Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “space computing”

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.

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

At least 37 records · Page 2

Computational discovery of stable and metastable ternary oxynitrides

Materials design from first principles enables exploration of uncharted chemical spaces. Extensive computational searches have been performed for mixed-cation ternary compounds, but mixed-anion systems are gaining increased interest as well. Central to computational discovery is the crystal structure prediction, where the trade-off between reliance on prototype structures and size limitations of unconstrained sampling has to be navigated. We approach this challenge by letting two complementary structure sampling approaches compete. We use the kinetically limited minimization approach for high-throughput unconstrained crystal structure prediction in smaller cells up to 21 atoms. On the other hand, ternary—and, more generally, multinary—systems often assume structures formed by atomic ordering on a lattice derived from a binary parent structure. Thus, we additionally sample atomic configurations on prototype lattices with cells up to 56 atoms. Using this approach, we searched 65 different charge-balanced oxide–nitride stoichiometries, including six known systems as the control sample. The convex hull analysis is performed both for the thermodynamic limit and for the case of synthesis with activated nitrogen sources. We identified 34 phases that are either on the convex hull or within a viable energy window for potentially metastable phases. We further performed structure sampling for “missing” binary nitrides whose energies are needed for the convex hull analysis. Among these, we discovered metastable Ce3N4 as a nitride analog of the tetravalent cerium oxide, which becomes stable under slightly activated nitrogen condition µN > +0.07 eV. Given the outsize role of CeO2 in research and application, Ce3N4 is a potentially important discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Experimental and computational study of phase space dynamics in strongly coupled plasmas with steep density gradients

Understanding how plasmas thermalize when density gradients are steep remains a fundamental challenge in plasma physics, with direct implications for fusion experiments and astrophysical phenomena. Standard hydrodynamic models break down in these regimes, and kinetic theories make predictions that have never been directly tested. Here, we present the first detailed phase-space measurements of a strongly coupled plasma as it evolves from sharp density gradients to thermal equilibrium. Using laser-induced fluorescence imaging of an ultracold calcium plasma, we track the complete ion distribution function f(x,v,t). We discover that commonly used kinetic models (Bhatnagar–Gross–Krook and Lenard–Bernstein) overpredict thermalization rates, even while correctly capturing the initial counterstreaming plasma formation. Our measurements reveal that the initial ion acceleration response scales linearly with electron temperature, and that the simulations underpredict the initial ion response. In our geometry we demonstrate the formation of well-controlled counterpropagating plasma beams. This experimental platform enables precision tests of kinetic theories and opens new possibilities for studying plasma stopping power and flow-induced instabilities in strongly coupled systems.

Bergeson, Scott (ORCID:0000000231249226)↗

Validating pyORBIT for Modeling Beam Dynamics in the IOTA Ring

The Integrable Optics Test Accelerator (IOTA) ring is a new Fermilab facility dedicated to beam physics experiments, currently operating with 150 MeV electrons. Space charge effects are expected to be significant when it operates with 2.5 MeV protons. In this contribution, we present results of a suite of validation tests of PyORBIT, a PICstyle space charge code. Single particle dynamics of quasiintegrable optics using an octupole string in IOTA is compared with MADX, and shown to be in good agreement. Requirements for the convergence of space charge computations are systematically established and when possible, tests involving space charge are compared with theoretical predictions.

Li, Runze↗

An introduction to neuromorphic computing and its potential impact for unattended ground sensors

Neuromorphic computers are hardware systems that mimic the brain’s computational process phenomenology. This is in contrast to neural network accelerators, such as the Google TPU or the Intel Neural Compute Stick, which seek to accelerate the fundamental computation and data flows of neural network models used in the field of machine learning. Neuromorphic computers emulate the integrate and fire neuron dynamics of the brain to achieve a spiking communication architecture for computation. While neural networks are brain-inspired, they drastically oversimplify the brain’s computation model. Neuromorphic architectures are closer to the true computation model of the brain (albeit, still simplified). Neuromorphic computing models herald a 1000x power improvement over conventional CPU architectures. Sandia National Labs is a major contributor to the research community on neuromorphic systems by performing design analysis, evaluation, and algorithm development for neuromorphic computers. Space-based remote sensing development has been a focused target of funding for exploratory research into neuromorphic systems for their potential advantage in that program area; SNL has led some of these efforts. Recently, neuromorphic application evaluation has reached the NA-22 program area. This same exploratory research and algorithm development should penetrate the unattended ground sensor space for SNL’s mission partners and program areas. Neuromorphic computing paradigms offer a distinct advantage for the SWaP-constrained embedded systems of our diverse sponsor-driven program areas.

