SEARCH · Engineering Papers

Results for “computers”

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 1,333 records · Page 74

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Adaptive simulations enable computational design of electron beam processing of nanomaterials with supersonic micro-jet precursor

Focused Electron Beam Induced Processing (FEBIP) is a powerful tool for the “direct-write” of nanomaterials with the possibility of atomistic control on suspended 2D material substrates. FEBIP capabilities have been significantly expanded by using a localized jet-based delivery of precursors, and especially when a thermally energized supersonic micro-jet enhances the delivery of mass flux along with controlling the far-from-equilibrium thermodynamic state of adsorbed adatoms. The possibilities of growing nanomaterials with “dialed-in” composition with ultra-high growth rates and aspect ratios up to 100:1 have been demonstrated using the supersonic micro-jet-FEBIP. Bringing this scientific discovery to the level of maturity required for practical applications in additive nanomanufacturing requires simulation tools that are capable of capturing the complex flow physics of micro-jet-substrate interactions that bridge a wide range of flow regimes from the high-density gas micro-jet expanding into the vacuum environment of FEBIP. To address this significant computational challenge, a new approach has been developed and described in this work for a multiscale adaptive DSMC (Direct Simulation Monte Carlo) algorithm to predict the micro-jet gas dynamics in an FEBIP environment, spanning the full range of flow regimes from low Knudsen (Kn) number O(0.01) continuum flow to high Kn of O(10) for the molecular flow within a unified computational framework. Here, the fundamental principles of the adaptive DSMC algorithm are described, its viability as a DSMC technique is demonstrated, and computational improvements for the benchmark cases are discussed in the context of advancing 3D nanofabrication with FEBIP. Ultimately, combining the first principle simulations via adaptive DSMC with the complementary experimental data will enable the creation of the powerful CAD tools for in silico design and optimal operation of micro-jet-FEBIP.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A Poisson equation method for prescribing fully developed non-Newtonian inlet conditions for computational fluid dynamics simulations in models of arbitrary cross-section

Prescribing inlet boundary conditions for computational fluid dynamics (CFD) simulations of internal flow in complex geometries such as anatomical vascular models is challenging. In the absence of patient-specific inlet velocity data, a common approach for long blood vessels is to assume that the inlet flow is fully developed. In vessels of irregular cross section, however, prescribing fully developed conditions is complicated due to the lack of a general closed-form analytical solution. In this study, we develop a simple Poisson equation method for prescribing fully developed inlet conditions for the flow of either Newtonian or non-Newtonian fluids in CFD models of arbitrary cross-section. We first derive the generalized Poisson equation for fully developed flow of a non-Newtonian fluid and we then develop and verify a methodology for numerically computing the solution on any planar boundary domain. In addition, we develop a simple extension of the method for prescribing a non-orthogonal inlet velocity that represents fully developed flow from an upstream tube that is connected to the CFD inlet at a non-orthogonal angle. This may be used to investigate a common source of uncertainty in CFD simulations of internal flow that is due to a lack of information concerning the exact streamwise flow direction at the inlets. Comparison to several Newtonian and non-Newtonian benchmark verification solutions shows the method to be extremely accurate. As a practical demonstration case, we use the method to prescribe fully developed conditions on multiple non-circular inlets for the non-Newtonian flow of blood in a patient-specific model of the inferior vena cava (IVC). Finally, we further demonstrate the utility of the method by performing a sensitivity study using the patient-specific IVC model, wherein we investigate the influence of inlet velocity flow direction on the non-Newtonian IVC hemodynamics. Given its simplicity and computational efficiency, the method is shown to be far superior to alternative approaches for prescribing fully developed inlet conditions in such complicated geometries. In conclusion, to facilitate the adoption of our Poisson equation method, we have distributed our OpenFOAM source code and the associated test cases from this study as open-source software.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Computational quantum chemistry of metal–organic frameworks

Metal–organic frameworks (MOFs) have premium exceptional properties for a variety of functions, such as gas separation and storage and catalysis. The large variety of possible inorganometallic nodes and organic linkers provide an almost unlimited number of combinations for assembling MOFs, which makes the experimental characterization and examination of all potentially useful combinations practically impossible. Furthermore, experimental studies of MOFs typically fall short in uncovering crucial details regarding their mechanisms of action or the molecular details responsible for their functional properties, such as the nature of adsorbate binding or the structures of transition states. Computational modeling has, therefore, become an efficient and important tool for strategizing the functionalization of MOFs and explicating the mechanisms of their functions. Furthermore, we review the computational methodologies used for computational studies of MOFs, especially Kohn–Sham density functional theory and combined quantum mechanical and molecular mechanical methods for calculating their structural, electronic, and magnetic properties, as well as for understanding the mechanisms of MOFs' applications to magetic devices, thermal conduction, gas adsorption, separation, storage, and sensing, thermal catalysis, photocatalysis, and electrocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Advanced Computing is at the Forefront of a New “Moonshot” Revolutionizing the North American Power Grid

