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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 19 records

Variable-Domain Displacement Transfer Functions for Converting Surface Strains into Deflections for Structural Deformed Shape Predictions

Variable-Domain Displacement Transfer Functions were formulated for shape predictions of complex wing structures, for which surface strain-sensing stations must be properly distributed to avoid jointed junctures, and must be increased in the high strain gradient region. Each embedded beam (depth-wise cross section of structure along a surface strain-sensing line) was discretized into small variable domains. Thus, the surface strain distribution can be described with a piecewise linear or a piecewise nonlinear function. Through discretization, the embedded beam curvature equation can be piece-wisely integrated to obtain the Variable-Domain Displacement Transfer Functions (for each embedded beam), which are expressed in terms of geometrical parameters of the embedded beam and the surface strains along the strain-sensing line. By inputting the surface strain data into the Displacement Transfer Functions, slopes and deflections along each embedded beam can be calculated for mapping out overall structural deformed shapes. A long tapered cantilever tubular beam was chosen for shape prediction analysis. The input surface strains were analytically generated from finite-element analysis. The shape prediction accuracies of the Variable- Domain Displacement Transfer Functions were then determined in light of the finite-element generated slopes and deflections, and were fofound to be comparable to the accuracies of the constant-domain Displacement Transfer Functions

displacement transfer functions↗

Improving Sim-to-Real Transfer in Vision-Based Robot Navigation Via Instance-Level GAN-Based Data Augmentation

Achieving robust vision-based robotic tasks requires large amounts of data, which are often difficult to obtain in real-world scenarios. Simulators and synthetic data offer a cost-effective alternative, but the visual gap between simulation and reality hinders the performance of models when deployed in real-world environments. In this paper, we present a data augmentation pipeline that integrates a foundation model (Segment Anything Model) with an unsupervised image-to-image translation model (CycleGAN) for instance-level domain transfer from simulation to reality. This pipeline enables the generation of realistic labeled data from synthetic images for training supervised machine learning models in vision-based navigation tasks. We evaluate our approach on real-world data for ego-vehicle pose estimation, a critical autonomous navigation task involving the prediction of cross-track position and heading angle relative to road center line markings. The results of our tests show that our GAN-based data augmentation pipeline significantly outperforms models trained solely on simulation data or on data processed with standard image augmentation methods for sim-to-real transfer, enhancing model robustness and generalizability in real-world scenarios. Our method provides a scalable and flexible data augmentation tool for leveraging large synthetic datasets to enhance vision-based robotic navigation tasks.

artificial intelligence↗

Correlation of AH-1G airframe flight vibration data with a coupled rotor-fuselage analysis

The formulation and features of the Rotor-Airframe Comprehensive Analysis Program (RACAP) is described. The analysis employs a frequency domain, transfer matrix approach for the blade structural model, a time domain wake or momentum theory aerodynamic model, and impedance matching for rotor-fuselage coupling. The analysis is applied to the AH-1G helicopter, and a correlation study is conducted on fuselage vibration predictions. The purpose of the study is to evaluate the state-of-the-art in helicopter fuselage vibration prediction technology. The fuselage vibration predicted using RACAP are fairly good in the vertical direction and somewhat deficient in the lateral/longitudinal directions. Some of these deficiencies are traced to the fuselage finite element model.

Sangha, K.↗

Human operator identification model and related computer programs

Four computer programs which provide computational assistance in the analysis of man/machine systems are reported. The programs are: (1) Modified Transfer Function Program (TF); (2) Time Varying Response Program (TVSR); (3) Optimal Simulation Program (TVOPT); and (4) Linear Identification Program (SCIDNT). The TV program converts the time domain state variable system representative to frequency domain transfer function system representation. The TVSR program computes time histories of the input/output responses of the human operator model. The TVOPT program is an optimal simulation program and is similar to TVSR in that it produces time histories of system states associated with an operator in the loop system. The differences between the two programs are presented. The SCIDNT program is an open loop identification code which operates on the simulated data from TVOPT (or TVSR) or real operator data from motion simulators.

