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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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An analytical technique is described for predicting the plume impingement force during tandem stage separation, including the reversed flow effects. Comparisons of predicted and measured impingement forces for various geometric and flow conditions show good correlation between the analytical technique and experimental data.
An optimal control approach has been used to model pilot performance during STOL flare and landing. The model is used to predict pilot landing performance for three STOL configurations, each having a different level of automatic control augmentation. Model predictions are compared with flight simulator data. It is concluded that the model can be effective design tool for studying analytically the effects of display modifications, different stability augmentation systems, and proposed changes in the landing area geometry.
A series of computer codes were developed to predict solid rocket motor produced contamination to spacecraft sensitive surfaces. Subscale and flight test data have confirmed some of the analytical results. Application of the analysis tools to a typical spacecraft has provided early identification of potential spacecraft contamination problems and provided insight into their solution; e.g., flight plan modifications, plume or outgassing shields and/or contamination covers.
An incremental numerical procedure based on lamination theory is developed to predict creep and creep rupture of general laminates. Existing unidirectional creep compliance and delayed failure data is used to develop analytical models for lamina response. The compliance model is based on a procedure proposed by Findley which incorporates the power law for creep into a nonlinear constitutive relationship. The matrix octahedral shear stress is assumed to control the stress interaction effect. A modified superposition principle is used to account for the varying stress level effect on the creep strain. The lamina failure model is based on a modification of the Tsai-Hill theory which includes the time dependent creep rupture strength. A linear cumulative damage law is used to monitor the remaining lifetime in each ply.
Fundamental experimental data base for turbulent flow mixing models is provided and better prediction of the more complex turbulent chemical reacting flows. Analytical application to combustor design is provided and a better fundamental understanding of the combustion process.
The purpose of this work is to present an experimental validation via fixed-base simulation of analytical methods for predicting handling qualities of flight vehicles. Different augmented dynamics are considered and analytical predictions of performance and pilot rating obtained. A flight simulation is then performed and comparison between experiment and theoretical data is shown.
Analytic predictions of X-wing rotor noise levels in hover without circulation control are presented and compared with measured data at two observer locations. Agreement of predictions and data is best in the rotor plane, due to the dominance of thickness noise terms. Predictions are made for the X-wing and a conventional type rotor system and are compared at two locations at the same tip speed and at each rotor's design tip speed. The acoustic signal of the X-wing is more heavily influenced by thickness noise terms than the conventional rotor. The comparison of each rotor's noise levels depends on the observer location and the amount of circulation control blowing employed.
The design of a two wheel bulk material transport vehicle is described in detail. The design consists of a modified cylindrical bowl, two independently controlled direct drive motors, and two deformable wheels. The bowl has a carrying capacity of 2.8 m (100 ft) and is constructed of aluminum. The low speed, high HP motors are directly connected to the wheels, thus yielding only two moving parts. The wheels, specifically designed for lunar applications, utilize the chevron tread pattern for optimum traction. The vehicle is maneuvered by varying the relative angular velocities of the wheels. The bulk material being transported is unloaded by utilizing the motors to oscillate the bowl back and forth to a height at which dumping is achieved. The analytical models were tested using a scaled prototype of the lunar transport vehicle. The experimental data correlated well with theoretical predictions. Thus, the design established provides a feasible alternative for the handling of bulk material on the moon.
Two complimentary studies were performed to determine the effects of stress and physical aging on the matrix dominated time dependent properties of IM7/8320 composite. The first of these studies, experimental in nature, used isothermal tensile creep/aging test techniques developed for polymers and adapted them for testing of the composite material. From these tests, the time dependent transverse (S22) and shear (S66) compliance's for an orthotropic plate were found from short term creep compliance measurements at constant, sub-T(sub g) temperatures. These compliance terms were shown to be affected by physical aging. Aging time shift factors and shift rates were found to be a function of temperature and applied stress. The second part of the study relied upon isothermal uniaxial tension tests of IM7/8320 to determine the effects of physical aging on the nonlinear material behavior at elevated temperature. An elastic/viscoplastic constitutive model was used to quantify the effects of aging on the rate-independent plastic and rate-dependent viscoplastic response. Sensitivity of the material constants required by the model to aging time were determined for aging times up to 65 hours. Verification of the analytical model indicated that the effects of prior aging on the nonlinear stress/strain/time data of matrix dominated laminates can be predicted.
This report discusses the uncertainty analysis performed in support of a new test analysis and performance prediction model for the Space Shuttle Main Engine. The new model utilizes uncertainty estimates for experimental data and for the analytical model to obtain the most plausible operating condition for the engine system. This report discusses the development of the data sets and uncertainty estimates to be used in the development of the new model. It also presents the application of uncertainty analysis to analytical models and the uncertainty analysis for the conservation of mass and energy balance relations is presented. A new methodology for the assessment of the uncertainty associated with linear regressions is presented.
An experimental setup was developed for data acquisition from IPMC strips subjected to various tip mass levels and in parallel an analytical model was developed to predict ther material response.
Prediction of battery health from electrochemical impedance spectroscopy (EIS) data can enable rapid measurement of battery state in real-world applications without using additional sensors or time-consuming performance measurements. However, deconvoluting the effect of capacity, state of charge, and temperature on EIS response is complicated analytically. Here, various machine-learning models, such as linear, Gaussian process, random forest, and artificial neural network regression, are utilized to predict capacity from EIS using hundreds of capacity, direct current (DC) resistance, and EIS measurements recorded under varying conditions of health, temperature, and state of charge (SOC). Several feature extraction and selection methods from traditional electrochemical analysis and statistical modeling are explored using machine-learning pipelines. EIS data from just two frequencies can accurately predict capacity, and interrogation shows that the optimal set of frequencies is not usually intuitive. Best results are achieved with an ensemble model, which predicts battery capacity with a mean absolute error of 1.9% on data from unobserved cells.
