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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2019

On the Susceptibility of Deep Neural Networks to Natural Perturbations

Deep learning systems are increasingly being adopted for safety critical tasks such as autonomous driving. These systems can be exposed to adverse weather conditions such as fog, rain and snow. Vulnerability of deep learning systems to synthetic adversarial attacks has been extensively studied and demonstrated, but the impact of natural weather conditions on these systems has not been studied in detail. In this paper, we study the effects of fog on classification accuracy of the popular Inception deep learning model. We use stereo images from the Cityscapes dataset and computer graphics techniques to mimic realistic naturally occurring fog. We show that the Inception deep learning model is vulnerable to the addition of fog in images.

Ozdag, Mesut↗
NASA NTRS2005

Autonomous and Autonomic Swarms

A watershed in systems engineering is represented by the advent of swarm-based systems that accomplish missions through cooperative action by a (large) group of autonomous individuals each having simple capabilities and no global knowledge of the group s objective. Such systems, with individuals capable of surviving in hostile environments, pose unprecedented challenges to system developers. Design and testing and verification at much higher levels will be required, together with the corresponding tools, to bring such systems to fruition. Concepts for possible future NASA space exploration missions include autonomous, autonomic swarms. Engineering swarm-based missions begins with understanding autonomy and autonomicity and how to design, test, and verify systems that have those properties and, simultaneously, the capability to accomplish prescribed mission goals. Formal methods-based technologies, both projected and in development, are described in terms of their potential utility to swarm-based system developers.

Hinchey, Michael G.↗
NASA NTRS2003

Use of a Prototype Airborne Separation Assurance System for Resolving Near-Term Conflicts During Autonomous Aircraft Operations

NASA is currently investigating a new concept of operations for the National Airspace System, designed to improve capacity while maintaining or improving current levels of safety. This concept, known as Distributed Air/Ground Traffic Management (DAGTM), allows appropriately equipped autonomous aircraft to maneuver freely for flight optimization while resolving conflicts with other traffic and staying out of special use airspace and hazardous weather. In order to perform these tasks, pilots use prototype conflict detection, prevention, and resolution tools, collectively known as an Airborne Separation Assurance System (ASAS). While ASAS would normally allow pilots to resolve conflicts before they become hazardous, evaluation of system performance in sudden, near-term conflicts is needed in order to determine concept feasibility. An experiment was conducted in NASA Langley's Air Traffic Operations Lab to evaluate the prototype ASAS for enabling pilots to resolve near-term conflicts and examine possible operational effects associated with the use of lower separation minimums. Sixteen commercial airline pilots flew a total of 32 traffic scenarios that required them to use prototype ASAS tools to resolve close range pop-up conflicts. Required separation standards were set at either 3 or 5 NM lateral spacing, with 1000 ft vertical separation being used for both cases. Reducing the lateral separation from 5 to 3 NM did not appear to increase operational risk, as indicated by the proximity to the intruder aircraft. Pilots performed better when they followed tactical guidance cues provided by ASAS than when they didn't follow the guidance. In an effort to improve compliance rate, ASAS design changes are currently under consideration. Further studies will of evaluate these design changes and consider integration issues between ASAS and existing Airborne Collision Avoidance Systems (ACAS).

Barhydt, Richard↗
DOE OSTI2024

Convergence of Emerging Technologies - EAGL Test Information

The Emergency Automatic Gunshot Detection and Lockdown (EAGL) system provides automatic, autonomous, and timely gunshot detection in both indoor and outdoor environments. This system uses both wired and wireless devices. Self-contained wireless EAGL sensors passively “listen” for gunshot events. These devices also perform a single, daily supervisory heartbeat (HB) function to include a device self-check with reporting capability. Transmissions are received by an assigned EAGL Gateway, which translates the RF sensor data to a PoE network format solely for use by the EAGL system server. The server then performs additional processes after data receipt, which include but are not limited to: event validation and logging, GUI presentation, notifications, and other independent operations.

47 OTHER INSTRUMENTATION↗
NASA NTRS1991

RoMPS concept review automatic control of space robot, volume 2

Topics related to robot operated materials processing in space (RoMPS) are presented in view graph form and include: (1) system concept; (2) Hitchhiker Interface Requirements; (3) robot axis control concepts; (4) Autonomous Experiment Management System; (5) Zymate Robot Controller; (6) Southwest SC-4 Computer; (7) oven control housekeeping data; and (8) power distribution.

Dobbs, M. E.↗
NASA NTRS1999

A Self-Tuning Kalman Filter for Autonomous Navigation Using the Global Positioning System (GPS)

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS (Global Positioning Systems) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.

Truong, Son H.↗
NASA NTRS1999

A Self-Tuning Kalman Filter for Autonomous Navigation using the Global Positioning System (GPS)

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.

Truong, S. H.↗
NASA NTRS2004

OCEANIDS: Autonomous Data Acquisition, Management and Distribution System

OCEANIDS is a clearinghouse for mission essential and near-real-time satellite data streams. This viewgraph presentation describes this mission, and includes the following topics: 1) OCEANIDS Motivation; 2) High-Level Architecture; 3) OCEANIDS Features; 4) OCEANIDS GUI: Nodes; 5) OCEANIDS GUI: Cluster; 6) Data Streams; 7) Statistics; and 8) GHRSST-PP.

automated monitoring↗
NASA NTRS2019

In-Time System-Wide Safety Assurance (ISSA) Concept of Operations

Emerging operations involving Urban Air Mobility (UAM) poses a challenge to safety assurance and accessibility to the NAS. In particular, the public has a low tolerance for risk in aviation and the current NAS tends to be labor-intensive with limited ability to scale up for UAM. In response to this landscape, NASA is collaborating with industry to define an In-time Aviation Safety Management System (IASMS) Concept of Operations (ConOps) for a scalable UAM along with a service-oriented architecture. This architecture would better focus safety investments for technological solutions that overcome safety related barriers for emerging operations. By working with industry, consensus can be reached on desirable system traits that are based on integration of data and leverage increasingly autonomous and automated systems. These complex systems can identify anomalies, precursors, and trends that together enable more proactive management of operational risks.

