Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “computer experiment”

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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Investigation of structural effects of aromatic compounds on sooting tendency with mechanistic insight into ethylphenol isomers

Small aromatic molecules with oxygen-containing functional groups are a promising class of fuel additives, as they can be readily sourced from depolymerized lignin. These oxygenated aromatic compounds (OACs) show a lower sooting tendency than aromatic hydrocarbons, but OACs having alkyl groups such as ethylphenol show a higher sooting tendency than other OACs such as phenol and anisole, despite the oxygen moiety. In this study, we investigate the relationship between chemical structure and soot precursor formation to explain observed differences in the sooting tendency of OACs and to gain insight into how alkyl or oxygenated substituents on the aromatic ring affect soot precursor formation. The weakest bond for 15 aromatic compounds was identified and cleavage of these bonds was shown to generate either benzyl or phenoxy radicals. A linear relationship between standard enthalpy of formation (ΔH f o ) of these radicals and the yield sooting index (YSI) was found, and thus ΔH f ° can be applied as a metric to estimate YSIs of various aromatic compounds; higher ΔH f o of a radical indicates an increase in the radical reactivity and leads to more soot precursor formation. Flow reactor experiments were performed for 2-ethylphenol and 3-ethylphenol to elucidate how ortho and meta substitution effects the sooting tendency. Soot precursors were identified from the experiment and their formation pathways were investigated computationally. 2-ethylphenol produces more oxygenated products than 3-ethylphenol since the ortho position has increased resonance stabilization of radical intermediates, which leads to lower YSI. Overall, these results further inform the selection of potential biomass-derived fuel blendstocks that have favorable sooting tendencies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Observation of Conformational Simplification upon N-methylation on Amino Acid Iodide Clusters

This work reports a surprising observation that methylation of glycine-iodide cluster leads to less conformations and spectroscopic simplicity. We used cryogenic “iodide-tagging” negative ion photoelectron spectroscopy (NIPES) to probe specific binding sites of three N-methylated glycine derivatives, i.e., N-methylglycine (sarcosine, Sar), N,N-dimethylglycine (Dmg), and N,N,N-trimethylglycine (glycine betaine, Bet). NIPES reveals a progressive spectral simplification of the iodide clusters with increasing methylation due to the presence of fewer contributing structures, a reduction in the number of NH and OH hydrogen bond donating sites, and an increase in the basicity of the amine. Low energy conformers and tautomers of each cluster are computationally identified, and those observed in the experiments are assigned based on excellent agreement between the NIPE spectra and theoretical simulations. Clusters with zwitterionic structures for Gly, Sar, and Dmg are found to be less stable than their canonical forms and do not contribute to the observed spectra. This work demonstrates the power of “iodide-tagging” NIPES in probing conformational and tautomeric structures of amino acid-iodide clusters and provides a molecular level understanding on the effect of methyl substitution on the active binding sites of amino acids.

Cao, Wenjin↗
DOE OSTI2023

The Structure of Boron Monoxide

Boron monoxide (BO), prepared by the thermal condensation of tetrahydroxydiboron, was first reported in 1955; however, its structure could not be determined. With the recent attention on boron-based two-dimensional materials, such as borophene and hexagonal boron nitride, there is renewed interest in BO. A large number of stable BO structures have been computationally identified, but none are supported by experiments. The consensus is that the material likely forms a boroxine-based two-dimensional material. Herein, we apply advanced 11 B NMR experiments to determine the relative orientations of B(B)O 2 centers in BO. We find that the material is composed of D 2h -symmetric O 2 B–BO 2 units that organize to form larger B 4 O 2 rings. Further, powder diffraction experiments additionally reveal that these units organize to form two-dimensional layers with a random stacking pattern. This observation is in agreement with earlier density functional theory (DFT) studies that showed B 4 O 2 -based structures to be the most stable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Storm Surge Modeling as an Application of Local Time-Stepping in MPAS-Ocean

