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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Evaluation of the Reconstruction Accuracy of the Ultrasound Model–Based Image Reconstruction (U-MBIR) Method for Concrete to Optimize Damage Detection

Reinforced concrete is a critical structural material used to construct nuclear power plants (NPPs). As such, its safety and performance must be thoroughly examined throughout the life cycle of the NPP infrastructure system. Ultrasonic measurements have been an industry standard for both surface and subsurface inspections. To support the development of these techniques, Oak Ridge National Laboratory (ORNL) has researched and developed advanced image reconstruction algorithms to capture internal damage. The results and discussion presented herein summarize the current state of the ultrasonic model–based iterative reconstruction (U-MBIR) algorithm developed at ORNL. In the work documented in this report, the U-MBIR methodology was applied to four sets of ultrasonic data collected from concrete specimens. The results demonstrate that the U-MBIR algorithm can successfully detect defects within the four concrete samples. The reconstruction images help identify the specimen thickness, regions of delamination, and location of rebar embedded within the concrete. The reconstruction images allow engineers and technicians to characterize the internal defects within concrete specimens and structural members. Ultimately, this knowledge can guide engineers in making informed decisions regarding the performance, safety, and reliability of structural materials (i.e., reinforced concrete) throughout the life cycle of NPPs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

The DISTINCTIVE University Consortium: Structural Integrity - 18272

The Engineering and Physical Sciences Research Council (EPSRC) sponsored DISTINCTIVE consortium (Decommissioning, Immobilisation and Storage Solutions for Nuclear Waste Inventories) is developing technologies for civil infrastructure repair, in-situ subsurface waste immobilisation, and groundwater protection during construction and decommissioning. The consortium has contributed to the development of skilled cross-disciplinary civil engineers and scientists, that have the knowledge and experience required to develop engineering solutions tailored for application within radiologically contaminated sites. The Structural Integrity Theme focuses on challenges ranging from site-scale infrastructure preservation and restoration, through injectable ground barriers for risk mitigation, to the remote characterisation and handling of individual waste packages. The main aim of the theme is to develop novel engineering solutions, tailored for use on radiologically contaminated sites, for: ground protection; infrastructure characterisation; concrete restoration and waste characterisation. Technologies should minimise current, and future, radiation exposure of the workforce whilst providing economically viable engineering solutions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites

Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the “chassis” organism’s metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A Detonator Design Guide and Knowledge Capture System [Slides]

Engineering design--good design--is a blend of engineering and science; this is especially true for of explosive detonator design. Detonators are complex components with interrelated mechanical, electrical, and chemical attributes, all of which must be balanced to achieve a robust and reliable, working whole. This design guide presents an integrated, lifecycle approach to the design process, bringing together engineering design with science, emphasizing elegance and subtlety in the design process.

42 ENGINEERING↗
DOE OSTI2025

Phylogenomic discovery and engineering of nitrogen fixation into the bioenergy woody crop poplar

Biological nitrogen fixation (BNF) is a key process enabling plants in specific lineages to convert atmospheric dinitrogen (N₂) into bioavailable ammonia through symbioses with diazotrophic microbes. Expanding this capability beyond native nitrogen-fixing clades into non-nodulating crops would reduce synthetic fertilizer use, lowering energy inputs and environmental impacts in agriculture. Supported by DOE Funding Award DE-SC0018247, the NitFix project advanced foundational knowledge required to engineer root-nodule symbioses in new host species. The team generated the most comprehensive phylogenomic analysis to date of all known nodulating lineages, resolving the evolutionary history of nitrogen-fixing symbiosis and identifying core gene suites retained across nodulating taxa. Through multimodal genomics, transcriptomics, and functional analyses in Medicago truncatula and related species, the project mapped regulatory networks underlying nodule organogenesis, bacterial infection, and nitrogen-fixation efficiency. Key discoveries include the identification of conserved signaling modules for rhizobial recognition, transcription factors controlling nodule differentiation, and metabolic pathways integrating fixed nitrogen into plant growth. The project also developed enabling tools—including optimized transformation pipelines, gene-editing workflows, and imaging-based phenotyping—to accelerate engineering efforts in emerging models. Together, these results refine the mechanistic framework of symbiotic nitrogen fixation and highlight transferable components essential for rewiring these traits into non-nodulating crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Assessment of Cold Spray Technology for Nuclear Power Applications

