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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques

Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition- Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation as in Particle Filters (PF), provides a powerful tool to integrate the diagnosis and prognosis of battery health. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for model identification, while the PF framework uses the learnt model, statistical estimates of noise and anticipated operational conditions to provide estimates of remaining useful life (RUL) in the form of a probability density function (PDF). This type of prognostics generates a significant value addition to the management of any operation involving electrical systems.

Saha, Bhaskar↗
NASA NTRS2015

Solar Alpha Rotary Joint (SARJ) Lubrication Interval Test and Evaluation (LITE). Post-Test Grease Analysis

The Solar Alpha Rotary Joint (SARJ) is a mechanism of the International Space Station (ISS) that orients the solar power generating arrays toward the sun as the ISS orbits our planet. The orientation with the sun must be maintained to fully charge the ISS batteries and maintain all the other ISS electrical systems operating properly. In 2007, just a few months after full deployment, the starboard SARJ developed anomalies that warranted a full investigation including ISS Extravehicular Activity (EVA). The EVA uncovered unexpected debris that was due to degradation of a nitride layer on the SARJ bearing race. ISS personnel identified the failure root-cause and applied an aerospace grease to lubricate the area associated with the anomaly. The corrective action allowed the starboard SARJ to continue operating within the specified engineering parameters. The SARJ LITE (Lubrication Interval Test and Evaluation) program was initiated by NASA, Lockheed Martin, and Boeing to simulate the operation of the ISS SARJ for an extended time. The hardware was designed to test and evaluate the exact material components used aboard the ISS SARJ, but in a controlled area where engineers could continuously monitor the performance. After running the SARJ LITE test for an equivalent of 36+ years of continuous use, the test was opened to evaluate the metallography and lubrication. We have sampled the SARJ LITE rollers and plate to fully assess the grease used for lubrication. Chemical and thermal analysis of these samples has generated information that has allowed us to assess the location, migration, and current condition of the grease. The collective information will be key toward understanding and circumventing any performance deviations involving the ISS SARJ in the years to come.

Golden, Johnny L.↗
NASA NTRS2015

Hand Controller Assembly

A user input device for a vehicular electrical system is provided. The user input device includes a handle sized and shaped to be gripped by a human hand and a gimbal assembly within the handle. The gimbal assembly includes a first gimbal component, a second gimbal component coupled to the first gimbal component such that the second gimbal component is rotatable relative to the first gimbal component about a first axis, and a third gimbal component coupled to the second gimbal component such that the third gimbal component is rotatable relative to the second gimbal component about a second axis.

Bandera, Pablo↗
NASA NTRS2012

Nuclear Propulsion through Direct Conversion of Fusion Energy: The Fusion Driven Rocket

The future of manned space exploration and development of space depends critically on the creation of a dramatically more proficient propulsion architecture for in-space transportation. A very persuasive reason for investigating the applicability of nuclear power in rockets is the vast energy density gain of nuclear fuel when compared to chemical combustion energy. Current nuclear fusion efforts have focused on the generation of electric grid power and are wholly inappropriate for space transportation as the application of a reactor based fusion-electric system creates a colossal mass and heat rejection problem for space application.

Propulsion↗
NASA NTRS2017

A Simulation Based Investigation of High Latency Space Systems Operations

This study was the first in a series of planned tests to use physics-based subsystem simulations to investigate the interactions between a spacecraft's crew and a ground-based mission control center for vehicle subsystem operations across long communication delays. The simulation models the life support system of a deep space habitat. It contains models of an environmental control and life support system, an electrical power system, an active thermal control systems, and crew metabolic functions. The simulation has three interfaces: 1) a real-time crew interface that can be use to monitor and control the subsystems; 2) a mission control center interface with data transport delays up to 15 minute each way; and 3) a real-time simulation test conductor interface used to insert subsystem malfunctions and observe the interactions between the crew, ground, and simulated vehicle. The study was conducted at the 21st NASA Extreme Environment Mission Operations (NEEMO) mission. The NEEMO crew and ground support team performed a number of relevant deep space mission scenarios that included both nominal activities and activities with system malfunctions. While this initial test sequence was focused on test infrastructure and procedures development, the data collected in the study already indicate that long communication delays have notable impacts on the operation of deep space systems. For future human missions beyond cis-lunar, NASA will need to design systems and support tools to meet these challenges. These will be used to train the crew to handle critical malfunctions on their own, to predict malfunctions and assist with vehicle operations. Subsequent more detailed and involved studies will be conducted to continue advancing NASA's understanding of space systems operations across long communications delays.

