Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “predictive mapping”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Fortuna Tessera, Venus - Evidence of horizontal convergence and crustal thickening

Structural and tectonic patterns mapped in Fortuna Tessera are interpreted to reflect a change in the style and intensity of deformation from east to west, beginning with simple tessera terrain at relatively low topographic elevations in the east and progressing through increasingly complex deformation patterns and higher topography to Maxwell Montes in the West. These morphologic and topographic patterns are consistent with east-to-west convergence and compression and the increasing elevations are interpreted to be due to crustal thickening processes associated with the convergent deformational environment. Using an Airy isostatic model, crustal thicknesses of approximately 35 km for the initial tessera terrain, and crustal thicknesses of over 100 km for the Maxwell Montes region are predicted. Detailed mapping with Magellan data will permit the deconvolution of individual components and structures in this terrain.

Vorder Bruegge, R. W.↗
DOE OSTI2022

DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network

Abstract Background Estimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality of protein models. Inter-residue distances are key information for predicting protein’s tertiary structures and therefore have good potentials to predict the quality of protein structural models. However, few methods have been developed to fully take advantage of predicted inter-residue distance maps to estimate the accuracy of a single protein structural model. Result We developed an attentive 2D convolutional neural network (CNN) with channel-wise attention to take only a raw difference map between the inter-residue distance map calculated from a single protein model and the distance map predicted from the protein sequence as input to predict the quality of the model. The network comprises multiple convolutional layers, batch normalization layers, dense layers, and Squeeze-and-Excitation blocks with attention to automatically extract features relevant to protein model quality from the raw input without using any expert-curated features. We evaluated DISTEMA’s capability of selecting the best models for CASP13 targets in terms of ranking loss of GDT-TS score. The ranking loss of DISTEMA is 0.079, lower than several state-of-the-art single-model quality assessment methods. Conclusion This work demonstrates that using raw inter-residue distance information with deep learning can predict the quality of protein structural models reasonably well. DISTEMA is freely at https://github.com/jianlin-cheng/DISTEMA

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Scan strategies in EBM-printed IN718 and the physics of bulk 3D microstructure development

Three-dimensional (3D) characterization provides opportunities for understanding processing-structure relationships in additively manufactured (AM) materials. Bulk samples of Inconel 718 were fabricated via electron beam melting (EBM) in order to study microstructural development as a function of energy input and beam scan strategy. TriBeam tomography of bulk Inconel 718 microstructures built under steady-state growth conditions reveals the sensitivity of microstructure formation and evolution to machine process parameters. Here, samples manufactured using a narrow range of energy input per unit build area result in varied grain morphologies and crystallographic textures. Using TRUCHAS, a thermal simulation software, the thermal history of bulk scan strategies was predicted, and combined with a calibrated microstructure-processing map to accurately predict bulk grain morphologies. The solidification parameters and the 3D measured nucleation density are used to predict the transition between columnar and equiaxed grain morphologies, providing a process map to guide AM parameter choices to locally control as-printed microstructure. A two-dimensional metric for characterizing bulk grain morphology was also found to agree well with predictions from the process map calibrated by 3D data. Combined with 3D tomography and thermal modelling, the physics of structure development were understood at a new level of detail with respect to the competing processes of grain nucleation and epitaxial growth.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Unraveling the Correlation between Raman and Photoluminescence in Monolayer MoS 2 through Machine‐Learning Models

Abstract 2D transition metal dichalcogenides (TMDCs) with intense and tunable photoluminescence (PL) have opened up new opportunities for optoelectronic and photonic applications such as light‐emitting diodes, photodetectors, and single‐photon emitters. Among the standard characterization tools for 2D materials, Raman spectroscopy stands out as a fast and non‐destructive technique capable of probing material's crystallinity and perturbations such as doping and strain. However, a comprehensive understanding of the correlation between photoluminescence and Raman spectra in monolayer MoS 2 remains elusive due to its highly nonlinear nature. Here, the connections between PL signatures and Raman modes are systematically explored, providing comprehensive insights into the physical mechanisms correlating PL and Raman features. This study's analysis further disentangles the strain and doping contributions from the Raman spectra through machine‐learning models. First, a dense convolutional network (DenseNet) to predict PL maps by spatial Raman maps is deployed. Moreover, a gradient boosted trees model (XGBoost) with Shapley additive explanation (SHAP) to bridge the impact of individual Raman features in PL features is applied. Last, a support vector machine (SVM) to project PL features on Raman frequencies is adopted. This work may serve as a methodology for applying machine learning to characterizations of 2D materials.