In the 50+ years since the first humans landed on the moon, computing has grown at breakneck speed. We are faced with another challenge that is just as daunting, and just as important to overcome-modernizing the North American electric power grid-and high-performance computing (HPC) systems with specialized software will be an important element in rising to this challenge. We describe at a high level how software developed in the ExaSGD project addresses this "moonshot" goal by utilizing exascale computing and a novel high performance solver software stack to support the mission of decarbonizing power grid operations in an environment of uncertain weather and climate. To reach the exascale benchmark the team has made a number of first-of-their-kind innovations, including novel method for stochastic optimization, fine grained parallel methods for modeling power systems, and GPU resident sparse numerical linear solvers.

17 WIND ENERGY↗

DOE OSTI2025

Adaptive Computing for Scale-Up Problems

Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocation adaptive to some objective such as optimization or model training. The framework enables the characterization and management of uncertainties associated with predictive models of complex systems when scale-up questions lead to significant model extrapolation. A key advancement of this framework is its integration of multi-fidelity surrogate modeling, uncertainty management, and automated orchestration of various computing and experimentation resources into a single integrated software package. This enables efficient multi-fidelity modeling across multiple computing resources by incorporating real-world constraints such as relative queue times and throughput on individual machines into the multi-fidelity sampling decision. We discuss applications of this framework to problems in the renewable energy space, including biofuels production, material synthesis, perovskite crystal growth, and building electrical loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2001

Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction

Electroencephalographic (EEG) recordings were made while 16 participants performed versions of a personal-computer-based flight simulation task of low, moderate, or high difficulty. As task difficulty increased, frontal midline theta EEG activity increased and alpha band activity decreased. A participant-specific function that combined multiple EEG features to create a single load index was derived from a sample of each participant's data and then applied to new test data from that participant. Index values were computed for every 4 s of task data. Across participants, mean task load index values increased systematically with increasing task difficulty and differed significantly between the different task versions. Actual or potential applications of this research include the use of multivariate EEG-based methods to monitor task loading during naturalistic computer-based work.

NASA Discipline Space Human Factors↗

DOE OSTI2025

Advanced Computing Annual Report 2024

In fiscal year (FY) 2024, the National Renewable Energy Laboratory (NREL) took a major leap forward with the completed full buildout of Kestrel, the Office of Energy Efficiency and Renewable Energy's newest high-performance computing (HPC) system. Kestrel is already supporting science across the portfolio, bringing roughly 44 petaflops of computing power, which is more than five times the capacity of our previous supercomputer, Eagle. By delivering greater GPU capacity, Kestrel enables faster progress in artificial intelligence (AI) and opens new avenues in energy research - from defining long-term planning scenarios to accommodate a growing power system to material discovery to improving energy efficiency in photovoltaics (PV). Across the portfolio, research is being accelerated by Kestrel's impressive power. During FY24, 427 projects and more than 700 researchers used NREL's HPC, supporting the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy across 13 funding areas. Through these collaborations, researchers produced more than 450 technical outputs, including 195 articles in peer-reviewed publications, pushing the boundaries of science and engineering. This year's report features new sections spotlighting the expanding roles of Artificial Intelligence and Accelerated Computing. We also introduce an early career section to celebrate the accomplishments of our up-and-coming researchers, whose pioneering work is shaping the future of energy. We hope you enjoy the new insights and discoveries highlighted in these pages.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A Computational Framework to Accelerate the Discovery of Perovskites for Solar Thermochemical Hydrogen Production: Identification of Gd Perovskite Oxide Redox Mediators

