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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

TPSAS-NF1676L-15194-DND

Technical Motivation - 1950s: Indication of large lateral (roll/yaw) acceleration (“β-dot”) dynamic derivatives for configurations with strong vortex flows in the stall region (Lichtenstein, Campbell, Riley)! - 1960s: Increased emphasis on use of long-range missiles tempered by aircraft losses in close-in air combat - 1970s: - Effect of lateral acceleration derivatives on aircraft stability (Nguyen) - Characteristics and air combat advantages of rapid controlled large-amplitude maneuvering at low speeds near maximum lift shown in piloted simulations (Herbst) - 1980s: - Dynamic (forced-oscillation, rotary balance, curved- and rolling-flow) wind tunnel tests (Grafton, Lutze, Hultberg) - Incorporation of test data into computer simulations (Kalviste) - Technologies developed for controlled maneuvers at stall, post-stall angles of attack - Concern that conventional math modeling inadequately represented the dynamic component of these motions - Need to evaluate dependency of control laws on accurate computer simulations and motion prediction

Marilyn E Ogburn↗
NASA NTRS1995

Development of a Prototype Simulation Executive with Zooming in the Numerical Propulsion System Simulation

A major difficulty in designing aeropropulsion systems is that of identifying and understanding the interactions between the separate engine components and disciplines (e.g., fluid mechanics, structural mechanics, heat transfer, material properties, etc.). The traditional analysis approach is to decompose the system into separate components with the interaction between components being evaluated by the application of each of the single disciplines in a sequential manner. Here, one discipline uses information from the calculation of another discipline to determine the effects of component coupling. This approach, however, may not properly identify the consequences of these effects during the design phase, leaving the interactions to be discovered and evaluated during engine testing. This contributes to the time and cost of developing new propulsion systems as, typically, several design-build-test cycles are needed to fully identify multidisciplinary effects and reach the desired system performance. The alternative to sequential isolated component analysis is to use multidisciplinary coupling at a more fundamental level. This approach has been made more plausible due to recent advancements in computation simulation along with application of concurrent engineering concepts. Computer simulation systems designed to provide an environment which is capable of integrating the various disciplines into a single simulation system have been proposed and are currently being developed. One such system is being developed by the Numerical Propulsion System Simulation (NPSS) project. The NPSS project, being developed at the Interdisciplinary Technology Office at the NASA Lewis Research Center is a 'numerical test cell' designed to provide for comprehensive computational design and analysis of aerospace propulsion systems. It will provide multi-disciplinary analyses on a variety of computational platforms, and a user-interface consisting of expert systems, data base management and visualization tools, to allow the designer to investigate the complex interactions inherent in these systems. An interactive programming software system, known as the Application Visualization System (AVS), was utilized for the development of the propulsion system simulation. The modularity of this system provides the ability to couple propulsion system components, as well as disciplines, and provides for the ability to integrate existing, well established analysis codes into the overall system simulation. This feature allows the user to customize the simulation model by inserting desired analysis codes. The prototypical simulation environment for multidisciplinary analysis, called Turbofan Engine System Simulation (TESS), which incorporates many of the characteristics of the simulation environment proposed herein, is detailed.

Reed, John A.↗
DOE OSTI2018

Ductile Fracture - Gradient Regularization

Nuclear weapons alteration (ALT) and life extension programs (LEP) are of primary interest to the mission of Sandia National Laboratories. These programs continue to require experimental exploration and computational simulation of ductile failure scenarios to address qualification. Therefore, we invest in generating understanding about ductile failure as demonstrated though experimental procedures and computational simulation of engineering environments. In particular, we study an approach to ductile failure that incorporates the notion of phase-field fracture into our models of inelasticity appropriate for structural alloys. This report covers the formulations of the constitutive model and fracture models used within the phase-field approach and provides some numerical examples highlighting features and the state of the capability.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2018

Decoding Oxyanion Aqueous Solvation Structure: A Potassium Nitrate Example at Saturation

