Krylov-Subspace Recycling via the POD-Augmented Conjugate Gradient Method.
Abstract not provided.
Carlberg, Kevin Thomas↗
SEARCH · Engineering Papers
Results for “Dimensionality Reduction”
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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Bismuthene for highly efficient carbon dioxide electroreduction reaction
Bismuth (Bi) has been known as a highly efficient electrocatalyst for CO 2 reduction reaction. Stable free-standing two-dimensional Bi monolayer (Bismuthene) structures have been predicted theoretically, but never realized experimentally. Here, we show the first simple large-scale synthesis of free-standing Bismuthene, to our knowledge, and demonstrate its high electrocatalytic efficiency for formate (HCOO - ) formation from CO 2 reduction reaction. The catalytic performance is evident by the high Faradaic efficiency (99% at -580 mV vs. Reversible Hydrogen Electrode (RHE)), small onset overpotential (<90 mV) and high durability (no performance decay after 75 h and annealing at 400 °C). Density functional theory calculations show the structure-sensitivity of the CO2 reduction reaction over Bismuthene and thicker nanosheets, suggesting that selective formation of HCOO - indeed can proceed easily on Bismuthene (111) facet due to the unique compressive strain. This work paves the way for the extensive experimental investigation of Bismuthene in many different fields.
Science & Technology - Other Topics↗
Wall effects in wind tunnels
A synthesis of current trends in the reduction and computation of wall effects is presented. Some of the points discussed include: (1) for the two-dimensional, transonic tests, various control techniques of boundary conditions are used with adaptive walls offering high precision in determining reference conditions and residual corrections. A reduction in the boundary layer effects of the lateral walls is obtained at T2; (2) for the three-dimensional tests, the methods for the reduction of wall effects are still seldom applied due to a lesser need and to their complexity; (3) the supports holding the model of the probes have to be taken into account in the estimation of perturbatory effects.
Chevallier, J. P.↗
Statistical Learning for Nonlinear Model Reduction from Local Simulations of Stochastic and Particle- and Agent-Based Systems
Stochastic physical systems across the sciences that have very high-dimensional state spaces, with a large number of fast degrees of freedom that force direct simulators to proceed by integration steps that are orders of magnitude smaller than events of interests (e.g., particle collisions). Examples range from molecular motion to dynamics of large populations of cells. A grand challenge in the simulation and understanding of such systems is the systematic construction of accurate, interpretable, reduced models, enabling faster simulations, revealing fundamental properties of the dynamics, and predicting phenomena of interest that the original simulator could not reached with sufficient accuracy or within a given computational budget. In this projected we developed novel statistical estimation/machine learning techniques for analyzing and building empirical reduced models for important families of high-dimensional stochastic systems, in particular: - we developed techniques for estimating interaction kernels in interacting particle- and agent-based systems, which are ubiquitous in Physics, Biology and many other sciences, given observed trajectories of the system; - we developed techniques for nonlinear model reduction for high-dimensional stochastic systems that have a small number of unknown, nonlinear slow variables, and a large number of fast modes, that are possibly of large magnitude, given observed short trajectories of the system in the form of bursts of trajectories from different initial conditions; - we developed novel techniques for estimating linear dynamical systems on graphs when both the dynamics and the underlying graph are unknown, and we have a sparse set of space-time observations; - we considered the problem of estimating an unknown nonlinear observation function of a standard process (e.g. Brownian motion), so that we can recognized if an observed dynamics is "just" a nonlinear version of a known dynamics; we also developed benchmarks for learning algorithms aimed at learning and classifying diffusion processes.
