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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

NASA DEVELOP’s Approach to Co-Production and Collaborative STEM Engagement

The NASA DEVELOP Program conducts 50-60 projects each year with the goal of bringing the benefits of NASA Earth science to local decision-making challenges. DEVELOP, part of NASA’s Applied Sciences’ Capacity Building Program, connects end users with students, recent graduates, and early and transitioning career professionals through 10-week rapid feasibility studies. These projects are a collaboration between NASA, DEVELOP office host locations (universities and other federal installations), project partners (federal agencies, state and local governments, non-profit and for-profit organizations, and international organizations), and the project teams who conduct them. The projects take place under the guidance of science advisors from NASA, academia, and partner organizations, and introduces communities to new applications of NASA Earth observations data with the desired outcome of informed decision-making. This presentation will highlight the DEVELOP model of co-production and programmatic collaboration, lessons learned in partnering, and evaluation activities that look at the impact of the program’s efforts.

Capacity Building↗
DOE OSTI2023

Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments

Offloading Internet of Things (IoT) tasks to the cloud for further processing might not always lead to an optimal execution time, particularly in situations such as resource contention, under-provisioning, over-provisioning, and fragmentation. In addition, dynamically optimizing the number of Virtual Machines (VMs) for resource scheduling in order to meet application requirements remains a major research challenge. Further, existing resource scheduling algorithms focus primarily on minimizing operational costs while maximizing resource sharing and utilization. Considering energy utilization as part of the resource allocation and scheduling process as an optimization objective for maintaining load balancing has often been neglected. To address these challenges and more, we propose a cooperative energy-aware resource allocation and scheduling strategy based on a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making method. Here we used the Grid Workloads Archive dataset to evaluate our proposed approach named TOPREAL. Experimental results with respect to the allocation of VM resources when considering processing a large segment of tasks indicate that TOPREAL outperforms existing algorithms in terms of energy savings, with an average improvement of 40.25%, while maintaining an average improvement of 16.21% when it comes to execution time. Results also demonstrate that our method can save an average of 78.06 processing hours and 63,215kJ of energy when compared to existing scheduling algorithms. These results demonstrate the effectiveness of our proposed model and the viability of using multi-criteria decision-making techniques such as TOPSIS to solve the resource allocation and scheduling problem in edge environments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

Integrating Earth Observations and Socioeconomic Data to Address Health, Equity, and Environmental Justice

Access to reliable data about the characteristics of populations is a crucial component of decision making, policy development, and the assessment of progress towards strategic goals. Social determinants of health provide insight into the status of social and economic conditions within populations that have profound effects on public health, equity, and vulnerability. International frameworks such as the Sustainable Development Goals (SDGs) fundamentally focus on equality, but to efficiently address targets and indicators, socioeconomic data and Earth observations must be integrated to help identify populations most vulnerable to the impacts of climate change, natural disasters, health disparities, and poor policy planning. The ability to identify vulnerable populations with data analysis has the potential to empower community stakeholders with spatial awareness needed to inform decision-making that addresses equity and environmental justice issues within their communities. The EPA defines environmental justice (EJ) as “the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation and enforcement of environmental laws, regulations and policies.” NASA’s Earth Science Division (ESD) recognizes the benefits that Earth observations with NASA satellites create by equipping individuals with the knowledge to address community challenges. NASA’s Socioeconomic Data and Applications Center (SEDAC) supports the integration of socioeconomic and Earth science data as an “informational gateway.” This integration of data is advancing health, equity, and environmental justice initiatives.