We present that the ability to probe the structure of a salt solution at the atomic scale is fundamentally important for our understanding of many chemical reactions and their mechanisms. The capability of neutron diffraction to “see” hydrogen (or deuterium) and other light isotopes is exceptional for resolving the structural complexity around the dissolved solutes in aqueous electrolytes. We have made measurements using oxygen isotopes on aqueous nitrate to reveal a small hydrogen-bonded water coordination number (3.9 ± 1.2) around a nitrate oxyanion. This is compared to estimates made using the existing method of nitrogen isotope substitution and those of computational simulations (>5–6 water molecules). The low water coordination number, combined with a comparison to classical molecular dynamics simulations, suggests that ion-pair formation is significant. Finally, this insight demonstrates the utility of experimental diffraction data for benchmarking atomistic computer simulations, enabling the development of more accurate intermolecular potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗
NASA NTRS2002

Geometry Modeling and Grid Generation for Computational Aerodynamic Simulations Around Iced Airfoils and Wings

Issues associated with analysis of 'icing effects' on airfoil and wing performances are discussed, along with accomplishments and efforts to overcome difficulties with ice. Because of infinite variations of ice shapes and their high degree of complexity, computational 'icing effects' studies using available software tools must address many difficulties in geometry acquisition and modeling, grid generation, and flow simulation. The value of each technology component needs to be weighed from the perspective of the entire analysis process, from geometry to flow simulation. Even though CFD codes are yet to be validated for flows over iced airfoils and wings, numerical simulation, when considered together with wind tunnel tests, can provide valuable insights into 'icing effects' and advance our understanding of the relationship between ice characteristics and their effects on performance degradation.

Choo, Yung K.↗
NASA NTRS1992

Helium bubble flow visualization of the spanwise separation on a NACA 0012 with simulated glaze ice

Research was performed to experimentally visualize and document the flow separation due to simulated glaze ice accretion on a NACA 0012 semispan with 30 deg sweep using helium bubbles as flow tracers. Results are compared to Navier-Stokes computational simulations for different angles of attack. Prior to acquiring data for the semispan model, a two dimensional experiment was conducted to determine the accuracy of using the helium bubbles as flow tracers. Results from the three dimensional experiment compare well to the computational simulations.

Kerho, M.↗
NASA NTRS1992

Helium bubble flow visualization of the spanwise separation on a NACA 0012 with simulated glaze ice

Research has been performed to experimentally visualize and document the flow separation due to simulated glaze ice accretion on a NACA 0012 semispan with 30-deg sweep using helium bubbles as flow tracers. Results are compared to Navier-Stokes computational simulations for different angles of attack. Prior to acquiring data for the semispan model, a two-dimensional experiment was conducted to determine the accuracy of using the helium bubbles as flow tracers. Results from the 3D experiment compare well to the computational simulations.

Kerho, M.↗
DOE OSTI2022

Classification of computed thermal tomography images with deep learning convolutional neural network

Thermal tomography (TT) is a computational method for the reconstruction of depth profile of the internal material defects from Pulsed Infrared Thermography (PIT) nondestructive evaluation. Here, the PIT method consists of recording material surface temperature transients with a fast frame infrared camera, following thermal pulse deposition on the material surface with a flashlamp and heat diffusion into material bulk. TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of the subsurface internal defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to the heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify the size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. The performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shapes imported from scanning electron microscopy images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on the TT images of elliptical defects is capable of classifying the shape and orientation of irregular defects.