Lu, Ang‐Yu↗
DOE OSTI2025

Mechanically induced thermal runaway severity analysis of Li-ion batteries and continuous energy release monitoring

The large-scale deployment of Li-ion batteries in stationary energy storage and electrical vehicle applications demands a strong focus on safety, particularly on the thermal runaway risk and severity evaluation. A standardized single-side mechanical indentation test protocol was developed to induce an internal short-circuit (ISC) and evaluate cells' thermal runaway severity at different state of charge (SOC). The observed hazard severity (OHS in five categories) and evaluated scores in this work have a comprehensive consideration of each cell's capacity, initial voltage, SOC, temperature and voltage change, allowing a better evaluation of the cells' thermal runaway potential. This method was applied to about 200 Li-ion batteries in order to build an extensive thermal runaway database covering various SOCs, capacities and chemistries. In this study, we monitored the transitions of stored electrochemical energy and applied mechanical energy into both thermal energy and acoustic emissions (AE). The surface temperature and mechanical failures were monitored by infrared imaging and AE to capture critical events within battery cells throughout the mechanical indentation tests. Furthermore, the initial temperature maps can predict two types of follow-up events: thermal runaway or gradual heat release via conduction. Analyzing each cell's severity, AEs, and leveraging the evolving database offer insights into predicting occurrences of thermal runaway. The test method, thermal runaway severity evaluation and prediction, and the corresponding database provide battery designers, manufacturers, and end-users a clear overview of Li-ion batteries' thermal runaway potential under mechanical abuse, advancing the safety design of Li-ion batteries.

Acoustic emission↗
DOE OSTI2020

Machine learning for endoleak detection after endovascular aortic repair

Diagnosis of endoleak following endovascular aortic repair (EVAR) relies on manual review of multi-slice CT angiography (CTA) by physicians which is a tedious and time-consuming process that is susceptible to error. We evaluate the use of a deep neural network for the detection of endoleak on CTA for post-EVAR patients using a novel data efficient training approach. 50 CTAs and 20 CTAs with and without endoleak respectively were identified based on gold standard interpretation by a cardiovascular subspecialty radiologist. The Endoleak Augmentor, a custom designed augmentation method, provided robust training for the machine learning (ML) model. Predicted segmentation maps underwent post-processing to determine the presence of endoleak. The model was tested against 3 blinded general radiologists and 1 blinded subspecialist using a held-out subset (10 positive endoleak CTAs, 10 control CTAs). Model accuracy, precision and recall for endoleak diagnosis were 95%, 90% and 100% relative to reference subspecialist interpretation (AUC = 0.99). Accuracy, precision and recall was 70/70/70% for generalist1, 50/50/90% for generalist2, and 90/83/100% for generalist3. The blinded subspecialist had concordant interpretations for all test cases compared with the reference. In conclusion, our ML-based approach has similar performance for endoleak diagnosis relative to subspecialists and superior performance compared with generalists.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Knowledge graph-aided Bayesian active learning for top- K genetic interaction discovery

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompassing all possible pairwise knockdowns of over 350 host genes associated with HIV infection. We demonstrate that our framework rapidly identifies the most effective gene knockdown pairs for reducing viral load. Furthermore, we show that incorporating side information enhances performance during the early stages of active learning (low data regime), while our batch diversification strategy significantly boosts performance in later stages (high data regime). This framework is general and can be adapted to explore gene interactions in other contexts, such as synthetic lethality prediction and mapping epistatic effects across quantitative trait loci.

Computational biology and bioinformatics↗
DOE OSTI2023

Topological structure of complex predictions

Abstract Current complex prediction models are the result of fitting deep neural networks, graph convolutional networks or transducers to a set of training data. A key challenge with these models is that they are highly parameterized, which makes describing and interpreting the prediction strategies difficult. We use topological data analysis to transform these complex prediction models into a simplified topological view of the prediction landscape. The result is a map of the predictions that enables inspection of the model results with more specificity than dimensionality-reduction methods such as tSNE and UMAP. The methods scale up to large datasets across different domains. We present a case study of a transformer-based model previously designed to predict expression levels of a piece of DNA in thousands of genomic tracks. When the model is used to study mutations in the BRCA1 gene, our topological analysis shows that it is sensitive to the location of a mutation and the exon structure of BRCA1 in ways that cannot be found with tools based on dimensionality reduction. Moreover, the topological framework offers multiple ways to inspect results, including an error estimate that is more accurate than model uncertainty. Further studies show how these ideas produce useful results in graph-based learning and image classification.

Computer Science↗
DOE OSTI2025

1 × 1 km maps of abundances of eight enzyme functional classes for soil C, N, and P cycling across the CONUS

This dataset includes eight 1 × 1 km maps of the abundances of eight enzyme functional classes (EFC) for soil C, N, and P cycling across the CONUS. These mappings are predicted by the machine learning model trained using metagenomics and the corresponding environmental data. This item corresponds to our article: Fan, C., Song, Y., Mishra, U., Gautam, S., & Mayes, M. A. (2025). Harnessing the Power of Machine Learning and Omics to Identify Environmental Regulation on Microbial Functional Composition for Soil C, N, and P Cycling. Journal of Geophysical Research: Biogeosciences, 130(10).