97 MATHEMATICS AND COMPUTING↗
An implicit algorithm for the transonic full-potential equation in conservative form
A fast, implicit approximate factorization algorithm for the solution of the conservative full-potential equation for transonic flow in two and three dimensions is presented. Stability in supersonic regions is maintained by the use of an upwind evaluation of the density coefficient along all coordinate directions, providing an effective upwind difference of the streamwise terms for any orientation of the velocity vector and thereby enhancing the reliability of the algorithm. The algorithm is shown to provide rapid convergence for the computation of certain difficult two-dimensional test cases, including cases with fishtail shock patterns, demonstrating the reliability and efficiency of the procedure. Surface pressure coefficient distributions obtained by the present method are also found to be in good agreement with those computed by successive-line overrelaxation and a hybrid direct-solver/successive-line overrelaxation scheme, with significant reductions in CPU time required. A three-dimensional solution for a swept wing mounted between parallel walls is also presented which demonstrates the high convergence rate of the algorithm in three dimensions as well as two.
Holst, T.↗
Relaxation dynamics in quantum dissipative systems: The microscopic effect of intramolecular vibrational energy redistribution
We investigate the effect of inter-mode coupling on the vibrational relaxation dynamics of molecules in weak dissipative environments. The simulations are performed within the reduced density matrix formalism in the Markovian regime, assuming a Lindblad form for the system-bath interaction. The prototypical two-dimensional model system representing two CO molecules approaching a Cu(100) surface is adapted from an ab initio potential, while the diatom-diatom vibrational coupling strength is systematically varied. In the weak system-bath coupling limit and at low temperatures, only first order non-adiabatic uni-modal coupling terms contribute to surface-mediated vibrational relaxation. Since dissipative dynamics is non-unitary, the choice of representation will affect the evolution of the reduced density matrix. Two alternative representations for computing the relaxation rates and the associated operators are thus compared: the fully coupled spectral basis, and a factorizable ansatz. The former is well-established and serves as a benchmark for the solution of Liouville-von Neumann equation. In the latter, a contracted grid basis of potential-optimized discrete variable representation is tailored to incorporate most of the inter-mode coupling, while the Lindblad operators are represented as tensor products of one-dimensional operators, for consistency. This procedure results in a marked reduction of the grid size and in a much more advantageous scaling of the computational cost with respect to the increase of the dimensionality of the system. The factorizable method is found to provide an accurate description of the dissipative quantum dynamics of the model system, specifically of the time evolution of the state populations and of the probability density distribution of the molecular wave packet. The influence of intra-molecular vibrational energy redistribution appears to be properly taken into account by the new model on the whole range of coupling strengths. It demontrates that most of the mode mixing during relaxation is due to the potential part of the Hamiltonian and not to the coupling among relaxation operators.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
High-Temperature Mechanical Properties of the P/M Extruded Mg-SiC{sub p} Composites
In the present study, pure magnesium reinforced with 0, 5, 10 and 15 vol.% SiC particulates was successfully prepared by powder metallurgy technique before being hot extruded. The average 14 μm grain size of the composite specimens remained almost unchanged after addition of SiC particles, while their dimensional stability was improved because of the reduction in the coefficient of thermal expansion (CTE) from 28.6 × 10{sup −6} K{sup −1} in pure Mg to 27.3, 25.3 and 23.4 × 10{sup −6} K{sup −1} in the Mg-5% SiC, Mg-10% SiC and Mg-15% SiC composites, respectively. Mechanical properties of the specimens were investigated in the temperature range of 298-498 K, implementing shear punch testing and hot hardness techniques. Depending on the test temperature, addition of SiC particles to the pure Mg matrix increased shear yield stress and ultimate shear strength of the materials by 5-25 and 6-23 MPa, respectively. The shear strength improvement was mainly attributed to the CTE mismatch strengthening mechanism (9.5-25.5 MPa), and to a lesser extent (1-4.5 MPa), to the load transfer mechanism. Finally, using the modified Clyne model, the contribution of different strengthening mechanisms to the total shear strength improvement in the composites was evaluated.
