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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 883 records · Page 49

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NASA NTRS1998

Turbulent Radiation Effects in HSCT Combustor Rich Zone

A joint UTRC-University of Connecticut theoretical program was based on describing coupled soot formation and radiation in turbulent flows using stretched flamelet theory. This effort was involved with using the model jet fuel kinetics mechanism to predict soot growth in flamelets at elevated pressure, to incorporate an efficient model for turbulent thermal radiation into a discrete transfer radiation code, and to couple die soot growth, flowfield, and radiation algorithm. The soot calculations used a recently developed opposed jet code which couples the dynamical equations of size-class dependent particle growth with complex chemistry. Several of the tasks represent technical firsts; among these are the prediction of soot from a detailed jet fuel kinetics mechanism, the inclusion of pressure effects in the soot particle growth equations, and the inclusion of the efficient turbulent radiation algorithm in a combustor code.

Hall, Robert J.↗

NASA NTRS2007

The EPEC Algorithm for Vision Guided Manipulation: Analysis and Validation

This paper describes the simulated performance and experimental validation of a computationally efficient algorithm for improving positioning accuracy of robot arms using low speed feedback from fixed stereo cameras. The algorithm, called End-Effector Position Error Compensation (EPEC) is robust to visual occlusion of the end-effector and does not require high fidelity calibration of either the arm or stereo camera. The algorithm works by calculating an error vector between the locations of a fiducial on the arm's end-effector as predicted by arm kinematics and detected by a stereo camera triangulation. With this knowledge, the commanded target pose is adjusted to compensate for positioning errors. A simulation environment where arbitrary error can be introduced into arm-camera systems is introduced and used to provide an assessment of the performance of the algorithm under both ideal and degraded conditions.

manipulations↗

NASA NTRS1978

Monitoring changes in upper ocean heat storage from satellites

A one-dimensional model of the upper ocean mixed-layer was developed to determine how the parameters which can be measured from satellites affect the development of the layer. The results show that the form of the dissipation term is important in achieving cyclic annual states, that the layer deepending rate depends on the averaging period for the surface heat flux and wind stress, that wind direction, as well as magnitude, can affect the deepening rate and that horizontal advective effects cannot simply be superimposed on the model results. An algorithm is given which uses satellite derived wind stress and sea surface temperature data to predict real time changes in upper ocean heat storage during the cooling seasons.

Miller, J. R.↗

NASA NTRS1987

New approaches to some methodological problems of meteor science

Several low cost approaches to continuous radioscatter monitoring of the incoming meteor flux are described. Preliminary experiments were attempted using standard time frequency stations WWVH and CHU (on frequencies near 15 MHz) during nighttime hours. Around-the-clock monitoring using the international standard aeronautical beacon frequency of 75 MHz was also attempted. The techniques are simple and can be managed routinely by amateur astronomers with relatively little technical expertise. Time series analysis can now be performed using relatively inexpensive microcomputers. Several algorithmic approaches to the analysis of meteor rates are discussed. Methods of obtaining optimal filter predictions of future meteor flux are also discussed.

Meisel, David D.↗

NASA NTRS1988

Robust vector quantization for noisy channels

The paper briefly discusses techniques for making vector quantizers more tolerant to tranmsission errors. Two algorithms are presented for obtaining an efficient binary word assignment to the vector quantizer codewords without increasing the transmission rate. It is shown that about 4.5 dB gain over random assignment can be achieved with these algorithms. It is also proposed to reduce the effects of error propagation in vector-predictive quantizers by appropriately constraining the response of the predictive loop. The constrained system is shown to have about 4 dB of SNR gain over an unconstrained system in a noisy channel, with a small loss of clean-channel performance.

Demarca, J. R. B.↗

NASA NTRS1987

Performance prediction of concurrent systems

Concurrent systems are computers that use multiple processors to solve a single problem. A means to predict the application performance on these systems is a useful tool in many areas of concurrent system research. A computationally efficient and accurate method to predict performance for a class of parallel computations on concurrent systems is described. A parallel computation is modeled as a task system with precedence relationships expressed as a series parallel directed acyclic graph. Resources in concurrent systems are modeled as service centers in queueing network models. Using these two models as inputs, the method outputs predictions of both the time to complete the computation and the concurrent system utilization. The algorithm used is based on the approximate Mean Value Analysis in queueing network modeling with extensions to model concurrency in the computation. The new algorithm was validated against both detailed simulation and actual execution on a commercial multiprocessor.

