1 (NDM-1), a bacterial enzyme that degrades β-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures of l - and d -amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC 50 values across seven designed NDM-1- inhibiting peptides. We were able to determine X-ray crystal structures of three of the designed inhibitors in complex with NDM-1, and in all three the conformation of the peptide is very close to the computationally designed model. In two of the three structures, the binding mode with NDM-1 is also very similar to the design model, while in the third, we observed an alternative binding mode likely arising from internal symmetry in the shape of the design combined with flexibility of the target. Although challenges remain in robustly predicting target backbone changes, binding mode, and the effects of mutations on binding affinity, our methods for designing ordered, binding-competent macrocycles should have broad applicability to a wide range of therapeutic targets.

42 ENGINEERING↗
DOE OSTI2012

Mesh Convergence Issues

(U) This document is written to describe issues that impact on the ability of codes to converge to a unique answer. Several issues that could be discussed in the area of mesh convergence and resolution the topics include the resolution of geometry, stress gradients, shock propagation, material softening, and localization of deformation. After a quick discussion of what mesh convergence means, this paper presents a review of some of the criteria for mesh convergence. With an introductory level understanding of these criteria, two problems are presented to demonstrate convergence and non-­convergence. The first example is a simple convergent hemisphere of gas, known as the Noh problem. With this problem convergence is shown. The Noh problem is also used to investigate the impact of mesh stiffening, or viscosity. The second problem is that of an explosively driven hemisphere. The hemisphere made of U6Nb with an inner radius of 40 mm and an outer radius of 41 mm was filled with PBX9501 and tested. Test results included surface temperature and information on the breakup, such as distribution of fines. Convergence computational methods are discussed along with some proposals for obtaining mesh convergence for a wide range of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Parallel High Order Accuracy Methods Applied to Non-Linear Hyperbolic Equations and to Problems in Materials Sciences

Note: This grant was originally awarded to Professor David Gottlieb and the majority of the work envisioned reflects his original ideas. However, when Prof Gottlieb passed away in December 2008, Professor Hesthaven took over as PI to ensure proper mentoring of students and postdoctoral researchers already involved in the project. This unusual circumstance has naturally impacted the project and its timeline. However, as the report reflects, the planned work has been accomplished and some activities beyond the original scope have been pursued with success. Project overview and main results: The effort in this project focuses on the development of high order accurate computational methods for the solution of hyperbolic equations with application to problems with strong shocks. While the methods are general, emphasis is on applications to gas dynamics with strong shocks.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fast calculation of diffraction patterns from an ensemble of aligned molecules

We report an algorithm to calculate electron diffraction patterns for molecules with anisotropic angular distribution, which is significantly faster than existing methods. The algorithm uses a transform to convert the molecular orientation distribution, which is a function of three Euler angles, to the atom-pair distribution functions which depend on the polar and azimuthal angles. The diffraction signal can then be calculated from the atom-pair distributions. We demonstrate the computation method numerically by calculating electron diffraction patterns for a symmetric top molecule (trifluoroiodomethane) and an asymmetric top molecule (formaldehyde) and show that it reduces the calculation time by approximately two orders of magnitude compared to the standard brute-force method. Here, the method can also be applied to the calculation of x-ray diffraction patterns.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2017

Joint the Center for Applied Scientific Computing

The Center for Applied Scientific Computing serves as Livermore Lab’s window to the broader computer science, computational physics, applied mathematics, and data science research communities. In collaboration with academic, industrial, and other government laboratory partners, we conduct world-class scientific research and development on problems critical to national security. CASC applies the power of high-performance computing and the efficiency of modern computational methods to the realms of stockpile stewardship, cyber and energy security, and knowledge discovery for intelligence applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Estimating the joint distribution of rate parameters across multiple reactions in the absence of experimental data

