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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Multiresolution Localization with Temporal Scanning for Super-Resolution Diffuse Optical Imaging of Fluorescence

A super-resolution optical imaging method is introduced that relies on the distinct temporal information associated with each fluorescent optical reporter to determine its spatial position to high precision with measurements of heavily scattered light. This multiple-emitter localization approach uses a diffusion equation forward model in a cost function, and has the potential to achieve micron-scale spatial resolution through centimeters of tissue. Utilizing some degree of temporal separation for the reporter emissions, position and emission strength are determined using a computationally efficient time stripping multiresolution algorithm. The method circumvents the spatial resolution challenges faced by earlier optical imaging approaches using a diffusion equation forward model, and is promising for in vivo applications. For example, in principle, the approach could be used to localize individual neurons firing throughout a rodent brain, enabling direct imaging of neural network activity.

42 ENGINEERING↗
DOE OSTI2019

The Trillion Pixel GeoAI Challenge Workshop

Rapid innovations in satellite and airborne remote sensing capabilities holds the promise of collecting high-resolution imagery with daily, even hourly cadence across the entire planet. Availability of such earth observation data streams at varying spatial and temporal scales, coupled with astonishing progress in AI and transformational advances in high performance computing bring into view the possibility of mapping and interpreting the surface of our planet at unprecedented detail. The implications of such capability for science, technology, policy, and security are far reaching. Moreover, even at a modest 5m resolution, 100 trillion pixels will describe the surface of the Earth every day. Consequently, monitoring the pulse of our planet will entail collecting, refining, analyzing, and curating those 100 trillion pixels within a 24-hour period. That is a challenge with unknown solutions as of today’s advances and therefore serves as a key motivation for this workshop. The GEOINT and AI communities have a unique opportunity to enable detailed monitoring the pulse of our planet and obtain new insights into how humans occupy, interact with, and alter the surface of the Earth over time. From a science and technology standpoint, this is a modern-day moonshot that presents numerous challenges particularly related to scaling the analysis to every pixel covering the planet. The challenge of making sense of all these pixels in a timely fashion include image processing at scale, selective analysis of only the pixels that matter, generalizing AI models across heterogeneous locations, computing on hardware that is memory constrained, and deploying automated feature extraction on edge devices. The designing of capable GeoAI workflows and data pipelines will require interdisciplinary efforts and partnerships. The Trillion Pixel Challenge for GeoAI workshop was is promoted as a first community effort to discuss priority research and development directions and to establish the partnerships and collaborations needed to make progress on critical scientific and operational applications from local to regional to planet scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Development and Validation of Southeast Asia Solar Resource Data [Slides]

Lack of access to high-quality, publicly available, time series solar data to inform decisions that will transform energy sectors in Southeast Asia is a challenge. The solution is to level the playing field by offering free, high-quality, robust solar data to inform private sector investment and policymaking. This is done by (1) leveraging deep NREL expertise in atmospheric science, solar resource assessment, high-performance computing, and cloud-based data dissemination, (2) producing and validating high spatial and temporal resolution solar resource data, (3) making data available on the USAID-funded global Renewable Energy Data Explorer platform, (4) providing capacity building for data and applications, and (5) informing future demand-driven tool development.

14 SOLAR ENERGY↗
DOE OSTI2023

Extend an innovative HPC-Compatible Multiple Temporal-spatial Resolution Concurrent Finite Element Modeling Approach to Guide Laser Powder Bed Fusion Additive

Laser power bed fusing (PBF) additive manufacturing is a key enabling technology to manufacture highly complex and integrated automotive structures. However, the geometric complexity of PBF-AM technique also leads to highly non-uniform heating and cooling rate in the manufactured part, which may cause flaw formation and produce excessive and nonuniform residual stresses, which increase quality uncertainties and manufacture issues, leading to increases in cost and energy consumption in the form of rejected parts. In this research project, we developed an innovative Multi-Spatial-Temporal-Resolution Finite Element (MUST-FE) method and completed the corresponding high performance computation (HPC) platform-based in-house code, which enables high accuracy prediction of temperature and residual stress fields for component-scale PBF-AM manufacture in efficient computation time. The MUST-FE model is calibrated and validated with a “2D pad” AlSi10Mg experiments by matching the melt pool shape and dimension, and with a “XY-cross” AlSi10Mg experiment by matching the thermal distortion and residual stress. The innovative multi-resolution and concurrent modeling approach adopted in this code ensures accuracy and computational efficiency, which will enable energy-efficient and high-yield, low-cost manufacturing of optimized, qualifiable automotive structures and contribute towards reaching technical targets outlined in AMO’s Program Plan to develop additive manufacturing systems that deliver consistently reliable parts with predictable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modeling bacterial microcompartment architectures for enhanced cyanobacterial carbon fixation

