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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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The advanced battery management system described uses the capabilities of an on-board microprocessor to: (1) monitor the state of the battery on a cell by cell basis; (2) compute the state of charge of each cell; (3) protect each cell from reversal; (4) prevent overcharge on each individual cell; and (5) control dual rate reconditioning to zero volts per cell.
Several battery models are under development at JPL based on first principles. The recent models are based on NiH2 and NiMH chemistries. Performance results and computation requirements are discussed.
We have developed a comprehensive system model for hydroxide exchange membrane fuel cell (HEMFC)-based light-duty vehicles which allows us to determine the material and system developments needed to enable affordable HEMFC-based cars for the mass market.
Preflight and flight battery system problems in flight programs at NASA created high-level concern and interest in the current battery technology status. As a result, NASA conducted an in-house review of problems experienced both internally and by other government users. The derived issues which encompassed the programmatic scope from cell manufacturing to in-flight operations of the system are discussed. From the identified deficiencies, a modestly scaled battery program was established to alleviate or minimize the risks of future occurrences.
This training manual is designed to provide end users with a comprehensive knowledge base for the safe and effective use of the Autonomous Radiation Cartographer (ARC) System. The ARC is a fully autonomous radiation detection robot based on the Spot Robot platform manufactured by Boston Dynamics.
Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 90's cannot enjoy an increased level of autonomy without the efficient use of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real time demands are met for large expert systems. Speed-up via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial labs in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems was surveyed. The survey is divided into three major sections: (1) multiprocessors for parallel expert systems; (2) parallel languages for symbolic computations; and (3) measurements of parallelism of expert system. Results to date indicate that the parallelism achieved for these systems is small. In order to obtain greater speed-ups, data parallelism and application parallelism must be exploited.
The High-Resolution Microwave Survey (HRMS) Sky Survey project will be implemented on the DSS 24 beam waveguide (BWG) antenna over the frequency range of 2.86 to 10 GHz. Two wideband, ring-loaded, corrugated feed horns were designed to cover this range. The horns match the frequency-dependent gain requirements for the DSS 24 BWG system. The performance of the feed horns and the calculated system performance of DSS 24 are presented.
The 1998 NASA Seal/Secondary Air System Workshop was divided into three major areas with limited materials presented in Volume II: (1) overviews of the (NASAs high speed research (HSR) and DOE's advanced turbine engine systems (ATS)) gas turbine programs and the general aviation program (GAP) with emphasis on sealing methods and results; (2) sealing concepts and methods and results including experimental facilities and numerical predictions; and (3) reviews of the numerical engine simulation and aerospace vehicles and concepts (Trailblazer, Bantam, and X-38).
The battery was a three ampere hour nickel cadmium prismatic cell. The battery consists of 20 cells connected in series and there were two batteries per spacecraft. The battery operations (voltage and temperature) that the spacecraft sees during a normal operational day are discussed.
