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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Residual Dose and Environmental Monitoring for the Fermilab Main Injector Tunnel Using the Data Acquisition Logging Engine (Dale)

The Recycler and the Main Injector are part of the Fermilab Accelerator complex used to deliver proton beam to the different experiments. It is very important to control and minimize losses in both machines during operation, to reduce personnel dose from residual activation and to preserve component lifetime. To minimize losses, we need to identify the loss points and adjust the components accordingly. The Data Acquisition Loss Engine (DALE) platform has been developed within the Main Injector department and upgraded throughout the years. DALE is used to survey the entire enclosure for residual dose rates and environmental readings when unrestricted access to the enclosure is possible. Currently DALE has two radiation meters, which are aligned along each machine, so loss points can be identified for both at the same time. DALE attaches to the enclosure carts and is continuously in motion monitoring dose rates and other environmental readings. In this paper we will describe how DALE is used to provide radiation maps of the residual dose rates in the enclosure. We will also compare the loss points with the Beam Loss monitor data.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Preventing Reverse Engineering of Critical Industrial Data with DIOD

Business analytics augmented by artificial intelligence and machine learning (AI/ML) have revolutionized the role of data in the modern world. In recent years, businesses have incorporated data into their decision-making process for better prediction, risk-assessment, content creation, etc. While such businesses often seek to leverage the full use of their data through third-party AI/ML services, they are often hampered by the risks of data leaks, reverse-engineering, stolen technology, etc. that often have disastrous consequences for businesses and their stakeholders alike. Thus, there arises a need for data masking prior to its transmission that obfuscates proprietary information while preserving the information relevant for AI/ML applications. In order to meet the needs of industrial data which are significantly different from those of data warehouses, previous work proposed an efficient time and space-scalable data masking paradigm known as the deceptive infusion of data (DIOD) methodology. The present work expands upon this work by leveraging existing reverse-engineering capabilities to facilitate the decomposition of industrial data into its proprietary and AI/ML-relevant parts, referred to as fundamental and inference metadata respectively. Both sets of metadata are further obfuscated in accordance with the DIOD methodology to create the DIOD rendition of the industrial data, which is rendered immune to reverse-engineering by discarding proprietary information and only preserving AI/ML-relevant information. Additionally, constraints of the original DIOD manuscript are relaxed using mutual information by configuring the methodology to the target AI/ML application to unlock the full potential of the DIOD methodology. As an example, data from a nuclear reactor is transformed into that from a nonlinear spring-mass system with different levels of data masking as required by the generic system and the target application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Data-Driven Molecular Engineering of Solar-Powered Windows

Buildings are the centerpiece of modern living, with more than half of the world’s population now living in urban environments. This demographic evolution has led to building use be-coming the main drain of our energy resources. According to the US Energy Information Administration, in 2016, 40 percent of the total energy consumption in the United States came from building use. However, we could overcome this energy drain by embedding new environmental technologies into future cities to realize energy-sustainable buildings.

42 ENGINEERING↗
DOE OSTI2015

DataHawk 1 data from COALA Engineering Evaluation

These are raw (uncorrected) measurements from the DataHawk 1 sensor package. This is a level 0 data product and only minimal quality control measures have been applied. It is likely that substantial further corrections will be required to use these in a physically meaningful way. Please contact the PI if interested in using the dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

A pilot “big data” education modular curriculum for engineering graduate education: Development and implementation

Engineering higher education increasingly produces data in the volume, variety, velocity, and need for veracity such that the output of the research is considered “Big Data”. While engineering faculty members do conceive of and direct the research producing this data, there may be gaps in faculty members’ knowledge in training graduate and undergraduate research assistants in the management of Big Data. The project described herein details the development of a Big Data education module for a group of graduate researchers and undergraduate research assistants in Electrical and Computer Engineering. This project has the following objectives: to document and describe current data management practices; to identify gaps in knowledge that need to be addressed in order for research assistants to successfully manage Big Data; and to create curricular interventions to address these gaps. This paper details the motivation, relevant literature, research methodology, curricular intervention, and pilot presentation of the module. Results indicate that, generally, students involved in Big Data projects need comprehensive introduction to the topic, which will be most effective when contextualized to the work that they are performing in the research or classroom environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

