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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learningautonomous systems; flight simulat↗
NASA NTRSDate not supplied

Predicting Melt Properties Using Atomistic Simulations With A Highly Accurate Physically Informed Neural Network Interatomic Potential

The use of a recently developed machine learning (ML) interatomic potential for molecular dynamics simulations of aluminum melt properties will be presented. Such properties are critical for process modeling in additive manufacturing, including the melt pool size, solidification, and formation of solidification microstructures. Direct first-principles modeling of these processes is computationally prohibitive whereas simulations employing ML potentials combine the high accuracy of quantum-mechanical methods with high computational speeds. The physically-informed neural network (PINN) method used herein, integrates a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential. PINN potentials can accurately reproduce many properties of aluminum in both crystalline-solid and liquid phases. We examine the accuracy of a PINN Al potential in predicting the density, self-diffusivity, viscosity, and the tension of the liquid surface and liquid-solid interfaces. Comparison with experimental data and ab initio molecular dynamics calculations shows very good agreement for all properties tested.

molecular dynamics↗
NASA NTRS2013

Optical Strain and Crack-Detection Measurements on a Rotating Disk

The development of techniques for the in-situ measurement and structural health monitoring of the rotating components in gas turbine engines is of major interest to NASA. As part of this on-going effort, several experiments have been undertaken to develop methods for detecting cracks and measuring strain on rotating turbine engine like disks. Previous methods investigated have included the use of blade tip clearance sensors to detect the presence of cracks by monitoring the change in measured blade tip clearance and analyzing the combined disk-rotor system's vibration response. More recently, an experiment utilizing a novel optical Moiré based concept has been conducted on a subscale turbine engine disk to demonstrate a potential strain measurement and crack detection technique. Moiré patterns result from the overlap of two repetitive patterns with slightly different spacing. When this technique is applied to a rotating disk, it has the potential to allow for the detection of very small changes in spacing and radial growth in a rotating disk due to a flaw such as a crack. This investigation was a continuation of previous efforts undertaken in 2011 to 2012 to validate this optical concept. The initial demonstration attempted on a subscale turbine engine disk was inconclusive due to the minimal radial growth experienced by the disk during operation. For the present experiment a new subscale Aluminum disk was fabricated and improvements were made to the experimental setup to better demonstrate the technique. A circular reference pattern was laser etched onto a subscale engine disk and the disk was operated at speeds up to 12 000 rpm as a means of optically monitoring the Moiré created by the shift in patterns created by the radial growth due the presence of the simulated crack. Testing was first accomplished on a clean defect free disk as a means of acquiring baseline reference data. A notch was then machined in to the disk to simulate a crack and testing was repeated for the purposes of demonstrating the concept. Displacement data was acquired using external blade tip clearance and shaft displacement sensors as a means of confirming the optical data and for validating other sensor based crack detection techniques.

Woike, Mark↗
NASA NTRS2013

Optical Strain and Crack-Detection Measurements on a Rotating Disk

The development of techniques for the in-situ measurement and structural health monitoring of the rotating components in gas turbine engines is of major interest to NASA. As part of this on-going effort, several experiments have been undertaken to develop methods for detecting cracks and measuring strain on rotating turbine engine like disks. Previous methods investigated have included the use of blade tip clearance sensors to detect the presence of cracks by monitoring the change in measured blade tip clearance and analyzing the combined disk-rotor system's vibration response. More recently, an experiment utilizing a novel optical Moiré based concept has been conducted on a subscale turbine engine disk to demonstrate a potential strain measurement and crack detection technique. Moiré patterns result from the overlap of two repetitive patterns with slightly different spacing. When this technique is applied to a rotating disk, it has the potential to allow for the detection of very small changes in spacing and radial growth in a rotating disk due to a flaw such as a crack. This investigation was a continuation of previous efforts undertaken in 2011-2012 to validate this optical concept. The initial demonstration attempted on a subscale turbine engine disk was inconclusive due to the minimal radial growth experienced by the disk during operation. For the present experiment a new subscale Aluminum disk was fabricated and improvements were made to the experimental setup to better demonstrate the technique. A circular reference pattern was laser etched onto a subscale engine disk and the disk was operated at speeds up to 12 000 rpm as a means of optically monitoring the Moiré created by the shift in patterns created by the radial growth due the presence of the simulated crack. Testing was first accomplished on a clean defect free disk as a means of acquiring baseline reference data. A notch was then machined in to the disk to simulate a crack and testing was repeated for the purposes of demonstrating the concept. Displacement data was acquired using external blade tip clearance and shaft displacement sensors as a means of confirming the optical data and for validating other sensor based crack detection techniques.

