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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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Modeling Empirical Size Relationships on Load-Displacement Behavior and Failure in Threaded Fasteners.
Abstract not provided.
Vortex interactions in multiple vortex wakes behind aircraft
A flow visualization technique has been developed which allows the nature of lift-generated wakes behind aircraft models to be investigated. Several different configurations of a 0.61-m span model of a Boeing 747-type transport aircraft were tested to allow observation of typical vortex interactions and merging in multiple vortex wakes. The vortices were identified by emitting tracer dyes from selected locations on the model. Wing span loading and model attitude were found to effect both vortex motions within the wake and resulting far-field wake velocity. Landing gear deployment caused a far-field reformation of vorticity behind a model configuration which dissipated concentrated vorticity in the near-field wake. A modified landing configuration was developed which appeared to significantly alleviate the concentrated wake vorticity.
Energy consumption program: A computer model simulating energy loads in buildings
The JPL energy consumption computer program developed as a useful tool in the on-going building modification studies in the DSN energy conservation project is described. The program simulates building heating and cooling loads and computes thermal and electric energy consumption and cost. The accuracy of computations are not sacrificed, however, since the results lie within + or - 10 percent margin compared to those read from energy meters. The program is carefully structured to reduce both user's time and running cost by asking minimum information from the user and reducing many internal time-consuming computational loops. Many unique features were added to handle two-level electronics control rooms not found in any other program.
An Investigation of the Experimental Aerodynamic Loading on a Model Helicopter Rotor Blade
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Water Landing Investigation of a Hydro-ski Model at Beam Loadings of 18.9 and 4.4
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Transonic Wind-tunnel Measurements of Static Lateral and Directional Stability and Vertical-tail Loads for a Model with a 45 Degree Sweptback Wing
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Evaluation of a Strain Based Failure Criterion for the Multi-Constituent Composite Model Under Shock Loading.
Abstract not provided.
Wound Ballistics Modeling for Blast Loading Blunt Force Impact and Projectile Penetration.
Abstract not provided.
THREE DIMENSIONAL MODELING OF MAGLIF LOADS ON THE Z GENERATOR USING THE GORGON MHD CODE.
Abstract not provided.
Evaluation of a Strain Based Failure Criterion for the Multi-Constituent Composite Model Under Shock Loading.
Abstract not provided.
Computational investigation of a blast loaded surrogate head model with application towards traumatic brain injury.
Abstract not provided.
Wound Ballistics Modeling for Blast Loading Blunt Force Impact and Projectile Penetration.
Abstract not provided.
Computational Investigation of a Blast Loaded Surrogate Head Model With Application Towards Traumatic Brain Injury.
Abstract not provided.
A mass additive technique for modal testing as applied to the Space Shuttle ASTRO-1 payload
Traditionally, a fixed base modal test has been performed as a means of verifying the coupled loads math model for Space Shuttle flight payloads. An alternate method, a free-free configured payload using mass loaded boundary conditions, is presented as a means of verifying the coupled loads model of the ASTRO-1 flight payload. This method allows evaluation of the influence of local load paths into the frequency range of the free-free test. The method is cost effective and does not contaminate the modal test results with fixture coupled modes or boundary condition uncertainties. This paper describes the mass additive modal test technique as applied to the Space Shuttle ASTRO-1 flight payload.
Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico
Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.
Modeling a constant power load for nickel-hydrogen battery testing using SPICE
The effort to design and model a constant power load for the HST (Hubble Space Telescope) nickel-hydrogen battery tests is described. The constant power load was designed for three different simulations on the batteries: life cycling, reconditioning, and capacity testing. A dc-dc boost converter was designed to act as this constant power load. A boost converter design was chosen because of the low test battery voltage (4 to 6 VDC) generated and the relatively high power requirement of 60 to 70 W. The SPICE model was shown to consistently predict variations in the actual circuit as various designs were attempted. It is concluded that the confidence established in the SPICE model of the constant power load ensures its extensive utilization in future efforts to improve performance in the actual load circuit.
NESC Peer Review of Exploration Systems Development (ESD) Integrated Vehicle Modal Test, Model Correlation, Development Flight Instrumentation (DFI) and Flight Loads Readiness; Uncertainty Propagation for Model Validation Sub-task
This report details a sub-task (regarding Uncertainty Propagation for Model Validation) from a NASA Engineering and Safety Center assessment that is a multi-year activity spanning the complete development of the Space Launch System integrated vehicle structural dynamic models, and the development of the certification of flight readiness for the Artemis 1 and Artemis 2 vehicles and their variants.