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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Boundary layer roll circulations during FIRE

The probable mechanism underlying the development of boundary layer roll circulations are studied using wind and temperature profiles measured by the National Center for Atmospheric Research (NCAR) Electra during the stratocumulus phase of the First ISCCP Regional Experiment (FIRE). The expected, or preferred, roll orientations, horizontal wavelengths, and propagation periods are determined by finding the minimum values of the dynamic and thermodynamic forcing parameters, which here are the eddy Reynolds number (Re) and moist Rayleigh number (Ra sub m). These minimum values depend on the height z sub T of the capping temperature inversion and on the values of the Fourier coefficients of the background height-dependent vector wind profile. As input to our nonlinear spectral model, descent and ascent runs by the Electra provide for initial estimates of the inversion height and the wind profiles. In the first phase of the investigation presented here, a mechanism is said to be a probable contributor to the development of roll circulations within the stratocumulus-topped boundary layer if the modeled roll orientation and wavelengths agree with their observed values. Preliminary results using the 14-coefficient model of Haack-Hirschberg (1988) are discussed for the 7 July 1987 Electra Mission 188-A (Flight 5). This mission was flown across a sharp cloud boundary that was within a LANDSAT/SPOT scene. The stratocumulus deck was relatively solid in the eastern part of the scene, while there was a rapid decrease in cloud cover to scattered cumulus clouds aligned in streets to the west. These cloud streets were oriented nearly parallel to the mean wind direction in the layer, which was approximately 340 degrees. The hypothesis that roll circulations occurred in both the relatively clear and the cloudy regions is investigated using as model input a descent profile obtained in the relatively clear air and an ascent profile obtained in the cloudy air. Initial results for the clear air case are that the pure inflection point mode is not possible and the pure thermal mode was oriented 35 degrees to the right of the mean wind direction. The origin of this unacceptably large discrepancy between the observed and modeled results will be investigated further and the conclusions reported at the next FIRE workshop.

Shirer, Hampton N.↗
NASA NTRS2016

Biomechanical Modeling of the Deadlift Exercise to Improve the Efficacy of Resistive Exercise Microgravity Countermeasures

During long-duration spaceflight missions, astronauts exposure to microgravity without adequate countermeasures can result in losses of muscular strength and endurance, as well as loss of bone mass. As a countermeasure to this challenge, astronauts engage in resistive exercise during spaceflight to maintain their musculoskeletal function. The Hybrid Ultimate Lifting Kit (HULK) has been designed as a prototype exercise device for an exploration-class vehicle; the HULK features a much smaller footprint than previous devices such as the Advanced Resistive Exercise Device (ARED) on the International Space Station (ISS), which makes the HULK suitable for extended spaceflight missions in vehicles with limited volume. As current ISS exercise countermeasure equipment represents an improvement over previous generations of such devices, the ARED is being employed as a benchmark of functional performance. This project involves the development of a biomechanical model of the deadlift exercise, and is novel in that it is the first exercise analyzed in this context to include the upper limbs in the loading path, in contrast to the squat, single-leg squat, and heel raise exercises also being modeled by our team. OpenSim software is employed to develop these biomechanical models of humans performing resistive exercises to assess and improve the new exercise device designs. Analyses include determining differences in joint and muscle forces when using different loading strategies with the device, comparing and contrasting with the ARED benchmark, and determining whether the loading is sufficient to maintain musculoskeletal health. During data collection, the number of repetitions, load, cadence, stance, and grip width are controlled in order to facilitate comparisons between loading configurations. To date, data have been collected for two human subjects performing the deadlift exercise on the HULK device using two different loading conditions. Recorded data include motion capture, electromyography (EMG), ground reaction forces, device load cell data, photos and videos, and anthropometric data. Work is ongoing to perform biomechanical analyses including inverse kinematics and inverse dynamics to compare different versions of the deadlift model in order to determine which provides an appropriate level of detail to study this exercise. This work is supported by the National Space Biomedical Research Institute through NCC 9-58.

physical exercise↗
DOE OSTI2022

LICS Modeling [Slides]

