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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

L1 Adaptive Control Augmentation System with Application to the X-29 Lateral/Directional Dynamics: A Multi-Input Multi-Output Approach

This paper presents an L(sub 1) adaptive control augmentation system design for multi-input multi-output nonlinear systems in the presence of unmatched uncertainties which may exhibit significant cross-coupling effects. A piecewise continuous adaptive law is adopted and extended for applicability to multi-input multi-output systems that explicitly compensates for dynamic cross-coupling. In addition, explicit use of high-fidelity actuator models are added to the L1 architecture to reduce uncertainties in the system. The L(sub 1) multi-input multi-output adaptive control architecture is applied to the X-29 lateral/directional dynamics and results are evaluated against a similar single-input single-output design approach.

Griffin, Brian Joseph↗
NASA NTRS2013

Understanding How Kurtosis Is Transferred from Input Acceleration to Stress Response and Its Influence on Fatigue Llife

High cycle fatigue of metals typically occurs through long term exposure to time varying loads which, although modest in amplitude, give rise to microscopic cracks that can ultimately propagate to failure. The fatigue life of a component is primarily dependent on the stress amplitude response at critical failure locations. For most vibration tests, it is common to assume a Gaussian distribution of both the input acceleration and stress response. In real life, however, it is common to experience non-Gaussian acceleration input, and this can cause the response to be non-Gaussian. Examples of non-Gaussian loads include road irregularities such as potholes in the automotive world or turbulent boundary layer pressure fluctuations for the aerospace sector or more generally wind, wave or high amplitude acoustic loads. The paper first reviews some of the methods used to generate non-Gaussian excitation signals with a given power spectral density and kurtosis. The kurtosis of the response is examined once the signal is passed through a linear time invariant system. Finally an algorithm is presented that determines the output kurtosis based upon the input kurtosis, the input power spectral density and the frequency response function of the system. The algorithm is validated using numerical simulations. Direct applications of these results include improved fatigue life estimations and a method to accelerate shaker tests by generating high kurtosis, non-Gaussian drive signals.

Kihm, Frederic↗
NASA NTRS2015

High Input Voltage, Silicon Carbide Power Processing Unit Performance Demonstration

A silicon carbide brassboard power processing unit has been developed by the NASA Glenn Research Center in Cleveland, Ohio. The power processing unit operates from two sources: a nominal 300 Volt high voltage input bus and a nominal 28 Volt low voltage input bus. The design of the power processing unit includes four low voltage, low power auxiliary supplies, and two parallel 7.5 kilowatt (kW) discharge power supplies that are capable of providing up to 15 kilowatts of total power at 300 to 500 Volts (V) to the thruster. Additionally, the unit contains a housekeeping supply, high voltage input filter, low voltage input filter, and master control board, such that the complete brassboard unit is capable of operating a 12.5 kilowatt Hall effect thruster. The performance of the unit was characterized under both ambient and thermal vacuum test conditions, and the results demonstrate exceptional performance with full power efficiencies exceeding 97%. The unit was also tested with a 12.5kW Hall effect thruster to verify compatibility and output filter specifications. With space-qualified silicon carbide or similar high voltage, high efficiency power devices, this would provide a design solution to address the need for high power electric propulsion systems.

Power↗
NASA NTRS2015

High Input Voltage, Silicon Carbide Power Processing Unit Performance Demonstration

A silicon carbide brassboard power processing unit has been developed by the NASA Glenn Research Center in Cleveland, Ohio. The power processing unit operates from two sources - a nominal 300-Volt high voltage input bus and a nominal 28-Volt low voltage input bus. The design of the power processing unit includes four low voltage, low power supplies that provide power to the thruster auxiliary supplies, and two parallel 7.5 kilowatt power supplies that are capable of providing up to 15 kilowatts of total power at 300-Volts to 500-Volts to the thruster discharge supply. Additionally, the unit contains a housekeeping supply, high voltage input filter, low voltage input filter, and master control board, such that the complete brassboard unit is capable of operating a 12.5 kilowatt Hall Effect Thruster. The performance of unit was characterized under both ambient and thermal vacuum test conditions, and the results demonstrate the exceptional performance with full power efficiencies exceeding 97. With a space-qualified silicon carbide or similar high voltage, high efficiency power device, this design could evolve into a flight design for future missions that require high power electric propulsion systems.

