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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Compressive deformation behavior and strain rate sensitivity of Al-cenosphere hybrid foam with mono-modal, bi-modal and tri-modal cenosphere size distribution

Highlights: • Cenospheres of different size ranges and distributions are used in hybrid aluminium foam. • Microstructure and deformation response of hybrid foam cenosphere size distribution. • Hybrid foam with bi or tri-modal cenosphere distribution exhibits higher strength and energy absorption. • The plateau stresses and energy absorption increased marginally with strain rate. • In case of bi- or tri-modal size distribution, strain rate sensitivity and strain rate sensitivity parameter is marginally lower. - Abstract: AlSi12Cu1Mg1 aluminium alloy-cenosphere closed cell hybrid foams with different kinds of cenosphere size distribution (mono-modal, bi-modal and tri-modal) are used to see the effect of cenosphere size distribution on their deformation behavior under quasi static compressive loading conditions at different strain rates. The stress strain curves are almost similar type irrespective of the cenosphere size distribution and strain rate. However, the plateau stress and energy absorption increased significantly when one used bi-modal and or tri-modal cenosphere size distribution instead of mono-modal cenosphere size distribution. These plateau stresses and energy absorption also increased marginally with strain rate. The densification strain remained almost invariant with strain rate and cenosphere size distribution. The strain rate sensitivity and strain rate sensitivity parameter is relatively lower in case of bi-modal and tri-modal cenosphere size distribution particularly at higher relative density.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Suppressing simulation bias in multi-modal data using transfer learning

Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

X-ray beam induced current (XBIC) measurements allow mapping of the nanoscale performance of electronic devices such as solar cells. Ideally, XBIC is employed simultaneously with other techniques within a multi-modal X-ray microscopy approach. An example is given herein combining XBIC with X-ray fluorescence to enable point-by-point correlations of the electrical performance with chemical composition. For the highest signal-to-noise ratio in XBIC measurements, lock-in amplification plays a crucial role. By this approach, the X-ray beam is modulated by an optical chopper upstream of the sample. The modulated X-ray beam induced electrical signal is amplified and demodulated to the chopper frequency using a lock-in amplifier. By optimizing low-pass filter settings, modulation frequency, and amplification amplitudes, noise can efficiently be suppressed for the extraction of a clear XBIC signal. A similar setup can be used to measure the X-ray beam induced voltage (XBIV). Beyond standard XBIC/XBIV measurements, XBIC can be measured with bias light or bias voltage applied such that outdoor working conditions of solar cells can be reproduced during in-situ and operando measurements. Ultimately, the multi-modal and multi-dimensional evaluation of electronic devices at the nanoscale enables new insights into the complex dependencies between composition, structure, and performance, which is an important step towards solving the materials' paradigm.

14 SOLAR ENERGY↗
DOE OSTI2018

Time-resolved PIV measurements in a low-aspect ratio facility of randomly packed spheres and flow analysis using modal decomposition

This work experimentally investigated the flow characteristics in a facility with randomly packed spheres at a low-aspect ratio of 4.4. Velocity fields in the near-wall region and in the pores between spheres were obtained by employing the matched-index-of-refraction (MIR) and time-resolved particle image velocimetry (TR-PIV) techniques for Reynolds numbers of 340, 520, and 720. From the obtained TR-PIV velocity vector fields, flow characteristics including first- and second-order statistics, such as mean velocity, root-mean-square fluctuating velocity, and Reynolds stress profiles, were computed. The effects of the wall enclosure and Reynolds numbers on the flow patterns were investigated by comparing the computed flow statistics and evaluating two-point cross correlations of the velocities measured adjacent to and far from the wall. Comparisons of the mean velocities, root-mean-square fluctuating velocities, and Reynolds stress component showed an increase in flow mixing and turbulent intensity in the gaps between spheres in the packed bed. The re-circulation-region sizes, however, were found to be independent from an increase in Reynolds numbers. Finally, flow modal decompositions, such as the proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), were applied to the vorticity fields extracted from sub-regions located near and far from the wall to reveal the most dominant POD and DMD flow structures.

