Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “temporal 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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

AMReX

AMReX is a software framework for the development and implementation of massively parallel algorithms using block-structured adaptive mesh refinement (AMR). Block-structured AMR provides the basis for the temporal and spatial discretization strategy for a large variety of applications of interest to the DOE. AMReX provides a unified infrastructure with the functionality needed for applications to be able to effectively and efficiently utilize machines from laptops to exascale accelerator-based architectures. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving accurate descriptions of different physical processes in complex multi-physics algorithms. AMReX supports algorithms that solve systems of partial differential equations (PDEs) in simple or complex geometries, and those that use particles and/or particle-mesh operations to represent component physical processes.

Zhang, Weiqun↗
DOE OSTI2014

WE-G-BRD-06: Variation in Dynamic Positron Emission Tomography Imaging of Tumor Hypoxia in Early Stage Non-Small Cell Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy

Purpose: Tumor hypoxia is correlated with treatment failure. To date, there are no published studies investigating hypoxia in non-small cell lung cancer (NSCLC) patients undergoing SBRT. We aim to use 18F-fluoromisonidazole (18F-FMISO) positron emission tomography (PET) imaging to non-invasively quantify the tumor hypoxic volume (HV), to elucidate potential roles of reoxygenation and tumor vascular response at high doses, and to identify an optimal prognostic imaging time-point. Methods: SBRT-eligible patients with NSCLC tumors >1cm were prospectively enrolled in an IRB-approved study. Computed Tomography and dynamic PET images (0–120min, 150–180min, and 210–240min post-injection) were acquired using a Siemens BiographmCT PET/CT scanner. 18F-FMISO PET was performed on a single patient at 3 different time points around a single SBRT delivery of 18 Gy and HVs were compared using a tumor-to-blood ratio (TBR)>1.2 and rate of influx (Ki)>0.0015 (Patlak). Results: Results from our first patient showed substantial temporal changes in HV following SBRT. Using a TBR threshold >1.2 and summed images 210–240min, the HVs were 19%, 31% and 13% of total tumor volume on day 0, 2 (48 hours post-SBRT), and 4 (96 hours post-SBRT). The absolute volume of hypoxia increased by nearly a factor of 2 after 18 Gy and then decreased almost to baseline 96 hours later. Selected imaging timepoints resulted in temporal changes in HV quantification obtained with TBR. Ki, calculated using 4-hour dynamic data, evaluated HVs as 22%, 75% and 21%, respectively. Conclusions: ith the results of only one patient, this novel pilot study highlights the potential benefit of 18F-FMISO PET imaging as results indicate substantial temporal changes in tumor HV post-SBRT. Analysis suggests that TBR is not a robust parameter for accurate HV quantification and heavily influenced by imaging timepoint selection. Kinetic modeling parameters are more sensitive and may aid in future treatment individualization based on patient-specific biological information.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

High temporal frequency data from a four turbine, blade-resolved wind farm simulation with ExaWind

The data was generated with ExaWind (https://github.com/Exawind) which couples AMR-Wind (https://github.com/Exawind/amr-wind/), Nalu-Wind (https://github.com/Exawind/nalu-wind), TIOGA (https://github.com/Exawind/tioga), and OpenFAST (https://github.com/OpenFAST/openfast). This is a large-scale simulation of a blade-resolved wind farm using the ExaWind software stack. ExaWind couples together a background flow solver, AMR-Wind, and a near-body solver, Nalu-Wind, through an overset technique from the TIOGA application. Another application, OpenFAST, handles the structural dynamics of the turbine blades and towers, which informs the fluid-structure interaction of the wind turbines with the flow solvers. This particular simulation includes four blade-resolved wind turbines operating in a turbulent atmospheric boundary layer. The AMR-Wind solver uses 500 million cells and is being solved on 256 AMD GPUs of the Oakridge Leadership Computing Facility Frontier supercomputer. Each turbine is assigned its own Nalu-Wind solver with over 13 million elements per turbine and solved using 448 CPU cores, for a total of 1792 CPU cores. For each node, 56 cores contain Nalu-Wind, while 8 cores correspond to AMR-Wind operations on the GPUs. Consequently, ExaWind is entirely utilizing the CPUs and the GPUs of the nodes concurrently. The data used in the visualization is full flow field data output from the simulation. It is lossy-compressed to a specific accuracy using ZFP and written to disk every 16 time-steps to enable real-time flow visualization. The flow fields are sampled at a high temporal frequency to enable real-time, 24fps visualization. The flow fields are sampled every 12 simulation time steps (every 0.04132s).

