SEARCH · Engineering Papers

Results for “knowledge engineering”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20

All sources NASA NTRS DOE OSTI

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), ↗

NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Text-based information about past engineering failures presents one possible solution by facilitating the retrieval of information that can inform new designs. However, identifying documents containing relevant information and extracting required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing-based framework is proposed to discover relevant knowledge from documents containing failure-related design information. Documents containing usable information are filtered using sentiment analysis based on a custom lexicon specialized for engineering design and by filtering out documents containing only irrelevant topics. Next, from the identified usable documents, information relating to engineering failures, contributing factors that can be controlled at design time (“risk factors”), and recommended preventative actions are extracted. Semantic similarity is then used to group similar pieces of extracted information for improved generalizability. The proposed framework is applied to NASA’s Lessons Learned Information System (LLIS). The framework can be used to identify documents containing usable failure-related design information from other databases, extract relevant information from these documents, and generalize the acquired knowledge such that it can be applied to novel systems.

Sequoia R. Andrade↗

NASA NTRS2002

The New NASA Orbital Debris Engineering Model ORDEM2000

The NASA Orbital Debris Program Office at Johnson Space Center has developed a new computer-based orbital debris engineering model, ORDEM2000, which describes the orbital debris environment in the low Earth orbit region between 200 and 2000 km altitude. The model is appropriate for those engineering solutions requiring knowledge and estimates of the orbital debris environment (debris spatial density, flux, etc.). ORDEM2000 can also be used as a benchmark for ground-based debris measurements and observations. We incorporated a large set of observational data, covering the object size range from 10 mm to 10 m, into the ORDEM2000 debris database, utilizing a maximum likelihood estimator to convert observations into debris population probability distribution functions. These functions then form the basis of debris populations. We developed a finite element model to process the debris populations to form the debris environment. A more capable input and output structure and a user-friendly graphical user interface are also implemented in the model. ORDEM2000 has been subjected to a significant verification and validation effort. This document describes ORDEM2000, which supersedes the previous model, ORDEM96. The availability of new sensor and in situ data, as well as new analytical techniques, has enabled the construction of this new model. Section 1 describes the general requirements and scope of an engineering model. Data analyses and the theoretical formulation of the model are described in Sections 2 and 3. Section 4 describes the verification and validation effort and the sensitivity and uncertainty analyses. Finally, Section 5 describes the graphical user interface, software installation, and test cases for the user.

Liou, Jer-Chyi↗

NASA NTRSDate not supplied

Standards Roadmapping for Mission Assurance in Commercial Spaceflight

This paper explores the application of established roadmapping approaches in standards planning and roadmap development to support mission assurance objectives. Mission assurance requires proactive planning across multiple domains including technical, schedule, cost, organizational, and policy. The intersection of these domains happens at the enterprise level, and enterprise systems engineering processes can systematically guide mission assurance activities, including development of standards roadmaps. Standards planning and development is a critical contributor to an effective mission assurance strategy, although standards are often developed and adopted reactively. Technology and standards planning is a key Enterprise Process Management activity, as identified by the Systems Engineering Body of Knowledge, and technology and standards roadmaps are strategic tools used to enable that process. Technology roadmapping has proven effective in guiding enterprise architecture and concept design, and standards planning supports enterprise requirements definition and management. However, there are limited methodologies to guide such standards planning activities. The application of technology roadmapping principles to standards planning formalizes the process and establishes a repeatable framework for standards development and integration. The framework development described in this paper fills a methodological gap by adapting proven roadmapping techniques to standards planning. Through a comparison of roadmapping methodologies and a case study analysis, this approach outlines a systematic process for anticipating and planning standards needs. In the case of developing a standards integration roadmap for NASA’s Office of Safety and Mission Assurance (OSMA), technology roadmapping principles were adapted to develop a roadmap framework to allow NASA and OSMA to define an agency-wide standards integration plan for commercial and industry safety and mission assurance standards. The outcome highlights key adaptations required for roadmapping in a standards context and describes the process steps to formalize a standards planning activity. The roadmapping framework adapted for standards development and integration enables more strategic, forward-looking standards planning and allows for better alignment between evolving capabilities and mission assurance requirements. Beyond its application to mission assurance, this methodology can be transferred to other domains requiring standards development, providing a systematic approach that integrates systems engineering principles into policy strategy and decision-making processes.

