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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Jefferson Laboratory C100 Superconducting Radio-Frequency Cavity Fault Data, 2020

The dataset was created to train machine learning models for the task of identifying the (1) cavity and (2) fault type from C100-type cryomodules at the Thomas Jefferson National Accelerator Facility (Jefferson Lab), thereby replacing the time-consuming efforts of a subject matter expert. Superconducting radio-frequency (SRF) cavity trips represent a significant source of accelerator downtime. Real-time – rather than post-mortem – identification of the offending cavity and classification of the fault type would give control room operators valuable feedback for corrective action planning. The anticipated benefit is increased beam-on-target time for users and provides performance metrics that can be used to improve future cavity designs. A series of 17 RF signals are recorded for each of the 8 cavities in a C100 cryomodule every time a cavity trips. These time-series signals are written to file using a specially designed data acquisition system. The dataset represents fault events recorded during Continuous Electron Beam Accelerator Facility (CEBAF) beam operations between January 18, 2019 and March 9, 2020. The following filtering steps were applied to collected data; (1) only 4 of the 17 signals per cavity are retained (GMES, GASK, CRFP, DETA2) (2) only events with data from each of the eight cavities in the cryomodule are kept, (3) only events that were sampled at 5 kHz were kept, (4) events from cryomodule 0L04 were neglected, (5) events occurring between February 4, 3PM and February 5, 12PM were neglected. As a result of preprocessing, the dataset is comprised of 2,375 unique events. The full dataset is comprised of three files: features.csv, cavity_labels.csv, fault_labels.csv. Each instance in faults.csv includes a timestamp (“date_time”), a label for the cryomodule which experienced the trip (“zone_label”), and 192 features (“feature_1”, “feature_2”... “feature_192”). The features correspond to 6 autoregressive features for each of 4 signals per cavity for each of the 8 cavities (6 × 4 signals/cavity × 8 cavities/cryomodule = 192). To deal with the large variation of signal amplitudes, time-series standardization via the z-score (standard score) function was applied prior to computing the features. For each instance, there is an associated label for the (1) cavity which faulted first (cavity_labels.csv) and (2) the type of fault that caused the trip (fault_labels.csv). The cavity identification can take values of [0, 1, 2, 3, 4, 5, 6, 7, 8] and the fault type can take values of [‘Microphonics’, ‘Quench_100ms’, ‘Controls_Fault’, ‘E_Quench’, ‘Quench_3ms’, ‘Single_Cav_Turn_Off’ , ‘Heat_Riser_Choke’, ‘Multi_Cav_Turn_Off’].

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

The False Dilemma: Rethinking AF Science and Technology Officer Talent Management

