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NASA NTRSDate not supplied

A Decision Support System for Extravehicular Operations Under Significant Communication Latency

Within the next few decades, humanity hopes to perform extravehicular activities (EVAs) on the surface of Mars; however, several technical and operational challenges must first be overcome. Foremost among these challenges is managing a significant two-way communication latency between Earth and Mars. Current and historical paradigms of EVA operations have required near-real-time communication between the crewmember(s) performing an EVA and an Earth-based mission control. Next-generation operational paradigms for supporting deep space exploration will necessitate a distributed decision authority system, including delayed Earth-based mission control, the on-planet extravehicular crewmember(s), and intermediate mission support from intravehicular crewmember(s) within real-time communication range. This latter group is of particular interest: they must provide operations support without the plentiful resources available to mission control on Earth. For this purpose, NASA is developing the Personalized EVA Informatics and Decision Support (PersEIDS) software platform. PersEIDS is designed to bolster operator situational awareness and offload operator workload by automating the tracking and projection of consumables usage over an EVA timeline, providing real-time probabilistic safety assessments of an EVA timeline given consumables constraints, and recommending alternative EVA timeline(s) when the active timeline is not expected to be completed under consumables limits. The PersEIDS concept of operations, use cases, and models will be presented. A limited version of PersEIDS was demonstrated during a three-day-long study where each day a roughly four-hour-long simulated Martian EVA was performed in virtual reality at the NASA Johnson Space Center. The first day was a control trial without PersEIDS support; the second and third days represented different levels of decision support provided by PersEIDS to the intravehicular crewmember acting as mission control. With PersEIDS support, the IV crewmember was able to manage the mission to completion faster and with more remaining consumables; however, additional testing is required to understand confounding factors, e.g. training bias.

Mars↗

NASA NTRS2011

Concept of Operations for Integrated Intelligent Flight Deck Displays and Decision Support Technologies

The document describes a Concept of Operations for Flight Deck Display and Decision Support technologies which may help enable emerging Next Generation Air Transportation System capabilities while also maintaining, or improving upon, flight safety. This concept of operations is used as the driving function within a spiral program of research, development, test, and evaluation for the Integrated Intelligent Flight Deck (IIFD) project. As such, the concept will be updated at each cycle within the spiral to reflect the latest research results and emerging developments

Bailey, Randall E.↗

NASA NTRSDate not supplied

On the Use of Resilience Models as Digital Twins for Operational Support and In time Decision Making

Human error is a major contributor to accidents and performance losses in complex engineered systems. If one examines these human error caused failures further, a specific cause, the lack of situation awareness, has dominated as a major cause of human errors that instigate latent or catastrophic failures in complex systems. Studies of aviation accidents involving major air carriers revealed that situation awareness was the root cause of around 90% of accidents involving pilot error. Another study explored offshore drilling accidents involving human error and found that 40% of accidents were directly attributed to the loss of situation awareness. Studies of human errors in other domains such as nuclear power, air traffic control, process industry, and advanced driving show that loss of SA was a root cause in a majority of the events. Situation awareness-related failures are not only common but also costly and fatal (e.g., Bhopal Gas Leak, Air France 447 Flight Crash). Thus, the concept of situation awareness has emerged as an important construct in human factors, resulting in numerous models and measurement methods to aid in promoting appropriate levels of situation awareness.

Lukman Irshad↗

NASA NTRSDate not supplied

Impact, A Tool Suite for Crew Health and Performance System Trade Analyses and Decision to Support - Transition to Operations