Natasha Johnson-Griffin↗
NASA NTRS2004

Autonomous In-Situ Resources Prospector

This presentation will describe the concept of an autonomous, intelligent, rover-based rapid surveying system to identify and map several key lunar resources to optimize their ISRU (In Situ Resource Utilization) extraction potential. Prior to an extraction phase for any target resource, ground-based surveys are needed to provide confirmation of remote observation, to quantify and map their 3-D distribution, and to locate optimal extraction sites (e.g. ore bodies) with precision to maximize their economic benefit. The system will search for and quantify optimal minerals for oxygen production feedstock, water ice, and high glass-content regolith that can be used for building materials. These are targeted because of their utility and because they are, or are likely to be, variable in quantity over spatial scales accessible to a rover (i.e., few km). Oxygen has benefits for life support systems and as an oxidizer for propellants. Water is a key resource for sustainable exploration, with utility for life support, propellants, and other industrial processes. High glass-content regolith has utility as a feedstock for building materials as it readily sinters upon heating into a cohesive matrix more readily than other regolith materials or crystalline basalts. Lunar glasses are also a potential feedstock for oxygen production, as many are rich in iron and titanium oxides that are optimal for oxygen extraction. To accomplish this task, a system of sensors and decision-making algorithms for an autonomous prospecting rover is described. One set of sensors will be located in the wheel tread of the robotic search vehicle providing contact sensor data on regolith composition. Another set of instruments will be housed on the platform of the rover, including VIS-NIR imagers and spectrometers, both for far-field context and near-field characterization of the regolith in the immediate vicinity of the rover. Also included in the sensor suite are a neutron spectrometer, ground-penetrating radar, and an instrumented cone penetrometer for subsurface assessment. Output from these sensors will be evaluated autonomously in real-time by decision-making software to evaluate if any of the targeted resources has been detected, and if so, to quantify their abundance. Algorithms for optimizing the mapping strategy based on target resource abundance and distribution are also included in the autonomous software. This approach emphasizes on-the-fly survey measurements to enable efficient and rapid prospecting of large areas, which will improve the economics of ISRU system approaches. The mature technology will enable autonomous rovers to create in-situ resource maps of lunar or other planetary surfaces, which will facilitate human and robotic exploration.

Dissly, R. W.↗
DOE OSTI2022

Advances in the Application and Utility of Subseasonal-to-Seasonal Predictions

The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

The Real Time Mission Monitor: A Situational Awareness Tool For Managing Experiment Assets

The NASA Real Time Mission Monitor (RTMM) is a situational awareness tool that integrates satellite, airborne and surface data sets; weather information; model and forecast outputs; and vehicle state data (e.g., aircraft navigation, satellite tracks and instrument field-of-views) for field experiment management RTMM optimizes science and logistic decision-making during field experiments by presenting timely data and graphics to the users to improve real time situational awareness of the experiment's assets. The RTMM is proven in the field as it supported program managers, scientists, and aircraft personnel during the NASA African Monsoon Multidisciplinary Analyses experiment during summer 2006 in Cape Verde, Africa. The integration and delivery of this information is made possible through data acquisition systems, network communication links and network server resources built and managed by collaborators at NASA Dryden Flight Research Center (DFRC) and Marshall Space Flight Center (MSFC). RTMM is evolving towards a more flexible and dynamic combination of sensor ingest, network computing, and decision-making activities through the use of a service oriented architecture based on community standards and protocols.

Blakeslee, Richard↗
NASA NTRS2011

Investigation of the Impact of User Gaming in the Next Generation National Airspace System

Over the past three decades, growth in the demand for air transportation has exceeded the growth in the national airspace system (NAS) capacity. Systems operating near capacity inevitably have delays and NAS d elays have increased in recent years. The desire to minimize delay costs has placed attention on the NAS air traffic management (ATM) syste m.One initiative that has helped to provide user representation in the ATM solution is the collaborative decision making (CDM) process. CDM addresses this issue by bringing users (referred to here as airline operation centers [AOCs]) and ATM providers together for information e xchange and cooperative planning. Such cooperative planning has been instituted, for instance, for the purpose of planning airport slot control strategies and rerouting strategies. While the CDM initiatives ha ve met with much success, they have also introduced the potential for AOCs to manipulate the system in unforeseen, unintended, and perhaps undesirable ways, from a system-wide, synoptic perspective. This type of manipulation is sometimes referred to as "gaming" the system. This study uses a high-fidelity simulation tool to investigate several models of user decision making behavior which could be considered to be gaming behavior and the emergent system dynamics and interactions between AOCs and traffic management.

Hunter, George C.↗
NASA NTRSDate not supplied

Enhancing In-Flight Structural Health Monitoring of Vertical Lift Vehicles Operating in an Urban Environment

