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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Attitude determination using an adaptive multiple model filtering Scheme

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations.

Lam, Quang↗
DOE OSTI2022

Consolidated Hydropower Data Repository: Value and Opportunities

Hydropower is one of several types of generating assets that provides energy, capacity, and services to electric power systems. It does so under rubrics and objectives—market driven and regulated, internal and external to asset and fleet owners—that address reliability, cost, price, and, increasingly, flexibility of output. The aggregation of data from multiple hydropower units can provide insights into asset operations and maintenance practices and needs and assist in meeting hydropower objectives. This paper examines the concept and potential benefits of aggregating hydropower asset data—primarily supervisory control and data acquisition (SCADA) information—with examples of insights developed from data aggregated by the Hydropower Research Institute (HRI). Data aggregation as discussed herein, and as implemented by the HRI, extends beyond multiple units in a powerhouse and beyond multiple hydropower facilities in an electric utility fleet or river system. Examples of research and analytics from such aggregated datasets range from unit load dependency analyses to modeling sensor measurements to detect and diagnose anomalies in assets. These examples showed the benefits of utilizing the entire dataset for insights into how the sensor layout of a single unit or set of units compares to the hydropower industry overall. Such insights include whether additional sensors are needed to complete analyses or to make decisions. In addition, utilizing multiple sensors of the same kind within a unit can provide an indication of possible current or upcoming problems with equipment. Although other analyses are possible, their use requires the development of complex models and, potentially, access to types of data that are currently not included with the example dataset used in this study. However, the examples studied herein confirmed the value of data aggregation in the fleet and unit contexts, and the value extends beyond multiple units in a powerhouse and beyond multiple hydropower facilities in an electric utility fleet or river system. The assessments also provided insights into potential extensions to the data aggregation concept that could further add to their value to the hydropower community, and these are included in this document as a set of recommendations.

13 HYDRO ENERGY↗
NASA NTRS2011

Thermal protection system ablation sensor

An isotherm sensor tracks space vehicle temperatures by a thermal protection system (TPS) material during vehicle re-entry as a function of time, and surface recession through calibration, calculation, analysis and exposed surface modeling. Sensor design includes: two resistive conductors, wound around a tube, with a first end of each conductor connected to a constant current source, and second ends electrically insulated from each other by a selected material that becomes an electrically conductive char at higher temperatures to thereby complete an electrical circuit. The sensor conductors become shorter as ablation proceeds and reduced resistance in the completed electrical circuit (proportional to conductor length) is continually monitored, using measured end-to-end voltage change or current in the circuit. Thermocouple and/or piezoelectric measurements provide consistency checks on local temperatures.

Gorbunov, Sergey↗
NASA NTRSDate not supplied

Entry, Descent, and Landing GN&C System Evaluation via Cable-Driven Emulation Robotics

An innovative cable-driven parallel robot named the Six Degree-of-Freedom Tendon Actu- ated Robot (STAR), recently developed by researchers at NASA Johnson Space Center, was built to enable entry, descent, and landing (EDL) emulation experiments. Upon functional testing and system verification, STAR employed several simulated EDL trajectories to validate velocimeter LIDAR-based sensor models for cooperative terrain relative navigation applica- tions for its first testing campaign. The STAR lab also hosted researchers from Astrobotic to evaluate the real-time performance of a newly developed sensor payload, UltraNav, through hardware in-the-loop-testing (HWIL). This work introduces the STAR system along with its accompanying testing utilities and capabilities, and deliver the experimental methodology and results of test campaigns performed with the STAR facility to date.

navigation↗
NASA NTRS2011

Ares I Scale Model Acoustic Tests Instrumentation for Acoustic and Pressure Measurements

The Ares I Scale Model Acoustic Test (ASMAT) was a development test performed at the Marshall Space Flight Center (MSFC) East Test Area (ETA) Test Stand 116. The test article included a 5% scale Ares I vehicle model and tower mounted on the Mobile Launcher. Acoustic and pressure data were measured by approximately 200 instruments located throughout the test article. There were four primary ASMAT instrument suites: ignition overpressure (IOP), lift-off acoustics (LOA), ground acoustics (GA), and spatial correlation (SC). Each instrumentation suite incorporated different sensor models which were selected based upon measurement requirements. These requirements included the type of measurement, exposure to the environment, instrumentation check-outs and data acquisition. The sensors were attached to the test article using different mounts and brackets dependent upon the location of the sensor. This presentation addresses the observed effect of the sensors and mounts on the acoustic and pressure measurements.

