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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Human Systems Integration Approach in Implementing Voice-Control of Future Spacecraft Systems

Crewed spacecraft and habitats of the future will require more automation and autonomy to support complex missions. However, these complex systems require a more efficient command and control input method. Speech recognition along with visual or auditory feedback is an alternative, providing an extra pair of hands and eyes for the crew. Yet, speech recognition demands a highly integrated development approach to ensure a successful system implementation. To ensure the voice control application is developed correctly will require a Human Systems Integration (HSI) approach. This paper provides an insight into the development of a speech/voice control application of a spacecraft system that encompasses automation and autonomy through an HSI approach. Results of the voice control experiment of the Space Shuttle camera system are provided as lessons learned about voice control on a spacecraft. Limitations and challenges of the technology are addressed as well as how HSI can help develop these types of voice control command and control systems.

Voice control↗
NASA NTRSDate not supplied

A NASA Perspective on Quantum AI, Error Correction, and Beyond

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. We discuss the prospects for quantum computing and AI, highlighting advances in algorithms, both near- and longer-term. Quantum error correction is critical to the realization of any such vision. The talk with touch on some recent exciting advanced in quantum error correction, particularly in dynamical codes. The talk will conclude with an example of how the combination of quantum computing and artificial intelligence can help probe fundamental aspect of quantum physics.

quantum optimization algorithms and sampling↗
NASA NTRS2002

Boosting with Averaged Weight Vectors

AdaBoost is a well-known ensemble learning algorithm that constructs its constituent or base models in sequence. A key step in AdaBoost is constructing a distribution over the training examples to create each base model. This distribution, represented as a vector, is constructed to be orthogonal to the vector of mistakes made by the previous base model in the sequence. The idea is to make the next base model's errors uncorrelated with those of the previous model. Some researchers have pointed out the intuition that it is probably better to construct a distribution that is orthogonal to the mistake vectors of all the previous base models, but that this is not always possible. We present an algorithm that attempts to come as close as possible to this goal in an efficient manner. We present experimental results demonstrating significant improvement over AdaBoost and the Totally Corrective boosting algorithm, which also attempts to satisfy this goal.

Oza, Nikunj C.↗
NASA NTRS2016

Preliminary Characterization Results from the DebriSat Project

The DebriSat project is a continuing effort sponsored by NASA and DoD to update existing break-up models using data obtained from two separate hypervelocity impact tests used to simulate on-orbit collisions. To protect the fragments resulting from the impact tests, "soft-catch" arenas made of polyurethane foam panels were utilized. After each impact test, the test chamber was cleaned and debris resulting from the catastrophic demise of the test article were collected and shipped to the University of Florida for post-impact processing. The post-impact processing activities include collecting, characterizing, and cataloging of the fragments. Since the impact tests, a team of students has been working to characterize the fragments in terms of their mass, size, shape, color and material content. The focus of the 20 months since the impact tests has been on the collection of 2 millimeters- and larger fragments resulting from impact test on the 56 kilogram-representative LEO (Low Earth Orbit) satellite referred to as DebriSat. To date we have recovered in excess of 115,000 fragments, 30,000 more than the prediction of 85,000 fragments from the existing model. We continue to collect fragments but have transitioned to the characterization phase of the post-impact activities. Since the start of the characterization phase, the focus has been to utilize automation to (i) expedite fragment characterization process and (ii) minimize human-in-the- loop. We have developed and implemented such automated processes; e.g., we have automated the data entry process to reduce operator errors during transcription of the measurement data. However, at all steps of the process, there is human oversight to ensure the integrity of the data. Additionally, we have developed and implemented repeatability and reproducibility tests to ensure that the instrumentation used in the characterization process is accurate and properly calibrated. In this paper, the implemented processes are described and preliminary results presented. Additionally, lessons learned from the implemented automations and their impacts on the integrity of the results are discussed.