The carboxysome is a bacterial microcompartment (BMC) which plays a central role in the cyanobacterial CO 2 -concentrating mechanism. These proteinaceous structures consist of an outer protein shell that partitions Rubisco and carbonic anhydrase from the rest of the cytosol, thereby providing a favorable microenvironment that enhances carbon fixation. The modular nature of carboxysomal architectures makes them attractive for a variety of biotechnological applications such as carbon capture and utilization. In silico approaches, such as molecular dynamics (MD) simulations, can support future carboxysome redesign efforts by providing new spatio-temporal insights on their structure and function beyond in vivo experimental limitations. However, specific computational studies on carboxysomes are limited. Fortunately, all BMC (including the carboxysome) are highly structurally conserved which allows for practical inferences to be made between classes. Here, we review simulations on BMC architectures which shed light on (1) permeation events through the shell and (2) assembly pathways. These models predict the biophysical properties surrounding the central pore in BMC-H shell subunits, which in turn dictate the efficiency of substrate diffusion. Meanwhile, simulations on BMC assembly demonstrate that assembly pathway is largely dictated kinetically by cargo interactions while final morphology is dependent on shell factors. Overall, these findings are contextualized within the wider experimental BMC literature and framed within the opportunities for carboxysome redesign for biomanufacturing and enhanced carbon fixation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Modeling the Impacts of Material Properties on Oscillatory Neuron Behavior

In this study, neuromorphic computing, which mimics the functions of biological brains, offers improvements in both latency and energy efficiency over typical von Neumann computing architectures. Spiking neural networks can be especially power-efficient because they encode information temporally and can use more sparse electrical inputs. Here, we study the design of volatile memristors (variable resistors with memory) for neuronal devices, with particular consideration toward the feasibility of all-on-chip oscillation using built-in capacitance. We use circuit simulations to model the behavior of oscillator neurons with a range of realistic material properties. We find that energy inputs increase with insulating-phase resistivity, thermal conductivity, and device aspect ratio. However, we also find that the minimum capacitance needed for oscillation decreases with increasing insulating-phase resistivity, which opposes the constraints for power efficiency. Based on published data on NbO 2 , VO 2 , and EuNiO 3 , we find that existing materials can be engineered for all-on-chip spiking using their parasitic capacitance.

36 MATERIALS SCIENCE↗
DOE OSTI2014

TEMPORAL SELF-ORGANIZATION IN GALAXY FORMATION

We report on the discovery of a relation between the number of star formation (SF) peaks per unit time, ν{sub peak}, and the size of the temporal smoothing window function, Δt, used to define the peaks: ν{sub peak}∝Δt {sup 1} {sup –} {sup φ} (φ ∼ 1.618). This relation holds over the range of Δt = 10-1000 Myr that can be reliably computed here, using a large sample of galaxies obtained from a state-of-the-art cosmological hydrodynamic simulation. This means that the temporal distribution of SF peaks in galaxies as a population is fractal with a Hausdorff fractal dimension equal to φ – 1. This finding reveals, for the first time, that the superficially chaotic process of galaxy formation is underlined by temporal self-organization up to at least one gigayear. It is tempting to suggest that, given the known existence of spatial fractals (such as the power-law two-point function of galaxies), there is a joint spatio-temporal self-organization in galaxy formation. From an observational perspective, it will be urgent to devise diagnostics to probe the SF histories of galaxies with good temporal resolution to facilitate a test of this prediction. If confirmed, it would provide unambiguous evidence for a new picture of galaxy formation that is interaction driven, cooperative, and coherent in and between time and space. Unravelling its origin may hold the key to understanding galaxy formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

An Experimental and Computational Study of a Single Diesel Droplet Impinging on a Dry Surface

This work presents experiments and computational simulations of a single diesel droplet impinging on a dry surface to provide a better understanding of the dynamics of the impingement process. The experimental work was carried out at several substrate surface temperatures where a single diesel droplet was dropped from a precision syringe pump to provide a specific droplet diameter and impact velocity. The effect of the surface temperature on the temporal evolution of the droplet spreading factor, dynamic contact angle, and contact line velocity was studied. The experimental study on the temporal evolution of the droplet-wall interaction was used to develop a dynamic contact angle model for computational simulations. Using an in-house multiphase flow solver, simulations of the single diesel droplet impingement were performed. The simulation results of the spreading factor were compared to the experimental results and the effects of liquid temperature and contact angle model parameters were investigated. The development of a dynamic contact angle model for diesel droplets enhances the ability to study diesel droplet impingement, an important factor in diesel fuel injection. With an accurate dynamic contact angle model, future studies can be performed at scales and velocities representative of the conditions present in internal combustion engines.

