SEARCH · Engineering Papers

Results for “linear scaling”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Wakefield generation in hydrogen and lithium plasmas at FACET-II: Diagnostics and first beam-plasma interaction results

Plasma wakefield acceleration provides ultrahigh acceleration gradients of tens of GeV/m, providing a novel path toward efficient, compact, TeV-scale linear colliders, and high brightness free electron lasers. Critical to the success of these applications is demonstrating simultaneously high gradient acceleration, high energy transfer efficiency, and preservation of emittance, charge, and energy spread. Experiments at the FACET-II National User Facility at SLAC National Accelerator Laboratory aim to achieve all of these milestones in a single-stage plasma wakefield accelerator, providing a 10 GeV energy gain in a < 1 m plasma with high energy transfer efficiency. Such a demonstration depends critically on diagnostics able to measure emittance with mm mrad accuracy, energy spectra to determine both percent level energy spread, and broadband energy gain and loss, incoming longitudinal phase space, and matching dynamics. This paper discusses the experimental setup at FACET-II, including the incoming beam parameters from the FACET-II linac, plasma sources, and diagnostics developed to meet this challenge. Initial progress on the generation of beam ionized wakes in meter-scale hydrogen gas is discussed as well as commissioning of the plasma sources and diagnostics. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Weak influence of anthropogenic emissions on aerosol, cloud, and rain in the wet season of the Amazon rainforest

Anthropogenic emissions have been shown to affect new particle formation, aerosol concentrations, and clouds. Such effects vary with region, environmental conditions, and cloud types. In the wet season of Amazonia, anthropogenic emissions emitted from Manaus, Brazil, can significantly increase the cloud condensation nuclei (CCN) concentrations compared to the background of mainly natural aerosols. However, the regional response of cloud and rain to anthropogenic emissions in Amazonia remains very uncertain. Here, we aim to quantify how aerosol concentration, cloud, and rain respond to changes in anthropogenic emissions through parameterized new particle formation and primary aerosol emission in the Manaus region and to understand the underlying mechanisms. We ran the atmosphere-only configuration of the HadGEM3 climate model with a nested regional domain that covers most of the rainforest region (720 km by 1200 km with 3 km resolution) under scaled regional emissions. The 7 d simulations show that, in the areas that are affected by anthropogenic emissions, when aerosol and precursor gas emissions are doubled from the baseline emission inventories, aerosol number concentrations increase by 13 %. The nucleation rate that involves sulfuric acid and biogenic compounds generally increases with pollution levels. However, nucleation is suppressed very close to the pollution source, resulting in lower nucleation and soluble Aitken mode aerosol number concentrations. We also found that doubling the anthropogenic emission can increase the cloud droplet number concentrations ( N d ) by 9 %, but cloud water and rain mass mixing ratios do not change significantly. Even very strong reductions in aerosol number concentrations by a factor of 4, which is an unrealistic condition, cause only a 4 % increase in rain over the domain. If we assume our simulation has a fine enough grid resolution and an accurate representation of the relevant atmospheric processes, the simulated weak and non-linear response of cloud and rain properties to linearly scaled anthropogenic emissions suggests that the interactions among aerosol, cloud, and precipitation in the Amazonian convective environment are buffered by microphysical processes. It also implies that the convective environment is resilient to the changes in Nd that occur in response to localized anthropogenic aerosol perturbations.

Wang, Xuemei [University of Leeds (United Kingdom)↗

DOE OSTI2021

encore : an O ( N g2) estimator for galaxy N -point correlation functions

ABSTRACT We present a new algorithm for efficiently computing the N-point correlation functions (NPCFs) of a 3D density field for arbitrary N. This can be applied both to a discrete spectroscopic galaxy survey and a continuous field. By expanding the statistics in a separable basis of isotropic functions built from spherical harmonics, the NPCFs can be estimated by counting pairs of particles in space, leading to an algorithm with complexity $\mathcal {O}(N_\mathrm{g}^2)$ for Ng particles, or $\mathcal {O}(N_\mathrm{FFT}\log N_\mathrm{FFT})$ when using a Fast Fourier Transform with NFFT grid-points. In practice, the rate-limiting step for N > 3 will often be the summation of the histogrammed spherical harmonic coefficients, particularly if the number of radial and angular bins is large. In this case, the algorithm scales linearly with Ng. The approach is implemented in the encore code, which can compute the 3PCF, 4PCF, 5PCF, and 6PCF of a BOSS-like galaxy survey in ${\sim}100$ CPU-hours, including the corrections necessary for non-uniform survey geometries. We discuss the implementation in depth, along with its GPU acceleration, and provide practical demonstration on realistic galaxy catalogues. Our approach can be straightforwardly applied to current and future data sets to unlock the potential of constraining cosmology from the higher point functions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Machine Learning-Driven Conservative-to-Primitive Conversion in Hybrid Piecewise Polytropic and Tabulated Equations of State

