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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Chapter 14 - Reliability of Wind Turbines

The global wind energy industry has grown at a fast pace during the past half-decade. Advancements from design and manufacturing to operation and maintenance have led to reduced capital and maintenance costs, which make wind power an indispensable source for a comprehensive solution to global electricity needs. Once wind turbines are installed, the opportunity to lower wind power costs is mainly through improved operation and maintenance practices. Modern wind turbines are equipped with tens or hundreds of measurement channels and are generating an abundance of data, with lot of efforts being put into data analysis by both the research community and the industry. One type of analysis is through the exploration of reliability engineering methods based on readily available data or maintenance records collected at typical wind power plants. If adopted and conducted appropriately, these analyses can quickly save operation and maintenance costs in a potentially impactful manner. The wind industry has adopted this discipline more broadly in recent years. This chapter discusses wind turbine reliability by highlighting the methodology of reliability engineering life data analysis. It first briefly discusses the fundamentals of wind turbine reliability and the current industry status. Then, the reliability engineering method for life analysis, including data collection, model development, and forecasting, is presented in detail and illustrated through two case studies. The chapter concludes with some remarks on potential opportunities to improve wind turbine reliability. An owner and operator's perspective is taken and mechanical components are used to exemplify the potential benefits of reliability engineering analysis to improve wind turbine reliability and availability.

database↗
DOE OSTI2020

Power-oriented bus encoding for data transmission

Systems, apparatuses, and methods for reducing the toggle rates on buses are disclosed. A computing system includes a source which provides packets for transmission on a bus. The packet is compressed by a compression engine. The compressed data format of the packet includes locations (bit positions) referred to as holes which do not include valid data. A bus configuration module identifies the locations of the holes and replaces the holes with information from a previous packet transmitted earlier on the bus. The bus configuration module also determines a new transmission bus width for the packet for lowering the bus toggle rate on the bus during transmission.

42 ENGINEERING↗
DOE OSTI2022

Sensing Electrical Networks Securely & Economically (SENSE)

The growing adoption of distributed energy resources (DERs) like battery energy storage systems and roof top solar/PV and the rapid penetration of electric vehicles (EVs), the electric grid is undergoing a major transformation with elevated stress on legacy grid assets. Despite a lot of expenditure to address these challenges, both in dollars and manpower, utilities have not been able to receive the value that was promised. The gains have been most visible at the transmission and substation level, especially where the main objective was improving operational and economic efficiency for the utility. Improving visibility and control at a few select points enhances the existing and established paradigm of centralized command and control. With changing load patterns, load types and the overall transition to an “active grid”, the centralized control and coordination paradigm gets challenged. To address the challenges, a new architecture and mechanism is needed, one that supports decentralized control and decision making, extracting value streams at the grid edge, particularly as the changes are fueled by transitions occurring in the distribution system. To address this, a communications and data processing platform, “GAMMA” was developed and demonstrated through the project. At the heart of the platform, are distributed, intelligent edge nodes with sensing and compute capabilities, that can record and analyze information locally. They are embedded in sensors and actuators specific to different distribution system applications. Phase 1 of the project focused on developing novel sensor technology that can be used for monitoring utility pole top distribution transformers. The sensors were designed with the objective of being low-cost, communicating with the GAMMA cloud using novel “delay-tolerant” networking using Bluetooth and a secure mobile application. They were non-intrusive in nature so that they can be installed quickly in the field, resulting in overall low cost of deployment and operations. Following the successful completion of Phase 1, the team manufactured 100 units for a field demonstration in Phase 2. The field demonstration was carried out on two real feeder systems with the local utility partner. In total, 100 sensors were installed and operated over a period of 6 months in the state of Georgia. The platform is operational end to end, with the cloud infrastructure deployed on a distributed, serverless environment that can serve multiple data streams, an analytics engine and a portal to securely view the data from multiple assets. The data collected through the GAMMA Mobile Phone app showcased the viability of the novel delay tolerant networking architecture, and the data processing algorithms developed through the course of the project, were successful in extracting important information about the overall network, improving the utility’s visibility and situational awareness in the distribution feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Effects of Critical Compression Ratio on Rating Gasoline Knock Propensity