In-situ airframe sensors have long been considered a potential solution for structural health monitoring that could change the design, certification, operation and maintenance paradigms of flight vehicles. In this approach, large networks of sensors covering the entire, or most of, an airframe throughout its operational lifetime would support real-time decisions on airworthiness and obviate the need to overbuild components or perform multiple cycles of structural qualification testing and inspections. This concept would go beyond the current practice of placing select sensors in strategic locations or relying on such sensors only during airframe qualification flights and inspections. For vehicles in the emerging urban air mobility space, reducing weight associated with overbuilds and shortening down-time associated with inspections are critical for improving safety and affordability. In practice, the wide-scale use of in-situ sensors as primary assurance for structural health has not been demonstrated to be feasible or the best solution. A sensor integration testbed was developed as a platform to evaluate the potential of multiple sensor types to enable decision making on airworthiness. We report on the initial runs of this testbed with multiple sensors attached to a common test article. Metal foil strain gauges, eddy current, fiber optic, guided wave (acoustic emission and ultrasonic)and carbon nanotube roving sensors were affixed to the test article. Baseline as well as post damage initiation and fatiguing data are presented and discussed. Despite the relative simplicity of the test article, the interpretation of the as-captured test data was generally not conclusive or did not have wide enough coverage. This result emphasizes the challenges and current limitations both in testing and the practical broad application of embedded sensors as the determinative elements in critical decision making on wide-scale structural health.

Vertical Lift Vehicles↗
NASA NTRSDate not supplied

Enhancing In-Flight Structural Health Monitoring of Vertical Lift Vehicles Operating in an Urban Environment

In-situ airframe sensors have long been considered a potential solution for structural health monitoring that could change the design, certification, operation, and maintenance paradigms of flight vehicles. In this approach, large networks of sensors covering the entire, or most of, an airframe throughout its operational lifetime would support real-time decisions on airworthiness and obviate the need to overbuild components or perform multiple cycles of structural qualification testing and inspections. This concept would go beyond the current practice of placing select sensors in strategic locations or relying on such sensors only during airframe qualification flights and inspections. For vehicles in the emerging urban air mobility space, reducing weight associated with overbuilds and shortening down-time associated with inspections are critical for improving safety and affordability. In practice, the wide-scale use of in-situ sensors as primary assurance for structural health has not been demonstrated to be feasible or the best solution. A sensor integration testbed was developed as a platform to evaluate the potential of multiple sensor types to enable decision making on airworthiness. We report on the initial runs of this testbed with multiple sensors attached to a common test article. Metal foil strain gauges, eddy current, fiber optic, guided wave (acoustic emission and ultrasonic) and carbon nanotube roving sensors were affixed to the test article. Baseline as well as post damage initiation and fatiguing data are presented and discussed. Despite the relative simplicity of the test article, the interpretation of the as-captured test data was generally not conclusive or did not have wide enough coverage. This result emphasizes the challenges and current limitations both in testing and the practical broad application of embedded sensors as the determinative elements in critical decision making on wide-scale structural health.

Vertical Lift Vehicles↗
DOE OSTI2022

Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration

This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to optimize the restoration. Due to the complexities stemming from the large policy search space, renewable uncertainty, and nonlinearity in a complex grid control problem, directly applying RL algorithms to train a satisfactory policy requires extensive tuning to be successful. To address this challenge, this paper leverages the curriculum learning (CL) technique to design a training curriculum involving a simpler steppingstone problem that guides the RL agent to learn to solve the original hard problem in a progressive and more effective manner. We demonstrate that compared with direct learning, CL facilitates controller training to achieve better performance. To study realistic scenarios where renewable forecasts used for decision-making are in general imperfect, the experiments compare the trained RL controllers against two model predictive controllers (MPCs) using renewable forecasts with different error levels and observe how these controllers can hedge against the uncertainty. Results show that RL controllers are less susceptible to forecast errors than the baseline MPCs and can provide a more reliable restoration process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO)

Inherent in interplanetary space travel are unprecedented challenges that could threaten mission success and negatively impact crew health and performance. Return to definitive care is essentially untenable and resources will be constrained with practically no re-supply capability. Access to ground-based medical expertise will be significantly delayed under nominal conditions with exacerbation during conjunction or prolonged dust storms. Taken together, these challenges necessitate the development of a progressively autonomous medical operational support system to assist the crew medical officer (CMO). While support from ground based medical experts will remain indispensable for pre-mission planning, the approach to management of acute/emergent medical contingencies will require a gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. To progressively enable EIMO, a series of meetings were convened with subject matter experts from within NASA, academia and industry to facilitate mapping of the path to support autonomous medical operations. Topics explored in these meetings included the scope of data (storage capacity, usage, transmission rate and bandwidth, computing capacity), CMO training, supply and resource management and task load balance. Recommendations from these meetings will inform the EIMO Concept of Operations and definition of the associated requirements culminating in updates to the NASA 3001 standards. The EIMO project team will work with stakeholders to conceptualize a clinical decision support system (CDSS) to assist the CMO in response to medical contingencies when terrestrial support is delayed or otherwise unavailable. The CDSS will be a system of systems that will utilize data from numerous input vectors. Successful deployment of the CDSS will facilitate medical decision making while decreasing the cognitive load leading to an improvement in task load balancing. Additional benefits of the envisioned CDSS include assistance with inventory management, locating resources, storage/retrieval of medical records, highlighting trends in recorded data, in addition to providing a consult for diagnosis and treatment. More advanced features might include passive monitoring to identify early warning signs of behavioral or medical anomalies to possibly pre-empt onset of conditions that would compromise crew health and performance.

