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

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Modelling decision-making by pilots

Our scientific goal is to understand the process of human decision-making. Specifically, a model of human decision-making in piloting modern commercial aircraft which prescribes optimal behavior, and against which we can measure human sub-optimality is sought. This model should help us understand such diverse aspects of piloting as strategic decision-making, and the implicit decisions involved in attention allocation. Our engineering goal is to provide design specifications for (1) better computer-based decision-aids, and (2) better training programs for the human pilot (or human decision-maker, DM).

Patrick, Nicholas J. M.↗
NASA NTRS2010

Achieving a Risk-Informed Decision-Making Environment at NASA: The Emphasis of NASA's Risk Management Policy

This slide presentation reviews the evolution of risk management (RM) at NASA. The aim of the RM approach at NASA is to promote an approach that is heuristic, proactive, and coherent across all of NASA. Risk Informed Decision Making (RIDM) is a decision making process that uses a diverse set of performance measures along with other considerations within a deliberative process to inform decision making. RIDM is invoked for key decisions such as architecture and design decisions, make-buy decisions, and budget reallocation. The RIDM process and how it relates to the continuous Risk Management (CRM) process is reviewed.

Dezfuli, Homayoon↗
DOE OSTI2024

Opportunities and Challenges in the Visualization of Energy Scenarios for Decision-Making: Preprint

Scenario studies are a technique for representing a range of possible complex decisions through time, and analyzing the impact of those decisions on future outcomes of interest. It is common to use scenarios as a way to study potential pathways towards future build-out and decarbonization of energy systems. The results of these studies are often used by diverse energy system stakeholders - such as community organizations, power system utilities, and policymakers - for decision-making using data visualization. However, the role of visualization in facilitating decision-making with energy scenario data is not well understood. In this work, we review visualization designs employed in energy scenario studies found in the literature and publicly accessible online sources. We discuss the effectiveness of existing techniques particularly in regards to decision-making, and present opportunities and challenges in the visualization of energy system scenario data.

decision-making↗
NASA NTRS2019

Tactical Scientific Decision-Making during Crewed Astrobiology Mars Missions

The limitations placed upon human explorers on the surface of Mars will necessitate a methodology for scientific exploration that is different from standard approaches to terrestrial fieldwork and prior crewed exploration of the Moon. In particular, the data transmission limitations and communication latency between Earth and Mars create a unique situation for surface crew in contact with a terrestrial science team. The BASALT research program simulated a series of extravehicular activities (EVAs) in Mars analog terrains under various Mars-relevant bandwidth and latency conditions to investigate how best to approach this problem. Here we discuss tactical decision-making under these conditions, that is, how the crew on Mars interacts with a team of scientists and support personnel on Earth to collect samples of maximum scientific interest. We describe the strategies, protocols, and tools tested in BASALT EVAs and give recommendations on how best to conduct human exploration of Mars with support from Earth-based scientists. We find that even with scientists supporting them, the crew performing the exploration must be trained in the appropriate scientific disciplines in order to provide the terrestrial scientists with enough information to make decisions, but that with appropriate planning and structure, and tools such as a ‘‘dynamic leaderboard,’’ terrestrial scientists can add scientific value to an EVA, even under Mars communication latency

Decision-making↗
NASA NTRS1977

A queueing model of pilot decision making in a multi-task flight management situation

Allocation of decision making responsibility between pilot and computer is considered and a flight management task, designed for the study of pilot-computer interaction, is discussed. A queueing theory model of pilot decision making in this multi-task, control and monitoring situation is presented. An experimental investigation of pilot decision making and the resulting model parameters are discussed.