Kessler, K. M.↗

Hardware realization of sampled-data controllers

This paper examines the hardware implementation process for digital controllers (filters), beginning with the desired z-domain transfer function of the digital compensator and ending in the selection of a hardware realization scheme. The final hardware realization is shown to be affected by quantization noise, dynamic range considerations, limit cycles, coefficient roundoff, and filter structure. A design example is included to illustrate the implementation process for a digital rate filter for vehicle control of the space shuttle. This paper develops open-loop techniques which may be extended to the closed-loop case.

Nagle, H. T., Jr.↗

Theoretical linear approach to the combined man-manipulator system in manual control of an aircraft

An approach to the calculation of the dynamic characteristics of the combined man manipulator system in manual aircraft control was derived from a model of the neuromuscular system. This model combines the neuromuscular properties of man with the physical properties of the manipulator system which is introduced as pilot manipulator model into the manual aircraft control. The assumption of man as a quasilinear and time invariant control operator adapted to operating states, depending on the flight phases, of the control system gives rise to interesting solutions of the frequency domain transfer functions of both the man manipulator system and the closed loop pilot aircraft control system. It is shown that it is necessary to introduce the complete precision pilot manipulator model into the closed loop pilot aircraft transfer function in order to understand the well known handling quality criteria, and to derive these criteria directly from human operator properties.

Brauser, K.↗

Experimental implementation of adaptive control for flexible space structures

On-going research at The Aerospace Corporation studying the feasibility of applying adaptive control methodologies to the control of flexible space structures is described. A laboratory testbed was established to test system identification and control approaches. The laboratory set-up and controller design approach are discussed. The ARX least squares parameter estimation technique is analyzed in terms of frequency domain transfer function bias error. This analysis approach enables the determination of the effects of sampling rate, sensor type, and data prefiltering on the estimation performance. The ability to identify space structure dynamics over a range of frequencies is shown to be heavily dependent on these factors.

Mcgraw, Gary A.↗

Comparison of Five System Identification Algorithms for Rotorcraft Higher Harmonic Control

This report presents an analysis and performance comparison of five system identification algorithms. The methods are presented in the context of identifying a frequency-domain transfer matrix for the higher harmonic control (HHC) of helicopter vibration. The five system identification algorithms include three previously proposed methods: (1) the weighted-least- squares-error approach (in moving-block format), (2) the Kalman filter method, and (3) the least-mean-squares (LMS) filter method. In addition there are two new ones: (4) a generalized Kalman filter method and (5) a generalized LMS filter method. The generalized Kalman filter method and the generalized LMS filter method were derived as extensions of the classic methods to permit identification by using more than one measurement per identification cycle. Simulation results are presented for conditions ranging from the ideal case of a stationary transfer matrix and no measurement noise to the more complex cases involving both measurement noise and transfer-matrix variation. Both open-loop identification and closed- loop identification were simulated. Closed-loop mode identification was more challenging than open-loop identification because of the decreasing signal-to-noise ratio as the vibration became reduced. The closed-loop simulation considered both local-model identification, with measured vibration feedback and global-model identification with feedback of the identified uncontrolled vibration. The algorithms were evaluated in terms of their accuracy, stability, convergence properties, computation speeds, and relative ease of implementation.

Jacklin, Stephen A.↗

Code for Multiblock CFD and Heat-Transfer Computations

The NASA Glenn Research Center General Multi-Block Navier-Stokes Convective Heat Transfer Code, Glenn-HT, has been used extensively to predict heat transfer and fluid flow for a variety of steady gas turbine engine problems. Recently, the Glenn-HT code has been completely rewritten in Fortran 90/95, a more object-oriented language that allows programmers to create code that is more modular and makes more efficient use of data structures. The new implementation takes full advantage of the capabilities of the Fortran 90/95 programming language. As a result, the Glenn-HT code now provides dynamic memory allocation, modular design, and unsteady flow capability. This allows for the heat-transfer analysis of a full turbine stage. The code has been demonstrated for an unsteady inflow condition, and gridding efforts have been initiated for a full turbine stage unsteady calculation. This analysis will be the first to simultaneously include the effects of rotation, blade interaction, film cooling, and tip clearance with recessed tip on turbine heat transfer and cooling performance. Future plans call for the application of the new Glenn-HT code to a range of gas turbine engine problems of current interest to the heat-transfer community. The new unsteady flow capability will allow researchers to predict the effect of unsteady flow phenomena upon the convective heat transfer of turbine blades and vanes. Work will also continue on the development of conjugate heat-transfer capability in the code, where simultaneous solution of convective and conductive heat-transfer domains is accomplished. Finally, advanced turbulence and fluid flow models and automatic gridding techniques are being developed that will be applied to the Glenn-HT code and solution process.