This report provides an overview of cold spray (CS) technology as it relates to repair and fabrication of metal alloys in the nuclear power industry. CS is a relatively new technology with applications established in other industries (e.g., aerospace, defense), and greater use of CS is anticipated within the nuclear power industry. This report is prepared in support of the United States Nuclear Regulatory Commission (NRC) Action Plan for Advanced Manufacturing Technologies (AMTs) Rev. 1, which includes objectives to identify the AMTs most likely to be used for nuclear power applications that require NRC approval, and to prepare NRC staff to review regulatory submittals containing components manufacturing using AMTs. In addition to providing an overview of CS technology, this report highlights engineering and scientific knowledge gaps of CS and rates the size of the knowledge gaps. It also assesses importance rankings of the knowledge gaps as they relate to several application categories. The application categories considered in this exercise include factory-applied CS for chloride induced stress corrosion cracking (CISCC) mitigation, field CISCC repair, light water reactor (LWR) factory structural fabrication, and LWR field dimensional restoration and corrosion protection. The combination of a knowledge gap size rating and importance ranking for an application can be used to prioritize additional data or information gathering efforts.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Developing a Fundamental Knowledge Base for the Interaction of Ruthenium with Natural and Engineered Iron-Bearing Materials

The intent of this project is to build a fundamental knowledge base concerning the redox coupling between Ru and Fe in heterogeneous systems with predictive applications towards the fate, speciation, and transport of Ru in the environment and its capture using available technologies. RuNOCl 3 and Fe 3+ are redox active over a broad range of pH. Initial assessment of effect of redox coupling between Ru and Fe indicates that Fe(III) in ferrihydrite can oxidize Ru 2+ in RuNOCl3 to Ru(IV) during coprecipitation at ambient conditions and alkaline pH. Further, evidence suggests that Ru(IV) is incorporated in the structure of fh. Likewise, Fe 3+ (aq) also reduces Ru 2+ in RuNOCl 3 in the homogenous system at acidic pH although the kinetics of reaction appear to be slow. Rufh annealed to 500 0C transforms to Ru(III) doped hematite. AIMD informed EXAFS indicates that Ru substitutes for Fe(III) in regular octahedral sites of hematite but requires three distinct configurations involving edge-sharing protonated Fe vacancies and straight substitution with no local defects. Fe 2+ (aq) appears to oxidize at the surface of RuO 2 , but the amount is too low to reasonably expect detectable reduced Ru. Future experiments at higher pH are warranted to make Fe 2+ (aq) less stable (i.e., make more redox active). We also demonstrated that we can functionalize Si aerogels with Fe(II) and Fe(III) but likely need to go to higher T to make Feo nanoparticles.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Digital Analytics, Causal Knowledge Acquisition and Reasoning for Technical Language Processing

Complex engineering systems such as nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) data elements that contain information on the status of components, assets, and systems. Some of this information is textual in form and can be found in documents such as incident reports (IRs) and work orders (WOs). Analyses of textual data in current NPPs-using natural language processing (NLP) methods-have been expanded over the last decade, and it is only recently that the true potential of such analyses has emerged. So far, applications of NLP methods have mostly been limited to classification and prediction, the goal being to identify the nature of the textual element (e.g., safety or non-safety related). Here, we target a more complex problem: automatically extracting knowledge from a textual element in order to assist system engineers in conducting system health assessments. Knowledge extraction is a very broad concept, and its definition may vary depending on the application context. Our methods are a blend of both rule-based and machine learning (ML) algorithms. For our purposes, knowledge extraction means identifying the systems or assets mentioned in a given textual element, as well as the type of event described (e.g., component failure or maintenance activity). In addition, we want to capture details such as measured quantities and the temporal/cause-effect relations between events. In this tool, we also demonstrate how textual data elements are preprocessed in order to handle typos, acronyms, and abbreviations. One main feature of these methods is that they are not based solely on data, but are in fact model-based. In other words, they also rely on MBSE models that are designed to capture-from a functional point of view-the architecture of the systems/assets under consideration. The main purpose of such models is to digitally emulate system engineers' knowledge of system and asset architecture and to identify dependencies among systems, assets, and components. Provided these models, analyses of textual and numeric ER data can be performed by first identifying the OPM model elements to which the ER data elements are referring. The relationships between ER data elements are then identified by checking for any temporal or logical dependencies.