We present a novel machine learning (ML)-based method to accelerate conservative-to-primitive inversion, focusing on hybrid piecewise polytropic and tabulated equations of state. Traditional root-finding techniques are computationally expensive, particularly for large-scale relativistic hydrodynamics simulations. To address this, we employ feedforward neural networks (NNC2PS and NNC2PL), trained in PyTorch (2.0+) and optimized for GPU inference using NVIDIA TensorRT (8.4.1), achieving significant speedups with minimal accuracy loss. The NNC2PS model achieves 𝐿 1 and 𝐿 ∞ errors of 4.54 × 10 −7 and 3.44 × 10−6, respectively, while the NNC2PL model exhibits even lower error values. TensorRT optimization with mixed-precision deployment substantially accelerates performance compared to traditional root-finding methods. Specifically, the mixed-precision TensorRT engine for NNC2PS achieves inference speeds approximately 400 times faster than a traditional single-threaded CPU implementation for a dataset size of 1,000,000 points. Ideal parallelization across an entire compute node in the Delta supercomputer (dual AMD 64-core 2.45 GHz Milan processors and 8 NVIDIA A100 GPUs with 40 GB HBM2 RAM and NVLink) predicts a 25-fold speedup for TensorRT over an optimally parallelized numerical method when processing 8 million data points. Moreover, the ML method exhibits sub-linear scaling with increasing dataset sizes. We release the scientific software developed, enabling further validation and extension of our findings. By exploiting the underlying symmetries within the equation of state, these findings highlight the potential of ML, combined with GPU optimization and model quantization, to accelerate conservative-to-primitive inversion in relativistic hydrodynamics simulations.

conservative-to-primitive conversion↗

DOE OSTI2023

Full forward model of galaxy clustering statistics with AbacusSummit light cones

ABSTRACT Novel summary statistics beyond the standard 2-point correlation function (2PCF) are necessary to capture the full astrophysical and cosmological information from the small-scale (r < 30h−1Mpc) galaxy clustering. However, the analysis of beyond-2PCF statistics on small scales is challenging because we lack the appropriate treatment of observational systematics for arbitrary summary statistics of the galaxy field. In this paper, we develop a full forward modelling pipeline for a wide range of summary statistics using the large high-fidelity AbacusSummit light cones that account for many systematic effects as well as remain flexible and computationally efficient to enable posterior sampling. We apply our forward model approach to a fully realistic mock galaxy catalog and demonstrate that we can recover unbiased constraints on the underlying galaxy–halo connection model using two separate summary statistics: the standard 2PCF and the novel k-th nearest neighbour (kNN) statistics, which are sensitive to correlation functions of all orders. We will demonstrate its strong constraining power on extended galaxy–halo connection models and cosmology in follow up papers. We expect this to become a powerful approach when applying to upcoming surveys such as DESI where we can leverage a multitude of summary statistics across a wide redshift range to maximally extract information from the non-linear scales.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Peeling-ballooning modes in spherical tokamaks: Multi-branch instabilities and effects beyond ideal MHD

A number of important physics effects on the stability of relatively high-n (n is the toroidal mode number) peeling-ballooning modes (PBMs) are investigated based on an equilibrium reconstructed from a NSTX discharge, utilizing extended magnetohydrodynamic (MHD) eigenvalue solvers. For a given toroidal mode number n, multiple branches of instabilities are computed, with the total number of unstable branches roughly linearly scaling with n. Most of the unstable branches are located in the plasma core region, but edge-localized branches, i.e., PBMs, are also identified at higher n-numbers. For the single-fluid-wise most unstable PBM with n = 19⁠, stabilizing/destabilizing effects due to various physics beyond ideal MHD are systematically investigated. Plasma toroidal flow is found to be weakly stabilizing. Local flow shear is generally stabilizing as well, with the degree of stabilization depending on the initial growth rate (without flow shear) of the mode. The plasma resistivity can strongly destabilize the PBM within the single-fluid framework. Anisotropic thermal transport, strong parallel sound wave damping, as well as two-fluid effects are all stabilizing to the mode. In particular, diamagnetic stabilization (within the two-fluid model) is found to be very strong for this mode.

