Search NASA⌕ Search

SEARCH · Search NASA

Results for “geometric search”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Thermal Image Processing for Feature Extraction from Encapsulated Phase Change Materials

Encapsulated inorganic particles with high melting points (>300 °C) are desired as high-temperature Phase Change Materials (PCMs) for next-generation Latent Heat Thermal Energy Storage (LHTES) systems. One of the many challenges during the development of PCMs is to achieve a high throughput that in turn depends on accurately modeling the relation between process parameters and geometric & thermal properties of the PCMs particle. During the production of the PCMs, a high-speed infrared camera is used to acquire images of the encapsulated material under controlled illumination conditions. This research article focuses on the development of image processing techniques for both geometric and thermal feature extraction during the development of the PCMs. A user-friendly GUI has been designed in MATLAB and preliminary experimental results have demonstrated that the method is fast, accurate and reliable for a high throughput production. The extracted features will be used to develop Machine Learning (ML) models to predict the geometric and thermal properties of the PCM based on the process parameter settings. The ML model will accelerate the search for the optimized process settings to boost the throughput of the production.

25 ENERGY STORAGE↗
DOE OSTI2021

Piston Bowl Geometry Effects on Gasoline Compression Ignition in a Heavy-Duty Diesel Engine

A design optimization campaign was conducted to search for improved combustion profiles that enhance gasoline compression ignition in a heavy-duty diesel engine with a geometric compression ratio of 17.3. A large-scale design of experiments approach was used for the optimization, employing three-dimensional computational fluid dynamics simulations. The main parameters explored include geometric features, injector specifications, and swirl motion. Both stepped-lip and re-entrant bowls were included in order to assess their respective performance implications. A total of 256 design candidates were prepared using the software package CAESES for automated and simultaneous geometry generation and combustion recipe perturbation. The design optimization was conducted for three engine loads representing light to medium load conditions. The design candidates were evaluated for fuel efficiency, emissions, fuel-air mixing, and global combustion behavior. Simulation results showed that the optimum designs were all stepped-lip bowls, due to improvements in fuel-air mixing, as well as reduced heat loss and emissions formation. Improvements in indicated specific fuel consumption of up to 3.2% were achieved while meeting engine-out NOx emission targets of 1-1.5 g/kW center dot h. Re-entrant bowls performed worse compared to the baseline design, and significant performance variations occurred across the load points. Specifically, the re-entrant bowls were on par with the stepped-lip bowls under light load conditions, but significant deteriorations occurred under higher load conditions. As a final task, selected optimized designs were then evaluated under full-load conditions.

CFD↗
DOE OSTI2020

Transfer Factors for the FRMAC Assessment Manual and Turbo FRMAC to Improve Radiological Dose Assessment

The Turbo FRMAC analysis tool is used to perform complex calculations to quickly evaluate radiological consequences and aid in decision making during an emergency response by assessing impacts to the public, workers, and the food supply. Turbo FRMAC calculations are based on methods established by the Federal Radiological Monitoring and Assessment Center (FRMAC). To be able to assess impacts, input data called transfer factors that describe radionuclide uptake by local plants and animals are required. During the code application exercises in late 2016, identifying, finding, and validating nonstandard transfer factors proved to be time consuming and diverted the teams’ activity away from other critical tasks. As a result, a task was undertaken to dramatically expand the list of available transfer factors (food and non-food) and incorporate these factors into Turbo FRMAC. This will ultimately result in improved efficiency of the assessment team to perform calculations during times when the FRMAC is activated and provide more defensible, vetted data from which to calculate results. As a result of these and subsequent exercises, transfer factors were needed for the following items: bell peppers, Christmas tree, deer, flowers, fresh cucumbers, tomatoes, grapefruit, lichens, mushrooms, oranges, snap beans, squash, strawberries, sugarcane for sugar and seed, sweet corn, tea, tobacco, tree bark, and watermelon. To expand the applicability of the information tables, generic transfer data were also provided for common categories and recommendations were made for expanding the list of chemicals based on chemical similarity. The data presented in this report were compiled from recent literature with most of the data encompassing the period from 2000 to 2018. The following radionuclides were targeted during the literature search: elements associated with reactor accidents or nuclear detonations (Sr 89/90, Cs 134/137, Ce 141/144, Ru 103/106, I 129/131/133, Pu 238/239, Am 241, Zr 95, and Nb 95) and elements associated with industrial accidents or dirty bombs (Ir 192 and Co 60). Data from other elements were evaluated if they were identified during the literature search. In addition, reports were evaluated that were recommended by the research consultants. For each plant or animal transfer factor, the goal was to determine the geometric mean, the geometric standard deviation, the minimum, the maximum, and the number of measurements used. If only one measurement was available that was presented as the mean. Concentration ratios with large geometric standard deviations (GSDs) were generally the result of a paucity of measurements or a few disparate measurements. Measurement disparity was observed for data from different soil types. The geometric mean (GM) is a good reference value for planning and responses purposes, but the location-specific concentrations are unlikely to be similar to the model results. Recommendations for further work include: segregating the data to reflect the influence of soil type, developing approaches to incorporate animal data based on aggregated transfer measurements, and including data for foliar deposition on plants.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