Here, a procedure for determining the joint uncertainty of Arrhenius parameters across multiple combustion reactions of interest is demonstrated. This approach is capable of constructing the joint distribution of the Arrhenius parameters arising from the uncertain measurements performed in specific target experiments without having direct access to the underlying experimental data. The method involves constructing an ensemble of hypothetical data sets with summary statistics consistent with the available information reported by the experimentalists, followed by a fitting procedure that learns the structure of the joint parameter density across reactions using this consistent hypothetical data as evidence. The procedure is formalized in a Bayesian statistical framework, employing maximum-entropy and approximate Bayesian computation methods and utilizing efficient Markov chain Monte Carlo techniques to explore data and parameter spaces in a nested algorithm. We demonstrate the application of the method in the context of experiments designed to measure the rates of selected chain reactions in the H 2 -O 2 system and highlight the utility of this approach for revealing the critical correlations between the parameters within a single reaction and across reactions, as well as for maximizing consistency when utilizing rate parameter information in predictive combustion modeling of systems of interest.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials (Second Annual Progress Report)

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. Thermal tomography is a computational method for heat diffusion-based imaging of solids, which provides 3D visualization of data from flash thermography measurements. We investigate thermal tomography imaging and nondestructive evaluation of stainless steel and nickel super alloy metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing (AM) process. Metallic structures produced with LPBF contain defects, and there are limited capabilities to evaluate these structures non-destructively. Thermal tomography reconstruction of 3D apparent spatial effusivity provides information about AM structure geometry and internal material flaws. We study performance of thermal tomography in imaging of metallic structures through COMSOL computer simulations of transient heat transfer, and through reconstruction of data obtained from experimental measurements. Reconstruction of internal defects is investigated using a stainless steel 316L specimen with flat bottom hole (FBH) indentations, and Inconel 718 plate produced with laser powder bed fusion (LPBF) method, which contains imprinted hemispherical shape low density regions containing non-sintered metallic powder. The FBH’s have the same sizes as the imprinted defects in the LPBF specimens, but offer better imaging contrast. Thermal tomography reconstructions provide visualizations of internal defects, and allow for estimation of their sizes and locations. Detection sensitivity of TT is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, we investigate TT of AM stainless steel 316L specimen with imprinted internal porosity defects using relatively low-cost, small form factor infrared (IR) camera based on uncooled micro bolometer detector. Sparse coding related K-means singular value decomposition (SVD) machine learning, image processing algorithms are developed to improve quality of TT images through removal of Additive white Gaussian noise without blurring the images. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact TT can also be used for in-service nondestructive evaluation (NDE). With capability to perform in-service NDE of the AM component lifecycle, TT data can be used for development of a component digital twin.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Interface Problem Formulation Improvements with Application to Nuclear Fuel Performance Analysis

The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation Program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. This report documents new developments and robustness improvements in mechanical and thermal (gap heat transfer) contact formulations. The improvements range from the migration of industrial level (“assessment”) nuclear fuel model setups to the usage of mortar formulations, the addition of frictional contact to one-dimensional layered representations of fuel and cladding components, and the addition of the Petrov-Galerkin approach to dual mortar, which improves performance on curved, relatively coarse meshes. In addition, the Lagrange-multiplier enforcement of mechanical mortar contact constraints has been extended to two additional types of enforcement: penalty and augmented Lagrange-Uzawa. We show that the latter approach yields the same interface results as dual mortar in the Multiphysics Object-Oriented Simulation Environment, with the advantage of not worsening the condition number of the system matrix—thereby enabling the use of some general implementations of iterative preconditioners, at the expense of additional system evaluations (i.e., augmentations).

42 ENGINEERING↗
DOE OSTI2018

Controlling Interfacial Properties of Lithium-Ion Battery Cathodes with Alkylphosphonate Self-Assembled Monolayers

In this paper, the preparation and characterization of modified LiMn 2 O 4 (LMO) cathodes utilizing chemisorbed alkylphosphonic acids to chemically modify their surfaces are reported. Electrochemical methods to study ionic and molecular mobility through the alkylphosphonate self-assembled monolayers (SAMs) for different alkyl chain compositions, in order to better understand their impact on the lithium-ion electrochemistry, are utilized. Electrochemical trends for different chains correlate to trends observed in contact angle measurements and solvation energies obtained from computational methods, indicating that attributes of the microscopic wettability of these interfaces with the battery electrolyte have an important impact on ionic mobility. The effects of surface modification on Mn dissolution are also reported. The alkylphosphonate layer provides an important mode of chemical stabilization to the LMO, suppressing Mn dissolution by 90% during extended immersion in electrolytes. A more modest reduction in dissolution is found upon galvanostatic cycling, in comparison to pristine LMO cathodes. Finally, taken together, the data suggest that alkylphosphonates provide a versatile means for the surface modification of lithium-ion battery cathode materials allowing the design of specific interfaces through modification of organic chain functionalities.