Mission planners, systems engineers, and clinicians that support crew health and performance face very difficult choices on upcoming exploration missions. Given that there will be a heavily constrained mass and volume allocation for a medical system on these missions, what medical capability should be manifested to minimize both medical risk and mission risk? Given that not all promising research and technology proposals can be funded, how can proposals be prioritized so that those funded research investments produce the maximum benefit in reducing overall medical risk? The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) project seeks to answer these kinds of questions and others to support upcoming exploration missions. IMPACT enables risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This presentation will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include an updated medical database, consisting of an Evidence Library for medical conditions and a medical item database (MedID) for medical resources, dynamic Probabilistic Risk Assessment (PRA) capabilities, System Modeling Language (SysML) models, and contextual data visualizations of output data. IMPACT is the result of a multi-center collaborative effort. The trade space analyses performed by IMPACT can directly inform mission, vehicle, and habitat development by quantifying medical risk, given a design reference mission, crew attributes and a set of medical capabilities. This presentation will update the audience on the development status of the IMPACT tool suite as it comes out of its System Acceptance Review (SAR) and nears Transition to Operations (TTO). It will review IMPACT’s constituent parts, briefly discuss typical outputs, and outline the plans for transitioning to operations, currently scheduled for later in FY24. Recent development successes on the IMPACT project include the integration of the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) v2.0 to accommodate segmented missions with multiple carriers and medical systems, full onboarding of the IMPACT Medical Database (IMPACT-MD), clustering medical resources and skills into medical capabilities and mutually-dependent bundles, verification of IMPACT-MD, and the ability to perform trade analyses on different medical sets, different design reference missions (DRM), with different crew complements and extra-vehicular activity (EVA) schedules.

IMPACT↗

NASA NTRS2013

Towards a Decision Support System for Space Flight Operations

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) has put in place a Model Based Systems Engineering (MBSE) technological framework for the development and execution of the Flight Production Process (FPP). This framework has provided much added value and return on investment to date. This paper describes a vision for a model based Decision Support System (DSS) for the development and execution of the FPP and its design and development process. The envisioned system extends the existing MBSE methodology and technological framework which is currently in use. The MBSE technological framework currently in place enables the systematic collection and integration of data required for building an FPP model for a diverse set of missions. This framework includes the technology, people and processes required for rapid development of architectural artifacts. It is used to build a feasible FPP model for the first flight of spacecraft and for recurrent flights throughout the life of the program. This model greatly enhances our ability to effectively engage with a new customer. It provides a preliminary work breakdown structure, data flow information and a master schedule based on its existing knowledge base. These artifacts are then refined and iterated upon with the customer for the development of a robust end-to-end, high-level integrated master schedule and its associated dependencies. The vision is to enhance this framework to enable its application for uncertainty management, decision support and optimization of the design and execution of the FPP by the program. Furthermore, this enhanced framework will enable the agile response and redesign of the FPP based on observed system behavior. The discrepancy of the anticipated system behavior and the observed behavior may be due to the processing of tasks internally, or due to external factors such as changes in program requirements or conditions associated with other organizations that are outside of MOD. The paper provides a roadmap for the three increments of this vision. These increments include (1) hardware and software system components and interfaces with the NASA ground system, (2) uncertainty management and (3) re-planning and automated execution. Each of these increments provide value independently; but some may also enable building of a subsequent increment.

Meshkat, Leila↗

NASA NTRS2013

Towards a decision support system for space flight operations

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) has put in place a Model Based Systems Engineering (MBSE) technological framework for the development and execution of the Flight Production Process (FPP). This framework has provided much added value and return on investment to date. This paper describes a vision for a model based Decision Support System (DSS) for the development and execution of the FPP and its design and development process. The envisioned system extends the existing MBSE methodology and technological framework which is currently in use. The MBSE technological framework currently in place enables the systematic collection and integration of data required for building an FPP model for a diverse set of missions. This framework includes the technology, people and processes required for rapid development of architectural artifacts. It is used to build a feasible FPP model for the first flight of spacecraft and for recurrent flights throughout the life of the program. This model greatly enhances our ability to effectively engage with a new customer. It provides a preliminary work breakdown structure, data flow information and a master schedule based on its existing knowledge base. These artifacts are then refined and iterated upon with the customer for the development of a robust end-to-end, high-level integrated master schedule and its associated dependencies. The vision is to enhance this framework to enable its application for uncertainty management, decision support and optimization of the design and execution of the FPP by the program. Furthermore, this enhanced framework will enable the agile response and redesign of the FPP based on observed system behavior. The differences between the anticipated system behavior and the observed behavior may be due to the processing of tasks internally, or due to external factors such as changes in program requirements or conditions associated with other organizations that are outside of MOD. The paper provides a roadmap for the four increments of this vision. These increments include (1) the existing capabilities (2) hardware and software system components and interfaces with the NASA ground system, (3) uncertainty management and (4) re-planning and automated execution. Each of these increments provides value independently; but some may also enable building of a subsequent increment.