Rivero, M.↗
NASA NTRS2008

Applying EVM to Satellite on Ground and In-Orbit Testing - Better Data in Less Time

Using Error Vector Magnitude (EVM) in satellite integration and test allows rapid verification of the Bit Error Rate (BER) performance of a satellite link and is particularly well suited to measurement of low bit rate satellite links where it can result in a major reduction in test time (about 3 weeks per satellite for the Geosynchronous Operational Environmental Satellite [GOES] satellites during ground test) and can provide diagnostic information. Empirical techniques developed to predict BER performance from EVM measurements and lessons learned about applying these techniques during GOES N, O, and P integration test and post launch testing, are discussed.

Peters, Robert↗
NASA NTRS2013

Formal Validation of Fault Management Design Solutions

The work presented in this paper describes an approach used to develop SysML modeling patterns to express the behavior of fault protection, test the model's logic by performing fault injection simulations, and verify the fault protection system's logical design via model checking. A representative example, using a subset of the fault protection design for the Soil Moisture Active-Passive (SMAP) system, was modeled with SysML State Machines and JavaScript as Action Language. The SysML model captures interactions between relevant system components and system behavior abstractions (mode managers, error monitors, fault protection engine, and devices/switches). Development of a method to implement verifiable and lightweight executable fault protection models enables future missions to have access to larger fault test domains and verifiable design patterns. A tool-chain to transform the SysML model to jpf-Statechart compliant Java code and then verify the generated code via model checking was established. Conclusions and lessons learned from this work are also described, as well as potential avenues for further research and development.

Statechart↗
NASA NTRS2020

Global Aerodynamic Modeling Using Automated Local Model Networks in Real Time

A novel method is presented for automated real-time global aerodynamic modeling using local model networks, known as Smoothed Partitioning with Localized Trees in Real Time (SPLITR), as part of NASA’s Learn-to-Fly technology development initiative. The global nonlinear aerodynamics are partitioned into several local regions known as cells, with the dimension, location, and timing of each partition automatically selected based on a residual characterization procedure, under the constraints of real-time operation. Regression trees represent the successive partitioning of the global flight envelope and describe the evolution of the cell structure. Recursive equation-error least-squares parameter estimation in the time domain is used to estimate a model that represents the local aerodynamics in each region, so that it can be updated independently with non-contiguous data in the range of each cell over time. A weighted superposition of these piecewise local models across the flight envelope forms a global nonlinear model that also accurately captures the local aerodynamics. The SPLITR approach is demonstrated using both simulation and flight data, and the results are analyzed in terms of model predictive capabilities as well as interpretability. The results show that SPLITR can be used to automatically partition complex nonlinear aerodynamic behavior, produce an accurate model, and provide valuable physical insight into the local and global aerodynamics.

Weinstein, Rose↗
NASA NTRSDate not supplied

Flow Reconstruction in A Transonic Turbine Cascade Using Physics-Informed Neural Networks (PINNS)

This paper investigates the application of Physics-Informed Neural Networks (PINNs) for the analysis of turbine blades in a transonic cascade. The 2-D flow field in a transonic turbine cascade is reconstructed in three ways: the traditional forward approach (PINN not trained on experimental data), by training the PINN using discrete sets of experimentally measured pressure at midspan, and in the inverse sense where no inlet or outlet pressure boundary conditions are applied. Comparisons between the PINN solutions to measured data are made. This is repeated for three different turbine blades with distinct loading characteristics. Good agreement is shown between a CFD calculation of the CMC7 blade, and the PINN model trained with all data. The PINN is trained utilizing all available data, half the available data, data from only the leading edge region, and data from only the trailing edge region. The forward problem results deviate the most from experimental data but show promise. Solutions from the assisted training cases show that the PINN can reconstruct the flow field with acceptable accuracy when trained on measurements along the entire blade. In the inverse case, it is shown that to simultaneously achieve acceptable errors for inlet Mach number and outlet isentropic Mach number, the PINN must be trained on the static pressure data along the entire blade.

