SEARCH · Search NASA

Results for “DECISION”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10

All sources NASA NTRS DOE OSTI

NASA NTRS2012

Method and apparatus for determining and utilizing a time-expanded decision network

A method, apparatus and computer program for determining and utilizing a time-expanded decision network is presented. A set of potential system configurations is defined. Next, switching costs are quantified to create a "static network" that captures the difficulty of switching among these configurations. A time-expanded decision network is provided by expanding the static network in time, including chance and decision nodes. Minimum cost paths through the network are evaluated under plausible operating scenarios. The set of initial design configurations are iteratively modified to exploit high-leverage switches and the process is repeated to convergence. Time-expanded decision networks are applicable, but not limited to, the design of systems, products, services and contracts.

Silver, Matthew↗

NASA NTRS2012

Using M and S to Improve Human Decision Making and Achieve Effective Problem Solving in an International Environment

In the international arena, decision makers are often swayed away from fact-based analysis by their own individual cultural and political bias. Modeling and Simulation-based training can raise awareness of individual predisposition and improve the quality of decision making by focusing solely on fact vice perception. This improved decision making methodology will support the multinational collaborative efforts of military and civilian leaders to solve challenges more effectively. The intent of this experimental research is to create a framework that allows decision makers to "come to the table" with the latest and most significant facts necessary to determine an appropriate solution for any given contingency.

Christie, Vanessa L.↗

NASA NTRS2013

Collaborative Platforms Aid Emergency Decision Making

Terra. Aqua. Cloudsat. Landsat. NASA runs and partners in many missions dedicated to monitoring the Earth, and the tools used in these missions continuously return data on everything from shifts in temperature to cloud formation to pollution levels over highways. The data are of great scientific value, but they also provide information that can play a critical role in decision making during times of crisis. Real-time developments in weather, wind, ocean currents, and numerous other conditions can have a significant impact on the way disasters, both natural and human-caused, unfold. "NASA has long recognized the need to make its data from real-time sources compatible and accessible for the purposes of decision making," says Michael Goodman, who was Disasters Program manager at NASA Headquarters from 2009-2012. "There are practical applications of NASA Earth science data, and we d like to accelerate the use of those applications." One of the main obstacles standing in the way of eminently practical data is the fact that the data from different missions are collected, formatted, and stored in different ways. Combining data sets in a way that makes them useful for decision makers has proven to be a difficult task. And while the need for a collaborative platform is widely recognized, very few have successfully made it work. Dave Jones, founder and CEO of StormCenter Communications Inc., which consults with decision makers to prepare for emergencies, says that "when I talk to public authorities, they say, If I had a nickel for every time someone told me they had a common operating platform, I d be rich. But one thing we ve seen over the years is that no one has been able to give end users the ability to ingest NASA data sets and merge them with their own."

Source record↗

NASA NTRS2013

Closed-Loop Analysis of Soft Decisions for Serial Links

We describe the benefit of using closed-loop measurements for a radio receiver paired with a counterpart transmitter. We show that real-time analysis of the soft decision output of a receiver can provide rich and relevant insight far beyond the traditional hard-decision bit error rate (BER) test statistic. We describe a Soft Decision Analyzer (SDA) implementation for closed-loop measurements on single- or dual- (orthogonal) channel serial data communication links. The analyzer has been used to identify, quantify, and prioritize contributors to implementation loss in live-time during the development of software defined radios. This test technique gains importance as modern receivers are providing soft decision symbol synchronization as radio links are challenged to push more data and more protocol overhead through noisier channels, and software-defined radios (SDRs) use error-correction codes that approach Shannon's theoretical limit of performance.