Linear stability analysis↗

DOE OSTI2025

Leveraging operator learning to accelerate convergence of the preconditioned conjugate gradient method

We propose a new deflation strategy to accelerate the convergence of the preconditioned conjugate gradient (PCG) method for solving parametric large-scale linear systems of equations. Unlike traditional deflation techniques that rely on eigenvector approximations or recycled Krylov subspaces, we generate the deflation subspaces using operator learning, specifically the Deep Operator Network (DeepONet). To this aim, we introduce two complementary approaches for assembling the deflation operators. The first approach approximates near-null space vectors of the discrete PDE operator using the basis functions learned by the DeepONet. The second approach directly leverages solutions predicted by the DeepONet. To further enhance convergence, we also propose several strategies for prescribing the sparsity pattern of the deflation operator. Here, a comprehensive set of numerical experiments encompassing steady-state, time-dependent, scalar, and vector-valued problems posed on both structured and unstructured geometries is presented and demonstrates the effectiveness of the proposed DeepONet-based deflated PCG method, as well as its generalization across a wide range of model parameters and problem resolutions.

Deflation↗

DOE OSTI2023

Giant Spontaneous Magnetostriction in MnTe Driven by a Novel Magnetostructural Coupling Mechanism

A comprehensive x-ray scattering study of spontaneous magnetostriction in hexagonal MnTe, an antiferromagnetic semiconductor with a Néel temperature of T N = 307 K, is presented. The largest spontaneous magnetovolume effect known for an antiferromagnet is observed, reaching a volume contraction of |ΔV/V| > 7 × 10 -3 . This can be justified semiquantitatively by considering bulk material properties, the spatial dependence of the superexchange interaction, and the geometrical arrangement of magnetic moments in MnTe. The highly unusual linear scaling of the magnetovolume effect with the short-range magnetic correlations, beginning in the paramagnetic state well above T N , points to a novel physical mechanism, which is explained in terms of a trilinear coupling of the elastic strain with superposed distinct domains of the antiferromagnetic order parameter. Furthermore, this novel mechanism for coupling lattice strain to robust short-range magnetic order casts new light on magnetostrictive phenomena and also provides a template by which the exceptional magnetostrictive properties of MnTe might be realized in a wide range of other functional materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Propane Dehydrogenation on Pt x Zn y Active Sites in Silicalite‐1

Abstract The improvement of Pt‐based catalysts for propane dehydrogenation (PDH) has progressed by recent investigations that have identified Zn as a promising promoter for Pt subnanometer catalysts. It is desirable to gain insights into the structure, stability, and activity of such active sites and the factors that influence them, such as Zn : Pt ratio, Pt coordination and nuclearity. Here, we employ density functional theory and microkinetic simulations to investigate the stability of Pt x Zn y ( x =1–3, y=0–3) active sites grafted on silanols of Silicalite‐1 and the PDH activity of Pt. We find that the coordination of a Pt atom to a nest of grafted Zn(II) atoms increases the stability of the Pt 1 Zn y sites, whose activity is similar for y=0–2 and drops dramatically for y>2. We further demonstrate, via linear scaling relations and microkinetic simulations, that the turnover frequency obeys a volcano law as a function of propylene binding strength. The Pt 2 Zn 1 and Pt 3 Zn 1 sites are stable and exhibit activity similar to Pt 1 Zn 2 , but only Pt 1 Zn 2 manifests reaction kinetics consistent with experimental data, strongly suggesting the active site composition in the synthesized catalyst samples. The methodology presented here suggests a general strategy for deducing active site information such as composition through simple kinetic experiments.