Mak, Victor W. K.↗

NASA NTRS1990

Diffraction Analysis Of Distorted Reflector Antennas

Effects of systematic distortions of surfaces on radiation patterns predicted. Computer program for Diffraction Analysis of Reflector Antennas Subject to Systematic Distortions predicts performance of reflector antennas subject to sinusoidal, thermal, or gravitational distortions. Provides local interpolation algorithm readily applied to nonregular distribution of data. Developed in UNIVAC FORTRAN 77 for UNIVAC computer.

Rahmat-Samii, Yahya↗

NASA NTRS1992

The 32nd CDC: System identification using interval dynamic models

Motivated by the recent explosive development of results in the area of parametric robust control, a new technique to identify a family of uncertain systems is identified. The new technique takes the frequency domain input and output data obtained from experimental test signals and produces an 'interval transfer function' that contains the complete frequency domain behavior with respect to the test signals. This interval transfer function is one of the key concepts in the parametric robust control approach and identification with such an interval model allows one to predict the worst case performance and stability margins using recent results on interval systems. The algorithm is illustrated by applying it to an 18 bay Mini-Mast truss structure.

Keel, L. H.↗

NASA NTRS1988

Optimal experiment design for on-orbit identification

An optimal on-orbit experiment is designed to extract the most information from an on-orbit test, subject to the constraints of the testing environment. However, simply jumping in and optimizing standard measures of information with respect to the experiment design can cause severe problems if attention is not paid to the specific needs and properties of the problem at hand. The actual criteria to be optimized depends on (among other things) the particular ID algorithm and parametrization being used. Two parametric techniques are the focus of this presentation: recursive prediction error method (RPEM) and maximum likelihood estimation (MLE).

Bayard, David S.↗

NASA NTRS1995

Stress Prediction System

NASA wanted to know how astronauts' bodies would react under various gravitational pulls and space suit weights. Under contract to NASA, the University of Michigan's Center for Ergonomics developed a model capable of predicting what type of stress and what degree of load a body could stand. The algorithm generated was commercialized with the ISTU (Isometric Strength Testing Unit) Functional Capacity Evaluation System, which simulates tasks such as lifting a heavy box or pushing a cart and evaluates the exertion expended. It also identifies the muscle group that limits the subject's performance. It is an effective tool of personnel evaluation, selection and job redesign.

Source record↗

NASA NTRS2008

Estimating the Need for Medical Intervention due to Sleep Disruption on the International Space Station

During ISS and shuttle missions, difficulties with sleep affect more than half of all US crews. Mitigation strategies to help astronauts cope with the challenges of disrupted sleep patterns can negatively impact both mission planning and vehicle design. The methods for addressing known detrimental impacts for some mission scenarios may have a substantial impact on vehicle specific consumable mass or volume or on the mission timeline. As part of the Integrated Medical Model (IMM) task, NASA Glenn Research Center is leading the development of a Monte Carlo based forecasting tool designed to determine the consumables required to address risks related to sleep disruption. The model currently focuses on the International Space Station and uses an algorithm that assembles representative mission schedules and feeds this into a well validated model that predicts relative levels of performance, and need for sleep (SAFTE Model, IBR Inc). Correlation of the resulting output to self-diagnosed needs for hypnotics, stimulants, and other pharmaceutical countermeasures, allows prediction of pharmaceutical use and the uncertainty of the specified prediction. This paper outlines a conceptual model for determining a rate of pharmaceutical utilization that can be used in the IMM model for comparison and optimization of mitigation methods with respect to all other significant medical needs and interventions.

Myers, Jerry G.↗

NASA NTRS2007

Protecting Against Faults in JPL Spacecraft

A paper discusses techniques for protecting against faults in spacecraft designed and operated by NASA s Jet Propulsion Laboratory (JPL). The paper addresses, more specifically, fault-protection requirements and techniques common to most JPL spacecraft (in contradistinction to unique, mission specific techniques), standard practices in the implementation of these techniques, and fault-protection software architectures. Common requirements include those to protect onboard command, data-processing, and control computers; protect against loss of Earth/spacecraft radio communication; maintain safe temperatures; and recover from power overloads. The paper describes fault-protection techniques as part of a fault-management strategy that also includes functional redundancy, redundant hardware, and autonomous monitoring of (1) the operational and health statuses of spacecraft components, (2) temperatures inside and outside the spacecraft, and (3) allocation of power. The strategy also provides for preprogrammed automated responses to anomalous conditions. In addition, the software running in almost every JPL spacecraft incorporates a general-purpose "Safe Mode" response algorithm that configures the spacecraft in a lower-power state that is safe and predictable, thereby facilitating diagnosis of more complex faults by a team of human experts on Earth.

