SEARCH · Engineering Papers

Results for “feature exploration”

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.

SourceResearch connectionFrom yearThrough year

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

At least 145 records · Page 8

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Exploration of advanced porous organic polymers as a platform for biomimetic catalysis and molecular recognition

Nature has long been a dominant source of inspiration in the area of chemistry, serving as prototypes for the design of materials with proficient performance. In this Feature article, we present our efforts to explore porous organic polymers (POPs) as a platform for the construction of biomimetic materials to enable new technologies to achieve efficient conversions and molecular recognition. For each aspect, we first present the chemical basis of nature, followed by depicting the principles and design strategies involved for functionalizing POPs along with a summary of critical requirements for materials, culminating in a demonstration of unique features of POPs. Our endeavours in using POPs to address the fundamental scientific problems related to biomimetic catalysis and adsorption are then illustrated to show their enormous potential and capabilities for applications ranging from concerted catalysis to radionuclide sequestration. To conclude, we present a personal perspective on the challenges and opportunities in this emerging field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Guiding Principles for the Rational Design of Hybrid Materials: Use of DFT Methodology for Evaluating Non‐Covalent Interactions in a Uranyl Tetrahalide Model System

Abstract Together with the synthesis and experimental characterization of 14 hybrid materials containing [UO 2 X 4 ] 2− (X=Cl − and Br − ) and organic cations, we report on novel methods for determining correlation trends in their formation enthalpy (ΔH f ) and observed vibrational signatures. Δ H f values were analyzed through isothermal acid calorimetry and a Density Functional Theory+Thermodynamics (DFT+T) approach with results showing good agreement between theory and experiment. Three factors (packing efficiency, cation protonation enthalpy, and hydrogen bonding energy [ ]) were assessed as descriptors for trends in Δ H f . Results demonstrated a strong correlation between and Δ H f , highlighting the importance of hydrogen bonding networks in determining the relative stability of solid‐state hybrid materials. Lastly, we investigate how hydrogen bonding networks affect the vibrational characteristics of uranyl solid‐state materials using experimental Raman and IR spectroscopy and theoretical bond orders and find that hydrogen bonding can red‐shift U≡O stretching modes. Overall, the tightly integrated experimental and theoretical studies presented here bridge the trends in macroscopic thermodynamic energies and spectroscopic features with molecular‐level details of the geometry and electronic structure. This modeling framework forms a basis for exploring 3D hydrogen bonding as a tunable design feature in the pursuit of supramolecular materials by rational design.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Guiding Principles for the Rational Design of Hybrid Materials: Use of DFT Methodology for Evaluating Non‐Covalent Interactions in a Uranyl Tetrahalide Model System

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Big Data Seismology

The discipline of seismology is based on observations of ground motion that are inherently undersampled in space and time. Our basic understanding of earthquake processes and our ability to resolve 4D Earth structure are fundamentally limited by data volume. Currently, Big Data Seismology is an emergent revolution involving the use of large, data-dense inquiries that is providing new opportunities to make fundamental advances in these areas. This article reviews recent scientific advances enabled by Big Data Seismology through the context of three major drivers: the development of new data-dense sensor systems, improvements in computing, and the development of new types of techniques and algorithms. Each driver is explored in the context of both global and exploration seismology, alongside collaborative opportunities that combine the features of long-duration data collections (common to global seismology) with dense networks of sensors (common to exploration seismology). The review explores some of the unique challenges and opportunities that Big Data Seismology presents, drawing on parallels from other fields facing similar issues. Finally, recent scientific findings enabled by dense seismic data sets are discussed, and we assess the opportunities for significant advances made possible with Big Data Seismology. This review is designed to be a primer for seismologists who are interested in getting up-to-speed with how the Big Data revolution is advancing the field of seismology.

58 GEOSCIENCES↗

DOE OSTI2021

Characterizing Signatures of Geothermal Exploration Data with Machine Learning Techniques: An Application to the Nevada Play Fairway Analysis

We are introducing machine learning methods to the play fairway analysis to generate geothermal potential maps to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Nevada Great Basin region. Our project aims to identify new ways to combine the play fairway data and empirically organize relationships between feature weights and labels in an improved workflow. As a means of doing this, we introduce machine learning methods to evaluate the influence of certain geological and geophysical features/feature sets in predicting geothermal favorability. This report highlights promising approaches based on supervised and unsupervised learning methods. First, we demonstrate a filter method applied to supervised classification modeling. The supervised filter method is based on permutation analysis to evaluate every possible feature combination/drop out scenario and rank feature influence based on the performance variance of supervised classification models. Additionally, we present an unsupervised factor analysis based on principal component analysis coupled with a semi-supervised kmeans clustering algorithm. This analysis allows us to identify the optimal number of groups/clusters for training sites and structural settings to identify feature patterns including correlation, variance, and latent and dominant feature relationships. The results from these methods offer a promising avenue for identifying favorable sources of predictive information to identify the locations of blind geothermal systems and furthering our understanding of complex geothermal feature and label relationships in the Great Basin region and beyond.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Deep learning interfacial momentum closures in coarse-mesh CFD two-phase flow simulation using validation data

