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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 NTRS2005

Twist Model Development and Results From the Active Aeroelastic Wing F/A-18 Aircraft

Understanding the wing twist of the active aeroelastic wing F/A-18 aircraft is a fundamental research objective for the program and offers numerous benefits. In order to clearly understand the wing flexibility characteristics, a model was created to predict real-time wing twist. A reliable twist model allows the prediction of twist for flight simulation, provides insight into aircraft performance uncertainties, and assists with computational fluid dynamic and aeroelastic issues. The left wing of the aircraft was heavily instrumented during the first phase of the active aeroelastic wing program allowing deflection data collection. Traditional data processing steps were taken to reduce flight data, and twist predictions were made using linear regression techniques. The model predictions determined a consistent linear relationship between the measured twist and aircraft parameters, such as surface positions and aircraft state variables. Error in the original model was reduced in some cases by using a dynamic pressure-based assumption and by using neural networks. These techniques produced excellent predictions for flight between the standard test points and accounted for nonlinearities in the data. This report discusses data processing techniques and twist prediction validation, and provides illustrative and quantitative results.

Lizotte, Andrew↗
NASA NTRS2007

Twist Model Development and Results from the Active Aeroelastic Wing F/A-18 Aircraft

Understanding the wing twist of the active aeroelastic wing (AAW) F/A-18 aircraft is a fundamental research objective for the program and offers numerous benefits. In order to clearly understand the wing flexibility characteristics, a model was created to predict real-time wing twist. A reliable twist model allows the prediction of twist for flight simulation, provides insight into aircraft performance uncertainties, and assists with computational fluid dynamic and aeroelastic issues. The left wing of the aircraft was heavily instrumented during the first phase of the active aeroelastic wing program allowing deflection data collection. Traditional data processing steps were taken to reduce flight data, and twist predictions were made using linear regression techniques. The model predictions determined a consistent linear relationship between the measured twist and aircraft parameters, such as surface positions and aircraft state variables. Error in the original model was reduced in some cases by using a dynamic pressure-based assumption. This technique produced excellent predictions for flight between the standard test points and accounted for nonlinearities in the data. This report discusses data processing techniques and twist prediction validation, and provides illustrative and quantitative results.

Lizotte, Andrew M.↗
NASA NTRS2012

Airborne Measurements of Atmospheric Pressure made Using an IPDA Lidar Operating in the Oxygen A-Band

We report airborne measurements of atmospheric pressure made using an integrated path differential absorption (IPDA) lidar that operates in the oxygen A-band near 765 nm. Remote measurements of atmospheric temperature and pressure are needed for NASA s Active Sensing of CO2 Emissions Over Nights, Days, and Seasons (ASCENDS) mission to measure atmospheric CO2. Accurate measurements of tropospheric CO2 on a global scale are very important in order to better understand its sources and sinks and to improve our predictions of climate change. The goal of ASCENDS is to determine the CO2 dry mixing ratio with lidar measurements from space at a level of ~1 ppm. Analysis to date shows that with current weather models, measurements of both the CO2 column density and the column density of dry air are needed. Since O2 is a stable molecule that uniformly mixed in the atmosphere, measuring O2 absorption in the atmosphere can be used to infer the dry air density. We have developed an airborne (IPDA) lidar for Oxygen, with support from the NASA ESTO IIP program. Our lidar uses DFB-based seed laser diodes, a pulsed modulator, a fiber laser amplifier, and a non-linear crystal to generate wavelength tunable 765 nm laser pulses with a few uJ/pulse energy. The laser pulse rate is 10 KHz, and average transmitted laser power is ~20 mW. Our lidar steps laser pulses across a selected line O2 doublet near 764.7 nm in the Oxygen A-band. The direct detection lidar receiver uses a 20 cm diameter telescope, a Si APD detector in Geiger mode, and a multi-channel scalar to detect and record the time resolved laser backscatter in 40 separate wavelength channels. Subsequent analysis is used to estimate the transmission line shape of the doublet for the laser pulses reflected from the ground. Ground based data analysis allows averaging from 1 to 60 seconds to increase SNR in the transmission line shape of the doublet. Our retrieval algorithm fits the expected O2 lineshapes against the measurements and determines the atmospheric pressure by minimizing the error between the observations and model. We first demonstrated our airborne lidar during flights during summer 2010. We made several improvements and made measurements during the Ascends flights during July 2011. More information about the technique, lidar instrument, airborne measurements, and pressure estimates will be described in the presentation.

