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Winds in excess of 74.5 knots could cause severe damage to a space shuttle on the launch pad. Current plans exist for rollback to the Vehicle Assembly Building, but require 48 hour leadtime to implement. Decisions based upon cost/loss are evaluated to ascertain whether predetermined forecast probabilities for rollback/rideout decisions can be made far in advance of hurricane seasons for use in decision making.
The paper describes an experiment using the Flight Planning Testbed (FPT) in which 27 airline dispatchers were studied. Five general questions was addresses in the study: Under what circumstances does the introduction of computer-generated suggestions (flight plans) influence the planning behavior of dispatchers; what is the nature of such influences; How beneficial are the general design concepts underlying FPT; How effective are the specific implementation decisions made in realizing these general design concepts; How effectively do dispatchers evaluate situations requiring replanning and how effectively do they identify appropriate solutions to these situations. The study leaves little doubt that the introduction of computer-generated suggestions for solving a flight planning problem can have a marked impact on the cognitive processes of the user and on the ultimate plan selected.
It is difficult in a large, complex computer program to ensure that it meets the specified requirements. As the program evolves over time, a11 program constraints originally elicited during the requirements phase must be maintained. In addition, during the life cycle of the program, requirements typically change and the program must consistently reflect those changes. Imagine the following scenario. Company X wants to develop a system to automate its assembly line. With such a large system, there are many different stakeholders, e.g., managers, experts such as industrial and mechanical engineers, and end-users. Requirements would be elicited from all of the stake holders involved in the system with each stakeholder contributing their point of view to the requirements. For example, some of the requirements provided by an industrial engineer may concern the movement of parts through the assembly line. A point of view provided by the electrical engineer may be reflected in constraints concerning maximum power usage. End-users may be concerned with comfort and safety issues, whereas managers are concerned with the efficiency of the operation. With so many points of view affecting the requirements, it is difficult to manage them, communicate information to relevant stakeholders. and it is likely that conflicts in the requirements will arise. In the coding process, the implementors will make additional assumptions and interpretations on the design and the requirements of the system. During any stage of development, stakeholders may request that a requirement be added or changed. In such a dynamic environment, it is difficult to guarantee that the system will preserve the current set of requirements. Tracing, the mapping between objects in the artifacts of the system being developed, addresses this issue. Artifacts encompass documents such as the system definition, interview transcripts, memoranda, the software requirements specification, user's manuals, the functional specifications, design reports, and system code. Tracing helps 1) validate system features against, the requirement specification, 2) identify error sources and, most importantly, 3) manage change. With so many people involved in the development of the system, it becomes necessary to identify the reasons behind the design requirements or the implementation decisions. This paper is concerned with an approach that maps documents to constraints that capture properties of and relationships between the objects being modeled by the program. Section 2 provides the reader with a background on traceability tools. Section 3 gives a brief description of the context monitoring system on which the approach suggested in this paper is based. Section 4 presents an overview of our approach to providing traceability. The last section presents our future direction of research.
The Orion spacecraft utilizes a system of Compact Unique Identifiers (CUIs) to identify telemetry measurements in the process of communicating with the ground. While avionic systems are still in development, simulation of telemetric data must be used for the development of dependent systems. The SuperScript Language (SSL) developed by Honeywell Aerospace provides users a programming language for simulation of this data. The use of tools that provide SSL generation capabilities at a higher level allows for faster and more efficient generation of simulation waveforms. Previous SSL Generator implementations such as Orion Scripted Interface Generator (OrionSIG) relied on software supported routines to generate simulation points for the SSL scripts. This provided relevant simulation capabilities, but was reduced in flexibility by the available software routines available. For this approach, software supported routines have been entirely dropped. Rather, the user is required to enter their own simulation waveform input. This allows arbitrarily complex functions to be utilized simulated with maximum degree of freedom for the user.: The idea for this version of the SSL Generator was a mapping of arbitrary waveforms to Orion CUI telemetry data. The SSL Generator implemented offers a higher degree of freedom to the user than previous versions while attempting to maintain abstraction from the underlying SSL script and the Orion CUI data types. As such, many mappings of waveforms to CUIs will simply not work. Certain implementation decisions were made to minimize the damage caused by these unintended side-effects, such as saturation of values exceeding the maximum or minimum bounds of the data type. Other details, such as signed values being represented in unsigned data types, present problems which cant be fixed to an acceptable degree of intended behavior without user input. Awareness of the limitations of all layers of the software stack will increase the productivity of the end-user.