Technology↗
NASA NTRS2017

Method and System for Physiologically Modulating Videogames and Simulations which Use Gesture and Body Image Sensing Control Input Devices

Method for physiologically modulating videogames and simulations includes utilizing input from a motion-sensing video game system and input from a physiological signal acquisition device. The inputs from the physiological signal sensors are utilized to change the response of a user's avatar to inputs from the motion-sensing sensors. The motion-sensing system comprises a 3D sensor system having full-body 3D motion capture of a user's body. This arrangement encourages health-enhancing physiological self-regulation skills or therapeutic amplification of healthful physiological characteristics. The system provides increased motivation for users to utilize biofeedback as may be desired for treatment of various conditions.

Pope, Alan T.↗
NASA NTRS2018

Dynamic Modeling using Output-Error Parameter Estimation based on Frequency Responses Estimated with Multisine Inputs

A method is developed for estimating model parameters, such as nondimensional stability and control derivatives, by fitting transfer function or state-space models to empirical frequency response data using the output-error approach. The frequency response data were computed using Fourier transforms of measured input and output data. The control surfaces were excited with periodic multisine inputs which facilitated time-ecient estimation of multiple-input multiple-output frequency responses. The method was applied to lateral data from a nonlinear flight dynamics simulation of the F-16 aircraft, and to longitudinal data from multiple repeated flight test maneuvers of the NASA T-2 subscale aircraft. Results using simulation data showed the frequency response method compared well to other standard methods for parameter estimation. In addition to including all the available inputs, outputs, and harmonic frequencies in the estimation, relatively small subsets of the measured data could also be used to focus on identifying specific parts of the model. Results from flight test data showed that parameter estimates and uncertainties determined from repeated maneuvers were accurate and in statistical agreement with each other.

Grauer, Jared A.↗
NASA NTRS2019

Identification of Bare-Airframe Dynamics from Closed-Loop Data Using Multisine Inputs and Frequency Responses

Amethod is presented for computing multiple-input multiple-output frequency responses of bare-airframe dynamics for systems excited using orthogonal phase-optimized multisines and including correlated data arising from control mixing or feedback control. The estimation was posed as the solution to an underdetermined system of linear equations, for which additional information was supplied using interpolation of the frequency responses. A simulation model of the NASA T-2 aircraft having two inputs and two outputs was used to investigate the method in the open-loop configuration and under closed-loop control. The method was also applied to flight test data from the X-56A aeroelastic demonstrator having five inputs and ten outputs and flying under closed-loop control with additional control allocation mixing. Results demonstrated that the proposed method accurately estimates the bare airframe frequency responses in the presence of correlated data from control mixing and feedback control. Results also agreed with estimates obtained using different methods that are less sensitive to correlated inputs.

Grauer, Jared A.↗
NASA NTRS2019

3+2+X: What Is the Most Useful Depolarization Input for Inverting Lidar Measurements of Non-Spherical Particles to Microphysical Properties?

The typical multiwavelength aerosol lidar data set for inversion of optical to microphysical parameters is composed of three backscatter coefficients (β ) at 355, 532, and 1064 nm and two extinction coefficients (α ) at 355 and 532 nm. This data combination is referred to as 3β +2α or 3+2 data set. This set of data is sufficient for retrieving some important microphysical particle parameters if the particles have spherical shape. Here, we investigate the effect of including the particle linear depolarization ratio (δ) as a third input parameter to the inversion of lidar data. The inversion algorithm is generally not used if measurements show values of δ that exceed 0.10 at 532 nm, i.e. in the presence of non-spherical particles such as desert dust, volcanic ash, and under special circumstances biomass-burning smoke.We use experimental data collected with instruments that are capable of measuring δ at all three lidar wavelengths with an inversion routine that uses the theory of light scattering by randomly oriented spheroids to replicate scattering properties of non-spherical particles. This is the first systematic test of the effect of using all theoretically possible combinations of δ taken at 355, 532, and 1064 nm as input in the lidar data inversion. We find that depolarization information at least at one wavelength already provides useful information in the in version of optical data that describe light-scattering by nonspherical particles. However, any choice of δ(λ) will give lower values of the single-scattering albedo than the traditional 3+2 data set. We find that input data sets that include 355 give a non-spherical fraction that closely resembles the dust ratio we obtain from using β(532) and δ(532) in a methodology applied in aerosol-type separation. The use of 355 in data sets of two or three reduces the fraction of non-spherical particles that is retrieved when using δ(532) and δ(1064). Use of the latter two without accounting for 355 generally leads to high fractions of non-spherical particles that we consider not trustworthy. The use of three δ(λ) instead of two δ(λ) including the constraint that one of these is measured at 355 nm does not provide any advantage over using 3+2+δ(355). Because of the technical challenges involved with accurately measuring δ(1064) we conclude that — depending on measurement capability — the future standard input for inversion using spheroid kernels might be 3+2+δ(355) or 3+2+δ(355)+δ(532).