Benjamin Easter↗
DOE OSTI2022

A multi-criteria CCUS screening evaluation of the Gulf of Mexico, USA

Continued research into reservoir characterization along with offshore carbon dioxide (CO 2 ) transportation and infrastructure assets is needed to facilitate development of safe and successful carbon capture, utilization, and storage (CCUS) projects. This paper outlines a multi-criteria evaluation methodology that incorporates disparate sets of quantitative, spatially variable data into a decision-making framework for screening the Gulf of Mexico (GOM) outer continental shelf (OCS) for potentially viable CO 2 storage and enhanced oil recovery (EOR) sites. Criteria categories include favorable geologic characteristics, logistics, and potential risks. Data compiled for 14 criteria from several publicly available geographic information system (GIS) layers was aggregated over 2559 spatially balanced points across the study area using the National Energy Technology Laboratory (NETL)-developed Cumulative Spatial Impact Layers™ (CSIL) GIS tool. Criteria are weighted by qualitative expert opinion relative to their perceived importance to given scenarios— the output of combined criteria values and weights enables regional CO 2 storage suitability differentiation. The methodology considers both technical and non-technical factors impacting CCUS decision-making. The flexible methodology enables a systematic approach to regional ranking at high spatial resolution over a large study domain. Additionally, the framework enables high-grading of priority sites that warrant further characterization and follow-on analysis. Areas along the Louisiana coast and Mississippi River Delta consistently rank high for all scenarios largely a result of the favorable geology with the potential for stacked storage, as well as the density of existing pipelines and platforms, and proximity to several onshore CO 2 sources. High-graded regions for the CO 2 EOR-related scenarios are typically located further offshore towards the middle and edge of the OCS compared to higher priority regions for the geologic storage scenarios which fall closer to the Louisiana coastline.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Event Report for The Ethical Artificial Intelligence Quantification Workshop

Artificial Intelligence (AI) is a powerful emerging technology area which requires special attention to using it ethically. AI ethics is still an emerging field, and the partners for this workshop and report seek to move AI ethics discussion ahead by experimenting with ways to measure AI ethics criteria. The following document describes the outcomes and learnings from The Ethical Artificial Intelligence Quantification Workshop held at the National Institute for Aerospace (NIA), Hampton, Virginia on May 12th, 2022. The purpose of the workshop was for participants to evaluate and experiment-with the methodology and process presented by AIEthics.World in cooperation with Intel Corporation. The meeting participants learned about the Ethical AI Certification and Maturity Model™ and applied the methodology to selected notional AI systems. The workshop facilitated the evaluation of the maturity of the AI system according to ethical considerations relevant to NASA, NIA and other participants. The workshop consisted of three main phases. The first phase focused on understanding and summarizing NASA’s ethical approaches, mission and values based on published documentation, discussions and individual insights & opinions of participants. This information was prioritized, weighted, ordered, and quantified in phase two, to formulate an alignment between human values (ethics) and their applicability to AI systems during all lifecycle phases. The first two phases were summarized as a form of ethical genealogy for artificial intelligence, specific to NASA’s ethical approaches. In the third and last phase of the workshop the participants evaluated notional examples of artificial intelligence to qualify and quantify its ability to adhere to the organizational ethics approaches, using the Ethical AI Certification and Maturity Model™. The workshop uses the concept of genealogy, in the traditional sense: the study and traceability of lines of ancestors in the process of evolutionary development from earlier forms. However, as it is applied to an Ethical AI definition, it is providing the insights to the necessary and mandatory traceability of content, data, metrics, telemetry, elements, and structures which are used in the AI’s lifecycle to foster and measure AI ethics in all steps of its lifecycle. The Ethical Artificial Intelligence Quantification Workshop provided NASA with the opportunity to apply the Ethical AI Certification and Maturity Model™, in combination with existing and well-known decision-making and quality control methods to identify the metrics and measurements for an Ethical AI and assess its ethical condition and quality aligned with NASA ethics approaches. The result of the workshop is the capacity for NASA to apply the maturity model assessment to its AI Systems as desired and if necessary, publish the ability of these AI Systems to adhere to the organizational ethical goals. AI ethics frameworks need to be customized for each application domain, for example, individual NASA Mission Directorates. General principles that work in one area such as AI/Machine Learning-based text analysis (the ethics of information-extraction) may need to be adapted for another such as sense-and-avoid decision-making in a flight environment. The workshop was conducted among approximately twenty NASA subject matter experts, so the elements noted above should be considered examples, not definitive NASA ethical AI principles, genealogy, etc. Generating a definitive AI ethics framework for an organization as diverse as NASA would require far more discussion, debate, review, etc. However, the workshop provided valuable insight into mechanisms and processes for quantifying AI ethical qualities.

