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At least 37 records · Page 2

Utility-Scale Power Tower Solar Systems: Performance Acceptance Test Guidelines

The purpose of these Guidelines is to provide direction for conducting performance acceptance testing for large power tower solar systems that can yield results of a high level of accuracy consistent with good engineering knowledge and practice. The recommendations have been developed under a National Renewable Energy Laboratory (NREL) subcontract and reviewed by stakeholders representing concerned organizations and interests throughout the concentrating solar power (CSP) community. An earlier NREL report provided similar guidelines for parabolic trough systems. These Guidelines recommend certain methods, instrumentation, equipment operating requirements, and calculation methods. When tests are run in accordance with these Guidelines, we expect that the test results will yield a valid indication of the actual performance of the tested equipment. But these are only recommendations--to be carefully considered by the contractual parties involved in the Acceptance Tests--and we expect that modifications may be required to fit the particular characteristics of a specific project.

14 SOLAR ENERGY↗

ORD FWP NRAP FY13 Annual Report

The U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) is conducting research to advance the science and engineering knowledge base for technologies that will accelerate the business case for CO 2 capture and storage, including prediction and quantification of risks that may relate to potential liabilities. As part of this effort, NETL, through its Office of Research and Development (ORD), is leading a multi-laboratory effort that leverages broad technical capabilities across the DOE complex: the National Risk Assessment Partnership (NRAP). NRAP involves five DOE national laboratories: NETL, Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), Los Alamos National Laboratory (LANL), and Pacific Northwest National Laboratory (PNNL). This team is working together to develop a science-based method for quantifying the likelihood of risks (and associated potential liabilities) for CO 2 storage sites.

20 FOSSIL-FUELED POWER PLANTS↗

Investigating the Fundamental Scientific Issues Affecting the Long-term Geologic Storage of Carbon Dioxide

The Zero Emissions Research and Technology (ZERT) collaborative was formed to address basic science and engineering knowledge gaps relevant to geologic carbon sequestration. The original funding round of ZERT (ZERT I) identified and addressed many of these gaps. ZERT II has focused on specific science and technology areas identified in ZERT I that showed strong promise and needed greater effort to fully develop.

54 ENVIRONMENTAL SCIENCES↗

Tools for Design and Scale-Up of Solar Thermochemical Reactors: Cooperative Research and Development Final Report, CRADA Number CRD-13-00530

NREL will be collaborating with the Participant on a United States - Australia Solar Energy Collaboration (USASEC) Project Number 1-USO034 "Tools for design and scale-up of solar thermochemical reactors." The grant funds for the Participant's 3.5 year project number 1-US034 commencing on 1 February 2013 have been awarded to the Participant by the Australian Renewable Energy Agency and NREL will be collaborating with the Participant during the final 28 months of this project. This project seeks to provide basic knowledge required to design solar thermochemical reactors able to perform the required energy conversions. In several proposed and demonstrated reactors, concentrated sunlight directly irradiates small solid particles suspended in fluid, enabling very high heat transfer rates to the particles which are the sites of chemical reaction. The reactors, therefore, involve the complex and couple dynamics of turbulent, chemically reacting, particle-laden flows and their interaction with concentrating solar radiation. A strong understanding of these coupled interactions will be crucial important in predicting and optimizing the performance of prototype reactors, but this understanding does not yet exist, since they have never been studied in any fundamental way. The project has a assembled an internationally leading team from The University of New South Wales (UNSW) and the University of Adelaide in Australia and the NREL in the United States to address this key gap in available know-how. The project will use U.S. Dept. of Energy (DOE) supercomputers, among the most powerful available worldwide, with cutting-edge software tools to perform first-principles simulations of the relevant interactions. These studies will be combined with detailed laser-based measurements in Australia to provide the first comprehensive databases concerning the governing phenomena in directly irradiated solar-thermochemical reactors. The outcomes will be the basic scientific knowledge, engineering knowhow and modeling tools necessary to design new reactor concepts and then scale up from the laboratory bench to practical size systems.

