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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Overview of Active Flow Control at NASA Langley Research Center

The paper summarizes Active Flow Control projects currently underway at the NASA Langley Research Center. Technology development is being pursued within a multidisciplinary, cooperative approach, involving the classical disciplines of fluid mechanics, structural mechanics, material science, acoustics, and stability and control theory. Complementing the companion papers in this session, the present paper will focus on projects that have the goal of extending the state-of-the-art in the measurement, prediction, and control of unsteady, nonlinear aerodynamics. Toward this goal, innovative actuators, micro and macro sensors, and control strategies are considered for high payoff flow control applications. The target payoffs are outlined within each section below. Validation of the approaches range from bench-top experiments to wind-tunnel experiments to flight tests. Obtaining correlations for future actuator and sensor designs are implicit in the discussion. The products of the demonstration projects and design tool development from the fundamental NASA R&D level technology will then be transferred to the Applied Research components within NASA, DOD, and US Industry. Keywords: active flow control, separation control, MEMS, review

Pack, L. G.↗
NASA NTRS2008

Close Approach Prediction Analysis of the Earth Science Constellation with the Fengyun-1C Debris

Routine satellite operations for the Earth Science Constellation (ESC) include collision risk assessment between members of the constellation and other orbiting space objects. Each day, close approach predictions are generated by a U.S. Department of Defense Joint Space Operations Center Orbital Safety Analyst using the high accuracy Space Object Catalog maintained by the Air Force's 1" Space Control Squadron. Prediction results and other ancillary data such as state vector information are sent to NASAJGoddard Space Flight Center's (GSFC's) Collision Risk Assessment analysis team for review. Collision analysis is performed and the GSFC team works with the ESC member missions to develop risk reduction strategies as necessary. This paper presents various close approach statistics for the ESC. The ESC missions have been affected by debris from the recent anti-satellite test which destroyed the Chinese Fengyun- 1 C satellite. The paper also presents the percentage of close approach events induced by the Fengyun-1C debris, and presents analysis results which predict the future effects on the ESC caused by this event. Specifically, the Fengyun-1C debris is propagated for twenty years using high-performance computing technology and close approach predictions are generated for the ESC. The percent increase in the total number of conjunction events is considered to be an estimate of the collision risk due to the Fengyun-1C break- UP.

Duncan, Matthew↗
DOE OSTI2022

Acoustic Data Analytics For Predictive Technologies

The software utilizes frequency, amplitude, and phase features from sensor signals (either raw or processed) that are extracted using recursive frequency analysis, enabling (1) understanding of reactor operations under different operating conditions, (2) identifying of quiescent frequency ranges for sensor development, (3) system controls (4) predictive analytics, (5) structural health monitoring, and (6) loose part monitoring.

Smith, JamesA.↗
DOE OSTI2012

Modeling Local Control After Hypofractionated Stereotactic Body Radiation Therapy for Stage I Non-Small Cell Lung Cancer: A Report From the Elekta Collaborative Lung Research Group

Purpose: Hypofractionated stereotactic body radiation therapy (SBRT) has emerged as an effective treatment option for early-stage non-small cell lung cancer (NSCLC). Using data collected by the Elekta Lung Research Group, we generated a tumor control probability (TCP) model that predicts 2-year local control after SBRT as a function of biologically effective dose (BED) and tumor size. Methods and Materials: We formulated our TCP model as follows: TCP = e{sup [BED10-c Asterisk-Operator L-TCD50]/k} Division-Sign (1 + e{sup [BED10-c Asterisk-Operator L-TCD50]/k}), where BED10 is the biologically effective SBRT dose, c is a constant, L is the maximal tumor diameter, and TCD50 and k are parameters that define the shape of the TCP curve. Least-squares optimization with a bootstrap resampling approach was used to identify the values of c, TCD50, and k that provided the best fit with observed actuarial 2-year local control rates. Results: Data from 504 NSCLC tumors treated with a variety of SBRT schedules were available. The mean follow-up time was 18.4 months, and 26 local recurrences were observed. The optimal values for c, TCD50, and k were 10 Gy/cm, 0 Gy, and 31 Gy, respectively. Thus, size-adjusted BED (sBED) may be defined as BED minus 10 times the tumor diameter (in centimeters). Our TCP model indicates that sBED values of 44 Gy, 69 Gy, and 93 Gy provide 80%, 90%, and 95% chances of tumor control at 2 years, respectively. When patients were grouped by sBED, the model accurately characterized the relationship between sBED and actuarial 2-year local control (r=0.847, P=.008). Conclusion: We have developed a TCP model that predicts 2-year local control rate after hypofractionated SBRT for early-stage NSCLC as a function of biologically effective dose and tumor diameter. Further testing of this model with additional datasets is warranted.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Understanding the Structure and Dynamics of Conjugated Polymers by Advancing Deuteration Chemistry and Neutron Scattering (Final Report)