Multiphase flow phenomena have been widely observed in the industrial applications while it remains a challenging yet unsolved problems. Three-dimensional computational fluid dynamics (CFD) approaches resolve the flow fields on a finer special and temporal scales which can complement the dedicated experimental study. However, closures have to be introduced to reflect the underlying physics in multiphase flow. Among them, the interfacial forces, including drag, lift, turbulent dispersion and wall lubrication forces, play in important role on the bubble’s distribution and migration in liquid-vapor two-phase flow. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions which usually cannot deliver a universal solution across wide range of flow conditions. In this paper, a data-driven approach, named as Feature Similarity Measurement (FSM), is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach. Interfacial momentum transfer in adiabatic bubbly flow serves as the focus of the present study. Both a mature and a simplified set of interfacial closures are taken as the low fidelity data. Experimental data and fine mesh CFD simulations results are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper which reveals that FSM can substantially improve the prediction of coarse mesh CFD model regardless of the choice of interfacial closures and it provides scalability and consistency across discontinuous flow regimes. Furthermore, it demonstrates that data-driven method can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Impacts of Superalloys on the Surface Quality of Additively Manufactured Channels

Abstract Gas turbines feature many components that require superalloys capable of handling extreme thermal environments. Increasing the selection of materials available for these components is important to their use in these extremely high-temperature environments. This study investigated two recently developed materials intended to be used for additive manufacturing (AM), with one superalloy based on cobalt and the other on nickel. Sets of four test coupons were built using the materials, in addition to the commonly used Inconel-718, on multiple laser powder bed fusion machines. Several build conditions were varied between coupon sets, including coupon orientation, contour settings, and upskin and downskin treatment. Each set of test coupons featured four unique cooling designs to explore how different cooling technologies would be impacted by the variations in build conditions. After being built, coupons were computed tomography (CT) scanned to determine accuracy to design intent and quantify the surface roughness. The CT scans indicated that horizontally built test coupons had a significantly higher deviation from design intent and higher surface roughness than those built vertically. Results also indicated that the cobalt-based alloy consistently had a smoother surface quality with lower surface roughness compared to the nickel-based alloy. After geometric characterization, the cooling performance of the test coupons was measured experimentally. Pressure losses were found to correlate with increases in surface roughness; however, in some cases, the convective heat transfer did not increase proportionally to the pressure loss as a result of surface features significantly blocking the flow without proportionally increasing convective heat transfer.

Engineering↗

DOE OSTI2025

Interstitial Atoms and the Frustrated and Allowed Structural Transitions Principle: Tunability in the Electronic Structure of AuCu 3 ‐type Frameworks in Dy 4 T 1− x Ga 12 (T = Ag, Ir)

In this Article, we explore how the chemical pressure (CP) features of an intermetallic phase may provide opportunities to couple perturbations in electron count with the stabilization of the underlying geometrical structure. AuCu 3 ‐type LnGa 3 (Ln = lanthanide or group 3 metal) phases contain octahedral cavities of negative CP held open by overly compressed Ln–Ga interactions, leading to a series of transition metal‐stuffed derivatives. We present new additions to this family with the synthesis and crystal structures of Dy 4 T 1−x Ga 12 with (T, x) = (Ag, 0.29) and (Ir, 0.15), adopting Y 4 PdGa 12 ‐type superstructures of the AuCu 3 ‐type. density functional Ttheory (DFT)‐CP calculations, when adjusted to avoid dipolar CP features, affirm that T atom incorporation provides a mechanism for the relief of packing tensions, while electronic density of states distributions illustrate that the T atoms serve largely as electron or hole donors to the band structure, as needed for them to attain d 10 configurations. The maximum obtainable value for x may be limited by a mismatch between the Fermi energy and pseudogap, in line with the balance of factors envisioned by the frustrated and allowed structural transitions principle. Furthermore, trends in resistivity measurements on T = Ir, Pd, and Ag compounds are interpretable in terms of the varying degrees of disorder arising from x < 1.0.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

W-induced morphological modification of NiFe layered double hydroxides as efficient electrocatalysts for overall water splitting