97 MATHEMATICS AND COMPUTING↗

Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process

Abstract Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces to discover optimal experimental conditions and regions of interest in the image space or parameter space of computational models. The direct grid search of the parameter space tends to be extremely time-consuming, leading to the development of strategies balancing exploration of unknown parameter spaces and exploitation towards required performance metrics. However, classical Bayesian optimization (BO) strategies based on the Gaussian process (GP) do not readily allow for the incorporation of the known physical behaviors or past knowledge. Here we explore a hybrid optimization/exploration algorithm created by augmenting the standard GP with a structured probabilistic model of the expected system’s behavior. This approach balances the flexibility of the non-parametric GP approach with a rigid structure of physical knowledge encoded into the parametric model. The fully Bayesian treatment of the latter allows additional control over the optimization via the selection of priors for the model parameters. The method is demonstrated for a noisy version of a standard univariate test function used to evaluate optimization algorithms and further extended to physical lattice models. This methodology is expected to be universally suitable for injecting prior knowledge in the form of physical models and past data in the BO framework.

42 ENGINEERING↗

The Memory Scaling of Reverse-Mode Differentiation in Particle Accelerator Simulations with Space Charge

The recent development of differentiable simulation codes for particle accelerators has enabled gradient-based workflows that promise finer control and more realistic modeling of accelerator facilities. However, when using reverse-mode automatic differentiation, the memory usage continuously increases during the simulation, and can potentially exceed the available hardware memory - especially when costly space charge computation is included. To study the memory requirements for differentiable simulations, we have implemented space charge in Cheetah, a PyTorch-based beam tracking code that supports reverse-mode differentiation. We find that the memory usage for reverse-mode differentiation grows linearly with the number of macroparticles and cells, and that it is proportional to the number of space charge kicks involved in the simulation. This general scaling can be used to evaluate whether a given differentiable simulation is feasible given hardware memory constraints.

Dhamrait, Arjun↗

Simulation of Proton Beams in the AGS Booster with Space Charge

As higher intensity accelerators are designed, built and operated, the resonances and instabilities caused by space charge can become dominant effects in limiting the intensity of the beam that can be delivered. Particle loss has many causes but space charge can be a major factor in reducing beam intensity and generating emittance growth, particularly at low energies and in the lower energy stages of accelerators such as the AGS Booster. Successful operation relies on a good understanding of basic particle dynamics and it becomes an important challenge to ascertain how the beam evolves when charged particles experience both external forces through the fields of the magnets combined with internal forces through Coulomb interactions. A possible approach to such a study is to include calculations of space charge in computer simulation codes that track the motions of charged particles in 6D phase space. Unfortunately, repeated space charge calculations in 6D phase space are computationally intensive even with the most modern computing facilities. Study with traditional models of space charge is then either limited to a short real-time scale, or sacrifices accuracy and often omits some of the physics. The study described in this report attempts to explore all aspects of the Booster from injection, through RF capture and acceleration to the extraction energy. The simulations are allied to real machine settings. They do not attempt to model effects that are well known and for which accurate experimental data are available, but they try in many cases to extend beyond the normal Booster parameter range so as to understand the limitations on operations and suggest areas for improvement.