We propose to design a new diagnostic based on laser inverse Compton scattering (LICS) to study the dynamics of runaway electron formation during killer-pellet triggered disruptions in DIII-D, and their subsequent loss. Obtaining a better understanding of transient runaway electrons (and how to suppress them) is one of the highest priorities of the US fusion program. To enable this, we need better measurements of the runaway electron population during and after disruptions. We intend to measure point-wise time-resolved spatial profile and energy distributions of the runaway electrons while they are in the core of the tokamak plasma. While LICS measurements of relativistic e-beams have been successfully made on accelerators, they have never been done in a tokamak. We bring two innovations to this diagnostic concept which should enable 10 times better signal-to-noise ratios than previously envisioned. In particular, we will marry bright short pulse (10’s of picosecond) laser technologies with the availability of short-pulse gated x-ray imagers developed at Los Alamos for the National Ignition Facility (NIF), to reject noise generated by the tokamak and plasma environment. We will generate a Conceptual Physics Basis, a Preliminary Design, and a Final Design package, with associated DIII-D reviews, along with prototype component testing for the diagnostic over the course of the two year duration of this proposal.

42 ENGINEERING↗
NASA NTRS1988

A noise source identification technique using an inverse Helmholtz integral equation method

A technique is developed which utilizes numerical models and field pressure information to characterize acoustic fields and identify acoustic sources. The numerical models are based on boundary element numerical procedures. Either pressure, velocity, or passive boundary conditions, in the form of impedance boundary conditions, may be imposed on the numerical model. Alternatively, if no boundary information is known, a boundary condition can be left unspecified. Field pressure data may be specified to overdetermine the numerical problem. The problem is solved numerically for the complete sound field from which the acoustic sources may be determined. The model can then be used to idenfify acoustic intensity paths in the field. The solution can be modified and the model used to evaluate design alternatives. In this investigation the method is tested analytically and verified. In addition, the sensitivity of the method to random and bias error in the input data is demonstrated.

Gardner, B. K.↗
NASA NTRS2021

Satellite Constraints on the Latitudinal Distribution and Temperature Sensitivity of Wetland Methane Emissions

Wetland methane (CH4) emissions comprise about one-third of the global CH4 source. The latitudinal distribution and climate sensitivity of wetland CH4 fluxes are the key determinants of the global CH4-climate feedback. However, large differences exist between bottom-up estimates, informed by ground-based flux measurements, and top-down estimates derived from spaceborne total column CH4. Despite the extensive coverage of satellite CH4 concentration observations, challenges remain with using top-down estimates to test bottom-up models, mainly because of the uncertainties in the satellite retrievals, the model representation errors, the variable prior emissions, and the confounding role of the posterior error covariance structures. Here, we use satellite-based top-down CH4 flux estimates (2010–2012) to test and refine 42 bottom-up estimates of wetland emissions that use a range of hypothesized wetland extents and process controls. Our comparison between bottom-up models and satellite-based fluxes innovatively accounts for cross-correlations and spatial uncertainties typically found in top-down inverse estimates, such that only the information from satellite observations and the atmospheric transport model is kept as a constraint. We present a satellite-constrained wetland CH4 ensemble product derived from assembling the highest-performance bottom-up models, which estimates global wetland CH4 emissions of 148 (117–189, 5th–95th percentile) Tg CH4 yr−1. We find that tropical wetland emissions contribute 72% (63%–85%) to the global wetland total. We also find that a lower-than-expected temperature sensitivity agrees better with atmospheric CH4 measurements. Overall, our approach demonstrates the potential for using satellites to quantitatively refine bottom-up wetland CH4 emission estimates, their latitudinal distributions, and their sensitivity to climate.

wetland ch4↗
NASA NTRS2024

Harnessing Collaborative Learning Automata to Guide Multi-objective Optimization based Inverse Analysis for Structural Damage Identification

Structural damage identification based on physical models is often transformed into an optimization problem that minimizes the difference between measurement information of structure being monitored and the model prediction in the parametric space. However, the objective function in this context often exhibits multimodality, involving high-dimensional variables due to the reliance on finite element models for damage identification. These features pose challenges to optimization algorithms, where entrapment in local solutions can lead to false positives and false negatives in damage identification. In this research, we propose a reinforcement learning based multi-swarm optimizer to tackle such challenges in pursuit of a small yet diverse solution set that can capture the true damage scenario as one of the solutions. The proposed method leverages the flexibility of the particle swarm optimizer and incorporates novel strategies of metaheuristics to realize targeted improvement. To enable the particle swarm to adaptively select the appropriate search strategy based on the current environment, we adopt the learning automata technique, which sidesteps the need for reward strategy selection that is usually ad hoc at each step of the search. The integration harnesses the automatic learning and self-adaptation capabilities of learning automata, enabling the particles to navigate based on environmental signals. This leads to accumulated probabilities tied to advantageous movements, fostering an adaptive exploration of particles in the search space. The proposed approach is first validated through implementing into benchmark test cases with comparisons. It is then applied to structural damage identification with piezoelectric admittance experimental signals. `The results highlight the capability of the algorithm to identify a small solution set with high accuracy to match the actual damage scenario.