A high-throughput computational framework to identify novel multinary perovskite redox mediators is presented, and this framework is applied to discover the Gd-containing perovskite oxide compositions Gd 2 BB'O 6 , GdA'B 2 O 6 , and GdA'BB'O 6 that split water. The computational scheme uses a sequence of empirical approaches to evaluate the stabilities, electronic properties, and oxygen vacancy thermodynamics of these materials, including contributions to the enthalpies and entropies of reduction, ΔH TR and ΔS TR . This scheme uses the machine-learned descriptor τ to identify compositions that are likely stable as perovskites, the bond valence method to estimate the magnitude and phase of BO 6 octahedral tilting and provide accurate initial estimates of perovskite geometries, and density functional theory including magnetic- and defect-sampling to predict STCH-relevant properties. Eighty-three promising STCH candidate perovskite oxides down-selected from 4392 Gd-containing compositions are reported, three of which are referred to experimental collaborators for characterization and exhibit STCH activity. Our results demonstrate that the high-throughput computational scheme described herein—which is used to evaluate Gd-containing compositions but can be applied to any multinary perovskite oxide compositional space(s) of interest—accelerates the discovery of novel STCH active redox mediators with reasonable computational expense.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Harnessing High‐Throughput Computational Methods to Accelerate the Discovery of Optimal Proton Conductors for High‐Performance and Durable Protonic Ceramic Electrochemical Cells

Abstract The pursuit of high‐performance and long‐lasting protonic ceramic electrochemical cells (PCECs) is impeded by the lack of efficient and enduring proton conductors. Conventional research approaches, predominantly based on a trial‐and‐error methodology, have proven to be demanding of resources and time‐consuming. Here, this work reports the findings in harnessing high‐throughput computational methods to expedite the discovery of optimal electrolytes for PCECs. This work methodically computes the oxygen vacancy formation energy (E V ), hydration energy (E H ), and the adsorption energies of H 2 O and CO 2 for a set of 932 oxide candidates. Notably, these findings highlight BaSn x Ce 0.8‐x Yb 0.2 O 3‐δ (BSCYb) as a prospective game‐changing contender, displaying superior proton conductivity and chemical resilience when compared to the well‐regarded BaZr x Ce 0.8‐x Y 0.1 Yb 0.1 O 3‐δ (BZCYYb) series. Experimental validations substantiate the computational predictions; PCECs incorporating BSCYb as the electrolyte achieved extraordinary peak power densities in the fuel cell mode (0.52 and 1.57 W cm −2 at 450 and 600 °C, respectively), a current density of 2.62 A cm −2 at 1.3 V and 600 °C in the electrolysis mode while demonstrating exceptional durability for over 1000‐h when exposed to 50% H 2 O. This research underscores the transformative potential of high‐throughput computational techniques in advancing the field of proton‐conducting oxides for sustainable power generation and hydrogen production.

08 HYDROGEN↗

DOE OSTI2021

An identity privacy scheme for blockchain‐based on edge computing

Abstract Blockchain has decentralization characteristics and requires more targeted security schemes to protect user privacy. In contrast, existing signature schemes have many high‐complexity operations and impose an enormous computational burden on wireless nodes. This article proposes a light‐weighted identity privacy scheme for blockchain‐based on edge computing. We construct linkable identity privacy and non‐linkable identity privacy, which can resist collusion attacks while virtually guaranteeing blockchain nodes' identity privacy. Since edge computing offloads heavily, the proposed scheme has lower computational complexity than the existing techniques.

Mu, Rui↗

DOE OSTI2021

Physics‐based iterative reconstruction for dual‐source and flying focal spot computed tomography

Purpose For single‐source helical Computed Tomography (CT), both Filtered‐Back Projection (FBP) and statistical iterative reconstruction have been investigated. However, for dual‐source CT with flying focal spot (DS‐FFS CT), a statistical iterative reconstruction that accurately models the scanner geometry and acquisition physics remains unknown to researchers. Therefore, our purpose is to present a novel physics‐based iterative reconstruction method for DS‐FFS CT and assess its image quality. Methods Our algorithm uses precise physics models to reconstruct from the native cone‐beam geometry and interleaved dual‐source helical trajectory of a DS‐FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture. In addition, we design forward system models to compute the locations of deflected focal spots, the dimension, and sensitivity of voxels and detector units, as well as the length of intersection between x‐rays and voxels. The forward system models further represent the coordinated movement between the dual sources by computing their x‐ray coverage gaps and overlaps at an arbitrary helical pitch. With the above models, we reconstruct images by an advanced Consensus Equilibrium (CE) numerical method to compute the maximum a posteriori estimate to a joint optimization problem that simultaneously fits all models. Results We compared our reconstruction with Siemens ADMIRE, which is the clinical standard hybrid iterative reconstruction (IR) method for DS‐FFS CT, in terms of spatial resolution, noise profile, and image artifacts through both phantoms and clinical scan datasets. Experiments show that our reconstruction has a higher spatial resolution, with a Task‐Based Modulation Transfer Function (MTF task ) consistently higher than the clinical standard hybrid IR. In addition, our reconstruction shows a reduced magnitude of image undersampling artifacts than the clinical standard. Conclusions By modeling a precise geometry and avoiding data rebinning or interpolation, our physics‐based reconstruction achieves a higher spatial resolution and fewer image artifacts with smaller magnitude than the clinical standard hybrid IR.