This paper presents the first practical application of local time-stepping (LTS) schemes in the Model for Prediction Across Scales-Ocean (MPAS-O). We use LTS schemes in a single-layer, global ocean model that predicts the storm surge around the eastern coast of the United States during Hurricane Sandy. The variable-resolution meshes used are of unprecedentedly high resolution in MPAS-O, containing cells as small as 125 m wide in Delaware Bay. It is shown that a particular, third-order LTS scheme (LTS3) produces sea-surface height solutions that are of comparable quality to solutions produced by the classical four-stage, fourth-order Runge-Kutta method (RK4) with a uniform time step on the same meshes. Furthermore, LTS3 is up to 35% faster in the best cases considered, where the number of cells using the coarse time-step relative to those using the fine time-step is as low as 1:1. This shows that LTS schemes are viable for use in MPAS-O with the added benefit of substantially less computational cost. The results of these performance experiments inform us of the requirements for efficient mesh design and configuration of LTS regions for LTS schemes. In particular, we see that for LTS to be efficient on a given mesh, it is important to have enough cells using the coarse time-step relative to those using the fine time-step, typically at least 1:5 to see an increase in performance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ising meson spectroscopy on a noisy digital quantum simulator

Abstract Quantum simulation has the potential to be an indispensable technique for the investigation of non-perturbative phenomena in strongly-interacting quantum field theories (QFTs). In the modern quantum era, with Noisy Intermediate Scale Quantum (NISQ) simulators widely available and larger-scale quantum machines on the horizon, it is natural to ask: what non-perturbative QFT problems can be solved with the existing quantum hardware? We show that existing noisy quantum machines can be used to analyze the energy spectrum of several strongly-interacting 1+1D QFTs, which exhibit non-perturbative effects like ‘quark confinement’ and ‘false vacuum decay’. We perform quench experiments on IBM’s quantum simulators to compute the energy spectrum of 1+1D quantum Ising model with a longitudinal field. Our results demonstrate that digital quantum simulation in the NISQ era has the potential to be a viable alternative to numerical techniques such as density matrix renormalization group or the truncated conformal space methods for analyzing QFTs.

Science & Technology - Other Topics↗
DOE OSTI2023

Boundaries of quantum supremacy via random circuit sampling

Abstract Google’s quantum supremacy experiment heralded a transition point where quantum computers can evaluate a computational task, random circuit sampling, faster than classical supercomputers. We examine the constraints on the region of quantum advantage for quantum circuits with a larger number of qubits and gates than experimentally implemented. At near-term gate fidelities, we demonstrate that quantum supremacy is limited to circuits with a qubit count and circuit depth of a few hundred. Larger circuits encounter two distinct boundaries: a return of a classical advantage and practically infeasible quantum runtimes. Decreasing error rates cause the region of a quantum advantage to grow rapidly. At error rates required for early implementations of the surface code, the largest circuit size within the quantum supremacy regime coincides approximately with the smallest circuit size needed to implement error correction. Thus, the boundaries of quantum supremacy may fortuitously coincide with the advent of scalable, error-corrected quantum computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions

Particle collisions at accelerators like the Large Hadron Collider (LHC), recorded by experiments such as ATLAS and CMS, enable precise standard model measurements and searches for new phenomena. Simulating these collisions significantly influences experiment design and analysis but incurs immense computational costs, projected at millions of CPU-years annually during the high luminosity LHC (HL-LHC) phase. Currently, simulating a single event with Geant4 consumes around 1000 CPU seconds, with calorimeter simulations especially demanding. To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). Our RBM architecture is tailored for D-Wave’s Pegasus-structured advantage quantum annealer for sampling, leveraging the flux bias for conditioning. This approach combines classical RBMs as universal approximators for discrete distributions with quantum annealing’s speed and scalability. We also introduce an adaptive method for efficiently estimating effective inverse temperature, and validate our framework on Dataset 2 of CaloChallenge.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Pitfalls in parameters: practical process development in chemical vapor processing of SiC