It is common practice in the automotive industry to explore the knock limits of fuels on an engine by a comparison of the knock limited spark advance (KLSA) at threshold knock intensity. However, the knock propensity of gasolines can be rated by changing one of three metrics on a variable compression ratio Cooperative Fuels Research (CFR) octane rating engine while holding the other two variables constant: knock intensity, spark timing, and critical compression ratio. The operational differences between the standard research octane number (RON) rating and modern engine operation have been explored in three parts. The first part focused on the effects of lambda and knock characterization. The second part studied the effects of spark timing. This third part explores the knock ratings of several gasolines by comparing the critical compression ratios at constant combustion phasing and knock intensity. The threshold knock intensity was based on the standard octane rating D1 pickup or by maximum amplitude of pressure oscillations (MAPO) measured by a piezoelectric cylinder pressure transducer. Several Fuels for Advanced Combustion Engines (FACE) gasolines, primary reference fuels (PRFs), and toluene standardization fuels (TSFs) were tested on a CFR octane rating engine with advanced data acquisition equipment and a piezoelectric cylinder pressure transducer. These tests deviated from the ASTM D2699 standard octane rating procedure. For each test fuel, the CFR engine was operated at stoichiometry at a constant combustion phasing (CA50) and the compression ratio was modified until a threshold knock intensity was realized. It was found that the chemical composition of the fuels affected the relationship of critical compression ratios between the D1 knockmeter and piezoelectric pressure transducer knock intensity thresholds, as well as the measured combustion maximum pressure rise rate and spark timing setting for constant CA50. For highly aromatic fuels tested at a constant MAPO knock intensity threshold, it was found that the maximum pressure rise rate was two to three times higher than that of highly paraffinic fuels with similar RON and the spark advance was several crank angle degrees less for constant combustion phasing.

Kolodziej, Christopher P↗
DOE OSTI2023

Persistence Control of Engineered Functions in Complex Soil Microbiomes (PerCon SFA), Secure Biosystems Design Project Data Catalog at PNNL DataHub

The Persistence Control of Engineered Functions in Complex Soil Microbiomes Project (PerCon SFA) at Pacific Northwest National Laboratory (PNNL) is a Genomic Sciences Program Biosystems Design, Science Focus Area research project consortium. Collaborating across highly integrated institutions, PerCon SFA scientists are exploring how environmental niches can be sculpted using the mechanisms of genome reduction and metabolic addiction to drive secure rhizosphere community design for robust biomass cropping in challenging environments. The PerCon SFA DataHub project repository contains publication-relevant digital dataset and metadata DOI packages, enabling exploration and download of integrated experimental dataset catalogs publicly available to a global scientific community. and metadata repository allow for exploring and downloading integrated experimental biodesign omics dataset catalogs, including experimental protocols and/or workflows, raw and/or processed data, as required by the repository, and other relevant supporting materials and/or metadata required for research reproducibility and reporting.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Performance of direct injected propane and gasoline in a high stroke-to-bore ratio SI engine: Pathways to diesel efficiency parity with ultra low soot

This work explores pathways to achieve diesel-like, high-efficiency combustion with stoichiometric 3-way catalyst compatible combustion in a single-cylinder spark ignition (SI) research engine. A unique high stroke-to-bore engine design (1.5:1) with cooled exhaust gas recirculation (EGR) and high compression ratio ( r c ) was used to improve engine efficiency by up to 30% compared with a production turbocharged gasoline direct injection spark ignition engine. Engine experiments were conducted with both 91 RON E10 gasoline and liquified petroleum gas (LPG) (i.e. autogas) and were compared to legacy gasoline data on the production engine. Geometric compression ratio ( r c ) of 13.3:1 was used for both fuels with additional experiments at 16.8:1 for LPG only. Measurements of exhaust soot particle size and number concentrations were made with both fuels. Significant reduction in soot particles across the whole particle size range were achieved with LPG due to the elimination of in-cylinder liquid films. The effects of EGR, late intake valve closing (IVC) and fuel characteristics were investigated through their effects on efficiency, combustion stability and soot production. Results of 47% gross thermal efficiency, and 45% net thermal efficiency at stoichiometric engine operation, at up to 17 bar IMEP and 2000 r/min with 16.8:1 r c were achieved with LPG. Estimated brake efficiency values were compared to a contemporary medium duty diesel engine illustrating the benefits of the chosen path for achieving diesel efficiency parity.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