Enterprise Systems Engineering↗

NASA NTRS1989

Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events, and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system. The artificial intelligence approach is useful because it provides (1) the use of human experts' knowledge of sensor behavior and faulty engine conditions in interpreting data; (2) the use of engine design knowledge and physical sensor locations in establishing relationships among the events of multiple sensors; (3) the use of stored analysis of past data of faulty engine conditions; and (4) the use of knowledge-based reasoning in distinguishing sensor failure from actual faults. The neural network approach appears promising because neural nets (1) can be trained on extremely noisy data and produce classifications which are more robust under noisy conditions than other classification techniques; (2) avoid the necessity of noise removal by digital filtering and therefore avoid the need to make assumptions about frequency bands or other signal characteristics of anomalous behavior; (3) can, in effect, generate their own feature detectors based on the characteristics of the sensor data used in training; and (4) are inherently parallel and therefore are potentially implementable in special-purpose parallel hardware.

Ali, Moonis↗

NASA NTRS2003

Evaluation and Characterization Study of Dual Pulse Laser-Induced Spark (DPLIS) for Rocket Engine Ignition System Application

This paper addresses the progress of technology development of a laser ignition system at NASA Marshall Space Flight Center (MSFC). Laser ignition has been used at MSFC in recent test series to successfully ignite RP1/GOX propellants in a subscale rocket chamber, and other past studies by NASA GRC have demonstrated the use of laser ignition for rocket engines. Despite the progress made in the study of this ignition method, the logistics of depositing laser sparks inside a rocket chamber have prohibited its use. However, recent advances in laser designs, the use of fiber optics, and studies of multi-pulse laser formats3 have renewed the interest of rocket designers in this state-of the-art technology which offers the potential elimination of torch igniter systems and their associated mechanical parts, as well as toxic hypergolic ignition systems. In support of this interest to develop an alternative ignition system that meets the risk-reduction demands of Next Generation Launch Technology (NGLT), characterization studies of a dual pulse laser format for laser-induced spark ignition are underway at MSFC. Results obtained at MSFC indicate that a dual pulse format can produce plasmas that absorb the laser energy as efficiently as a single pulse format, yet provide a longer plasma lifetime. In an experiments with lean H2/air propellants, the dual pulse laser format, containing the same total energy of a single laser pulse, produced a spark that was superior in its ability to provide sustained ignition of fuel-lean H2/air propellants. The results from these experiments are being used to optimize a dual pulse laser format for future subscale rocket chamber tests. Besides the ignition enhancement, the dual pulse technique provides a practical way to distribute and deliver laser light to the combustion chamber, an important consideration given the limitation of peak power that can be delivered through optical fibers. With this knowledge, scientists and engineers at Los Alamos National Laboratory and CFD Research Corporation have designed and fabricated a miniaturized, first-generation optical prototype of a laser ignition system that could be the basis for a laser ignition system for rocket applications. This prototype will be tested at MSFC in future subscale rocket ignition tests.

