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Results for “Dimensionality”

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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Three dimensional radioisotope battery and methods of making the same

According to one embodiment, a product includes an array of three dimensional structures, where each of the three dimensional structure includes a semiconductor material; a cavity region between each of the three dimensional structures; and a first material in contact with at least one surface of each of the three dimensional structures, where the first material is configured to provide high energy particle and/or ray emissions.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Biomolecular patterning of three dimensional tissue scaffolds

The invention provides methods and compositions for making and using collagen-glycosaminoglycan three-dimensional scaffolds immobilized with biomolecules that are spatially and temporally patterned. The method comprises adding benzophenone to a collagen-glycosaminoglycan three dimensional scaffold in the dark; adding one or more biomolecules to one or more areas of the collagen-glycosaminoglycan three-dimensional scaffold (which can be done optionally in the dark or in the light); and exposing the collagen-2glycosaminoglycan three-dimensional scaffold to light at a wavelength of about 350 to about 365 nm.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Offset-aligned three-dimensional integrated circuit

A method for manufacturing a three-dimensional integrated circuit includes attaching a first side of a first die to a first carrier wafer. The method includes preparing a second side of the first die to generate a prepared second side of the first die. The method includes attaching the prepared second side of the first die to a second carrier wafer. The method includes removing the first carrier wafer from the first side of the first die to form a transitional three-dimensional integrated circuit. The method includes attaching a third carrier wafer to a first side of the transitional three-dimensional integrated circuit. The method includes attaching a first side of the second die to a second side of the transitional three-dimensional integrated circuit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Detailed characterization of a five-dimensional phase space distribution

We image the five-dimensional phase space distribution of a hadron beam in unprecedented detail. The resolution and dynamic range of the measurement are sufficient to resolve sharp, high-dimensional features in low-density regions of phase space. We develop several visualization techniques, including non-planar slicing, to facilitate the identification and analysis of such features. We use these techniques to examine the transverse dependence of longitudinal hollowing and the longitudinal dependence of transverse hollowing in the distribution. Our results strengthen the claim that low-dimensional projections do not adequately characterize high-dimensional phase space distributions.

Hoover, Austin↗
DOE OSTI2021

A Significant Increase in Detection of High-resolution Emission Spectra Using a Three-dimensional Atmospheric Model of a Hot Jupiter

High-resolution spectroscopy has opened the way for new, detailed study of exoplanet atmospheres. There is evidence that this technique can be sensitive to the complex, three-dimensional (3D) atmospheric structure of these planets. In this work, we perform cross-correlation analysis of high-resolution (R ∼ 100,000) CRIRES/VLT emission spectra of the hot Jupiter HD 209458b. We generate template emission spectra from a 3D atmospheric circulation model of the planet, accounting for temperature structure and atmospheric motions—winds and planetary rotation—missed by spectra calculated from one-dimensional models. In this first-of-its-kind analysis, we find that using template spectra generated from a 3D model produces a more significant detection (6.9σ) of the planet’s signal than any of the hundreds of one-dimensional models we tested (maximum of 5.1σ). We recover the planet’s thermal emission, its orbital motion, and the presence of CO in its atmosphere at high significance. Additionally, we analyzed the relative influences of 3D temperature and chemical structures in this improved detection, including the contributions from CO and H{sub 2}O, as well as the role of atmospheric Doppler signatures from winds and rotation. This work shows that the hot Jupiter’s 3D atmospheric structure has a first-order influence on its emission spectra at high resolution and motivates the use of multidimensional atmospheric models in high-resolution spectral analysis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Kernel Manifolds: Nonlinear‐Augmentation Dimensionality Reduction Using Reproducing Kernel Hilbert Spaces

This paper generalizes recent advances on quadratic manifold (QM) dimensionality reduction by developing kernel methods-based nonlinear-augmentation dimensionality reduction. QMs, and more generally feature map-based nonlinear corrections, augment linear dimensionality reduction with a nonlinear correction term in the reconstruction map to overcome approximation accuracy limitations of purely linear approaches. While feature map-based approaches typically learn a least squares optimal polynomial correction term, we generalize this approach by learning an optimal nonlinear correction from a user-defined reproducing kernel Hilbert space. Our approach allows one to impose arbitrary nonlinear structure on the correction term, including polynomial structure, and includes feature map and radial basis function-based corrections as special cases. Furthermore, our method has relatively low training cost and has monotonically decreasing error as the latent space dimension increases. In conclusion, we compare our approach to proper orthogonal decomposition and several recent QM approaches on data from several example problems.