Tesche, M.↗
DOE OSTI2022

Providing Levelized Cost and Waiting Time Inputs for HDV Hydrogen Refueling Station Planning: A Case Study of U.S. I-75 Corridor

Widespread use of diesel fuels in freight transportation leads to greenhouse gas (GHG) emissions that play an important role in air pollution. The air pollution, as well as the energy crisis, drives the transition from diesel fuel to cleaner and more energy-efficient fuels such as hydrogen fuels. With such a transition, the strategic planning and optimization of supporting refueling infrastructure along the national highway for heavy-duty vehicles (HDVs) become a necessity. Cost and service level are two essential factors to be considered in hydrogen refueling station location and capacity optimization. The service level in this study is assessed by the delay (waiting time) at fueling stations as well as the fueling demand fulfilled. To this end, this study presents a methodology to provide waiting time (delay) and levelized hydrogen fuel cost inputs under different station configurations (number of dispensers and fill rates) for hydrogen refueling station planning. Determining the station configurations based on the trade-off between the two inputs is further discussed. Particularly, this study points out that the levelized cost and waiting time are affected by hourly demand patterns and estimates the two inputs under different peak hour fueling demand scenarios. The results suggest that, with the same daily demand, the cost, as well as the waiting time, increases obviously with the peak hour demand. In the case study, the cost grows at least 30% when the peak hour visit frequency of the daily total visits increases from 5% (evenly distributed pattern) to 10% (the most common pattern for existing diesel fueling stations along I-75). Accounting for this peak hour effect in hydrogen refueling station planning is recommended. Overall, the waiting time model and cost analysis provide key inputs for optimizing hydrogen refueling station location and configurations based on anticipated refueling demand patterns. The paper is the second in a series that aims to build a comprehensive modeling plan and optimize hydrogen refueling infrastructure along the Interstate 75 (I-75) corridor for HDVs.

Liu, Yuandong↗
DOE OSTI2023

Gramian-Based Characterization of Network Vulnerability to Nodal Impulse Inputs: Preprint

Impulsive inputs applied at influential nodes of a network system may result in undesirable behavior and degrade performance. This paper proposes the notion of vulnerability matrix (VM) to characterize the effect of impulse inputs on a network following either discrete-time or continuous-time dynamics. The VM describes the first-order effects of impulse inputs on edge flows and is based on the controllability Gramian. We provide explicit expressions for the elements of the vulnerability matrix for the class of directed line networks in terms of the edge weights. Simulations validate our results and highlight the utility of the proposed metric in capturing the transient effects of nodal impulse inputs on edge flows.

controllability Gramian↗
DOE OSTI2023

Detection of False Data Injection Attacks in Battery Stacks Using Input Noise-Aware Nonlinear State Estimation and Cumulative Sum Algorithms

Grid-scale battery energy storage systems (BESSs) are vulnerable to false data injection attacks (FDIAs), which could be used to disrupt state of charge (SoC) estimation. Inaccurate SoC estimation has negative impacts on system availability, reliability, safety, and the cost of operation. In this article a combination of a Cumulative Sum (CUSUM) algorithm and an improved input noise-aware extended Kalman filter (INAEKF) is proposed for the detection and identification of FDIAs in the voltage and current sensors of a battery stack. The series-connected stack is represented by equivalent circuit models, the SoC is modeled with a charge reservoir model and the states are estimated using the INAEKF. Further, the root mean squared error of the states’ estimation by the modified INAEKF was found to be superior to the traditional EKF. By employing the INAEKF, this article addresses the research gap that many state estimators make asymmetrical assumptions about the noise corrupting the system. Additionally, the INAEKF estimates the input allowing for the identification of FDIA, which many alternative methods are unable to achieve. The proposed algorithm was able to detect attacks in the voltage and current sensors in 99.16% of test cases, with no false positives. Utilizing the INAEKF compared to the standard EKF allowed for the identification of FDIA in the input of the system in 98.43% of test cases.