42 ENGINEERING↗
DOE OSTI2021

Wave energy resources assessment for the multi-modal sea state of Hawaii

This paper describes development and validation of a 32-year wave hindcast for Hawaii as part of the wave energy resources assessment for the Exclusive Economic Zones of the US and affiliated territories. The nested model system comprises structured WAVEWATCH III models for the entire globe and the Central North Pacific as well as an unstructured SWAN model around the Hawaiian Islands with wind forcing from the Global Forecast System Reanalysis and its regional downscaling. The development effort follows the standards introduced by the International Electrotechnical Commission (IEC). The hindcast dataset was firstly validated with buoy measurements in terms of the six IEC wave energy resources parameters including the omnidirectional wave power ????, significant wave height ????????0, energy period ????????, spectral width ????0, direction of maximum directionally resolved wave power ?????????? ????????, and directionality coefficient ????. The comparison shows good overall agreement but with greater uncertainties for the spectral parameters ????0, ???????????????????? , and ???? formulated for narrow band spectra. The multimodal sea state in Hawaii always includes a mix of short-period wind seas, long-period swells from the North and South Pacific as well as waves with moderate periods from multiple sources. We partition the hindcast spectra based on the respective period ranges and directions and validate the partitioned spectral energy and parameters with measured values for implementation in wave energy resources assessment. The partitioned wave parameters from the hindcast allow separate seasonal descriptions of wind seas, north and south swells, and moderate-period waves for in-depth understanding of the wave climate and resources in Hawaii.

Li, Ning↗
DOE OSTI2024

CrossMP: Enabling Cross-Modality Translation between Single-Cell RNA-Seq and Single-Cell ATAC-Seq through Web-Based Portal

In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Detection of multi-modal Doppler spectra – Part 1: Establishing characteristic signals in radar moment data

Vertically pointing millimeter-wavelength radars provide a wealth of information about cloud and precipitation particle properties. Doppler spectral data can inform on how particles of varying vertical velocities contribute to the total backscattered power observed. It is more computationally cost effective to process moment data instead of spectra data, but doing so leaves valuable information on the cutting room floor. To confidently identify a multi-modal spectra event, in which two or more modes are present within a layer, Doppler spectral data are essential. This means long-term identification of layers featuring multi-modal spectra can be cost prohibitive. To address this, we explore three multi-modal spectra cases from winter precipitation events to determine characteristic signatures of these layers in the moment data averaged over short time periods (∼ 145 s) and explore how these layers differ from the rest of the vertical profiles. We find that the mean spectrum width and the standard deviation of mean Doppler velocity can be used to determine whether or not a layer is multi-modal. In particular, multi-modal layers in mixed-phase and ice clouds feature larger mean spectrum width (exceeding 0.17 m s −1 ) and smaller standard deviation of the mean Doppler velocity (below 0.1 m s −1 ). In Part 1 of this study, the identification criteria and methods are described. In Part 2 (Wugofski and Kumjian, 2025), we perform a verification of the method for three years of vertically pointing radar data, and explore the meteorological conditions associated with identified multi-modal spectral events.

Wugofski, Sarah [Pennsylvania State Univ., Univers↗
DOE OSTI2023

Sensitivity Analysis of Modal Parameters of a Jacket Offshore Wind Turbine to Operational Conditions

Accurate estimation of offshore wind turbine (OWT) modal parameters has a prominent effect on the design loads, lifetime prediction, and dynamic response of the system. Modal parameters can vary during the operation of OWTs. This paper studies the variation and sensitivity analysis of an OWT’s modal parameters with respect to operational and environmental conditions. Three finite element models of a jacket-supported OWT at the Block Island Wind Farm are created within the OpenSees, SAP2000, and OpenFAST platforms and validated using experimental measurements. The OpenFAST model is used to simulate the modal parameters of the turbine under various wind speed, rotor speed, power, yaw angle, mean sea level, blade pitch angle, and soil spring values. The model-predicted modal parameters of the first fore–aft (FA) and side–side (SS) modes are compared to those identified from experimental measurements. Results from the simulations show that the first FA natural frequency and damping ratio mostly depend on the rotor speed and wind speed, respectively, while yaw angle and mean sea level do not have a visible effect. It is observed that there is about 8% stiffening in the first FA frequency and an aerodynamic damping of 7.5% during the operation of the OWT.

17 WIND ENERGY↗