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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

CMC Research at NASA Glenn in 2021: Recent Progress and Plans

As part of NASA’s Aeronautics research, Glenn Research Center has developed SiC/SiC Ceramic Matrix Composites for 2700°F turbine engine applications in the next generation of ultra-efficient aircraft. In this presentation, the development and characterization of constituents and fabrication processes that enabled this advancement will be reviewed, and the resulting improvements in CMC mechanical properties and durability will be summarized. Progress toward the development and experimental validation of analytical models predicting the effects of engine operating conditions on the durability of Ceramic Matrix Composites with Environmental Barrier Coatings will be summarized. Results from current collaborative research with industry, universities and other government agencies will be reviewed. Research plans for 2021 and opportunities for future collaborations with NASA will also be summarized.

Ceramic Matrix Composites↗
DOE OSTI2022

DPC Direct Disposal Postclosure Thermal Modeling

Performance of geologic radioactive waste repositories depends on near-field and far-field processes, including km-scale flow and transport in engineered and natural barriers, that may require simulations of up to 1 M years of regulatory period. For a relatively short time span (less than 1000 years), the thermohydro-mechanical-chemical (THMC) coupled processes caused by heat from the waste package will influence near-field multiphase flow, chemical/reactive transport, and mechanical behaviors in the repository system. This study integrates the heat-driven perturbations in thermo-hydro-mechanical characteristics into thermo-hydro-chemical simulations using PFLOTRAN to reduce dimensionality and improve computational efficiency by implementing functions of stress-dependent permeability and saturation-temperature-dependent thermal conductivity. These process couplings are developed for spent nuclear fuel in dual-purpose canisters in two different hypothetical repositories: a shale repository and a salt repository.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2011

Lunar Wormbot: Design and Development of a Ground Base Robotic Tunneling Worm for Operation in Harsh Environments

From 1969 to 1972, the National Aeronautics and Space Administration (NASA) sent Apollo missions to the moon to conduct various exploration experiment. A few of the missions were directed to the study and sampling of moon soil, otherwise known as lunar regolith. The extent of the sample acquisition was limited due to the astronauts' limited ability to penetrate the moon's surface to a depth greater than three meters. However. the samples obtained were sufficient enough to provide key information pertaining to lunar regolith material properties that would further assist in future exploration endeavors. Analysis of the collected samples showed that the properties of lunar regolith may lead to knowledge of processed materials that will be beneficial for future human exploration or colonization. However, almost 40 years after the last Apollo mission, limited infonnation is known about regions underneath the moon's surface. Future lunar missions will require hardware that possesses the ability to burrow to greater depths in order to collect samples for subsequent analysis. During the summer of 2010, a team (Dr. Jessica Gaskin, Michael Kuhlman. Blaze Sanders, and Lafe Zabowski) from the NASA Robotics Academy at Marshall Space Flight Center (MSFC) was given the task of designing a robot to function as a soil collection and analysis device. Working with the National Space Science and Technology Center (NSSTC), the team was able to propose an initial design, build a prototype, and test the various subsystems of the prototype to be known as the "Lunar Wormbot" (LW). The NASA/NSSTC team then transferred the project to a University of Alabama in Huntsville (UAH) Mechanical and Aerospace Engineering (MAE) senior design class for further development. The UAH team was to utilize the NASA Systems Engineering Engine Design Process in the continuance of the Lunar Wormbot project. This process was implemented in order to coordinate the efforts of the team and guide the design of the project to ensure a high quality product that met requirements within the academic year timeframe. When the transition from the NASA NSSTC team to the UAH team occurred in August 2010, the scope and requirements were provided to the UAH team. The main objective for the UAH team was to design and fabricate a robotic burrowing prototype using peristaltic or earthworm-like motion with the purpose of collecting soil samples. The team was tasked with the design of a sub-system of the LW called the locomotive, or active, segment. Through the design process, the team extensively reviewed the requirements and functions to be performed of the LW, which led to the proposal of a final design. The present paper provides the details of the development of the design up to and including the Critical Design Review (CDR) of the Lunar Wormbot. This document briefly describes thc overall system and its function but primarily focuses on the design and implementation of the locomotive segment. Content presented includes: general design and system functionality, technical drawings, system analysis, manufacturing methods, and general project costs.