25 ENERGY STORAGE↗
DOE OSTI2024

Lyapunov-based nonlinear control of nonautonomous systems with individual input constraints

A control algorithm that can locally stabilize a specific class of multi-input multi-output nonautonomous nonlinear dynamical systems while satisfying individual input constraints is developed. The proposed Lyapunov-based state-feedback control law inherently accounts for the actuator amplitude saturation limits without the need for computationally expensive real-time optimization techniques. In addition to the control law, a formal definition for the local “controllable region” within which the controller can asymptotically drive the system states to the origin and satisfy the input saturation limits is also presented. The nonautonomous nature of the system dynamics implies that the “controllable region” continuously evolves with time. Therefore, a sufficient condition to maintain the system states within the “controllable region” is proposed in this work to make practical implementation feasible. The effectiveness of the controller is tested for a specific control problem arising in tokamaks, which are toroidal devices that use strong magnetic fields to confine a plasma (hot ionized gas). Here, the primary emphasis of tokamak research is to regulate the plasma properties around predetermined values to achieve stable plasma confinement. Nonlinear simulations show that the proposed controller can achieve the desired plasma control objectives in a DIII-D tokamak scenario.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

GeoVision: Harnessing the Heat Beneath Our Feet - Analysis Inputs and Results

This submission includes input and results data from analysis done as part of the Geothermal Technology Office's Geothermal Vision Study (GeoVision). The submission includes data for both analysis of the electricity sector and the heating and cooling sector. For the electricity sector, the submission includes geothermal resource potential and development cost inputs for the ReEDS model and ReEDS model results of projected installed capacity and generation of geothermal and other technologies under the GeoVision scenarios. It also includes results from environmental and social impacts analysis. For the cooling sector, the submission includes geothermal resource potential and development cost inputs for the dGeo model and dGeo model results of economic, market, and deployment potential.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Pilgrim Hot Springs: GEOPHIRES Inputs and Outputs for Direct-Use Geothermal District Heating and Cooling

This dataset includes files for a techno-economic analysis conducted using the GEOPHIRES simulator to examine the feasibility of expanding a larger district heating site in a remote location: Pilgrim Hot Springs, Alaska. Files included here are GEOPHIRES inputs and outputs for five different scenarios with varying demand, cycle, and system design characteristics to analyze. Also included is the link to the GEOPHIRES GitHub, as well as a link to the dataset that contains the energy modelling used to determine the heating demand for the district. For a list of the differences between scenarios, see the included "Input Overview.txt" file. Fields included in the input files are: subsurface technical parameters, surface technical parameters, financial parameters, capital and O&M parameters, as well as simulation parameters. The output files are case reports that summarize all equipment, reservoir characteristics, costs, and heating profiles.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

The path from litter to soil: Insights into soil C cycling from long-term input manipulation and high-resolution mass spectrometry

The pathways carbon (C) takes from plant litter to soil organic matter (SOM) underlies our understanding of how soil C stocks and microbial decomposition will respond to climate change, and determining whether natural soil C sinks can be enhanced to offset potential losses. Long-term ecosystem-scale litter manipulations and innovations in the molecular characterization of SOM provide a unique opportunity to explore these dynamics. We incubated soils from a 20-year litter-input experiment for 525 days and asked how litter quantity and source (i.e., roots versus aboveground litter) affected soil C cycling, including microbial function and the size and molecular composition of C pools defined by degree of mineral association. Input exclusion led to a 30% loss of soil C, attributable largely to the non-mineral-associated C fraction, and was accompanied by declines in soil C decomposition. The absence of roots led to a shift in the microbial catabolic profile, though there was no evidence that root litter was preferentially stabilized. Carbon did not accumulate under litter addition. However, direct examination of the finest mineral fraction with Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) revealed dramatic changes to the chemical composition of carbon. In particular, lipid content increased proportionally with input addition, and was subsequently mineralized during incubation, indicating that this apparently stable fraction is metabolically active. Moreover, non-metric dimensional scaling (NMS) showed that both litter treatments and incubation caused the molecular composition of SOM to diverge, and explained 79% of the variance in soil respiration. We conclude that the path from litter to soil is more complex than previously thought and mineral-associated SOM thought to be stable may be microbially available.