PanDA: Production and Distributed Analysis System

The Production and Distributed Analysis (PanDA) system is a data-driven workload management system engineered to operate at the LHC data processing scale. The PanDA system provides a solution for scientific experiments to fully leverage their distributed heterogeneous resources, showcasing scalability, usability, flexibility, and robustness. The system has successfully proven itself through nearly two decades of steady operation in the ATLAS experiment, addressing the intricate requirements such as diverse resources distributed worldwide at about 200 sites, thousands of scientists analyzing the data remotely, the volume of processed data beyond the exabyte scale, dozens of scientific applications to support, and data processing over several billion hours of computing usage per year. PanDA’s flexibility and scalability make it suitable for the High Energy Physics community and wider science domains at the Exascale. Beyond High Energy Physics, PanDA’s relevance extends to other big data sciences, as evidenced by its adoption in the Vera C. Rubin Observatory and the sPHENIX experiment. As the significance of advanced workflows continues to grow, PanDA has transformed into a comprehensive ecosystem, effectively tackling challenges associated with emerging workflows and evolving computing technologies. The paper discusses PanDA’s prominent role in the scientific landscape, detailing its architecture, functionality, deployment strategies, project management approaches, results, and evolution into an ecosystem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Physics guided machine learning using simplified theories

Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines. The chief idea in our approach is to augment the knowledge of the simplified theories with the underlying learning process. To emphasize their physical importance, our architecture consists of adding certain features at intermediate layers rather than in the input layer. To demonstrate our approach, we select a canonical airfoil aerodynamic problem with the enhancement of the potential flow theory. We include the features obtained by a panel method that can be computed efficiently for an unseen configuration in our training procedure. By addressing the generalizability concerns, our results suggest that the proposed feature enhancement approach can be effectively used in many scientific machine learning applications, especially for the systems where we can use a theoretical, empirical, or simplified model to guide the learning module.

42 ENGINEERING↗
DOE OSTI2021

NuScale Systems Engineering Program Overview and Status

NuScale systems engineering program overview, including technical data management and model-based engineering, product structures, architecture views, and model-based systems engineering. Includes a discussion on technical risk and opportunity management and technical readiness levels, as well as risk-informed performance-based principles and methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Privacy policy robustness to reverse engineering

Differential privacy policies allow one to preserve data privacy while sharing and analyzing data. However, these policies are susceptible to an array of attacks. In particular, often a portion of the data desired to be privacy protected is exposed online. Access to these pre-privacy protected data samples can then be used to reverse engineer the privacy policy. With knowledge of the generating privacy policy, an attacker can use machine learning to approximate the full set of originating data. Bayesian inference is one method for reverse engineering both model and model parameters. We present a methodology for evaluating and ranking privacy policy robustness to Bayesian inference-based reverse engineering, and demonstrated this method across data with a variety of temporal trends.

Kusne, Aaron Gilad↗
DOE OSTI2025

Engineered Accumulation of Protocatechuate in Corn Biomass to Enhance Biomanufacturing

The in-planta accumulation of coproducts in crops can enhance the value of lignocellulosic biomass and facilitate a sustainable bioeconomy. Corn stover represents a major renewable source of lignocellulose for the production of advanced biofuels and bioproducts. In this study, we engineered corn with a bacterial gene encoding a dehydroshikimate dehydratase (QsuB) to overproduce protocatechuate (DHBA). Transgenic corn lines accumulate up to 2.9% DHBA on a dry weight basis in leaf and stem biomass. DHBA occurs in the form of glucosides that are extractable from biomass using aqueous methanol as the solvent. The analysis of lignin did not show any evidence for the incorporation of DHBA; however, an increase in the lignin syringyl to guaiacyl ratio and a higher relative abundance of p-coumarate groups compared with total lignin units were observed in QsuB-modified corn. Alkaline hydrolysates prepared from QsuB corn were enriched in DHBA compared to the hydrolysates obtained from wild-type biomass, which contained mostly p-coumarate and ferulate. Using engineered Novosphingobium aromaticivorans as a production host, a 375% improvement in 2-pyrone-4,6-dicarboxylate titers was achieved through biological upgrading of alkaline hydrolysates derived from QsuB corn compared to unmodified biomass. Our data demonstrate an engineering strategy to overproduce DHBA in corn that can facilitate sustainable manufacturing of other valuable bioproducts using stover as a feedstock.