Li, Zu Qun↗
NASA NTRS2017

A Simulation Based Investigation of High Latency Space Systems Operations

This study was the first in a series of planned tests to use physics-based subsystem simulations to investigate the interactions between a spacecraft's crew and a ground-based mission control center for vehicle subsystem operations across long communication delays. The simulation models the life support system of a deep space habitat. It contains models of an environmental control and life support system, an electrical power system, an active thermal control system, and crew metabolic functions. The simulation has three interfaces: 1) a real-time crew interface that can be use to monitor and control the subsystems; 2) a mission control center interface with data transport delays up to 15 minute each way; and 3) a real-time simulation test conductor interface used to insert subsystem malfunctions and observe the interactions between the crew, ground, and simulated vehicle. The study was conducted at the 21st NASA Extreme Environment Mission Operations (NEEMO) mission. The NEEMO crew and ground support team performed a number of relevant deep space mission scenarios that included both nominal activities and activities with system malfunctions. While this initial test sequence was focused on test infrastructure and procedures development, the data collected in the study already indicate that long communication delays have notable impacts on the operation of deep space systems. For future human missions beyond cis-lunar, NASA will need to design systems and support tools to meet these challenges. These will be used to train the crew to handle critical malfunctions on their own, to predict malfunctions, and to assist with vehicle operations. Subsequent more detailed and involved studies will be conducted to continue advancing NASA's understanding of space systems operations across long communications delays.

Li, Zu Qun↗
NASA NTRS2017

NASA-Supported Research on Electric Materials

A brief overview of NASA supported research on electronic materials is presented to the Electrical Materials panel of the Inter-agency Advanced Power Group Electrical Systems Working Group.

NASA↗
NASA NTRS2018

Best Practices: Power Quality and Integrated Testing at JSC

This presentation discusses Best Practices for Power Quality and Integrated Testing at JSC in regards to electrical systems. These high-level charts include mostly generic information; however, a specific issue is discussed involving flight hardware that could have been discovered prior to flight with an integrated test.

Davis, Lydia↗
NASA NTRS2023

Subsonic Ultra Green Aircraft Research: Phase IV Final Report – Volume I Mach 0.80 Transonic Truss-Braced Wing High-Speed Design Report

This design report summarizes work performed by the Boeing Subsonic Ultra-Green Aircraft Research (SUGAR) team from December 2016 through June 2018. In Phase IV of the SUGAR program, Boeing is working to improve the productivity of a TTBW configuration by updating the Mach 0.745 vehicle concept to operate efficiently at Mach 0.80. Due to the aerodynamic and structural design challenges associated with adapting the design to a higher speed, design tasks have been broken into several subtasks. The information in this report (Volume I) documents the development of a Mach 0.80 vehicle outer mold line, maturation of TTBW structural concepts, and presents a preliminary high-liftsystem assessment. Results from these studies were integrated into an aircraft-level performance code that estimates the benefits of TTBW technology, both on its own and as a part of the SUGAR integrated technology suite. The aerodynamic performance results presented herein detail the high cruise efficiency of the TTBW concept operating at M=0.80. Results from the RANS code OVERFLOW indicate that the TTBW is aerodynamically well suited for operation at the design M=0.80 transonic cruise Mach number. Aircraft-level performance predictions show that the TTBW offers a 7.2% improvement in fuel burn (per seat) for an economic mission of 900nm, and a 9.0% improvement in fuel burn (per seat) for a 3500nm mission with full passenger payload, relative to an aspect ratio 13 conventional tube and wing configuration of equivalent technology. Integration of the TTBW performance benefits into the SUGAR technology suite shows a 55.9% reduction in fuel burn (for the economic mission) relative to the reference (SUGAR Free) aircraft, which is a consistently sized traditional cantilever wing, single-aisle transport typical of the 2008 fleet. This benefit does not include the performance improvement potential of hybrid-electric systems studied in earlier phases of the SUGAR program Structural concept development work completed in Phase IV has identified several strut attachment concepts that are worth additional development activity – an inboard strut attachment swivel joint concept (which enables the strut to carry axial loads while avoiding strut bending induced by wing twist during flight) significantly simplifies the construction of the main strut. An outboard offset strut attachment concept holds significant potential for further reductions in wing-strut interference drag. Based on work performed to date on Phase IV and the previous phases of the SUGAR program, the TTBW concept continues to remain a viable concept for significantly improving the performance of future aircraft. High-fidelity aerodynamic analysis of the Mach 0.80 TTBW aircraft has shown fuel burn improvement that has exceeded initial expectations despite the increase in cruise Mach number. Structural concept maturation has continued to refine the TTBW structure, and has found no obvious ‘show-stoppers’ to date. Therefore, the potential benefit of improved performance through the use of a very high aspect ratio strut-braced wing continues to warrant additional study