14 SOLAR ENERGY↗

The Astronomy Commons Platform: A Deployable Cloud-based Analysis Platform for Astronomy

Abstract We present a scalable, cloud-based science platform solution designed to enable next-to-the-data analyses of terabyte-scale astronomical tabular data sets. The presented platform is built on Amazon Web Services (over Kubernetes and S3 abstraction layers), utilizes Apache Spark and the Astronomy eXtensions for Spark for parallel data analysis and manipulation, and provides the familiar JupyterHub web-accessible front end for user access. We outline the architecture of the analysis platform, provide implementation details and rationale for (and against) technology choices, verify scalability through strong and weak scaling tests, and demonstrate usability through an example science analysis of data from the Zwicky Transient Facility’s 1Bn+ light-curve catalog. Furthermore, we show how this system enables an end user to iteratively build analyses (in Python) that transparently scale processing with no need for end-user interaction. The system is designed to be deployable by astronomers with moderate cloud engineering knowledge, or (ideally) IT groups. Over the past 3 yr, it has been utilized to build science platforms for the DiRAC Institute, the ZTF partnership, the LSST Solar System Science Collaboration, and the LSST Interdisciplinary Network for Collaboration and Computing, as well as for numerous short-term events (with over 100 simultaneous users). In a live demo instance, the deployment scripts, source code, and cost calculators are accessible. 4 4 http://hub.astronomycommons.org/

79 ASTRONOMY AND ASTROPHYSICS↗

Experimental and Computational Investigations of Plenum-to-Plenum Heat Transfer and Gas Dynamics under Natural Circulation in a Prismatic Very High Temperature Reactor

All the goals and the objectives set for the project were successfully executed and achieved and all the milestones have been successfully completed. The results that have been obtained for the first time advance the scientific and engineering knowledge and understanding of the plenum-to plenum natural convection of prismatic block nuclear reactors that is encountered during accident or abnormal operation. These have been accomplished by developing and implementing for the first time unique and flexible scaled-down separate and integrated effects experimental plenumto- plenum facility (P2PF) with dual channels at this time that has been equipped with sophisticated measurement techniques integrated in a novel way on the heated and cooled channels. The unique facility is an asset now that can be extended to research multiple channels and to study the effects of hot plumes in the plena for future projects if funding will be available. It can also be modified to research natural convection of pebble bed reactors. Hence, it complement the HTTF at Oregon State University. However, in this study, heat transfer coefficients from the inner wall surface to the flowing gas (both helium and air were used) and the radial temperature and gas velocity profiles have been measured and investigated along the height of the heated and cooled channels using in house developed wall flush mounted heat transfer probes, thermocouple with in house developed adjuster for radial movement with 1 mm increment inside the channel and hot wire anemometry with also in house developed adjuster for 1 mm radial movement inside the channel, respectively. Also advanced tracer technique has been developed to quantify also for the first time the dispersion of the gas dynamics of the hot and cold channels. The research has provided new knowledge and new benchmarking data that can be used to validate computational fluid dynamics (CFD) codes with conjugate heat transfer. The work and its results that have been performed within the budget have demonstrated their superior technical effectiveness and high economic feasibility to perform needed studies for safety analysis and assessment at least cost for these types of gas cooled very high temperature 4th generation nuclear reactors. Accordingly, the results obtained in this project and the unique facility and techniques that have been developed will benefit greatly the public by advancing the technology of the prismatic block nuclear reactors toward commercialization and to ensure they will be designed and operated safely by utilizing the obtained knowledge and having well validated CFD simulations integrated with heat transfer computations

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Public Resistance is Waste-Based and What to Do About That - 13412

'Nuclear Communicators' connect a highly advanced sci-tech world with the world of everyday living. One challenge is helping stakeholders fit together three Big Ideas: (1) the valuable nuclear energy resource, (2) nuclear energy's invisible mortal dangers and potential bad-guy threats, and (3) critical scientific and engineering knowledge that is far over the heads of average (grade 8) USA reading levels. This article provides an overview of what does - and does not - work in our public communications. What does not work: 1. Going off topic. Address what concerns people most: how to manage nuclear wastes. 2. Underestimating public intelligence. What works: 1. Doing your homework on community history regarding nuclear materials. 2. Having discussion forums and public meetings with special guests from industry, government, and local leaders. 3. Regular cycles of communication with community groups to build a long-term dialogue. Solutions to some communication challenges require handling four gaps and one jungle: 1. Facts gap - Honest information about waste disposition. 2. Time gap - The timelines associated with waste management include 300 years, 10,000 years, and 703.8 million years. How do we talk about this? 3. Money gap - Who pays for new waste management challenges and technologies? 4. Confidence gap - Link local options to regional, national, and global knowledge. 5. Decision jungle - How can we make waste management rules and infrastructure more logical and transparent? Public communication needs to be grounded in facts for people who want to be credible actors in the new nuclear world. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗

A Model Based Approach to Extract Health Information from Textual Data

In current nuclear power plants (NPPs) a large amount of condition-based data is being generated and stored to assess and monitor component health and performance. The format of this data can be either numeric (e.g., pump vibration data) or textual (e.g., condition report which assess component health). While assessing component health from numeric data can be performed with a large variety of methods, the extraction of information from textual data still remains a challenge. Natural language processing (NLP) methods are starting to be deployed in current NPPs mainly to filter out incident reports (IRs) that are not safety related by employing supervised machine learning methods. However, these methods do not really provide the quantitative information that might be contained in IRs. This paper presents an approach to extract information from textual data (e.g., from IRs, maintenance reports) that is based on NLP data analytics methods coupled with model-based system engineer (MBSE) models. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence; such analysis includes: part of speech (POS) tagging (i.e., identification of grammatic elements of each string - e.g., nouns, verbs), named entity recognition (i.e., identification of text entities - e.g., names, dates, events), and relation extraction (e.g., coreference resolution). On the other hand, semantic analysis is designed to analyze the logic structure of a sentence. Through a specific set of rules, our methods can identify whether a sentence contains health information of a component (e.g., degraded performance, anomaly behavior) or the causal relationship between two events (i.e., a cause-effect pair). An innovative element of our approach is that semantic analysis relies on MBSE models to identify links between textual elements. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. This paper presents in detail how the integration of NLP methods and MBSE models is performed. Few analysis examples focusing on centrifugal pumps are presented.

97 - MATHEMATICS AND COMPUTING↗

Engineering integration in elementary science classrooms: Effects of disciplinary language scaffolds on English learners' content learning and engineering identity

Abstract Background Limited research examines the effects of integrated science and engineering (SE) instruction emphasizing disciplinary literacy and language activities on engineering identity and content understanding. Far fewer studies target English learners (ELs). Purpose The impact of an SE intervention on the development of science, engineering, and technology knowledge as well as engineering identity was examined. To address ELs' learning needs, the curricular design was built on a validated SE model by integrating (1) developmental, (2) language scaffolds, and (3) culturally based accommodations. Design/Method Separate analysis of variance examined the effects of the intervention on science, engineering, and technology knowledge as well as engineering identity. The relationship among engineering identity and content outcomes was also examined. ELs from kindergarten to second grade classrooms were randomly assigned to the integrated SE group or control group. Results Integrated SE instruction significantly increased ELs' science, engineering, and technology knowledge as well as a substantially developed engineering identity. Overall, ELs' engineering identity is associated with an increase in science, engineering, and technology content knowledge. However, second grade girls' identity development was not associated with learning measures. These correlations suggest the context of the engineering activity may have reinforced gendered stereotypes and reduced the effects for girls' engineering attitudes. Conclusions Integrated SE instruction emphasizing disciplinary literacy and cultural accommodations increases early elementary ELs' learning and engineering identity. Future studies should examine the unique effects of language scaffolds and cultural modifications on student learning and the impact of gender stereotypes on girls' engineering attitudes.

Aguirre‐Muñoz, Zenaida↗

Grid Energy-Storage Projects: Engineers Building and Using Knowledge in Emerging Projects

At this stage of the rapidly evolving role of grid-connected battery energy-storage systems (BESSs), actual experience with grid-connected BES projects is limited as is experience in how to control these complex systems for effective application and economic benefits. Furthermore our coauthors share their unique experiences of working at the forefront of BESs on the grid: an emerging and expanding new tool being applied across all grid domains, e.g., energy production, transmission, distribution, and utilization.