NRIC Asset Suite Engineering and Operations Data Integration Plan

The National Reactor Innovation Center (NRIC), established by the U.S. Department of Energy (DOE) in August 2019, accelerates the demonstration and deployment of advanced nuclear energy through its mission to inspire stakeholders and the public, empower innovators, and deliver successful outcomes through efficient coordination of partners and resources. NRIC is a national program led by Idaho National Laboratory (INL), enabling collaborators to harness the world-class capabilities of the U.S. National Laboratory System. Committed to demonstrating advanced reactors by the end of 2025, NRIC is designed to bridge the gap between research, development, and the marketplace to help convert some of the Nation’s most promising advanced nuclear reactors into commercial applications by 2030. To meet these needs, NRIC is developing two reactor demonstration test beds at Idaho National Laboratory (INL), the Laboratory for Operation and Testing in the United States (LOTUS) and the Demonstration and Operation of Microreactor Experiments (DOME) test bed. Each test bed involves the modification of existing facilities at INL’s Materials and Fuels Complex (MFC). Retrofitting these facilities to accommodate novel reactors, as well as subsequent but similar NRIC projects, is a non-trivial engineering task and is expected to generate a significant amount of new and revised documentation. Leveraging digital engineering practices, this documentation will be managed in a purpose-built data management tool to facilitate origination, review, and approval while coordinating with the design contractor. The subsequent upload and review of documentation to INL’s existing document control system comprises a long and unnecessarily manual task. An opportunity exists to automate this upload process and save job-hours while continuing to adhere to INL/MFC document management procedures.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Study of the Effects of Various Injection Geometries on the Operation of a Rotating Detonation Engine

Test data for operation of a rotating detonation engine using Hydrogen-air mixtures at air mass fluxes of 40-115 kg/m2s and a range of global equivalence ratios using three different injection schemes are presented and discussed. Stable rotation of at least one detonation wave traveling at speeds of 1380-1650 m/s is achieved for each injector under certain conditions. A variety of stable, unstable, and transitory combustion processes are seen, and data collected from high-speed pressure transducers is used to characterize this behavior. Further insight into what drives the operating mode for each injector is provided with the aid of static pressure measurements and a simple model to estimate injector jet Mach numbers and momentum fluxes. Comparisons are made between the injectors using average pressure measurements taken along the detonation channel. When these data are normalized by the feed system stagnation pressure self similar profiles are seen and clear trends in pressure losses among the injectors emerge.

Duvall, James↗
DOE OSTI2021

Application of Data Science and Engineering

Metal additive manufacturing (AM) processes exhibit significant variability in the quality and properties of components that are produced. This variability has prevented the widespread adoption of AM in industry. The need for more advanced and descriptive process monitoring, part qualification, and process control has led to an increasing number of sensors on machines and subsequent data to analyze. Increasingly, data science principles are being leveraged in each of these domains in order to process this data and better understand the causes of variability and the corresponding quality inconsistencies that occur in additive manufacturing.

Halsey, William↗
DOE OSTI2016

Brief 76 Nuclear Engineering Enrollments and Degrees Survey, 2015 Data

The 2015 Nuclear Engineering Enrollments and Degrees Survey reports degrees granted between September 1, 2014 and August 31, 2015. Enrollment information refers to the fall term 2015. The enrollments and degrees data comprises students majoring in nuclear engineering or in an option program equivalent to a major. Thirty-five academic programs reported having nuclear engineering programs during 2015, and data was received from all thirty-five programs. The report includes enrollment information on undergraduate students and graduate students and information by degree level for post-graduation plans.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Revealing Physical Activity of GRB Central Engine with Macronova/Kilonova Data

