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

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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

A rule-based systems approach to spacecraft communications configuration optimization

An experimental rule-based system for optimizing user spacecraft communications configurations was developed at NASA to support mission planning for spacecraft that obtain telecommunications services through NASA's Tracking and Data Relay Satellite System. Designated Expert for Communications Configuration Optimization (ECCO), and implemented in the OPS5 production system language, the system has shown the validity of a rule-based systems approach to this optimization problem. The development of ECCO and the incremental optimizatin method on which it is based are discussed. A test case using hypothetical mission data is included to demonstrate the optimization concept.

Rash, James L.↗
NASA NTRS1990

Fault injection experiments using FIAT

The results of several experiments conducted using the fault-injection-based automated testing (FIAT) system are presented. FIAT is capable of emulating a variety of distributed system architectures, and it provides the capabilities to monitor system behavior and inject faults for the purpose of experimental characterization and validation of a system's dependability. The experiments consist of exhaustively injecting three separate fault types into various locations, encompassing both the code and data portions of memory images, of two distinct applications executed with several different data values and sizes. Fault types are variations of memory bit faults. The results show that there are a limited number of system-level fault manifestations. These manifestations follow a normal distribution for each fault type. Error detection latencies are found to be normally distributed. The methodology can be used to predict the system-level fault responses during the system design stage.

Barton, James H.↗
NASA NTRS2002

Verification and Validation of Neural Networks for Aerospace Systems

The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: Overview of Adaptive Systems and V&V Processes/Methods.

Mackall, Dale↗
NASA NTRS2002

Verification and Validation of Neural Networks for Aerospace Systems

The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: 1) Overview of Adaptive Systems; and 2) V&V Processes/Methods.

Mackall, Dale↗
NASA NTRS2005

Addressing the Real-World Challenges in the Development of Propulsion IVHM Technology Experiment (PITEX)

The Propulsion IVHM Technology Experiment (PITEX) has been an on-going research effort conducted over several years. PITEX has developed and applied a model-based diagnostic system for the main propulsion system of the X-34 reusable launch vehicle, a space-launch technology demonstrator. The application was simulation-based using detailed models of the propulsion subsystem to generate nominal and failure scenarios during captive carry, which is the most safety-critical portion of the X-34 flight. Since no system-level testing of the X-34 Main Propulsion System (MPS) was performed, these simulated data were used to verify and validate the software system. Advanced diagnostic and signal processing algorithms were developed and tested in real-time on flight-like hardware. In an attempt to expose potential performance problems, these PITEX algorithms were subject to numerous real-world effects in the simulated data including noise, sensor resolution, command/valve talkback information, and nominal build variations. The current research has demonstrated the potential benefits of model-based diagnostics, defined the performance metrics required to evaluate the diagnostic system, and studied the impact of real-world challenges encountered when monitoring propulsion subsystems.

Maul, William A.↗
NASA NTRS1996

Knowledge Acquisition, Validation, and Maintenance in a Planning System for Automated Image Processing

A key obstacle hampering fielding of AI planning applications is the considerable expense of developing, verifying, updating, and maintainting the planning knowledge base (KB). Planning systems must be able to compare favorably in terms of software lifecycle costs to other means of automation such as scripts or rule-based expert systems. This paper describes a planning application of automated imaging processing and our overall approach to knowledge acquisition for this application.

artificial intelligence image processing knowledge↗
NASA NTRS2006

Technology developments integrating a space network communications testbed

As future manned and robotic space explorations missions involve more complex systems, it is essential to verify, validate, and optimize such systems through simulation and emulation in a low cost testbed environment. The goal of such a testbed is to perform detailed testing of advanced space and ground communications networks, technologies, and client applications that are essential for future space exploration missions. We describe the development of new technologies enhancing our Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) that enables its integration in a distributed space communications testbed. MACHETE combines orbital modeling, link analysis, and protocol and service modeling to quantify system performance based on comprehensive considerations of different aspects of space missions.