SUGAR↗
NASA NTRS2022

Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model Approach

During periods of rapidly changing geomagnetic conditions electric fields form within the Earth’s surface and induce currents known as geomagnetically induced currents(GICs), which interact with unprotected electrical systems our society relies on. In this study, we train multi-variate Long-Short Term Memory neural networks to predict magnitude of north-south component of the geomagnetic field (|BN|) at multiple ground magnetometer stations across Alaska provided by the SuperMAG database with a future goal of predicting geomagnetic field disturbances. Each neural network is driven by solar wind and interplanetary magnetic field inputs from the NASA OMNI database spanning from 2000–2015 and is fine tuned for each station to maximize the effectiveness in predicting |BN|. The neural networks are then compared against multivariate linear regression models driven with the same inputs at each station using Heidke skill scores with thresholds at the 50, 75, 85, and 99 percentiles for |BN|. The neural network models show significant increases over the linear regression models for |BN| thresholds. We also calculate the Heidke skill scores for d|BN|/dt by deriving d|BN|/dt from |BN| predictions. However, neural network models do not show clear outperformance compared to the linear regression models. To retain the sign information and thus predict BN instead of |BN|, a secondary so-called polarity model is utilized. The polarity model is run in tandem with the neural networks predicting geomagnetic field in a coupled model approach and results in a high correlation between predicted and observed values for all stations. We find this model a promising starting point for a machine learned geomagnetic field model to be expanded upon through increased output time history and fast turnaround times.

Matthew Blandin↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRS2023

The Impact of Soft Magnetic Materials in Electrified Aircraft Applications

Electrical systems with high power and high power density are integral to many aerospace and energy applications. These systems, enabled by power electronics with wide bandgap semiconductor devices, require management of both efficiency/thermal aspects and electromagnetic interference that are problems specific to high power and high frequency applications, respectively. In addition to advanced active components, these applications also require passive components (inductors, capacitors, conductors) capable of operation under challenging conditions. This work surveys the state of the art of magnetic components and soft magnetic materials used as inductive cores. The wide variety of motor and converter topologies translates to a variety of requirements in the magnetics including mechanical properties, permeabilities, and frequency response. The scaling impact of material properties will be surveyed with respect to component performance and sizing, using both data from experiment and the published literature. Variations on simulations, both in lumped circuits and finite element models, will also be discussed as these are important aspects of the design phase of modern power electronics.

Alex Leary↗
NASA NTRSDate not supplied

Artemis Crewed Surface Operations Training Dust Mitigation Test/Training Facility (DMTF)

Based on several Apollo mission(s) reports and lessons learned1 on lunar dust impacts, it is imperative for successful Artemis missions, to develop dust mitigation methods & train crew in dust cleaning techniques for efficient use of lunar surface time and for crew health. Longer durations and increased number of Extra Vehicular Activities (EVAs) for Artemis need better training. Apollo missions lacked training in a dust facility. Current facilities do not provide adequate fidelity to train crew in dust cleaning operations in suited, pressurized environments. Mimicking dust cleaning actions in existing facilities (eg- Neutral Buoyancy lab) does not provide in depth understanding of actions and time needed to clean suit or positions crew need to take to remove dust from suit. Available dust bins do not support suited/pressurized ops, personnel not trained in suited ops, more overhead/travel. A local facility at Johnson Space Center (JSC) w/ adequate fidelity supporting suited/pressurized ops, personnel in suited ops is a crucial need for rapid development/testing and training. This facility will provide NASA capabilities to: 1. Provide a dust containment bin to perform suited pressurized crewed training/testing activities for dust interaction/mitigation leveraging existing JSC facility infrastructure and 2. Perform end-to-end simulated lunar dust testing of fluid, mechanical, and electrical systems to provide “test like you fly” confidence for industry developed solutions.

