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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

SynopFrame: Multiscale time-dependent visual abstraction framework for analyzing DNA nanotechnology simulations

We present an open-source framework, SynopFrame, that allows DNA nanotechnology (DNA-nano) experts to analyze and understand molecular dynamics simulation trajectories of their designs. We use a multiscale multi-dimensional abstraction space, connect the representations to a projected conformational space plot of the structure’s temporal sequence, and thus enable experts to analyze the dynamics of their structural designs and, specifically, failure cases of the assembly. In addition, our time-dependent abstraction representation allows the biologists, for the first time in a smooth and structurally clear way, to identify and observe temporal transitions of a DNA-nano design from one configuration to another, and to highlight important periods of the simulation for further analysis. We realize SynopFrame as a dashboard of the different synchronized 3D spatial and 2D schematic visual representations, with a color overlay to show essential properties such as the status of hydrogen bonds. The linking of the spatial, schematic, and abstract views ensures that users can effectively analyze the high-frequency motion. We also categorize the status of the hydrogen bonds into a new format to allow us to color-encode it and overlay it on the representations. To demonstrate the utility of SynopFrame, we describe example usage scenarios and report user feedback.

Abstraction space↗
DOE OSTI2022

Fiber Optic Distributed Acoustic Sensing in DFPS East Well

Fiber optic files in *.hdf format, acquired in the Devine Fracture Pilot Site East well, using Fotech interrogator. There was no change in channel spacing or acquisition settings during the project. Channel spacing is 0.68 m.

Electrically active proppant,Electromagnetic,Fiber↗
DOE OSTI2024

Deep kernel methods learn better: from cards to process optimization

Abstract The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards dataset and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The Jupyter Notebooks that encapsulate the complete analysis accompany the article.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Perceptions of Value in Process-based Engineering in LANL's Intelligence and Space Research Division [Capstone Project]

This project studied a perceived aversion to process-based engineering in ISR Division. Thirty interviews were conducted with ISR staff to explore research questions including perceived advantages and disadvantages of process and ISR strengths and weaknesses. Results were analyzed for synergies between the research questions. Results indicate an unexpected willingness to adopt process so long as that process does not stifle creativity and is flexible enough that it can be applied if and when it provides the most value.

42 ENGINEERING↗
DOE OSTI2023

Semiclassical theory and the Koopman-van Hove equation *

Abstract The phase space Koopman-van Hove (KvH) equation can be derived from the asymptotic semiclassical analysis of partial differential equations. Semiclassical theory yields the Hamilton–Jacobi equation for the complex phase factor and the transport equation for the amplitude. These two equations can be combined to form a nonlinear semiclassical version of the KvH equation in configuration space. There is a natural injection of configuration space solutions into phase space and a natural projection of phase space solutions onto configuration space. Hence, every solution of the configuration space KvH equation satisfies both the semiclassical phase space KvH equation and the Hamilton–Jacobi constraint. For configuration space solutions, this constraint resolves the paradox that there are two different conserved densities in phase space. For integrable systems, the KvH spectrum is the Cartesian product of a classical and a semiclassical spectrum. If the classical spectrum is eliminated, then, with the correct choice of Jeffreys–Wentzel–Kramers–Brillouin (JWKB) matching conditions, the semiclassical spectrum satisfies the Einstein–Brillouin–Keller quantization conditions which include the correction due to the Maslov index. However, semiclassical analysis uses different choices for boundary conditions, continuity requirements, and the domain of definition. For example, use of the complex JWKB method allows for the treatment of tunneling through the complexification of phase space. Finally, although KvH wavefunctions include the possibility of interference effects, interference is not observable when all observables are approximated as local operators on phase space. Observing interference effects requires consideration of nonlocal operations, e.g. through higher orders in the asymptotic theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations

Physics-informed neural networks have emerged as an alternative method for solving partial differential equations. However, for complex problems, the training of such networks can still require high-fidelity data which can be expensive to generate. To reduce or even eliminate the dependency on high-fidelity data, we propose a novel multi-fidelity architecture which is based on a feature space shared by the low- and high-fidelity solutions. In the feature space, the projections of the low-fidelity and high-fidelity solutions are adjacent by constraining their relative distance. The feature space is represented with an encoder and its mapping to the original solution space is effected through a decoder. In conclusion, the proposed multi-fidelity approach is validated on forward and inverse problems for steady and unsteady problems described by partial differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Gradual Decline of Star Formation since Cluster Infall: New Kinematic Insights into Environmental Quenching at 0.3 < z < 1.1