An experimental and analytical study of the thermal behavior of spiral bevel gears is presented. Experimental data were taken using thermocoupled test hardware and an infrared microscope. Many operational parameters were varied to investigate their effects on the thermal behavior. The data taken were also used to validate the boundary conditions applied to the analytical model. A finite element-based solution sequence was developed. The three-dimensional model was developed based on the manufacturing process for these gears. Contact between the meshing gears was found using tooth contact analysis to describe the location, curvatures, orientations, and surface velocities. This information was then used in a three-dimensional Hertzian contact analysis to predict contact ellipse size and maximum pressure. From these results, an estimate of the heat flux magnitude and the location on the finite element model was made. The finite element model used time-averaged boundary conditions to permit the solution to attain steady state in a computationally efficient manner.Then time- and position-varying boundary conditions were applied to the model to analyze the cyclic heating and cooling due to the gears meshing and transferring heat to the surroundings, respectively. The model was run in this mode until the temperature behavior stabilized. The transient flash temperature on the surface was therefore described. The analysis can be used to predict the overall expected thermal behavior of spiral bevel gears. The experimental and analytical results were compared for this study and also with a limited number of other studies. The experimental and analytical results attained in the current study were basically within 10% of each other for the cases compared. The experimental comparison was for bulk thermocouple locations and data taken with an infrared microscope. The results of a limited number of other studies were compared with those obtained herein and predicted the same basic behavior.
This paper reviews a parameter identification methodology developed to investigate the hypersonic longitudinal trim misprediction apparent in the NASA Space Shuttle Orbiter entry flights. The method combines an analysis using a measured versus predicted technique in conjunction with a multilinear regression analysis to identify prediction deficiencies using quasi-static longitudinal data in the hypersonic flight regime (Mach 6 through 26). In general, the results of this extraction confirm results previously obtained by other Shuttle investigators with the exception of elevon effectiveness. Further analysis and/or flight data will be required to resolve the conflicting elevon results. A combination of this analytical tool and other flight data will enable flight data interpretation with the potential for identifying the sources of the Shuttle's hypersonic trim misprediction to an accuracy consistent with updating preflight prediction methodology for future spacecraft.
Attaining Industry 4.0 for manufacturing operations requires advanced monitoring systems and real-time data analytics of plant data, among other topics. We propose a Probabilistic Bidirectional Recurrent Network (PBRN) for industrial process monitoring for the early detection of faults. The model is based on a Gated Recurrent Unit (GRU) neural network that allows the model to retain long-term dependencies between sensor data along a time horizon, hence learning the dynamic behavior of the process. To reduce the false-positive detection rate of the model, we compel the model to learn from a highly noisy sensor reading while outputting noise-free sensor outputs. The performance of the proposed model is compared to other data-driven statistical process monitoring schemes using real plant data from an industrial Air Separations Unit (ASU) containing noisy sensor readings. We show that the model can learn from noisy data without reducing its performance. Using two different fault cases, we demonstrate the model’s ability to carry out early fault detection with average false-positive rates of 2.9% and 4.9% for both fault cases. The missed detection rates are 0.1% and 0.2%, respectively.
This article highlights fiscal year 2002 work performed by NASA Glenn Research Center personnel to validate algorithms and data developed in-house to predict shadowing effects on the International Space Station (ISS) solar arrays power generation. The validation effort utilized video footage and on-orbit telemetry for cases spanning a 1-yr period. Validation was required because of the uncertainty of various aspects involved in shadowing analysis. Results show that a good comparison exists between actual and predicted shadowed power system performance for solar array front and backside shadowing.
Focal Area(s): 3. Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI; & 1. Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Science Challenge: Interactions between water, land, and energy systems are complex and occur on a variety of scales, ranging from local to basinal to regional. Accurately predicting the behavior of ground water and surface water systems for 5-10 years and beyond requires an understanding of the current system and the ability to model both the natural system at scale and human-induced forcings related to energy and other activities. Artificial intelligence and machine learning (AI/ML) combined with modern compilation and integration efforts for U.S. groundwater and surface water systems present potential solutions to bolstering detailed physics-based models of these systems. Big data tied with ML and physics-based modeling can drive breakthroughs in understanding the earth system, but research is often impeded by data access (e.g., privacy issues), quality, formats, gaps, multi-source, multi-scale, integration, and spatiotemporal challenges. Effective integration of real data and simulated (synthetic) data that fill gaps is critical. Overcoming these complex data and model integration challenges will enable a transformational approach to acquiring enhanced understanding of environmental systems.
Current models of human visual search have extended the traditional serial/parallel search dichotomy. Two successful models for predicting human visual search are the Guided Search model and the Signal Detection Theory model. Although these models are inherently different, it has been difficult to compare them because the Guided Search model is designed to predict response time, while Signal Detection Theory models are designed to predict performance accuracy. Moreover, current implementations of the Guided Search model require the use of Monte-Carlo simulations, a method that makes fitting the model's performance quantitatively to human data more computationally time consuming. We have extended the Guided Search model to predict human accuracy in target-localization search tasks. We have also developed analytic expressions that simplify simulation of the model to the evaluation of a small set of equations using only three free parameters. This new implementation and extension of the Guided Search model will enable direct quantitative comparisons with human performance in target-localization search experiments and with the predictions of Signal Detection Theory and other search accuracy models.