The overarching goal of the proposed work was to set up a partnership between the University of Southern Mississippi (USM) and Oak Ridge National Laboratory (ORNL) to develop novel approaches to measure the backbone rigidity of conjugated polymers (CPs) and understand the critical role of sidechains on the backbone conformation and the materials macroscopic property. The backbone rigidity greatly influences the electronic properties of CPs, which ultimately determines the functionality and performance of these materials. Improvements in the electronic properties of CPs would allow for enhanced charge transport in semiconductor devices, improved photovoltaic performance, recycling of waste heat in thermoelectrics, and discovery of new phenomena that will enable the next generation of energy technologies. Although significant progress has been made to optimize the optical and electronic properties of CPs, largely through Edisonian methodologies, it remains a challenge to experimentally characterize conjugated backbone conformation (chain rigidity, torsion, planarity, and short-range order) and relate these to the fundamental optical and electronic properties (electronic coupling, charge transport, etc.). This has left fundamental gaps in our knowledge of the most basic structure/property relationships within these systems, precluded the study of fundamental physical phenomena, and constrained the design and realization of new electronic and device functionalities. Thus, the major goal of this work is to use novel deuteration methodologies via systematic synthetic approaches, and neutron scattering techniques to comprehensively characterize the structural and dynamic properties of CPs in contrast-matching solvents. Our work would, for the first time, elucidate the relationship between backbone rigidity and macroscopic properties. They will also allow a rational formulation of design principles for next-generation CPs that are resilient to disorder through precise control of the delocalized electrons along the polymer backbone. Overall, this project will advance our understanding of the structure, dynamics, and fundamental physics of these materials, which is crucial for enabling the prediction, design, control, and manipulation of current and emerging material electronic properties.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Sensor-model prediction, monitoring and in-situ control of liquid RTM advanced fiber architecture composite processing

In-situ frequency dependent electromagnetic sensors (FDEMS) and the Loos resin transfer model have been used to select and control the processing properties of an epoxy resin during liquid pressure RTM impregnation and cure. Once correlated with viscosity and degree of cure the FDEMS sensor monitors and the RTM processing model predicts the reaction advancement of the resin, viscosity and the impregnation of the fabric. This provides a direct means for predicting, monitoring, and controlling the liquid RTM process in-situ in the mold throughout the fabrication process and the effects of time, temperature, vacuum and pressure. Most importantly, the FDEMS-sensor model system has been developed to make intelligent decisions, thereby automating the liquid RTM process and removing the need for operator direction.

Kranbuehl, D.↗
NASA NTRS2017

Progress Towards a Microgravity CFD Validation Study Using the ISS SPHERES-SLOSH Experiment

Understanding, predicting, and controlling fluid slosh dynamics is critical to safety and improving performance of space missions when a significant percentage of the spacecrafts mass is a liquid. Computational fluid dynamics simulations can be used to predict the dynamics of slosh, but these programs require extensive validation. Many CFD programs have been validated by slosh experiments using various fluids in earth gravity, but prior to the ISS SPHERES-Slosh experiment1, little experimental data for long-duration, zero-gravity slosh existed. This paper presents the current status of an ongoing CFD validation study using the ISS SPHERES-Slosh experimental data.