Lansdowne, Chatwin A.↗

NASA NTRS2013

Soft Decision Analyzer

Lansdowne, Chatwin↗

NASA NTRS2016

Decision Analysis Methods Used to Make Appropriate Investments in Human Exploration Capabilities and Technologies

NASA is transforming human spaceflight. The Agency is shifting from an exploration-based program with human activities in low Earth orbit (LEO) and targeted robotic missions in deep space to a more sustainable and integrated pioneering approach. Through pioneering, NASA seeks to address national goals to develop the capacity for people to work, learn, operate, live, and thrive safely beyond Earth for extended periods of time. However, pioneering space involves daunting technical challenges of transportation, maintaining health, and enabling crew productivity for long durations in remote, hostile, and alien environments. Prudent investments in capability and technology developments, based on mission need, are critical for enabling a campaign of human exploration missions. There are a wide variety of capabilities and technologies that could enable these missions, so it is a major challenge for NASA's Human Exploration and Operations Mission Directorate (HEOMD) to make knowledgeable portfolio decisions. It is critical for this pioneering initiative that these investment decisions are informed with a prioritization process that is robust and defensible. It is NASA's role to invest in targeted technologies and capabilities that would enable exploration missions even though specific requirements have not been identified. To inform these investments decisions, NASA's HEOMD has supported a variety of analysis activities that prioritize capabilities and technologies. These activities are often based on input from subject matter experts within the NASA community who understand the technical challenges of enabling human exploration missions. This paper will review a variety of processes and methods that NASA has used to prioritize and rank capabilities and technologies applicable to human space exploration. The paper will show the similarities in the various processes and showcase instances were customer specified priorities force modifications to the process. Specifically, this paper will describe the processes that the NASA Langley Research Center (LaRC) Technology Assessment and Integration Team (TAIT) has used for several years and how those processes have been customized to meet customer needs while staying robust and defensible. This paper will show how HEOMD uses these analyses results to assist with making informed portfolio investment decisions. The paper will also highlight which human exploration capabilities and technologies typically rank high regardless of the specific design reference mission. The paper will conclude by describing future capability and technology ranking activities that will continue o leverage subject matter experts (SME) input while also incorporating more model-based analysis.

Williams-Byrd, Julie↗

NASA NTRS2018

A Markov Decision Process Framework for Optimal Airport Reconfiguration

The airport runway configuration is defined as a combination set of runways for arrivals and departures used at a point during operation of the airport. An optimal configuration of these runways depends on a number of factors, including traffic demand, wind magnitude and direction, other adverse weather conditions, and noise restrictions, among others. Based on the current state of these factors and predictions of traffic demand and weather conditions, runway configuration changes are made and coordinated between tower controller, other air traffic control facilities, pilots, and ground personnel. Reconfigurations can be quite disruptive to airport operations; minimizing their frequency and scheduling them well in advance is essential for mitigating some of the added workload for controllers and pilots. Unfortunately, deciding on an appropriate time to change is challenging for human decision makers. Not only do multiple factors need to be evaluated, but the uncertainty in their forecasts must also be considered. Previous optimization methods, such as mixed linear integer programming, have been proposed. Although these methods can reason over a large set of variables, they do not systematically handle the uncertainty associated with weather movement, traffic demands, and other variables. In this work, we introduce a Markov Decision Process (MDP)-based decision making framework which can reason effectively over the inherent uncertainties and make optimal decisions on if/when to change the airport configuration. In a prototype implementation, we present a single runway with three aircraft and utilize knowledge of the forecasted wind speed and direction to determine whether to keep or change the current runway configuration. Our aim through this work is to present a framework for airport reconfiguration which can be scalable to additional aircraft, multiple runways, and various input parameters. This technique will optimize the airport reconfiguration procedure by providing a proactive approach, optimizing not just at the next optimal opportunity for a reconfiguration based on varying atmospheric and traffic conditions in the terminal airspace, but also anticipating future necessary reconfigurations. This will eliminate the inefficiencies of frequent changes currently associated with runway reconfiguration procedures.

runway reconfiguration↗

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 NTRS2019

Onboard Decision-Making for Nominal and Contingency sUAS Flight

This study presents an onboard decision-making architecture for small unmanned aerial systems (sUAS). The decision-maker is part of NASA's SAFE50 project that is working under the UAS Traffic Management (UTM) Technical Capability Level (TCL) 4 to provide autonomous point-to-point UAV flight in BVLOS, high-density urban environments. The decision-maker monitors various metrics to determine the safety and feasibility of the mission and categorizes flight states as Nominal, Off-Nominal, Alternate Land, and Land Now in a finite state machine. Changes in the monitored metrics serve as transitions in the state machine and trigger replanning. Navigation degradation and communication failure are simulated to show the feasibility of the decision-maker framework in appropriately switching the flight state.