Woike, Mark↗
NASA NTRS2022

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗
NASA NTRS2026

Development and Experimental Validation of a Path-Dependent Spin Forming Finite Element Model

Spin forming is an advanced manufacturing process widely used in the aerospace and defense sectors to produce lightweight, high-strength cylindrical components with tight dimensional tolerances. This study explores the applicability of the path-dependent Mechanical Threshold Stress (MTS) constitutive model by simulating the evolution of geometry, machining forces, and plastic deformation during the spin forming of a 10-mm thick 6061-O aluminum cylinder. While numerical modeling of spin forming has advanced substantially over the past decade, systematic verification and experimental validation of material models remain limited, particularly in predicting through-thickness process evolution. The MTS model, incorporating a Voce hardening rule, is employed for its ability to represent cyclic loading, rapidly varying temperature fields, and strain rates characteristic of spin forming. Numerical convergence analysis indicates discretization uncertainties between 0.3% and 9.2% for key quantities of interest. Experimental validation demonstrates that the MTS model, when implemented with a verified mesh, accurately reproduces both elastic and plastic behavior of 6061-O aluminum, predicting peak roller loads within 11–18% of measurements, geometric tolerances within 3%, and plastic strain distributions within 10% of experimental values. Collectively, these results establish a validated computational framework for predictive spin-forming simulations with quantified confidence, providing a foundation for extension to other alloys, geometries, and forming conditions.

Spin forming↗
NASA NTRS1994

Simulation of Unsteady Combustion in a Ramjet Engine Using a Highly Parallel Computer

Combustion instability in ramjets is a complex phenomenon that involve nonlinear interaction between acoustic waves, vortex motion and unsteady heat release in the combustor. To numerically simulate this 3-D, transient phenomenon, enormous computer resources (time, memory and disk storage) are required. Although current generation vector supercomputers are capable of providing adequate resources for simulations of this nature, their high cost and limited availability, makes such machines less than satisfactory for routine use. The primary focus of this study is to assess the feasibility of using highly parallel computer systems as a cost-effective alternative for conducting such unsteady flow simulations. Towards this end, a large-eddy simulation model for combustion instability was implemented on the Intel iPSC/860 and a careful study was conducted to determine the benefits and the problems associated with the use of such machines for transient simulations. Details of this study along with the results obtained from the unsteady combustion simulations carried out on the iPSC/860 are discussed in this paper.

Menon, Suresh↗
NASA NTRS1989

OMV mission simulator

The Orbital Maneuvering Vehicle (OMV) will be remotely piloted during rendezvous, docking, or proximity operations with target spacecraft from a ground control console (GCC). The real-time mission simulator and graphics being used to design a console pilot-machine interface are discussed. A real-time orbital dynamics simulator drives the visual displays. The dynamics simulator includes a J2 oblate earth gravity model and a generalized 1962 rotating atmospheric and drag model. The simulator also provides a variable-length communication delay to represent use of the Tracking and Data Relay Satellite System (TDRSS) and NASA Communications (NASCOM). Input parameter files determine the graphics display. This feature allows rapid prototyping since displays can be easily modified from pilot recommendations. A series of pilot reviews are being held to determine an effective pilot-machine interface. Pilots fly missions with nominal to 3-sigma dispersions in translational or rotational axes. Console dimensions, switch type and layout, hand controllers, and graphic interfaces are evaluated by the pilots and the GCC simulator is modified for subsequent runs. Initial results indicate a pilot preference for analog versus digital displays and for two 3-degree-of-freedom hand controllers.