Mandelli, Diego [Idaho National Laboratory (INL), ↗
DOE OSTI2022

Evidence of Nonlinear Seismic Effects in the Earth from Downhole Distributed Acoustic Sensors

Seismic velocities and elastic moduli of rocks are known to vary significantly with applied stress, which indicates that these materials exhibit nonlinear elasticity. Monochromatic waves in nonlinear elastic media are known to generate higher harmonics and combinational frequencies. Such effects have the potential to be used for broadening the frequency band of seismic sources, characterization of the subsurface, and safety monitoring of civil engineering infrastructure. However, knowledge on nonlinear seismic effects is still scarce, which impedes the development of their practical applications. To explore the potential of nonlinear seismology, we performed three experiments: two in the field and one in the laboratory. The first field experiment used two vibroseis sources generating signals with two different monochromatic frequencies. The second field experiment used a surface orbital vibrator with two eccentric motors working at different frequencies. In both experiments, the generated wavefield was recorded in a borehole using a fiber-optic distributed acoustic sensing cable. Both experiments showed combinational frequencies, harmonics, and other intermodulation products of the fundamental frequencies both on the surface and at depth. Laboratory experiments replicated the setup of the field test with vibroseis sources and showed similar nonlinear combinations of fundamental frequencies. Amplitudes of the nonlinear signals observed in the laboratory showed variation with the saturating fluid. These results confirm that nonlinear components of the wavefield propagate as body waves, are likely to generate within rock formations, and can be potentially used for reservoir fluid characterization.

58 GEOSCIENCES↗
DOE OSTI2020

Accommodation of Silicon in an Interconnected Copper Network for Robust Li-Ion Storage

Silicon (Si)-based materials are one of the most promising anodes to be applied in rechargeable lithium ion batteries. However, the active Si/electrolyte interface causes continuous side reactions and poor conductivity, which significantly decreases the cycling stability. Cu is the only metallic current collector that has been known to promote electron conduction and lithium-ion transfer without alloying reaction occurrence. However, to the best current knowledge, scalable interface engineering incorporating Cu has not been reported. In this study, this conductive Cu interface (CCI) is constructed through a self-assembly carbothermic reduction method to achieve efficient protection of Si/electrolyte interfaces while allowing for fast Li + diffusion. The energy barrier of lithium-ion diffusion through Cu is calculated to be 0.1965 eV, which is much lower than that through Au, Fe, and Ni films. Benefiting from the enhanced interfacial protection and kinetics of Si with CCI, a fading rate of only 0.068% is maintained for 1000 cycles and an aerial capacity of 4.78 mAh cm -2 is achieved after 280 cycles, which is comparable to the industry standards required for practical application.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A deep learning-based battery sizing optimization tool for hybridizing generation plants

Hybrid generation and energy storage systems offer the ability to increase flexibility of the combined asset. This flexibility can be used to increase provision of services already provided by the generation asset, such as timing sale of electricity to the energy market during high price periods, and also enable provision of additional services, such as ancillary services or contribute to resource adequacy. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that, among other factors, maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary (i.e., based on “rules of thumb”) or too complex to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run). This work presents a novel deep learning-based battery sizing optimization tool that is designed to help generation asset owners easily assess preliminary sizing considerations for potential battery investments to hybridize their generation facility. The tool uses deep learning to predict revenue over a broad search space of potential battery sizes, estimates capital and operating costs (including accounting for battery degradation), and computes financial performance of each potential battery system investment, recommending a system with maximum financial performance. The tool is tested and validated for hydropower assets. Finally, this tool will help a greater cross-section of industry consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.

13 HYDRO ENERGY↗
DOE OSTI2023

Knowledge-driven design of solid-electrolyte interphases on lithium metal via multiscale modelling

Abstract Due to its high energy density, lithium metal is a promising electrode for future energy storage. However, its practical capacity, cyclability and safety heavily depend on controlling its reactivity in contact with liquid electrolytes, which leads to the formation of a solid electrolyte interphase (SEI). In particular, there is a lack of fundamental mechanistic understanding of how the electrolyte composition impacts the SEI formation and its governing processes. Here, we present an in-depth model-based analysis of the initial SEI formation on lithium metal in a carbonate-based electrolyte. Thereby we reach for significantly larger length and time scales than comparable molecular dynamic studies. Our multiscale kinetic Monte Carlo/continuum model shows a layered, mostly inorganic SEI consisting of LiF on top of Li 2 CO 3 and Li after 1 µs. Its formation is traced back to a complex interplay of various electrolyte and salt decomposition processes. We further reveal that low local Li + concentrations result in a more mosaic-like, partly organic SEI and that a faster passivation of the lithium metal surface can be achieved by increasing the salt concentration. Based on this we suggest design strategies for SEI on lithium metal and make an important step towards knowledge-driven SEI engineering.