2-pyrone-4,6-dicarboxylate↗
DOE OSTI2022

Employing Technology to Enable Remote Research Charrettes as a Method for Engaging Industry and Uncovering Best Practices: A Novel Approach for a Post-COVID-19 World

Methods to collect data in construction engineering and management (CEM) research are evolving, informed by recent technological advancements. One such method is research charrettes that allow effective interactions and knowledge sharing between expert industry practitioners and academic researchers, all colocated in a single venue, enabling rich data collection and live communication. A pivot point in technological evolution occurred with the COVID-19 pandemic, forcing a global shift to remote work. Hence, planned in-person research charrettes had to shift to remote sessions, relying on virtual conferencing platforms and online data collection mechanisms. Technology-enabled charrettes have allowed the authors to collect significantly richer data sets and ensure a more diverse representation of participants, while saving tremendous amounts of time. With the continuing emergence of technological applications, the world might not go back to functioning fully in person. The authors believe remote research charrettes (RRCs) will still be used in a post-COVID-19 world because of their superior performance. This paper builds on a previous publication that described traditional research charrettes as a method to enhance CEM research a decade ago; it offers a significantly updated and improved RRC method based on the knowledge gained from transitioning a dozen in-person charrettes into RRCs. It also presents performance comparisons between RRCs and traditional charrettes by quantifying metrics indicating how RRCs are more time-efficient and cost-saving, harness more participants from more diverse locations, and enable the collection of richer data sets and four times more industry comments and expert feedback. This paper also provides guidance on the integration of technology with traditional research charrettes, hence contributing to the CEM body of knowledge.

42 ENGINEERING↗
DOE OSTI2021

A Rapid Compression Machine Study of 2-Phenylethanol Autoignition at Low-To-Intermediate Temperatures

To meet the increasing anti-knocking quality demand of boosted spark-ignition engines, fuel additives are considered an effective approach to tailor fuel properties for satisfying the performance requirements. Thus, screening/developing bio-derived fuel additives that are best-suited for advanced spark-ignition engines has become a significant task. 2-Phenylethanol (2-PE) is an attractive candidate that features high research octane number, high octane sensitivity, low vapor pressure, and high energy density. Recognizing that the low temperature autoignition chemistry of 2-PE is not well understood and the need for fundamental experimental data at engine-relevant conditions, rapid compression machine (RCM) experiments are therefore conducted herein to measure ignition delay times (IDTs) of 2-PE in air over a wide range of conditions to fill this fundamental void. These newly acquired IDT data at low-to-intermediated temperatures, equivalence ratios of 0.35–1.5, and compressed pressures of 10–40 bar are then used to validate the 2-PE model developed by Shankar et al. (2017). It is found that this literature model greatly overpredicts the current RCM data. The comparison of experimental and simulated results also provides insights into 2-PE autoignition behaviors at varying conditions. Further chemical kinetic analyses demonstrate that the absence of the O2-addition pathway of β-R. radical in the 2-PE model of Shankar et al. (2017) could account for the model discrepancies observed at low-to-intermediated temperatures.