EVA training↗
NASA NTRSDate not supplied

Extending ISS Life Beyond 2030

This paper presents an overview of the International Space Station life extension project, focusing on the analytical approach used to assess its primary structure. Also addressed are life extension approaches for other affected sub-systems, including secondary structure, materials, Environmental Control and Life Support Systems (ECLSS), Electrical Power Systems (EPS), and Logistics and Maintenance (L&M). The United States On-orbit Segment (USOS) of the International Space Station (ISS) was initially designed for a 15-year on-orbit life, but with the realization of its continued importance, efforts were made to extend its operations through 2030. The paper discusses the various analyses conducted, including future operations planning, load simulation, material degradation studies, thermal analyses, and fracture analyses. The results demonstrate the feasibility and achievability of extending the ISS's life, ensuring its on-going role as a hub for scientific research, international cooperation, and educational endeavors. With the life extension process in place, continuous operations beyond 2030 are possible to maintain an uninterrupted human presence in Low Earth Orbit, facilitating the testing of new technologies, and allow for a seamless transition to new commercially owned and operated destinations.

design life↗
NASA NTRSDate not supplied

Battery Pack Shape Optimization using Transient Heat Conduction Coupled with Cell-Discharge Analysis

Battery electric systems exhibit significant time-dependence, especially when evaluated in the context of an aircraft mission profile with continually changing power demands. Additionally, when evaluating battery-powered aircraft concepts, it is important to accurately compute the temperature of the batteries and properly characterize the thermal response of the system. The temperature of the batteries has a significant impact on cell performance, in addition to safety considerations of maintaining battery temperatures below their operating limit. Because of these considerations, battery models for preliminary design and optimization of aircraft should include the capability to accurately compute the temperature distribution within the battery pack. Furthermore, battery pack designs should be as light-weight as possible to maximize the pack energy density, while also considering battery temperature limits. Here, we demonstrate a simultaneous trajectory and shape optimization of a battery pack concept, using a transient heat transfer finite element model coupled with a time-varying cell-discharge battery model to provide this capability. Including the transient finite element problem in the loop enables accurate temperatures that can be passed back to the cell discharge model, while the cell discharge model can supply the finite element model with time-varying heat boundary conditions to the finite element problem, further benefiting the fidelity of the thermal response of the batteries. We first demonstrate the coupling capability between the battery cell-discharge model and the transient finite-element heat transfer through an optimization which computes the optimal current profile for the battery pack while ensuring the battery temperatures remain below their operational limit. We then build on this optimization by adding shape optimization to the problem, which allows us to consider a composite objective function which also minimizes the mass of the battery pack, while also producing an optimal current discharge profile.

Optimization↗
NASA NTRSDate not supplied

Battery Pack Shape Optimization using Transient Heat Conduction Coupled with Cell-Discharge Analysis

Battery electric systems exhibit significant time-dependence, especially when evaluated in the context of an aircraft mission profile with continually changing power demands. Additionally, when evaluating battery-powered aircraft concepts, it is important to accurately compute the temperature of the batteries and properly characterize the thermal response of the system. The temperature of the batteries has a significant impact on cell performance, in addition to safety considerations of maintaining battery temperatures below their operating limit. Because of these considerations, battery models for preliminary design and optimization of aircraft should include the capability to accurately compute the temperature distribution within the battery pack. Furthermore, battery pack designs should be as light-weight as possible to maximize the pack energy density, while also considering battery temperature limits. Here, we demonstrate a simultaneous trajectory and shape optimization of a battery pack concept, using a transient heat transfer finite element model coupled with a time-varying cell-discharge battery model to provide this capability. The transient finite-element analysis is done using TACS, and the cell-discharge battery model uses OpenMDAO and dymos. Including the transient finite element problem in the loop enables accurate temperatures that can be passed back to the cell discharge model, while the cell discharge model can supply the finite element model with time-varying heat boundary conditions, further benefiting the fidelity of the thermal response of the batteries. We first demonstrate the coupling capability between the battery cell-discharge model and the transient finite-element heat transfer through an optimization which computes the optimal current profile for the battery pack while ensuring the battery temperatures remain below their operational limit. We then build on this optimization by adding shape optimization to the problem, which allows us to consider a composite objective function which also minimizes the mass of the battery pack, while also producing an optimal current discharge profile.

Optimization↗