Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ENERGY SCIENCES NETWORK”

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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Towards a Scalable and Adaptive Application Support Platform for Large-Scale Distributed E-Sciences in High-Performance Network Environments

The advent of large-scale collaborative scientific applications has demonstrated the potential for broad scientific communities to pool globally distributed resources to produce unprecedented data acquisition, movement, and analysis. System resources including supercomputers, data repositories, computing facilities, network infrastructures, storage systems, and display devices have been increasingly deployed at national laboratories and academic institutes. These resources are typically shared by large communities of users over Internet or dedicated networks and hence exhibit an inherent dynamic nature in their availability, accessibility, capacity, and stability. Scientific applications using either experimental facilities or computation-based simulations with various physical, chemical, climatic, and biological models feature diverse scientific workflows as simple as linear pipelines or as complex as a directed acyclic graphs, which must be executed and supported over wide-area networks with massively distributed resources. Application users oftentimes need to manually configure their computing tasks over networks in an ad hoc manner, hence significantly limiting the productivity of scientists and constraining the utilization of resources. The success of these large-scale distributed applications requires a highly adaptive and massively scalable workflow platform that provides automated and optimized computing and networking services. This project is to design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a web-based user interface specially tailored for a target application, a set of user libraries, and several easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in heterogeneous high-performance network environments. SWAMP will enable the automation and management of the entire process of scientific workflows with the convenience of a few mouse clicks while hiding the implementation and technical details from end users. Particularly, we will consider two types of applications with distinct performance requirements: data-centric and service-centric applications. For data-centric applications, the main workflow task involves large-volume data generation, catalog, storage, and movement typically from supercomputers or experimental facilities to a team of geographically distributed users; while for service-centric applications, the main focus of workflow is on data archiving, preprocessing, filtering, synthesis, visualization, and other application-specific analysis. We will conduct a comprehensive comparison of existing workflow systems and choose the best suited one with open-source code, a flexible system structure, and a large user base as the starting point for our development. Based on the chosen system, we will develop and integrate new components including a black box design of computing modules, performance monitoring and prediction, and workflow optimization and reconfiguration, which are missing from existing workflow systems. A modular design for separating specification, execution, and monitoring aspects will be adopted to establish a common generic infrastructure suited for a wide spectrum of science applications. We will further design and develop efficient workflow mapping and scheduling algorithms to optimize the workflow performance in terms of minimum end-to-end delay, maximum frame rate, and highest reliability. We will develop and demonstrate the SWAMP system in a local environment, the grid network, and the 100Gpbs Advanced Network Initiative (ANI) testbed. The demonstration will target scientific applications in climate modeling and high energy physics and the functions to be demonstrated include workflow deployment, execution, steering, and reconfiguration. Throughout the project period, we will work closely with the science communities in the fields of climate modeling and high energy physics including Spallation Neutron Source (SNS) and Large Hadron Collider (LHC) projects to mature the system for production use.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Concept of Operations: National Technology Test Bed Network for Water Resource Recovery

There is an urgency to advancing wastewater technologies due to aging water infrastructure and emerging regulations. A crosscutting working group proposes a conceptual design for a test bed network to understand and evaluate wastewater technologies to drive acceptance and deployment of new technologies to enhance performance. The working group includes contributors from the U.S. Department of Energy, the U.S. Environmental Protection Agency, the U.S. National Science Foundation, and the Water Research Foundation (formerly known as the Water Environment & Reuse Foundation). Upgrading today's aging wastewater treatment infrastructure to a new generation of water resource recovery facilities (WRRFs) requires the development and deployment of innovative technologies. The path to technology adoption depends on expensive, time consuming, and often repetitive cycles of testing and validation. Existing water resource recovery technology testing facilities (e.g., test beds) are underutilized and cannot, by themselves, broaden technology uptake. It has been proposed that technology adoption could be accelerated by a network linking these test facilities with innovators, manufacturers, utilities, regulators, policymakers, and educators. This document proposes a structure for a National Test bed Network (TBN) for water resource recovery. The concepts for its operation are explored herein. This document identifies drivers to launch a TBN and critical needs for successful implementation of the network. An important driver to technology implementation at WRRFs is the relationship between local utilities and state regulators. The TBN could provide access to validated performance data and facilitate deployment. To link the stakeholders and accelerate deployment, the TBN will require standards for methods, data quality, data management, and data security. These standards will enable validation of performance and will expand the “toolbox” that utilities and regulators could consider. Design, set up, and launch of this toolbox is beyond the scope of operations of utilities and regulators and will likely require external support to achieve full operation. Herein, a pathway is proposed to achieve this full operation. This document defines the function of a WRRF TBN. It also proposes a structure for the TBN to achieve its stated goals and mission. Finally, this document summarizes findings from several sources, including a series of workshops about the TBN concept, operators of test beds in ancillary industries, and discussions with regulators and permitting agencies. A description of this work was published in the peer-reviewed literature (Mihelcic 2017).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Water Dynamics in Polyacrylamide Hydrogels