In 2019, Secretary of the Air Force (SECAF) Heather Wilson launched the 2030 Science and Technology (S&T) Strategy by stating, “The advantage will go to those who create the best technologies and who integrate and field them in creative operational ways that provide military advantages.” In 2021, 15% of Air Force general officers responsible for creating and integrating this technological edge had a science, technology, engineering, and math (STEM) graduate degree, and less than 1% of generals had a STEM doctorate. As one point of comparison, at least 32% and 13% of founding CEOs of Fortune 500 technology companies had a master’s and doctoral STEM degree, respectively. The gulf is larger when you consider that these CEOs used their technical degrees throughout their careers whereas most general officers do not. In large organizations, if the leader does not possess technical knowledge, it is difficult to drive innovation systems and connect ideas to reality. The evidence for this disconnect is abundant in acquisition challenges for high-tech systems, significant pushback to the “revolution in military affairs”, and failure of the “Third Offset” to take hold and deliver capabilities to offset Chinese and Russian capabilities. Indeed, when the former Air Force and Space Force Chief Software Officer, Nicolas M. Chaillan, offered his resignation, one of the main reasons for leaving was “the failure of OSD and the Joint Staff to deliver on their own alleged top ‘priority’, JADC2 – they couldn’t ‘walk the walk.’” Senior leaders have recognized this disconnect, and several inquiries and studies regarding STEM competency have been conducted. Many have posed a false dilemma: a large, STEM-cognizant or small, STEM-expert force. However, current and future STEM human capital needs are managed at the unit and functional area level. This leads to “silos” and the tactical “needs of the present” dominating the strategic “needs of the future”, highlighted by only two functional areas systematically tracking future STEM needs. Therefore, the decision on force structure appears to have been historically made by default through these and other perceived structural realities, and a vast majority of recommendations have focused on the small STEM community – an exercise in diminishing returns. In essence, the solution often boils down to finding unicorns – officers that have chartered the unforgiving pathway through the traditional “gates” to general officer while obtaining sufficient STEM proficiency along the way. Fortunately for the Air Force, these officers exist, albeit at a rate below what the evidence would suggest is necessary. However, this approach fundamentally limits the ability to develop a deep pool of officers with the ability to create and integrate technologies and ensures the Dunning-Kruger effect is prevalent. The Dunning-Kruger effect is a cognitive bias where those with limited knowledge in an area lack the expertise necessary to recognize their lack of expertise and consequently are prone to overestimate their knowledge and performance. In short, we need less leaders overestimating their STEM knowledge and more with hard-earned STEM competency required to “walk the walk” to create and integrate technologies for military advantage. This will not be accomplished by restricting the emphasis to the traditional scientists and engineering (S&E) career fields – the pool, only 10% of line officers, and general officer progression is just too small. Fortunately, the recent SECAF’s Management Initiatives and Chief of Staff’s (CSAF) Action Orders, both containing emphasis on organic expertise to “accrue advantage in military-technological competition,” present an opportunity to truly develop a framework for the force of the future.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Use of Historical EM Practices and Policies for Management of Cleanup Program and Address GAO Concerns - 20222

As Department of Energy (DOE) Environmental Management (EM) program policies and management practices are revised over time, it might be useful to periodically reflect on the DOE EM program's past to identify practices that have worked and those that might be appropriate for possible reuse in the future. An added impetus to examine alternative approaches is the recent Government Accountability Office (GAO) report entitled 'Nuclear Waste Cleanup - DOE Could Improve Program and Project Management by Better Classifying Work and Following Leading Practices.' The February 2019 GAO report states that EM categorizes most of its work in a way that does not adequately involve independent experts and DOE senior leadership. It also noted that EM has not followed leading practices for program and project management that could help keep the cleanup efforts on schedule and control costs. The GAO report recommended that EM, working with DOE Office of Project Management (PM): - Establish requirements and then assess EM's ongoing operations activities to determine if some activities should be reclassified as capital asset projects based on these newly established requirements. - Review and revise EM's 2017 cleanup policy to include program management leading practices related to scope, cost, schedule performance, and independent reviews. - Update the cleanup policy to require that earned value management (EVM) systems be maintained and used in a way that follows EVM best practices. - Develop a policy to ensure that work is categorized as level of effort (LOE) only in appropriate, specified circumstances, such as when work is not measurable or when measurement is impractical. - Integrate EVM data into EM's performance metrics for operations activities. To assist with improving program performance and implementing audit recommendations, this paper reviewed past successful cleanups such as Weldon Springs, Rocky Flats, Fernald, Ashtabula, Battelle Columbus (abs, and other historical resources and people with experience during the early years of EM program. As such, there are several past practices which may well be suited for reuse today. Some of these practices include: - In 1990's, as the DOE EM cleanup program, known as Environmental Restoration came into effect, the known cleanup scope at each geographic location or site was organized into four levels ranging from overall site-wide cleanup effort to the individual 'release sites', providing high granularity to the program. - The cleanup program was organized by geography (Northwest, Southwest, Eastern Area Programs, etc.). These 'sub-program' managers provided a degree of Headquarters oversight and management allowing upper management (EM-1, 2, and 3) to focus on major decisions and issues. - Budget levels at selected cleanup sites were funded above 'minimum-safety' levels to accelerate actual cleanup work. - Past practice utilized EVM techniques and uniform EM oversight over all discrete project-like activities, providing for a coordinated cleanup effort. - EM managed LOE activities using benchmarking and best in class management practice techniques to help manage costs. - EM managed and reviewed entire site cleanup baselines, not only individual projects, focusing on a successful overall outcome. - Sites had a 'Road map' or 'Management Action Plan' that provided the overall strategy or plan for that site's cleanup - this helped with understanding and coordination of efforts. - EM had a robust lessons-learned program and database to capture and transmit worthy ideas. - EM had well-structured cleanup baseline change control policies to control cost and schedule changes, at the contractor and higher Headquarters levels. Finally, some of the GAO observations may be valid, however it is not clear as to their extent. For example, for some operation types of work, the use of the Level of Effort (LOE) earned value method to track progress may be appropriate. Also, to ensure independence of thought, and to obtain useful insight on past practices, it may be suitable to have former EM employees to assist with understanding past practices or to verify that activities have been properly identified. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Addressing Consequence within Operational Risk (O.T. Gagnon III) 9-18-2024