Vapor processing of high-value materials, such as silicon carbide (SiC), is of interest for many industries, including aerospace and energy production. Chemical vapor infiltration (CVI) of additively manufactured components is an especially attractive manufacturing process currently in development. Here, a novel development workflow is demonstrated with the H 2 –CH 3 SiCl 3 gas system for SiC to accelerate the process optimization of CVI SiC. A combination of calculated thermodynamics and high-throughput experimental chemical vapor deposition (CVD) coatings substantially reduced the experiments required with slow CVI processes. The computational results accurately predicted changes in the thermodynamic conditions tested, while CVD coatings – characterized by Raman spectroscopy – addressed changes in kinetic parameters. This workflow is also applicable to other vapor-processing systems, such as pyrolytic carbon, ZrC, or Si 3 N 4 .

Lamm, Benjamin W. [Oak Ridge National Laboratory (↗
DOE OSTI2025

Improving noisy free-energy measurements by adding more noise

Estimating free-energy differences using nonequilibrium work relations, such as the Jarzynski equality, is hindered by poor convergence when work fluctuations are large. For systems governed by overdamped Langevin dynamics, we propose the counterintuitive approach of adding noise in order to increase the precision of such calculations. Here, by introducing additional stochastic fluctuations to the system and rescaling its potential energy accordingly, we leave the thermodynamics of the system unchanged while increasing its relaxation rate. For a given time-dependent protocol this modification reduces the dissipated reduced work, leading to more accurate free-energy estimates. The method is designed to be used in experiment, and we illustrate its operation using computer simulations applied to two model systems. However, the regime of applicability of this strategy is likely limited, because it requires control of the system's potential energy in a way that is feasible in only a few experimental settings.

Whitelam, Stephen [Lawrence Berkeley National Labo↗
DOE OSTI2020

Efficient I/O for Neural Network Training with Compressed Data

FanStore is a shared object store that enables efficient and scalable neural network training on supercomputers. By providing a global cache layer on node-local burst buffers using a compressed representation, it significantly enhances the processing capability of deep learning (DL) applications on existing hardware. In addition, FanStore allows POSIX-compliant file access to the compressed data in user space. We investigate the tradeoff between runtime overhead and data compression ratio using real-world datasets and applications, and propose a compressor selection algorithm to maximize storage capacity given performance constraints. We consider both asynchronous (i.e., with prefetching) and synchronous I/O strategies, and propose mechanisms for selecting compressors for both approaches. Using FanStore, the same storage hardware can host 2-13x more data for example applications without significant runtime overhead. Empirically, our experiments show that FanStore scales to 512 compute nodes with near linear performance scalability.

Zhang, Zhao↗
DOE OSTI2026

From Reproducible Edge–Cloud Experimentation to Real-World Practice: The E2Clab Experience

Reproducibility is already difficult in distributed systems; on the computing continuum, it becomes substantially harder. Applications that span sensing devices, edge and fog resources, and cloud platforms must be evaluated across heterogeneous hardware, variable network conditions, cross-layer orchestration decisions, and long-running workflow lifecycles. We use E2Clab as a case study to examine these challenges and their implications for experimental methodology. We explain why reproducible experimentation is harder on the continuum, then revisit E2Clab as an initial response based on explicit modeling of infrastructure, workflow lifecycle, and artifacts. Lastly, we discuss how its evolution toward more realistic application settings can be understood through the lens of Translational Computer Science. We argue that reproducible continuum experimentation requires methods that are rigorous enough for research while remaining adaptable to real-world practice.

42 ENGINEERING↗
DOE OSTI2022

AutodiDAQt v1.1.0

AutodiDAQt automates and simplifies writing data acquisition software for spectroscopy and microscopy. After defining only how to communicate with instruments and hardware, autodiDAQt generates user interfaces for long running acquisition applications, handlings data collation and retention, and provides remote communication to analysis computers. This reduces the time to get experiments running from months to hours and increases reliability for scientific experiments. AutodiDAQt metaprograms from instrument drivers directly, where possible.