The Event Detection System in the NEXT-White Detector

This article describes the event detection system of the NEXT-White detector, a 5 kg high pressure xenon TPC with electroluminescent amplification, located in the Laboratorio Subterráneo de Canfranc (LSC), Spain. The detector is based on a plane of photomultipliers (PMTs) for energy measurements and a silicon photomultiplier (SiPM) tracking plane for offline topological event filtering. The event detection system, based on the SRS-ATCA data acquisition system developed in the framework of the CERN RD51 collaboration, has been designed to detect multiple events based on online PMT signal energy measurements and a coincidence-detection algorithm. Implemented on FPGA, the system has been successfully running and evolving during NEXT-White operation. The event detection system brings some relevant and new functionalities in the field. A distributed double event processor has been implemented to detect simultaneously two different types of events thus allowing simultaneous calibration and physics runs. This special feature provides constant monitoring of the detector conditions, being especially relevant to the lifetime and geometrical map computations which are needed to correct high-energy physics events. Other features, like primary scintillation event rejection, or a double buffer associated with the type of event being searched, help reduce the unnecessary data throughput thus minimizing dead time and improving trigger efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Automated Adsorption Workflow for Semiconductor Surfaces and the Application to Zinc Telluride

Surface adsorption is a crucial step in numerous processes, including heterogeneous catalysis, where the adsorption of key species is often used as a descriptor of efficiency. We present here an automated adsorption workflow for semiconductors which employs density functional theory calculations to generate adsorption data in a high-throughput manner. Starting from a bulk structure, the workflow performs an exhaustive surface search, followed by an adsorption structure construction step, which generates a minimal energy landscape to determine the optimal adsorbate-surface distance. An extensive set of energy-based, charge-based, geometric, and electronic descriptors tailored toward catalysis research are computed and saved to a personal user database. Finally, the application of the workflow to zinc telluride, a promising CO 2 reduction photocatalyst, is presented as a case study to illustrate the capabilities of this method and its potential as a material discovery tool.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

An optimization-based approach to tailor the mechanical response of soft metamaterials undergoing rate-dependent instabilities

An optimization-based design framework is proposed to tune the response of soft metamaterials involving both geometric instabilities and nonlinear viscoelastic material behavior. Designing the response of soft metamaterials to harness instabilities and undergo large, tailored configuration changes will enable advancements in soft robotics, shock and vibration mitigation, and flexible electronics. In line with the metamaterial concept, the response of these materials is governed to a large extent by the geometric and topological makeup of their small-scale features. However, the link between structure and response is less intuitive for soft metamaterials due to their reliance upon highly nonlinear responses triggered by geometric instabilities. This is further complicated by the effects of viscoelastic relaxation, which recent studies have shown to alter the emergence of instabilities in non-intuitive ways. Here, these effects are accounted for in our framework to achieve various design objectives, including tailored force–displacement response and maximized energy absorption from both geometric and material effects. To fully automate this process, it is essential to have a completely robust equation solver for forward problems involving instabilities and viscoelastic relaxation. We achieve this by casting the search for stable mechanical equilibrium — i.e. the forward problem — as a minimization problem and utilize a trust region algorithm to robustly handle instabilities and follow energetically-favorable equilibrium paths through critical points.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Weakly supervised anomaly detection with event-level variables