36 MATERIALS SCIENCE↗
Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference
Operator inference learns low-dimensional dynamical-system models with polynomial nonlinear terms from trajectories of high-dimensional physical systems (non-intrusive model reduction). Here, this work focuses on the large class of physical systems that can be well described by models with quadratic and cubic nonlinear terms and proposes a regularizer for operator inference that induces a stability bias onto learned models. The proposed regularizer is physics informed in the sense that it penalizes higher-order terms with large norms and so explicitly leverages the polynomial model form that is given by the underlying physics. This means that the proposed approach judiciously learns from data and physical insights combined, rather than from either data or physics alone. Additionally, a formulation of operator inference is proposed that enforces model constraints for preserving structure such as symmetry and definiteness in linear terms. Numerical results demonstrate that models learned with operator inference and the proposed regularizer and structure preservation are accurate and stable even in cases where using no regularization and Tikhonov regularization leads to models that are unstable.
97 MATHEMATICS AND COMPUTING↗
Characterizing the surface compositions of supported bimetallic PtSn clusters: Effects of cluster-support interactions and surface adsorbates
PtSn bimetallic clusters on TiO2(110) and highly oriented pyrolytic graphite (HOPG) surfaces have been characterized by scanning tunneling microscopy, low energy ion scattering (LEIS), Xray photoelectron spectroscopy, and temperature programmed desorption (TPD); density functional theory (DFT) calculations have also been performed to better understand adsorption of CO and D2 on the PtSn surfaces. On TiO2 at coverages of 2 ML of Pt and 2 ML of Sn, exclusively bimetallic clusters are formed for both orders of deposition because clusters of the first metal completely cover the surface such that all atoms of the second metal are incorporated into the existing clusters. In contrast, on HOPG, the high mobility and weak cluster-support interactions on HOPG result in much larger 2 ML monometallic clusters (~30 Å high) that do not completely cover the surface, and deposition of the second metal produces larger clusters as well as smaller ones. Despite the difference in cluster morphologies for the different orders of deposition and supports, the LEIS experiments demonstrate that in all cases, the PtSn clusters are rich in Sn at the surface, as expected based on the lower surface free energy for Sn compared to Pt. Furthermore, the +0.2 eV shift in the Sn(3d5/2) binding energy observed on all surfaces in the presence of Pt is consistent with PtSn alloy formation. Deposition of 2 ML of Sn on TiO2 produces two-dimensional clusters with oxidation of Sn and reduction of titania at the clustersupport interface, but addition of Pt to the Sn clusters causes Sn to diffuse away from this interface, leaving Sn in the metallic state. TPD experiments on 2 ML Pt/TiO2 with increasing coverages of Sn show that the number of adsorption sites for D2 sharply decreases to nearly zero at 0.5 ML, while CO adsorption decreases to zero only at much higher Sn coverages of 2 ML. DFT studies for Sn modified Pt surfaces and bulk structures demonstrate that for CO adsorption at low Sn coverages (<0.25 ML), the strong Pt-CO interactions induce diffusion of Pt to the cluster surface and the formation of a bulk Pt3Sn alloy, whereas D2 adsorption does not lead to interactions with the Pt surface that are strong enough to induce alloy formation. A single Sn adatom prevents D2 adsorption on four neighboring Pt atoms via site-blocking and the donation of electron density to Pt.
Li, Fangliang↗
Energy Spotlight: Advances in Solar Cells and Solar Fuels
Our editorial advisory board members Kai Zhu, Dongling Ma, and Shannon W. Boettcher provide highlights of three articles published in the August issue. They highlight new advances in solar cells and solar fuels that include phenethylammonium iodide treatment on suppressing ion migration in perovskite solar cells, nitride heterostructures for water splitting, and three-dimensional electrode (Si microarray) for CO 2 reduction.