Yang Zhang↗
NASA NTRS1981

An analytic solution to the classical two-body problem with drag

The two body problem was analyzed with a specific drag model. The model treats drag as a force proportional to the vector velocity and inversely proportional to the distance to the center of attraction. The solution is expressed in terms of known functions and is of a simple and compact form. The time of flight is expressed as a quadrature in the true anomaly. The results are: (1) development of a vector differential equation which allows analysis of an infinite number of gravitational and drag models; and (2) obtaining the solution of a linear differential equation using the inverse method of laplace transforms.

Mittleman, D.↗
NASA NTRS1987

Adaptive control of robotic manipulators

The author presents a novel approach to adaptive control of manipulators to achieve trajectory tracking by the joint angles. The central concept in this approach is the utilization of the manipulator inverse as a feedforward controller. The desired trajectory is applied as an input to the feedforward controller which behaves as the inverse of the manipulator at any operating point; the controller output is used as the driving torque for the manipulator. The controller gains are then updated by an adaptation algorithm derived from MRAC (model reference adaptive control) theory to cope with variations in the manipulator inverse due to changes of the operating point. An adaptive feedback controller and an auxiliary signal are also used to enhance closed-loop stability and to achieve faster adaptation. The proposed control scheme is computationally fast and does not require a priori knowledge of the complex dynamic model or the parameter values of the manipulator or the payload.

Seraji, H.↗
NASA NTRS2012

A Regional CO2 Observing System Simulation Experiment Using ASCENDS Observations and WRF-STILT Footprints

Knowledge of the spatiotemporal variations in emissions and uptake of CO2 is hampered by sparse measurements. The recent advent of satellite measurements of CO2 concentrations is increasing the density of measurements, and the future mission ASCENDS (Active Sensing of CO2 Emissions over Nights, Days and Seasons) will provide even greater coverage and precision. Lagrangian atmospheric transport models run backward in time can quantify surface influences ("footprints") of diverse measurement platforms and are particularly well suited for inverse estimation of regional surface CO2 fluxes at high resolution based on satellite observations. We utilize the STILT Lagrangian particle dispersion model, driven by WRF meteorological fields at 40-km resolution, in a Bayesian synthesis inversion approach to quantify the ability of ASCENDS column CO2 observations to constrain fluxes at high resolution. This study focuses on land-based biospheric fluxes, whose uncertainties are especially large, in a domain encompassing North America. We present results based on realistic input fields for 2007. Pseudo-observation random errors are estimated from backscatter and optical depth measured by the CALIPSO satellite. We estimate a priori flux uncertainties based on output from the CASA-GFED (v.3) biosphere model and make simple assumptions about spatial and temporal error correlations. WRF-STILT footprints are convolved with candidate vertical weighting functions for ASCENDS. We find that at a horizontal flux resolution of 1 degree x 1 degree, ASCENDS observations are potentially able to reduce average weekly flux uncertainties by 0-8% in July, and 0-0.5% in January (assuming an error of 0.5 ppm at the Railroad Valley reference site). Aggregated to coarser resolutions, e.g. 5 degrees x 5 degrees, the uncertainty reductions are larger and more similar to those estimated in previous satellite data observing system simulation experiments.

Wang, James S.↗
NASA NTRS1990

System Identification for Nonlinear Control Using Neural Networks

An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.

Stengel, Robert F.↗
NASA NTRS1987

The magnetic field of Uranus

Aspherical harmonic model of the planetary magnetic field of Uranus is obtained from the Voyager 2 encounter observations using generalized inverse techniques which allow partial solutions to complex (underdetermined) problems. The Goddard Space Flight Center 'Q3' model is characterized by a large dipole tilt (58.6 deg) relative to the rotation axis, a dipole moment of 0.228 G R(Uranus radii cubed) and an unusually large quadrupole moment. Characteristics of this complex model magnetic field are illustrated using contour maps of the field on the planet's surface and discussed in the context of possible dynamo generation in the relatively poorly conducting 'ice' mantle.