The environments where galaxies reside crucially shape their star formation histories. We investigate a large sample of 1626 cluster galaxies located within 105 galaxy clusters spanning a large range in redshift (0.26 < z < 1.13). The galaxy clusters are massive (M 500 ≳ 2 × 10 14 M ⊙ ) and uniformly selected from the SPT and ACT Sunyaev–Zel'dovich surveys. With spectra in hand for thousands of cluster members, we use the galaxies' position in projected phase space as a proxy for their infall times, which provides a more robust measurement of environment than quantities such as projected clustercentric radius. We find clear evidence for a gradual age increase of the galaxy's mean stellar populations (~0.71 ± 0.4 Gyr based on a 4000 Å break, D n 4000) with the time spent in the cluster environment. This environmental quenching effect is found regardless of galaxy luminosity (faint or bright) and redshift (low or high-z), although the exact stellar age of galaxies depends on both parameters at fixed environmental effects. Such a systematic increase of D n 4000 with infall proxy would suggest that galaxies that were accreted into hosts earlier were quenched earlier due to longer exposure to environmental effects such as ram pressure stripping and starvation. Compared to the typical dynamical timescales of 1–3 Gyr of cluster galaxies, the relatively small age increase (~0.71 ± 0.4 Gyr) found in our sample galaxies seems to suggest that a slow environmental process such as starvation is the dominant quenching pathway. Our results provide new insights into environmental quenching effects spanning a large range in cosmic time (~5.2 Gyr, z = 0.26–1.13) and demonstrate the power of using a kinematically derived infall time proxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Dimensional Reduction for Sampled Priors and Application to Photometric Redshift Distributions

A typical Bayesian inference on the values of some parameters of interest q from some data D involves running a Markov Chain (MC) to sample from the posterior $p$($q$,$n$|$D$) $\propto$ $\mathcal{L}$($D$|$q$,$n$)$p$(q)$p$($n$), where n are some nuisance parameters with a separable prior. In some cases, the nuisance parameters are high-dimensional, and their prior p(n) is itself defined only by a set of samples that have been drawn from some other MC. The MC for the posterior will typically require evaluation of p(n) at arbitrary values of n, i.e., one needs to provide a density estimator over the full n space from the provided samples. But the high dimensionality of n hinders both the density estimation and the efficiency of the MC for the posterior. We describe a solution to this problem: a linear compression of the n space into a much lower-dimensional space u, which projects away directions in n space that cannot appreciably alter $\mathcal{L}$. The algorithm for doing so is a slight modification to principal components analysis, and is less restrictive on p(n) than other proposed solutions to this issue. We demonstrate this “mode projection” technique using the analysis of 2-point correlation functions of weak lensing fields and galaxy density in the Dark Energy Survey, where n is a binned representation of the redshift distribution n(z) of the galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Exploring the stellar populations of backsplash galaxies

Backsplash galaxies are those that traverse and overshoot cluster cores as they fall into these structures. They are affected by environment, and should stand out in contrast to the infalling population. We target galaxies in the vicinity of clusters (R≳R 200 ) and select a sample in projected phase space (PPS), from the compilation of Sampaio et al. based on SDSS data. We present a statistical analysis, comparing two regions in PPS, with the same projected distance to the cluster but different velocity. The analysis relies on the presence of variations in the stellar population content of backsplash galaxies. We find a lower limit in the fractional contribution of ∼5 per cent with respect to the general sample of infalling galaxies at similar group-centric distance when using single line strength analysis, or ∼15-30 per cent when adopting bivariate distributions. The stellar populations show a subtle but significant difference towards older ages, and a higher fraction of quiescent galaxies. We also compare this set with a general field sample, where a substantially larger difference in galaxy properties is found, with the field sample being consistently younger, metal poorer and with a lower fraction of quiescent galaxies. Noting that our ‘cluster’ sample is located outside of the virial radius, we expect this difference to be caused by pre-processing of the infalling galaxies in the overall higher density regions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A Subspace-Inclusive Sampling Method for the Computational Design of Compositionally Graded Alloys

Abstract Compositionally graded alloys, a subclass of functionally graded materials (FGMs), utilize localized variations in composition with a single metal part to achieve higher performance than traditional single material parts. In previous work [Kirk, T., Galvan, E., Malak, R., and Arroyave, R., 2018, “Computational Design of Gradient Paths in Additively Manufactured Functionally Graded Materials,” J. Mech. Des., 140, p. 111410. 10.1115/1.4040816], the authors presented a computational design methodology that avoids common issues which limit a gradient alloy’s feasibility, such as deleterious phases, and optimizes for performance objectives. However, the previous methodology only samples the interior of a composition space, meaning designed gradients must include all elements in the space throughout the gradient. Because even small amounts of additional alloying elements can introduce new deleterious phases, this characteristic often neglects potentially simpler solutions to otherwise unsolvable problems and, consequently, discourages the addition of new elements to the state space. The present work improves upon the previous methodology by introducing a sampling method that includes subspaces with fewer elements in the design search. The new method samples within an artificially expanded form of the state space and projects samples outside the true region to the nearest true subspace. This method is evaluated first by observing the sample distribution in each subspace of a 3D, 4D, and 5D state space. Next, a parametric study in a synthetic 3D problem compares the performance of the new sampling scheme to the previous methodology. Lastly, the updated methodology is applied to design a gradient from stainless steel to equiatomic NiTi that has practical uses such as embedded shape memory actuation and for which the previous methodology fails to find a feasible path.

