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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.

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At least 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Vertically Aligned Carbon Nanofiber based Biosensor Platform for Glucose Sensor

Vertically aligned carbon nanofibers (VACNFs) have recently become an important tool for biosensor design. Carbon nanofibers (CNF) have excellent conductive and structural properties with many irregularities and defect sites in addition to exposed carboxyl groups throughout their surfaces. These properties allow a better immobilization matrix compared to carbon nanotubes and offer better resolution when compared with the FET-based biosensors. VACNFs can be deterministically grown on silicon substrates allowing optimization of the structures for various biosensor applications. Two VACNF electrode architectures have been employed in this study and a comparison of their performances has been made in terms of sensitivity, sensing limitations, dynamic range, and response time. The usage of VACNF platform as a glucose sensor has been verified in this study by selecting an optimum architecture based on the VACNF forest density. Read More: http://www.worldscientific.com/doi/abs/10.1142/S0129156414500062

Al Mamun, Khandaker A.↗
DOE OSTI2018

Scalable Cloning on Large-Scale GPU Platforms with Application to Time-Stepped Simulations on Grids

Cloning is a technique to efficiently simulate a tree of multiple what-if scenarios that are unraveled during the course of a base simulation. However, cloned execution is highly challenging to realize on large, distributed memory computing platforms, due to the dynamic nature of the computational load across clones, and due to the complex dependencies spanning the clone tree. In this paper, we present the conceptual simulation framework, algorithmic foundations, and runtime interface of CloneX, a new system we designed for scalable simulation cloning. It efficiently and dynamically creates whole logical copies of a dynamic tree of simulations across a large parallel system without full physical duplication of computation and memory. The performance of a prototype implementation executed on up to 1,024 graphical processing units of a supercomputing system has been evaluated with three benchmarks—heat diffusion, forest fire, and disease propagation models—delivering a speed up of over two orders of magnitude compared to replicated runs. Finally, the results demonstrate a significantly faster and scalable way to execute many what-if scenario ensembles of large simulations via cloning using the CloneX interface.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

BCL: A Cross-Platform Distributed Data Structures Library

One-sided communication is a useful paradigm for irregular parallel applications, but most one-sided programming environments, including MPI's one-sided interface and PGAS programming languages, lack application-level libraries to support these applications. We present the Berkeley Container Library, a set of generic, cross-platform, high-performance data structures for irregular applications, including queues, hash tables, Bloom filters and more. BCL is written in C++ using an internal DSL called the BCL Core that provides one-sided communication primitives such as remote get and remote put operations. The BCL Core has backends for MPI, OpenSHMEM, GASNet-EX, and UPC++, allowing BCL data structures to be used natively in programs written using any of these programming environments. Along with our internal DSL, we present the BCL ObjectContainer abstraction, which allows BCL data structures to transparently serialize complex data types while maintaining efficiency for primitive types. We also introduce the set of BCL data structures and evaluate their performance across a number of high-performance computing systems, demonstrating that BCL programs are competitive with hand-optimized code, even while hiding many of the underlying details of message aggregation, serialization, and synchronization.

Brock, Benjamin↗
DOE OSTI2020

Caspian: A Neuromorphic Development Platform

Current neuromorphic systems often may be difficult to use and costly to deploy. There exists a need for a simple yet flexible neuromorphic development platform which can allow researchers to quickly prototype ideas and applications. Caspian offers a high-level API along with a fast spiking simulator to enable the rapid development of neuromorphic solutions. It further offers an FPGA architecture that allows for simplified deployment -- particularly in SWaP (size, weight, and power) constrained environments. Leveraging both software and hardware, Caspian aims to accelerate development and deployment while enabling new researchers to quickly become productive with a spiking neural network system.

Mitchell, Parker↗
DOE OSTI2020

Safe and Private Forward-trading Platform for Transactive Microgrids

Power grids are evolving at an unprecedented pace due to the rapid growth of distributed energy resources (DER) in communities. These resources are very different from traditional power sources, as they are located closer to loads and thus can significantly reduce transmission losses and carbon emissions. However, their intermittent and variable nature often results in spikes in the overall demand on distribution system operators (DSO). To manage these challenges, there has been a surge of interest in building decentralized control schemes, where a pool of DERs combined with energy storage devices can exchange energy locally to smooth fluctuations in net demand. Building a decentralized market for transactive microgrids is challenging, because even though a decentralized system provides resilience, it also must satisfy requirements such as privacy, efficiency, safety, and security, which are often in conflict with each other. As such, existing implementations of decentralized markets often focus on resilience and safety but compromise on privacy. In this article, we describe our platform, called TRANSAX, which enables participants to trade in an energy futures market, which improves efficiency by finding feasible matches for energy trades, enabling DSOs to plan their energy needs better. TRANSAX provides privacy to participants by anonymizing their trading activity using a distributed mixing service, while also enforcing constraints that limit trading activity based on safety requirements, such as keeping planned energy flow below line capacity. We show that TRANSAX can satisfy the seemingly conflicting requirements of efficiency, safety, and privacy. We also provide an analysis of how much trading efficiency is lost. Trading efficiency is improved through the problem formulation, which accounts for temporal flexibility, and system efficiency is improved using a hybrid-solver architecture. Lastly, we describe a testbed to run experiments and demonstrate its performance using simulation results.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Advancing Quantum Many-Body GW Calculations on Exascale Supercomputing Platforms