kernel methods↗
DOE OSTI2023

Probabilistic partition of unity networks for high–dimensional regression problems

We explore the probabilistic partition of unity network (PPOU-Net) model in the context of high-dimensional regression problems and propose a general framework focusing on adaptive dimensionality reduction. With the proposed framework, the target function is approximated by a mixture of experts model on a low-dimensional manifold, where each cluster is associated with a fixed-degree polynomial. We present a training strategy that leverages the expectation maximization (EM) algorithm. During the training, we alternate between (i) applying gradient descent to update the DNN coefficients; and (ii) using closed-form formulae derived from the EM algorithm to update the mixture of experts model parameters. Under the probabilistic formulation, step (ii) admits the form of embarrassingly paralleliazable weighted least-squares solves. The PPOU-Nets consistently outperform the baseline fully-connected neural networks of comparable sizes in numerical experiments of various data dimensions. Here, we also explore the proposed model in applications of quantum computing, where the PPOU-Nets act as surrogate models for cost landscapes associated with variational quantum circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Geodesic analysis and black hole shadows on a general non-extremal rotating black hole in five-dimensional gauged supergravity

In this work, motivated by the fact that higher dimensional theories predict the existence of black holes which differ from their four dimensional counterpart, we analyse the geodesics and black hole shadow cast by a general non-extremal five dimensional black hole. The system under consideration corresponds to the Chong–Cvetič–Lü–Pope (Chong et al., 2005), which has the Myers–Perry black hole as a limit.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Non-dimensional performance and safety parameters for heat pipes

The use of heat pipes in safety-critical systems such as nuclear microreactors dictates the development of generalized, practical, scalable performance and safety parameters. Traditional dimensional metrics, while informative, lack the universality required for comparative analysis across varying designs and operating regimes. Here, this work introduces a comprehensive set of non-dimensional parameters to characterize heat pipe performance and safety, including capillary performance, effective thermal conductivity, response time, exergetic efficiency, allowable temperature gradients, allowable rate of temperature change, priming coefficients, and factor of safety. A reference heat pipe design representative of microreactor applications was analyzed via the developed parameters using both traditional analytical models and Sockeye simulations under transient and steady-state conditions. Sodium, potassium, and water were evaluated as working fluids to demonstrate the applicability of the framework across a broad temperature range. The proposed non-dimensional parameters effectively captured key thermal-hydraulic behaviors and safety concerns, as was demonstrated via Sockeye simulations. This framework supports the development of design optimization strategies, operational protocols, and safety assurance practices for advanced reactor systems and other high-reliability applications.

42 - ENGINEERING↗
DOE OSTI2020

Efficient bifurcation and tabulation of multi-dimensional combustion manifolds using deep mixture of experts: An a priori study

This work describes and validates an approach for autonomously bifurcating turbulent combustion man-ifolds to divide regression tasks amongst specialized artificial neural networks (ANNs). This approach relies on the mixture of experts (MoE) framework, where each neural network is trained to be specialized in a given portion of the input space. The assignment of different input regions to the experts is determined by a gating network, which is a neural network classifier. In some previous studies, it has been demonstrated that bifurcation of a complex combustion manifold and fitting different ANNs for each part leads to better fits or faster inference speeds. However, the manner of bifurcation in these studies was based on heuristic approaches or clustering techniques. In contrast, the proposed technique enables automatic bifurcation using non-linear planes in high-dimensional turbulent combustion manifolds that are often associated with complex behavior due to different dominating physics in various zones. Furthermore, the proposed concept is validated using 4-dimensional (4D) and 5D flamelet tables, showing that the errors obtained with a given network size, or conversely the network size required to achieve a given accuracy, is considerably reduced. The effect of the number of experts on inference speed is also investigated, showing that by increasing the number of experts from 1 to 8, the inference time can be approximately reduced by a factor of two. Moreover, it is shown that the MoE approach divides the input manifold in a physically intuitive manner, suggesting that the MoE framework can elucidate high-dimensional datasets in a physically meaningful way.