43 PARTICLE ACCELERATORS↗

ML-based Dimension Reduction Strategies

Deep learning (DL)--based surrogate models have achieved success in various applications in carbon capture and storage (CCS). However, the model training on high-dimensional spaces is computationally expensive and impractical for large-scale and complex geological models, because the models usually contain hundreds of thousands to millions of grid cells, each with a set of parameters. Furthermore, the high cost of generating training data with sufficient variation is another limitation of model training on high-dimensional spaces, which may result in overfitting and reduce the model efficiency and prediction performance. We proposed the workflow incorporating dimension reduction methods and deep learning models, which aim to extract the latent variables of input parameters and output state variables, and then build the mapping function at the latent spaces. The proposed workflow can significantly reduce the computational complexity in solving both forward and inverse problems compared to models trained on high-dimensional spaces. Dimensionality reduction models showed great potential in workflows for fast reservoir simulation, history matching, prior model generation, visualization, and more, ultimately enhancing DL model performance in related SMART Work Packages.

Hosseini, Seyyed↗

Computationally efficient subglacial drainage modelling using Gaussian process emulators: GlaDS-GP v1.0

Subglacial drainage models represent water flow at the ice–bed interface through coupled distributed and channelized systems to determine water pressure, discharge, and drainage system geometry. While they are used to understand processes such as the relationship between surface melt and ice flow, the number of uncertain model parameters and the computational cost of running models makes it difficult to adequately explore the high-dimensional parameter space and evaluate uncertainty in model predictions. Here, we develop Gaussian process (GP) emulators that make fast predictions with associated uncertainty of subglacial drainage model outputs. Using a truncated principal component (PC) basis representation, we construct a GP emulator for diurnally averaged subglacial water pressure. We also explore emulation of scalar variables describing drainage efficiency and configuration. We train the emulators using ensembles of up to 512 simulations varying eight parameters of the Glacier Drainage System (GlaDS) model on a synthetic domain intended to represent an ice-sheet margin. The emulators make predictions ∼ 1000 times faster than GlaDS simulations, with errors <3 % for the water pressure field and ∼ 5 %–9 % for drainage efficiency and configuration. We apply the emulators to explore the eight-dimensional parameter space by computing variance-based parameter sensitivity indices, finding that three parameters (ice flow coefficient, bed bump aspect ratio, and the subglacial cavity system conductivity) explain 90 % of the variance in modelled water pressure in response to parameter changes. The GP emulator approach described here is well suited to integrating observational data with models to make calibrated, credible predictions of subglacial drainage.

58 GEOSCIENCES↗

Krylov spaces for truncated spectrum methodologies

We propose herein an extension of truncated spectrum methodologies, a nonperturbative numerical approach able to elucidate the low energy properties of quantum field theories. TSMs, in their various flavors, involve a division of a computational Hilbert space, H , into two parts, one part, H 1 that is “kept” for the numerical computations, and one part, H 2 , that is discarded or “truncated.” Even though H 2 is discarded, truncated spectrum methodologies will often try to incorporate the effects of H 2 in some effective way. In these terms, we propose to keep the dimension of H 1 small. We pair this choice of H 1 with a Krylov subspace iterative approach able to take into account the effects of H 2 . This iterative approach can be taken to arbitrarily high order and so offers the ability to compute quantities to arbitrary precision. In many cases it also offers the advantage of not needing an explicit UV cutoff. To compute the matrix elements that arise in the Krylov iterations, we employ a Feynman diagrammatic representation that is then evaluated with Monte Carlo techniques. Each order of the Krylov iteration is variational and is guaranteed to improve upon the previous iteration. The first Krylov iteration is akin to the next-to-leading order approach of Elias-Miró [NLO renormalization in the Hamiltonian truncation, ]. To demonstrate this approach, we focus on the ( 1 + 1 d )-dimensional ϕ 4 model and compute the bulk energy and mass gaps in both the Z 2 -broken and unbroken sectors. We estimate the critical ϕ 4 coupling in the broken phase to be g c = 0.2645 ± 0.002 . Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Solving the Bernstein-Vazirani problem using Majorana-based topological quantum algorithms