Ruszkowski, James↗

NASA NTRS2015

Evaluation of Pushback Decision-Support Tool Concept for Charlotte Douglas International Airport Ramp Operations

This paper proposes a new departure pushback decision-support tool (DST) for airport ramp-tower controllers. It is based on NASA's Spot and Runway Departure Advisor (SARDA) collaborative decision-making concept, except with the modification that the gate releases now are controlled by tactical pushback (or gate-hold) advisories instead of strategic pre-assignments of target pushback times to individual departure flights. The proposed ramp DST relies on data exchange with the airport traffic control tower (ATCT) to coordinate pushbacks with the ATCT's flow-management intentions under current operational constraints, such as Traffic Management Initiative constraints. Airlines would benefit in reduced taxi delay and fuel burn. The concept was evaluated in a human-in-the-loop simulation experiment with current ramp-tower controllers at the Charlotte Douglas International Airport as participants. The results showed that the tool helped reduce taxi time by one minute per flight and overall departure flight fuel consumption by 10-12% without reducing runway throughput. Expect Departure Clearance Time (EDCT) conformance also was improved when advisories were provided. These benefits were attained without increasing the ramp-tower controllers' workload. Additionally, the advisories reduced the ATCT controllers' workload.

aerospace human factors↗

NASA NTRS1993

DESSY: Making a real-time expert system robust and useful

As the complexity and expected life-span of modern space systems continue to increase, the need for real-time data monitoring and failure analysis becomes more critical to their successful operation. The DEcision Support SYstem (DESSY) is a joint effort by the Intelligent Systems Branch/ER2 and the Remote Manipulator System (RMS) Section/DF44 to develop an expert system for the monitoring of the Payload Deployment and Retrieval System (PDRS). DESSY users, the RMS flight controllers, are provided with user interface enhancements and automated monitoring of system state (physical orientation) and status (operational health). Currently, a DESSY prototype for the Manipulator Positioning Mechanism (MPM) and Manipulator Retention Latches (MRL) of the PDRS has been developed and successfully demonstrated during the STS-49 and STS-46 missions. Expert systems for monitoring real-time operations must not only accurately represent domain knowledge, but also address the challenges of using unfiltered real-time data as input. This paper describes the methods and design strategies developed to overcome problems with real-time data in the NASA Mission Control Center. Types of data problems addressed are as follows: (1) loss of data; (2) erratic data; and (3) data lags and irregularities during state transition. Methods used to handle data problems include rule disabling for ignoring data when data quality is uncertain, context-sensitive bounded pattern recognition for minimizing incorrect conclusions based on bad data, and graceful recovery through system correction when reliable data returns. This combination of methods with an object-based modular DESSY design assures a robust program capable of lengthy periods of uninterrupted use in operations.

Land, Sherry A.↗

NASA NTRS2007

Decision Support Systems for Launch and Range Operations Using Jess

The virtual test bed for launch and range operations developed at NASA Ames Research Center consists of various independent expert systems advising on weather effects, toxic gas dispersions and human health risk assessment during space-flight operations. An individual dedicated server supports each expert system and the master system gather information from the dedicated servers to support the launch decision-making process. Since the test bed is based on the web system, reducing network traffic and optimizing the knowledge base is critical to its success of real-time or near real-time operations. Jess, a fast rule engine and powerful scripting environment developed at Sandia National Laboratory has been adopted to build the expert systems providing robustness and scalability. Jess also supports XML representation of knowledge base with forward and backward chaining inference mechanism. Facts added - to working memory during run-time operations facilitates analyses of multiple scenarios. Knowledge base can be distributed with one inference engine performing the inference process. This paper discusses details of the knowledge base and inference engine using Jess for a launch and range virtual test bed.