Stansbury, Conrad↗
DOE OSTI2023

EPiC-GAN: Equivariant point cloud generation for particle jets

With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.

Buhmann, Erik↗
DOE OSTI2020

Nanostructures for Electrical Energy Storage (NEES) (2020 Final Technical Report)

Nanostructures for Electrical Energy Storage (NEES, www.efrc.umd.edu) was an Energy Frontier Research Center supported by the DOE Office of Science, Basic Energy Sciences, from 8/1/2009 to 7/31/2020. Led by the University of Maryland, NEES enjoyed extensive collaborations with its funded partners, including two DOE Laboratories and six universities. The NEES vision has been to reveal a set of scientific insights and design principles that can underpin a next-generation electrical energy storage approach, building on advances in nanoscale science and technology to achieve simultaneous high power and high energy over extended charge/discharge cycling. The vision is motivated by the recognition that scaling into the nano regime opens the door to new physical phenomena and that the tools enlisted in nanoscale research provide major new opportunities for the synthesis not only of materials at molecular scale but for structures at nano scale and above. NEES has translated this vision into its research program based on two observations. First, while the behavior of ions and electrons in electrolytes and in electrode materials is crucial to electrical energy storage (or more appropriately electrochemical energy storage), it is the transport of ion and electron charge between different structural components of a storage device that ultimately determine its performance. With it well recognized that the choice of electrode materials typically constrain ion transport kinetics as well as maximum ion concentration, the search for better electrode materials has been a primary driver of battery research. At the same time the synthesis of electrodes is typically based on aggregation of particles with varying size, shape, and orientation in the electrode. Together with the presence of additional materials to impart electrical conductivity and cohesion to the composite electrode, change in electrode materials is necessarily accompanied by structural changes at the nano/micro scale that are difficult to categorize and manage. From the beginning, NEES’ vision has been to create and study simpler, highly controlled spatial arrangements of known materials as battery components (electrodes, current collectors, and electrolyte) and to understand how design and structure above the molecular scale determines the energy storage performance available from known materials. Second, advances in nanoscience dramatically expanded the portfolio of synthesis methods, structural motifs, and new phenomena available for research. Some of these gave rapid access to new building blocks at the deep nanoscale (e.g., carbon nanotubes grown by self-assembly, nanoscale arrays formed by electrochemical self-alignment, monolayer films controlled by self-limiting reaction). Such advances served as the enabler for the NEES vision to be pursued experimentally through study of 3D structures created and controlled at the nano, micro, and meso scales. Here, we use meso as in the BES MESO Report, implying not only intermediate or varying length scales, but very much the way behavior is influenced by other factors including aggregation of nanocomponents at different densities and spatial configurations, statistical variations in the aggregates, hierarchical architectures in which they can be assembled, or local 3D configurations that result from the architectures. Over its life cycle, NEES has pursued two overarching goals: (1) to understand the scientific fundamentals of electrochemical storage from the nanoscale to the mesoscale; and (2) to create and learn from innovative, controlled, heterogeneous nanostructures, where such nanostructures can enable the first goal and serve as models for future paradigms in energy storage. Specific goals have included: Synthesize heterogeneous nanostructures comprised of multiple materials arranged in controlled fashion and characterize their behavior; Demonstrate and elucidate design principles for achieving simultaneous high power and high energy; Develop materials processes which enable precision control of thin layers and 3D structures; Investigate the impact of artificial interphases on electrode stability during ion insertion/deinsertion; Create dense arrays of nanostructures to understand how the architecture of these assemblies, along with nanostructure design, influences energy storage behavior at the mesoscale; Identify and understand the consequences of nanoconfinement and local inhomogeneities in 3D mesoscale arrays; Develop and apply computational models to stimulate, guide and interpret experiments.