Walden, R. S.↗
NASA NTRS2024

Climate Extremes and Risks: Links Between Climate Science and Decision-Making

The World Climate Research Programme (WCRP) envisions a future where actionable climate information is universally accessible, supporting decision makers in preparing for and responding to climate change. In this perspective, we advocate for enhancing links between climate science and decision-making through a better and more decision-relevant understanding of climate impacts. The proposed framework comprises three pillars: climate science, impact science, and decision-making, focusing on generating seamless climate information from sub-seasonal, seasonal, decadal to century timescales informed by observed climate events and their impacts. The link between climate science and decision-making has strengthened in recent years, partly owing to undeniable impacts arising from disastrous weather extremes. Enhancing decision-relevant understanding involves utilizing lessons from past extreme events and implementing impact-based early warning systems to improve resilience. Integrated risk assessment and management require a comprehensive approach that encompasses good knowledge about possible impacts, hazard identification, monitoring, and communication of risks while acknowledging uncertainties inherent in climate predictions and projections, but not letting the uncertainty lead to decision paralysis. The importance of data accessibility, especially in the Global South, underscores the need for better coordination and resource allocation. Strategic frameworks should aim to enhance impact-related and open-access climate services around the world. Continuous improvements in predictive modeling and observational data are critical, as is ensuring that climate science remains relevant to decision makers locally and globally. Ultimately, fostering stronger collaborations and dedicated investments to process and tailor climate data will enhance societal preparedness, enabling communities to navigate the complexities of a changing climate effectively.

climate extremes↗
DOE OSTI2025

First-of-a-Kind Risk-Informed Digital Twin for Operational Decision Making

A digital twin (DT) is a digital model or a collection of models of a physical entity. DTs in the nuclear arena can be used from plant design through decommissioning. Decisions are typically a priori or made offline. Risk-informed decision making is identifying what can go wrong, its frequency, and the consequences of its failure. Ideally risk-informed decision making reflects the current state of the plant and provides a decision in real time. Traditionally, probabilistic risk assessments (PRAs) evaluate the failures of safety systems, the risk of core damage, and the offsite dose as the consequence. However, this DT evaluates the decisions on the control side rather than the protection side. It uses the same risk methods to probabilistically inform the decision-making process but in a different way. Rather than evaluating the risk of core damage, this DT evaluates the likelihood of avoiding a trip set point while maintaining plant safety. Performance-based assessments are identified via its probabilistic evaluation of operational alternatives based on system status. Because the purpose of the control system is to maintain system variables within prescribed operating ranges, upsets or challenges that can exceed a trip set point resulting in a plant transient and a challenge to plant mitigating systems based on actual plant conditions, are evaluated to safely maintain the plant within the operating ranges. The probabilistic portion of the model is autonomously and automatically adjusted, and the metric of interest (i.e. likelihood of avoiding a trip set point) is recalculated. The digital representation of the physical system (i.e. the DT) performs a deterministic performance–based assessment of the probabilistically identified alternatives identified to validate the probabilistic assessment. A decision-making algorithm selects the appropriate option based on the probabilistic and deterministic assessments and transmits a control signal to a component(s) to initiate a corrective action or informs an operator of its decision.

digital twin↗
NASA NTRS1990

Distributed decision-making for space operations

A programmatic and technical perspective in the context of future space applications is presented, that includes some of the management challenges that arise as the decision-making process becomes increasingly more decentralized. Three challenges are discussed: (1) the degree to which the planners must communicate with each other and with those who are seeking space operations resources, (2) the collection, management, employment and dissemination of the information needed to make decisions, and (3) the challenges connected with schedule integration. The technical perspective presented leads to recommended adaptations to the normal scheduling algorithms that retain the 'degrees of freedom' in the planning result. It is shown that these adaptations are specific technical responses to the programmatic challenges discussed.

Hornstein, Rhoda Shaller↗
NASA NTRS1979

Monitoring and decision making by people in man machine systems

The analysis of human monitoring and decision making behavior as well as its modeling are described. Classic and optimal control theoretical, monitoring models are surveyed. The relationship between attention allocation and eye movements is discussed. As an example of applications, the evaluation of predictor displays by means of the optimal control model is explained. Fault detection involving continuous signals and decision making behavior of a human operator engaged in fault diagnosis during different operation and maintenance situations are illustrated. Computer aided decision making is considered as a queueing problem. It is shown to what extent computer aids can be based on the state of human activity as measured by psychophysiological quantities. Finally, management information systems for different application areas are mentioned. The possibilities of mathematical modeling of human behavior in complex man machine systems are also critically assessed.