We introduce a new topology for weakly supervised anomaly detection searches, diobject plus X. In this topology, one looks for a resonance decaying to two standard model particles produced in association with other anomalous event activity (X). This additional activity is used for classification. We demonstrate how anomaly detection techniques which have been developed for dijet searches focusing on jet substructure anomalies can be applied to event-level anomaly detection in this topology. To robustly capture event-level features of multiparticle kinematics, we employ new physically motivated variables derived from the geometric structure of a collision’s phase space manifold. As a proof of concept, we explore the application of this approach to several benchmark signals in the di-𝜏 and di-𝜇 plus X final states. We demonstrate that our anomaly detection approach can reach discovery-level significances for signals that would be missed in a conventional bump-hunt approach.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Athermal phonon collection efficiency in diamond crystals for low mass dark matter detection

Here, we explored the efficacy of lab-grown diamonds as potential target materials for the direct detection of sub-GeV dark matter (DM) using metallic magnetic calorimeters (MMCs). Diamond, with its excellent phononic properties and the low atomic mass of the constituent carbon, can play a crucial role in detecting low mass dark matter particles. The relatively long electron-hole pair lifetime inside the crystal may provide discrimination power between the DM-induced nuclear recoil events and the background-induced electron recoil events. Utilizing the fast response times of the MMCs and their unique geometric versatility, we deployed a novel methodology for quantifying phonon dynamics inside diamond crystals. We demonstrated that lab-grown diamond crystals fabricated via the chemical vapor deposition (CVD) technique can satisfy the stringent quality requirements for sub-GeV dark matter searches. The high-quality polycrystalline CVD diamond showed a superior athermal phonon collection efficiency compared to that of the reference sapphire crystal, and achieved energy resolution 62.7 eV at the 8.05 keV copper fluorescence line. With this energy resolution, we explored the low-energy range below 100 eV and confirmed the existence of so-called low-energy excess (LEE) reported by multiple cryogenic experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Physics Opportunities in the Far-forward Region at the Future Electron–Ion Collider

The Electron–Ion Collider provides the opportunity to drastically advance our understanding of QCD and the multidimensional structure of both protons and nuclei. An essential component of the EIC physics program is the identification and characterization of exclusive, diffractive, and tagged events using detectors integrated with the outgoing hadron beamline, the so-called “far-forward” detectors. The ePIC experiment includes a suite of far-forward detectors designed to deliver the necessary geometric coverage and resolution required to achieve the exclusive physics program envisioned at the EIC. Additionally, to the multidimensional imaging program at the EIC, topics such as spectator tagging in e + d and e + 3 He reactions to access structure functions and searches for gluon saturation in e + A collisions are also enabled by this experimental apparatus. In these proceedings, the ePIC far-forward detectors will be briefly introduced, and a few selected physics topics focused on tagged deep-inelastic scattering will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid–liquid mixtures

Exploring mesoscopic physical phenomena has always been a challenge for brute-force all-atom molecular dynamics simulations. Although recent advances in computing hardware have improved the accessible length scales, reaching mesoscopic timescales is still a significant bottleneck. Coarse-graining of all-atom models allows robust investigation of mesoscale physics with a reduced spatial and temporal resolution but preserves desired structural features of molecules, unlike continuum-based methods. Here, we present a hybrid bond-order coarse-grained forcefield (HyCG) for modeling mesoscale aggregation phenomena in liquid–liquid mixtures. The intuitive hybrid functional form of the potential offers interpretability to our model, unlike many machine learning based interatomic potentials. We parameterize the potential with the continuous action Monte Carlo Tree Search (cMCTS) algorithm, a reinforcement learning (RL) based global optimizing scheme, using training data from all-atom simulations. The resulting RL-HyCG correctly describes mesoscale critical fluctuations in binary liquid–liquid extraction systems. cMCTS, the RL algorithm, accurately captures the mean behavior of various geometrical properties of the molecule of interest, which were excluded from the training set. The developed potential model along with the RL-based training workflow could be applied to explore a variety of other mesoscale physical phenomena that are typically inaccessible to all-atom molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Reconstruction of cosmic-ray muon events with CUORE