hybrid simulations↗
NASA NTRS2006

Propulsion IVHM Technology Experiment

The Propulsion IVHM Technology Experiment (PITEX) successfully demonstrated real-time fault detection and isolation of a virtual reusable launch vehicle (RLV) main propulsion system (MPS). Specifically, the PITEX research project developed and applied a model-based diagnostic system for the MPS of the X-34 RLV, a space-launch technology demonstrator. The demonstration was simulation-based using detailed models of the propulsion subsystem to generate nominal and failure scenarios during captive carry, which is the most safety-critical portion of the X-34 flight. Since no system-level testing of the X-34 Main Propulsion System (MPS) was performed, these simulated data were used to verify and validate the software system. Advanced diagnostic and signal processing algorithms were developed and tested in real time on flight-like hardware. In an attempt to expose potential performance problems, the PITEX diagnostic system was subjected to numerous realistic effects in the simulated data including noise, sensor resolution, command/valve talkback information, and nominal build variations. In all cases, the PITEX system performed as required. The research demonstrated potential benefits of model-based diagnostics, defined performance metrics required to evaluate the diagnostic system, and studied the impact of real-world challenges encountered when monitoring propulsion subsystems.

Chicatelli, Amy K.↗
NASA NTRS2007

NASA CEV On-Orbit GN&C Analysis

The Orion spacecraft orbit GN&C system will be required to perform both ISS servicing missions and Lunar sortie and outpost crew transportation. While certain aspects of these two missions are complementary, the two missions are also distinct in many ways. Common between the missions is a Low Earth Orbit (LEO) rendezvous, one with the ISS And the other with the LSAM/EDS stack prior to trans-lunar insertion. The lunar missions will additionally require Orion to perform orbit maintenance in LLO, perform contingency lunar orbit operations including RPOD with the LSAM, perform the TEI maneuver sequence, and execute the trans-Earth cruise. The NASA-led team developed a reference configuration orbit GN&C system capable of executing all of these activities with the same navigation sensor suite and control effectors while fully meeting the requirements being developed for System Requirements Review in advance of prime contractor selection. This paper will present an overview of the analyses performed to support system trade studies, demonstrate the feasibility of the NASA reference configuration, and validate the Orion system requirements. These analyses include linear covariance techniques, 3-DOF Monte Carlo simulations, 6-DOF Monte Carlo simulations, and analytical evaluations of GN&C systems performed by the NASA-led Orion team to characterize the sensitivities of Orion s various missions and assess the unique challenges of each. The first part of this paper will describe the trade studies that were performed in order to characterize the RPOD system performance for both the ISS and the lunar missions including development of an RPOD operations concept, RPOD trajectories, contingency scenarios, docking mechanism and associated contact conditions, post-contact thrust issues, relative navigation sensors, relative navigation sensor target infrastructure. The paper will also discuss concepts for both manually-piloted and automatically executed scenarios, as well as the remotely-piloted contingency scenario in LLO. The second part of the paper will discuss the analyses performed for lunar and cislunar GN&C performance including the accuracy of the post-insertion lunar orbit, the ability of Orion to hit a particular Earth return entry corridor, attitude control solutions for long-duration surface operations, and autonomous navigation in support of TEI without ground tracking.