The Jet Propulsion Laboratory Innovation Foundry has established a new approach for exploring, developing, and evaluating early concepts with a group called the Architecture Team. The Architecture Team combines innovative collaborative methods and facilitated sessions with subject matter experts and analysis tools to help mature mission concepts. Science, implementation, and programmatic elements are all considered during an A-Team study. In these studies, Concept Maturity Levels are used to group methods. These levels include idea generation and capture (Concept Maturity Level 1), initial feasibility assessment (Concept Maturity Level 2), and trade space exploration (Concept Maturity Level 3). Methods used for exploring the science objectives, feasibility, and scope will be described including the use of a new technique for understanding the most compelling science, called a Science Return Diagram. In the process of developing the Science Return Diagram, gradients in the science trade space are uncovered along with their implications for implementation and mission architecture. Special attention is paid toward developing complete investigations, establishing a series of logical claims that lead to the natural selection of a measurement approach. Over 20 science-focused A-Team studies have used these techniques to help science teams refine their mission objectives, make implementation decisions, and reveal the mission concept's most compelling science. This article will describe the A-Team process for exploring the mission concept's science trade space and the Science Return Diagram technique.
The Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) is a 2-year study by the U.S. Department of Energy's (DOE's) Grid Deployment Office and six national laboratories to comprehensively analyze stakeholder-driven pathways to Puerto Rico's clean energy future. In Year 1 of the study, the PR100 team rigorously modeled and analyzed scenarios that meet Puerto Rico's renewable energy targets and achieve short-term recovery goals and long-term energy resilience. This presentation will be showcased in the form of a public webinar, which summarizes PR100 progress in Year 1, provides considerations that can inform potential funding and implementation decisions by key federal and local agencies and stakeholders. The presentation follows the publication in July 2022 of a PR100 Six-Month Progress Update (in English and Spanish), as well as public webinars in February 2022 to kick off the study and in July 2022 to present the 6-month update. A final written report and web-based visuals will be published in late 2023. All publications and public events associated with the study will be available in Spanish and English. This report is also available in Spanish: https://www.nrel.gov/docs/fy23osti/85201.pdf.
Abstract The effects of climate change, population growth, and future hydrologic uncertainties necessitate increased water conservation, new water resources, and a shift towards sustainable urban water supply portfolios. Diversifying water portfolios with non-traditional water sources can play a key role. Rooftop harvested rainwater (RHRW), atmospheric and condensate harvesting, stormwater, recycled wastewater and greywater, and desalinated seawater and brackish water are all currently utilized and rapidly emerging non-traditional water sources. This review explores the status and trends around these non-traditional water sources, and reviews approaches and models for prioritizing, predicting, and quantifying metrics of concern. The analysis presented here suggests that understanding the challenges of location specific scenarios, socioeconomic knowledge gaps, water supply technologies, and/or water management structure is the crucial first step in establishing a model or framework approach to provide a strategy for improvement going forward. The findings of this study also suggest that clear policy guidance and onsite maintenance is necessary for variable water quality concerns of non-traditional sources like harvested rainwater and greywater. In addition, use of stormwater or reuse of wastewater raises public health concerns due to unknown risks and pathogen levels, thus rapid monitoring technologies and transparent reporting systems can facilitate their adoption. Finally, cost structure of desalination varies significantly around the world, largely due to regulatory requirements and local policies. Further reduction of its capital cost and energy consumption is identified as a hurdle for implementation. Overall, models and process analyses highlight the strength of comparative assessments across scenarios and water supply options.
An all digital multiple axis, multiple input, multiple output pilot model flies five different tasks in aircraft of different classes while encountering turbulence represented by the Dryden spectral model. Six degree of freedom linearized aircraft equations of motion are used together with a generalized stability augmentation system. The pilot model consists of two parts: (1) a decision maker, and (2) a control action implementer. This decision maker selects the critical variable and predicts the error at action implementation. The control action implementer decides the magnitude of control to be applied and applies it. This system has been successfully compared with hybrid/analog man in the loop simulations of the F-5, A-7, T-33, and 707 aircraft, thus validating the all digital simulation and the decision and control pilot model.