Liu, Yilang↗

DOE OSTI2024

Propane Dehydrogenation on Pt x Zn y Active Sites in Silicalite‐1

Liu, Yilang↗

DOE OSTI2022

Fluxionality of Subnano Clusters Reshapes the Activity Volcano of Electrocatalysis

The Sabatier activity volcano provides intuitive guide for catalyst design, but also imposes fundamental limitations on the composition and maximal activity of catalysts. Here we show that the ORR activity volcano is shifted and reshaped by the potential-dependent fluxionality of subnano cluster catalysts. Fluxionality causes the typically under-binding Ag/Au to gain optimal activity in the cluster form, and surpass Pt/Pd. Furthermore, isomerization of clusters as a function of the potential breaks linear scaling relationships, enabling surpassing the volcano “apex” relative to the bulk. The effect is likely general for fluxional cluster catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning

Abstract Brønsted‐Evans‐Polanyi (BEP) relationships, i. e., a linear scaling between reaction and activation energies, lie at the core of computational design of heterogeneous catalysts. However, BEPs are not general and often require reparameterization for each class of reactions. Here we construct generalized BEPs (gBEPs), which can predict activation energies for a diverse dataset of reactions of C, O, N and H containing molecules on metal surfaces. In a first step we develop a set of descriptors based on scaling relationships that can capture the change in chemical identity of reactants during the reaction. Subsequently, we use the reaction energy, these descriptors and a single descriptor for the surface structure to parameterize machine learning based regression approaches for the prediction of activation energies. The best approach we developed shows a Mean Absolute Error (MAE) of 0.11 eV for the training set (80 % of the data set) and 0.23 eV for the test set (20 % of the data set). The methodology presented here allows to calculate activation energies within fractions of seconds on a typical personal computer and due to its generality, accuracy and simplicity in application it might prove to be useful in transition metal catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

The Significance of the 'Insignificant': Non-covalent Interactions in CO 2 Reduction Reactions with 3C-TM (TM=Sc-Zn) Single-Atom Catalysts

With energy shortages and excessive CO 2 emissions driving climate change, converting CO 2 into high-value-added products offers a promising solution for carbon recycling. We investigate CO 2 reduction reactions (CO2RR) catalyzed by 10 single-atom catalysts (SACs), incorporating weak non-covalent interactions, specifically lone pair-π and H-π interactions. The SACs, consisting of transition metals coordinated by three carbon atoms in a defective graphene substrate (3C-TM, TM=Sc-Zn), leverage these interactions to influence the energy fluctuations of intermediates and the limiting potentials of CO 2 RR, without altering the overall reaction pathway. Further, our findings show that SACs based on early transition metals (Sc, Ti, V, Cr) can serve as catalysts for C 1 products, including HCOOH, HCHO, CH 3 OH, and CH 4 , while those based on Fe and Co are suitable for CO formation. Driving force analysis helps bridge theoretical results with experimental observations and propose a modified approach for assessing hydrogen evolution reactions (HER) competition. SACs based on Ni and Cu exhibit moderate HER tolerance, while early transition metals excel in selective CO 2 reduction. We also identify a linear scaling relationship between the free energies of *COOH and *CO. This study offers valuable insights for future experimental studies and large-scale computational screenings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Parallel performance of algebraic multigrid domain decomposition

Algebraic multigrid (AMG) is a widely used scalable solver and preconditioner for large-scale linear systems resulting from the discretization of a wide class of elliptic PDEs. While AMG has optimal computational complexity, the cost of communication has become a significant bottleneck that limits its scalability as processor counts continue to grow on modern machines. This article examines the design, implementation, and parallel performance of a novel algorithm, algebraic multigrid domain decomposition (AMG-DD), designed specifically to limit communication. The goal of AMG-DD is to provide a low-communication alternative to standard AMG V-cycles by trading some additional computational overhead for a significant reduction in communication cost. Numerical results show that AMG-DD achieves superior accuracy per communication cost compared with AMG, and speedup over AMG is demonstrated on a large GPU cluster.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Steric Modulation of Protein‐Mediated Nanoparticle Assembly: Controlling Cluster Size, Polydispersity, and FRET Responses by Rebalancing Short‐ and Long‐Range Interactions

Understanding and manipulating protein-nanoparticle interactions is of broad interest to fields ranging from nanomedicine to the biological fabrication of functional hierarchical materials. This study investigates how steric forces introduced by a pegylated derivative of superfolder green fluorescent protein (sfGFP) that is monofunctional for silica binding modulate the delicate interplay of long-range (electrostatic and van der Waals) and short-range (protein-mediated) interactions in pH-responsive silica nanoparticle (SiNP) assembly by bifunctional silica-binding sfGFP. Increasing the length of the PEG segment and pre-incubating SiNPs with increasing concentrations of pegylated proteins enables precise control over cluster size within the 800–1450 nm range with a sixfold decrease in polydispersity index to a remarkable 0.1 endpoint. Weakening short-range attractive interactions via mutagenesis extends this control to clusters in the 50–250 nm range and reveals that the Förster resonance energy transfer (FRET) efficiency of clusters scales linearly with cluster diameter below 230 nm but increases only by 15% as clusters grow to 1450 nm. Furthermore, these findings enable the development of a system that provides an optical readout to dynamic changes in solution conditions enacted by a combination of pH adjustment and ion charge screening.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2026