Thirumalainambi, Rajkumar↗

NASA NTRS2013

Towards a Decision Support System for Space Flight Operations

No abstract available

Uncertainty Management↗

NASA NTRSDate not supplied

GeoNEX: A geostationary earth observatory at NASA Earth eXchange: Earth monitoring from operational geostationary satellite systems

The latest generation of geostationary satellites (Himawari 8/9, GOES-16/17, FY-4, GK-2A) carries sensors that closely mimic the spatial and spectral characteristics of widely used polar-orbiting, global monitoring sensors such as MODIS and VIIRS. When combined, data from various currently operating/planned geostationary platforms provide a geo-ring of hyper-temporal (5-10 minutes), multispectral observations at spatial resolutions as high as 500 m. These high frequency observations offer exciting new possibilities for monitoring our planet, including better retrievals of geophysical variables by overcoming cloud cover, enabling studies of diurnally varying phenomena in the atmosphere, land, and the oceans, and support operational decision-making in agriculture, hydrology and disaster management. The NASA Earth Exchange (NEX) team, in collaboration with scientists from JAXA, KARI, NOAA and other international institutions, created the GeoNEX (www.nasa.gov/geonex) pipeline to integrate data from all available geostationary platforms and produce and distribute spatially, temporally, and radiometrically consistent data for the earth science community. We envision various institutions adapting the Geo component (e.g., GeoNOAA, GeoKARI, GeoChiba, GeoJAXA, GeoCMA) and customizing the pipeline and downstream products to serve the local/regional research and applied science communities.

Ramakrishna R Nemani↗

NASA NTRSDate not supplied

GeoNEX: A Geostationary Earth Observatory

GeoNEX↗

NASA NTRSDate not supplied

Using fmdtools Models as Low-fidelity Digital Twins for Operations Support and In-time Decision Making

Explore the source record for details and available documents.

Lukman Irshad↗

NASA NTRSDate not supplied

Satellite-assisted Evaluation of Irrigation Application Efficiency for Lettuce in the Salinas Valley

The Salinas Valley region of California’s Central Coast is a majorU.S. vegetable producer. Lettuce is a leading commodity, grown on about 100,000 acres with annual value near $1.4B (2022 Monterey County Ag Report). Vegetables crops in the area aretypically well watered and fertilized in pursuit of commercial yield and quality standards. Information on irrigation application efficiency, meanwhile, can enhance waterresource sustainability and mitigate impacts of nitrate infiltrationon groundwater quality. The Crop Consumptive Use Fraction (CCUF), previously recommended by the California Department of Water Resources, is a metric that expresses evapotranspiration (ET) of applied water relative to total applied water. More recently under Assembly Bill 1668 addressing water conservation and drought planning, the metric can be used to help quantify application efficiency to support preparation of agricultural water management plans in the state. This study will report CCUF evaluations from several commercial lettuce plantings in the Salinas Valley during the 2022 dry season (late spring-early fall). The evaluations were derived from satellite-based observations of cropET from the OpenETcloud application, and applied water monitored by in-line flowmetersand supplemented by grower reports of irrigation system runtime. The study will also compare observed (satellite-based) ET with modeled ET from the CropManage irrigationand nutrient management decision-support system operated by Cooperative Extension. Additional study in cool-season vegetables is ongoing in 2023.