Baculi, Joshua↗

NASA NTRS2018

Evaluating the Socioeconomic Impacts of Rapid Assembly and Deployment of Geospatial Data in Wildfire Emergency Response Planning: A Case Study Using the NASA RECOVER Decision Support System (DSS)

Today’s extended fire seasons and large fire footprints have prompted state and federal land management agencies to devote increasingly larger portions of their budget to wildfire management. As fire costs continue to rise, timely and comprehensive fire information becomes increasingly critical to response and rehabilitation efforts. The NASA Rehabilitation Capability Convergence for Ecosystem Recovery (RECOVER) post-fire decision support system is a server-based application designed to rapidly provide land managers with the information needed to develop a comprehensive rehabilitation plan. This study tested the efficacy of RECOVER through structured interviews with land managers (n=15) who used RECOVER and were responsible for post-fire rehabilitation efforts on over 645 000 ha of fire-affected lands. Although the benefit of better-informed decisions is difficult to quantify, the results of this study illustrate RECOVER’s decision support capabilities provided information to land managers that either validated or altered their decisions on post-fire treatments estimated at over $1.2 million (USD) and saved nearly 800 hours of staff time by streamlining data collection as well as communication with local stakeholders and partnering agencies.

William Toombs↗

NASA NTRSDate not supplied

UAM Decision Making

NASA, in collaboration with the industry and FAA, is conducting research on Urban Air Mobility (UAM). UAM introduces unique and evolving operational characteristics unaccounted for within current transportation planning tools. This evolving modality requires a unique and comprehensive tool that integrates new and existing planning methodologies to provide a holistic solution for decision makers. NASA has identified a number of barriers and research areas related to aircraft, airspace, and communities as well as infrastructure requirements. The research will identify requirements related to urban capable aircraft and airspace technologies. While civil aviation authorities are responsible for safety and structure of operations through the air, the local and regional authorities are responsible for decisions related to location of vertiports, helipads, and airports. The implementation of UAM vertiports will consider diverse regional system categories such as weather, airspace restrictions, noise acceptability, surface traffic, availability of power, vertipad locations, routes, impact on surface traffic, safety and risks, economic impact, ingress/egress for electric/hybrid VTOL aircraft, applicable fire codes, evacuation strategy, zoning requirements, emergency preparedness, interactions with surface traffic, and community acceptance. Therefore, regional implementation bodies need a decision making tool to assess systemic dependencies in preparation for UAM impact on the region. We are developing a simulation and modeling tool that allows regional authorities to consider many factors while deciding the location of vertiport and UAM operations. The objective of this paper is to present the conceptual design of a comprehensive decision making tool to assist planning bodies in developing UAM infrastructure. Specifically, the UAM planning tool will simultaneously consider all relevant local/regional considerations to identify for vertiport locations and UAM operations for a region.

Parimal Kopardekar↗

NASA NTRSDate not supplied

Decision Framework for Classifying the State of Knowledge for Pharmaceutical Stability in Spaceflight