Particle‐Size‐Dependent Lithium‐Ion Transport in PEO/LLZO Composite Electrolytes

Lithium-metal batteries with solid electrolytes can deliver higher energy density and improved safety than conventional Li-ion batteries. Among solid electrolyte candidates, polymer/ceramic composite electrolytes are attractive because they combine polymer flexibility with the high ionic conductivity of ceramics. However, whether ceramic fillers synergistically reduce polarization losses in the polymer matrix remains unclear. A central unknown is the critical polymer/ceramic interfacial resistance (Rint,crit), below which adding ceramics lowers electrolyte overpotential. Here, we present the first macroscale model framework to quantify R int,crit for composite electrolytes based on polyethylene oxide (PEO) and Ta-doped Li 7 La 3 Zr 2 O 12 (LLZO). A 1D model for DC-polarization of tri-layer cells (PEO-LiTFSI/LLZO/PEO-LiTFSI) shows that LLZO surface functionalization reduces the PEO/LLZO interfacial resistance, consistent with electrochemical impedance measurements. Extending to a 2D composite model, we show notably that Rint,crit scales linearly with LLZO particle diameter and shifts toward experimentally accessible values (e.g., 28.8 Ωcm 2 ) as particle size increases. At fixed ceramic volume fraction, larger LLZO particles reduce the number of interfacial crossings, driving more current through the ceramic phase and lowering concentration polarization. In contrast, R int,crit is largely independent of ceramic volume fraction. These results demonstrate that ceramic filler-size engineering can enable synergistic, energy-efficient transport in polymer/ceramic composite electrolytes.

25 ENERGY STORAGE↗

DOE OSTI2021

The FMO-DFTB Method

Although the fragment molecular orbital (FMO) method enables electronic structure calculations with near-linear scaling behavior with respect to system size, the computational cost of ab initio methods typically employed in conjunction with FMO is still prohibitive for routine calculations of very large systems or long timescale molecular dynamics simulations. We, therefore, combined the FMO and density-functional tight-binding (DFTB) method, which is one of the emerging semi-empirical quantum chemical methods, and have demonstrated that FMO-DFTB is capable of performing geometry optimizations for systems containing up to one million atoms using limited computational resources. In this chapter, we will review the basics of the DFTB method first before introducing FMO-DFTB, focusing on the relationship with density functional theory and other FMO methodologies. We also demonstrate the latest scalings of FMO-DFTB2 and DFTB3 using three-dimensional water clusters, showing that the most favorable scaling is O(N1.16). Applications of FMO-DFTB to various systems are briefly summarized, and an outlook to future applications is provided.

Nishimoto, Yoshio↗

DOE OSTI2025

Looking at extremal black holes from very far away

Near-extremal black holes are subject to large quantum effects, which modify their low-temperature thermodynamic behavior. Hitherto, these quantum effects were analyzed by separating the geometry into the near-horizon region and its exterior. It is desirable to understand and reproduce such corrections from the full higher-dimensional asymptotically flat or AdS geometry’s perspective. We address this question in this article and fill this gap. Specifically, we find off-shell eigenmodes of the quadratic fluctuation operator of the Euclidean gravitational dynamics, with eigenvalues that vanish linearly with temperature. We illustrate this for BTZ and neutral black holes with hyperbolic horizons in AdS in Einstein-Hilbert theory, and for the charged black holes in Einstein-Maxwell theory. The linear scaling with Matsubara frequency, which is a distinctive feature of the modes, together with the fact that their wavefunctions localize close to the horizon as we approach extremality, identifies them as responsible for the aforementioned quantum effects. We provide a contour prescription to deal with the sign indefiniteness of the Euclidean Einstein-Maxwell action, which we derive to aid our analysis. We also resolve a technical puzzle regarding modes associated with rotational isometries in stationary black hole spacetimes.

AdS-CFT Correspondence↗