Polymeric hydrogels have wide applications including electrophoresis, biocompatible materials, water superadsorbents, and contact lenses. The properties of hydrogels involve the poorly characterized molecular dynamics of water and solutes trapped within the three-dimensional cross-linked polymer networks. In this study, we apply ultrafast infrared two-dimensional infrared (2D IR) vibrational echo and polarization-selective pump-probe (PSPP) spectroscopies to investigate the ultrafast molecular dynamics of water and a small molecular anion solute, selenocyanate (SeCN - ), in polyacrylamide hydrogels. For all mass concentrations of polymer studied (5% and above), the hydrogen bonding network reorganization (spectral diffusion) dynamics and reorientation dynamics reported by both water and SeCN - solvated by water are significantly slower than in bulk water. As the polymer mass concentration increases, molecular dynamics in the hydrogels slow further. The magnitudes of the slowing, measured with both water and SeCN - , are similar. However, the entire hydrogen bonding network of water molecules appear to slow down as a single ensemble, without a difference between the core water population and the interface water population at the polymer-water surface. In contrast, the dissolved SeCN - do exhibit two-component dynamics, where the major component is assigned to the anions fully solvated in the confined water nanopools. Lastly, the slower component has a small amplitude which is correlated with the polymer mass concentration, and is assigned to adsorbed anions strongly interacting with the polymer fiber networks.

14 SOLAR ENERGY↗
DOE OSTI2023

Cyber Framework for Steering and Measurements Collection Over Instrument-Computing Ecosystems

We propose a framework to develop cyber solutions to support the remote steering of science instruments and measurements collection over instrument-computing ecosystems. It is based on provisioning separate data and control connections at the network level, and developing software modules consisting of Python wrappers for instrument commands and Pyro server-client codes that make them available across the ecosystem network. We demonstrate automated measurement transfers and remote steering operations in a microscopy use case for materials research over an ecosystem of Nion microscopes and computing platforms connected over site networks. The proposed framework is currently under further refinement and being adopted to science workflows with automated remote experiments steering for autonomous chemistry laboratories and smart energy grid simulations.

Al Najjar, Anees↗
DOE OSTI2012

Stuff- The Materials that Shape our World - Experimental Learning Opportunities

Making Stuff is a four-part series that explores how materials changed history and are shaping the future. To further enhance public engagement in and understanding of materials science, the project convened an extensive network of community coalitions across the country that hosted Making Stuff outreach activities and events, science cafes, and educator workshops in their local areas. Department Of Energy funding enabled us to increase the number of communities formally involved in the project, from 10 to 20 community hubs. Department of Energy funding also made it possible to develop a collection of materials science resources, activities and hands-on demonstrations for use in a variety of formal and informal settings, and Making Stuff activities were presented at science conferences and festivals around the country. The design, printing and national dissemination of the Making Stuff afterschool activity guide were also developed with DOE funding, as well as professional webinar trainings for scientists and educators to help facilitate many of the community activities and other online and print materials. Thanks to additional funding from the Department of Energy, we were able to expand the reach and scope of the project's outreach plan, specifically in the areas of: 1) content development, 2) training/professional development, 3) educational activities and 4) community partnerships. This report documents how the following DOE project goals were met: (1) Train scientists and provide teachers and informal educators with resources to engage youth with age appropriate information about materials science; (2) Provide activities and resources to five selected communities with ties to DOE researchers; (3) Increase interest in STEM.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institutional equipment orchestration, (2) intelligent data management with FAIR compliance, (3) AI-agent driven orchestration grounded in scientific principles, (4) interoperable agent communication interfaces, and (5) AI/ML-integrated scientific education. By connecting autonomous agents across institutional boundaries, autonomous science can unlock research spaces inaccessible to traditional approaches while democratizing cutting-edge technologies. This paradigm shift toward collaborative autonomous science promises breakthroughs in sustainable energy, materials development, and public health.