25 ENERGY STORAGE↗
DOE OSTI2022

Design of Transition-Metal/Zeolite Catalysts for Direct Conversion of Coal-derived CO 2 to Aromatics

This project introduces a lab-scale, stand-alone process to produce aromatics from CO 2 . Besides reducing the amount of coal-derived CO 2 emission, this process can provide new approaches to produce valuable chemicals such as benzene, toluene, and xylene (BTX). These chemicals are the starting material in various industries such as pharmaceuticals, polymers, paints, and coatings. The focus of the project is on developing a suitable reaction configuration, feed composition, and catalyst combination that leads to high aromatics selectivity and CO 2 conversions. To achieve these objectives, computational catalysis will accompany hands-on experiments to provide insights to potential transition metals or alloys that may serve as optimal active sites for different steps of the reaction mechanism, such as reduction of CO 2 and dehydration (in the case of alcohol intermediates) and oligomerization (in the case of paraffins or olefins) of intermediate hydrocarbons.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Design of Transition-Metal/Zeolite Catalysts for Direct Conversion of Coal-derived CO 2 to Aromatics

This project introduces a lab-scale, stand-alone process to produce aromatics from CO 2 . Besides reducing the amount of coal-derived CO 2 emission, this process can provide new approaches to produce valuable chemicals such as benzene, toluene, and xylene (BTX). These chemicals are the starting material in various industries such as pharmaceuticals, polymers, paints, and coatings. The focus of the project is on developing a suitable reaction configuration, feed composition, and catalyst combination that leads to high aromatics selectivity and CO 2 conversions. To achieve these objectives, computational catalysis will accompany hands-on experiments to provide insights to potential transition metals or alloys that may serve as optimal active sites for different steps of the reaction mechanism, such as reduction of CO 2 and dehydration (in the case of alcohol intermediates) and oligomerization (in the case of paraffins or olefins) of intermediate hydrocarbons.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

A reactive tracer method for predicting EGS reservoir geometry and thermal lifetime: development and field validation

The project summarized here (DOE Award No. DE-EE0006764) was intended to develop a methodology for predicting advective heat transfer in fracture-dominated crystalline rock. Our goal was to determine if a combination of inert and reactive tracers could adequately constrain the effective heat transfer surface area between an injection-production well pair. Our approach consisted of: 1. developing a novel computational framework; 2. Performing heat and tracer experiments at meso-scale; and 3. Comparing predictions of advective heat transfer to the “true” thermal breakthrough measured at the Altona site. Below is a summary of project activities/findings, a summary of project tasks, and a conclusion

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Spark-Assisted HCCI Residential CHP

A transformative small spark ignition (SI) internal combustion (IC) engine fueled with natural gas was developed for combined heat and power (CHP) applications using a combination of cycle simulations, computational fluid dynamics (CFD) modeling, and engine experiments. The resulting 1 kW CHP engine was demonstrated to achieve 36.1% brake thermal efficiency (BTE) while meeting aggressive exhaust emissions targets using a low-cost three-way catalyst. Starting from a “clean-sheet” design, modeling tools were used to select the optimal engine parameters and operating characteristics. The disruptive technologies developed for the CHP system have a target lifetime of at least 10 years. The engine cost at volume of 10,000 units was estimated to be $\$$1,050. A combination of experimental and simulation results were used to identify a path to the program target of 38.6% BTE. Base engine design parameters (i.e., displacement, speed, bore, stroke, valve timings, and compression ratio) were optimized to reduce friction, heat transfer, and exhaust losses. The base clean sheet prototype engine achieved 32% BTE. Optimization of the lube oil and coolant temperatures added 1.8% BTE. The addition of exhaust gas recirculation added 1.1% BTE. Novel thermal barrier coatings (TBCs) developed over the course of the project added 1.2% BTE. Further improvements in engine friction reduction (1.3% BTE) and improved combustion (1.2% BTE) are estimated to meet the program target 38.6% BTE, while also meeting the stringent criteria pollutant emissions targets.

42 ENGINEERING↗