14 SOLAR ENERGY↗
Analysis of ultra-high sensitivity configuration in chip-integrated photonic crystal microcavity bio-sensors
We analyze the contributions of quality factor, fill fraction, and group index of chip-integrated resonance microcavity devices, to the detection limit for bulk chemical sensing and the minimum detectable biomolecule concentration in biosensing. We analyze the contributions from analyte absorbance, as well as from temperature and spectral noise. Slow light in two-dimensional photonic crystals provide opportunities for significant reduction of the detection limit below 1 × 10{sup −7} RIU (refractive index unit) which can enable highly sensitive sensors in diverse application areas. We demonstrate experimentally detected concentration of 1 fM (67 fg/ml) for the binding between biotin and avidin, the lowest reported till date.
36 MATERIALS SCIENCE↗
Neutrino-nucleon scattering in supernova matter from the virial expansion
Here we extend our virial approach to study the neutral-current neutrino response of nuclear matter at low densities. In the long-wavelength limit, the virial expansion makes model-independent predictions for neutrino-nucleon scattering rates and the density S V and spin S A responses. We find that S A is significantly reduced from one even at low densities. We provide a simple fit $S^f_A$(n,T,Y p ) of the axial response as a function of density n, temperature T, and proton fraction Y p , which can be incorporated into supernova simulations in a straightforward manner. This fit reproduces our virial results at low densities and the Burrows and Sawyer random-phase approximation (RPA) model calculations at high densities. Preliminary one-dimensional supernova simulations suggest that the virial reduction in the axial response may enhance neutrino heating rates in the gain region during the accretion phase of a core-collapse supernovae.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning
Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.
58 GEOSCIENCES↗
EFIT‐AI: Machine Learning and Artificial Intelligence Assisted Equilibrium Reconstruction for Tokamak Experiments and Burning Plasmas (Final Report)
The EFIT-AI project is creating a modern advanced equilibrium reconstruction code suitable for tokamak experiments of burning plasmas. EFIT [1,2] was the first and is the most extensively used equilibrium reconstruction code in the world. This project builds on the production-level experience and adds key elements as follows. 1. A Model Order Reduction (MOR) version of the two-dimensional (2D) Grad-Shafranov equation solver (EFIT-MORNN) using physics-informed neural networks. 2. Improved optimization and data analysis capabilities using a Bayesian framework enhanced with machine learning. 3. A MOR version of the three-dimensional (3D) perturbed equilibrium reconstruction tool.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Active Subspaces for Wind Plant Surrogate Modeling
Understanding the uncertainty in wind plant performance is crucial to their cost-effective design and operation. However, conventional approaches to uncertainty quantification (UQ), such as Monte Carlo techniques or surrogate modeling, are often computationally intractable for utility-scale wind plants because of poor congergence rates or the curse of dimensionality. In this paper we demonstrate that wind plant power uncertainty can be well represented with a low-dimensional active subspace, thereby achieving a significant reduction in the dimension of the surrogate modeling problem. We apply the active sub-spaces technique to UQ of plant power output with respect to uncertainty in turbine axial induction factors, and find a single active subspace direction dominates the sensitivity in power output. When this single active subspace direction is used to construct a quadratic surrogate model, the number of model unknowns can be reduced by up to 3 orders of magnitude without compromising performance on unseen test data. We conclude that the dimension reduction achieved with active subspaces makes surrogate-based UQ approaches tractable for utility-scale wind plants.
17 WIND ENERGY↗
Image coding by coding contours
Two dimensional data array contour tracing algorithm for reduction of image coding bits number
Wilkins, L. C.↗
Preliminary assessment of the potential impact of solid-fueled rocket engines in the stratosphere
Free chlorine atoms produced from HCl deposited by solid-fueled rocket engines react to destroy odd oxygen in the stratosphere. Using a one-dimensional photochemical model, we have computed the reduction of ozone likely to be caused by long-term operation of such vehicles powered by large solid-fueled rocket engines. It is likely that ozone reduction will not be greater than a few tenths of a per cent, although the value is dependent upon ambient NO(x) concentrations.
Whitten, R. C.↗