Ferreira da Silva, Rafael [Oak Ridge National Labo↗
DOE OSTI2020

The Brachypodium ENCODE Project – from sequence to function: Predicting physiological responses in grasses to facilitate engineering of biofuel crops

The overarching goal of this proposal was to gain deep knowledge of chromatin dynamics and gene networks related to stress responses in grasses. To accomplish this goal our Brachypodium ENCODE project (1) coupled drought-focused physiological analysis with multiple layers of ENCODE ‘omics’ profiling; (2) obtained fundamental information about the Brachypodium transcriptome, epigenome, chromatin dynamics, and gene regulatory networks; and (3) elucidated the regulatory dynamics of gene networks that can be manipulated to increase energy grass crop productivity.

09 BIOMASS FUELS↗
DOE OSTI2020

SNPeffect: identifying functional roles of SNPs using metabolic networks

Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine-learning accelerated geometry optimization in molecular simulation

Geometry optimization is an important part of both computational materials and surface science because it is the path to finding ground state atomic structures and reaction pathways. These properties are used in the estimation of thermodynamic and kinetic properties of molecular and crystal structures. This process is slow at the quantum level of theory because it involves an iterative calculation of forces using quantum chemical codes such as density functional theory (DFT), which are computationally expensive and which limit the speed of the optimization algorithms. It would be highly advantageous to accelerate this process because then one could do either the same amount of work in less time or more work in the same time. Here, we provide a neural network (NN) ensemble based active learning method to accelerate the local geometry optimization for multiple configurations simultaneously. We illustrate the acceleration on several case studies including bare metal surfaces, surfaces with adsorbates, and nudged elastic band for two reactions. In all cases, the accelerated method requires fewer DFT calculations than the standard method. In addition, we provide an Atomic Simulation Environment (ASE)-optimizer Python package to make the usage of the NN ensemble active learning for geometry optimization easier.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Efficient partnership models for energy technology startups enabled by artificial intelligence that hyper-targets ecosystem connectivity

PartnerAI is a platform that utilizes the tools of the rapidly advancing field of Artificial Intelligence (AI) to understand and identify the connections between founders and potential partners or investors in the clean energy space. The platform makes use of data science tools, including clustering and NLP libraries, to evaluate scraped text and data, and build a real-time network of potential connections.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Vehicle Platooning: An Energy Consumption Perspective

Urban traffic congestion is a chronic problem faced by many cities in the US and worldwide. It results in inefficient infrastructure use as well as increased vehicle fuel consumption and emission levels. Excessive fuel consumptions add extra costs to commuters as well as transportation businesses. Consuming less fuel and thus reducing costs by a single percentage digit can have a significant impact on the balance sheet as well as the protection of the environment. Researchers have developed, and continue to develop, tools and systems to optimize the operations of fleets as well as engines in order to burn less fuel and therefore generate less CO2 emissions. Platooning is one such tool that attempts to maintain relatively small distances (i.e. pre-determined time gap) between consecutive vehicles. It has the potential to increase the capacity of the road as well as reduce the consumed fuel. In this paper, we use a fuel consumption model for internal combustion light-duty vehicles, electric vehicles, hybrid electric vehicles, buses and trucks in order to determine and quantify the effects of platooning on a fleet fuel consumption. The results suggest that a reduction of up to 3%, 3.5%, 4.5 %, 10%, and 15% in fuel consumption can be achieved for internal combustion engine vehicles, hybrid electric vehicles, electric vehicles, buses and trucks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Interweaving 3D Network Binder for High-Areal-Capacity Si Anode through Combined Hard and Soft Polymers

