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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Spin Effects in Low Dimensional Correlated Systems (Final Technical Report)

Our program focuses on the behavior of two-dimensional superconducting systems that have been placed in high magnetic fields and/or in contact with magnetic materials. Our specific research agenda is organized into two separate but related areas. The first is a study of disorder and correlation effects in extremely thin superconducting aluminum, beryllium, and rhenium films that have been subjected to very high magnetic fields. We are particularly interested in understanding how the coupling between the applied field and the electron spins affects the system’s ability to form superconducting state. We now have good evidence that as the electron spins in these superconductors attempt to align to the magnetic field, a new superconducting phase emerges near the critical field transition and that this phase has a significant influence on the character of the transition. Thus, the spin behavior near the transition is much more complex than expected. The second class of systems is proximity structures comprised of superconducting/ferromagnetic (SC/FM) and superconducting/heavy metal (SC/HM) bilayers. We have developed spin-resolved tunneling probes that give us a direct measure of the proximity-induced exchange field in the SC/FM structures, as well as the spin-orbit scattering rate in SC/HM structures. Recently we have demonstrated that the magnitude of the exchange field can be modulated with an external gate, thereby producing a magneto-electric response in the bilayers. We plan to optimize this magneto-electric effect by getting a better understanding of the microscopic mechanism of the interface-induce exchange field, as well as improving the gate barriers and/or the bilayer interface quality. The ultimate goal is to develop a device such as a voltage-controlled superconducting switch or a spin- polarized electron source with a voltage-tunable Zeeman splitting.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Uncertainty Quantification and Sensitivity Analysis of Low-Dimensional Manifold via Co-Kurtosis PCA in Combustion Modeling

For multi-scale multi-physics applications e.g., the turbulent combustion code Pele, robust and accurate dimensionality reduction is crucial to solving problems at exascale and beyond. A recently developed technique, Co-Kurtosis based Principal Component Analysis (CoK-PCA) which leverages principal vectors of co-kurtosis, is a promising alternative to traditional PCA for complex chemical systems. To improve the effectiveness of this approach, we employ Artificial Neural Networks for reconstructing thermo-chemical scalars, species production rates, and overall heat release rates corresponding to the full state space. Our focus is on bolstering confidence in this deep learning based non-linear reconstruction through Uncertainty Quantification (UQ) and Sensitivity Analysis (SA). UQ involves quantifying uncertainties in inputs and outputs, while SA identifies influential inputs. One of the noteworthy challenges is the computational expense inherent in both endeavors. To address this, we employ the Monte Carlo methods to effectively quantify and propagate uncertainties in our reduced spaces while managing computational demands. Our research carries profound implications not only for the realm of combustion modeling but also for a broader audience in UQ. By showcasing the reliability and robustness of CoK-PCA in dimensionality reduction and deep learning predictions, we empower researchers and decision-makers to navigate complex combustion systems with greater confidence.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Low‐dimensional manifold learning for uncertainty quantification in complex multi‐scale stochastic systems

Broadly speaking, the goals of the project are to develop techniques to use manifold learning to develop reduced‐order and surrogate models for "hyper‐reduction" of very high‐dimensional complex multi‐scale systems. This is being achieved by employing a newly proposed form of manifold projection and learning that leverages recent advancements in computational geometry and data‐driven modeling. In particular, we are applying a manifold projection technique to project the solutions of very high‐dimensional systems onto the so‐called Grassmannmanifold, a Reimannian manifold comprised of orthonormal matrices. We then apply data‐driven machine learning techniques to classify the solutions on the manifold (e.g. clustering techniques) according to their proximity on the manifold and leverage a further nonlinear dimension reduction to organize the structured data on the manifold. Finally, we are developing novel techniques that enable us to directly interpolate the hyper‐reduced data such that we can predict the solution of the complex, high‐ dimensional system without need to call the full expensive computational model. Given their adherence to the underlying structure of the solution of the physical system, it is expected that these approximate solutions will be sufficiently constrained so as to (approximately) adhere to physical principles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Deciphering and Manipulating Low Dimensional Magnetism