Executing quantum algorithms using Majorana zero modes—a major milestone for the field of topological quantum computing—requires a platform that can be scaled to large quantum registers, can be controlled in real time and space, and a braiding protocol that uses the unique properties of these exotic particles. Here, we demonstrate the first successful simulation of a Majorana-based, fault-tolerant quantum algorithm to solve the Bernstein-Vazirani problem in two-dimensional magnet-superconductor hybrid structures from initialization to read-out of the final many-body state. Utilizing the Majorana zero modes’ topological properties, we introduce an optimized braiding protocol for the algorithm and a scalable architecture for its implementation with an arbitrary number of qubits. We visualize the algorithm protocol in real time and space by computing the non-equilibrium density of states, which is proportional to the time-dependent differential conductance, and the non-equilibrium charge density, which assigns a unique signature to each final state of the algorithm.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Localized growth and remodelling drives spongy mesophyll morphogenesis

The spongy mesophyll is a complex, porous tissue found in plant leaves that enables carbon capture and provides mechanical stability. Unlike many other biological tissues, which remain confluent throughout development, the spongy mesophyll must develop from an initially confluent tissue into a tortuous network of cells with a large proportion of intercellular airspace. How the airspace in the spongy mesophyll develops while the tissue remains mechanically stable is unknown. Here, we use computer simulations of deformable polygons to develop a purely mechanical model for the development of the spongy mesophyll tissue. By stipulating that cell wall growth and remodelling occurs only near void space, our computational model is able to recapitulate spongy mesophyll development observed in Arabidopsis thaliana leaves. We find that robust generation of pore space in the spongy mesophyll requires a balance of cell growth, adhesion, stiffness and tissue pressure to ensure cell networks become porous yet maintain mechanical stability. The success of this mechanical model of morphogenesis suggests that simple physical principles can coordinate and drive the development of complex plant tissues like the spongy mesophyll.

Science & Technology - Other Topics↗

Coupling of multiscale imaging analysis and computational modeling for understanding thick cathode degradation mechanisms

Here, using a thick NMC811 (LiNi 0.8 Mn 0.1 Co 0.1 O 2 ) electrode as an example, we present a macro- to nanoscale 2D and 3D imaging analysis approach coupled with 4D (space + time) computational modeling to probe its degradation mechanism in a lithium-ion battery cell. Particle cracking increases and contact loss between particles and carbon-binder domain are observed to correlate with the cell degradation. This study unravels that the reaction heterogeneity within the thick cathode caused by the unbalanced electron conduction is the main cause of the battery degradation over cycling. The increased heterogeneity in the system will entail more cathode regions where the degree of active material utilization is uneven, leading to higher probabilities of particle cracking. These findings shed light on the crucial role of the electronic and ionic transportation networks in the performance deterioration of the thick cathode. They also provide guidance for cathode architecture optimization and performance improvement.

25 ENERGY STORAGE↗

Computational discovery of ultra-strong, stable, and lightweight refractory multi-principal element alloys. Part I: design principles and rapid down-selection

Abstract Refractory metal-based multi-principal element alloys (MPEAs) are compelling materials for high-temperature (1000–2000 K) structural applications. However, only a minuscule fraction of their vast and heterogeneous compositional design space has been explored, leaving many potentially interesting alloys undiscovered. In this two-part work, a large region of the 11-element Al-Cr-Fe-Hf-Mo-Nb-Ta-Ti-V-W-Zr design space is computationally explored to identify refractory MPEAs with simultaneously high yield strength or specific yield strength and body-centered cubic (BCC) solid solution stability. In Part I , two case studies illuminate key factors and considerations in the yield strength versus phase stability tradeoff, provide guidelines for narrowing the expansive design space, and identify many candidates predicted to be stronger than refractory MPEAs reported to date, with BCC phase stability. Our findings indicate that medium entropy ternary alloys can outperform alloys with more elements and highlight the importance of exploring regions away from the equiatomic center of composition space.