Addressing Consequence within Operational Risk: Why threats and security are just not that important! When dealing with cyber or physical risk within any critical infrastructure (CI) environment, don’t concern yourself with vulnerabilities and threats, at least not at first! Also, don’t be overly fixated on “securing the systems” within the organization. The endeavor of tackling operational risk focused on consequences in any critical infrastructure environment to include the complex Aviation ecosystem is challenging even for the most resourced entity but can be advanced though a simplified approach: identifying, binning, and prioritizing the infrastructure environment. While no two entities within a single element of the 16 critical infrastructure sectors are exactly alike when it comes to risk, there is a basic process to move toward a greater understanding of operational risk through becoming more informed about the infrastructure environment in which the entity exists. The process starts with bringing internal and external stakeholders and subject matter experts together to analyze key areas such as Information Technology (IT) and Operational Technology (OT) components and points of convergence, analyzing internal and external cyber and physical dependencies, accounting for explosive growth in devices and wireless technology, and leveraging the contributions of people inside and outside the operational environment. Attaining a common understanding of the infrastructure environment as part of addressing consequences within operational risk is not easy to do or resource light, but the process outlined provides the framework to further any entity’s efforts in this space. When it comes to cyber risks, before an organization can consider vulnerabilities within and threats to its operations, it must first have a solid understanding of the consequences existing inside its infrastructure environment. Idaho National Lab’s Consequence-Driven, Cyber-Informed Engineering is offered as an example of this approach to effective and efficient cyber risk mitigation.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

The Archaeal Proteome Project advances knowledge about archaeal cell biology through comprehensive proteomics

Abstract While many aspects of archaeal cell biology remain relatively unexplored, systems biology approaches like mass spectrometry (MS) based proteomics offer an opportunity for rapid advances. Unfortunately, the enormous amount of MS data generated often remains incompletely analyzed due to a lack of sophisticated bioinformatic tools and field-specific biological expertise for data interpretation. Here we present the initiation of the Archaeal Proteome Project (ArcPP), a community-based effort to comprehensively analyze archaeal proteomes. Starting with the model archaeon Haloferax volcanii , we reanalyze MS datasets from various strains and culture conditions. Optimized peptide spectrum matching, with strict control of false discovery rates, facilitates identifying > 72% of the reference proteome, with a median protein sequence coverage of 51%. These analyses, together with expert knowledge in diverse aspects of cell biology, provide meaningful insights into processes such as N-terminal protein maturation, N -glycosylation, and metabolism. Altogether, ArcPP serves as an invaluable blueprint for comprehensive prokaryotic proteomics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Sequential Decision Making (SDM) for Mesh Refinement and Model Selection in Multiscale, Multi-Physics Applications