42 ENGINEERING↗
DOE OSTI2024

Chemical Reactivity Through Adaptive Quantum Mechanics/Many-Body Representations: Theoretical Development, Software Implementation, and Applications (Final Report)

The main objective of this research project was the development and application of a new theoretical/computational framework to model chemical transformations and electronic excitations in fluid mixtures across different phases. Our theoretical/computational framework combines our data-driven many-body (DD-MB) potentials representing molecular interactions with adaptive schemes for modeling chemical reactions in solution. The combination of these two components resulted in an adaptive quantum mechanics/many-body (adQM/MB) method that largely suppresses discontinuities between QM and MM regions, which affect existing QM/MM methods, and thus provides an accurate representation of both quantum mechanical and environmental effects through a rigorous description of mutual polarization between QM and MM regions. The implementation of our DD-MB potentials and adQM/MB method in popular software enabled computer simulations of solvation phenomena, reactive processes, and electronic excitations in fluid mixtures with chemical and spectroscopic accuracy, representing a major step toward realistic computer simulations of a wide range of molecular systems relevant to the DOE mission.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1987

SP-100 control system modeling

SP-100 Control Systems modeling was done using a thermal hydraulic transient analysis model called ARIES-S. The ARIES-S Computer Simulation provides a basis for design, integration and analysis of the reactor including the control and protection systems. It is a modular digital computer simulation written in FORTRAN that operates interactively in real time on a VAX minicomputer.

Meyer, R. A.↗
NASA NTRS1991

Microfracture in high temperature metal matrix crossply laminates

Microfracture (fiber/matrix fracture, interphase debonding and inter-ply delamination) in high temperature metal matrix composites (HTMMC), subjected to both mechanical and thermal loading, is computationally simulated. A crossply 0.3 fiber volume ratio SiC/Ti15 composite with 0/90/0 layup is evaluated for microfracture using a multicell finite element model. A computational simulation procedure based on strain energy release rates is used to predict the fracture process and establish the hierarchy of fracture modes. Microfracture results for various loading cases are presented and discussed.

Mital, Subodh K.↗
NASA NTRS1998

Damage Progression in Bolted Composites

Structural durability,damage tolerance,and progressive fracture characteristics of bolted graphite/epoxy composite laminates are evaluated via computational simulation. Constituent material properties and stress and strain limits are scaled up to the structure level to evaluate the overall damage and fracture propagation for bolted composites. Single and double bolted composite specimens with various widths and bolt spacings are evaluated. The effect of bolt spacing is investigated with regard to the structural durability of a bolted joint. Damage initiation, growth, accumulation, and propagation to fracture are included in the simulations. Results show the damage progression sequence and structural fracture resistance during different degradation stages. A procedure is outlined for the use of computational simulation data in the assessment of damage tolerance, determination of sensitive parameters affecting fracture, and interpretation of experimental results with insight for design decisions.

Minnetyan, Levon↗
NASA NTRS1998

Damage Progression in Bolted Composites

Structural durability, damage tolerance, and progressive fracture characteristics of bolted graphite/epoxy composite laminates are evaluated via computational simulation. Constituent material properties and stress and strain limits are scaled up to the structure level to evaluate the overall damage and fracture propagation for bolted composites. Single and double bolted composite specimens with various widths and bolt spacings are evaluated. The effect of bolt spacing is investigated with regard to the structural durability of a bolted joint. Damage initiation, growth, accumulation, and propagation to fracture are included in the simulations. Results show the damage progression sequence and structural fracture resistance during different degradation stages. A procedure is outlined for the use of computational simulation data in the assessment of damage tolerance, determination of sensitive parameters affecting fracture, and interpretation of experimental results with insight for design decisions.