42 ENGINEERING↗
DOE OSTI2021

Local Lattice Deformation of Tellurene Grain Boundaries by Four-Dimensional Electron Microscopy

Two-dimensional (2D) tellurene is a promising competitor for the fabrication of ultrathin optoelectronic devices belonging to a new family of monoelemental 2D materials. Furthermore, the precise fabrication and characterization of tellurene and its lattice defects is of utmost importance to determine device reliability and predict functionality, yet it remains experimentally challenging. The rapid growth of the four-dimensional scanning transmission electron microscopy (4D-STEM) technique as well as postprocessing tools now allows for structural analysis with nanometer-scale resolution over a broad range of length scales. Here, we use 4D-STEM to reveal the three-dimensional (3D) atomic structure of the characteristic grain boundary in 2D tellurium formed through a new microwave-enabled chemical self-assembly. Strain and lattice parameter maps permit the reconstruction of the roughness of the grain boundary and suggest that its formation is promoted by a wedge of helical atomic chains along the [0001] crystallographic direction. The observation of wrinkles at the grain boundary is found to be the dominant relaxation mechanism. Insights into the formation mechanism are elucidated by mapping the lattice parameters as the first demonstration of local in-plane and out-of-plane unit cell variation in a nanometer-by-nanometer real-space array.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data mining and computational screening of Rashba-Dresselhaus splitting and optoelectronic properties in two-dimensional perovskite materials

Recent developments highlighting the promise of two-dimensional perovskites have vastly increased the compositional search space in the perovskite family. This presents a great opportunity for the realization of highly performant devices and practical challenges associated with the identification of candidate materials. High-fidelity computational screening offers great value in this regard. In this study, we carry out a multiscale computational workflow, generating a dataset of two-dimensional perovskites in the Dion-Jacobson and Ruddlesden-Popper phases. Our dataset comprises ten B-site cations, four halogens, and over 20 organic cations across over 2000 materials. We compute electronic properties, thermoelectric performance, and numerous geometric characteristics. Furthermore, we introduce a framework for the high-throughput computation of Rashba-Dresselhaus splitting. Finally, we use this dataset to train machine learning models for the accurate prediction of band gaps, candidate Rashba-Dresselhaus materials, and partial charges. The work presented herein can aid future investigations of two-dimensional perovskites with targeted applications in mind.

14 SOLAR ENERGY↗
DOE OSTI2020

Two-dimensional image edge enhancement using two orders of Bragg diffraction

The process of two-dimensional image edge enhancement with an acousto-optical spatial filter using two orders of light diffraction is studied. It is theoretically shown that the diffraction that occurs in a paratellurite crystal on a ‘slow’ acoustic wave propagating orthogonally to the optical axis of the crystal allows enhancing a two-dimensional edge of an image, transferred by optical radiation with a wavelength of 0.63 μm in a wide band of acoustic frequencies 25 – 50 MHz. Enhancement of a two-dimensional image edge at an acoustic frequency of 29 MHz is experimentally demonstrated. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Strain modulation using defects in two-dimensional MoS 2

We investigate the nature of strain in MoS 2 and correlate it to defect types and densities, while systematically assessing the tolerance of this low dimensional material to He and Au ion irradiations. Through a series of theoretical predictions and experimental observations, we establish the onset of the crystalline-to-amorphous transition in MoS 2 and identify sulfur vacancies as the most favorable defects introduced during irradiation. We note the presence of both tensile and compressive strains, which depend on the types of defects introduced into the lattice and vary with increasing fluence. In conclusion, the results show that defects can be used to tune strain in two-dimensional materials and provide an exciting pathway for using external stimuli to control properties of low dimensional materials.