Morgan, Paula↗

NASA NTRS2010

Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

Daigle, Matthew↗

NASA NTRS2011

Current-Sensitive Path Planning for an Underactuated Free-Floating Ocean Sensorweb

This work investigates multi-agent path planning in strong, dynamic currents using thousands of highly under-actuated vehicles. We address the specific task of path planning for a global network of ocean-observing floats. These submersibles are typified by the Argo global network consisting of over 3000 sensor platforms. They can control their buoyancy to float at depth for data collection or rise to the surface for satellite communications. Currently, floats drift at a constant depth regardless of the local currents. However, accurate current forecasts have become available which present the possibility of intentionally controlling floats' motion by dynamically commanding them to linger at different depths. This project explores the use of these current predictions to direct float networks to some desired final formation or position. It presents multiple algorithms for such path optimization and demonstrates their advantage over the standard approach of constant-depth drifting.

oceanography↗

NASA NTRS2019

Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia

Snow is an important component of the terrestrial freshwater budget in high mountainAsia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despitethe importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (1Tb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASALand Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMsserve as a nonlinear map between the geophysical space (modeled in Noah-MP) andthe observation space (1Tb as measured by the radiometer). Advanced MicrowaveScanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm.

high mountain Asia↗

NASA NTRSDate not supplied

Jet Noise Prediction for Chevron Nozzle Flows with Wall-Modeled Large-Eddy Simulation

This paper presents results from ongoing research on jet noise prediction with wall-modeled large eddy simulations (WMLES) performed with the LAVA computational framework. In particular the focus of this study is on mixing enhancements from a single stream chevron nozzle at Reynolds number of 1×10(exp 6). Although the concept of chevron nozzles to reduce jet noise is not new, our understanding of its impact on the overall noise is still not well understood. As a first step towards predicting noise reduction due to mixing enhancement concepts from first principles with WMLES, we simulate the noise generated by a single stream chevron nozzle SMC001 as well as its equivalent axisymmetric round jet nozzle SMC000. Detailed comparisons are made with a dedicated experiment conducted at NASA’s Glenn Research Center and good agreement was achieved. Two different approaches to introduce a turbulent boundary layer were compared but show no major impact on the results. This is especially important given future work were multi-stream nozzles are considered and extended costs of resolving the internal BL would have a bigger cost impact. A permeable Ffowcs Williams Hawkings (FWH) surface enclosing the jet is used to predict far-field noise from the simulated flow-field and excellent comparison to microphone array measurements is achieved within the resolved frequency band. Sensitivity of far-field noise predictions to grid resolution is systematically documented. Near-field comparisons to PIV data shows great agreement for both velocity and normal stresses, however a systematic TKE overshoot at the nozzle exit is seen in the shear-layer. The paper shows a cost reduction of an order of magnitude compared to an earlier study of this configuration due to algorithmic and software improvements and demonstrates that WMLES can be used as a cost-competitive approach for jet noise predictions.

CST↗

NASA NTRSDate not supplied

Integrated Modeling for Payload Test of the Roman Space Telescope

The Nancy Grace Roman Telescope (RST) is a NASA observatory designed to unravel the secrets of dark energy and dark matter, search for and image exoplanets, and explore many topics in infrared optics. Scheduled to launch in no earlier than October 2026, this 2.4 meter aperture telescope has a field of view 100 times greater than the Hubble Space Telescope. The mission is currently in its construction phase, where integrated modeling between thermal, structural, and optical models of the observatory is necessary to demonstrate science quality images over the range of operational parameters. This presentation discusses the most recent integrated modeling analysis cycle for Roman, including model correlation with our instrument level testing. We include a discussion on improved processes of the handling of the various flows of data between the modeling disciplines and discipline specific monte-carlo analysis predictions. We will finish with the predicted uncertainties and expected performance for our upcoming observatory alignment verification test using machine learning algorithms.

telescope↗

NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

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.

Diagnostics↗