Layered double hydroxides (LDHs) are one of the most efficient electrocatalysts for water splitting due to their nanosheet features and compositional flexibilities. This work explored the impact of W precursor concentration (0 ~ 10 mM) on LDH morphologies and performance in hydrogen production. Using an electrodeposition W-doping process, W-induced NiFe LDHs (NiFeW-LDHs) were in-situ grown on carbon fiber papers for water splitting. A performance peak was found at a W doping of 5 mM with well-aligned nanosheets, which not only boosted the charge transfer ability and gas evolution but also offered more than a four-fold electrochemical surface area increase compared to film-like NiFe hydroxides. The NiFeW-LDHs exhibited remarkable performance compared to NiFe hydroxides, showing decreased overpotentials of 31 mV and 114 mV for the oxygen evolution reactions (OERs) and hydrogen evolution reactions (HERs) at 10 mA/cm 2 and -10 mA/cm 2 , respectively, in alkaline media. The performance enhancement at 5 mM W-doping was linked to the well-aligned NiFeW-LDH nanosheets; smaller, less-textured nanosheets were observed with lower or higher W precursor concentrations (2.5 mM or >7.5 mM), leading to inferior OER and HER performances. Hence, an appropriate W doping is crucial to generating the morphologies that contribute to the higher performance of NiFeW-LDHs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Activity Convergence between Continuous- and Pulsed-Deposition NiFe Hydroxide Anodes in Liquid Alkaline Electrolyzers

Improving the activity of anodes for the alkaline oxygen evolution reaction (OER) is of interest because of the importance of the reaction in electrochemical technology. There is an abundance of studies which confirm that NiFe hydroxide, often prepared by electrodeposition, is the most active catalyst for the alkaline OER. This relatively high level of confidence in the optimal OER catalyst chemistry suggests that exploration of methods which improve on features besides the chemistry of the films, such as their microstructure, could access new heights of activity. In this study, the possible benefits of pulsed current deposition relative to the conventional continuous current approach to the deposition of NiFe hydroxides were investigated. Pulsed deposition densified the film surfaces by preventing metal ion depletion at the electrode surface during film formation. The Fe content, redox reversibility, and OER activity were higher for the pulsed deposition films relative to their continuous deposition counterparts. Though pulsed deposition imparted a higher OER performance of the films compared to continuous deposition at the three-electrode level, this improved performance was not retained during electrolyzer operation. Rather, a convergence of the cell performance is seen irrespective of the deposition approach. This phenomenon was attributed to the way electrolyzer conditionsrelatively high temperature, base concentration, and current densitycan drive alternate mechanisms for observed performance.

08 HYDROGEN↗

DOE OSTI2025

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles

Iron carbide (Fe x C y ) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as high-temperature Fischer–Tropsch synthesis. However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe 182 C 88 NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe 182 C 88 NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (H ads ), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting H ads . Furthermore, using a combined ML and quantum chemistry approach, our work demonstrates a general and efficient procedure for generating and probing complex surface phenomena on binary nanoparticles.

Adsorption↗

DOE OSTI2024

Searching for Exploding black holes

Abstract The observation of the final stages of the evaporation of a light black hole, which Hawking referred to as “black hole explosion”, would offer critical insights on quantum gravity and high-energy physics phenomena. Here, we explore, review, and revisit the observational features and rates expected for nearby, light, evaporating black holes, and we assess and compare the expected sensitivity of a broad range of observatories. We then focus on the search for candidate black hole explosions in archival data from the Fermi Large Area Telescope and Gamma-ray Burst Monitor, and outline possible future observational campaigns.

Astronomy & Astrophysics↗

DOE OSTI2021

Subaru Hyper Suprime-Cam excavates colossal over- and underdense structures over 360 deg 2 out to z = 1

Subaru Strategic Program with the Hyper-Suprime Cam (HSC-SSP) has proven to be successful with its extremely wide area coverage in past years. Taking advantages of this feature, we report initial results from exploration and research of expansive over- and underdense structures at z = 0.3–1 based on the second Public Data Release where optical 5-band photometric data for ~ eight million sources with i < 23 mag are available over ~360 deg 2 . We not only confirm known superclusters but also find candidates of titanic over- and underdense regions out to z = 1. The mock data analysis suggests that the density peaks would involve one or more massive dark matter haloes (>1014 M ⊙ ) of the redshift, and the density troughs tend to be empty of massive haloes over >10 comoving Mpc. Besides, the density peaks and troughs at z ≲ 0.6 are in part identified as positive and negative weak lensing signals respectively, in mean tangential shear profiles, showing a good agreement with those inferred from the full-sky weak lensing simulation. The coming extensive spectroscopic surveys will be able to resolve these colossal structures in 3D space. Finally, the number density information over the entire survey field is available as grid-point data on the website of the HSC-SSP data release (https://hsc.mtk.nao.ac.jp/ssp/data-release/).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Radio AGN in nearby dwarf galaxies: the important role of AGN in dwarf galaxy evolution