Minnetyan, Levon↗
NASA NTRS2004

Sensory-Feedback Exoskeletal Arm Controller

An electromechanical exoskeletal arm apparatus has been designed for use in controlling a remote robotic manipulator arm. The apparatus, called a force-feedback exoskeleton arm master (F-EAM) is comfortable to wear and easy to don and doff. It provides control signals from the wearer s arm to a robot arm or a computer simulator (e.g., a virtual-reality system); it also provides force and torque feedback from sensors on the robot arm or from the computer simulator to the wearer s arm. The F-EAM enables the wearer to make the robot arm gently touch objects and finely manipulate them without exerting excessive forces. The F-EAM features a lightweight design in which the motors and gear heads that generate force and torque feedback are made smaller than they ordinarily would be: this is achieved by driving the motors to power levels greater than would ordinarily be used in order to obtain higher torques, and by providing active liquid cooling of the motors to prevent overheating at the high drive levels. The F-EAM (see figure) includes an assembly that resembles a backpack and is worn like a backpack, plus an exoskeletal arm mechanism. The FEAM has five degrees of freedom (DOFs) that correspond to those of the human arm: 1. The first DOF is that of the side-to-side rotation of the upper arm about the shoulder (rotation about axis 1). The reflected torque for this DOF is provided by motor 1 via drum 1 and a planar four-bar linkage. 2. The second DOF is that of the up-and-down rotation of the arm about the shoulder. The reflected torque for this DOF is provided by motor 2 via drum 2. 3. The third DOF is that of twisting of the upper arm about its longitudinal axis. This DOF is implemented in a cable remote-center mechanism (CRCM). The reflected torque for this DOF is provided by motor 3, which drives the upper-arm cuff and the mechanism below it. A bladder inflatable by gas or liquid is placed between the cuff and the wearer s upper arm to compensate for misalignment between the exoskeletal mechanism and the shoulder. 4. The fourth DOF is that of flexion and extension of the elbow. The reflected torque for this DOF is provided by motor 4 and drum 4, which are mounted on a bracket that can slide longitudinally by a pin-and-slot engagement with the upper-arm cuff to compensate for slight variations in the position of the kinematic center of the elbow. Attached to drum 4 is an adapter plate to which is attached a CRCM for the lower arm. 5. The lower-arm CRCM implements the fifth DOF, which is the twist of the forearm about its longitudinal axis. Motor 5 provides the reflected torque for this DOF by driving the lower-arm cuff. A rod transmits twist and torsion between the lower-arm cuff and the hand cuff. With this system, the motion of the wearer s joints and the reflected torques applied to these joints can be measured and controlled in a relatively simple manner. This is because the anthropomorphic design of the mechanism imitates the kinematics of the human arm, eliminating the need for kinematic conversion of joint-torque and joint-angle data.

An, Bin↗
DOE OSTI2025

Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments

Artificial Intelligence (AI) combined with simulations and experiments has great potential to accelerate scientific discovery across technology and pharmaceuticals. However, the gap between simulations and experiments is challenging due to disparities in time and scale, making it difficult to estimate properties like energy and electronic states from experiments, and to provide feedback based on theoretical insights.Our research addresses the challenge by developing unique deep kernel based surrogate models that learns from microscopic images, mapping structural features to energy differences from defect formation. We start with full-training using simulated images to determine optimal settings, establishing a baseline for active learning. Using these settings from the baseline, active learning is trained, and predicts structures along simulation trajectories based on uncertainty and energetic stability, thus reducing data requirements, simulation time and computational costs. The results demonstrate that the model achieves a low average error margin of approximately 0.03 meV, indicating good performance. To enhance feature extraction and reconstruction capabilities, we developed an autoencoder-decoder as additional surrogate to create latent space to capture essential features, enabling precise comparisons between simulations and experiments. The results from this model achieved a reconstruction loss of around 0.2 and accurately reconstructed molecular structures.Overall, this work advances the steering of experiments through computational simulations by employing a surrogate models that actively predicts the trajectories of structural evolution, achieving time-to-solution comparable to experimental measurements.

Saranathan, Gayathri [Hewlett-Packard]↗
NASA NTRS1987

The cortex transform - Rapid computation of simulated neural images

With a goal of providing means for accelerating the image processing, machine vision, and testing of human vision models, an image transform was designed, which makes it possible to map an image into a set of images that vary in resolution and orientation. Each pixel in the output may be regarded as the simulated response of a neuron in human visual cortex. The transform is amenable to a number of shortcuts that greatly reduce the amount of computation.

Watson, Andrew B.↗