17 WIND ENERGY↗
DOE OSTI2015

A Bayesian, multivariate calibration for Globigerinoides ruberMg/Ca

The use of Mg/Ca in marine carbonates as a paleothermometer has been challenged by observations that implicate salinity as a contributing influence on Mg incorporation into biotic calcite and that dissolution at the sea-floor alters the original Mg/Ca. Yet, these factors have not yet been incorporated into a single calibration model. In this paper, we introduce a new Bayesian calibration for Globigerinoides ruber Mg/Ca based on 186 globally distributed core top samples, which explicitly takes into account the effect of temperature, salinity, and dissolution on this proxy. Our reported temperature, salinity, and dissolution (here expressed as deep-water ΔCO 2- 3 ) sensitivities are (±2σ) 8.7±0.9%/°C, 3.9±1.2%/psu, and 3.3±1.3%/μmol.kg -1 below a critical threshold of 21 μmol/kg in good agreement with previous culturing and core-top studies. We then perform a sensitivity experiment on a published record from the western tropical Pacific to investigate the bias introduced by these secondary influences on the interpretation of past temperature variability. This experiment highlights the potential for misinterpretations of past oceanographic changes when the secondary influences of salinity and dissolution are not accounted for. Finally, multiproxy approaches could potentially help deconvolve the contributing influences but this awaits better characterization of the spatio-temporal relationship between salinity and δ 18 O sw over millennial and orbital timescales.

58 GEOSCIENCES↗
DOE OSTI2019

Sub-grid scale model classification and blending through deep learning

In this article we detail the use of machine learning for spatio-temporally dynamic turbulence model classification and hybridization for large eddy simulations (LES) of turbulence. Here, our predictive framework is devised around the determination of local conditional probabilities for turbulence models that have varying underlying hypotheses. As a first deployment of this learning, we classify a point on our computational grid as that which requires the functional hypothesis, the structural hypothesis or no modelling at all. This ensures that the appropriate model is specified from a priori knowledge and an efficient balance of model characteristics is obtained in a particular flow computation. In addition, we also utilize the conditional-probability predictions of the same machine learning to blend turbulence models for another hybrid closure. Our test case for the demonstration of this concept is given by Kraichnan turbulence, which exhibits a strong interplay of enstrophy and energy cascades in the wavenumber domain. Our results indicate that the proposed methods lead to robust and stable closure and may potentially be used to combine the strengths of various models for complex flow phenomena prediction.

42 ENGINEERING↗
DOE OSTI2025

Echo state network for coarsening dynamics of charge density waves

An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer. Compared with other recurrent neural networks, one great advantage of ESN is the simplicity of its training process. Yet, despite the seemingly restricted learnable parameters, ESN has been shown to successfully capture the spatial-temporal dynamics of complex patterns. Here we build an ESN to model the coarsening dynamics of charge-density waves (CDWs) in a semiclassical Holstein model, which exhibits a checkerboard electron density modulation at half-filling stabilized by a commensurate lattice distortion. The inputs to the ESN are local CDW order parameters in a finite neighborhood centered around a given site, while the output is the predicted CDW order of the center site at the next time step. Special care is taken in the design of couplings between hidden layer and input nodes to ensure lattice symmetries are properly incorporated into the ESN model. Since the model predictions depend only on CDW configurations of a finite domain, the ESN is scalable and transferrable in the sense that a model trained on dataset from a small system can be directly applied to dynamical simulations on larger lattices. Furthermore, our work opens avenues for efficient dynamical modeling of pattern formations in functional electron materials.

2-dimensional systems↗
DOE OSTI2018

TopicOnTiles: Tile-Based Spatio-Temporal Event Analytics via Exclusive Topic Modeling on Social Media

Detecting anomalous events of a particular area in a timely manner is an important task. Geo-tagged social media data are useful resource for this task; however, the abundance of everyday language in them makes this task still challenging. To address such challenges, we present TopicOnTiles, a visual analytics system that can reveal information relevant to anomalous events in a multi-level tile-based map interface by using social media data. To this end, we adopt and improve a recently proposed topic modeling method that can extract spatio-temporally exclusive topics corresponding to a particular region and a time point. Furthermore, we utilize a tile-based map interface to efficiently handle large-scale data in parallel. Our user interface effectively highlights anomalous tiles using our novel glyph visualization that encodes the degree of anomaly computed by our exclusive topic modeling processes. To show the effectiveness of our system, we present several usage scenarios using real-world datasets as well as comprehensive user study results.