This program investigates the electronic structure and collective quantum phenomena of correlated magnetic materials using advanced angle-resolved photoemission spectroscopy (ARPES) and complementary probes, with a focus on three interrelated material families: (i) the semiconducting van der Waals magnet Cr₂Ge₂Te₆, (ii) metallic Fe-based van der Waals magnets including Fe₃GeTe₂ Fe₃GaTe₂, and Fe5GeTe₂, and (iii) kagome magnets such as FeGe, FeSn, and CsV₃Sb₅/CsCr₃Sb₅. In Cr₂Ge₂Te₆, our work established how spin excitations develop across a dimensional crossover as spins establish correlation to form long range order, providing a clean platform to isolate correlation effects. In metallic Fe-based magnets, we uncovered the dichotomy between flat and dispersive bands, revealed momentum-dependent electronic reconstructions tied to magnetic order, and demonstrated reversible, non-volatile electronic switching near room temperature, highlighting the strong coupling among spin, charge, and lattice degrees of freedom in metallic ferromagnets. In kagome magnets, we identified charge density wave formation, symmetry breaking, flat-band renormalization, and field-induced momentum-dependent electronic anisotropy, elucidating how geometric frustration and electronic correlations conspire to generate emergent quantum states. Collectively, these results establish a unified microscopic framework for understanding correlation-driven electronic reconstruction, symmetry breaking, and collective order across semiconducting and metallic magnetic systems, advancing DOE mission goals in quantum materials discovery and control.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1980

An experimental investigation of three dimensional low speed minimum interference wind tunnel for high lift wings

As a means to achieve a minimum interference correction wind tunnel, a partially actively controlled test section was experimentally examined. A jet flapped wing with 0.91 m (36 in) span and R = 4.05 was used as a model to create moderately high lift coefficients. The partially controlled test section was simulated using an insert, a rectangular box 0.96 x 1.44 m (3.14 x 4.71 ft) open on both ends in the direction of the tunnel air flow, placed in the University of Washington Aeronautical Laboratories (UWAL) 2.44 x 3.66 m (8 x 12 ft) wind tunnel. A tail located three chords behind the wing was used to measure the downwash at the tail region. The experimental data indicates that, within the range of momentum coefficient examined, it appears to be unnecessary to actively control all four sides of the test section walls in order to achieve the near interference free flow field environment in a small wind tunnel. The remaining wall interference can be satisfactorily corrected by the vortex lattice method.

Shindo, S.↗
NASA NTRS1989

Low dimensional models of the wall region of a turbulent boundary layer, and the possibility of control

A method of identification of coherent structures in a random turbulent flow is outlined with emphasis on the structure best correlated with a random energy-integrable field and application of the proper orthogonal decomposition to the shear flow of the wall region. The wall region of a pipe flow with almost pure glycerine is investigated experimentally, dynamic equations are obtained, and an energy-transfer model is employed. Numerical solutions are obtained, implications for the flow in the wall region are discussed with focus placed on the sets of solutions exhibiting intermittency. The most significant finding of the study is considered to be the suggestion of the etiology of the bursting phenomenon; presuming that abrupt transitions from one fixed point to the other can be identified with a burst, these bursts appear to be produced autonomously by the wall region, but be triggered by pressure signals from the outer layer.

Lumley, John L.↗
NASA NTRS1994

Low-dimensional description of the dynamics in separated flow past thick airfoils

Results are presented for the numerical simulation of unsteady viscous incompressible flow past thick airfoils. Specifically, flow past a NACA 4424 at an angle of attack of 2.5 deg and Reynolds numbers in the range of 1700-4000 has been simulated using the spectral element method. At these conditions the flow is separatedd and an unsteady wake is formed. Application of the method of empirical eigenfunction reveals the structure of the most energetic components of the flow. These are found to occur in pairs that, through phase exchange, are responsible for the vortex shedding. A set of ordinary differential equations is obtained for the amplitudes of these eigenfunctions by a Galerkin projection of the Navier-Stokes equations. The solutions of the model system are compared with the full simulation. The work is of relevance to the transition process and observed routes to chaos in airfoil wakes.

Deane, Anil E.↗
NASA NTRS1942

Investigation of a Low-Drag Gun Port in the NACA Two-Dimensional Low-Turbulence Tunnel

Tests were made in the NACA two-dimensional low-turbulence tunnel of three gun ports with a height of approximately 4 percent of the chord faired into an NACA 66,2-213 low-drag-airfoil section by bulging the section at the gun port. Gun ports faired in this manner had practically no effect on the maximum lift and the critical compressibility speed of the section and showed only small increase in the drag in the range of lift coefficients for high-speed and cruising-flight conditions.