25 ENERGY STORAGE↗

Synthetic Microbial Consortium for Biological Breakdown and Conversion of Lignin

The plant polymer lignin is the most abundant renewable source of aromatics on the planet and conversion of it to valuable fuels and chemicals is critical to the economic viability of a lignocellulosic biofuels industry and to meeting the DOE’s 2022 goal of $\$2.50$/gallon mean biofuel selling price. Presently, there is no efficient way of converting lignin into valuable commodities. Current biological approaches require mixtures of expensive ligninolytic enzymes and engineered microbes. This project was aimed at circumventing these problems by discovering commensal relationships among fungi and bacteria involved in biological lignin utilization and using this knowledge to engineer microbial communities capable of converting lignin into renewable fuels and chemicals. Essentially, we aimed to learn from, mimic and improve on nature. We discovered fungi that synergistically work together to degrade lignin, engineered fungal systems to increase expression of the required enzymes and engineered organisms to produce products such as biodegradable plastics precursors.

09 BIOMASS FUELS↗

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗

Enhancing Knowledge Capture and Management within a Knowledge-Driven Workforce [Capstone Project]

This project aimed to understand how the collective knowledge of a group of engineers or ‘knowledge-based workers’ can be baselined through use of qualitative questionnaires based on input from subject matter experts. The results of the questionnaires were then used to inform management and the group where to focus efforts on terms of training and mentoring. With use of re-administering the qualitative questionnaire to gauge progress over time. Additionally, the development a knowledge-based wiki was explored to help shape group culture from one where ideas and knowledge tend to be siloed, into one where information is traded freely across the group and with external customers and collaborators.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Engineering Yeast for the Production of Acids and Esters (CRADA Final Report)

NREL and Lygos have a mutual interest in engineering yeast for the production of acids and esters as intermediates towards fuels or bioproducts. In particular, the yeast Pichia kudriavzevii is a fantastic candidate for the production of acid and ester based products including butyric acid and ethyl butyrate. However, the strain is relatively uncommon in the biotechnological space and as such there are not an abundant amount of genetic tools that are publically available. However, Lygos has been working with this species in particular for several years and has developed genetic tools and physiological characteristics and are well versed in engineering this strain for acid based products. While currently focusing on malonic acid-derived products, Lygos is not currently targeting butyric acid and ethyl butyrate but have a long term interest in these molecules and derivatives therein. This CRADA proposal seeks to leverage NREL’s immediate focus on butyric acid and ethyl butyrate, and general bioprocess expertise with Lygos’ specific knowledge on engineering P. kudriavzevii for the production of acid-based products.

09 BIOMASS FUELS↗

Interdisciplinary research in climate and energy sciences

Due to the complex nature of climate change, interdisciplinary research approaches involving knowledge and skills from a broad range of disciplines have been adopted for studying changes in the climate system as well as strategies for mitigating climate change (i.e., greenhouse gas emissions reductions) and adapting to its impacts on society and natural systems. Harnessing of renewable energy sources to replace fossil fuels is widely regarded as a long-term mitigation strategy that requires the synthesis of knowledge from engineering, technology, and natural and social sciences. In this study, we examine how the adoption of interdisciplinary approaches has evolved over time and in different geographic regions. We conducted a comprehensive literature survey using an evaluation matrix of keywords, in combination with a word cloud analysis, to evaluate the spatiotemporal dynamics of scholarly discourse about interdisciplinary approaches to climate change and renewable energy research and development (R&D). Publications that discuss interdisciplinary approaches to climate change and renewable energy have substantially increased over the last 60 years; it appears, however, that the nature, timing, and focus of these publications vary across countries and through time. Over the most recent three decades, the country-level contribution to interdisciplinary research for climate change has become more evenly distributed, but this was not true for renewable energy research, which remained dominated by the United Sates and a few other major economies. The research topics have also evolved: Water resource management was emphasized from 1990s to 2000s, policy and adaptation were emphasized from the 2000s to 2010 – 2013, while vulnerability became prominent during the most recent years (2010 – 2013). Lastly, our analysis indicates that the rate of growth of interdisciplinary research for renewable energy lags behind that for climate change, possibly because knowledge emanating from climate change science has motivated the subsequent upswing in renewable energy R&D.

54 ENVIRONMENTAL SCIENCES↗