Satellite-assisted↗

NASA NTRS1992

Cognitive engineering models in space systems

NASA space systems, including mission operations on the ground and in space, are complex, dynamic, predominantly automated systems in which the human operator is a supervisory controller. The human operator monitors and fine-tunes computer-based control systems and is responsible for ensuring safe and efficient system operation. In such systems, the potential consequences of human mistakes and errors may be very large, and low probability of such events is likely. Thus, models of cognitive functions in complex systems are needed to describe human performance and form the theoretical basis of operator workstation design, including displays, controls, and decision support aids. The operator function model represents normative operator behavior-expected operator activities given current system state. The extension of the theoretical structure of the operator function model and its application to NASA Johnson mission operations and space station applications is discussed.

Mitchell, Christine M.↗

NASA NTRS2017

Beyond Control Centers

For NASA's Resource Prospector (RP) Lunar Rover Mission, we are moving away from a control center concept, to a fully distributed operation utilizing control nodes, with decision support from anywhere via mobile devices. This operations concept will utilize distributed information systems, notifications, mobile data access, and optimized mobile data display for off-console decision support. We see this concept of operations as a step in the evolution of mission operations from a central control center concept to a mission operations anywhere concept. The RP example is part of a trend, in which mission expertise for design, development and operations is distributed across countries and across the globe. Future spacecraft operations will be most cost efficient and flexible by following this distributed expertise, enabling operations from anywhere. For the RP mission we arrived at the decision to utilize a fully distributed operations team, where everyone operates from their home institution, based on evaluating the following factors: the requirement for physical proximity for near-real time command and control decisions; the cost of distributed control nodes vs. a centralized control center; the impact on training and mission preparation of flying the team to a central location. Physical proximity for operational decisions is seldom required, though certain categories of decisions, such as launch abort, or close coordination for mission or safety-critical near-real-time command and control decisions may benefit from co-location. The cost of facilities and operational infrastructure has not been found to be a driving factor for location in our studies. Mission training and preparation benefit from having all operators train and operate from home institutions.

Operations↗

NASA NTRS1991

Expert system decision support for low-cost launch vehicle operations

Progress in assessing the feasibility, benefits, and risks associated with AI expert systems applied to low cost expendable launch vehicle systems is described. Part one identified potential application areas in vehicle operations and on-board functions, assessed measures of cost benefit, and identified key technologies to aid in the implementation of decision support systems in this environment. Part two of the program began the development of prototypes to demonstrate real-time vehicle checkout with controller and diagnostic/analysis intelligent systems and to gather true measures of cost savings vs. conventional software, verification and validation requirements, and maintainability improvement. The main objective of the expert advanced development projects was to provide a robust intelligent system for control/analysis that must be performed within a specified real-time window in order to meet the demands of the given application. The efforts to develop the two prototypes are described. Prime emphasis was on a controller expert system to show real-time performance in a cryogenic propellant loading application and safety validation implementation of this system experimentally, using commercial-off-the-shelf software tools and object oriented programming techniques. This smart ground support equipment prototype is based in C with imbedded expert system rules written in the CLIPS protocol. The relational database, ORACLE, provides non-real-time data support. The second demonstration develops the vehicle/ground intelligent automation concept, from phase one, to show cooperation between multiple expert systems. This automated test conductor (ATC) prototype utilizes a knowledge-bus approach for intelligent information processing by use of virtual sensors and blackboards to solve complex problems. It incorporates distributed processing of real-time data and object-oriented techniques for command, configuration control, and auto-code generation.

Szatkowski, G. P.↗

NASA NTRS2018

Airplane Capabilities: Translating Non-Normal Information for Operational Decision-Making

We consider how a jet transport airplane interface supports the flight crew in managing airplane system failures (or non-normals) for continued safe flight and landing. The existing state of the art starts with a list of airplane system component failures and asks the flight crew to determine, with the help of non-normal procedures, the operational consequences of those failures. As airplane systems become more complex and interconnected, the flight crew's ability to determine operational consequences will become inadequate. We describe an approach that attempts to translate airplane system failures directly into airplane "capabilities," which is a set of basic airplane functions, such as the ability to stop after landing. This paper describes the overall framework for supporting flight crews in operational decision making and the initial efforts to develop a language and display concepts.

managing airplane system non-normals↗