Choi, Minsuk↗
DOE OSTI2025

Spatio-temporal Fourier Transformer for Long-term Dynamics Prediction (StFT) v1.0

We propose a novel machine learning model spatio-temporal Fourier transformer (StFT) to emulate long-term dynamics of multi-scale and multi-physics systems. Our method StFT overcomes the limitations of rapid error accumulation, particularly in long-term forecasting of systems characterized by complex and coupled dynamics. StFT achieves outstanding accuracy and computational efficiency by effectively capturing multi-scale interactions, and quantify the uncertainties inherent in the predictions. Our model leverages a structured hierarchy of StFT blocks, and explicitly captures dynamics across both macro- and micro- spatial scales. Evaluations conducted on three benchmark datasets (plasma, fluid, and atmospheric dynamics) demonstrate the advantages of our approach over state-of-the-art ML methods.

Bai, Zhe [Lawrence Berkeley National Laboratory (L↗
DOE OSTI2026

In situ quantum verification of polarization-stabilized optical channels

We demonstrate in situ benchmarking of classical polarization stabilization in deployed fiber-optic channels via coexistent ancilla-assisted process tomography, leveraging sliding windows to enhance temporal sensitivity to environmental fluctuations.

Stevens, Matthew L [Arizona State University]↗
DOE OSTI2015

Improving microbial fitness in the mammalian gut by in vivo temporal functional metagenomics

Abstract Elucidating functions of commensal microbial genes in the mammalian gut is challenging because many commensals are recalcitrant to laboratory cultivation and genetic manipulation. We present Temporal FU nctional Metagenomics sequencing ( TFUM seq), a platform to functionally mine bacterial genomes for genes that contribute to fitness of commensal bacteria in vivo . Our approach uses metagenomic DNA to construct large‐scale heterologous expression libraries that are tracked over time in vivo by deep sequencing and computational methods. To demonstrate our approach, we built a TFUM seq plasmid library using the gut commensal Bacteroides thetaiotaomicron (Bt) and introduced Escherichia coli carrying this library into germfree mice. Population dynamics of library clones revealed Bt genes conferring significant fitness advantages in E. coli over time, including carbohydrate utilization genes, with a Bt galactokinase central to early colonization, and subsequent dominance by a Bt glycoside hydrolase enabling sucrose metabolism coupled with co‐evolution of the plasmid library and E. coli genome driving increased galactose utilization. Our findings highlight the utility of functional metagenomics for engineering commensal bacteria with improved properties, including expanded colonization capabilities in vivo .

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Computational modeling of nanoscale and microscale particle deposition, retention and dosimetry in the mouse respiratory tract

Comparing effects of inhaled particles across rodent test systems and between rodent test systems and humans is a key obstacle to the interpretation of common toxicological test systems for human risk assessment. These comparisons, correlation with effects and prediction of effects, are best conducted using measures of tissue dose in the respiratory tract. Differences in lung geometry, physiology and the characteristics of ventilation can give rise to differences in the regional deposition of particles in the lung in these species. Differences in regional lung tissue doses cannot currently be measured experimentally. Regional lung tissue dosimetry can however be predicted using models developed for rats, monkeys, and humans. A computational model of particle respiratory tract deposition and clearance was developed for BALB/c and B6C3F1 mice, creating a cross-species suite of available models for particle dosimetry in the lung. Airflow and particle transport equations were solved throughout the respiratory tract of these mice strains to obtain temporal and spatial concentration of inhaled particles from which deposition fractions were determined. Particle inhalability (Inhalable fraction, IF) and upper respiratory tract (URT) deposition were directly related to particle diffusive and inertial properties. Measurements of the retained mass at several post-exposure times following exposure to iron oxide nanoparticles, micro- and nanoscale C60 fullerene, and nanoscale silver particles were used to calibrate and verify model predictions of total lung dose. Interstrain (mice) and interspecies (mouse, rat and human) differences in particle inhalability, fractional deposition and tissue dosimetry are described for ultrafine, fine and coarse particles.

Lung deposition, nanosized particles, particle↗
DOE OSTI2019

Channelrhodopsin-2 Function is Modulated by Residual Hydrophobic Mismatch with the Surrounding Lipid Environment

Channelrhodopsin-2 (ChR2) is a light-gated ion channel that conducts cations of multiple valencies down the electrochemical gradient. This light-gated property has made ChR2 a popular tool in the field of optogenetics, allowing for the spatial and temporal control of excitable cells with light. A central aspect of protein function is the interaction with the surrounding lipid environment. To further explore these membrane-protein interactions, we demonstrate the role of residual hydrophobic mismatch (RHM) as a mechanistically important component of ChR2 function. We combined computational and functional experiments to understand how RHM between the lipid environment and ChR2 alters the structural and biophysical properties of the channel. Analysis of our results revealed significant RHM at the intracellular/lipid interface of ChR2 from a triad of residues. The resulting energy penalty is substantial and can be lowered via mutagenesis to evaluate the functional effects of this change in lipid-protein interaction energy. The experimental measurement of channel stability, conductance and selectivity resulting from the reduction of the RHM energy penalty showed changes in progressive H+ permeability, kinetics and open-state stability, suggesting how the modulation of ChR2 by the surrounding lipid membrane can play an important biological role and contribute to the design of targeted optogenetic constructs for specific cell types.