The currently approved medication formulary for exploration-class spaceflight missions presently exceeds 200pharmaceuticals. However, these medications' physical and chemical stability during long-term exposure to the spaceflight environment remains uncharacterized. A multi-disciplinary team of pharmacy and spaceflight subject matter experts (SMEs) collaborated to develop a decision framework that will prioritize medications for future stability research. This framework prioritizes the physical stability, drug quality, and safety of the active pharmaceutical ingredient of finished drug products selected for specific design reference mission (DRM) drug formularies. The United States Pharmacopeia (USP) defines drug stability as "the extent to which a drug product retains, within specified limits, and throughout its period of storage and use, the same properties and characteristics that it possessed at the time of its manufacture.1"The candidate medication is assessed by applying a sequence of drug agnostic questions to evaluate the limits of stability information pertinent to spaceflight. The answers to these questions ultimately lead to one of three characterizations: green (not prioritized for further research for this DRM), yellow (requires further literature review/additional studies), or red (not suitable for this DRM based on current knowledge). The decision framework will rely on several information resources to inform the ultimate decision, including the ExMC Pharmacy Information Database, published literature, FDA/drug manufacturer monographs, and USP. The results of this framework will be used to classify the state of knowledge for pharmaceutical stability for any specified DRM and guide future research efforts accordingly. This presentation will provide an overview of how this decision framework was developed, detail the challenges and limitations of the pathways, and discuss how researchers can apply the lessons learned from this project to future work

S Kurian↗

NASA NTRSDate not supplied

Clinical Decision Support For Exploration Space Flight: Software Augmentation To Enhance Progressively Earth-Independent Medical Operations

This panel identifies the challenges of supporting medical events in deep space using integrated systems software to augment existing capabilities during increasingly Earth-independent missions where an asynchronous communication environment becomes routine. An interdisciplinary team of software designers, physicians, human factors engineers, and computational modelers applied their respective expertise to develop a roadmap for supporting the crew making medical decisions in more autonomous fashion with time-delayed ground support. The first presentation describes predictive modeling implemented to assist medical system design and address risk reduction using robust clinical decision support software. The second presentation details how operational and environmental challenges guide assumptions and, in turn, by what means requirements for medical decision-making in deep space are derived. The third presentation describes and defines the skills and capabilities an exploration spaceflight medical officer will need to perform effectively during extended-duration spaceflight missions. The fourth presentation covers the potential models and function of the software element for supporting clinical decisions. The final presentation covers how these elements may work together with ground support to provide comprehensive care despite deep space travel's extreme challenges and limitations.

Dana Levin↗

NASA NTRSDate not supplied

Ensuring Safe Decision-Making on the Moon and Mars: Cognitive Performance Assessment for Exploration Class Mission EVA

Extravehicular activity (EVA) is one of the most dangerous and cognitively demanding actions that astronauts can execute, and the cognitive demands associated with future partial gravity EVAs on the Moon and Mars are expected to be higher compared to microgravity EVAs currently conducted from the International Space Station. Decrements in cognitive performance present an important risk to crew safety during exploration mission class EVA. Yet there is currently insufficient data to characterize cognitive performance prior to, during, and following EVA. Furthermore, it is still unclear which cognitive domains are most important for conducting mission critical decisions with crew safety implications. To address this gap, we conducted a cognitive task analysis (CTA) of EVA to characterize the procedures, the cognitive demands required, and the critical safety decisions associated with decrements in cognitive performance. We conducted a cognitive task analysis with 15 astronauts and subject matter experts in EVA operations and research. Interviews focused on surface exploration EVA and elicited feedback from experts on the cognitive skills required for specific EVA tasks, including cognitive strategies, critical cues, and decision-making strategies. A cognitive demands table was assembled to consolidate and synthesize the information from all interviews. The information from this cognitive task analysis informs ongoing exploration EVA for Moon to Mars. This work identifies the specific cognitive challenges that astronauts are likely to encounter during surface exploration EVA, and provides the foundation for: (1) prioritized and targeted cognitive performance measurement and functional performance tests, (2) EVA simulation design at varying levels of cognitive workload, and (3) the development of training and other technologies that can improve safe decision-making and inform EVA planning on future spaceflight missions to the Moon and Mars.