25 ENERGY STORAGE↗
DOE OSTI2025

Electric Drive Technologies Research: ELT223 Component Modeling, Co-Optimization, and Trade-Space Evaluation Annual Report

This project is intended to support the development of new traction drive systems that meet the targets of 100 kW/L for power electronics and 50 kW/L for electric machines with reliable operation to 300,000 miles. To meet these goals, new designs must be identified that make use of state-of-the-art and next-generation electronic materials and design methods. Designs must exploit synergies between components, for example converters designed for high-frequency switching using wide band gap (WBG) devices and ceramic capacitors. This project included: (1) a survey of available technologies; (2) investigating new technologies, that for example, reduce volume of thermal management or magnetic components; (3) the development of computer aided design tools that consider the converter volume, reliability, and electrical performance; (4) exercising the design software to evaluate performance gaps and predict the impact of certain technologies and design approaches, i.e. GaN semiconductors, ceramic capacitors, ceramic thermal management components, and select topologies; (5) building and testing hardware prototypes to validate models and concepts. The design tools enable co-optimization of the power module and passive elements and provide some design guidance. At the end of the project, new advanced computing methods, such as machine learning approaches, were considered.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Data Science-Driven Discovery of Multimetallic Oxygen-cycle Electrocatalysts for Enhanced Energy Conversion

The overarching objective of this effort has been to combine state-of-the-art data science techniques, first principles analyses, and molecular-level characterization of electrocatalyst structure and reactivity to identify both in-situ mechanisms for degradation and transformation of electrocatalysts with highly complex catalytic structures and the impact of these transformations on catalytic activity. The primary catalysts of interest have been multielemental alloys, including high entropy alloys (HEA’s), which are characterized by a high degree of disorder and up to 20 different elements within a single nanoparticle. We have applied these strategies primarily to energy-critical oxygen cycle electrocatalytic reactions, including oxygen reduction (ORR), but we have also considered extensions to non-electrochemical chemistries such as ammonia synthesis and decomposition. We have made strong progress in the development of computational methods on both the level of machine learning methods development as well as first principles-based treatments of HEA’s, and we have leveraged these insights to propose promising HEA catalysts for the ORR. On the experimental side, we developed new HEA synthesis and characterization protocols relevant to these reactions and developed a database combining our experimental results with corresponding computational tools.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Thermal Evaluation of Metal Oxides on Silica Supports

Atomic level engineering of nanoparticles (NPs) with ability to precisely tune its functional properties can help us realize many promising applications in catalytic science. Such a bottom-up approach would require fundamental level understanding of nucleation and subsequent chemical evolution of nanoparticles during synthesis process. In particular, transition metal based nanoparticles such as Cobalt (Co), Nickel (Ni), Iron (Fe), Paladium (Pd), and Platinum (Pt) can serve as catalysts for many important reactions such as syngas conversion, hydrogenation-dehydrogenation reactions and hydro-deoxygenation reactions. The porous SiO2 is widely used as support medium due to their chemical inertness under reducible and reactive chemical environments often encountered during catalytic process. Nevertheless, even chemically benign SiO2 can have interaction with catalyst particles under high temperatures and adversely affect catalytic activity due to the presence of reactive surface functional groups. Although the influence of the support on catalyst activity and the selectivity is widely studied with respect to the strong metal-support interaction (SMSI) and more recently on the electronic effect of metal-support interaction (EMSI), the choice of support and the catalyst preparation method are still mostly determined by the empirical optimization. For this study, we focus on local structural evolution of cobalt and nickel oxide nanoparticles and its interaction with SiO2 support using multi-modal spectroscopic approach involving X-ray absorption spectroscopy (XAS), nuclear magnetic resonance (NMR) and density functional theory (DFT) based computational methods.