CFD↗
NASA NTRS2017

Progress Towards a Microgravity CFD Validation Study Using the ISS SPHERES-SLOSH Experiment

Understanding, predicting, and controlling fluid slosh dynamics is critical to safety and improving performance of space missions when a significant percentage of the spacecrafts mass is a liquid. Computational fluid dynamics simulations can be used to predict the dynamics of slosh, but these programs require extensive validation. Many CFD programs have been validated by slosh experiments using various fluids in earth gravity, but prior to the ISS SPHERES-Slosh experiment, little experimental data for long-duration, zero-gravity slosh existed. This paper presents the current status of an ongoing CFD validation study using the ISS SPHERES-Slosh experimental data.

microgravity↗
NASA NTRS2017

Progress Towards a Microgravity CFD Validation Study Using the ISS SPHERES-SLOSH Experiment

Understanding, predicting, and controlling fluid slosh dynamics is critical to safety and improving performance of space missions when a significant percentage of the spacecrafts mass is a liquid. Computational fluid dynamics simulations can be used to predict the dynamics of slosh, but these programs require extensive validation. Many CFD programs have been validated by slosh experiments using various fluids in earth gravity, but prior to the ISS SPHERES-Slosh experiment1, little experimental data for long-duration, zero-gravity slosh existed. This paper presents the current status of an ongoing CFD validation study using the ISS SPHERES-Slosh experimental data.

Storey, Jedediah M.↗
DOE OSTI2021

Fully Convolutional Spatio-Temporal Models for Representation Learning in Plasma Science

We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma science. The onset of major disruptions is a critically important fusion energy science issue that must be resolved for advanced tokamak plasmas such as the $25B burning plasma international thermonuclear experimental reactor (ITER) experiment. While a variety of statistical methods have been used to address the problem of tokamak disruption prediction and control, recent approaches based on deep learning have proven particularly compelling. In the present paper, we introduce further improvements to the fusion recurrent neural network (FRNN) software suite, which delivered cross-machine disruption predictions with unprecedented accuracy using a large database of experimental signals from two major tokamaks. Up to now, FRNN was based on the long short-term memory (LSTM) variant of recurrent neural networks to leverage the temporal information in the data. Here, we implement and apply the "temporal convolutional neural network (TCN)" architecture to the time-dependent input signals. Furthermore, this allows highly optimized convolution operations to carry the majority of the computational load of training, thus enabling a reduction in training time, and the effective use of high-performance computing resources for hyperparameter tuning. At the same time, the TCN-based architecture achieves better predictive performance when compared with the LSTM architecture for various tasks for a representative fusion database.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2016

Experimental, Numerical, and Analytical Slosh Dynamics of Water and Liquid Nitrogen in a Spherical Tank

Understanding, predicting, and controlling fluid slosh dynamics is critical to safety and improving performance of space missions when a significant percentage of the spacecraft's mass is a liquid. Computational fluid dynamics simulations can be used to predict the dynamics of slosh, but these programs require extensive validation. Many experimental and numerical studies of water slosh have been conducted. However, slosh data for cryogenic liquids is lacking. Water and cryogenic liquid nitrogen are used in various ground-based tests with a spherical tank to characterize damping, slosh mode frequencies, and slosh forces. A single ring baffle is installed in the tank for some of the tests. Analytical models for slosh modes, slosh forces, and baffle damping are constructed based on prior work. Select experiments are simulated using a commercial CFD software, and the numerical results are compared to the analytical and experimental results for the purposes of validation and methodology-improvement.

slosh↗
DOE OSTI2015

Materials Frontiers to Empower Quantum Computing: A Report of a Workshop on Materials Opportunities for Quantum Computing

This is an exciting time at the nexus of quantum computing and materials research. The materials frontiers described in this report represent a significant advance in electronic materials and our understanding of the interactions between the local material and a manufactured quantum state. Simultaneously, directed efforts to solve materials issues related to quantum computing provide an opportunity to control and probe the fundamental arrangement of matter that will impact all electronic materials. An opportunity exists to extend our understanding of materials functionality from electronic-grade to quantum-grade by achieving a predictive understanding of noise and decoherence in qubits and their origins in materials defects and environmental coupling. Realizing this vision systematically and predictively will be transformative for quantum computing and will represent a qualitative step forward in materials prediction and control.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Optimal Battery Utilization Over Lifetime for Parallel Hybrid Electric Vehicle to Maximize Fuel Economy