2-dimensional systems↗
DOE OSTI2021

Physical properties of YbFe 5 P 3 with a quasi-one-dimensional crystal structure

We report basic physical properties of the quasi-one-dimensional heavy-fermion compound Yb Fe 5 P 3 . Its magnetic susceptibility follows a modified Curie-Weiss behavior at high temperatures with a Weiss temperature between - 5 and - 21 K depending on the direction of the applied magnetic field. Our single crystals of Yb Fe 5 P 3 are good metals with a room-temperature resistivity of ~ 140 μ Ω cm and a residual resistivity of less than 2 μ Ω cm. Below ~ 2 K the resistivity reflects the presence of strong quantum fluctuations, which is supported by specific heat measurements that show a similar strong enhancement in C / T at low temperatures reaching 1.5 J/mol K 2 below T = 0.5 K. No magnetic ordering is observed down to 80 mK. Magnetic fields up to 8 T rapidly suppress the magnetic fluctuations, while resistivity measurements under pressures up to 2.54 GPa indicate the enhancement of quantum fluctuations, although still no order is observed down to 0.3 K. Density functional theory calculations suggest the presence of electronically quasi-one-dimensional (1D) Fermi surface sheets together with three-dimensional electronic pockets. We suggest that the quasi-1D nature of this compound leads to an extended regime of large quantum fluctuations within the paramagnetic state prior to reaching a magnetic instability.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Manifold Learning-Based Polynomial Chaos Expansions for High-Dimensional Surrogate Models

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantities of interest of the computational or analytical model. For this purpose, we employ Grassmannian diffusion maps, a two-step nonlinear dimension reduction technique which allows us to reduce the dimensionality of the data and identify meaningful geometric descriptions in a parsimonious and inexpensive manner. Polynomial chaos expansion is then used to construct a mapping between the stochastic input parameters and the diffusion coordinates of the reduced space. An adaptive clustering technique is proposed to identify an optimal number of clusters of points in the latent space. The similarity of points allows us to construct a number of geometric harmonic emulators which are finally utilized as a set of inexpensive pretrained models to perform an inverse map of realizations of latent features to the ambient space and thus perform accurate out-of-sample predictions. Thus, the proposed method acts as an encoder-decoder system which is able to automatically handle very high-dimensional data while simultaneously operating successfully in the small-data regime. The method is demonstrated on two benchmark problems and on a system of advection-diffusion-reaction equations which model a first-order chemical reaction between two species. In all test cases, the proposed method is able to achieve highly accurate approximations which ultimately lead to the significant acceleration of UQ tasks.

42 ENGINEERING↗
DOE OSTI2024

Probing ligand conformation and net dimensionality in a series of tetraphenylethene-based metal–organic frameworks

Tetraphenylethene-based ligands with lowered symmetry are promising building blocks for the construction of novel luminescent metal–organic frameworks (MOFs). However, few examples have been reported, and predicting the ligand conformation and the dimensionality of the resulting MOF remains challenging. In order to uncover how synthetic conditions and accessible ligand conformations may affect the resulting MOF structure, four new MOF structures were synthesized under solvothermal conditions using the meta-coordinated tetraphenylethene-based ligand m-ETTC and paddlewheel SBUs composed of Co(II), Cu(II), and Zn(II). WSU-10 (WSU = Washington State University) is formed with either Zn or Cu comprising stacked psuedo-2D layers. The dimensionality of WSU-10 can be intentionally increased through the addition of pyrazine as a pillar ligand into the synthesis, forming the 3D structure WSU-11. The third structure, WSU-20, is formed by the combination of Zn or Co with m-ETTC and is intrinsically 3D without the use of a pillar ligand; interestingly, this is the result of a distortion in the paddlewheel SBU. Finally, Cu was also found to form a new structure (WSU-12), which displays an m-ETTC conformation unique from that found in the other isolated MOFs. Structural features are compared across the series and a mechanistic relationship between WSU-10 and -20 is proposed, providing insight into the factors that can encourage the generation of frameworks with increased dimensionality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Challenging the Curse of Dimensionality in Multidimensional Numerical Integration by Using a Low-Rank Tensor-Train Format

Numerical integration is a basic step in the implementation of more complex numerical algorithms suitable, for example, to solve ordinary and partial differential equations. The straightforward extension of a one-dimensional integration rule to a multidimensional grid by the tensor product of the spatial directions is deemed to be practically infeasible beyond a relatively small number of dimensions, e.g., three or four. In fact, the computational burden in terms of storage and floating point operations scales exponentially with the number of dimensions. This phenomenon is known as the curse of dimensionality and motivated the development of alternative methods such as the Monte Carlo method. The tensor product approach can be very effective for high-dimensional numerical integration if we can resort to an accurate low-rank tensor-train representation of the integrand function. In this work, we discuss this approach and present numerical evidence showing that it is very competitive with the Monte Carlo method in terms of accuracy and computational costs up to several hundredths of dimensions if the integrand function is regular enough and a sufficiently accurate low-rank approximation is available.

97 MATHEMATICS AND COMPUTING↗