ABSTRACT We combine deep optical and radio data, from the Hyper Suprime-Cam and the Low-Frequency Array (LOFAR), respectively, to study 78 radio active galactic nuclei (AGN) in nearby (z < 0.5) dwarf galaxies. Comparison to a control sample, matched in stellar mass and redshift, indicates that the AGN and controls reside in similar environments, show similar star formation rates (which trace gas availability) and exhibit a comparable incidence of tidal features (which indicate recent interactions). We explore the AGN properties by combining the predicted gas conditions in dwarfs from a cosmological hydrodynamical simulation with a Monte Carlo suite of simulated radio sources, based on a semi-analytical model for radio-galaxy evolution. In the subset of LOFAR-detectable simulated sources, which have a similar distribution of radio luminosities as our observed AGN, the median jet powers, ages, and accretion rates are ∼1035 W, ∼5 Myr, and ∼10−3.4 M⊙ yr−1, respectively. The median mechanical energy output of these sources is ∼100 times larger than the median binding energy expected in dwarf gas reservoirs, making AGN feedback plausible. Since special circumstances (in terms of environment, gas availability, and interactions) are not necessary for the presence of AGN, and the central gas masses are predicted to be an order of magnitude larger than that required to fuel the AGN, AGN triggering in dwarfs is likely to be stochastic and a common phenomenon. Together with the plausibility of energetic feedback, this suggests that AGN could be important drivers of dwarf galaxy evolution, as is the case in massive galaxies.

Davis, F. (ORCID:0000000285224980)↗

DOE OSTI2025

Coupling between collective modes in the deformed 98 Zr nucleus: Insights from consistent HFB + QRPA calculations with the Gogny interaction

The zirconium isotopes exhibit structural properties that present multiple challenges to nuclear theory. Investigations of the coupling present within isoscalar modes and within isovector modes are scarce but important for advancing our understanding of the microscopic picture of nuclei. To explore some of these underlying coupling features, and to test the predictive power of a state-of-the-art nuclear structure approach, we provide a detailed analysis of the properties of 90,96,98 Zr . This region includes a benchmarking case and offers insights into nuclear deformation phenomena. Here, to investigate the coupling between collective modes in deformed nuclei, we focused our analysis on the ground and excited-state properties of these isotopes, employing a consistent approach with the axially symmetric deformed Hartree-Fock-Bogoliubov (HFB) and the quasiparticle random phase approximation (QRPA) framework, both using the Gogny D1M force. This approach effectively describes both low-lying and giant-resonance states. We devoted special attention to the deformed 98 Zr nucleus, where we confirm the existence of coupling between monopole and quadrupole excitations through the 𝐾 𝜋 = 0 + QRPA components and demonstrate an analogous dipole-octupole coupling through the 𝐾 𝜋 = 0 − and 𝐾 𝜋 = 1 − components. Intrinsic transition densities and associated radial projections illustrate the coupling. Our work complements and extends earlier studies carried out using density-functional-based methods and notably, we included the complete Coulomb interaction also in the pairing fields, i.e., we treat terms exactly that are approximated in typical calculations that use the Gogny D1 and D2 interaction families.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

SNNVis: Visualizing Graph Embedding of Evolutionary Optimization for Spiking Neural Networks

While Spiking Neural Networks (SNNs) show a lot of promise, it is difficult to optimize them because applying traditional gradient-based optimization techniques is difficult. Even though evolutionary algorithms (EAs) have been shown to promise to optimize SNNs, understanding the relationship between evolving the characteristics of SNNs and their performance to improve the optimization algorithm is challenging because of the complex characteristics and huge population size. We propose visual analytics with novel graph embedding for evolutionary SNNs to address the challenges. While existing graph embedding techniques have limitations in preserving the specific features of the nodes and edges, our approach maintains them. Also, we develop visual analytics for understanding the relationship between the network performance and the features of nodes and edges and exploring and analyzing the evolving SNNs to build insights into improving the EA.

Chae, Junghoon [ORNL] (ORCID:0000000206016746)↗

DOE OSTI2024

Conditional Experts for Improved Building Damage Assessment Across Satellite Imagery View Angles

Rapid building damage assessment (BDA) is vital in guiding disaster response missions and estimating population distribution across impacted areas. While commercial satellite imagery providers have enabled near-daily monitoring of the Earth, near-realtime assessment of disaster scenarios frequently requires analysis of off-nadir imagery, as satellites are often far from impacted areas for at-nadir post-event imaging to occur Such scenarios are, however, underrepresented in existing BDA datasets and methodologies. With this motivation, we investigate generalization capabilities of current BDA practices across overhead view-angles and strategies for their improvement. Using a labeled dataset of images capturing conflict-related damages, we first train a baseline BDA architecture using imbalanced and balanced datasets with respect to view-angle. Then, we explore conditional convolutions parameterized on image features, image nadir, and their combination as a mechanism for conditioning on view-angles. Experiments demonstrate the limitations of current practice and the potential of conditional mechanisms to increase model robustness to view-angle variations.

Ambrozio Dias, Philipe↗