Fabian, John C.↗

Deep Domain Adaptation based Cloud Type Detection using Active and Passive Satellite Data

Domain adaptation techniques have been developed to handle data from multiple sources or domains. Most existing domain adaptation models assume that source and target domains are homogeneous, i.e., they have the same feature space. Nevertheless, many real world applications often deal with data from heterogeneous domains that come from completely different feature spaces. In our remote sensing application, data in source domain (from an active spaceborne Lidar sensor CALIOP onboard CALIPSO satellite) contain 25 attributes, while data in target domain (from a passive spectroradiometer sensor VIIRS onboard Suomi-NPP satellite) contain 20 different attributes. CALIOP has better representation capability and sensitivity to aerosol types and cloud phase, while VIIRS has wide swaths and better spatial coverage but has inherent weakness in differentiating atmospheric objects on different vertical levels. To address this mismatch of features across the domains/sensors, we propose a novel end-to-end deep domain adaptation with domain mapping and correlation alignment (DAMA) to align the heterogeneous source and target domains in active and passive satellite remote sensing data. It can learn domain invariant representation from source and target domains by transferring knowledge across these domains, and achieve additional performance improvement by incorporating weak label information into the model (DAMA-WL). Our experiments on a collocated CALIOP and VIIRS dataset show that DAMA and DAMA-WL can achieve higher classification accuracy in predicting cloud types.

domain adaptation↗

Application and comparison of on-line identification methods for flexible manipulator control

The application of on-line identification schemes to flexible manipulators is considered. The common objective of all methods, for control purposes, is a time domain parameterization of the system transfer function. Time domain methods generally require more computational load, are more sensitive to noise than frequency domain methods, and suffer from over- (under-) parameterization of the transfer function. Frequency domain methods parameterize the lightly damped transfer function in terms of its easily recognizable poles and zeros and offer alternatives to time domain methods. Several solutions to overcome the aforementioned problems are discussed and demonstrated through experimental studies on a one-link flexible manipulator.

Tzes, Anthony P.↗

Transfer of Satellite Rainfall Uncertainty from Gauged to Ungauged Regions at Regional and Seasonal Timescales

Hydrologists and other users need to know the uncertainty of the satellite rainfall data sets across the range of time/space scales over the whole domain of the data set. Here, uncertainty' refers to the general concept of the deviation' of an estimate from the reference (or ground truth) where the deviation may be defined in multiple ways. This uncertainty information can provide insight to the user on the realistic limits of utility, such as hydrologic predictability, that can be achieved with these satellite rainfall data sets. However, satellite rainfall uncertainty estimation requires ground validation (GV) precipitation data. On the other hand, satellite data will be most useful over regions that lack GV data, for example developing countries. This paper addresses the open issues for developing an appropriate uncertainty transfer scheme that can routinely estimate various uncertainty metrics across the globe by leveraging a combination of spatially-dense GV data and temporally sparse surrogate (or proxy) GV data, such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and the Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar. The TRMM Multi-satellite Precipitation Analysis (TMPA) products over the US spanning a record of 6 years are used as a representative example of satellite rainfall. It is shown that there exists a quantifiable spatial structure in the uncertainty of satellite data for spatial interpolation. Probabilistic analysis of sampling offered by the existing constellation of passive microwave sensors indicate that transfer of uncertainty for hydrologic applications may be effective at daily time scales or higher during the GPM era. Finally, a commonly used spatial interpolation technique (kriging), that leverages the spatial correlation of estimation uncertainty, is assessed at climatologic, seasonal, monthly and weekly timescales. It is found that the effectiveness of kriging is sensitive to the type of uncertainty metric, time scale of transfer and the density of GV data within the transfer domain. Transfer accuracy is lowest at weekly timescales with the error doubling from monthly to weekly.However, at very low GV data density (<20% of the domain), the transfer accuracy is too low to show any distinction as a function of the timescale of transfer.