Wang, Xiao↗

DOE OSTI2022

Virtual Infrastructure Twins: Software Testing Platforms for Computing-Instrument Ecosystems

Science ecosystems are being built by federating computing systems and instruments located at geographically distributed sites over wide-area networks. These computing-instrument ecosystems are expected to support complex workflows that incorporate remote, automated AI-driven science experiments. Their realization, however, requires various designs to be explored and software components to be developed, in order to support the orchestration of distributed computations and experiments. It is often too expensive, infeasible, or disruptive for the entire ecosystem to be available during the typically long software development and testing periods. We propose a Virtual Infrastructure Twin (VIT) of the ecosystem that emulates its network and computing components, and incorporates its instrument software simulators. It provides a software environment nearly identical to the ecosystem to support early development and testing, and design space exploration. We present a brief overview of previous digital infrastructure twins that culminated in the VIT concept, including (i) the virtual science network environment for developing software-defined networking solutions, and (ii) the virtual federated science instrument environment for testing the federation software stack and remote instrument control software. We briefly describe VITs for Nion microscope steering and access to GPU systems.

Rao, Nageswara↗

DOE OSTI2022

A Non-perturbative Approach to Computing Seismic Normal Modes in Rotating Planets

In this work, a continuous Galerkin method based approach is presented to compute the seismic normal modes of rotating planets. Special care is taken to separate out the essential spectrum in the presence of a fluid outer core using a polynomial filtering eigensolver. The relevant elastic-gravitational system of equations, including the Coriolis force, is subjected to a mixed finite-element method, while self-gravitation is accounted for with the fast multipole method. Our discretization utilizes fully unstructured tetrahedral meshes for both solid and fluid regions. The relevant eigenvalue problem is solved by a combination of several highly parallel and computationally efficient methods. We validate our three-dimensional results in the non-rotating case using analytical results for constant elastic balls, as well as numerical results for an isotropic Earth model from standard “radial” algorithms. We also validate the computations in the rotating case, but only in the slowly-rotating regime where perturbation theory applies, because no other independent algorithms are available in the general case. The algorithm and code are used to compute the point spectra of eigenfrequencies in several Earth and Mars models studying the effects of heterogeneity on a large range of scales.

58 GEOSCIENCES↗

DOE OSTI2026

Quantum computing approach for building surface sunlit in urban-scale energy modeling

Solar shadow calculations are needed in building energy modeling and performance simulation of PV systems installed on roofs or facades of buildings. We present a quantum computing approach for calculation of building surface sunlit fractions by recasting solar visibility as a binary optimization problem solved by quantum annealing. Each triangulated surface centroid is encoded as a binary qubit indicating sunlit or shaded status. Geometric visibility constraints are derived from the Möller-Trumbore intersection algorithm and converted into a constrained quadratic binary model compatible with contemporary quantum annealers. The coefficients were embedded to D-Wave quantum computer. To demonstrate feasibility, we conducted a case study in San Francisco for a target building with 52 triangles and roughly 2700 nearby triangles within 50 m evaluated at representative winter and summer solar positions. The results demonstrated that quantum annealing can reliably calculate and distinguish sunlit from shaded surfaces. Quantum samples achieved average accuracy exceeding 92.4 %, with the aggregate surface-level agreement approaching 99.9 %. The outputs of quantum computers agreed closely with classical algorithms, indicating practical feasibility and promising scalability. Finally, the hourly sunlit fractions of building surfaces can be obtained for urban energy modelling. This is the first study to apply quantum computing to the solar shadow and building surface sunlit calculation. It introduces a new paradigm that differs fundamentally from traditional approaches.

Deng, Zhipeng↗

DOE OSTI2020

Impact of the adhesion-force lever-arm “a” on the Rock ‘n’ Roll resuspension model and how to compute it from contact mechanics