Horton, Elmer A.↗
DOE OSTI2022

Grassmannian Diffusion Maps--Based Dimension Reduction and Classification for High-Dimensional Data

This work introduces the Grassmannian diffusion maps (GDMaps), a novel nonlinear dimensionality reduction technique that defines the affinity between points through their representation as low-dimensional subspaces corresponding to points on the Grassmann manifold. Here, the method is designed for applications, such as image recognition and data-based classification of constrained high-dimensional data where each data point itself is a high-dimensional object (i.e., a large matrix) that can be compactly represented in a lower-dimensional subspace. The GDMaps is composed of two stages. The first is a pointwise linear dimensionality reduction wherein each high-dimensional object is mapped onto the Grassmann manifold representing the low-dimensional subspace on which it resides. The second stage is a multipoint nonlinear kernel-based dimension reduction using diffusion maps to identify the subspace structure of the points on the Grassmann manifold. To this end, an appropriate Grassmannian kernel is used to construct the transition matrix of a random walk on a graph connecting points on the Grassmann manifold. Spectral analysis of the transition matrix yields low-dimensional Grassmannian diffusion coordinates embedding the data into a low-dimensional reproducing kernel Hilbert space. Further, a novel data classification/recognition technique is developed based on the construction of an overcomplete dictionary of reduced dimension whose atoms are given by the Grassmannian diffusion coordinates. Three examples are considered. First, a "toy" example shows that the GDMaps can identify an appropriate parametrization of structured points on the unit sphere. The second example demonstrates the ability of the GDMaps to revealing the intrinsic subspace structure of high-dimensional random field data. In the last ex- ample, a face recognition problem is solved considering face images subject to varying illumination conditions, changes in face expressions, and occurrence of occlusions. The technique presented high recognition rates (i.e., 95% in the best case) using a fraction of the data required by conventional methods.

42 ENGINEERING↗
DOE OSTI2021

Widespread Negative Longitudinal Piezoelectric Responses in Ferroelectric Crystals with Layered Structures

In this study, we investigate the underlying mechanisms of the universal negative piezoelectricity in low-dimensional layered materials by carrying out first-principles calculations. Two-dimensional layered ferroelectric CuInP 2 S 6 is analyzed in detail as a typical example, but the theory can be applied to any other low-dimensional layered piezoelectrics. Consistent with the theory proposed in [Phys. Rev. Lett. 119, 207601 (2017), the anomalous negative piezoelectricity in CuInP 2 S 6 also results from its negative clamped-ion term, which cannot be compensated by the positive internal-strain part. Here, we focus on a more general rule by proposing that having a negative clamped-ion term should be universal among piezoelectric materials, which is attributed to the “lag of Wannier center” effect. The internal-strain term, which is the change in polarization due to structural relaxation in response to strain, is mostly determined by the spatial structure and chemical bonding of the material. In a low-dimensional layered piezoelectric material such as CuInP 2 S 6 , the internal-strain term is approximately zero. This is because the internal structure of the molecular layers, which are bonded by the weak van der Waals interaction, responds little to the strain. As a result, the magnitude of the dipole, which depends strongly on the dimension and structure of the molecular layer, also has a small response with respect to strain. An equation bridging the internal strain responses in low-dimensional and three-dimensional piezoelectrics is also derived to analytically express this point. This work aims to deepen our understanding about this anomalous piezoelectric effect, especially in low-dimensional layered materials, and provide strategies for discovering materials with novel electromechanical properties.

2-dimensional systems↗
DOE OSTI2025

Detecting thermodynamic phase transition via explainable machine learning of photoemission spectroscopy

Identifying thermodynamic signatures of electronic phases, such as superconductivity, is challenging in low-dimensional materials due to strong fluctuations and low probing volume. Spectroscopic methods are often used to identify new bulk phases, but their main measurable quantity—electronic energy gaps—is no longer an effective order parameter in low-dimensional and fluctuating systems. Combining angle-resolved photoemission with a domain-adversarial neural network, we report a data-driven method to identify thermodynamic phase transitions solely based on single-particle spectra. We demonstrate 97.6% accuracy in cuprate superconductor Bi 2 Sr 2 CaCu 2 O 8+δ with strong superconducting fluctuations. This model notably compensates for the scarcity of experimental data by leveraging virtually inexhaustible simulated data. Further, its explainability reveals the crucial role of in-gap spectral weight in detecting phase fluctuations and thermodynamic transitions. Our work pinpoints the spectroscopic signatures of fluctuating orders and enables using spectroscopy for machine-learning-assisted material discovery for low-dimensional and strong coupling systems.

2D materials↗