This paper presents a control strategy to maximize fuel economy of a parallel hybrid electric vehicle over a target life of the battery. Many approaches to maximizing fuel economy of parallel hybrid electric vehicle do not consider the effect of control strategy on the life of the battery. This leads to an oversized and underutilized battery. There is a trade-off between how aggressively to use and 'consume' the battery versus to use the engine and consume fuel. The proposed approach addresses this trade-off by exploiting the differences in the fast dynamics of vehicle power management and slow dynamics of battery aging. The control strategy is separated into two parts, (1) Predictive Battery Management (PBM), and (2) Predictive Power Management (PPM). PBM is the higher level control with slow update rate, e.g. once per month, responsible for generating optimal set points for PPM. The considered set points in this paper are the battery power limits and State Of Charge (SOC). The problem of finding the optimal set points over the target battery life that minimize engine fuel consumption is solved using dynamic programming. PPM is the lower level control with high update rate, e.g. a second, responsible for generating the optimal HEV energy management controls and is implemented using model predictive control approach. The PPM objective is to find the engine and battery power commands to achieve the best fuel economy given the battery power and SOC constraints imposed by PBM. Simulation results with a medium duty commercial hybrid electric vehicle and the proposed two-level hierarchical control strategy show that the HEV fuel economy is maximized while meeting a specified target battery life. On the other hand, the optimal unconstrained control strategy achieves marginally higher fuel economy, but fails to meet the target battery life.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Prediction of continuous porosity gradients in ceramics using ZnO as a model material

Materials with gradient microstructures have a wide range of applications such as cutting tools, armor, and electronic devices. However, it is difficult to predict and control the gradient microstructure during processing. In the present work, a continuous porosity gradient was successfully achieved in ZnO material via spark plasma sintering with a large induced thermal gradient. The porosity is overestimated if isothermal prediction is applied. The current work proposed a more accurate prediction of the porosity by considering the stress-shielding effect caused by the thermal gradient. The shielding effect results from different stress states in the sample due to differential sintering: the hotter side of the specimen experiences a higher strain rate and more shrinkage while the colder side experiences a lower strain rate and less shrinkage simultaneously. Therefore, the axial strains are varied throughout the sample thickness. Using the constituent equations in advanced sintering analysis, the shield stress was calculated to be approximately 13 MPa for the viscoelastic assumption of sintering. To improve the accuracy of predicting porosity gradient, it is necessary to add a load to overcome the shield stress when the materials are sintered with a thermal gradient.

36 MATERIALS SCIENCE↗
NASA NTRS1981

Development and validation of the V/STOL aerodynamics and stability and control manual

A V/STOL Aerodynamics and Stability and Control Manual was developed to provide prediction methods which are applicable to a wide range of V/STOL configurations in hover and transition flight, in and out of ground effect. Propulsion-induced effects have been combined with unpowered aerodynamics in a buildup of total forces and moments for the jet-lift concept, so that total aerodynamics can be used to predict aircraft stability, control, and flying qualities characteristics. Results of longitudinal aerodynamic predictions have been compared with test data, and indicate that the methods are fast, inexpensive, and within the desired accuracy for the objective preliminary design stage.

Henderson, C.↗
DOE OSTI2012

Commercial Implementation of Model-Based Manufacturing of Nanostructured Metals

Computational modeling is an essential tool for commercial production of nanostructured metals. Strength is limited by imperfections at the high strength levels that are achievable in nanostructured metals. Processing to achieve homogeneity at the micro- and nano-scales is critical. Manufacturing of nanostructured metals is intrinsically a multi-scale problem. Manufacturing of nanostructured metal products requires computer control, monitoring and modeling. Large scale manufacturing of bulk nanostructured metals by Severe Plastic Deformation is a multi-scale problem. Computational modeling at all scales is essential. Multiple scales of modeling must be integrated to predict and control nanostructural, microstructural, macrostructural product characteristics and production processes.

36 MATERIALS SCIENCE↗