Vargas, Magda B.↗
NASA NTRS1993

Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors

Statistical modeling and evaluation of the performance of obstacle detection systems for Unmanned Ground Vehicles (UGVs) is essential for the design, evaluation, and comparison of sensor systems. In this report, we address this issue for imaging range sensors by dividing the evaluation problem into two levels: quality of the range data itself and quality of the obstacle detection algorithms applied to the range data. We review existing models of the quality of range data from stereo vision and AM-CW LADAR, then use these to derive a new model for the quality of a simple obstacle detection algorithm. This model predicts the probability of detecting obstacles and the probability of false alarms, as a function of the size and distance of the obstacle, the resolution of the sensor, and the level of noise in the range data. We evaluate these models experimentally using range data from stereo image pairs of a gravel road with known obstacles at several distances. The results show that the approach is a promising tool for predicting and evaluating the performance of obstacle detection with imaging range sensors.

Matthies, Larry↗
NASA NTRS1996

A dynamic response model for pressure sensors in continuum and high Knudsen number flows with large temperature gradients

This paper develops a dynamic model for pressure sensors in continuum and rarefied flows with longitudinal temperature gradients. The model was developed from the unsteady Navier-Stokes momentum, energy, and continuity equations and was linearized using small perturbations. The energy equation was decoupled from momentum and continuity assuming a polytropic flow process. Rarefied flow conditions were accounted for using a slip flow boundary condition at the tubing wall. The equations were radially averaged and solved assuming gas properties remain constant along a small tubing element. This fundamental solution was used as a building block for arbitrary geometries where fluid properties may also vary longitudinally in the tube. The problem was solved recursively starting at the transducer and working upstream in the tube. Dynamic frequency response tests were performed for continuum flow conditions in the presence of temperature gradients. These tests validated the recursive formulation of the model. Model steady-state behavior was analyzed using the final value theorem. Tests were performed for rarefied flow conditions and compared to the model steady-state response to evaluate the regime of applicability. Model comparisons were excellent for Knudsen numbers up to 0.6. Beyond this point, molecular affects caused model analyses to become inaccurate.

Whitmore, Stephen A.↗
NASA NTRS1990

Modeling of internal contaminant deposition on a cold instrument sensor

A model is developed to predict the deposition of contaminants on the CCD sensors of the Wide Field Planetary Camera to determine expected performance parameters. Contaminant deposition due to internal instrument sources is considered as is the mechanism of transport to the CCDs themselves. The Contamination Analysis Program (CAP) model considers deposition and reemission kinetics, nodal containment sources, and internodal shape factors related to line-of-sight transport. Indirect transport is also accounted for in the CAP model by considering the effect of diffuse reflection at internodal exchanges. The effective total transport factors for each node-to-node exchange is precalculated to reduce the effective number of nodes and reducing run time of the CAP program. The method is applied to four distinct conditions and is found to be suitable for the analysis of internal self-contamination. Cold traps, venting, and vacuumlike design are factors which are found to be important for cold-sensitive sensors.

Barengoltz, Jack B.↗
NASA NTRS2009

Investigation of Primary Mirror Segment's Residual Errors for the Thirty Meter Telescope

The primary mirror segment aberrations after shape corrections with warping harness have been identified as the single largest error term in the Thirty Meter Telescope (TMT) image quality error budget. In order to better understand the likely errors and how they will impact the telescope performance we have performed detailed simulations. We first generated unwarped primary mirror segment surface shapes that met TMT specifications. Then we used the predicted warping harness influence functions and a Shack-Hartmann wavefront sensor model to determine estimates for the 492 corrected segment surfaces that make up the TMT primary mirror. Surface and control parameters, as well as the number of subapertures were varied to explore the parameter space. The corrected segment shapes were then passed to an optical TMT model built using the Jet Propulsion Laboratory (JPL) developed Modeling and Analysis for Controlled Optical Systems (MACOS) ray-trace simulator. The generated exit pupil wavefront error maps provided RMS wavefront error and image-plane characteristics like the Normalized Point Source Sensitivity (PSSN). The results have been used to optimize the segment shape correction and wavefront sensor designs as well as provide input to the TMT systems engineering error budgets.

