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This paper describes an automated decision support system designed to facilitate the management of a continuously changing portfolio of technologies as new technologies are deployed and older technologies are decommissioned.
Natural environments were determined for each of the Shuttle mission phases as a guide to design and operational decisions. The phases were categorized as mission planning, prelaunch, launch/ascent, booster recovery, on-orbit, and descent and landing. The wind criteria included magnitude/height, gust factors, turbulence spectra, wind shear, and vector properties for all flight and ground phases. A maximum design risk of 1 percent was permitted for the windiest two weeks the Shuttle would have to stay on the pad at either KSC or Vandenberg. The ascent wind profile was determined with the Jimsphere Vector Wind profiles, which cover the wind speeds, shears, gusts, and turbulence up to 18 km altitude. The models used to define the environmental situations of the Shuttle comprised wind, thermodynamic, space, lightning, sea state, mission analysis, and ET ice data base formulations.
Sounds of Silence. Using a general systems approach, factors and components of the acoustical design process for an isolated, confined space community in a torus space enclosure are considered. These components include the following: organizational structure and its effect on alternatives; problem definition and limits; criteria and priorities; methods of data gathering; modelling and measurement of the whole system and its components; decision methods; and design scenario of the acoustics of the complex, socio-technical space community system with emphasis on the human factors.
The requirement for ultradependability of computer systems in future avionics and space applications necessitates a top-down, integrated systems engineering approach for design, implementation, testing, and operation. The functional analyses of hardware and software systems must be combined by models that are flexible enough to represent their interactions and behavior. The information contained in these models must be accessible throughout all phases of the system life cycle in order to maintain consistency and accuracy in design and operational decisions. One approach being taken by researchers at Ames Research Center is the creation of an object-oriented environment that integrates information about system components required in the reliability evaluation with behavioral information useful for diagnostic algorithms. Procedures have been developed at Ames that perform reliability evaluations during design and failure diagnoses during system operation. These procedures utilize information from a central source, structured as object-oriented fault trees. Fault trees were selected because they are a flexible model widely used in aerospace applications and because they give a concise, structured representation of system behavior. The utility of this integrated environment for aerospace applications in light of our experiences during its development and use is described. The techniques for reliability evaluation and failure diagnosis are discussed, and current extensions of the environment and areas requiring further development are summarized.
The NASA Ambassadors Program is designed to present the excitement and importance of NASA's programs to its customers, the general public. Those customers, which are identified in the "Science Communications Strategy" developed by the Space Sciences Laboratory at the MSFC, are divided into three categories: (1) Not interested and not knowledgeable; (2) Interested but not knowledgeable; and (3) Science attentive. In it they recognize that it makes the most sense to attempt to communicate with those described in the last two categories. However, their plan suggests that the media and the educational institutions are the only means of outreach. The NASA Ambassadors Program allows NASA to reach its target audience directly. Steps to be taken in order for the program to commence: (1) MSFC chooses to support the NASA Ambassadors Program - decision point; (2) Designate an "Office In Charge". (3) Assign the "Operation" phase to in-house MSFC personnel or to a contractor - decision point; (4) Name a point of contact; (5) Identify partners in the program and enlist their assistance; (6) Process an unsolicited proposal from an outside source to accomplish those tasks which MSFC chooses to out-source.
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.
Many companies are looking for new tools and techniques to aid a design manager in making decisions that can reduce the time and cost of a design cycle. One tool is the Design Manager's Aid for Intelligent Decomposition (DeMAID). Since the initial public release of DeMAID in 1989, much research has been done in the areas of decomposition, concurrent engineering, parallel processing, and process management; many new tools and techniques have emerged. Based on these recent research and development efforts, numerous enhancements have been added to DeMAID to further aid the design manager in saving both cost and time in a design cycle. The key enhancement, a genetic algorithm (GA), will be available in the next public release called DeMAID/GA. The GA sequences the design processes to minimize the cost and time in converging a solution. The major enhancements in the upgrade of DeMAID to DeMAID/GA are discussed in this paper. A sample conceptual design project is used to show how these enhancements can be applied to improve the design cycle.