Boyles, Charles↗
DOE OSTI2020

Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold

This paper introduces a surrogate modeling scheme based on Grassmannian manifold learning to be used for cost-efficient predictions of high-dimensional stochastic systems. The method exploits subspace-structured features of each solution by projecting it onto a Grassmann manifold. This point-wise linear dimensionality reduction harnesses the structural information to assess the similarity between solutions at different points in the input parameter space. The method utilizes a solution clustering approach in order to identify regions of the parameter space over which solutions are sufficiently similarly such that they can be interpolated on the Grassmannian. In this clustering, the reduced-order solutions are partitioned into disjoint clusters on the Grassmann manifold using the eigen-structure of properly defined Grassmannian kernels and, the Karcher mean of each cluster is estimated. Then, the points in each cluster are projected onto the tangent space with origin at the corresponding Karcher mean using the exponential mapping. For each cluster, a Gaussian process regression model is trained that maps the input parameters of the system to the reduced solution points of the corresponding cluster projected onto the tangent space. Using this Gaussian process model, the full-field solution can be efficiently predicted at any new point in the parameter space. In certain cases, the solution clusters will span disjoint regions of the parameter space. In such cases, for each of the solution clusters we utilize a second, density-based spatial clustering to group their corresponding input parameter points in the Euclidean space. The proposed method is applied to two numerical examples. Here, the first is a nonlinear stochastic ordinary differential equation with uncertain initial conditions where the surrogate is used to predict the time history solution. The second involves modeling of plastic deformation in a model amorphous solid using the Shear Transformation Zone theory of plasticity, where the proposed surrogate is used to predict the full strain field of a material specimen under large shear strains.

42 ENGINEERING↗
DOE OSTI2022

On analysis and stochastic modeling of the particle kinetic energy equation in particle-laden isotropic turbulent flows

In this study, we analyze three-dimensional particle-laden, isotropic turbulence to develop an understanding of inertial particle dynamics from a kinetic energy perspective. Data trends implying inhomogeneous sampling of the flow by particles are identified and used to support a proposed particle behavior: particles appear to accumulate in regions of low flow kinetic energy over time because they lose kinetic energy and slow down in such regions, ultimately causing them to spend more time there. To elucidate this behavior, we derive a particle kinetic energy equation from the particle momentum equation, which incorporates inertial effects through the Schiller–Naumann drag correlation. Upon extracting fundamental physics from this equation, hypotheses regarding the role of the Stokes number in the temporal change of particle kinetic energy and the previously proposed particle behavior are evaluated using simulation data considering three Stokes numbers. Finally, a Fokker–Planck equation is used to derive the steady-state probability density function of the particle kinetic energy. The model fits the simulation data well and provides a tool for further investigation into understanding preferential concentration, as well as a reduced order model for predicting particle kinetic energy in turbulent flows.

42 ENGINEERING↗
DOE OSTI2025

Time-Resolved Stochastic Dynamics of Quantum Thermal Machines

Steady-state quantum thermal machines are typically characterized by a continuous flow of heat between different reservoirs. However, at the level of discrete stochastic realizations, heat flow is unraveled as a series of abrupt quantum jumps, each representing an exchange of finite quanta with the environment. Here, in this work, we present a framework that resolves the dynamics of quantum thermal machines into cycles classified as enginelike, coolinglike, or idle. We analyze the statistics of individual cycle types and their durations, enabling us to determine both the fraction of cycles useful for thermodynamic tasks and the average waiting time between cycles of a given type. Central to our analysis is the notion of intermittency, which captures the operational consistency of the machine by assessing the frequency and distribution of idle cycles. Our framework offers a novel approach to characterizing thermal machines, with significant relevance to experiments involving mesoscopic transport through quantum dots.

full counting statistics↗
NASA NTRS1972

Cost reduction by integration of the assurance technologies.

It is shown how the cost of applying assurance disciplines to the development of complex systems can be reduced by integrating the mechanics of system engineering and system management into a 'Development Risk Control System'. The essential features of the integration process are examined. A 'Development Risk Management Model' is described, and the use of the model to integrate and thereby reduce the cost of other assurance disciplines is discussed and illustrated.