The modeling of Li-Paczyński macronova/kilonova signals gives a reasonable estimate on the neutron-rich material ejected during the neutron star mergers. Usually the accretion disk is more massive than the macronova ejecta, with which the efficiencies of converting the disk mass into prompt emission of three merger-driven GRBs can hence be directly constrained. Supposing the macronovae/kilonovae associated with GRB 050709, GRB 060614, and GRB 130603B arose from radioactive decay of the r -process material, the upper limit on energy conversion efficiencies are found to be as low as ∼10{sup −6}–10{sup −4}. Moreover, for all three events, neutrino annihilation is likely powerful enough to account for the brief gamma-ray flashes. Neutrino annihilation can also explain the “extended” emission lasting ∼100 s in GRB 050709, but does not work for the one in GRB 060614. These progresses demonstrate that the macronova can serve as a novel probe of the central engine activity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

High-dimensional data analytics in civil engineering: A review on matrix and tensor decomposition

Recent developments in sensing and monitoring techniques have led to the generation of high-dimensional data in the field of civil engineering. High-dimensional data analytics methods have thus been developed to interpret such complex data. Among the different high-dimensional data analytics techniques, matrix and tensor decomposition methods have acquired a notable interest in the civil engineering community over the past decade. Due to their unique ability to deal with highly redundant and correlated data, these methods are establishing themselves as promising and efficient tools to analyze high-dimensional data in the civil engineering arena. In this paper, high-dimensional data is referred to as a data set in which the number of features is comparable or larger than the number of observations. This review paper aims to summarize the applications of matrix and tensor decomposition methods in civil engineering over the last decade. The survey begins with a general overview of matrix and tensor decomposition followed by highlighting their significance in the field. Afterward, various applications of these high-dimensional data analytics methods in civil engineering are presented, while the advantages offered by these methods are discussed. Lastly, challenges and potential research avenues for employing matrix and tensor decomposition and future emerging trends for their novel use are highlighted.

42 ENGINEERING↗
DOE OSTI2022

Data for Metabolic Engineering of Oleaginous Yeast Rhodotorula toruloides for Overproduction of Triacetic Acid Lactone

The plant-sourced polyketide triacetic acid lactone (TAL) has been recognized as a promising platform chemical for the biorefinery industry. However, its practical application was rather limited due to low natural abundance and inefficient cell factories for biosynthesis. Here, we report the metabolic engineering of oleaginous yeast Rhodotorula toruloides for TAL overproduction. We first introduced a 2-pyrone synthase gene from Gerbera hybrida (GhPS) into R. toruloides and investigated the effects of different carbon sources on TAL production. We then systematically employed a variety of metabolic engineering strategies to increase the flux of acetyl-CoA by enhancing its biosynthetic pathways and disrupting its competing pathways. We found that overexpression of ATP-citrate lyase (ACL1) improved TAL production by 45% compared to the GhPS overexpressing strain, and additional overexpression of acetyl-CoA carboxylase (ACC1) further increased TAL production by 29%. Finally, we characterized the resulting strain I12-ACL1-ACC1 using fed-batch bioreactor fermentation in glucose or oilcane juice medium with acetate supplementation and achieved a titer of 28 or 23 g/L TAL, respectively. This study demonstrates that R. toruloides is a promising host for the production of TAL and other acetyl-CoA-derived polyketides from low-cost carbon sources.

Conversion↗
DOE OSTI2022

Data for Metabolic Engineering of Rhodotorula toruloides IFO0880 Improves C16 and C18 Fatty Alcohol Production from Synthetic Media