Intelligent automation and decision support are needed to enhance computational efficiency and robustness in multiscale and multi-physics problems, including materials science, manufacturing, and climate and weather modeling. Current scientific computing approaches for enabling decisions by scientists fail to explore the role of learning, reasoning, and probabilistic planning. Often these decisions are not performed in real-time during the computation but are made prior to the start of the computation, which must be interrupted in order to make changes to the prior choices. Such interruptions at different stages of the computation increase the total computing time and the need for a human expert to frequently monitor the results. State of art scientific computing methods consist of rule-based algorithms that cannot automatically adapt to a dynamically changing computing environment. The development of a Sequential Decision Making (SDM) framework will automate scientific computing by optimizing the policies for mesh refinement, time-stepping, model and algorithm selection, resource allocation, and pre and post-processing. Our agent SDM framework for scientific computing will consist of data-driven learning (Classifier), automated reasoning (contextual knowledge), and probabilistic planning (Reinforcement Learning). In this project, we focused on three problems to demonstrate our SDM framework on a set of ordinary and partial differential equations. Classification of Lorenz system regions using Feed-Forward Neural Networks examined learning in the SDM framework. On the other hand, reasoning and planning in the SDM framework were used in two problems: adaptive time-stepping for nonlinear ODEs using on-policy RL algorithms, and adaptive mesh refinement for 2-D PDEs using off-policy RL algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ESAC (EQ-SANS Assisting Chatbot): Application of large language models and retrieval-augmented generation for enhanced user experience at EQ-SANS

Neutron scattering experiments have played vital roles in exploring materials properties in the past decades. While user interfaces have been improved over time, neutron scattering experiments still require specific knowledge or training by an expert due to the complexity of such advanced instrumentation and the limited number of experiments each person may perform each year. This paper introduces an innovative chatbot application that leverages Large Language Models(LLM) and Retrieval-Augmented Generation (RAG) technologies to significantly enhance the user experience at the EQ-SANS, a small-angle neutron scattering instrument at the Spallation Neutron Source of Oak Ridge National Laboratory. Through a user-centric design approach, the EQ-SANS Assisting Chatbot (ESAC) serves as an interactive reference for users, thereby facilitating the use of the instrument by visiting scientists. By bridging the gap between the users of EQ-SANS and the control systems required to perform their experiments, the ESAC sets a new standard for interactive learning and support for the scientific community using large-scale scientific facilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Energy Emergency and Preparedness Data: Frequently Asked Questions (FAQs) and Quick Guidance on Crisis Communications

State Energy Offices and Public Utility Commissions rely on timely, accurate, and actionable information to perform their energy emergency response duties and execute their roles as state energy security planners. In support of this need, the National Association of State Energy Officials (NASEO) and the National Association of Regulatory Utility Commissions (NARUC) hosted an Energy Security and Data Analysis Workshop in Washington, DC to identify energy security response and planning data sources; and to share successful methods of data use and integration in state, federal, and private sector tools. Following the workshop, NASEO and NARUC hosted two topical data-centric webinars based on state priorities to identify best practices in Geographic Information Systems (GIS) and Crisis Communications programs leveraged in energy assurance planning and response. The Crisis Communications webinar covered best practice tactics for how states can respond during energy emergencies and other crises. Based on the workshop and webinars, this document summarizes commonly used data sources and includes frequently asked questions which may be used by state energy officials (i.e., consisting of staff from Public Utility Commissions and Governor-designated State Energy Offices) to help guide them in developing or improving their Crisis Communications capabilities. A strong public information program is a key crisis management tool. Timely and accurate information helps prevent confusion and uncertainty and encourages public support and cooperation. As energy subject matter experts, state energy officials are vital in the distilling, clarifying, and conveying energy sector information and implications to decision-makers and the public. Other participants in an effective public information program include the Governor’s Office, other state agencies, local governments, energy providers, local businesses, state legislature, and the federal government. It is essential to provide stakeholders and the public with information about the nature, severity, and duration of an emergency because inadequate understanding and awareness can lead to undesirable actions that could further exacerbate the situation. Before a state government can provide information to the public, it must gather information, describe the emergency accurately, and develop recommendations to manage the situation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Molten Salt Reactor Fundamental Safety Function PIRT