Riris, Haris↗
NASA NTRS2006

Pressure Effects on Oxygen Concentration Flammability Thresholds of Materials for Aerospace Applications

Spacecraft materials selection is based on an upward flammability test conducted in a quiescent environment in the highest-expected oxygen-concentration environment. However, NASA s advanced space exploration program is anticipating using various habitable environments. Because limited data is available to support current program requirements, a different test logic is suggested to address these expanded atmospheric environments through the determination of materials self-extinguishment limits. This paper provides additional pressure effects data on oxygen concentration and partial pressure self-extinguishment limits under quiescent conditions. For the range of total pressures tested, the oxygen concentration and oxygen partial pressure flammability thresholds show a near linear function of total pressure. The oxygen concentration/oxygen partial pressure flammability thresholds depend on the total pressure and appear to increase with increasing oxygen concentration (and oxygen partial pressure). For the Constellation Program, the flammability threshold information will allow NASA to identify materials with increased flammability risk because of oxygen concentration and total pressure changes, minimize potential impacts, and allow for development of sound requirements for new spacecraft and extraterrestrial landers and habitats.

Hirsch, David↗
NASA NTRS1994

Understanding software faults and their role in software reliability modeling

This study is a direct result of an on-going project to model the reliability of a large real-time control avionics system. In previous modeling efforts with this system, hardware reliability models were applied in modeling the reliability behavior of this system. In an attempt to enhance the performance of the adapted reliability models, certain software attributes were introduced in these models to control for differences between programs and also sequential executions of the same program. As the basic nature of the software attributes that affect software reliability become better understood in the modeling process, this information begins to have important implications on the software development process. A significant problem arises when raw attribute measures are to be used in statistical models as predictors, for example, of measures of software quality. This is because many of the metrics are highly correlated. Consider the two attributes: lines of code, LOC, and number of program statements, Stmts. In this case, it is quite obvious that a program with a high value of LOC probably will also have a relatively high value of Stmts. In the case of low level languages, such as assembly language programs, there might be a one-to-one relationship between the statement count and the lines of code. When there is a complete absence of linear relationship among the metrics, they are said to be orthogonal or uncorrelated. Usually the lack of orthogonality is not serious enough to affect a statistical analysis. However, for the purposes of some statistical analysis such as multiple regression, the software metrics are so strongly interrelated that the regression results may be ambiguous and possibly even misleading. Typically, it is difficult to estimate the unique effects of individual software metrics in the regression equation. The estimated values of the coefficients are very sensitive to slight changes in the data and to the addition or deletion of variables in the regression equation. Since most of the existing metrics have common elements and are linear combinations of these common elements, it seems reasonable to investigate the structure of the underlying common factors or components that make up the raw metrics. The technique we have chosen to use to explore this structure is a procedure called principal components analysis. Principal components analysis is a decomposition technique that may be used to detect and analyze collinearity in software metrics. When confronted with a large number of metrics measuring a single construct, it may be desirable to represent the set by some smaller number of variables that convey all, or most, of the information in the original set. Principal components are linear transformations of a set of random variables that summarize the information contained in the variables. The transformations are chosen so that the first component accounts for the maximal amount of variation of the measures of any possible linear transform; the second component accounts for the maximal amount of residual variation; and so on. The principal components are constructed so that they represent transformed scores on dimensions that are orthogonal. Through the use of principal components analysis, it is possible to have a set of highly related software attributes mapped into a small number of uncorrelated attribute domains. This definitively solves the problem of multi-collinearity in subsequent regression analysis. There are many software metrics in the literature, but principal component analysis reveals that there are few distinct sources of variation, i.e. dimensions, in this set of metrics. It would appear perfectly reasonable to characterize the measurable attributes of a program with a simple function of a small number of orthogonal metrics each of which represents a distinct software attribute domain.

Munson, John C.↗
NASA NTRS1972

Riding and handling qualities of light aircraft: A review and analysis

Design procedures and supporting data necessary for configuring light aircraft to obtain desired responses to pilot commands and gusts are presented. The procedures employ specializations of modern military and jet transport practice where these provide an improvement over earlier practice. General criteria for riding and handling qualities are discussed in terms of the airframe dynamics. Methods available in the literature for calculating the coefficients required for a linearized analysis of the airframe dynamics are reviewed in detail. The review also treats the relation of spin and stall to airframe geometry. Root locus analysis is used to indicate the sensitivity of airframe dynamics to variations in individual stability derivatives and to variations in geometric parameters. Computer programs are given for finding the frequencies, damping ratios, and time constants of all rigid body modes and for generating time histories of aircraft motions in response to control inputs. Appendices are included presenting the derivation of the linearized equations of motion; the stability derivatives; the transfer functions; approximate solutions for the frequency, damping ratio, and time constants; an indication of methods to be used when linear analysis is inadequate; sample calculations; and an explanation of the use of root locus diagrams and Bode plots.