warping harness↗
NASA NTRSDate not supplied

Lunar Navigation Performance Using the Deep Space Network and Alternate Solutions to Support Precision Landing

As human exploration once again targets the surface of the Moon, questions continue to emerge regarding the necessity of Earth-based tracking systems, such as the Deep Space Network, for spacecraft navigation in support of lunar descent and landing. This paper will derive an extensive Deep Space Network sensor model for use in linear covariance analysis and demonstrate the resulting trajectory dispersions and navigation performance in comparison with alternate solutions, such as terrain relative navigation. An in-depth trade study with considerations for various trajectory profiles, time allocated to ground tracking, number of active ground stations, and interaction with other sensors will be conducted to shed significant insight into sensor suite requirements to ensure safe and precise landing on the Moon.

Navigation↗
NASA NTRS1992

Evaluation of electrolytic tilt sensors for measuring model angle of attack in wind tunnel tests

The results of a laboratory evaluation of electrolytic tilt sensors as potential candidates for measuring model attitude or angle of attack in wind tunnel tests are presented. The performance of eight electrolytic tilt sensors was compared with that of typical servo accelerometers used for angle-of-attack measurements. The areas evaluated included linearity, hysteresis, repeatability, temperature characteristics, roll-on-pitch interaction, sensitivity to lead-wire resistance, step response time, and rectification. Among the sensors being evaluated, the Spectron model RG-37 electrolytic tilt sensors have the highest overall accuracy in terms of linearity, hysteresis, repeatability, temperature sensitivity, and roll sensitivity. A comparison of the sensors with the servo accelerometers revealed that the accuracy of the RG-37 sensors was on the average about one order of magnitude worse. Even though a comparison indicates that the cost of each tilt sensor is about one-third the cost of each servo accelerometer, the sensors are considered unsuitable for angle-of-attack measurements. However, the potential exists for other applications such as wind tunnel wall-attitude measurements where the errors resulting from roll interaction, vibration, and response time are less and sensor temperature can be controlled.

Wong, Douglas T.↗
NASA NTRS1988

Super magnets for large tunnels

The development and use of a large Magnetic Suspension and Balance System (MSBS) in wind tunnels is examined, discussing NASA-sponsored research to develop a large MSBS, and the 2 MSBS already in use. The MSBS holds the model in an arbitrary position in the wind tunnel test section and measures the forces and moments acting on the model. Technologies for building a large MSBS have been developed, and research is being done to reduce building costs. Magnetic suspension is advantageous because it completely eliminates interference from mechanical support systems such as stings or struts and it allows for easy rotation and translation of the model. Advances allowing operation at higher temperatures, probably using liquid nitrogen instead of liquid helium may reduce the cost of MSBS operation. A large MSBS would require power controllers with capacity up to 10 MW, efficiency approaching 98 percent and the ability to reverse the sign of both voltage and current. Technologies include the electro-magnetic position sensor, model positioning sensing, digital controller, and a precalibrated resistance strain-gage balance.

Boyden, Richmond P.↗
NASA NTRSDate not supplied

Advances in EDL Flight Mechanics Modeling & Simulation

Flight mechanics modeling and simulation is an area of continuous development. One of the industry-standard simulation tools in the Program to Optimize Spacecraft Trajectories II (POST2) is developed and maintained in-house at Langley Research Center. Recent uses of this tool include assessing GN&C performance of human-scale Lunar and Mars landing systems using generalized low- and medium-fidelity navigation sensor models required for precision landing. This talk will review recent POST2 simulation developments, as well as how current and planned advances in GN&C modeling enable rapid assessment of spacecraft integrated performance.