The phenomenon identification and ranking table (PIRT) process was employed to evaluate the current capability to assess the ability of liquid-salt–fueled molten salt reactors to achieve their fundamental safety functions (FSFs). The PIRT process provides a structured mechanism to elicit and document expert opinions on the most important phenomena and the corresponding level of knowledge with regard to achieving the FSFs. The PIRT panel included reactor developers, accident progression evaluation tool developers, US Department of Energy (DOE) national laboratory technical staff, university researchers, and US Nuclear Regulatory Commission (NRC) staff. The information-gathering process concentrated on phenomena related to accidents in which the fuel salt—including any in the cover gas—has been released from the first barrier layer. This information-gathering process included identifying all potential accidents that could result in the release of a substantial amount of radionuclides that could breach or bypass the first barrier. The PIRT did not identify any previously unrecognized systemic vulnerabilities. However, the elicitation process identified several areas with low levels of knowledge and significant potential impact on accident progression. These areas either require additional research to improve the state of knowledge or additional design conservatism to accommodate the remaining unknowns.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Neutrons in Structural Biology: Challenges and Opportunities (Workshop Report)

Gaining a thorough understanding of biological systems requires building our knowledge about biological processes from the level of atoms and electrons, and up to whole organisms. Such comprehensive knowledge will allow for a predictive understanding of complex biological systems behavior. It will guide us in the design and development of novel therapeutics and vaccines to tackle existing health threats and to prepare for future pandemics, and it will provide information necessary to create new biomaterials and bio-inspired technologies through manipulation of biological macromolecules, their assemblies, single cells and even microorganisms. Reaching these goals will require a synergistic combination of multiple experimental techniques with molecular calculations and predictive simulations, and the design and development of new techniques and capabilities that bridge current knowledge and technology gaps. Neutron scattering provides unique information about the biomacromolecular structure and function and can play a major role in achieving these goals. A workshop was held to engage the scientific community in identifying pressing challenges in biochemistry, structural biology, enzymology and structure-guided drug design not solved with the current neutron scattering technologies or utilizing other structural biology techniques such as X-ray crystallography, NMR, and cryo-EM. The workshop brought together structural biology, biochemistry and computational experts, as well as early career researchers and students, creating a forum for discussing scientific advancement and collaboration. The workshop included a one-day satellite training workshop where graduate students and postdoctoral researchers were educated in the application of neutron crystallography and small-angle scattering in structural biology. Furthermore, the Instrument Scientific Advisory Board (ISAB) for the development of a macromolecular neutron diffractometer at ORNL’s Second Target Station was introduced at the workshop. The major outcome was that neutrons can provide atomic-level understanding of biomacromolecular structure, function and dynamics which is of paramount importance for addressing the identified challenges. Neutron crystallography, in particular, can resolve long-standing biochemical issues regarding enzyme function by delineating the underlying chemistry and can have a major impact on the design of small-molecule therapeutics, especially in combination with molecular computation (quantum chemistry and molecular dynamics simulations) and the emerging artificial intelligence (AI)-assisted drug design technologies. The unique properties of neutrons, including their high sensitivity to hydrogen and their non-destructive nature, make them ideal probes of biological matter. There is a palpable need in the scientific community to expand and enhance the impact of neutron sciences on biology. Neutron crystallography is the only structural biology method capable of determining positions of all hydrogen atoms in proteins, nucleic acids and their complexes at near-physiological temperatures and of unstable species at cryogenic temperatures. Moreover, neutron analysis is non-ionizing, non-destructive and does not perturb the structure or redox chemistry of active site metal centers and clusters in proteins, which can be invaluable for studying radiation-sensitive metalloprotein complexes. Further, neutron energies used in scattering applications are similar to atomic motions, permitting neutron spectroscopies to characterize the dynamics of biomacromolecules on the picosecond to microsecond timescales. The different sensitivities of neutrons to protium (H) and deuterium (D) isotopes of hydrogen allow enhanced visibility of specific parts of biological complexes through isotopic labeling. The impact of neutrons will be most powerful when neutron scattering is combined with complementary experimental techniques that use photons and electrons, and with high-performance computing. The interconnection and mutuality of the experimental and theoretical capabilities will drive discoveries in biological and health sciences to generate more complete picture of complex biological systems. The major limitation in the field of biological neutron crystallography has been signal-to-noise, demanding large samples that are difficult to produce for the majority of biomacromolecules and limiting the applicability of this technique in biological sciences. A neutron crystallography instrument at the Second Target Station will revolutionize biological science with neutrons by engaging a large scientific community of structural biologists, enabling successful neutron diffraction experiments from radically smaller biomacromolecular crystals, resolving unanswered biochemical questions, and meaningfully contributing to rational drug design. The meeting highlighted 10 grand challenges that will be addressed with this advanced capability over the next decade and beyond, and the recommendations required to help address them are given below.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Automated identification of dominant physical processes