We report that Si anodes suffer an inherent volume expansion problem. The consensus is that hydrogen bonds in these anodes are preferentially constructed between the binder and Si powder for enhanced adhesion and thus can improve cycling performance. There has been little research done in the field of understanding the contribution of the binder's mechanical properties to performance. Herein, a simple but effective strategy is proposed, combining hard/soft polymer systems, to exploit a robust binder with a 3D interpenetrating binding network (3D-IBN) via an in situ polymerization. The 3D-IBN structure is constructed by interweaving a hard poly(furfuryl alcohol) as the skeleton with a soft polyvinyl alcohol (PVA) as the filler, buffering the dramatic volume change of the Si anode. The resulting Si anode delivers an areal capacity of >10 mAh cm -2 and enables an energy density of >300 Wh kg -1 in a full lithium-ion battery (LIB) cell. The component of the interweaving binder can be switched to other polymers, such as replacing PVA by thermoplastic polyurethane and styrene butadiene styrene. Such a strategy is also effective for other high-capacity electroactive materials, e.g., Fe 2 O 3 and Sn. In conclusion, this finding offers an alternative approach in designing high-areal-capacity electrodes through combined hard and soft polymer binders for high-energy-density LIBs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Solving sparse finite element problems on neuromorphic hardware

The finite element method (FEM) is one of the most important and ubiquitous numerical methods for solving partial differential equations (PDEs) on computers for scientific and engineering discovery. Applying the FEM to larger and more detailed scientific models has driven advances in high-performance computing for decades. Here we demonstrate that scalable spiking neuromorphic hardware can directly implement the FEM by constructing a spiking neural network that solves the large, sparse, linear systems of equations at the core of the FEM. We show that for the Poisson equation, a fundamental PDE in science and engineering, our neural circuit achieves meaningful levels of numerical accuracy and close to ideal scaling on modern, inherently parallel and energy-efficient neuromorphic hardware, specifically Intel’s Loihi 2 neuromorphic platform. We illustrate extensions to irregular mesh geometries in both two and three dimensions as well as other PDEs such as linear elasticity. Our spiking neural network is constructed from a recurrent network model of the brain’s motor cortex and, in contrast to black-box deep artificial neural network-based methods for PDEs, directly translates the well-understood and trusted mathematics of the FEM to a natively spiking neuromorphic algorithm.

Applied mathematics↗
DOE OSTI2014

SU-C-BRE-07: Sensitivity Analysis of the Threshold Energy for the Creation of Strand Breaks and of Single and Double Strand Break Clustering Conditions

Purpose: To analyse the sensitivity of the creation of strand breaks (SB) to the threshold energy (Eth) and thresholding method and to quantify the impact of clustering conditions on single strand break (SSB) and double strand break (DSB) yields. Methods: Monte Carlo simulations using Geant4-DNA were conducted for electron tracks of 280 eV to 220 keV in a geometrical DNA model composed of nucleosomes of 396 phospho-diester groups (PDGs) each. A strand break was created inside a PDG when the sum of all energy deposits (method 1) or energy transfers (method 2) was higher than Eth or when at least one interaction deposited (method 3) or transferred (method 4) an energy higher than Eth. SBs were then clustered into SSBs and DSBs using clustering scoring criteria from the literature and compared to our own. Results: The total number of SBs decreases as Eth is increased. In addition, thresholding on the energy transfers (methods 2 and 4) produces a higher SB count than when thresholding on energy deposits (methods 1 and 3). Method 2 produces a step-like function and should be avoided when attempting to optimize Eth. When SBs are grouped into damage patterns, clustering conditions can underestimated SSBs by up to 18 % and DSBs can be overestimated by up to 12 % compared to our own implementation. Conclusion: We show that two often underreported simulation parameters have a non-negligible effect on overall DNA damage yields. First more SBs are counted when using energy transfers to the PDG rather than energy deposits. Also, SBs grouped according to different clustering conditions can influence reported SSB and DSB by as much as 20%. Careful handling of these parameters is required when trying to compare DNA damage yields from different authors. Research funding from the governments of Canada and Quebec. PP acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290)

60 APPLIED LIFE SCIENCES↗