Machine Learning↗
NASA NTRS2011

Lessons Learned from AIRS: Improved Determination of Surface and Atmospheric Temperatures Using Only Shortwave AIRS Channels

This slide presentation reviews the use of shortwave channels available to the Atmospheric Infrared Sounder (AIRS) to improve the determination of surface and atmospheric temperatures. The AIRS instrument is compared with the Infrared Atmospheric Sounding Interferometer (IASI) on-board the MetOp-A satellite. The objectives of the AIRS/AMSU were to (1) provide real time observations to improve numerical weather prediction via data assimilation, (2) Provide observations to measure and explain interannual variability and trends and (3) Use of AIRS product error estimates allows for QC optimized for each application. Successive versions in the AIRS retrieval methodology have shown significant improvement.

Susskind, Joel↗
NASA NTRS1971

Foundations of planetary quarantine.

Discussion of some of the problems in microbiology and engineering involved in the implementation of planetary quarantine. It is shown that the solutions require new knowledge in both disciplines for success at low cost in terms of both monetary outlay and man's further exploration of the planets. A related problem exists in that engineers are not accustomed to the wide variation of biological data and microbiologists must learn to work and think in more exact terms. Those responsible for formulating or influencing national and international policies must walk a tightrope with delicate balance between unnecessarily stringent requirements for planetary quarantine on the one hand and prevention of contamination on the other. The success of planetary quarantine measures can be assured only by rigorous measures, each checked, rechecked, and triple-checked to make sure that no errors have been made and that no factor has been overlooked.

Hall, L. B.↗
NASA NTRSDate not supplied

TechEdSat-13: The First Flight of an AI/ML Neuromorphic Processor

Neuromorphic processors, inspired by the wiring of the brain, permit certain classes of Artificial Intelligence/Machine Learning (AI/ML) algorithms to run far more efficiently. Ultimately, such systems will make the small- and nano-satellite platforms even more useful in terms of greatly improved power, communication and internal data management. In addition, on-board processing of images and data will help to not only rapidly interpret the information, but also reduce the amount of data that needs to be transmitted to ground stations. This initial flight experiment uses the Intel/ Loihi processor combined with a custom interface board and three communication channels to run the AI/ML scripts. These will vary with increasing length and complexity during the course of the mission. The algorithms will use the (at first) limited sensor data to ‘learn’ - with comparisons to similar architecture in comparable ground experiments. Some of the applications for successor flights in the TES-n flight series include Cognitive Communications, whereby the overall communication system is optimized per overflight – by optimizing timing and data transmission functions. Lastly, the performance of the Loihi 14nm process technology will be monitored for performance in the LEO radiation environment, thus looking for induced hardware and software errors. This information will help guide future radiation protection techniques to extend the lifetime and overall utility. The TES-13 is a 3U nanosat successfully launched by the Virgin Orbit Launcher-1 on January 13, 2022, and will presage more flights and AI/ML applications to come.

TechEdSat↗
NASA NTRS2022

TechEdSat-13: The First Flight of a Neuromorphic Processor

Neuromorphic processors, inspired by the wiring of the brain, permit certain classes of Artificial Intelligence/Machine Learning (AI/ML) algorithms to run far more efficiently. Ultimately, such systems will make the small- and nano-satellite platforms even more useful in terms of greatly improved power, communication and internal data management. In addition, on-board processing of images and data will help to not only rapidly interpret the information, but also reduce the amount of data that needs to be transmitted to ground stations. This initial flight experiment uses the Intel/Loihi processor combined with a custom interface board and three communication channels to run the AI/ML scripts. These will vary with increasing length and complexity during the course of the mission. The algorithms will use the (at first) limited sensor data to ‘learn’ - with comparisons to similar architecture in comparable ground experiments. Some of the applications for successor flights in the TES-n flight series include Cognitive Communications, whereby the overall communication system is optimized per overflight – by optimizing timing and data transmission functions. Lastly, the performance of the Loihi 14nm process technology will be monitored for performance in the LEO radiation environment, thus looking for induced hardware and software errors. This information will help guide future radiation protection techniques to extend the lifetime and overall utility. The TES-13 is a 3U nanosat successfully launched by the Virgin Orbit Launcher-1 on January 13, 2022, and will presage more flights and AI/ML applications to come.