36 MATERIALS SCIENCE↗

Assessment of the burning-plasma operational space in ITER by using a control-oriented core-SOL-divertor model

In future tokamaks, the control of burning plasmas will require careful regulation of the plasma density and temperature. Along with the design of effective burn-control systems, understanding how the fusion power varies in the density-temperature space is vital for the operation of fusion power plants. Here in this work, the steady-state operational space of ITER is studied using a control-oriented core-plasma model coupled to a two-point model of the scrape-off-layer (SOL) and divertor regions. The two models are coupled through the exchange of input-output parameters. The deuterium and tritium recycling from the wall are output parameters of the SOL-divertor model that are used as input parameters in the core-plasma density balance. Furthermore, the separatrix temperature, which is an output parameter of the SOL-divertor model, is incorporated into the radial core-plasma temperature profiles. Therefore, the temperature-dependent power balance of the plasma core is intimately linked to the SOL-divertor model. Both the power entering the SOL from the core, as determined by the core-plasma power balance, and the separatrix density, as dictated by the core-plasma density balance, are input parameters to the SOL-divertor model. They are control knobs in the SOL-divertor model that can be regulated using the core-plasma actuators: auxiliary power and pellet injection. There are various operational limitations, such as the saturation of the aforementioned actuators, that will prevent ITER from accessing certain high-fusion plasma regimes. The achievable tritium concentration in the fueling lines and the maximum sustainable heat load on the divertor will impose further restrictions. By accounting for these limitations, the ITER operational space is computed based on the coupled core-SOL-divertor model and visualized using Plasma Operation Contour (POPCON) plots that map performance metrics, such as the fusion to auxiliary power ratio, over the density-temperature space. Comparisons are drawn between plasmas with different recycling, confinement, and SOL-divertor conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Robust Implicit Adaptive Low Rank Time-Stepping Methods for Matrix Differential Equations

In this work, we develop implicit rank-adaptive schemes for time-dependent matrix differential equations. The dynamic low rank approximation (DLRA) is a well-known technique to capture the dynamic low rank structure based on Dirac–Frenkel time-dependent variational principle. In recent years, it has attracted a lot of attention due to its wide applicability. Our schemes are inspired by the three-step procedure used in the rank adaptive version of the unconventional robust integrator (the so called BUG integrator) (Ceruti et al. in BIT Numer Math 62(4):1149–1174, 2022) for DLRA. First, a prediction (basis update) step is made computing the approximate column and row spaces at the next time level. Second, a Galerkin evolution step is invoked using an implicit solves for the small core matrix. Finally, a truncation is made according to a prescribed error threshold. Since the DLRA is evolving the differential equation projected on to the tangent space of the low rank manifold, the error estimate of the BUG integrator contains the tangent projection (modeling) error which cannot be easily controlled by mesh refinement. This can cause convergence issue for equations with cross terms. To address this issue, we propose a simple modification, consisting of merging the row and column spaces from the explicit step truncation method together with the BUG spaces in the prediction step. In addition, we propose an adaptive strategy where the BUG spaces are only computed if the residual for the solution obtained from the prediction space by explicit step truncation method, is too large. Here, we prove stability and estimate the local truncation error of the schemes under assumptions. We benchmark the schemes in several tests, such as anisotropic diffusion, solid body rotation and the combination of the two, to show robust convergence properties.

97 MATHEMATICS AND COMPUTING↗

Principal Landau determinants

We reformulate the Landau analysis of Feynman integrals with the aim of advancing the state of the art in modern particle-physics computations. We contribute new algorithms for computing Landau singularities, using tools from polyhedral geometry and symbolic/numerical elimination. Inspired by the work of Gelfand, Kapranov, and Zelevinsky (GKZ) on generalized Euler integrals, we define the principal Landau determinant of a Feynman diagram. We illustrate with a number of examples that this algebraic formalism allows to compute many components of the Landau singular locus. We adapt the GKZ framework by carefully specializing Euler integrals to Feynman integrals. For instance, ultraviolet and infrared singularities are detected as irreducible components of an incidence variety, which project dominantly to the kinematic space. We compute principal Landau determinants for the infinite families of one-loop and banana diagrams with different mass configurations, and for a range of cutting-edge Standard Model processes. Furthermore, our algorithms build on the Julia package this http URL and are implemented in the new open-source package this http URL available at this https URL.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