Murugesan, Vijayakumar↗
DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Design of Reduction Process of SnO 2 by CH 4 for Efficient Sn Recovery

We design a novel method for the CH 4 reduction of SnO 2 for the efficient recovery of Sn from SnO 2 through a study combining theory and experiment. The atomic-level process of CH 4 -SnO 2 interaction and temperature-dependent reduction behavior of SnO 2 were studied with a combination of a multi-scale computational method of thermodynamic simulations and density functional theory (DFT) calculations. We found that CH 4 was a highly efficient and a versatile reducing agent, as the total reducing power of CH 4 originates from the carbon and hydrogen of CH 4 , which sequentially reduce SnO 2 . Moreover, as a result of the CH 4 reduction of SnO 2 , a mixture of CO and H 2 was produced as a gas-phase product (syngas). The relative molar ratio of the produced gas-phase product was controllable by the reduction temperature and the amount of supplied CH 4 . The laboratory-scale experimental study confirmed that CH 4 actively reduces SnO 2 , producing 99.34% high-purity Sn and H 2 and CO. Our results present a novel method for an efficient, green, and economical recycling strategy for Sn with economic value added that is held by the co-produced clean energy source (syngas).

Science & Technology - Other Topics↗
DOE OSTI2024

High throughput, accurate gene annotation through AI and HPC-enabled structural analysis

With the advances in next generation sequencing technologies, the number of sequenced genomes is growing exponentially, resulting in a technology bottleneck for the translation of sequence information into usable hypotheses about the function of each gene. We have proposed leveraging our leadership high-performance computing (HPC) resources to help break this annotation bottleneck. Here we design an HPC-based framework to infer gene function from gene sequence by incorporating information about protein structure and interactions predicted by deep learning approaches. Accurate functional prediction and gene annotation using computational methods will facilitate breakthroughs in the genomic sciences essential to understanding and harnessing life processes in bacteria, fungi and plants. The development and applications of the state-of-the-art deep neural networks to protein structural modeling, interaction prediction, sequence comparison, and quality assessment of protein structural models will be made possible by leadership computational resources. These HPC-enabled bioinformatics and molecular modeling tools will provide powerful insights into molecular functions of genes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Prospects and Challenges in the Management of Non-Retrievable Radioactive Waste in the Russian Federation - 17212

In 2011, the Federal Law 'On the Management of Radioactive Waste' was enacted in the Russian Federation setting a framework for the Unified State System for Radioactive Waste Management and establishing a mandatory requirement for safe radioactive waste (RW) disposal. Particular category of 'non-retrievable RW' was defined given the amount of RW generated by that time, the issues associated with relevant sites, as well as the experience gained during the implementation of nuclear legacy programs. It was specified that in situ disposal is the preferred management option for this category of waste rather than RW retrieval and re-disposal due to relevant radiation and financial aspects. In 2011 - 2015, a set of high-priority tasks on taking the inventory of accumulated RW was accomplished. This work also provided for demonstrating the feasibility and making key decisions on the final stages on their lifecycle and relevant end-state strategies. Facilities holding non-retrievable RW were divided into two categories: - subject to transition into near-surface storage facilities by constructing additional safety barriers; - subject to controlled long-term storage followed by remediation. Efforts on a number of surface liquid RW storage water reservoirs falling under the first category launched back in the 20. century were completed. The new federal program provides for a set of follow-up efforts to be completed at 12 facilities. As it comes to the second category, the main task was to develop a set of safety management options, as well as some computational methods to demonstrate the feasibility of the selected end-state strategies. The most considerable efforts were made at the Techa Cascade of water reservoirs being the largest liquid radioactive waste (LRW) storage facility in the world that holds over 360 million m{sup 3} of waste with uncertain legal status, the lack of relevant computational models to predict its state. The paper summarizes the data on defining the end states of non-retrievable RW, completed and scheduled efforts on implementing relevant long-term safety strategies. The paper focuses on subsurface repositories and near-surface LRW storage reservoirs considered as common types of facilities holding non-retrievable RW. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. Furthermore, this algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.

60 APPLIED LIFE SCIENCES↗