Advanced ab initio materials simulations face growing challenges as increasing systems and phenomena complexity requires higher accuracy, driving up computational demands. Quantum many-body GW methods are state-of-the-art for treating electronic excited states and couplings but often hindered due to the costly numerical complexity. Here, we present innovative implementations of advanced GW methods within the BerkeleyGW package, enabling large-scale simulations on Frontier and Aurora exascale platforms. Our approach demonstrates exceptional versatility for complex heterogeneous systems with up to 17,574 atoms, along with achieving true performance portability across GPU architectures. We demonstrate excellent strong and weak scaling to thousands of nodes, reaching double-precision core-kernel performance of 1.069 ExaFLOP/s on Frontier (9,408 nodes) and 707.52 PetaFLOP/s on Aurora (9,600 nodes), corresponding to 59.45% and 48.79% of peak, respectively. Our work demonstrates a breakthrough in utilizing exascale computing for quantum materials simulations, delivering unprecedented predictive capabilities for rational designs of future quantum technologies.

Zhang, Benran [University of Southern California, ↗
DOE OSTI2014

Electrolyte Development for Non-Aqueous Redox Flow Batteries Using a High-Throughput Screening Platform

Though 2,5-di-tert-butyl-1,4-bis(2-methoxyethoxy) benzene (DBBB) is a promising active species for non-aqueous redox flow batteries, the development and optimization of suitable electrolytes is still required to realize this potential. Given the breadth and diversity of non-aqueous electrolytes, combinatorial screening using automated platforms offers an efficient means of mapping out the design space. Employing an automated electrolyte synthesis and characterization system, we studied electrolytes consisting of DBBB, various alkali ion salts, and carbonate solvents, with an overarching goal of improving DBBB solubility and ionic conductivity. Via this high-throughput approach, mixtures of linear and cyclic carbonates were found to improve solubility and conductivity including certain mixtures that demonstrated both improved DBBB solubility (>0.5 M) and electrolyte conductivity (>5 mS/cm), over the base case scenario. In general, the addition of LiTFSI as a salt (or co-salt) improved DBBB solubility in electrolytes and the ionic conductivity of solutions. Furthermore, the electrochemical behavior of DBBB was systematically investigated over a range of concentrations (0.005 M to 0.3 M) in an optimized electrolyte using both macroelectrode and ultramicroelectrode techniques. Here, determining the trends of key electrochemical parameters may help bridge the gap between low concentration screening experiments and higher concentration energy storage applications.

25 ENERGY STORAGE↗
DOE OSTI2024

Field Testing of a Mixed Potential IoT Sensor Platform for Methane Quantification

Emissions of CH 4 from natural gas infrastructure must urgently be addressed to mitigate its effect on global climate. With hundreds of thousands of miles of pipeline in the US used to transport natural gas, current methods of surveying for leaks are inadequate. Mixed potential sensors are a low cost, field deployable technology for remote and continuous monitoring of natural gas infrastructure. We demonstrate for the first time a field trial of a mixed potential sensor device coupled with machine learning and internet-of-things platform at Colorado State University’s Methane Emissions Technology Evaluation Center (METEC). Emissions were detected from a simulated buried underground pipeline source. Sensor data was acquired and transmitted from the field test site to a remote cloud server. Quantification of concentration as a function of vertical distance is consistent with previously reported transport modelling efforts and experimental surveys of methane emissions by more sophisticated CH 4 analyzers.

03 NATURAL GAS↗
DOE OSTI2014

Standard Energy Efficiency Data Platform

The SEED platform is expected to be a building energy performance data management tool that provides federal, state and local governments, building owners and operators with an easy, flexible and cost-effective method to collect information about groups of buildings, oversee compliance with energy disclosure laws and demonstrate the economic and environmental benefits of energy efficiency. It will allow users to leverage a local application to manage data disclosure and large data sets without the IT investment of developing custom applications. The first users of SEED will be agencies that need to collect, store, and report/share large data sets generated by benchmarking, energy auditing, retro-commissioning or retrofitting of many buildings. Similarly, building owners and operators will use SEED to manage their own energy data in a common format and centralized location. SEED users will also control the disclosure of their information for compliance requirements, recognition programs such as ENERGY STAR, or data sharing with the Buildings Performance Database and/or other third parties at their discretion.