Connerney, J. E. P.↗
NASA NTRS2004

Improved Ionospheric Electrodynamic Models and Application to Calculating Joule Heating Rates

Improved techniques have been developed for empirical modeling of the high-latitude electric potentials and magnetic field aligned currents (FAC) as a function of the solar wind parameters. The FAC model is constructed using scalar magnetic Euler potentials, and functions as a twin to the electric potential model. The improved models have more accurate field values as well as more accurate boundary locations. Non-linear saturation effects in the solar wind-magnetosphere coupling are also better reproduced. The models are constructed using a hybrid technique, which has spherical harmonic functions only within a small area at the pole. At lower latitudes the potentials are constructed from multiple Fourier series functions of longitude, at discrete latitudinal steps. It is shown that the two models can be used together in order to calculate the total Poynting flux and Joule heating in the ionosphere. An additional model of the ionospheric conductivity is not required in order to obtain the ionospheric currents and Joule heating, as the conductivity variations as a function of the solar inclination are implicitly contained within the FAC model's data. The models outputs are shown for various input conditions, as well as compared with satellite measurements. The calculations of the total Joule heating are compared with results obtained by the inversion of ground-based magnetometer measurements. Like their predecessors, these empirical models should continue to be a useful research and forecast tools.

Weimer, D. R.↗
NASA NTRS2019

Eddy Compensation Dampens Southern Ocean Sea Surface Temperature Response to Westerly Wind Trends

Anthropogenic influences have led to a strengthening and poleward shift of westerly winds over the Southern Ocean (SO), especially during austral summer. We use observations, an idealized eddy‐resolving ocean‐sea ice channel model, and a global coupled model to explore the SO response to a step‐change in westerly winds. Previous work hypothesized a two timescale response for sea surface temperature. Initially, Ekman transport cools the surface before sustained upwelling causes warming on decadal timescales. The fast response is robust across our models and the observations: we find Ekman‐driven cooling in the mixed layer, mixing‐driven warming below the mixed layer, and a small upwelling‐driven warming at the temperature inversion. The long‐term response is inaccessible from observations. Neither of our models shows a persistent upwelling anomaly, or long‐term, upwelling‐driven subsurface warming. Mesoscale eddies act to oppose the anomalous wind‐driven upwelling, through a process known as eddy compensation, thereby preventing long‐term warming.

Southern Annular Mode↗
NASA NTRS2012

Spatial and Temporal Variations in Titan's Surface Temperatures from Cassini CIRS Observations

We report a wide-ranging study of Titan's surface temperatures by analysis of the Moon's outgoing radiance through a spectral window in the thermal infrared at 19 mm (530/cm) characterized by lower atmospheric opacity. We begin by modeling Cassini Composite Infrared Spectrometer (CIRS) far infrared spectra collected in the period 2004-2010, using a radiative transfer forward model combined with a non-linear optimal estimation inversion method. At low-latitudes, we agree with the HASI near-surface temperature of about 94 K at 101S (Fulchignoni et al., 2005). We find a systematic decrease from the equator toward the poles, hemispherically asymmetric, of approx. 1 K at 60 deg. south and approx. 3 K at 60 deg. north, in general agreement with a previous analysis of CIRS data and with Voyager results from the previous northern winter. Subdividing the available database, corresponding to about one Titan season, into 3 consecutive periods, small seasonal changes of up to 2 K at 60 deg N became noticeable in the results. In addition, clear evidence of diurnal variations of the surface temperatures near the equator are observed for the first time: we find a trend of slowly increasing temperature from the morning to the early afternoon and a faster decrease during the night. The diurnal change is approx. 1.5 K, in agreement with model predictions for a surface with a thermal inertia between 300 and 600 J/ sq. m s (exp -1/2) / K. These results provide important constraints on coupled surface-atmosphere models of Titan's meteorology and atmospheric dynamic.

titan↗
DOE OSTI2020

A Multiyear Gridded Data Ensemble of Surface Biogenic Carbon Fluxes for North America: Evaluation and Analysis of Results