Recognizing the significance of NASA remote sensing Earth science data in monitoring and better understanding our planet s natural environment, NASA has implemented the Decision Support Through Earth Science Research Results program (NASA ROSES solicitations). a) This successful program has yielded several monitoring, surveillance, and decision support systems through collaborations with benefiting organizations. b) The Goddard Space Flight Center (GSFC) Earth Sciences Data and Information Services Center (GES DISC) has participated in this program on two projects (one complete, one ongoing), and has had opportune ad hoc collaborations gaining much experience in the formulation, management, development, and implementation of decision support systems utilizing NASA Earth science data. c) In addition, GES DISC s understanding of Earth science missions and resulting data and information, including data structures, data usability and interpretation, data interoperability, and information management systems, enables the GES DISC to identify challenges that come with bringing science data to decision makers. d) The purpose of this presentation is to share GES DISC decision support system project experiences in regards to system sustainability, required data quality (versus timeliness), data provider understanding of how decisions are made, and the data receivers willingness to use new types of information to make decisions, as well as other topics. In addition, defining metrics that really evaluate success will be exemplified.
The goal of a Quality Management System (QMS) as specified in ISO 9001 and AS9100 is to provide assurance to the customer that end products meet specifications. Measuring devices, often called measuring and test equipment (MTE), are used to provide the evidence of product conformity to specified requirements. Unfortunately, processes that employ MTE can become a weak link to the overall QMS if proper attention is not given to the measurement process design, capability, and implementation. Documented "decision rules" establish the requirements to ensure measurement processes provide the measurement data that supports the needs of the QMS. Measurement data are used to make the decisions that impact all areas of technology. Whether measurements support research, design, production, or maintenance, ensuring the data supports the decision is crucial. Measurement data quality can be critical to the resulting consequences of measurement-based decisions. Historically, most industries required simplistic, one-size-fits-all decision rules for measurements. One-size-fits-all rules in some cases are not rigorous enough to provide adequate measurement results, while in other cases are overly conservative and too costly to implement. Ideally, decision rules should be rigorous enough to match the criticality of the parameter being measured, while being flexible enough to be cost effective. The goal of a decision rule is to ensure that measurement processes provide data with a sufficient level of quality to support the decisions being made - no more, no less. This paper discusses the basic concepts of providing measurement-based evidence that end products meet specifications. Although relevant to all measurement-based conformance tests, the target audience is the MTE end-user, which is anyone using MTE other than calibration service providers. Topics include measurement fundamentals, the associated decision risks, verifying conformance to specifications, and basic measurement decisions rules.
National Aeronautics and Space Administration (NASA)’s Exploration and In-Space Services (NExIS) Division of Goddard Space Flight Center has been developing technology for the On-orbit Servicing, Assembly and Manufacturing Mission 1 (OSAM-1) to robotically refuel heritage and new satellites on-orbit. OSAM-1, formerly known as Restore-L, successfully passed an important NASA milestone called Key Decision Point-C (KDP-C), receiving agency-level approval for its implementation. The decision point also establishes the mission’s official schedule and budget. The OSAM-1 spacecraft, Servicing Payload and the Space Infrastructure Dexterous Robot (SPIDER) payload will refuel a satellite in space, assemble a communications antenna and manufacture a beam. By demonstrating these capabilities, the mission is advancing never-before tested technologies for use in future missions (by NASA and other government organizations and private industry). The mission is funded by the Technology Demonstration Missions program within NASA’s Space Technology Mission Directorate. This paper covers a review of servicing extensibility and critical technologies that are being developed within NExIS with a focus on the fluid transfer refueling technology within the framework of the Propellant Transfer Subsystem (PTS). An overview of the planned initial technology demonstration servicing mission via the OSAM-1 Space Vehicle is provided as an extension of the technology development progress reported in 20181, and 20192. The general objectives, challenges, and key technologies are presented as an introduction to the context of the OSAM-1 mission, and a precursor to the OSAM-1 PTS specific development status. Development and progress of the Hose Management Assembly (HMA) and Propellant Transfer Assembly (PTA) are discussed. HMA risk reduction test results including those of thermal vacuum testing are presented. Important analytical results are discussed and progress of drawings and procedures developed for the fabrication and testing phase are shown. A summary of the PTS overall verification status and design activities prepared for the critical design peer review are presented. Procurement progress is shown with status on long, medium, and short term efforts to acquire the hardware required to support the mission objectives. Technology development, challenges, and testing status are discussed with particular regard to the OSAM-1 specific key assemblies including the PTA and HMA along with the overall integrated flight mockup of the spacecraft to client fluid transfer testing conducted in the servicing testbeds. The paper concludes with key milestones leading to the goal of on-orbit refueling to be demonstrated in 2024. The overall mission Launch Readiness Date (LRD) has been realigned to accommodate budget profiles and incorporation of additional on-orbit assembly and manufacturing demonstration efforts.