Engineering↗
DOE OSTI2020

Windowed least-squares model reduction for dynamical systems

Here we propose a windowed least-squares (WLS) approach for model reduction of dynamical systems. The proposed approach sequentially minimizes the time-continuous full-order-model residual within a low-dimensional space–time trial subspace over time windows. The approach comprises a generalization of existing model reduction approaches, as particular instances of the methodology recover Galerkin, least-squares Petrov–Galerkin (LSPG), and space–time LSPG projection. In addition, the approach addresses key deficiencies in existing model reduction techniques, e.g., the dependence of LSPG and space–time LSPG projection on the time discretization and the exponential growth in time exhibited by a posteriori error bounds for both Galerkin and LSPG projection. We consider two types of space–time trial subspaces within the proposed approach: one that reduces only the spatial dimension of the full-order model, and one that reduces both the spatial and temporal dimensions of the full-order model. For each type of trial subspace, we consider two different solution techniques: direct (i.e., discretize then optimize) and indirect (i.e., optimize then discretize). Numerical experiments conducted using trial subspaces characterized by spatial dimension reduction demonstrate that the WLS approach can yield more accurate solutions with lower space–time residuals than Galerkin and LSPG projection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

An Approach to High-Performance Affordable Housing Using Point-Source Space Conditioning

This project explored the use of minisplit ductless heat pumps in combination with small through-wall transfer fans to provide space conditioning in small single-story homes, including manufactured homes and homes built by Habitat for Humanity. The objectives were to reduce space-conditioning energy consumption by 50% compared to the 2009 International Energy Conservation Code (ICC 2009) while maintaining home affordability and thermal comfort. The strategy to achieve this goal also included a highly efficient thermal envelope to reduce space-conditioning loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

An Approach to High-Performance Affordable Housing Using Point-Source Space Conditioning

This project explored the use of minisplit ductless heat pumps in combination with small through-wall transfer fans to provide space conditioning in small single-story homes including manufactured homes and homes built by Habitat for Humanity. The objectives were to reduce space conditioning energy consumption by 50% compared to the 2009 International Energy Conservation Code while maintaining home affordability and thermal comfort. The strategy to achieve this goal also included a highly efficient thermal envelope to reduce space conditioning loads.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Space Charge Effects in Low Energy Magnetized Electron Beams

Magnetized electron cooling of the ion beam is one of the major approaches towards obtaining the required high luminosity in the proposed Electron-Ion Collider (EIC). To increase the cooling efficiency, a bunched electron beam with a high bunch charge and high repetition rate is required. However, these features can combine to enhance the collective interactions, such as the space charge effect. A magnetized electron beam was successfully generated at the Thomas Jefferson National Accelerator Facility (JLab) using a compact, 300 kV DC high voltage photo-gun with an inverted insulator geometry and bi-alkali antimonide photocathode. The beam magnetization was characterized using a modest diagnostic beamline to measure beam sizes, rotation angles, and normalized transverse emittance as a function of magnetic field at the photocathode, laser spot size, and gun high-voltage. Simultaneously, simulations were performed using the A Space Charge Tracking Algorithm (ASTRA) and General Particle Tracer (GPT) programs. Further, sustained high average current magnetized beam up to 28 mA was demonstrated, and the photocathode lifetime for different magnetized electron beam currents was investigated. In the second part of the project, the space charge effect in low-energy magnetized electron beams was studied and compared with GPT simulations. The high bunch charge studies showed evidence of space charge current limitations starting at 0.3 nC and limited the maximum delivered bunch charge to 0.7 nC. To reach the high bunch charge goal of a few nC, the existing DC photo-gun design was modified using CST Studio Suite’s electromagnetic field solver, and a way to cancel the beam deflection exerted by the non-symmetric nature of the inverted insulator photo-gun geometry was discovered. This thesis presents a detailed description of the generation, characterization, and simulation of high current, high bunch charge magnetized electron beams at JLab, as well as the modified photo-gun design. Together with measurements and simulations, the purpose of this thesis is to provide a solid background on the theoretical aspects and the experimental challenges associated with magnetized electron beams for accelerator applications.