1 × 1 km↗
NASA NTRS1972

Highly loaded multi-stage fan drive turbine: Plain blade configuration design

The constant-inside-diameter flowpath was scaled for testing in an existing turbine test facility. Blading detailed design is discussed, and design data are summarized. Predicted performance maps are presented. Steady-state stresses and vibratory behavior are discussed and the results of the mechanical design analysis are presented.

Evans, D. C.↗
NASA NTRS1982

Plasma injection and transport in the mid-altitude polar cusp

DE-1 hot plasma observations in the mid-altitude polar cusp have shown evidence of a significant velocity filtering phenomenon which is consistent with a latitudinally narrow region of plasma injection located at a geocentric distance of about eight earth radii (in a dipole approximation). Plasma convection from the injection region into the polar cap results in a V-shaped log E vs. observed pitch angle relation at geocentric distances near 4 earth radii. This velocity filtering effect allows the measurement of much smaller flow velocities (about 10 km/sec) than have heretofore been possible with hot plasma measurements. The flows thus determined are consistent with ionospheric flows measured nearly simultaneously by the DE-2 spacecraft, although the magnitudes of the higher altitude flows are higher by a factor of 2 or more than an approximately r to the 3/2 power dipole-field mapping would predict.

Burch, J. L.↗
NASA NTRS1983

A correlation analysis of percent canopy closure versus TMS spectral response for selected forest sites in the San Juan National Forest, Colorado

The correlation of canopy closure with the signal response of individual thematic mapper simulator (TMS) bands for selected forest sites in the San Juan National Forest, Colorado was investigated. Ground truth consisted of a photointerpreted determination of percent canopy closure of 0 to 100 percent for 32 sites. The sites selected were situated on plateaus at an elevation of approximately 3 km with slope or = 10 percent. The predominant tree species were ponderosa pine and aspen. The mean TMS response per band per site was calculated from data acquired by aircraft during mid-September, 1981. A correlation analysis of TMS response vs. canopy closure resulted in the following correlation coefficients for bands 1 through 7, respectively: -0.757, -0.663, -0.666, -0.088, -0.797, -0.763. Two model regressions were applied to the TMS data set to create a map of predicted percent forest canopy closure for the study area. Results indicated percent predictive accuracies of 71, 74, and 57 for percent canopy closure classes of 0-25, 25-75, and 75-100, respectively.

Butera, M. K.↗
NASA NTRS1986

Fifty year canon of solar eclipses: 1986-2035

A reference of moderately detailed eclipse predictions and maps for use by the professional astronomical community is provided. The general characteristics of every solar eclipse and a detailed set of cylindrical project world maps which show the umbral paths of every solar eclipse from 1901 to 2100 are presented. The geodetic path coordinates and local circumstance on the center line, and a series of orthographic projection maps which show the regions of visibility of both partial and central phases for every eclipse from 1986 through 2035 are also provided.

Espenak, Fred↗
NASA NTRS1991

Relaxation processes in a low-order three-dimensional magnetohydrodynamics model

The time asymptotic behavior of a Galerkin model of 3D magnetohydrodynamics (MHD) has been interpreted using the selective decay and dynamic alignment relaxation theories. A large number of simulations has been performed that scan a parameter space defined by the rugged ideal invariants, including energy, cross helicity, and magnetic helicity. It is concluded that time asymptotic state can be interpreted as a relaxation to minimum energy. A simple decay model, based on absolute equilibrium theory, is found to predict a mapping of initial onto time asymptotic states, and to accurately describe the long time behavior of the runs when magnetic helicity is present. Attention is also given to two processes, operating on time scales shorter than selective decay and dynamic alignment, in which the ratio of kinetic to magnetic energy relaxes to values 0(1). The faster of the two processes takes states initially dominant in magnetic energy to a state of near-equipartition between kinetic and magnetic energy through power law growth of kinetic energy. The other process takes states initially dominant in kinetic energy to the near-equipartitioned state through exponential growth of magnetic energy.

Stribling, Troy↗
NASA NTRS1992

An experimental study of heat transfer in a large-scale turbine rotor passage

An experimental study of the heat transfer distribution in a turbine rotor passage was conducted in a large-scale, ambient temperature, rotating turbine model. Heat transfer was measured for both the full-span suction and pressure surfaces of the airfoil as well as for the hub endwall surface. The objective of this program was to document the effects of flow three-dimensionality on the heat transfer in a rotating blade row (vs a stationary cascade). Of particular interest were the effects of the hub and tip secondary flows, tip leakage and the leading-edge horseshoe vortex system. The effect of surface roughness on the passage heat transfer was also investigated. Midspan results are compared with both smooth-wall and rough-wall finite-difference two-dimensional heat transfer predictions. Contour maps of Stanton number for both the rotor airfoil and endwall surfaces revealed numerous regions of high heat transfer produced by the three-dimensional flows within the rotor passage. Of particular importance are regions of local enhancement (as much as 100 percent over midspan values) produced on the airfoil suction surface by the secondary flows and tip-leakage vortices and on the hub endwall by the leading-edge horseshoe vortex system.

Blair, Michael F.↗