Reynolds, L L.↗
DOE OSTI2025

Robustness of Deep Learning Classification to Adversarial Input on GPUs: Asynchronous Parallel Accumulation Is a Source of Vulnerability

The ability of machine learning (ML) classification models to resist small, targeted input perturbations—known as adversarial attacks—is a key measure of their safety and reliability. We show that floating-point non associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any perturbation to the input. Additionally, we show that this misclassification is particularly significant for inputs close to the decision boundary and that standard adversarial robustness results may be overestimated up to 4.6 when not considering machine-level details. We first study a linear classifier, before focusing on standard Graph Neural Network (GNN) architectures and datasets used in robustness assessments. We develop a novel black-box attack using Bayesian optimization to discover external workloads that can change the instruction scheduling which bias the output of reductions on GPUs and reliably lead to misclassification. Motivated by these results, we present a new learnable permutation (LP) gradient-based approach to learning floating-point operation orderings that lead to misclassifications. The LP approach provides a worst-case estimate in a computationally efficient manner, avoiding the need to run identical experiments tens of thousands of times over a potentially large set of possible GPU states or architectures. Finally, using instrumentation-based testing, we investigate parallel reduction ordering across different GPU architectures under external background workloads, when utilizing multi-GPU virtualization, and when applying power capping. Our results demonstrate that parallel reduction ordering varies significantly across architectures under the first two conditions, substantially increasing the search space required to fully test the effects of this parallel scheduler-based vulnerability. These results and the methods developed here can help to include machine-level considerations into adversarial robustness assessments, which can make a difference in safety and mission critical applications.

Shanmugavelu, Sanjif [Maxeler Technologies, a Groq↗
DOE OSTI2025

An Analysis of Input Parameters for Film-Based Flash X-Ray Radiography

Flash X-ray radiography (flash) is a commonly used diagnostic technique in dynamic experiments. An analysis of the effects of input parameters on resulting metrics of image quality can aid the experimentalist in configuring the X-ray input parameters to produce the highest quality radiograph for a given experiment. Here, a flash X-ray test bed with HS800 film and a LANEX Medium F intensifier screen was used with an L3 450 kVp pulser and Scandiflash X-ray tube for this study. Input parameters including charge voltage, source filtering, and film-pack assembly were investigated for their impact on contrast-to-noise ratio (CNR), contrast, and contrast transfer function (CTF). Using VIDAR’s NDT Pro industrial film digitizer, scanner parameters such as optical density range, pixel spacing, scan mode, and digital bit-depth were also examined for their impact on image quality metrics. The highest CNR values were found with two LANEX intensifiers and no filtering. Charge voltage had no direct impact on CNR values. LANEX screen count and filtering resulted both in direct effects on CNR and interaction effects with each other and CNR value. Uncertainty bounds for CNR comparisons and repeatability of CTF evaluations are also discussed. Finally, the film results are compared with a previous study using other detector types, specifically Carestream INDUSTREX Flex GP, Flex HR, Flex XL Blue, and HPX-DR 3543.

dynamic radiography↗
DOE OSTI2019

In-situ characterization and quantification of melt pool variation under constant input energy density in laser powder bed fusion additive manufacturing process

Size and shape of a melt pool play a critical role in determining the microstructure in additively manufactured metals. However, it is very challenging to directly characterize the size and shape of the melt pool beneath the surface of the melt pool during the additive manufacturing process. Here, we report the direct observation and quantification of melt pool variation during the laser powder bed fusion (LPBF) additive manufacturing process under constant input energy density by in-situ high-speed high-energy x-ray imaging. We show that the melt pool can undergo different melting regimes and both the melt pool dimension and melt pool volume can have orders-of-magnitude change under a constant input energy density. Our analysis shows that the significant melt pool variation cannot be solely explained by the energy dissipation rate. We found that energy absorption changes significantly under a constant input energy density, which is another important cause of melt pool variation. Our further analysis reveals that the significant change in energy absorption originates from the separate roles of laser power and scan speed in depression zone development. The results reported here are important for understanding the laser powder bed fusion additive manufacturing process and guiding the development of better metrics for processing parameter design.

36 MATERIALS SCIENCE↗