The paper proposes a methodology to fill a gap in the context of mechanistic particle resuspension models related to the adhesion and lift force lever arms ( a ): to date, only one experimental observation exists from a centrifuge experiment with a specific particle/surface pair which found r P / a = 100 and assumed that the contact points were symmetric with respect to the particle center. However, the Reeks and Hall’s (2001) model is highly sensitive to this parameter, which therefore cannot be used as-is for different particle diameters or materials. This paper proposes a methodology to compute the adhesion and lift force lever arms ( a A and a L , respectively) based on Atomic Force Microcopy (AFM) surface roughness measurements and contact mechanics arguments. In practice, a spherical, perfectly smooth particle is brought in contact (computationally) with the measured surface roughness and is then rotated by the point of contact to determine the two or more asperities over which the particle would sit on the surface. A method to compute the asperity radius of curvature is devised to compute the asperity deformation and the correct partitioning of the total deformation between the particle and the asperity is presented. For the glass surface under consideration, the asperity deformation was negligible, i.e., it was below the van der Waals radius, which is the limit of applicability of contact mechanics. The Reeks and Hall’s (2001) model is then extended to account for the joint probability of a A and a L . Significant differences in particle resuspended after a day in common atmospheric conditions are found between the approach that uses the lever-arm distributions or their mean values. Also, important differences are found when comparing the lever-arm distribution and r P / a = 100. Hence, one should be cautious of using r P / a = 100 for surface/particle pairs differing from those in the original study. Finally, limits of applicability of the theories used are presented together with considerations regarding how to extend the method to non-spherical particles and different materials.

42 ENGINEERING↗

DOE OSTI2024

Direct, simple and efficient computation of all components of the virtual-casing magnetic field in axisymmetric geometries with Kapur–Rokhlin quadrature

In a recent publication (Toler et al., J. Plasma Phys., vol. 89, issue 2, 2023, p. 905890210), we demonstrated that for axisymmetric geometries, the Kapur–Rokhlin quadrature rule provided an efficient and high-order accurate method for computing the normal component, on the plasma surface, of the magnetic field due to the toroidal current flowing in the plasma, via the virtual-casing principle. The calculation was indirect, as it required the prior computation of the magnetic vector potential from the virtual-casing principle, followed by the computation of its tangential derivative by Fourier differentiation, to obtain the normal component of the magnetic field. Our approach did not provide the other components of the virtual-casing magnetic field. In this letter, we show that a more direct and more general approach is available for the computation of the virtual-casing magnetic field. The Kapur–Rokhlin quadrature rule accurately calculates the principal value integrals in the expression for all the components of the magnetic field on the plasma boundary, and the numerical error converges at a rate nearly as high as the indirect method we presented previously.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Rechargeable Alkali-Ion Battery Materials: Theory and Computation

Since its development in the 1970s, the rechargeable alkali-ion battery has proven to be a truly transformative technology, providing portable energy storage for devices ranging from small portable electronics to sizable electric vehicles. Here, we present a review of modern theoretical and computational approaches to the study and design of rechargeable alkali-ion battery materials. Additionally, starting from fundamental thermodynamics and kinetics phenomenological equations, we rigorously derive the theoretical relationships for key battery properties, such as voltage, capacity, alkali diffusivity, and other electrochemically relevant computable quantities. We then present an overview of computational techniques for the study of rechargeable alkali-ion battery materials, followed by a critical review of the literature applying these techniques to yield crucial insights into battery operation and performance. Finally, we provide perspectives on outstanding challenges and opportunities in the theory and computation of rechargeable alkali-ion battery materials.

25 ENERGY STORAGE↗

DOE OSTI2026

Reducing the Cost of CCSD Basis Set Extrapolation in Ab Initio Computational Thermochemistry

Here, a series of approximations to CCSD contributions in computational model chemistries is presented in the context of kcal mol –1 , kJ mol –1 , and 20 cm –1 theoretical predictions of total atomization energies, benchmarked within the HEAT+CH 4 test suite. A specific set of circumstances where MP2, without empirical scaling, may be used as an effective intermediate in the first two of these accuracy ranges was determined. However, SDQ-MP4, a method long used in pursuit of kcal mol –1 accuracy but relatively unstudied in the subchemical accuracy community, offers significant improvement over the quality of MP2 as a basis-set intermediate at significantly reduced cost compared to CCSD. Given this, we argue for SDQ-MP4 as the de facto CCSD basis-set intermediate in sub-chemical accuracy calculations when CCSD in a desired basis set becomes unaffordable. We additionally report on a “CBS-like” scheme, where MP2 and SDQ-MP4 are used in conjunction to create a “cheap” three-part approximation of large CCSD basis set limits. The data for the CCSD approximation schemes are organized in such a way that model chemistry developers can locate an analog of their current approach for the CCSD basis set limit and explore alternative intermediates that either decrease computational cost or increase computational accuracy. We also show, for a handful of molecules, that SDQ-MP4 shows promise as an effective basis-set intermediate for harmonic and fundamental frequency computations, allowing for zero-point corrections of nearly CCSD(T)/ANO1 quality using simple composite methods that only require CCSD(T)/ANO0.

Thorpe, James H. [Argonne National Laboratory (ANL↗