Smetana, F. O.↗
DOE OSTI2024

Parallel interior-point solver for block-structured nonlinear programs on SIMD/GPU architectures

Here, we investigate how to port the standard interior-point method to new exascale architectures for block-structured nonlinear programs with state equations. Computationally, we decompose the interior-point algorithm into two successive operations: the evaluation of the derivatives and the solution of the associated Karush-Kuhn-Tucker (KKT) linear system. Our method accelerates both operations using two levels of parallelism. First, we distribute the computations on multiple processes using coarse parallelism. Second, each process uses SIMD/GPU accelerators locally to accelerate the operations using fine-grained parallelism. The KKT system is reduced by eliminating the inequalities and the state variables from the corresponding equations. We demonstrate our method's capability on the supercomputer Polaris, a testbed for the future exascale Aurora system. Each node is equipped with four GPUs, a setup amenable to our two-level approach. Our experiments on the stochastic optimal power flow problem show that the reduction method is 50x faster than the sparse linear solver HSL MA57 running in serial on the CPU, and 6x faster than Pardiso running in parallel on CPU on the same number of processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Isotope Production Education and Research via a Systematic Study of Photo-nuclear Reaction Yields and Excitation Functions

A new collaboration between the New Mexico Institute of Mining and Technology, Idaho State University, and Idaho National Laboratory builds the domestic workforce in isotope production and related science and technologies by conducting research and education programs related to photo-nuclear reactions. Workforce development was identified by the Department of Energy’s Office of Science in the Reaching a New Energy Sciences Workforce (RENEW) program that seeks to engage underrepresented university students in isotope production activities. This collaboration includes coursework development, student training, and isotope production research utilizing photo-nuclear reactions at electron linear accelerators. Bremsstrahlung-weighted excitation functions and cross-sections will be measured and the use of nanomaterials to exploit kinematic recoil reactions will be investigated. Students will be hosted at the Idaho National Laboratory where mentors will provide opportunities to explore careers in nuclear science and technology. This presentation regards the ongoing progress of the research and education objectives of the collaboration that was performed under the Office of Science Isotopes Program under award number D000-22-2765.

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRS1982

The MLA program at NASA

The Thematic Mapper (TM) on Landsat-D represents a significant improvement compared to the Multispectral Scanner (MSS) of the current Landsat satellites. However, there is a need for even higher spatial and spectral resolution. Such developments would, for instance, provide a significantly greater capability for mineral identification and the detection of vegetation stress. The pushbroom mode of imaging utilizing solid state linear or area arrays is widely regarded as the best approach to overcome the inherent limitations of a scanning type system. The Multispectral Linear Array (MLA) has become the generic term for the sensor concept and the technology associated with solid state electronically scanned arrays operating in the pushbroom mode. A brief review is provided of the MLA instrument concepts, associated technology developments, program status, and future plans.

Ando, K. J.↗
NASA NTRS2006

DCS-Neural-Network Program for Aircraft Control and Testing

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Jorgensen, Charles C.↗
DOE OSTI2026

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

Ng, Max↗
NASA NTRS1973

Development of a two channel linear discriminant function for detecting and identifying surface water using ERTS-1 data

A method for detecting surface water using ERTS-1 multispectral scanner digital data is described. The several computer programs for processing multispectral data are described, along with the manner in which these programs were used. The use of the LARSAA program to test and demonstrate the discriminant function is explained. The discriminant function itself was later implemented in an operational program which performs only those specific operations necessary to test each data point for an indication of surface water in the scene. Several possible sources of difficulty in the use of the function are identified, and suggestions are made for avoiding or correcting those difficulties.

Source record↗
NASA NTRS1985

Linear and nonlinear dynamic analysis of redundant load path bearingless rotor systems

The bearingless rotorcraft offers reduced weight, less complexity and superior flying qualities. Almost all the current industrial structural dynamic programs of conventional rotors which consist of single load path rotor blades employ the transfer matrix method to determine natural vibration characteristics because this method is ideally suited for one dimensional chain like structures. This method is extended to multiple load path rotor blades without resorting to an equivalent single load path approximation. Unlike the conventional blades, it isk necessary to introduce the axial-degree-of-freedom into the solution process to account for the differential axial displacements in the different load paths. With the present extension, the current rotor dynamic programs can be modified with relative ease to account for the multiple load paths without resorting to the equivalent single load path modeling. The results obtained by the transfer matrix method are validated by comparing with the finite element solutions. A differential stiffness matrix due to blade rotation is derived to facilitate the finite element solutions.