Computer-generated human models have been used in aerospace design for a decade. They have come to be highly reliable for worksite analysis of certain types of EVA tasks. In many design environments, this analysis comes after the structural design is largely complete. However, the use of these models as a development tool is gaining acceptance within organizations that practice good systems engineering processes. The design of the United States Propulsion Module for the International Space Station provides an example of this application. The Propulsion Module will provide augmentation to the propulsion capability supplied by the Russian Service Module Zvezda. It is a late addition to the set of modules provided by the United States to the ISS Program, and as a result, faces design challenges that result from the level of immaturity of its integration into the Station. Among these are heat dissipation and physical envelopes. Since the rest of the Station was designed to maximize the use of the cooling system, little margin is available for the addition of another module. The Propulsion Module will attach at the forward end of the Station, and will be between the Orbiter and the rest of ISS. Since cargo must be removed from the Payload Bay and transferred to Station by the Canadarm, there is a potential for protrusions from the module, such as thruster booms, to interfere with robotic operations. These and similar engineering issues must be addressed as part of the development. In the implementation of good system design, all design solutions should be analyzed for compatibility with all affected subsystems. Human modeling has been used in this project to provide rapid input to system trades of design concepts. For example, the placement of radiators and avionics components for optimization of heat dissipation had to be examined for feasibility of EVA translation paths and worksite development. Likewise, the location of and mechanism for the retraction of thruster booms was partly driven by available Orbiter, robotic arm, and other module envelopes; worksite analysis was required for early assessment of task success. Since these trade studies included the EVA analysis as part of the decision criteria, the design had a high degree of assurance of EVA supportability from the outset. This approach contributes greatly to mission success.
Research and development organizations that push the innovation edge of technology frequently encounter challenges when attempting to identify an investment strategy and to accurately forecast the cost and schedule performance of selected projects. Fast moving and complex environments require managers to quickly analyze and diagnose the value of returns on investment versus allocated resources. Our Project Assessment Framework through Design (PAFTD) tool facilitates decision making for NASA senior leadership to enable more strategic and consistent technology development investment analysis, beginning at implementation and continuing through the project life cycle. The framework takes an integrated approach by leveraging design principles of useability, feasibility, and viability and aligns them with methods employed by NASA's Independent Program Assessment Office for project performance assessment. The need exists to periodically revisit the justification and prioritization of technology development investments as changes occur over project life cycles. The framework informs management rapidly and comprehensively about diagnosed internal and external root causes of project performance.
Abstract Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.
The MAESTRO system is a mission analysis tool designed to present to the user information necessary to make the various decisions required in the design and execution of a spaceflight mission. The system was designed so that it can be used in both the pre-launch mission planning phase of a mission and during the flight as an in-flight decision making tool. A description of each of the following modes is presented: (1) trajectory propagation mode; (2) retro-fire determination mode; (3) midcourse analysis determination mode; (4) Monte Carlo mode; (5) verification mode; (6) orbit stability mode; and (7) post injection trim mode. A description of the inputs necessary to run the program mode is given along with a sample case.
The Mapping Application for Penguin Populations and Projected Dynamics (MAPPPD) is a web-based, open access, decision-support tool designed to assist scientists, non-governmental organizations and policy-makers working to meet the management objectives as set forth by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) and other components of the Antarctic Treaty System (ATS) (that is, Consultative Meetings and the ATS Committee on Environmental Protection). MAPPPD was designed specifically to complement existing efforts such as the CCAMLR Ecosystem Monitoring Program (CEMP) and the ATS site guidelines for visitors. The database underlying MAPPPD includes all publicly available (published and unpublished) count data on emperor, gentoo, Adelie) and chinstrap penguins in Antarctica. Penguin population models are used to assimilate available data into estimates of abundance for each site and year.Results are easily aggregated across multiple sites to obtain abundance estimates over any user-defined area of interest. A front end web interface located at www.penguinmap.com provides free and ready access to the most recent count and modelled data, and can act as a facilitator for data transfer between scientists and Antarctic stakeholders to help inform management decisions for the continent.
Before returning humans to the Moon for mankind's seventh lunar landing, NASA will embark upon a series of robotic missions to prepare the way for further exploration. These missions, part of the Robotic Lunar Exploration Program (RLEP), are designed to acquire decisive knowledge about the moon as well as to develop infrastructure needed to sustain human exploration in the lunar environment. Here we focus on the second mission in the RLEP program, RLEP-2, the first dedicated to landing in the south polar region of the moon. Managed by NASA's Marshall Space Flight Center in Huntsville, Alabama, along with the Applied Physics Laboratory and NASA Goddard Space Flight Center, RLEP-2 will build upon knowledge gained from the Chandraayan-1 and Lunar Robotic Orbiter orbital missions, to help further the prospects for sustainable human exploration on the moon. This mission will characterize the lighting environment in the polar region, critically important to understanding the amount of power available and to the thermal design of hardware, as well as explore the nature and distribution of volatiles that may be present in permanently shadowed regions of polar craters. We shall review the current status of the mission, articulate the results of onoging trade studies in power, surface mobility, launch vehicles, measurements and instrumentation, and navigation/communication, as well as discuss the primary mission objectives in detail.