Cheifetz, D.Magnus↗
DOE OSTI2014

F3D Image Processing and Analysis for Many - and Multi-core Platforms

F3D is written in OpenCL, so it achieve[sic] platform-portable parallelism on modern mutli-core CPUs and many-core GPUs. The interface and mechanims to access F3D core are written in Java as a plugin for Fiji/ImageJ to deliver several key image-processing algorithms necessary to remove artifacts from micro-tomography data. The algorithms consist of data parallel aware filters that can efficiently utilizes[sic] resources and can work on out of core datasets and scale efficiently across multiple accelerators. Optimizing for data parallel filters, streaming out of core datasets, and efficient resource and memory and data managements over complex execution sequence of filters greatly expedites any scientific workflow with image processing requirements. F3D performs several different types of 3D image processing operations, such as non-linear filtering using bilateral filtering and/or median filtering and/or morphological operators (MM). F3D gray-level MM operators are one-pass constant time methods that can perform morphological transformations with a line-structuring element oriented in discrete directions. Additionally, MM operators can be applied to gray-scale images, and consist of two parts: (a) a reference shape or structuring element, which is translated over the image, and (b) a mechanism, or operation, that defines the comparisons to be performed between the image and the structuring element. This tool provides a critical component within many complex pipelines such as those for performing automated segmentation of image stacks. F3D is also called a "descendent" of Quant-CT, another software we developed in the past. These two modules are to be integrated in a next version. Further details were reported in: D.M. Ushizima, T. Perciano, H. Krishnan, B. Loring, H. Bale, D. Parkinson, and J. Sethian. Structure recognition from high-resolution images of ceramic composites. IEEE International Conference on Big Data, October 2014.

Source record↗
DOE OSTI2017

Transactive Energy Simulation Platform

Transactive Energy Simulation Platform (TESP) combines existing domain simulators in the electric power grid, buildings, and communication systems, with new transactive agents, growth models and evaluation scripts. The existing domain simulators include GridLAB-D for the distribution grid and single-family residential buildings, MATPOWER for transmission and bulk generation, EnergyPlus for large buildings and ns-3 for communication systems. More are planned for subsequent versions of TESP. The new elements are: TEAgents - simulate market participants and transactive systems for market clearing. Some of this functionality was extracted from GridLAB-D and implemented in Python for customization by PNNL and others. Growth Model - a means for simulating system changes over a multiyear period, including both normal load growth and specific investment decisions. Customizable in Python code. Evaluation Script - a means of evaluating different transactive systems through customizable post-processing in Python code. TESP will run on Linux, Windows and Mac OS X.

McDermott, Tom↗
DOE OSTI2017

Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform, Version 1.5 and 1.x.

EDGE bioinformatics was developed to help biologists process Next Generation Sequencing data (in the form of raw FASTQ files), even if they have little to no bioinformatics expertise. EDGE is a highly integrated and interactive web-based platform that is capable of running many of the standard analyses that biologists require for viral, bacterial/archaeal, and metagenomic samples. EDGE provides the following analytical workflows: quality trimming and host removal, assembly and annotation, comparisons against known references, taxonomy classification of reads and contigs, whole genome SNP-based phylogenetic analysis, and PCR analysis. EDGE provides an intuitive web-based interface for user input, allows users to visualize and interact with selected results (e.g. JBrowse genome browser), and generates a final detailed PDF report. Results in the form of tables, text files, graphic files, and PDFs can be downloaded. A user management system allows tracking of an individual’s EDGE runs, along with the ability to share, post publicly, delete, or archive their results.