Artificial Intelligence↗
NASA NTRS2012

Understanding Climate Policy Data Needs. NASA Carbon Monitoring System Briefing: Characterizing Flux Uncertainty, Washington D.C., 11 January 2012

Climate policy in the United States is currently guided by public-private partnerships and actions at the local and state levels. This mitigation strategy is made up of programs that focus on energy efficiency, renewable energy, agricultural practices and implementation of technologies to reduce greenhouse gases. How will policy makers know if these strategies are working, particularly at the scales at which they are being implemented? The NASA Carbon Monitoring System (CMS) will provide information on carbon dioxide fluxes derived from observations of earth's land, ocean and atmosphere used in state of the art models describing their interactions. This new modeling system could be used to assess the impact of specific policy interventions on CO2 reductions, enabling an iterative, results-oriented policy process. In January of 2012, the CMS team held a meeting with carbon policy and decision makers in Washington DC to describe the developing modeling system to policy makers. The NASA CMS will develop pilot studies to provide information across a range of spatial scales, consider carbon storage in biomass, and improve measures of the atmospheric distribution of carbon dioxide. The pilot involves multiple institutions (four NASA centers as well as several universities) and over 20 scientists in its work. This pilot study will generate CO2 flux maps for two years using observational constraints in NASA's state-of -the-art models. Bottom-up surface flux estimates will be computed using data-constrained land and ocean models; comparison of the different techniques will provide some knowledge of uncertainty in these estimates. Ensembles of atmospheric carbon distributions will be computed using an atmospheric general circulation model (GEOS-5), with perturbations to the surface fluxes and to transport. Top-down flux estimates will be computed from observed atmospheric CO2 distributions (ACOS/GOSAT retrievals) alongside the forward-model fields, in conjunction with an inverse approach based on the CO2 model of GEOS ]Chem. The forward model ensembles will be used to build understanding of relationships among surface flux perturbations, transport uncertainty and atmospheric carbon concentration. This will help construct uncertainty estimates and information on the true spatial resolution of the top-down flux calculations. The relationship between the top-down and bottom-up flux distributions will be documented. Because the goal of NASA CMS is to be policy relevant, the scientists involved in the flux modeling pilot need to understand and be focused on the needs of the climate policy and decision making community. If policy makers are to use CMS products, they must be aware of the modeling effort and begin to design policies that can be evaluated with information. Improving estimates of carbon sequestered in forests, for example, will require information on the spatial variability of forest biomass that is far more explicit than is presently possible using only ground observations. Carbon mitigation policies being implemented by cities around the United States could be designed with the CMS data in mind, enabling sequential evaluation and subsequent improvements in incentives, structures and programs. The success of climate mitigation programs being implemented in the United States today will hang on the depth of the relationship between scientists and their policy and decision making counterparts. Ensuring that there is two-way communication between data providers and users is important for the success both of the policies and the scientific products meant to support them..

Brown, Molly E.↗
NASA NTRSDate not supplied

Decentralized and Asynchronous Planning for Cooperative Practices in High Altitude Digitally Enabled Operations

Cooperative practices will likely be foundational to future digitally enabled airspace operations characterized by decentralized and asynchronous decision-making. This paper explores the implementation of cooperative practices in high-altitude airspace. The digitally enabled operations framework, under exploratory investigation by NASA as a potential new operating mode, which is enabled and dependent on digital technologies and information sharing, may create new operational possibilities for a wide range of vehicle types and missions. An essential element of the operating mode framework under investigation, cooperative practices, could empower operators to manage critical functions such as deconfliction, equitable airspace usage, and demand-capacity balancing. The use of cooperative practices would involve asynchronous flight information sharing and decentralized conflict management by operators, representing a major change from traditional air traffic services that use synchronous surveillance information to support centralized decision-making by air traffic controllers. This paper details three key components for implementing cooperative practices for decentralized, digitally enabled operations: (a) intent modeling and sharing, (b) conflict detection, and (c) cooperative deconfliction.