Richards, Ryan↗
DOE OSTI2024

Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects

This is the conference paper accompanying an oral presentation “Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects” at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. Carbon capture and storage (CCS) technology is critical for mitigating climate change but requires effective subsurface reservoir management to ensure safe containment of injected CO2. Accurate predictions of reservoir pressure and saturation are essential for assessing long-term CCS performance. Traditional numerical simulations, while effective, are computationally intensive, time-consuming, and constrained by data discretization. Previous work has shown the effectiveness of MeshGraphNets (MGN), a graph-based machine learning framework, as an innovative alternative for predicting reservoir behavior. MGN leverages graph neural networks (GNNs) and mesh representations to model complex geological formations, offering superior adaptability across different discretizations and reservoir configurations. Classic MGN implementations utilize an autoregressive technique to predict future behavior based on current predictions, but this technique is hampered by error accumulation over time. To enhance the model accuracy in time-series predictions, this study implemented a multi-step rollout strategy that integrates autoregressive predictions during training to stabilize prediction of saturation over time. Using the Illinois Basin – Decatur Project (IBDP) dataset, comprising 100 simulations of CO2 injection, pressure, and saturation changes, the framework demonstrated its ability to learn spatial dependencies and temporal dynamics. With inputs including permeabilities, porosities, and injection rates, MGN accurately predicted CO2 plume evolution over time, even with limited training data. Moreover, the addition of a multi-step rollout procedure during training improved the ability of MGN to predict stably over time by ~15%. This research positions MGN, enhanced with multi-step rollout capabilities, as a robust and efficient tool for CCS applications. It advances the field by enabling precise, computationally efficient predictions of reservoir behavior, providing a foundation for the broader adoption of machine learning frameworks in CCS and other geoscience domains.

Holcomb, Paul↗
DOE OSTI2018

Cyber-Infrastructure for Data-Intensive Geospatial Computing

With the recent advent of heterogeneous High-performance Computing (HPC) to handle EO “Big Data” workloads, there is a need for a unified Cyber-infrastructure (CI) platform that can bridge the best of many HPC worlds. In this chapter, we discuss such a CI platform being developed at Geographic Information Science and Technology (GIST) group using novel and innovative techniques, and emerging technologies that are scalable to large-scale supercomputers. The CI platform utilizes a wide variety of computing such as GPGPU, distributed, real-time and cluster computing, which are being brought together architecturally to enable data-driven analysis, scientific understanding of earth system models, and research collaboration. This development addresses the need for close integration of EO and other geospatial information in the face of growing volumes of the data, and facilitates spatio-temporal analysis of disparate and dynamic data streams. Horizontal scalability and linear throughput are supported in the heart of the platform itself. It is being used to support very broad application areas, ranging from high-resolution settlement mapping, national bioenergy infrastructure to urban information and mobility systems. The platform provides spatio-temporal decision support capabilities in planning, policy and operational missions for US federal agencies. Also, the platform is designed to be functionally and technologically sustainable for continued support of the US energy and environment mission for the coming decades.

Karthik, Rajasekar↗
DOE OSTI2017

Faxén form of time-domain force on a sphere in unsteady spatially varying viscous compressible flows

An explicit expression for the time-dependent force on a stationary, finite-sized spherical particle located in an unsteady inhomogeneous ambient flow is presented. The force expression accounts for both viscous and compressible effects. Towards this end, a time-harmonic plane travelling wave of a given frequency propagating in a viscous compressible flow over a sphere is considered. Linearized compressible Navier–Stokes equations are solved to obtain an analytical expression for the force exerted on the particle in the frequency domain. The force obtained in the Laplace space due to a travelling wave of a given frequency and wavenumber is then generalized to any arbitrary incoming flow. This is achieved by relating the radial and tangential velocity components in the Laplace space to the surface-averaged radial velocity and volume-averaged velocity vectors respectively in the time space. Moreover an expression relating the surface-averaged radial velocity and volume-averaged velocity vector has been provided. The total force is written as a summation of the undisturbed and disturbed force (quasi-steady, inviscid-unsteady and viscous-unsteady) contributions. The force contributions thus obtained are expressed as comprising of two parts – that arising due to spatial variation in the ambient flow and the other arising due to temporal variation. The current formulation is applicable to inhomogeneous ambient flows, however in the limit of negligible Reynolds and Mach numbers. The results are applicable even for particles of sizes larger than the acoustic wavelength. The accuracy of the explicit time-domain force expression is first tested by computing the force on an 80 mm diameter particle due to a weak planar expansion fan. Extension of this formulation when nonlinear effects become important is also proposed and tested by considering strong expansion fans. The results thus obtained are compared against corresponding axisymmetric numerical simulations.