We report the in-situ 3D reconstruction of through-going muons in the CUORE experiment, a cryogenic calorimeter array searching for neutrinoless double beta (0vββ) decay, leveraging the segmentation of the detector. Due to the slow time response of the detector, time-of-flight estimation is not feasible. Therefore, the track reconstruction is performed using a multi-objective optimization algorithm that relies on geometrical information from the detector as a whole. We measure the integral flux of cosmic-ray muons underground at the Laboratori Nazionali del Gran Sasso, and find our value to be in good agreement with other experiments that have performed a similar measurement. To our knowledge, this work represents the first demonstration of 3D particle tracking and reconstruction of through-going muons with per-event angular determination in a millikelvin cryogenic detector array. The analysis performed for this work will be critical for validating the muon-related background in CUPID, a next-generation 0vββ experiment, and for follow-up studies on detector response and on delayed products induced by cosmic-ray muons.

Adams, D. Q. [University of South Carolina]↗
DOE OSTI2024

Antiferroelectric Ceramics for Energy–Efficient Capacitors by Theory–Guided Discovery

Antiferroelectric ceramics, via the electric-field-induced antiferroelectric (AFE)–ferroelectric (FE) phase transitions, show great promise for high-energy-density capacitors. Yet, currently, only 70–80% energy release is found during a charge–discharge cycle. Here, for PbZrO 3 -based oxides, geometric nonlinear theory of martensitic phase transitions is applied (first used to guide supercompatible shape-memory alloys) to predict the reversibility of the AFE–FE transition by using density-functional theory to assess AFE/FE interfacial lattice-mismatch strain that assures ultralow electric hysteresis and extended fatigue lifetime. A good correlation of mismatch strain with electric hysteresis, hence, with energy efficiency of AFE capacitors is observed. Here, guided by theory, high-throughput material search is conducted and AFE compositions with a near-perfect charge–discharge energy efficiency (98.2%), i.e., near-zero hysteresis are discovered. And the fatigue life of the capacitor reaches 79.5 million charge–discharge cycles, a factor of 80 enhancement over AFE ceramics with large electric hysteresis.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Alchemy: A Model-Based Approach for 2D to 3D Autonomous Nuclear System Design

Engineering design of nuclear power plant (NPP) piping and equipment systems frequently bypasses crucial 2D system planning, instead moving straight to 3D modeling. This often leads to designs that exceed building envelope constraints, forcing expensive and time-consuming redesigns. When 2D modeling is employed, it typically involves labor-intensive manual workflows that convert 2D drawings into 3D models, resulting in inefficiencies and errors across design iterations. These workflows further suffer from poor software interoperability and dependence on proprietary software ecosystems, thereby contributing to schedule delays and cost overruns. This paper presents Alchemy, an autonomous framework that transforms 2D system definitions into Industry Foundation Classes (IFC)-compliant 3D building information models (BIMs) for expediting nuclear facility design at the conceptual preliminary phase. Using a model-based approach, the framework treats the 2D system diagram as the central reference model employed to automatically generate all subsequent outputs, ensuring consistency between the system definition and the resulting physical design. A web-based interface enables engineers to define hierarchical system topologies including associated equipment, geometric properties, and connectivity requirements. A two-phase equipment layout optimization algorithm automatically computes collision-free spatial configurations within predefined building envelopes. An artificial intelligence (AI)-assisted pipe routing module then generates orthogonal, collision-free routing paths, allowing the user to select either an A* search-based method or an Ant Colony Optimization (ACO)-based method. All outputs are authored natively in IFC format, relying on open-source technologies and standardized formats in order to ensure extensibility and eliminate proprietary software dependencies. The proposed framework is validated on two representative pressurized-water reactor (PWR)-based case studies, for which it autonomously generates IFC-compliant 3D models in minutes, drastically reducing workflows that typically require hours of manual effort. The generated model demonstrates topologically correct equipment placement, physically plausible spatial relationships, and collision-free pipe routing consistent with known PWR loop configurations. This work represents a foundational step toward digital engineering for nuclear facility preliminary design, with future ongoing development targeting design code compliance and expanded system complexity.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Field-controlled dynamics of skyrmions and monopoles