25 ENERGY STORAGE↗
DOE OSTI2020

Structure of disordered TiO 2 phases from ab initio based deep neural network simulations

Amorphous TiO 2 (a-TiO 2 ) is widely used in many fields, ranging from photo-electrochemistry to bio-engineering, hence detailed knowledge of its atomic structure is of scientific and technological interest. Here we use an ab initio-based deep neural network potential (DP) to simulate large scale atomic models of crystalline and disordered TiO 2 with molecular dynamics. Our DP reproduces the structural properties of all (11) TiO 2 crystalline phases, predicts the densities and structure factors of molten and amorphous TiO 2 with only a few percent deviation from experiments, and describes the pressure dependence of the amorphous structure in agreement with recent observations. Furthermore, it can be extended to model additional structures and compositions and can be thus of great value in the study of TiO 2 -based (nano-)materials.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Laboratory evolution reveals the metabolic and regulatory basis of ethylene glycol metabolism by Pseudomonas putida KT2440

Pollution from ethylene glycol, and plastics containing this monomer, represent a significant environmental problem. The investigation of its microbial metabolism therefore provides insights into the environmental fate of this pollutant, but also enables its utilization as a carbon source for microbial biotechnology. Here, we reveal the genomic and metabolic basis of ethylene glycol metabolism in Pseudomonas putida KT2440. Although this strain cannot grow on ethylene glycol as sole carbon source, it can be used to generate growth-enhancing reducing equivalents upon co-feeding with acetate. Mutants that utilize ethylene glycol as sole carbon source were isolated through adaptive laboratory evolution. Genomic analysis of these mutants revealed a central role of the transcriptional regulator GclR, which represses the glyoxylate carboligase pathway as part of a larger metabolic context of purine and allantoin metabolism. Secondary mutations in a transcriptional regulator encoded by PP_2046 and a porin encoded by PP_2662 further improved growth on ethylene glycol in evolved strains, likely by balancing fluxes through the initial oxidations of ethylene glycol to glyoxylate. With this knowledge we reverse engineered an ethylene glycol utilizing strain and thus, revealed the metabolic and regulatory basis that are essential for efficient ethylene glycol metabolism in P. putida KT2440.

09 BIOMASS FUELS↗
DOE OSTI2021

Toward sustainable production of value‐added bioenergy and industrial oils in oilseed and biomass feedstocks

Abstract Plant fatty acids are used for food, feed, fuel, and industrial materials. Structurally and chemically diverse fatty acids, referred to as unusual or specialized fatty acids, are found in the seed oils of diverse plant species. Many unusual fatty acids have potential use as alternative and renewable sources of biofuels and bio‐based industrial feedstocks due to their variant structures' physical or functional properties. Oils enriched in these fatty acids can increase the value of oilseed crops and provide co‐products that can be readily extracted from lignocellulosic materials in biomass crops. Here, we describe recent progress in strategies for enhancement of oil production and quality in oilseed crops and the extension of this knowledge for metabolic engineering of biomass crops. Successful implementation of these strategies, in part, through the use of emerging synthetic biology tools, will provide added product value for the economic sustainability of biomass crops as bioenergy feedstocks.

Park, Kiyoul↗
DOE OSTI2021

Chemo–mechanical properties of carbon fiber reinforced geopolymer interphase

Geopolymers, as a potentially environmentally friendly alternative to Portland cement, are increasingly attracting attention in the construction industry. Various methods have been applied for customizing the properties of geopolymers and improving their commercial viability. One of the promising methods for refining the properties of geopolymers such as their toughness is the use of short fibers. The effectiveness of a high-strength short fiber in the geopolymer matrix is largely dependent on the interfacial bonding between the fiber and its surrounding matrix. While the importance of this interfacial chemistry is highlighted in the literature, the characteristics of this bonding structure have not been fully understood. In this paper, we aim to investigate the bonding mechanism between the carbon fiber and metakaolin-based geopolymer matrix. For the first time, the existence and nature of the chemical bonding at the interfacial region (interphase) between carbon fiber and geopolymer matrix has been revealed. X-ray pair distribution function computed tomography (PDF-CT), field emission-scanning electron microscopy imaging, and nanoindentation techniques are employed to discern the chemo-mechanical properties of the interphase. PDF-CT results show the emergence of a new atom-atom correlation at the interfacial region (around 1.82 angstrom). This correlation is a characteristic of interfacial bonding between the fiber and its surrounding matrix, where the existence of chemical linkages (potentially Al-V-O-C) between fibers and the matrix contributes to the adhesion between the two constituents making up the composite. Due to such chemical bonding, the nanomechanical properties of the interfacial region fall between that of the carbon fiber and geopolymer. The combination of advanced techniques is proved useful for enhancing our understanding of the interfacial chemistry between fibers and the binding matrix. This level of knowledge facilitates the engineering of composite systems through the manipulation of their nanostructure.

36 MATERIALS SCIENCE↗