The identification of processes that locally and approximately dominate dynamical system behavior has enabled significant advances in understanding and modeling nonlinear differential dynamical systems. Conventional methods of dominant process identification involve piecemeal and ad hoc (non-rigorous, informal) scaling analyses to identify dominant balances of governing equation terms and to delineate the spatiotemporal boundaries (boundaries in space and/or time) of each dominant balance. For the first time, we present an objective global measure of the fit of dominant balances to observations, which is desirable for automation, and was previously undefined. Furthermore, we propose a formal definition of the dominant balance identification problem in the form of an optimization problem. Here, we show that the optimization can be performed by various machine learning algorithms, enabling the automatic identification of dominant balances. Our method is algorithm agnostic and it eliminates reliance upon expert knowledge to identify dominant balances which are not known beforehand.

42 ENGINEERING↗
DOE OSTI2025

Center of Excellence for Operational Technology

The Center of Excellence for Operational Technology Traditional Presentation Abstract 2025 National Laboratories Information Technology Summit | Denver, CO Traditional Presentation Session Managing cybersecurity risk in Operational Technology (OT) presents a significant challenge across the Department, and critically, at many of the national laboratories. This includes IT-OT convergence, aging OT systems, cost of updating OT systems, and increased Advanced Persistent Threat efforts against OT including the 16 critical infrastructure sectors as listed in Presidential Policy Directive 21. DoE’s Office of Science and NNSA’s Office of the Chief Information Officer are taking the lead in addressing this challenge to include critical systems, by establishing the Center of Excellence (CoE) for Operational Technology. Championed by NNSA Deputy Chief Information Officer Steven McAndrews and the Office of Science Chief Information Officer Shila Cooch, the CoE for OT was chartered in February 2025 to address the challenges of OT cybersecurity and compliance. The CoE for OT will create partnerships and leverage expertise from across the NNSA National Security Enterprise and DOE Labs, Plants and Sites. The CoE will also collaborate with colleagues in other government agencies, industry partners and academia. The CoE for OT discussion at the National Laboratories Information Technology Summit ’25 will include the genesis of the CoE, stated goals, organizational structure, and the effort to attract OT subject matter experts to join the CoE effort to share knowledge and expertise. The discussion will include opportunities to get involved and contribute to this important effort. This session will be led by CoE for OT Co-Chairs Matt Kwiatkowski, Fermi National Laboratory Chief Information Security Officer, and Steven Weldon, Savannah River National Laboratory Cyber Program Director at the Georgia Cyber Center. The session will be of particular interest to CIOs, CTOs, CISOs, as well as IT and OT practitioners.

Kwiatkowski, Matt [Fermilab]↗
DOE OSTI2021

Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review

Sensors are the key information source for fault detection and diagnostics (FDD) in buildings. However, sensors are often not properly designed, installed, calibrated, located, and maintained, which negatively impacts FDD performance. Several sensor-related FDD topics have been widely studied, covering a wide range of fault types and applications. However, it is difficult to get a clear picture of the technical development of sensor-related topics in FDD. A systematic review of sensor topics is needed to summarize the existing research in a logical way, draw conclusions on the current development, and predict the future development of sensors in building FDD. To address this gap, we conducted a comprehensive literature review of more than 100 FDD-sensor-related papers. In this article, we subdivide the FDD tasks into building-level, system-level, and component-level FDD, and review sensor-related topics in each category. Our major conclusions are: (a) current data-driven FDD research focuses more on FDD algorithms than sensors, (b) sensor “hardware” research topics are less studied than sensor “software” topics, (c) very few papers focus on sensor engineering as an integral aspect of FDD development, and (d) some important sensor topics, such as sensor cost-effectiveness and sensor schema/layout/location, are not well studied. Finally, we discuss the need for a systematic framework of FDD sensors and models to integrate sensor design/selection, sensor data analysis/mining, feature selection, physics-based or data-driven algorithm development, sensor fault detection, sensor calibration, and sensor maintenance. Finally, expert interviews are conducted to validate the above findings and conclusions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