TechEdSat↗
NASA NTRS2022

Landslide Likelihood Prediction using Machine Learning Algorithms

The supply of electricity via power plants is criticalto the operation of many critical infrastructure systems in mod-ern society. Natural hazards can disrupt the power supply, causepower outages that can halt economic growth, and impede emer-gency response until power is restored. The proposed work aimsto predict the landslides likelihood in these critical infrastructurelocations in the Northeastern USA using integrated databases ofexplanatory variables and machine learning algorithms. First,data related to landslides are obtained and merged, includingtopographic, soil moisture, and precipitation-related data. Fiveregression algorithms, namely: Random Forest, Extreme Gradi-ent Boosting (XGBoost), K-Nearest Neighbor regression (KNN),Linear Support Vector Regressor (SVR), and Linear regression,are utilized to predict the landslide probability and evaluatedon the dataset. The accuracy of the models is assessed by usingstatistical metrics such as mean absolute error (MAE), meansquared error (MSE), and root mean squared error (RMSE).The study results show that Random Forest outperformed othermodels with the mutual information feature selection method.It achieved an MSE of 0.0011 with mutual information-basedfeature selection and an MSE of 0.00157 without feature selection.KNN regressor outperformed the other models with an MSEof 0.00139 with correlation-based information selection. Theproposed landslide identification model with Random Forestalgorithm shows outstanding robustness and great potential intackling the landslide likelihood prediction by employing MLalgorithms.

Vasundhara Acharya↗
NASA NTRSDate not supplied

Where IMERG Goes Next: Version 08 and Beyond

With the Version 07 (V07) Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm finalized and production initiated, the focus turns to enhancements for Version 08. These include innovations not included in V07 due to time constraints, plus issues revealed by the initial V07 products. One high priority is to evaluate and revise the schemes in V07 that rectify temporal artifacts caused by the time interpolation that fills the gaps between the various passive microwave (PMW) sensor overpasses. A second priority is to improve the homogeneity between the TRMM and GPM eras by characterizing differences between the two eras, determining the causes of these differences, and applying corrections as feasible, perhaps by enforcing spatial scale consistency (an overarching issue). Certainly, we must account for GPROF and the Combined Radar-Radiometer Algorithm converting to Machine Learning schemes in V08. Other priority topics include additional automated quality control for artifacts in the IR brightness temperatures and PMW precipitation fields, revisions to the specification algorithm for the probability of liquid precipitation, and accommodating new PMW sensors, which include the next generation of small-sats. We also consider the post-V08 landscape; the final GPM reprocessing will be restricted to fixing known code or algorithmic errors. Nonetheless, there are several data sources on the horizon to consider, including more small-sat PMW radiometers, AVHRR-based precipitation estimates (most useful in high latitudes), and the ISCCP-Next Generation and GEO-Ring projects that could provide easy access to multiple geosynchronous satellite channels and enable significantly improved algorithms compared to GEO-IR alone.