The oleaginous, carotenogenic yeast Rhodotorula toruloides has been increasingly explored as a platform organism for the production of terpenoids and fatty acid derivatives. Fatty alcohols, a fatty acid derivative widely used in the production of detergents and surfactants, can be produced microbially with the expression of a heterologous fatty acyl-CoA reductase. Due to its high lipid production, R. toruloides has high potential for fatty alcohol production, and in this study several metabolic engineering approaches were investigated to improve the titer of this product. Fatty acyl-CoA reductase from Marinobacter aqueolei was co-expressed with SpCas9 in R. toruloides IFO0880 and a panel of gene overexpressions and Cas9-mediated gene deletions were explored to increase the fatty alcohol production. Two overexpression targets (ACL1 and ACC1, improving cytosolic acetyl-CoA and malonyl-CoA production, respectively) and two deletion targets (the acyltransferases DGA1 and LRO1) resulted in significant (1.8 to 4.4-fold) increases to the fatty alcohol titer in culture tubes. Combinatorial exploration of these modifications in bioreactor fermentation culminated in a 3.7 g/L fatty alcohol titer in the LRO1Δ mutant. As LRO1 deletion was not found to be beneficial for fatty alcohol production in other yeasts, a lipidomic comparison of the DGA1 and LRO1 knockout mutants was performed, finding that DGA1 is the primary acyltransferase responsible for triacylglyceride production in R. toruloides , while LRO1 disruption simultaneously improved fatty alcohol production, increased diacylglyceride and triacylglyceride production, and increased glucose consumption. The fatty alcohol titer of fatty acyl-CoA reductase-expressing R. toruloides was significantly improved through the deletion of LRO1, or the deletion of DGA1 combined with overexpression of ACC1 and ACL1. Disruption of LRO1 surprisingly increased both lipid and fatty alcohol production, creating a possible avenue for future study of the lipid metabolism of this yeast.

Conversion↗
DOE OSTI2023

Data for Metabolic Engineering of Low-pH-Tolerant Non-Model Yeast, Issatchenkia orientalis , for Production of Citramalate

Methyl methacrylate (MMA) is an important petrochemical with many applications. However, its manufacture has a large environmental footprint. Combined biological and chemical synthesis (semisynthesis) may be a promising alternative to reduce both cost and environmental impact, but strains that can produce the MMA precursor (citramalate) at low pH are required. A non-conventional yeast, Issatchenkia orientalis , may prove ideal, as it can survive extremely low pH. Here, we demonstrate the engineering of I. orientalis for citramalate production. Using sequence similarity network analysis and subsequent DNA synthesis, we selected a more active citramalate synthase gene (cimA) variant for expression in I. orientalis . We then adapted a piggyBac transposon system for I. orientalis that allowed us to simultaneously explore the effects of different cimA gene copy numbers and integration locations. A batch fermentation showed the genome-integrated-cimA strains produced 2.0 g/L citramalate in 48 h and a yield of up to 7% mol citramalate/mol consumed glucose. These results demonstrate the potential of I. orientalis as a chassis for citramalate production.

Conversion↗
DOE OSTI2025

Data for Metabolic Engineering of β-Oxidation to Leverage Thioesterases for Production of 2-Heptanone, 2-Nonanone, and 2-Undecanone

Medium-chain length methyl ketones are potential blending fuels due to their cetane numbers and low melting temperatures. Biomanufacturing offers the potential to produce these molecules from renewable resources such as lignocellulosic biomass. In this work, we designed and tested metabolic pathways in Escherichia coli to specifically produce 2-heptanone, 2-nonanone and 2-undecanone. We achieved substantial production of each ketone by introducing chain-length specific acyl-ACP thioesterases, blocking the β-oxidation cycle at an advantageous reaction, and introducing active β-ketoacyl-CoA thioesterases. Using a bioprospecting approach, we identified 15 homologs of E. coli β-ketoacyl-CoA thioesterase (FadM) and evaluated the in vivo activity of each against various chain length substrates. The FadM variant from Providencia sneebia produced the most 2-heptanone, 2-nonanone, and 2-undecanone, suggesting it has the highest activity on the corresponding β-ketoacyl-CoA substrates. We tested enzyme variants, including acyl-CoA oxidases, thiolases, and bi-functional 3-hydroxyacyl-CoA dehydratases to maximize conversion of fatty acids to β-keto acyl-CoAs for 2-heptanone, 2-nonanone, and 2-undecanone production. In order to address the issue of product loss during fermentation, we applied a 20% (v/v) dodecane layer in the bioreactor and built an external water cooling condenser connecting to the bioreactor heat-transferring condenser coupling to the condenser. Using these modifications, we were able to generate up to 4.4 g/L total medium-chain length methyl ketones.

Metabolic Engineering↗