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NASA’s Intelligent Systems Technology Program (IST) is driving a paradigm shift in Earth science through the development of Earth System Digital Twins (ESDT). These integrated information systems create a dynamic "digital replica" of the Earth by harmonizing continuous, multi-source observations with high-fidelity models and state-of-the-art artificial intelligence (AI) that enable “What now?”, “What next?”, and “What if?” scenario building. These scenarios are reflected in NASA IST’s series of ESDTs, from the Coastal Zone Digital Twin that integrates complex data on the current state of the Chesapeake Bay to the Terrestrial Environmental Rapid-Replication and Assimilation Hydrometeorological (TerraHydro) AI-based ESDT that forecasts water movement across Earth’s surface, to the Agriculture Land Information System (AgLIS) which can be used to assess optimal planting dates and crop yield estimates. By bridging the gap between vast data archives and actionable insights, these projects enable a system-of-systems approach to understanding complex, interacting Earth processes. This poster will highlight recent innovations and future directions from NASA’s ESDT initiatives: Continuous Data Assimilation & Multi-Source Fusion. A core requirement of the ESDT work is the transition from static models to dynamic "living" replicas. This involves creating frameworks for the continual assimilation of near-real-time data from uncoordinated, heterogeneous sources, including satellite observations and airborne assets, and ground-based Internet of Things (IoT) sensors. These systems link design, operational status, and environmental data, ensuring the digital twin accurately reflects the current state of the physical Earth system. High-Fidelity Hybrid Modeling & Computational Acceleration to enable interactive "what-if" explorations, programs are moving beyond traditional, slow physical solvers by developing fast surrogate machine learning models and Deep Generative Models (DGMs). These hybrid approaches use neural networks to emulate complex physics, such as cloud feedback or ocean dynamics, at a fraction of the original computing cost, often leveraging advanced hardware like Graphics Processing Units (GPUs) to achieve the necessary scale. Federated Ecosystems & Interoperable Frameworks rather than building isolated tools, NASA IST is moving toward federated ESDTs and reusable analytic collaborative frameworks. This theme focuses on interoperability standards and common ontologies that allow specialized digital twins to interact and share data. This system-of-systems architecture supports multi-discipline investigations, such as analyzing how upstream watershed changes impact downstream urban flooding or how wildfire emissions affect regional air quality. By leveraging these advancements, ESDTs empower researchers and decision-makers to conduct real-time analysis and run complex hypothetical scenarios, ultimately improving our understanding of Earth’s evolving systems and informing critical real-world applications.
A model to simulate nickel-cadmium battery performance and response in a spacecraft electrical power system energy balance calculation was developed. The voltage of the battery is given as a function of temperature, operating depth-of-charge (DOD), and battery state-of-charge. Also accounted for is charge inefficiency. A battery is modeled by analysis of the results of a multiparameter battery cycling test at various temperatures and DOD's.
We present preliminary results on the development of a fire detection system for partial and low gravity environments. A significant challenge is the design of a system with a low false alarm rate. The detection system therefore has to discriminate between combustion particles, cabin, and lunar dust. Aerosol properties are measured and cross referenced to the target detectors at the Gases and Aerosols from Smoldering Polymers (GASP) Laboratory located at the NASA Glenn Research Center. A computational fluid dynamics plume model is also employed to determine the smoke transport properties of the targeted habitats.
The development of a battery comprised of bipolar lead acid modules is discussed. The battery is designed to satisfy the requirements of the Advanced Launch System (ALS). The battery will have the following design features: (1) conventional lead acid chemistry; (2) thin electrode/active materials; (3) a thin separator; (4) sealed construction (gas recombinant); and (5) welded plastic frames for the external seal.
The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.
A summary of NASA Aerospace Flight Battery Systems Program Activities is presented. The NASA Aerospace Flight Battery Systems Program represents a unified NASA wide effort with the overall objective of providing NASA with the policy and posture which will increase the safety, performance, and reliability of space power systems. The specific objectives of the program are to: enhance cell/battery safety and reliability; maintain current battery technology; increase fundamental understanding of primary and secondary cells; provide a means to bring forth advanced technology for flight use; assist flight programs in minimizing battery technology related flight risks; and ensure that safe, reliable batteries are available for NASA's future missions.
Over a period of some 5 years Canadian Astronautics Limited (CAL) has developed a system to autonomously manage, and thus prolong the life of, secondary storage batteries. During the development, the system was aimed at the space vehicle application using nickel cadmium batteries, but is expected to be able to enhance the life and performance of any rechargeable electrochemical couple. The system handles the cells of a battery individually and thus avoids the problems of over, and under, drive that inevitably occur in a battery of cells managed by an averaging system. This individual handling also allow cells to be totally bypassed in the event of failure, thus avoiding the losses associated with low capacity, partial short circuit, and the catastrophe of open circuit. The system has an optional capability of managing redundant batteries simultaneously, adding the advantage of on line reconditioning of one battery, while the other maintains the energy storage capability of the overall system. As developed, the system contains a dedicated, redundant, microprocessor, but the capability exists to have this computing capability time shared, or remote, and operating through a data link. As adjuncts to the basic management system CAL has developed high efficiency, polyphase, power regulators for charge and discharge power conditioning.