Ball, L. W.↗
NASA NTRS1989

LEADER - An integrated engine behavior and design analyses based real-time fault diagnostic expert system for Space Shuttle Main Engine (SSME)

The LEADER expert system has been developed for automatic learning tasks encompassing real-time detection, identification, verification, and correction of anomalous propulsion system operations, using a set of sensors to monitor engine component performance to ascertain anomalies in engine dynamics and behavior. Two diagnostic approaches are embodied in LEADER's architecture: (1) learning and identifying engine behavior patterns to generate novel hypotheses about possible abnormalities, and (2) the direction of engine sensor data processing to perform resoning based on engine design and functional knowledge, as well as the principles of the relevant mechanics and physics.

Gupta, U. K.↗
NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗
NASA NTRS2014

NASA Engineering and Safety Center Technical Bulletin No. 14-03: COPV Mechanical Model Validation

Global and local deformation measurements should be incorporated into the composite overwrapped pressure vessel (COPV) design and analysis process to allow correlation of these measurements with finite element analysis (FEA) models. This correlation improves understanding of liner, liner/overwrap interface, and composite deformation response in COPVs. The improved accuracy reduces error in subsequent analyses, such as fracture, fatigue, and stress rupture that are critical for COPV qualification.

Composite Overwrap Pressure Vehicle↗
NASA NTRS1992

Influence of engineered interfaces on residual stresses and mechanical response in metal matrix composites

Because of the inherent coefficient of thermal expansion (CTE) mismatch between fiber and matrix within metal and intermetallic matrix composite systems, high residual stresses can develop under various thermal loading conditions. These conditions include cooling from processing temperature to room temperature as well as subsequent thermal cycling. As a result of these stresses, within certain composite systems, radial, circumferential, and/or longitudinal cracks have been observed to form at the fiber matrix interface region. A number of potential solutions for reducing this thermally induced residual stress field have been proposed recently. Examples of some potential solutions are high CTE fibers, fiber preheating, thermal anneal treatments, and an engineered interface. Here the focus is on designing an interface (by using a compensating/compliant layer concept) to reduce or eliminate the thermal residual stress field and, therefore, the initiation and propagation of cracks developed during thermal loading. Furthermore, the impact of the engineered interface on the composite's mechanical response when subjected to isothermal mechanical load histories is examined.

Arnold, Steven M.↗
NASA NTRS2001

Challenges Facing Design and Analysis Tools

The design and analysis of future aerospace systems will strongly rely on advanced engineering analysis tools used in combination with risk mitigation procedures. The implications of such a trend place increased demands on these tools to assess off-nominal conditions, residual strength, damage propagation, and extreme loading conditions in order to understand and quantify these effects as they affect mission success. Advances in computer hardware such as CPU processing speed, memory, secondary storage, and visualization provide significant resources for the engineer to exploit in engineering design. The challenges facing design and analysis tools fall into three primary areas. The first area involves mechanics needs such as constitutive modeling, contact and penetration simulation, crack growth prediction, damage initiation and progression prediction, transient dynamics and deployment simulations, and solution algorithms. The second area involves computational needs such as fast, robust solvers, adaptivity for model and solution strategies, control processes for concurrent, distributed computing for uncertainty assessments, and immersive technology. Traditional finite element codes still require fast direct solvers which when coupled to current CPU power enables new insight as a result of high-fidelity modeling. The third area involves decision making by the analyst. This area involves the integration and interrogation of vast amounts of information - some global in character while local details are critical and often drive the design. The proposed presentation will describe and illustrate these areas using composite structures, energy-absorbing structures, and inflatable space structures. While certain engineering approximations within the finite element model may be adequate for global response prediction, they generally are inadequate in a design setting or when local response prediction is critical. Pitfalls to be avoided and trends for emerging analysis tools will be described.

Knight, Norman F., Jr.↗
NASA NTRS2018

CMC Research at NASA Glenn in 2018: Recent Progress and Plans

As part of NASA's Aeronautics research, Glenn Research Center has developed SiC/SiC Ceramic Matrix Composites for 2700 °F turbine engine applications in the next generation of ultra-efficient aircraft. In this presentation, the development of fiber and matrix constituents and fabrication processes that enabled this advancement will be reviewed, and characterization of the resulting improvements in CMC mechanical properties and durability will be summarized. Progress toward the development and validation of models predicting the effects of the engine environment on durability of Ceramic Matrix Composites and Environmental Barrier Coatings will be summarized. Results from current collaborative research with industry and other government agencies will be reviewed. Research plans for 2018 and opportunities for future collaborations with NASA will also be summarized.