Mechanics↗
DOE OSTI2019

Spatial and Temporal Quantification of Spontaneous Imbibition

Spontaneous imbibition–the process of a wetting fluid displacing a non-wetting fluid purely by capillary forces–is a ubiquitous phenomenon in porous and fibrous materials. Here we present a new experimental method for quantification of spontaneous imbibition in geologic materials. This method makes it possible to perform spontaneous imbibition under elevated pressure conditions relevant to environmental and energy resource applications. In this work, computed tomography imaging reveals a new time-independent scaling relationship that describes local imbibition rates as a function of water saturation. Imbibition capillary pressure curves are calculated with this wetting-phase pressure and flowrate characterization, with no assumptions about the functional form, end point behavior, or scaling factors. Calculated end-point capillary pressure is nonzero, in agreement with recent pore-scale measurements of capillary pressure of trapped nonwetting phase. This work provides a new approach and insights into trapping and remobilization of non-wetting fluids in CO 2 storage reservoirs and contaminated groundwater aquifers.

58 GEOSCIENCES↗
DOE OSTI2022

Geranium Photodiodes for Hard Xray Detection

This report summarizes the investigations engaged at UC Davis under subcontract B632083, as related to the LLNL-LDRD Project “High-Speed X-Ray Imager Arrays.” The first section entails work done within the Electrical and Computer Engineering (ECE) Department. The purpose of the work done so far is to understand photodiode design and which parameters are conducive to the design goals, which are: I: Maximize the detector quantum efficiency. The device must have a quantum efficiency of roughly 5% at 80 keV. II: Minimize temporal response. Collection time is limited to 1ns. All charge must be collected in that time. III: Minimize dark current. In this document, various photodiode parameters and designs are explored, and their impact on these goals are tabulated. In addition to these, various instruments that have been constructed for either testing these parameters or helping teammates with their experiments. The second section entails work done with the Materials Science Engineering (MSE) Department. In accordance with the proposed development of the germanium ROIC backside imagers, a critical step in the micromachining of these devices was the removal of the substrate on which the PIN junctions were epitaxially grown. A common technique for the micromachining of silicon wafers is the use of chemical etching alongside an “etch-stop” layer. In this technique, a chemical etchant will remove material until it reaches the etch-stop layer, at which the etch rate will drop significantly. This high selectivity (ratio of etch rates) allows any unevenness in the initial etching to be effectively smoothed away in the event that part of the wafer surface reaches the etch-stop layer sooner than another. An initial literature review found a paper by Divan et al. in which the authors used ion implantation to dope germanium wafers and achieve selectivity on the order of 100, sufficient for a reliable etch-stop.

42 ENGINEERING↗
DOE OSTI2015

Continuum Mechanical and Computational Aspects of Material Behavior

Fluid flows are typically classified as laminar or turbulent. While the glassy, regular flow of water from a slightly opened tap is laminar, the sinuous, irregular flow of water from a fully opened tap is turbulent. In a laminar flow, the velocity and other relevant fields are deterministic functions of position and time. Photos taken at different times, no matter how far removed, of steady laminar flow from a tap will be identical. In a turbulent flow, the velocity and other relevant fields manifest complex spatial and temporal fluctuations. A video of steady turbulent flow from a tap will exhibit a constantly changing pattern and many length and time scales. In nature and technology, laminar flows are more the exception than the rule. Fluvial, oceanic, pyroclastic, atmospheric, and interstellar flows are generally turbulent, as are the flows of blood through the left ventricle and air in the lungs. Flows around land, sea, and air vehicles and through pipelines, heating, cooling, and ventilation systems are generally turbulent, as are most flows involved in industrial processing, combustion, chemical reactions, and crystal growth. Over the past year, a significant portion of our research activity has focused on numerical studies of Navier-Stokes-αβ model and extensions thereof. Our results regarding these and other approaches to turbulence modeling are described below.

42 ENGINEERING↗