Ellis, Kyle↗
NASA NTRSDate not supplied

Safety Assessment of a Machine Learning-Based Aircraft Emergency Braking System: A Case Study

Machine Learning (ML) is revolutionizing many technological fields, but its use in aviation remains restricted due to stringent certification requirements. Efforts by the aviation community to establish standards for certifying ML-based systems are progressing, yet challenges persist, particularly with safety assessment methods for ML-based systems. This research addresses these challenges through a case study of an autonomous emergency braking system utilizing a computer vision deep neural network (DNN). We demonstrate a safety assessment process tailored to ML-specific concerns, such as low integrity and performance variability in quantitative safety analysis. This study can serve as an illustrative example to facilitate the discussion and convergence on certification aspects for ML-based systems within the aviation community.

Safety certification↗
DOE OSTI2023

Autonomous sense and guide machine learning system

A system for generating a machine learning system to generate guidance information based on locations of objects is provided. The system accesses training data that includes training time-of-arrival (“TOA”) information of looks and guidance information for each look. The guidance information is based on a training collection of object locations. The TOA of a look represents, for each object location of a training collection of object locations, times between signals transmitted by transmitters and return signals received by receivers. The return signals represent signals reflected from an object at the object location. The system trains a machine learning system using the training data wherein the machine learning system inputs TOA information and outputs guidance information.

Beer, N. Reginald↗
DOE OSTI2022

Multi-channel high-speed converter clock synchronization with autonomous coherent deterministic latency

A system and corresponding method that achieves coherency and deterministic latency (CDL) autonomously upon power on is disclosed. The system, for example, a multi-channel RF system, may require CDL with respect to the digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) assigned to the channels in the system. CDL is achieved through a timed combination of external reference and synchronization signals, resetting and disabling of various clock dividers, and enabling clock generation. In addition to synchronizing all of the clocks, the data acquisition sequence must be synchronized across all of the channels, whether they are on chips, cards, or chassis. Data acquisition synchronization may be implemented using an initiator/target or a wired OR mode configuration.

Brady, Charles E.↗
NASA NTRS2012

Autonomous Cryogenics Loading Operations Simulation Software: Knowledgebase Autonomous Test Engineer

The Simulation Software, KATE (Knowledgebase Autonomous Test Engineer), is used to demonstrate the automatic identification of faults in a system. The ACLO (Autonomous Cryogenics Loading Operation) project uses KATE to monitor and find faults in the loading of the cryogenics int o a vehicle fuel tank. The KATE software interfaces with the IHM (Integrated Health Management) systems bus to communicate with other systems that are part of ACLO. One system that KATE uses the IHM bus to communicate with is AIS (Advanced Inspection System). KATE will send messages to AIS when there is a detected anomaly. These messages include visual inspection of specific valves, pressure gauges and control messages to have AIS open or close manual valves. My goals include implementing the connection to the IHM bus within KATE and for the AIS project. I will also be working on implementing changes to KATE's Ul and implementing the physics objects in KATE that will model portions of the cryogenics loading operation.

Wehner, Walter S.↗
DOE OSTI2024

A Neural Network-Based Power Mismatch Elimination Strategy for Integrated Solar and ESS AC/DC Systems (MARS)

The multiport autonomous reconfigurable solar power plant (MARS) is a promising concept for the integration of photovoltaic (PV) and energy storage system (ESS) to the transmission ac grid and a high-voltage direct current (HVdc) link. The presence of PV and ESS in each arm of the MARS results in uneven distribution of active power among different submodules (SMs), thereby leading to unbalanced SM capacitor voltages and potentially compromising the system stability. Moreover, in the case of partial shadings, shaded PV SMs will suffer from decreased power injections causing power mismatch in the MARS system. To address this issue, a neural-network-based power mismatch elimination (NNPME) strategy is proposed in this article. The proposed NNPME strategy optimizes ESS usage and leverages both dc and ac circulating currents to facilitate power transfer among the SMs, arms, and phases of the MARS system. Simulation and control hardware-in-the-loop (cHIL) experiments demonstrate the effectiveness of the proposed NNPME strategy. Compared with the traditional approaches, the proposed NNPME strategy can significantly enhance system efficiency and ensure stable and continuous operation, even in the presence of uneven power distribution within the MARS system.

14 SOLAR ENERGY↗
DOE OSTI2022

Developments in Online Monitoring Technologies for Autonomous Microreactor Operations

This report presents the results of an ongoing research and development (R&D) effort to develop an online monitoring (OLM) system to support autonomous microreactor operations. A key component of this work is an evaluation of Artificial Intelligence (AI) and Machine Learning (ML) techniques to identify, diagnose, and predict problems with sensors and processes of the reactor. As described herein, selected methods of AI/ML were used to identify and diagnose anomalous sensor and system behaviors using data from a thermal-hydraulic flow loop and from operating nuclear power plants. This work serves to further the state-of-the-art in OLM technologies for nuclear reactor applications and will ultimately result in a comprehensive system to enable OLM of critical structures, systems, components, and processes in microreactors.

Machine learning↗