Ceramic Matrix Composites↗
NASA NTRS2018

CMC Research at NASA Glenn in 2018: Recent Progress and Plans

As part of NASA's Aeronautics research, Glenn Research Center has developed SiC/SiC Ceramic Matrix Composites for 2700 °F turbine engine applications in the next generation of ultra-efficient aircraft. In this presentation, the development of fiber and matrix constituents and fabrication processes that enabled this advancement will be reviewed, and characterization of the resulting improvements in CMC mechanical properties and durability will be summarized. Progress toward the development and validation of models predicting the effects of the engine environment on durability of Ceramic Matrix Composites and Environmental Barrier Coatings will be summarized. Results from current collaborative research with industry and other government agencies will be reviewed. Research plans for 2018 and opportunities for future collaborations with NASA will also be summarized.

Ceramic Matrix Composites↗
NASA NTRS2018

CMC Research at NASA Glenn in 2018: Recent Progress and Plans

As part of NASA's Aeronautics research, Glenn Research Center has developed SiC/SiC Ceramic Matrix Composites for 2700°F turbine engine applications in the next generation of ultra-efficient aircraft. In this presentation, the development of fiber and matrix constituents and fabrication processes that enabled this advancement will be reviewed, and characterization of the resulting improvements in CMC mechanical properties and durability will be summarized. Progress toward the development and validation of models predicting the effects of the engine environment on durability of Ceramic Matrix Composites and Environmental Barrier Coatings will be summarized. Results from current collaborative research with industry and other government agencies will be reviewed. Research plans for 2018 and opportunities for future collaborations with NASA will also be summarized.

composites↗
NASA NTRS2019

CMC Research at NASA Glenn in 2019: Recent Progress and Plans

As part of NASA's Aeronautics research, Glenn Research Center has developed SiC/SiC Ceramic Matrix Composites (CMC) for 2700 degrees Fahrenheit turbine engine applications in the next generation of ultra-efficient aircraft. In this presentation, the development of fiber and matrix constituents and fabrication processes that enabled this advancement will be reviewed, and characterization of the resulting improvements in CMC mechanical properties and durability will be summarized. Progress toward the development and validation of models predicting the effects of the engine environment on durability of Ceramic Matrix Composites and Environmental Barrier Coatings will be summarized. Results from current collaborative research with industry and other government agencies will be reviewed. Research plans for 2019 and opportunities for future collaborations with NASA will also be summarized.

Composites↗
NASA NTRS2020

CMC / EBC Research at NASA Glenn in 2020: Recent Progress and Plans

As part of NASA's Aeronautics research, Glenn Research Center has developed SiC/SiC Ceramic Matrix Composites for 2700°F turbine engine applications in the next generation of ultra-efficient aircraft. In this presentation, the development of fiber and matrix constituents and fabrication processes that enabled this advancement will be reviewed, and characterization of the resulting improvements in CMC mechanical properties and durability will be summarized. Progress toward the development and validation of models predicting the effects of the engine environment on durability of Ceramic Matrix Composites and Environmental Barrier Coatings will be summarized. Results from current collaborative research with industry and other government agencies will be reviewed. Research plans for 2020 and opportunities for future collaborations with NASA will also be summarized.

Grady, Joseph E.↗
NASA NTRS2020

CMC / EBC Research at NASA Glenn in 2020: Recent Progress and Plans

As part of NASA's Aeronautics research, Glenn Research Center has developed SiC/SiC Ceramic Matrix Composites for 2700 °F turbine engine applications in the next generation of ultra-efficient aircraft. In this presentation, the development of fiber and matrix constituents and fabrication processes that enabled this advancement will be reviewed, and characterization of the resulting improvements in CMC mechanical properties and durability will be summarized. Progress toward the development and validation of models predicting the effects of the engine environment on durability of Ceramic Matrix Composites and Environmental Barrier Coatings will be summarized. Results from current collaborative research with industry and other government agencies will be reviewed. Research plans for 2020 and opportunities for future collaborations with NASA will also be summarized.

Grady, Joseph↗