Steven R Anderson↗

DOE OSTI2024

Decarbonization Dilemmas: Deliberating Difficult Decisions in Laboratory Design

Dive into the depths of design and decision-making, while we discuss the decarbonization of laboratory buildings! Delve into the dense domain of laboratory design and operations, where every development presents a diverse array of dilemmas and delights. Join us for these dynamic sessions focused on decoding the secrets of sustainable success. Dig deep into the dynamic world of heat pump designs and the delicate balance of heating and cooling loads. Debate between constant and variable fume hood designs, where these decisions determine outcomes. Discover the divergent paths of HVAC system implementation, from the deployment of chilled beams to the diverse array of different terminal unit types. But don't delay; decisive action is demanded for these goals! Dare to dream of decarbonization as we direct discussions on retrofitting existing building stock versus innovative new design approaches. Delve into the depths of debate and emerge with a decisive strategy for sustainable success. Discuss recent discoveries in development from experts associated with existing laboratory buildings with decarbonization goals. These insights and lessons learned will help determine the path forward in our industry's drive for decarbonization designs. Decarbonization is no easy task, but with determination, dedication, and devotion, we can defy the odds and forge a brighter future for laboratory design and operations. Let's dare to decarbonize together!

decarbonization↗

DOE OSTI2025

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Scenario Generation for Built Environment Decision Support under Uncertainty: Case Studies of Airflow Modeling and Climate-Resilient Infrastructure System Design

When confronted with unforeseen challenges, practicing informed decision making is crucial for enhancing resilience in the built environment. While scan-to-building information modeling (BIM) is a well-established approach for creating detailed digital representations of physical assets, its application in assessing and improving infrastructure resilience remains underexplored. This study addresses this gap by proposing a novel application of scan-to-BIM, namely, scan-to-BIM-to-digital twin (S-BIM-DT) workflow. By integrating reality capture and digital twin technologies, this workflow creates continuously updated and accurate digital representations of physical assets, enabling the generation of various scenarios. Unlike traditional methods, the S BIM-DT workflow facilitates continuous model refinement, supporting informed resilience strategies. By combining these technologies into a cohesive process, the workflow facilitates decision making under uncertainty, enabling stakeholders to evaluate and respond to various scenarios effectively. We demonstrate the implementation of the S-BIM-DT workflow through two use cases that highlight its capability to enhance resilience at different scales. The first use case involves the Combined Transportation, Emergency, and Communications Center (CTECC) in Austin, Texas. BIM-enriched computational fluid dynamics (CFD) modeling simulates airflow and develops alternative scenarios for optimizing the heating, ventilation, and air conditioning (HVAC) systems. This approach enhances resilience against airborne health threats in a postCOVID context. The second use case focuses on designated areas within Beaumont, Texas, as part of the Southeast Texas Urban Integrated Field Laboratory (SETx-UIFL) research. By developing inundation maps to assess extreme weather events, this modeling aids in preparedness efforts and informs the development of climate-resilient infrastructure in vulnerable neighborhoods. Results indicate that the S-BIM-DT workflow effectively generates scenarios that enhance resilience in the built environment by facilitating informed decision making. Furthermore, this study serves as a bridge between advanced scan-to-BIM methodologies and the practical strategies needed to improve built infrastructure resilience.

Built environment↗

NASA NTRS2005

Crew collaboration in space: a naturalistic decision-making perspective

Successful long-duration space missions will depend on the ability of crewmembers to respond promptly and effectively to unanticipated problems that arise under highly stressful conditions. Naturalistic decision making (NDM) exploits the knowledge and experience of decision makers in meaningful work domains, especially complex sociotechnical systems, including aviation and space. Decision making in these ambiguous, dynamic, high-risk environments is a complex task that involves defining the nature of the problem and crafting a response to achieve one's goals. Goal conflicts, time pressures, and uncertain outcomes may further complicate the process. This paper reviews theory and research pertaining to the NDM model and traces some of the implications for space crews and other groups that perform meaningful work in extreme environments. It concludes with specific recommendations for preparing exploration crews to use NDM effectively.

Review, Tutorial↗