Tang, Ling↗

Transfer-AE: A novel autoencoder-based impact detection model for structural digital twin

Accurately detecting the location and intensity of impacts is crucial for ensuring structural safety. Currently, AI-based structural impact detection methods are widely used for their excellent detection accuracy. However, their generalization capability is limited by the scenarios present in the training data. Many complex and dangerous impact scenarios are difficult to conduct real-world experiments on to collect sufficient samples. To capture all impact scenarios and fully leverage the advantages of AI-based detection technologies, advanced methods involve combining real-world structural monitoring data with corresponding numerical models to construct digital twins. These methods continuously refine the created numerical models with limited real-world data and provide diverse impact scenarios through numerical model simulations. However, there are inevitable differences between digital models and physical models that are challenging to correct through mechanical means. This discrepancy in data distribution between the two models significantly hinders the application of digital twin technology in impact/event identification tasks. To address this challenge, this study proposes a novel model based on autoencoders, named Transfer-AE. Transfer-AE encodes the common features of digital twins in the latent space to bridge the uncertainty gap at a macro scale between numerical models and physical models and synchronously fits the magnitude and location of the impact load in the decoder. This enables consistent detection results for the same impact event, whether the sample comes from the numerical model or the physical model. Transfer-AE includes two operating modes: Mode 1 has a fixed computational complexity with stable inference speed, but the training cost and difficulty increase with data distribution. Mode 2's computational complexity increases with data distribution, but it has a fixed training cost and speed. In both cases involving the geodesic dome structure simulating a deep space habitat and the IASC-ASCE benchmark structure, Transfer-AE demonstrated the best performance in impact localization and quantification tasks compared to mainstream domain-adaptive transfer models.

Chengjia Han↗

Radiative Transfer and Retrievals in EOF Domain

The Infrared Atmospheric Sounding Interferometer (IASI) is a hyperspectral sensor with 8461 spectral channels and a nominal spectral resolution of 0.25 cm(sup -1). It is computationally intensive to perform radiative transfer calculations and inversions using all these channels. We will present a Principal Component-based Radiative Transfer Model (PCRTM) and a retrieval algorithm which perform all the necessary calculations in EOF domain. Since the EOFs are orthogonal to each other, only about 100 principal components are needed to represent the information content of the 8461 channels. The PCRTM provides the EOF coefficients and associated derivatives with respect to atmospheric and surface parameters needed by the inversion algorithm. The inversion algorithm is based on a non-linear Levenberg-Marquardt method with climatology covariance and a priori information as constraints. The retrieved parameters include atmospheric temperature, moisture and ozone profiles, cloud parameters, surface skin temperature, and surface emissivities. To make the retrieval system even more compact and stable. The atmospheric vertical profiles are compressed into the EOF space as well. The surface emissivities are also compressed into EOF space.

Liu, Xu↗

Study of mathematical modeling of communication systems transponders and receivers

The modeling of communication receivers is described at both the circuit detail level and at the block level. The largest effort was devoted to developing new models at the block modeling level. The available effort did not permit full development of all of the block modeling concepts envisioned, but idealized blocks were developed for signal sources, a variety of filters, limiters, amplifiers, mixers, and demodulators. These blocks were organized into an operational computer simulation of communications receiver circuits identified as the frequency and time circuit analysis technique (FATCAT). The simulation operates in both the time and frequency domains, and permits output plots or listings of either frequency spectra or time waveforms from any model block. Transfer between domains is handled with a fast Fourier transform algorithm.

Walsh, J. R.↗