The Fusion of Simulation, Experiment, and Data (FuSED) team provides a set of tools for solving inverse problems in structural dynamics (InverseSD) and thermal physics (InverseAria), a sensor placement optimization tool via Optimal Experimental Design (OED), and a decision boundary tool using SVMs (TRACE). These methods are used for designing experiments, model calibration, and verification/validation analysis of systems. This document provides a user’s guide.
From the beginning of the Shuttle Program to its end, integrated loads and dynamics analyses and tests have been critical in shaping the vehicle design and operational decisions for NASA and its customers. Starting with scaled models and simple mathematical simulations of the structural dynamics, engineers defined the required structural stiffness and predicted the limit loads for each element of the system. Early structural tests provided reasonable confidence that the models and predictions were good. The first launch of the Space Shuttle brought surprises, though, when the ignition overpressure event caused a forward fuel tank support strut to buckle, among several unexpected effects. The launch pad and other ground equipment became an integral part of the system integration, especially where the acoustic and pressure environments of ignition and lift-off were concerned. Following the Challenger accident, operating limits were changed in response to new understandings of how the integrated system performed. Controlling loads while maximizing performance was a key tenet of the Performance Enhancement design process, which enabled construction of the International Space Station. During the return to flight after the Columbia accident, engineers grew to understand that loads during the roll maneuver were also important to the vehicle s structural margin and life. At this point the crawler transport from the Vehicle Assembly Building to the launch pad also became a part of the integrated loads analysis. Even in the last years of the Space Shuttle Program, new data still provided interesting insights into this complicated and fascinating spaceship. This paper will present some examples of the important findings by the team of specialists that supported the Integrated Loads and Dynamics Panel for the Space Shuttle Program.
NASA Langley has developed a multi-mode decision support system for pilots operating in a Distributed Air-Ground Traffic Management (DAG-TM) environment. An Autonomous Operations Planner (AOP) assists pilots in performing separation assurance functions, including conflict detection, prevention, and resolution. Ongoing AOP design has been based on a comprehensive human factors analysis and evaluation results from previous human-in-the-loop experiments with airline pilot test subjects. AOP considers complex flight mode interactions and provides flight guidance to pilots consistent with the current aircraft control state. Pilots communicate goals to AOP by setting system preferences and actively probing potential trajectories for conflicts. To minimize training requirements and improve operational use, AOP design leverages existing alerting philosophies, displays, and crew interfaces common on commercial aircraft. Future work will consider trajectory prediction uncertainties, integration with the TCAS collision avoidance system, and will incorporate enhancements based on an upcoming air-ground coordination experiment.
Future expeditions will enable exploration and study of the planetary surfaces of the Moon and Mars by performing extravehicular activity (EVA) operations. Present-day International Space Station (ISS) EVA operations require an intricate choreography of crew, space suits, tools, systems, and flight teams to plan, train, and execute with limited advanced informatics. In this paper, the Joint Augmented Reality Visual Informatics System (Joint AR) project team at NASA Johnson Space Center (JSC) characterizes the design space for developing a modular augmented reality (AR) device for a spacesuit form factor that can support crew decision-making for EVA. The Joint AR product was defined via trade studies and market analysis of previous EVA display efforts, various AR components such as optics, commercial AR systems, light engines, data interfaces, and graphics engine software. This paper outlines the defining architectural design decisions, including safety criticality considerations, interfaces, and computer architectures. The outcomes of these studies result in a prototype design which is defined here as the Joint AR product. This work aims to enable a community-wide discussion toward realizing necessary suit-compatible AR features and capabilities for future missions.
Future expeditions will enable exploration and study of the planetary surfaces of the Moon and Mars by performing extravehicular activity (EVA) operations. Present-day International Space Station (ISS) EVA operations require an intricate choreography of crew, space suits, tools, systems, and flight teams to plan, train, and execute with limited advanced informatics. In this paper, the Joint Augmented Reality Visual Informatics System (Joint AR) project team at NASA Johnson Space Center (JSC) characterizes the design space for developing a modular augmented reality (AR) device for a spacesuit form factor that can support crew decision-making for EVA. The Joint AR product was defined via trade studies and market analysis of previous EVA display efforts, various AR components such as optics, commercial AR systems, light engines, data interfaces, and graphics engine software. This paper outlines the defining architectural design decisions, including safety criticality considerations, interfaces, and computer architectures. The outcomes of these studies result in a prototype design which is defined here as the Joint AR product. This work aims to enable a community-wide discussion toward realizing necessary suit-compatible AR features and capabilities for future missions.