Johannsen, G.↗
NASA NTRS1988

Hierarchical neural networks for autonomous data analysis and decision making

A neural network based data analysis and decision making system to increase the autonomy of a planetary rover or similar exploratory vehicle is presented. A hierarchical series of neural networks for real time analysis of scientific images is used. The system under development emphasizes analysis of multispectral images by classifier and feature detector neural networks, to provide information on the mineral composition of a scene. A hierarchy of alternating analysis and decision making networks is being developed to allow increasingly fine scale analysis in regions of the image that are potentially important. It is noted that this system will facilitate both the selection of high priorty scientific information for transmission to earth, and the autonomous collection of rocks and soil for sample return.

Eberlein, Susan↗
NASA NTRS1993

Criteria for assessing problem solving and decision making in complex environments

Training crews to cope with unanticipated problems in high-risk, high-stress environments requires models of effective problem solving and decision making. Existing decision theories use the criteria of logical consistency and mathematical optimality to evaluate decision quality. While these approaches are useful under some circumstances, the assumptions underlying these models frequently are not met in dynamic time-pressured operational environments. Also, applying formal decision models is both labor and time intensive, a luxury often lacking in operational environments. Alternate approaches and criteria are needed. Given that operational problem solving and decision making are embedded in ongoing tasks, evaluation criteria must address the relation between those activities and satisfaction of broader task goals. Effectiveness and efficiency become relevant for judging reasoning performance in operational environments. New questions must be addressed: What is the relation between the quality of decisions and overall performance by crews engaged in critical high risk tasks? Are different strategies most effective for different types of decisions? How can various decision types be characterized? A preliminary model of decision types found in air transport environments will be described along with a preliminary performance model based on an analysis of 30 flight crews. The performance analysis examined behaviors that distinguish more and less effective crews (based on performance errors). Implications for training and system design will be discussed.

Orasanu, Judith↗
NASA NTRS2010

Risk-Informed Decision Making: Application to Technology Development Alternative Selection

NASA NPR 8000.4A, Agency Risk Management Procedural Requirements, defines risk management in terms of two complementary processes: Risk-informed Decision Making (RIDM) and Continuous Risk Management (CRM). The RIDM process is used to inform decision making by emphasizing proper use of risk analysis to make decisions that impact all mission execution domains (e.g., safety, technical, cost, and schedule) for program/projects and mission support organizations. The RIDM process supports the selection of an alternative prior to program commitment. The CRM process is used to manage risk associated with the implementation of the selected alternative. The two processes work together to foster proactive risk management at NASA. The Office of Safety and Mission Assurance at NASA Headquarters has developed a technical handbook to provide guidance for implementing the RIDM process in the context of NASA risk management and systems engineering. This paper summarizes the key concepts and procedures of the RIDM process as presented in the handbook, and also illustrates how the RIDM process can be applied to the selection of technology investments as NASA's new technology development programs are initiated.

Dezfuli, Homayoon↗
NASA NTRS2016

Using Multimodal Input for Autonomous Decision Making for Unmanned Systems

Autonomous decision making in the presence of uncertainly is a deeply studied problem space particularly in the area of autonomous systems operations for land, air, sea, and space vehicles. Various techniques ranging from single algorithm solutions to complex ensemble classifier systems have been utilized in a research context in solving mission critical flight decisions. Realized systems on actual autonomous hardware, however, is a difficult systems integration problem, constituting a majority of applied robotics development timelines. The ability to reliably and repeatedly classify objects during a vehicles mission execution is vital for the vehicle to mitigate both static and dynamic environmental concerns such that the mission may be completed successfully and have the vehicle operate and return safely. In this paper, the Autonomy Incubator proposes and discusses an ensemble learning and recognition system planned for our autonomous framework, AEON, in selected domains, which fuse decision criteria, using prior experience on both the individual classifier layer and the ensemble layer to mitigate environmental uncertainty during operation.

Neilan, James H.↗
DOE OSTI2024

A Tool to Incorporate Non-Energy Impacts in Energy Efficiency Investment Decision Making for Firms