Murthy, V. R.↗
NASA NTRS2005

Hard X-ray Spectroscopic, Microwave and H-alpha Linear Polarization Studies with Hard X-Ray Observations from HESSI

The Principal Investigator (P.I.) has been pursuing a three year grant under NASA's Sun-Earth Connection Guest Investigator Program in support of the Ramaty High Energy Solar Spectroscopic Imager (RHESSI). An objective of these efforts is to combine X-ray and other data on solar flares, coronal mass ejections and interplanetary particle events in order to obtain a more comprehensive recognition of signatures, and understanding of interplanetary proton events. Thus, part of these efforts are to investigate if signatures seen in hard X-rays and microwaves can lead to better predictions of interplanetary proton events that can be dangerous to astronauts and spacecraft. The original proposal was written in May, 2000 and it discusses a three-pronged approach for data comparisons with three new types of instrumentation observing at X-ray, microwave and optical wavelengths. The major impetus behind this work and the proposal is that the P.I. discovered a strong correlation between a particular type of hard X-ray signature seen in spectral evolutions and interplanetary proton events (Kiplinger, 1995). The basic signature is that hard X-ray flux peaks either exhibit spectra that soften on their decays (Le. show fewer and fewer high energy X-rays with time) or they harden during decays (i.e. high energy X-rays decay significantly slower that lower energy X-rays). This signature is called progressive hardening. Studies were conducted over an eight-year period of data from the Hard X-Ray Burst Spectrometer (HXRBS) of the Solar maximum mission. Out of the 750 well observed flares studied, 41 flares had major associated proton events. Of these, 29 events were predicted on the basis of progressive hardening for a hit rate of 71%. The 152 largest flares had a hit rate of 82%.

Kiplinger, Alan L.↗
NASA NTRS1990

Finding curvilinear features in speckled images

A method for finding curves in digital images with speckle noise is described. The solution method differs from standard linear convolutions followed by thresholds in that it explicitly allows curvature in the features. Maximum a posteriori (MAP) estimation is used, together with statistical models for the speckle noise and for the curve-generation process, to find the most probable estimate of the feature, given the image data. The estimation process is first described in general terms. Then, incorporation of the specific neighborhood system and a multiplicative noise model for speckle allows derivation of the solution, using dynamic programming, of the estimation problem. The detection of curvilinear features is considered separately. The detection results allow the determination of the minimal size of detectable feature. Finally, the estimation of linear features, followed by a detection step, is shown for computer-simulated images and for a SAR image of sea ice.

Samadani, Ramin↗
DOE OSTI2024

Solving differential‐algebraic equations in power system dynamic analysis with quantum computing

Abstract Power system dynamics are generally modeled by high dimensional non‐linear differential‐algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system non‐linearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.

computational complexity↗
NASA NTRS1996

Dose equivalent near the bone-soft tissue interface from nuclear fragments produced by high-energy protons

During manned space missions, high-energy nucleons of cosmic and solar origin collide with atomic nuclei of the human body and produce a broad linear energy transfer spectrum of secondary particles, called target fragments. These nuclear fragments are often more biologically harmful than the direct ionization of the incident nucleon. That these secondary particles increase tissue absorbed dose in regions adjacent to the bone-soft tissue interface was demonstrated in a previous publication. To assess radiological risks to tissue near the bone-soft tissue interface, a computer transport model for nuclear fragments produced by high energy nucleons was used in this study to calculate integral linear energy transfer spectra and dose equivalents resulting from nuclear collisions of 1-GeV protons transversing bone and red bone marrow. In terms of dose equivalent averaged over trabecular bone marrow, target fragments emitted from interactions in both tissues are predicted to be at least as important as the direct ionization of the primary protons-twice as important, if recently recommended radiation weighting factors and "worst-case" geometry are used. The use of conventional dosimetry (absorbed dose weighted by aa linear energy transfer-dependent quality factor) as an appropriate framework for predicting risk from low fluences of high-linear energy transfer target fragments is discussed.

NASA Discipline Number 45-10↗
DOE OSTI2024

Optimal Control of SOEC-Based Hydrogen Production Systems for Demand Response Using Deep Reinforcement Learning in Smart Grids

Solid oxide electrolysis cell (SOEC) hydrogen production technology can range in size from small, appliance-size equipment to large-scale, central production facilities that can be tied directly to renewable or non-greenhouse-gas-emitting forms of electricity production, making it an ideal resource for demand response (DR). The SOEC hydrogen production system is a complex integrated system that encompasses fluid dynamics, electrical dynamics, and electrochemical and thermal dynamics, all of which involve non-linearity and non-convexity. Proper control of the SOEC hydrogen production system is crucial to enable its participation in the DR program. Here, to overcome the difficulty of designing an explicit control law for such nonlinear systems with nonconvex optimization features in DR applications, deep reinforcement learning (DRL) is explored to achieve the optimal control of the SOEC system for DR participation. Specifically, a twin delayed deterministic policy gradient (TD3) control framework is applied to achieve optimal response performance during DR events by considering power tracking error and hydrogen production efficiency with a suitable reward function. Two case studies with grid connections for tracking different DR commands were investigated. The first case study involved operating conditions reaching the boundaries, while the second involved operating conditions within the boundaries. The results showed that the proposed DRL-based control for SOEC can track the DR signal in a timely manner while maintaining high energy efficiency.

08 HYDROGEN↗