Accurate and fine-scale estimates of biogenic carbon fluxes are critical for measuring and monitoring the biosphere's responses and feedback to the climate system. Currently available data products from flux towers and model-intercomparison projects struggle to adequately represent spatiotemporal dynamics of surface biogenic carbon fluxes, and to quantify their uncertainties, which also are crucial to atmospheric inversion systems. To address these gaps, we introduce a new perturbed-parameter model ensemble with the CASA model to estimate surface biogenic carbon fluxes at monthly and 3-hourly scales for North America at ~500-m and 5-km resolutions. We first use the Extended Fourier Amplitude Sensitivity Testing to choose the three most sensitive parameters to be perturbed, maximum light use efficiency (E max ), optimal temperature of photosynthesis (T opt ), and temperature response of respiration (Q 10 ). The initial range for each parameter is broadly sampled for the L1 ensemble, but then we pruned E max with site-level primary productivity to derive an L2 ensemble with narrower uncertainty ranges. Ensembles are strongly correlated with site-level results at both monthly and 3-hourly scales, and the spread across L1/L2 ensemble members encompasses the range of AmeriFlux observations. Monthly variability in the L2 ensemble mean is 85% of the observed variability. The L2 ensemble outperforms diverse data products with the highest Taylor skill scores at diurnal to annual scales. Finally, the ensemble's seasonality agrees well with other models for most biome types and in high and middle latitudes, but inconsistencies are found in subtropical and tropical ecoregions and for annual totals over North America.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Design of Materials with Alchemite

Machine learning models that establish the relationships between materials processing and properties can enable inverse design of materials through active learning. Alchemite is a commercial software that can perform inverse materials design on sparse data. Here we evaluate Alchemite’s performance on a dataset of shape memory alloys and a dataset of heat exchangers compared to baseline random forest models. Alchemite had higher accuracy when making predictions on sparse data and was more accurate or nearly as accurate as random forests on complete datasets while also quantifying uncertainty. The software was also used to suggest processing steps and design parameters to optimize properties and performance; however, physical validation of the suggested design parameters was beyond the scope of this work. Several useful design insights were gained about the impact of the design parameters on properties and performance including the importance of dopant choice and amount for shape memory alloys and the importance of height and weight on the thermal resistance of heat exchangers.

Machine learning↗
NASA NTRS2019

Implementation and Testing of Inverse Kinematics on Robotic Arm

COSIE (Coronal Spectrographic Imager in the Extreme Ultraviolet) is a proposed solar tracking ISS imaging payload that will help bridge the theoretical gap between the physics of the low corona and the heliosphere. This scientific instrument requires high pointing accuracy, on the order of arc seconds. The instrument is mounted on to a three revolute joint robotic arm in order to track the roll, pitch and yaw motion of the Sun. The goal of this project is to construct a prototype model of the robotic arm and implement the proposed analytical inverse kinematics algorithm. In robotics, the inverse kinematics problem is solving for the set of joint angles that achieve the desired end effect or location and/or orientation. In this case, orientation is the focus. Depending on the configuration, multiple sets of joint angle solutions may exist. Due to the complexity of robotics, typically iterative methods are used to solve for the joint angle solution sets. However, in this case, an analytical solution exists. A small robotic arm representative of the full size hardware was constructed. The inverse kinematics algorithm, originally in MATLAB/Simulink, was converted into C in order to interface with the motors. This C software was implemented on a Windows PC and micro-controller, and serial communication between the two was established, allowing the motors to be directly controlled by the inverse kinematics algorithm. Testing the inverse kinematics on a physical system will allow the validity and accuracy of the analytic solution to be verified.

Franz, Carter↗
DOE OSTI2025

Calibration of RAFM Micromechanical Model for Creep Using Bayesian Optimization for Functional Output

A Bayesian optimization procedure is presented for calibrating a multimechanism micromechanical model for creep to experimental data of F82H steel. Reduced activation ferritic martensitic (RAFM) steels based on Fe(8–9)%Cr are the most promising candidates for some fusion reactor structures. Although there are indications that RAFM steel could be viable for fusion applications at temperatures up to 600°C, the maximum operating temperature will be determined by the creep properties of the structural material and the breeder material compatibility with the structural material. Due to the relative paucity of available creep data on F82H steel compared to other alloys such as Grade 91 steel, micromechanical models are sought for simulating creep based on relevant deformation mechanisms. As a point of departure, this work recalibrates a model form that was previously proposed for Grade 91 steel to match creep curves for F82H steel. Due to the large number of parameters (9) and cost of the nonlinear simulations, an automated approach for tuning the parameters is pursued using a recently developed Bayesian optimization for functional output (BOFO) framework (Huang et al., 2021, “Bayesian optimization of functional output in inverse problems,” Optim. Eng., 22, pp. 2553–2574). Incorporating extensions such as batch sequencing and weighted experimental load cases into BOFO, a reasonably small error between experimental and simulated creep curves at two load levels is achieved in a reasonable number of iterations. In conclusion, validation with an additional creep curve provides confidence in the fitted parameters obtained from the automated calibration procedure to describe the creep behavior of F82H steel.

42 ENGINEERING↗