Fang, Ruozhou (ORCID:0000000348315100)↗
DOE OSTI2024

SMART Deliverable 6.1.2a: Application of the ORION tool to the IBDP Carbon Storage Site

Forecasting and managing potential induced seismic activity is one of the challenges facing commercialscale geologic carbon sequestration (GCS), as well as other geologic energy extraction and byproduct disposal technologies. Historically, the process to develop robust, science-based forecasts of induced seismicity has required an integrated effort from experts in seismology, geomechanics, and reservoir engineering to manage data, develop and evaluate models of subsurface processes, and to calibrate and interpret the results from a range of models to understand site behavior relative to prescribed standards and in the context of uncertainty in geologic characterization data, forecasting models, and operational scenario uncertainty. The Operational Forecasting of Induced Seismicity (ORION) toolkit is an open-source, observation-based forecasting toolkit that is being co-developed by two U.S. DOE-funded initiatives: the National Risk Assessment Partnership (NRAP) and the Scienceinformed Machine Learning for Accelerating Real Time Decisions in Subsurface Applications (SMART) Initiative. ORION is designed to provide functionality to support decision making about seismic hazard analysis and risk management for GCS stakeholders ranging from the public to site operators to expert seismologists. The tool, which is written as open-source code in the Python programming language, is composed of a desktop graphical user interface (GUI) and an underlying forecasting engine. The forecasting engine uses available reservoir properties, well and fluid injection scenario details, and observed seismic catalog data as inputs to produce a set of temporal and spatio-temporal seismic forecasts.

58 GEOSCIENCES↗
DOE OSTI2025

Data for "Land-based Resources for Engineered Carbon Dioxide Removal in the United States Exceed the Expected Needs"

Gigatonne-scale atmospheric carbon dioxide removal (CDR), alongside deep emission cuts, is critical to stabilizing the climate. However, some of the most scalable CDR technologies are also the most land intensive. Here, we examine whether adequate land resources exist in the contiguous United States to meet CDR targets when prioritizing grid emissions reduction, food production, and the protection of sensitive ecosystems. We focus on biomass carbon removal and storage (BiCRS) and direct air capture and storage (DACS) and show that suitable lands exceed the expected needs: 37.6 million hectares of land are available for BiCRS, resulting in 0.26 GtCO2 of CDR/year, and 34 million hectares are suitable for wind- and solar-powered DACS, resulting in 4.8 GtCO2 of CDR/year if facilities are co-located with geologic CO2 storage. We identify biomass and energy supply hotspots to meet CDR targets while ensuring land protection and minimizing land competition.

carbon↗
DOE OSTI2020

Data from Enhanced 2′-Fucosyllactose Production by Engineered Saccharomyces cerevisiae using Xylose as a Co-Substrate

2′-Fucosyllactose (2′-FL), a human milk oligosaccharide with confirmed benefits for infant health, is a promising infant formula ingredient. Although Escherichia coli , Saccharomyces cerevisiae , Corynebacterium glutamicum , and Bacillus subtilis have been engineered to produce 2′-FL, their titers and productivities need be improved for economic production. Glucose along with lactose have been used as substrates for producing 2′-FL, but accumulation of by-products due to overflow metabolism of glucose hampered efficient production of 2′-FL regardless of a host strain. To circumvent this problem, we used xylose, which is the second most abundant sugar in plant cell wall hydrolysates and is metabolized through oxidative metabolism, for the production of 2′-FL by engineered yeast. Specifically, we modified an engineered S. cerevisiae strain capable of assimilating xylose to produce 2′-FL from a mixture of xylose and lactose. First, a lactose transporter (Lac12) from Kluyveromyces lactis was introduced. Second, a heterologous 2′-FL biosynthetic pathway consisting of enzymes Gmd, WcaG, and WbgL from E. coli was introduced. Third, we adjusted expression levels of the heterologous genes to maximize 2′-FL production. The resulting engineered yeast produced 25.5 g/L of 2′-FL with a volumetric productivity of 0.35 g/L∙h in a fed-batch fermentation with lactose and xylose feeding to mitigate the glucose repression. Interestingly, the major location of produced 2′-FL by the engineered yeast can be changed using different culture media. While 72% of the produced 2′-FL was secreted when a complex medium was used, 82% of the produced 2′-FL remained inside the cells when a minimal medium was used. As yeast extract is already used as food and animal feed ingredients, 2′-FL enriched yeast extract can be produced cost-effectively using the 2′-FL-accumulating yeast cells.

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