Osborne, Robin↗

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 OSTI2015

Lead Toxicity to the Performance, Viability, And Community Composition of Activated Sludge Microorganisms

Lead (Pb) is a prominent toxic metal in natural and engineered systems. Current knowledge on Pb toxicity to the activated sludge has been limited to short-term (<= 24 h) toxicity. The effect of extended Pb exposure on process performance, bacterial viability, and community compositions remains unknown. We quantified the 24-h and 7-day Pb toxicity to chemical oxygen demand (COD) and NH3-N removal, bacterial viability, and community compositions using lab-scale experiments. Our results showed that 7-day toxicity was significantly higher than the short-term 24-h toxicity. Ammonia-oxidizing bacteria were more susceptible than the heterotrophs to Pb toxicity. The specific oxygen uptake rate responded quickly to Pb addition and could serve as a rapid indicator for detecting Pb pollutions. Microbial viability decreased linearly with the amount of added Pb at extended exposure. The bacterial community diversity was markedly reduced with elevated Pb concentrations. Surface analysis suggested that the adsorbed form of Pb could have contributed to its toxicity along with the dissolved form. Our study provides for the first time a systematic investigation of the effect of extended exposure of Pb on the performance and microbiology of aerobic treatment processes, and it indicates that long-term Pb toxicity has been underappreciated by previous studies.

Yuan, L↗

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 OSTI2015

Guest editorial: Special issue micro-and nanomachines.

The articles in this special section focus on the technologies and applications supported by micro- and nanomachines. The world of artificial micro- and nanomachines has greatly expanded over the last few years to include a range of disciplines from chemistry, physics, biology, to micro/nanoengineering, robotics, and theoretical physics. The dream of engineering nanomachines involves fabricating devices that mimic the mechanical action of biological motors that operate over multiple length scales: from molecular-scale enzymes and motors such as kinesins to the micro-scale biomachinery responsible for the motility of tiny organisms such as the flagella motors of E. coli. However, the design and fabrication of artificial nano- and micromachines with comparable performance as their biological counterparts is not a straightforward task. It requires a detailed understanding of the basic principles of the operation of biomotors and mechanisms that couple the dissipation of energy to mechanical motion. Furthermore, micro engineering and microfabrication knowledge is required in order to design efficient, small and even smart micro- and nanomachines.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

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↗

DOE OSTI2018

A comparative study on wave prediction for WECs

The idea of acausality for control of a wave energy converter (WEC) is a concept that has been popular since the birth of modern wave energy research in the 1970s. This concept has led to considerable research into wave prediction and feedforward WEC control algorithms. However, the findings in this report mostly negate the need for wave prediction to improve WEC energy absorption, and favor instead feedback driven control strategies. Feedback control is shown to provide performance that rivals a prediction-based controller, which has been unrealistically assumed to have perfect prediction. It is well known in classical control engineering that perfect knowledge of past and future events will always lead to higher performing systems. However, it is also well known that the underlying system must be well-designed; control cannot fix a bad design. Additionally, one must consider the practical application of a control design, which relies on measurements and actuation systems. There are major implications to cost and reliability when relying on remote sensors requiring real-time data-streaming (e.g., remote wave buoys). This report shows that for a well-designed WEC, in which closed loop dynamics is considered since early stages of design, a suboptimal controller using no prediction can achieve more than 90% of the theoretical maximum. A predictionless feedback resonating (FBR) controller performs within 0.1% percent of a controller with perfect future knowledge (something which is not practically attainable). Given the major challenges with accurate and robust wave prediction, this result provides a major argument and incentive for utilizing feedback for WEC control. Implementation of these feedback strategies is readily attainable, while the strategy requiring perfect wave prediction will demand an unknown number of additional years to research and develop, all in the service of a marginal 1% benefit.

16 TIDAL AND WAVE POWER↗

DOE OSTI2018

Sandia National Labs Summer Internship

I am working for Sandia National Laboratories in Albuquerque, New Mexico in the Summer Product Realization Institute for Nuclear Weapons (NW SPRINT). NW SPRINT focused on increasing agility and facilitating the development of novel concepts and ideas on a compressed schedule. The program focuses on using advanced manufacturing technologies to innovate and revolutionize the products that Sandia National Laboratories delivers. The program is a design challenge incorporating knowledge from various engineering fields to design and implement a working product. Multiple teams from different departments compete to develop and iterate the best design. I am working on a team of five with individual disciplines including Mechanical, Aerospace, and Electrical Engineering.

42 ENGINEERING↗