POST2↗
DOE OSTI2025

River Dissolved Oxygen Prediction Using Machine Learning Models and Wireless Sensor Measurements

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches River Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Real-Time Hardware-in-the-Loop Simulation of Ares I Launch Vehicle

The Ares Real-Time Environment for Modeling, Integration, and Simulation (ARTEMIS) has been developed for use by the Ares I launch vehicle System Integration Laboratory at the Marshall Space Flight Center. The primary purpose of the Ares System Integration Laboratory is to test the vehicle avionics hardware and software in a hardware - in-the-loop environment to certify that the integrated system is prepared for flight. ARTEMIS has been designed to be the real-time simulation backbone to stimulate all required Ares components for verification testing. ARTE_VIIS provides high -fidelity dynamics, actuator, and sensor models to simulate an accurate flight trajectory in order to ensure realistic test conditions. ARTEMIS has been designed to take advantage of the advances in underlying computational power now available to support hardware-in-the-loop testing to achieve real-time simulation with unprecedented model fidelity. A modular realtime design relying on a fully distributed computing architecture has been implemented.

Tobbe, Patrick↗
NASA NTRS2013

Dependence of Dynamic Modeling Accuracy on Sensor Measurements, Mass Properties, and Aircraft Geometry

The NASA Generic Transport Model (GTM) nonlinear simulation was used to investigate the effects of errors in sensor measurements, mass properties, and aircraft geometry on the accuracy of identified parameters in mathematical models describing the flight dynamics and determined from flight data. Measurements from a typical flight condition and system identification maneuver were systematically and progressively deteriorated by introducing noise, resolution errors, and bias errors. The data were then used to estimate nondimensional stability and control derivatives within a Monte Carlo simulation. Based on these results, recommendations are provided for maximum allowable errors in sensor measurements, mass properties, and aircraft geometry to achieve desired levels of dynamic modeling accuracy. Results using additional flight conditions and parameter estimation methods, as well as a nonlinear flight simulation of the General Dynamics F-16 aircraft, were compared with these recommendations

Grauer, Jared A.↗
NASA NTRS1992

Simulation model of a twin-tail, high performance airplane

The mathematical model and associated computer program to simulate a twin-tailed high performance fighter airplane (McDonnell Douglas F/A-18) are described. The simulation program is written in the Advanced Continuous Simulation Language. The simulation math model includes the nonlinear six degree-of-freedom rigid-body equations, an engine model, sensors, and first order actuators with rate and position limiting. A simplified form of the F/A-18 digital control laws (version 8.3.3) are implemented. The simulated control law includes only inner loop augmentation in the up and away flight mode. The aerodynamic forces and moments are calculated from a wind-tunnel-derived database using table look-ups with linear interpolation. The aerodynamic database has an angle-of-attack range of -10 to +90 and a sideslip range of -20 to +20 degrees. The effects of elastic deformation are incorporated in a quasi-static-elastic manner. Elastic degrees of freedom are not actively simulated. In the engine model, the throttle-commanded steady-state thrust level and the dynamic response characteristics of the engine are based on airflow rate as determined from a table look-up. Afterburner dynamics are switched in at a threshold based on the engine airflow and commanded thrust.

Buttrill, Carey S.↗
DOE OSTI2021

Feature Extraction: Improving Remote Sensor Classification of Non-Proliferation

This research focuses on developing algorithms for nuclear non-proliferation detection using remote sensor modeling. To improve the performance of classification models, we implemented a data pipeline with feature extraction. This pipeline takes raw data and transforms it into smaller data points called features that still describe the model. Improving this classification works towards the departments of energy’s missions of ensuring American’s security and prosperity by creating technology that addresses nuclear challenges. To conduct this analysis, we used the Python programming language and some key packages, including tsfresh and TSFEL. Originally tsfresh was selected because it has the most statistical features out of all the packages. Later TSFEL was incorporated due to the additional features it can extract from data, such as temporal and spectral. However, feature extraction becomes challenging in the presence of missing values. In this case, two additional Python packages were added to our workflow, NumPy and pandas, allowing for the feature extraction process to handle unknown values. Our data pipeline was tested on data collected from a simulation that describes the process state of a physical example. The results show the pipeline’s capability to consume and extract a total 17 features from tabular data. Future work includes producing classifications using decision tree-based models such as XGBoost and improving data collection by analyzing feature importance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