100+ RON Gasoline Blendstock for High Efficiency, Low Emissions (CRADA 493)

The PNNL/LanzaTech alcohol-to-Jet (ATJ) patented process converts ethanol from LanzaTech’s syngas fermentation process to low aromatic jet-range isoparaffins. Compounds are generated across a wide boiling range by the process. The project goal is to improve the viability of the ATJ process by increasing the RON of the gasoline fraction and determine what types of engines and vehicles can be fueled with this gasoline. PNNL experts’ team will investigate the impact of the process conditions and catalysts formulation on the blendstock composition in relation with the fuel properties. Co-optima capabilities for fuel properties measurements will be leveraged to assess the potential engines/ vehicles applications of the produced blendstock. A techno-economic-analysis will be conducted to assess the commercial viability of the process for generating a high RON gasoline-range fuel.

09 BIOMASS FUELS↗
DOE OSTI2020

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

Prognosis techniques for prediction of remaining useful life (RUL) are of crucial importance to the management of complex systems for they can lead to appropriate maintenance interventions and improvements in reliability. While various data-driven methods have been introduced to predict the remaining useful life (RUL) of machinery systems or batteries, no research has been reported on the remaining useful strength (RUS) prediction of silicon carbide fiber reinforced silicon carbide matrix (SiCf-SiCm) materials with pivotal role in its potential usage as a structural material in nuclear reactors and turbine engines. Knowledge of its degradation process is of the utmost importance to the manufacturers. For this purpose, two approaches based on the machine-learning techniques of random-forest (RF) and convolutional neural network (CNN) are proposed to predict the RUS of SiCf-SiCm using only acoustic emission (AE) signals generated during the material’s stress applying process. Experimental results show that the CNN models achieved better predictive performance than the RF models but the latter with expert-engineered features achieves better prediction for AE signals in the early stage of degradation. Additionally, our results demonstrate that both models can correctly predict the SiCf-SiCm RUS as evaluated by our robust testing method from which the best average root mean square error (RMSE) and Pearson correlation coefficient of 3.55 ksi units and 0.85 were obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2021

International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

Abstract The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high‐quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.

14 SOLAR ENERGY↗
DOE OSTI2021

New high-throughput endstation to accelerate the experimental optimization pipeline for synchrotron X-ray footprinting

Synchrotron X-ray footprinting (XF) is a growing structural biology technique that leverages radiation-induced chemical modifications via X-ray radiolysis of water to produce hydroxyl radicals that probe changes in macromolecular structure and dynamics in solution states of interest. The X-ray Footprinting of Biological Materials (XFP) beamline at the National Synchrotron Light Source II provides the structural biology community with access to instrumentation and expert support in the XF method, and is also a platform for development of new technological capabilities in this field. Hee, the design and implementation of a new high-throughput endstation device based around use of a 96-well PCR plate form factor and supporting diagnostic instrumentation for synchrotron XF is described. This development enables a pipeline for rapid comprehensive screening of the influence of sample chemistry on hydroxyl radical dose using a convenient fluorescent assay, illustrated here with a study of 26 organic compounds. The new high-throughput endstation device and sample evaluation pipeline now available at the XFP beamline provide the worldwide structural biology community with a robust resource for carrying out well optimized synchrotron XF studies of challenging biological systems with complex sample compositions.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

History of the Beer Game

This article describes the history of the Beer Game. By triangulating information from literature, archival analysis, and interviews with experts in the field, the main changes in the game over its almost 70-year history are identified. The article discusses three aspects of the game: 1) its structure (phases of its history, stocks and flows, parameters, etc.); 2) the process for playing the game; and 3) the game debrief. The structure of the Beer Game, and the process for running it, have stabilized over the years into what is now a de facto standard approach. Additional work is needed in the game debrief, specifically in the clarification of key insights and messages (depending on the context of the use of the game), in how to communicate such messages to different audiences, and in the development of support materials for its delivery. © 2024 UChicago Argonne, LLC. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

97 MATHEMATICS AND COMPUTING↗