Cok, Keith E.↗
NASA NTRSDate not supplied

Optimization-Based Parametric Design via High-Fidelity Simulation: Overview + Examples

Design-Build-Test approaches for developing spaceflight hardware are prohibitively time and cost intensive and often lead to suboptimal mechanism designs. Approaches that couple machine learning and high-fidelity physics simulation could eliminate the need for hardware prototyping and dramatically accelerate the engineering design cycle, ultimately reducing cost. This talk presents a modular NASA-developed toolchain to optimize hardware mechanisms in a virtual environment using numerical optimization and multi-body physics simulation and includes example applications related to rigid wheel design for autonomous rovers and computational fluid dynamics.

optimization↗
NASA NTRS1971

Finite-state machines as elements in control systems.

Demonstration that approximate solutions to certain classes of differential and difference equations can be expressed in form of finite state machines. Based on this result, a finite-state machine model of an adaptive gain changer in an aircraft stability augmentation system is developed. Results of simulated flights using the finite-state machine gain changer are presented.

Burgin, G. H.↗
NASA NTRS1972

The development of an inert simulant for HNS/teflon explosive

The report describes the development and evaluation of an inert simulant for the thermally stable, heat-resistant plastic-bonded explosive HNS/Teflon. The simulant is made by dry blending vinylidene fluoride, melamine and Teflon which when compared has a pressed density and thermal properties corresponding closely to the explosive. In addition, the machinability and handling characteristics of the simulant are similar to the explosive.

Elban, W. L.↗
NASA NTRS1971

Shuttle simulation

Development and characteristics of simulation concept to analyze man, machine, and environment aspects of space shuttle operation

King, A.↗
NASA NTRS1984

Model equation for simulating flows in multistage turbomachinery

A steady, three-dimensional average-passage equation system is derived for use in simulating multistage turbomachinery flows. These equations describe a steady, viscous flow that is periodic from blade passage to blade passage. From this system of equations, various reduced forms can be derived for use in simulating the three-dimensional flow field within multistage machinery. It is suggested that a properly scaled form of the average-passage equation system would provide an improved mathematical model for simulating the flow in multistage machines at design and, in particular, at off-design conditions.

Adamczyk, J. J.↗
NASA NTRS1983

A minicomputer based software system for the selection of optimal subsets of Thematic Mapper channels

A software system has been developed and implemented on a minicomputer for feature selection based on two inter-dependent methods. The first is an enhancement of the traditional approach based on optimizing interclass average separabilities. The second is based on a Monte Carlo simulation of multispectral data and machine classification with subsequent estimation of classification accuracy as a function of channel subset. The two methods are mutually supportive - the first allows rapid screening whereas the second is based on the more solid theoretical foundation of maximizing classification accuracy.

Card, D. H.↗
NASA NTRS1985

Model equation for simulating flows in multistage turbomachinery

A steady, three-dimensional average-passage equation system is derived for use in simulating multistage turbomachinery flows. These equations describe a steady, viscous flow that is periodic from blade passage to blade passage. From this system of equations, various reduced forms can be derived for use in simulating the three-dimensional flow field within multistage machinery. It is suggested that a properly scaled form of the averaged-passage equation system would provide an improved mathematical model for simulating the flow in multistage machines at design and, in particular, at off-design conditions.

Adamczyk, J. J.↗
NASA NTRS1987

Neural nets on the MPP

The Massively Parallel Processor (MPP) is an ideal machine for computer experiments with simulated neural nets as well as more general cellular automata. Experiments using the MPP with a formal model neural network are described. The results on problem mapping and computational efficiency apply equally well to the neural nets of Hopfield, Hinton et al., and Geman and Geman.

Hastings, Harold M.↗