Markt, D.↗
DOE OSTI2021

Learning from learning machines: improving the predictive power of energy-water-land nexus models with insights from complex measured and simulated data

Focal Area(s): Insights gleaned from complex data (observed and simulated) using AI; Predictive modeling through the use of AI techniques and AI-derived model components, including physics- and knowledge-informed models; Energy-water-land nexus and integrated energy systems – models of MultiSector dynamics. Science Challenge: Scientific communities are in need of tools for the computational integration of physics-based models, experimental data, and empirical/observational studies across a broad range of temporal and spatial scales to explore and meet EESSD grand challenges. We require AI algorithms for the discovery of process-drivers in the Earth-energy-human system and to link them with complex measured and simulated data. We aim to understand the energy-water-land nexus, particularly under extreme forcing scenarios and rare events, leveraging scale-aware AI process models, including probabilistic uncertainties, and benefiting from the emerging 5G-enabled landscape-scale sensing and edge computing capabilities. These models are needed to identify instabilities and tipping points that manifest extreme system behaviors with consequences for integrated energy systems and the environment. This work requires fundamental advances in uncertainty quantification, in particular, to identify and model unlikely but catastrophic outliers. Specifically, we need models that are interpretable to domain scientists and, essentially, explainable to public and private stake holders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Watermarks in stream processing systems: semantics and comparative analysis of Apache Flink and Google cloud dataflow

Streaming data processing is an exercise in taming disorder: from oftentimes huge torrents of information, we hope to extract powerful and timely analyses. But when dealing with streaming data, the unbounded and temporally disordered nature of real-world streams introduces a critical challenge: how does one reason about the completeness of a stream that never ends? In this paper, we present a comprehensive definition and analysis of watermarks, a key tool for reasoning about temporal completeness in infinite streams.First, we describe what watermarks are and why they are important, highlighting how they address a suite of stream processing needs that are poorly served by eventually-consistent approaches:• Computing a single correct answer, as in notifications.• Reasoning about a lack of data, as in dip detection.• Performing non-incremental processing over temporal subsets of an infinite stream, as in statistical anomaly detection with cubic spline models.• Safely and punctually garbage collecting obsolete inputs and intermediate state.• Surfacing a reliable signal of overall pipeline health.Second, we describe, evaluate, and compare the semantically equivalent, but starkly different, watermark implementations in two modern stream processing engines: Apache Flink and Google Cloud Dataflow.

Akidau, Tyler↗
DOE OSTI2016

In Situ Real-Time Radiographic Study of Thin Film Formation Inside Rotating Hollow Spheres

Hollow spheres with uniform coatings on the inner surface have applications in optical devices, time- or site controlled drug release, heat storage devices, and target fabrication for inertial confinement fusion experiments. The fabrication of uniform coatings, which is often critical for the application performance, requires precise understanding and control over the coating process and its parameters. Here, we report on in-situ real-time radiography experiments that provide critical spatio-temporal information about the distribution of fluids inside hollow spheres during uniaxial rotation. Image analysis and computer fluid dynamics simulations were used to explore the effect of liquid viscosity and rotational velocity on the film uniformity. The data were then used to demonstrate the fabrication of uniform sol-gel chemistry derived porous polymer films inside 2mm inner diameter diamond shells.