cooperative operating practics↗
NASA NTRSDate not supplied

The Emphasis of Design Patterns in Expressing Expert Knowledge From A Technical Solution – A Framework for Continued Research

Digital Engineering is a transformative strategy that leverages an integrated model-based approach to improve communication, decision making, design understanding, and acquisition efficiency of system development. As modern systems are derived from pre-existing systems, harvesting expert knowledge from proven systems in a useful, model-based way will reduce the experiential learning and cognition required for new system development, contributing to a Digital Engineering transformation. Motivated by performance gains observed during a multi-year, sequential development activity, this survey reviews knowledge, architecture, and pattern literature to establish a framework for research of architectural methods for expert knowledge identification and description using Model Based System Engineering. The multi-year sequential development activity is offered as the experimental system of interest for this research. This work aims to enable a digital engineering strategy that improves concept phase decision making, accelerates knowledge acquisition from lessons learned repositories, and eases the burden of generational knowledge loss.

Lithium Ion↗
DOE OSTI2020

How data science methods can improve the quality and efficiency of ICF and HEDP research

Data Science methods (many that are Bayesian based) are widely used in the physical sciences to estimate model parameters from experimental data, synthesize heterogeneous data, calibrate models, design experiments, and determine statistical significance of data. These methods provide a wealth of advantages over traditional analysis techniques because: 1) uncertainties are rigorously defined and propagated naturally through complex systems including covariance, 2) prior information is captured within the analysis framework (including rad-MHD and rad-hydro simulations), 3) competing models can be selected and/or ruled out using quantitative criteria, and 4) complex, heterogeneous data can be incorporated simultaneously. While these methods have been widely adopted as the gold standard in fields such as particle physics, astronomy, and biology, they have been slow to catch on in Inertial Confinement Fusion (ICF) and High Energy Density Physics (HEDP) research. Recently, several teams at LLNL, SNL, LANL, and the LLE have been exploring the use of these tools in their research and have found success. Here we propose that a concerted effort to consolidate these independent research efforts by developing and deploying common tools for use across the complex can revolutionize the way we approach data analysis, assimilation of theory and experiment, and decision making. The Bayesian formalism provides a means to accomplish this, but we are lacking certain infrastructure to make it happen on a large scale. Furthermore, once adopted, these techniques can be used to develop standards by which discoveries can be judged, similar to the so-called 5σ rule in high energy particle physics. Such standards may be used in the future to address the issue of unknown reproducibility in ICF and HED experiments caused by low shot rate and high cost per experiment. Our goals as a group are to advance the state of the art in HED measurement science by enabling: 1) better inferences from data with well-defined uncertainties, 2) better use of the data we have and continue to collect, 3) intelligent synthesis of data, 4) evaluation of the statistical significance of our data, and 5) informed decision making regarding the design of new experiments and instruments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Evaluation of HVAC & refrigeration system fault behaviors and impacts: A systematic review

Achieving the goals of green buildings critically depends on the fault-free operation of heating, ventilation and air conditioning and refrigeration (HVAC&R) systems. However, faults frequently occur in these systems, causing a range of negative consequences, including increased energy consumption, diminished operational performance, compromised indoor environmental quality, higher operational costs, and shortened system lifespan. The evaluation of fault behaviors and impacts plays a critical role in revealing fault characteristics and consequently supports many research areas, including the design of the high-performance equipment, development of fault detection and diagnostics (FDD) and robust control approaches, as well as the enhancement of maintenance decision-making activities. This paper systematically reviews 112 research publications that reported the analysis and evaluation of fault behaviors and impacts in HVAC&R systems over the past thirty years. Here, we designed a review approach to address five crucial research questions, namely: 1) the objectives of analysis and evaluation of fault behaviors and impacts, 2) data sources, 3) equipment/system types and fault types, 4) evaluation methods including evaluation measures and associated metrics, and 5) challenges and future directions in the research on evaluating of fault behaviors and impacts. In-depth discussions on these questions help bridge the gap between the evaluation of fault behaviors and impacts and their practical applications, such as the development of high-performance systems, fault models, FDD methods, and maintenance decision-making tools within the HVAC&R FDD domain.

Chen, Yimin [Oak Ridge National Laboratory (ORNL),↗