Energy efficiency is a key demand-side strategy for sustainability, recently identified by the United States Department of Energy as a pillar of industrial decarbonization. The increased focus on decarbonization and the requirement for efficiency to enable electrification, another decarbonization pillar, due to the spark spread between natural gas and electricity prices, make energy efficiency increasingly relevant. Still, industries face challenges in adopting energy efficiency measures. Researchers have long found a gap in adoption of even those measures with a profitable net present value, attributed to lack of strategic value among other barriers (see for rigorous exploration and taxonomy). One solution to facilitate energy efficiency projects is the inclusion of non-energy impacts, as this has been shown to double potential deployment of such projects at system level. Energy efficiency can provide valuable benefits outside of simple operating cost reductions, from decreased pollution to enhanced productivity. The inclusion of these benefits in decision making assessments faces hurdles due to inconsistency of ancillary benefits across projects, difficulties in quantifying impacts and the need for additional measurement to quantify them. The decision-making tools to support this have been designed primarily for the European context. We begin with a stakeholder engagement process to better characterize the U.S. decision making process surrounding adoption of energy efficiency investments. Characterization of non-energy impacts has developed substantially over recent decades. Cagno et al. provided a framework for studying the applicability of these impacts to energy efficiency projects, listing 120 key performance indicators focused mainly on reductions of costs/harms. Other researchers have included impacts on the strategic and revenue side that can be merged into this framework as well. We seek a tractable set of impacts that can be included in a decision-making tool in the US, and as such are well suited to US industry, management and decision making processes. We also seek to understand how to best quantify or characterize these impacts. This work will demonstrate the results of a survey conducted among US manufacturing industry decision makers to assess the decision making landscape of stakeholders as well as the most relevant performance indicators for energy efficiency projects.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
NASA NTRS1995

Multi-objective decision-making under uncertainty: Fuzzy logic methods

Fuzzy logic allows for quantitative representation of vague or fuzzy objectives, and therefore is well-suited for multi-objective decision-making. This paper presents methods employing fuzzy logic concepts to assist in the decision-making process. In addition, this paper describes software developed at NASA Lewis Research Center for assisting in the decision-making process. Two diverse examples are used to illustrate the use of fuzzy logic in choosing an alternative among many options and objectives. One example is the selection of a lunar lander ascent propulsion system, and the other example is the selection of an aeration system for improving the water quality of the Cuyahoga River in Cleveland, Ohio. The fuzzy logic techniques provided here are powerful tools which complement existing approaches, and therefore should be considered in future decision-making activities.

Hardy, Terry L.↗
NASA NTRS2019

Medical Decision Making in the Physician Hierarchy: A Pilot Pedagogical Evaluation

Context: Recently, The American College of Graduate Medical Education (ACGME) has included the medical decision making as a core competency in several specialties. To date, the ability to demonstrate and measure a pedagogical evolution of medical judgment in a medical education program has been limited. Objective: In this study we hope to examine differences in medical decision making ability of different physicians across their various stages of post-graduate hierarchy. Method: Physcians spanning a wide spectrum of scientific disciplines were recruited for three catagories: administrative physicians(AP) representing physcians with the most experience but mostly practice administratively; resident physicians completing their postgraduate medical training (RP) and seasoned attending physicians with mastery level experience (MP). Participants completed four medical simulations focused on abdominal pain: cholecystitis (CH) and renal colic(RC) and chest pain; Cardiac ischemia (STEMI) and pneumothorax (PX). Simulation were ordered randomly so that there was no systematic bias due to learning or to fatigue. The Medical judgment metric (MJM) was used to evaluate medical decision-making. Results: There were no significant differences between the AP, RP, and MP groups in the gender, race, ethnicity, education, and baseline heart rate. There was a significant (p=0.002) interaction effect for simulation time and RP group, 6.2 minutes (+/-1.58); MP group, 8.7 minutes (+/-2.46); and AP group, 10.3 minutes (+/-2.78). The RC MJM scores were significantly (P=0.10) worse in the AP group 12.3 (+/-2.66) then the RP 14.7(+/-1.15) and MP17.7 (+/-1.15) groups. In every simulation, the AP group MJM scores were worse on average (no significantly) compared to the MP and RP groups. The AP group was significantly (P=0.040) less likely to stabilize the subject in the RC simulation than MP and RP groups. Conclusion: There remains significant variability in the medical education and skill retention influences medical decision making throughout a physician's career.

Rosasco, John↗
NASA NTRS1971

The impact of management science on political decision making

The possible impact on public policy and organizational decision making of operations research/management science (OR/MS) is discussed. Criticisms based on the assumption that OR/MS will have influence on decision making and criticisms based on the assumption that it will have no influence are described. New directions in the analysis of analysis and in thinking about policy making are also considered.

White, M. J.↗