DSouza, Chris↗
NASA NTRS2006

Technology Developments Integrating a Space Network Communications Testbed

As future manned and robotic space explorations missions involve more complex systems, it is essential to verify, validate, and optimize such systems through simulation and emulation in a low cost testbed environment. The goal of such a testbed is to perform detailed testing of advanced space and ground communications networks, technologies, and client applications that are essential for future space exploration missions. We describe the development of new technologies enhancing our Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) that enable its integration in a distributed space communications testbed. MACHETE combines orbital modeling, link analysis, and protocol and service modeling to quantify system performance based on comprehensive considerations of different aspects of space missions. It can simulate entire networks and can interface with external (testbed) systems. The key technology developments enabling the integration of MACHETE into a distributed testbed are the Monitor and Control module and the QualNet IP Network Emulator module. Specifically, the Monitor and Control module establishes a standard interface mechanism to centralize the management of each testbed component. The QualNet IP Network Emulator module allows externally generated network traffic to be passed through MACHETE to experience simulated network behaviors such as propagation delay, data loss, orbital effects and other communications characteristics, including entire network behaviors. We report a successful integration of MACHETE with a space communication testbed modeling a lunar exploration scenario. This document is the viewgraph slides of the presentation.

hybrid simulation↗
NASA NTRS2012

A Model-Based Approach to Engineering Behavior of Complex Aerospace Systems

One of the most challenging yet poorly defined aspects of engineering a complex aerospace system is behavior engineering, including definition, specification, design, implementation, and verification and validation of the system's behaviors. This is especially true for behaviors of highly autonomous and intelligent systems. Behavior engineering is more of an art than a science. As a process it is generally ad-hoc, poorly specified, and inconsistently applied from one project to the next. It uses largely informal representations, and results in system behavior being documented in a wide variety of disparate documents. To address this problem, JPL has undertaken a pilot project to apply its institutional capabilities in Model-Based Systems Engineering to the challenge of specifying complex spacecraft system behavior. This paper describes the results of the work in progress on this project. In particular, we discuss our approach to modeling spacecraft behavior including 1) requirements and design flowdown from system-level to subsystem-level, 2) patterns for behavior decomposition, 3) allocation of behaviors to physical elements in the system, and 4) patterns for capturing V&V activities associated with behavioral requirements. We provide examples of interesting behavior specification patterns, and discuss findings from the pilot project.

SysML↗
NASA NTRS2013

State Machine Modeling of the Space Launch System Solid Rocket Boosters

The Space Launch System is a Shuttle-derived heavy-lift vehicle currently in development to serve as NASA's premiere launch vehicle for space exploration. The Space Launch System is a multistage rocket with two Solid Rocket Boosters and multiple payloads, including the Multi-Purpose Crew Vehicle. Planned Space Launch System destinations include near-Earth asteroids, the Moon, Mars, and Lagrange points. The Space Launch System is a complex system with many subsystems, requiring considerable systems engineering and integration. To this end, state machine analysis offers a method to support engineering and operational e orts, identify and avert undesirable or potentially hazardous system states, and evaluate system requirements. Finite State Machines model a system as a finite number of states, with transitions between states controlled by state-based and event-based logic. State machines are a useful tool for understanding complex system behaviors and evaluating "what-if" scenarios. This work contributes to a state machine model of the Space Launch System developed at NASA Ames Research Center. The Space Launch System Solid Rocket Booster avionics and ignition subsystems are modeled using MATLAB/Stateflow software. This model is integrated into a larger model of Space Launch System avionics used for verification and validation of Space Launch System operating procedures and design requirements. This includes testing both nominal and o -nominal system states and command sequences.

model-based systems engineering↗
NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Preliminary work into this field was promising but given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation (CoCEI) and an execution crowdsourcing platform partner to solicit machine learning framework developments from external contenders. NASA provided contenders with images and video clips of spacesuits with simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, the top five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The weighted scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy. Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA environments such as the NASA Active Response Gravity Offload System (ARGOS). After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗
DOE OSTI2023

NREL CGI and PHIL Platform for Validation of Multi-Technology Energy Systems at Scale

The presentation is an update on National Renewable Energy Laboratory Flatirons Campus' capabilities around multi-megawatt grid simulation and power hardware-in-the-loop. Recent improvements in operational and dynamic range of existing 7MVA Controllable Grid Interface (CGI) are described together with improvements in site-wide automation and data acquisition systems. In later part of presentation, a second 19.9MVA CGI that is currently under development is introduced. Its features are described further explaining future site's capabilities.

acquisition↗