George J. Huffman↗
NASA NTRS1995

Geometric modeling for computer aided design

The primary goal of this grant has been the design and implementation of software to be used in the conceptual design of aerospace vehicles particularly focused on the elements of geometric design, graphical user interfaces, and the interaction of the multitude of software typically used in this engineering environment. This has resulted in the development of several analysis packages and design studies. These include two major software systems currently used in the conceptual level design of aerospace vehicles. These tools are SMART, the Solid Modeling Aerospace Research Tool, and EASIE, the Environment for Software Integration and Execution. Additional software tools were designed and implemented to address the needs of the engineer working in the conceptual design environment. SMART provides conceptual designers with a rapid prototyping capability and several engineering analysis capabilities. In addition, SMART has a carefully engineered user interface that makes it easy to learn and use. Finally, a number of specialty characteristics have been built into SMART which allow it to be used efficiently as a front end geometry processor for other analysis packages. EASIE provides a set of interactive utilities that simplify the task of building and executing computer aided design systems consisting of diverse, stand-alone, analysis codes. Resulting in a streamlining of the exchange of data between programs reducing errors and improving the efficiency. EASIE provides both a methodology and a collection of software tools to ease the task of coordinating engineering design and analysis codes.

Schwing, James L.↗
NASA NTRS2013

Creating and Testing Simulation Software

The goal of this project is to learn about the software development process, specifically the process to test and fix components of the software. The paper will cover the techniques of testing code, and the benefits of using one style of testing over another. It will also discuss the overall software design and development lifecycle, and how code testing plays an integral role in it. Coding is notorious for always needing to be debugged due to coding errors or faulty program design. Writing tests either before or during program creation that cover all aspects of the code provide a relatively easy way to locate and fix errors, which will in turn decrease the necessity to fix a program after it is released for common use. The backdrop for this paper is the Spaceport Command and Control System (SCCS) Simulation Computer Software Configuration Item (CSCI), a project whose goal is to simulate a launch using simulated models of the ground systems and the connections between them and the control room. The simulations will be used for training and to ensure that all possible outcomes and complications are prepared for before the actual launch day. The code being tested is the Programmable Logic Controller Interface (PLCIF) code, the component responsible for transferring the information from the models to the model Programmable Logic Controllers (PLCs), basic computers that are used for very simple tasks.

Heinich, Christina M.↗
NASA NTRS1996

Accuracy and adaptation of reaching and pointing in pitched visual environments

Visually perceived eye level (VPEL) and the ability of subjects to reach with an unseen limb to targets placed at VPEL were measured in a statically pitched visual surround (pitchroom). VPEL was shifted upward and downward by upward and downward room pitch, respectively. Accuracy in reaching to VPEL represented a compromise between VPEL and actual eye level. This indicates that VPEL shifts reflect in part a change in perceived location of objects. When subjects were provided with terminal visual feedback about their reaching, accuracy improved rapidly. Subsequent reaching, with the room vertical, revealed a negative aftereffect (i.e., reaching errors that were opposite those made initially in the pitched room). In a second study, pointing accuracy was assessed for targets located both at VPEL and at other positions. Errors were similar for targets whether located at VPEL or elsewhere. Additionally, pointing responses were restricted to a narrower range than that of the actual target locations. The small size of reaching and pointing errors in both studies suggests that factors other than a change in perceived location are also involved in VPEL shifts.

NASA Center ARC↗
NASA NTRS1984

Modeling to predict pilot performance during CDTI-based in-trail following experiments

A mathematical model was developed of the flight system with the pilot using a cockpit display of traffic information (CDTI) to establish and maintain in-trail spacing behind a lead aircraft during approach. Both in-trail and vertical dynamics were included. The nominal spacing was based on one of three criteria (Constant Time Predictor; Constant Time Delay; or Acceleration Cue). This model was used to simulate digitally the dynamics of a string of multiple following aircraft, including response to initial position errors. The simulation was used to predict the outcome of a series of in-trail following experiments, including pilot performance in maintaining correct longitudinal spacing and vertical position. The experiments were run in the NASA Ames Research Center multi-cab cockpit simulator facility. The experimental results were then used to evaluate the model and its prediction accuracy. Model parameters were adjusted, so that modeled performance matched experimental results. Lessons learned in this modeling and prediction study are summarized.

Sorensen, J. A.↗