Chain, Patrick↗
DOE OSTI2018

Sandia Cloud Forensics and Incident Response Platform v 1.0

As systems and devices become virtualized and deployed in the cloud, the hypervisor becomes an increasingly appropriate place to collect performance data, system state, system landscape, function calls, transaction traces, and other characteristics. We developed a method by which an introspection application may be coupled with a hypervisor to “reach into” the VM with minimal intrusiveness to collect data critical to the reconstruction of events, files, and operations. Such a capability is required to take advantage of the hypervisor as an instrumentation platform and to integrate that data with more traditional collection mechanisms. The concept of a VM serviced by a lightweight hypervisor is a relatively new paradigm for forensic practitioners. Traditional forensic techniques, based on assumptions that the filesystem was directly interacting with the hardware through an abstraction, afforded the forensic practitioner the assumption that there was nothing controlling the application below the filesystem. This is not the case when using virtualized technologies. Hypervisors can covertly monitor, introspect and interact with the guest in a transparent fashion. The problems of storage and collection of actionable data are exhausting. The current challenge is most hypervisors do not expose a useful application programming interface (API) at a sufficient level to do transparent, fine-grained and customizable introspection. Scalable VM instrumentation and introspection at an in-depth level requires fast handling of events, as well as direct access to VM state. Furthermore, deep introspection benefits greatly from the ability to gather data from the hardware during the VM’s exit to the hypervisor. All of this requires identical access to the system as the hypervisor itself; improper use of this ability could easily cause system instability. It is for this reason we believe that the hypervisor developers have been hesitant to grant this much control through their APIs. However, our approach leverages other means to collect and monitor the guests in a targeted fashion. Virtual machine introspection (VMI) is a technique used to monitor the runtime state of a system-level virtual machine. The runtime state can be may include processor registers, memory, disk, network, and any other hardware-level events A review of research literature and current VMI technologies exposed several limitations and tradeoffs in VMI approaches, including: the use of in-guest agents; kernel to user space transitions (dramatically slowing down processing); VMI tool pre-configuration requirements; hypervisor version lock-in or source code patching; reliance on operating system symbols; limited processor features due to hypervisor (even if the hardware could do more). To address these constraints, a VMI tool was envisioned to provide the cloud forensic capabilities while having as few of these limitations as possible. The Kernel-based Virtual Machine Introspection (KVMi) tool was developed for hypervisors on Intel’s x86-64 architecture. To meet performance, scoping and use-case demands, the follow criteria were applied to KVMi to support live, forensic data collection: • Shall not require in-guest agents. • Shall work with any recent version of modern hypervisors. • Shall introspect upon any guest (VM) running modern versions of Windows, Linux and OS X. • Shall have the ability to find and track operating system artifacts • Shall not require operating system symbol files. • Shall be able to fully handle VM-exits, bypassing execution of the hypervisor if necessary, to facilitate new features the hypervisor may not support. • Shall be compilable/loadable on a running system with standard build tools.

Stout, WilliamM.S.↗
DOE OSTI2017

Basin Assessment Spatial Planning Platform

The tool is intended to facilitate hydropower development and water resource planning by improving synthesis and interpretation of disparate spatial datasets that are considered in development actions (e.g., hydrological characteristics, environmentally and culturally sensitive areas, existing or proposed water power resources, climate-informed forecasts). The tool enables this capability by providing a unique framework for assimilating, relating, summarizing, and visualizing disparate spatial data through the use of spatial aggregation techniques, relational geodatabase platforms, and an interactive web-based Geographic Information Systems (GIS). Data are aggregated and related based on shared intersections with a common spatial unit; in this case, industry-standard hydrologic drainage areas for the U.S. (National Hydrography Dataset) are used as the spatial unit to associate planning data. This process is performed using all available scalar delineations of drainage areas (i.e., region, sub-region, basin, sub-basin, watershed, sub-watershed, catchment) to create spatially hierarchical relationships among planning data and drainages. These entity-relationships are stored in a relational geodatabase that provides back-end structure to the web GIS and its widgets. The full technology stack was built using all open-source software in modern programming languages. Interactive widgets that function within the viewport are also compatible with all modern browsers.

Source record↗
DOE OSTI2018

Localization and Mapping Platform Software (LAMP) v3

The Localization and Mapping Platform (LAMP) software fuses radiation and contextual data to produce 3-D maps that locate and identify gamma-ray emitting sources in complex environments. It enables data collection, fusion, and processing onboard the LAMP system in handheld configurations, and enables near-real-time 3-D mapping from onboard an unmanned aerial system (UAS). This software enables a real-time 3-D gamma-ray mapping capability that is not available commercially.

Joshi, Tenzing↗
DOE OSTI2018

Localization and Mapping Platform Software (LAMP Software) v1

The Localization and Mapping Platform (LAMP) software fuses radiation and contextual data to produce 3-D maps that locate and identify gamma-ray emitting sources in complex environments. It enables data collection, fusion, and processing in near-real-time and on-board in several configurations, including handheld and unmanned aerial system (UAS) form factors.

Haefner, Andrew↗
DOE OSTI2021

Design, Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) v0.1.0

The DISPATCHES software will enable the design and optimization of Integrated Energy Systems (IES). The platform will support hybridizing existing generation facilities (such as nuclear and coal plants) as well as greenfield hybrid designs that incorporate advanced technologies (such as Advanced Modular Reactors and Coal FIRST), and it will enable the consideration of multiple technology options with different time constants. The design of the IES will be optimized for operation within the bulk power system via energy market signals (day ahead, real-time, and ancillary services) obtained from a production cost model. The goal of DISPATCHES is to identify flexible, dynamic designs that are responsive to grid market signals resulting in highly efficient, cost effective tightly coupled hybrid energy systems that can act as a "black box" to the grid to provide increased flexibility and resiliency.

Gunter, Daniel↗