Recognizing the significance of NASA remote sensing Earth science data in monitoring and better understanding our planet's natural environment, NASA Earth Applied Sciences has implemented the 'Decision Support Through Earth Science Research Results' program. Several applications support systems through collaborations with benefiting organizations have been implemented. The Goddard Earth Sciences Data and Information Services Center (GES DISC) has participated in this program on two projects (one complete, one ongoing), and has had opportune ad hoc collaborations utilizing NASA Earth science data. GES DISC's understanding of Earth science missions and resulting data and information enables the GES DISC to identify challenges that come with bringing science data to research applications. In this presentation we describe applications research projects utilizing NASA Earth science data and a variety of resulting GES DISC applications support system project experiences. In addition, defining metrics that really evaluate success will be exemplified.
This talk will provide an overview of an overall Mars Sample Return campaign architecture, and the current concepts and options for the architecture and design of a Mars Sample Retrieval Lander (called Sample Retrieval Lander, SRL). The overall SRL mission concept and key mission objectives will be described, including the mission’s concept of operations and a notional timeline from launch to entry, through surface operations, to delivery of the samples to Mars orbit. The overall lander vehicle concept will be described, including current options being evaluated. Key lander element options will be discussed, including a Mars Ascent Vehicle (MAV), Fetch Rover, Orbiting Sample container (OS), and tube transfer robotics systems. Details of the notional Fetch Rover functions, constraints and operations will be discussed. Specific challenges and approaches for addressing those challenges will be discussed, including key technical margins and planetary protection. Major trade studies and implementation approaches and a proposed schedule will also be discussed. The information provided about possible Mars sample return architectures and concepts is for planning and discussion purposes only. NASA and ESA have made no official decisions to implement Mars Sample Return.
This talk will provide an overview of an overall Mars Sample Return campaign architecture, and the current concepts and options for the architecture and design of a Mars Sample Retrieval Lander (called Sample Retrieval Lander, SRL). The overall SRL mission concept and key mission objectives will be described, including the mission’s concept of operations and a notional timeline from launch to entry, through surface operations, to delivery of the samples to Mars orbit. The overall lander vehicle concept will be described, including current options being evaluated. Key lander element options will be discussed, including a Mars Ascent Vehicle (MAV), Fetch Rover, Orbiting Sample container (OS), and tube transfer robotics systems. Details of the notional Fetch Rover functions, constraints and operations will be discussed. Specific challenges and approaches for addressing those challenges will be discussed, including key technical margins and planetary protection. Major trade studies and implementation approaches and a proposed schedule will also be discussed. The information provided about possible Mars sample return architectures and concepts is for planning and discussion purposes only. NASA and ESA have made no official decisions to implement Mars Sample Return.
We describe the implementation of Boosted Decision Trees in the hls4ml library, which allows the translation of a trained model into FPGA firmware through an automated conversion process. Thanks to its fully on-chip implementation, hls4ml performs inference of Boosted Decision Tree models with extremely low latency. With a typical latency less than 100 ns, this solution is suitable for FPGA-based real-time processing, such as in the Level-1 Trigger system of a collider experiment. These developments open up prospects for physicists to deploy BDTs in FPGAs for identifying the origin of jets, better reconstructing the energies of muons, and enabling better selection of rare signal processes.
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.
The development of autonomous systems requires a rigorous process that can guarantee a system’s reliability in critical applications. At its core, an autonomous system bases its behavior on a well-defined decision making system. In this paper, we present a methodological basis for the design, formalization and formal verification of Decision Making systems for autonomous agents. The approach is generally applicable to operational objectives that can be functionally decomposed and subsequently represented as Hierarchical Finite State Machines. As a case study, we present the application of this method to implement a Decision Making model in Simulink. Furthermore, we present how we use NASA’s FRET tool to write requirements in structured natural language and generate formal specifications that can be automatically digested by NASA’s CoCoSim tool. Finally, we present how, by leveraging CoCoSim, we perform formal verification against the Simulink model and present analysis results.
The development of autonomous systems requires a rigorous process that can guarantee a system’s reliability in critical applications. At its core, an autonomous system bases its behavior on a well-defined decision making system. In this paper, we present a methodological basis for the design, formalization and formal verification of Decision Making systems for autonomous agents. The approach is generally applicable to operational objectives that can be functionally decomposed and subsequently represented as Hierarchical Finite State Machines. As a case study, we present the application of this method to implement a Decision Making model in Simulink. Furthermore, we present how we use NASA’s FRET tool to write requirements in structured natural language and generate formal specifications that can be automatically digested by NASA’s CoCoSim tool. Finally, we present how, by leveraging CoCoSim, we perform formal verification against the Simulink model and present analysis results.