Magnetic monopoles, despite their ongoing experimental search as elementary particles, have inspired the discovery of analogous excitations in condensed matter systems. In chiral condensed matter systems, emergent monopoles are responsible for the onset of transitions between topologically distinct states and phases, such as in the case of transitions from helical and conical phase to A-phase comprising periodic arrays of skyrmions. By combining numerical modeling and optical characterizations, we describe how different geometrical configurations of skyrmions terminating at monopoles can be realized in liquid crystals and liquid crystal ferromagnets. We demonstrate how these complex structures can be effectively manipulated by external magnetic and electric fields. Furthermore, we discuss how our findings may hint at similar dynamics in other physical systems and their potential applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Exploring unsupervised top tagging using Bayesian inference

Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Beyond Standard Model physics as well. Although there exists outstanding top-tagger algorithms, their construction and their expected performance rely on Montecarlo simulations, which may induce potential biases. For these reasons we develop two simple unsupervised top-tagger algorithms based on performing Bayesian inference on a mixture model. In one of them we use as the observed variable a new geometrically-based observable \tilde{A}_{3} A ̃ 3 , and in the other we consider the more traditional \tau_{3}/\tau_{2} τ 3 / τ 2 N N -subjettiness ratio, which yields a better performance. As expected, we find that the unsupervised tagger performance is below existing supervised taggers, reaching expected Area Under Curve AUC \sim 0.80-0.81 ∼ 0.80 − 0.81 and accuracies of about 69% - − 75% in a full range of sample purity. However, these performances are more robust to possible biases in the Montecarlo that their supervised counterparts. Our findings are a step towards exploring and considering simpler and unbiased taggers.

Alvarez, Ezequiel↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Engineering of Ferroic Orders in Thin Films by Anionic Substitution

Multiferroics are a unique class of materials where magnetic and ferroelectric orders coexist. The research on multiferroics contributes significantly to the fundamental understanding of the strong correlations between different material degrees of freedom and provides an energy-efficient route toward the electrical control of magnetism. While multiple ABO 3 oxide perovskites are identified as being multiferroic, their magnetoelectric coupling strength is often weak, necessitating the material search in different compounds. Here, the observation of room-temperature multiferroic orders in multi-anion SrNbO 3-x N x thin films is reported. In these samples, the multi-anion state enables the room-temperature ferromagnetic ordering of the Nb d-electrons. Simultaneously, ferroelectric responses that originate from the structural symmetry breaking associated are found with both the off-center displacements of Nb and the geometric displacements of Sr, depending on the relative O-N arrangements within the Nb-centered octahedra. So findings not only diversify the available multiferroic material pool but also demonstrate a new multiferroism design strategy via multi-anion engineering.

thin-films↗
DOE OSTI2025

Additively-manufactured monocrystalline YBCO superconductor

Abstract Single-crystal microstructures enable high-performance YBa 2 Cu 3 O 7-x superconductors which are however limited to simple shapes due to their brittleness. Additive manufacturing can fabricate YBa 2 Cu 3 O 7-x superconductor with complex shapes, albeit with a polycrystalline microstructure. Here, we demonstrate a route to grow single-crystals from 3D-ink-printed, polycrystalline, sintered superconducting YBa 2 Cu 3 O 7-x (YBCO or Y123) + Y 2 BaCuO 5 (Y211), manufacturing objects with complex architectures displaying both high critical current density (J c =2.1 × 10 4 A . cm –2 , 77 K) and high critical temperature (T c = 88-89.5 K). An ink containing precursor powders (Y 2 O 3 , BaCO 3 , and CuO) is 3D-extruded into complex geometries and then reaction-sintered to obtain polycrystalline Y123 + Y211. A seed is then utilized to transform these 3D-printed parts from polycrystal to monocrystal via the melt growth method. The geometric details of 3D-printed parts survive the process without slumping, sagging or collapse, despite the long-term presence of liquid above the peritectic temperature. Origami structures can be created by sheet folding after 3D-printing. This additive approach enables the facile fabrication of superconducting devices with complex shapes and architectures, such as advanced undulator magnets to generate synchrotron radiation and microwave cavities for dark-matter axion search. This work highlights the potential of additive manufacturing for producing monocrystalline cuprate superconductors and opens the door to additive manufacturing of other monocrystalline functional ceramic or semiconductor materials.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