Wijethunga, W M Sajini↗
DOE OSTI2025

Symmetry-projected spin-AGP methods applied to spin systems

Symmetry-projected wave function methods capture static correlation by breaking and restoring the symmetries of a system. In this article, we present the symmetry-projected spin antisymmetrized geminal power (spin-AGP) state projected onto space group symmetry as well as complex conjugation, spin-flip, and time-reversal symmetries. The method is benchmarked on the 1D XXZ model and the 2D J 1 − J 2 model with square and triangular lattices. Our results indicate that symmetry projection methods provide a powerful tool for frustrated spin systems.

Antisymmetrized geminal power↗
DOE OSTI2024

Reduced-dimension Bayesian optimization for model calibration of transient vapor compression cycles

Development and calibration of first-principles dynamic models of vapor compression cycles (VCCs) is of critical importance for applications that include control design and fault detection and diagnostics. Nevertheless, the inherent complexity of models that are represented by large systems of differential–algebraic equations leads to significant challenges for model calibration processes that utilize classical gradient-based methods. Bayesian optimization (BO) is a sample-efficient and gradient-free approach using a probabilistic surrogate model and optimal search over a feasible parameter space. Despite the benefits of BO in reducing computational costs, challenges remain in dealing with a high-dimensional calibration task resulting from a large set of parameters that have significant impacts on system behavior and need to be calibrated simultaneously. This paper presents a reduced-dimension BO framework for calibrating transient VCCs models where the calibration space is projected to a low-dimensional subspace for accelerating convergence of the solution algorithm and consequently reducing the number of transient simulations. The proposed approach was demonstrated via two case studies associated with different VCC applications where 10 parameters were calibrated in each case using laboratory measurements. The reduced-dimension BO framework only required 1 / 8 th of the iterations associated with a standard BO method that deals with high-dimensional calibration parameters for converged solutions and yielded comparable accuracy. Furthermore, both calibrated models revealed significant accuracy improvements compared to uncalibrated models.

Ma, Jiacheng↗
DOE OSTI2022

Unsupervised learning from three-component accelerometer data to monitor the spatiotemporal evolution of meso-scale hydraulic fractures

Enhanced geothermal systems can provide a substantial share of the global energy demand. There exist several hurdles in the engineering implementations of such geothermal systems. One such hurdle is the accurate monitoring of the fracture networks created in subsurface through hydraulic stimulation of these systems. Micro seismicity associated with the stimulation is the primary means to locate the event hypocenters for estimating the stimulated rock volume. Existing methods for location the hypocenters are restricted to only the highest amplitude impulsive signals that are simultaneously detected on several sensors. Consequently, a large portion (usually ~99%) of the measurements are left unused. In this paper, an unsupervised manifold-approximation followed by clustering of 3-component accelerometer data is used to analyze the seismicity recorded on a monitoring well. With this method, a larger portion of the measured signal is used for the monitoring of the hydraulic fracture network. We analyze the EGS Collab experiment 1 microseismic data, recorded at the Sanford Underground Research Facility, South Dakota. Using the data from a single three-component accelerometer, the polarization features viz. Azimuth, incidence, rectilinearity, and planarity are used as inputs for the unsupervised manifold approximation followed by clustering. Our study shows that density-based clusters in the projected 3D space correspond to distinct types of hydraulically fractured zones around the injection point. Finally, we show that the temporal evolution of these clusters can be used to track fracture creation and propagation.

58 GEOSCIENCES↗
DOE OSTI2025

Optimal 3D chemical imaging with multimodal electron tomography

Accurate mapping of nanoscale chemistry in three dimensions (3D) has been a longstanding challenge. Modern electron microscopy provides chemical images by electron energy loss spectroscopy (EELS) and energy dispersive x-ray spectrometry (EDX) but requires high fluences that damage specimens. In 3D, the requirements are worse; electron tomography demands many high-fluence chemical maps for reconstruction, creating a tradeoff between resolution, accuracy, and sample survival. Fused multimodal electron tomography (MM-ET) alleviates this requirement by leveraging lower-fluence high-angle annular dark-field (HAADF) images alongside a few chemical maps to dramatically improve chemical resolution. Here, experimental and computational parameter space is systematically explored to determine when MM-ET performs best. Ideal imaging conditions balance sample survival with resolution and chemical specificity; we recommend a tilt range of at least ± 70°, acquiring 40 equally spaced HAADF projections (signal-to-noise > 10), and 7 EELS/EDX maps of each chemistry (signal-to-noise > 4).

36 MATERIALS SCIENCE↗