aerogel↗
DOE OSTI2023

Rolling Horizon Based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows

The offline pickup and delivery problem with time windows (PDPTW) is a classical combinatorial optimization problem in the transportation community, which has proven to be very challenging computationally. Due to the complexity of the problem, practical problem instances can be solved only via heuristics, which trade-off solution quality for computational tractability. Among the various heuristics, a common strategy is problem decomposition, that is, the reduction of a large-scale problem into a collection of smaller sub-problems, with spatial and temporal decompositions being two natural approaches. While spatial decomposition has been successful in certain settings, effective temporal decomposition has been challenging due to the difficulty of stitching together the sub-problem solutions across the decomposition boundaries. In this work, we introduce a novel temporal decomposition scheme for solving a class of PDPTWs that have narrow time windows, for which it is able to provide both fast and high-quality solutions. We utilize techniques that have been popularized recently in the context of online dial-a-ride problems along with the general idea of rolling horizon optimization. To the best of our knowledge, this is the first attempt to solve offline PDPTWs using such an approach. To show the performance and scalability of our framework, we use the optimization of paratransit services as a motivating example. Due to the lack of benchmark solvers similar to ours (i.e., temporal decomposition with an online solver), we compare our results with an offline heuristic algorithm using Google OR-Tools. In smaller problem instances (with an average of 129 requests per instance), the baseline approach is as competitive as our framework. However, in larger problem instances (approximately 2,500 requests per instance), our framework is more scalable and can provide good solutions to problem instances of varying degrees of difficulty, while the baseline algorithm often fails to find a feasible solution within comparable compute times.

Kim, Youngseo↗
DOE OSTI2018

Mining data in a dynamic PRA framework

Computational (also known as Dynamic) Probabilistic Risk Assessment (PRA) methods employs system simulation codes coupled with stochastic analysis tools in order to determine probabilities of certain outcomes such as system failure. In contrast to classical PRA methods (i.e., Event-Tree and Fault-Tree) in which timing and sequencing of events is set by the analyst, accident progression is dictated by the system control logic and its interaction with system temporal evolution. Due to the nature of the problem, Simulation-Based PRA methods require large amount of computationally expensive simulation runs. In addition, these methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). In this paper we investigate and apply several methods and algorithms to analyze these large amounts of time-dependent data. Furthermore, the scope of this article is to present a broad overview of methods and algorithms that can be used to improve data quality and to analyze and extract information from large data sets containing time dependent data. In this context, “extracting information” means constructing input-output correlations, finding commonalities, and identifying outliers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deep learning-based temporal deconvolution for photon time-of-flight distribution retrieval

The acquisition of the time of flight (ToF) of photons has found numerous applications in the biomedical field. Over the last decades, a few strategies have been proposed to deconvolve the temporal instrument response function (IRF) that distorts the experimental time-resolved data. However, these methods require burdensome computational strategies and regularization terms to mitigate noise contributions. Herein, we propose a deep learning model specifically to perform the deconvolution task in fluorescence lifetime imaging (FLI). The model is trained and validated with representative simulated FLI data with the goal of retrieving the true photon ToF distribution. Its performance and robustness are validated with well-controlled in vitro experiments using three time-resolved imaging modalities with markedly different temporal IRFs. The model aptitude is further established with in vivo preclinical investigation. Overall, these in vitro and in vivo validations demonstrate the flexibility and accuracy of deep learning model-based deconvolution in time-resolved FLI and diffuse optical imaging.

Pandey, Vikas (ORCID:0000000154771095)↗
DOE OSTI2025

Boosting Noise2Inverse via enhanced model selection for denoising computed tomography data

Synchrotron-based x-ray tomographic imaging enables the examination of the internal structure of materials at high spatial and temporal resolution. Experimental constraints can impose dose and time limits on the measurements, introducing a higher level of noise and artifacts in the reconstructed images. Deep learning has emerged as a powerful tool to remove noise from reconstructed images. Recently, the Noise2Inverse method was designed specifically for denoising reconstructed images without requiring paired noisy and clean images. This method creates multiple statistically independent reconstructions used to pair the data in which training involves transforming one reconstruction into the other, and vice versa. Originally designed to be used after a fixed number of epochs, we see in practice that this approach may not produce the optimal model and may unnecessarily waste computational resources. Therefore, we propose an alternative method of identifying the best model during training that aligns with the Noise2Inverse method. During validation, we compare the model output of the multiple reconstructions among each other. We hypothesize that the best model is the one that produces images with the highest similarity, implying a convergence in the predicted material properties and absorption values. To compare model outputs, we consider the absolute error, square error, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity. We evaluate our method on two simulated tomography datasets and two, real-world, low-contrast, high-energy x-ray tomography datasets. We show our approach is more effective at determining the best model, up to an increase of 12.50% and 12.53% in SSIM and PSNR, respectively, while only requiring a fifth of the training time compared to the original approach.

CT↗