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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Supporting mobile WEEE collection on demand: A method for multi-criteria vehicle routing, loading and cost optimisation

Highlights: • Multi-criteria method of WEEE mobile collection on demand. • Vehicle routing and loading problem included in the model and algorithm. • Optimisation of collecting vehicles routes including genetic algorithms and fuzzy logic. • Case study for heterogeneous fleet of collection vehicles for medium size city in Poland. • Possible application in a software supporting waste collection on demand. - Abstract: The task of increasing collection rates of waste electrical and electronic equipment (WEEE) is an important challenge in the global economy, and especially in the European Union where stiffer collection targets set out in a new WEEE directive are to be effective by 2019. As the circular economy approach replaces the linear model, resource recycling activities become a priority in waste management policy. As new techniques and possibilities of waste collection systems emerge, opportunities are created for improving efficiency for collection companies and affording benefits for the environment. A model proposed for mobile WEEE collection in this study considers a multi-criteria approach in developing a cost efficient method for pick up on demand from residents or electrical and electronic equipment (EEE) stores. The algorithm used in this model optimises vehicle routes and helps in selecting a number of vehicles from a heterogeneous fleet, incorporating the WEEE loading problem. Using genetic algorithm and fuzzy logic, this model optimises costs and resources required to complete the WEEE collection assuring timely pick up of the waste equipment. The numerical model is verified in a case study in Opole, a city in the south of Poland. The results show that the proposed model can handle the multiple parameter optimisation problem including operational costs, efficient use of vehicles from a fleet, efficient waste loading in vehicles and residents' satisfaction with timely pick up of the waste equipment from a household. Such system can be successfully applied even for large cities. The algorithm provides an opportunity for writing software or mobile apps design to be used by WEEE collection companies.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Rapidly Deployable Security System Final Report CRADA No. TC-2030-01

The ultimate objective of the LEADER and LLNL strategic partnership was to develop and commercialize_a security-based system product and platform for the use in protecting the substantial physical and economic assets of the government and commerce of the United States. The primary goal of this project was to integrate video surveillance hardware developed by LLNL with a security software backbone developed by LEADER. Upon completion of the project, a prototype hardware/software security system that is highly scalable was to be demonstrated.

42 ENGINEERING↗
DOE OSTI2018

Automated Management in Aerospace Production

Software for analysis of the efficiency of high-tech production is proposed. The software is based on a multidimensional model of product-information transformation during the manufacturing process, with a complex relation between the product parameters. Technological inheritance is taken into account.

42 ENGINEERING↗
DOE OSTI2022

Nuclear Quality Assurance Certification of the MCNP-ORIGEN Activation Automation tool

The MCNP-ORIGEN Activation Automation (MOAA) tool couples MCNP and ORIGEN-S, allowing to support ATR and TREAT experiments by predicting their composition and activity before their irradiation takes place. MOAA is developed and maintained by the Irradiation Experiment Neutronics Analysis group (C-150) at INL. This poster presents the motivation behind the development of MOAA, its main workflow, as well as the software quality assurance (SQA) efforts required by LWP-13620, "Managing Information Technology Assets."

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

SubDyn User's Guide and Theory Manual

SubDyn is a time-domain structural-dynamics module for multimember fixed-bottom substructures created by the National Renewable Energy Laboratory (NREL) through U.S. Department of Energy Wind and Water Power Program support. The module has been coupled into the FAST aero-hydro-servo-elastic computer-aided engineering (CAE) tool. Substructure types supported by SubDyn include monopiles, tripods, jackets, and other lattice-type substructures common for offshore wind installations in shallow and transitional water depths. SubDyn can also be used to model lattice support structures for land-based wind turbines. This document is organized as follows. Section 1 details how to obtain the SubDyn and FAST software archives and run both the stand-alone SubDyn or SubDyn coupled to FAST. Section 2 describes the SubDyn input files. Section 3 discusses the output files generated by SubDyn; these include echo files, a summary file, and the results file. Section 4 provides modeling guidance when using SubDyn. The SubDyn theory is covered in Section 5. Section 6 outlines future work, and Section 7 contains a list of references. Example input files are shown in Appendixes A and B. A summary of available output channels are found in Appendix C. Instructions for compiling the stand-alone SubDyn program are detailed in Appendix D. Appendix E tracks the major changes we have made to SubDyn for each public release.

17 WIND ENERGY↗
DOE OSTI2021

Developing a Hybrid Electric Vehicle Eco-Cooperative Adaptive Cruise Control System at Signalized Intersections.

This study develops an eco-driving strategy for hybrid electric vehicles (HEVs) in the vicinity of signalized intersections, entitled HEV Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I). The proposed system computes real-time, energy-optimized vehicle trajectories using HEV vehicle dynamics and energy consumption models. In the proposed system, a simple HEV energy model is used to compute the instantaneous fuel consumption. This HEV energy model is selected since it is general, transferable, and can be easily used to compute instantaneous energy consumption levels for HEVs without the additional input of vehicle engine data or complicated power control strategies. In addition, a vehicle dynamics model is used to capture the relationship between speed, acceleration level, and tractive/resistance forces on vehicles. The energy-optimum problem is formulated as an optimization problem with constraints, which is solved using a moving-horizon dynamic programming approach. The proposed HEV Eco-CACC-I system was tested to evaluate its performance for various speed limits, roadway grades, and signal timings. Lastly, the proposed HEV controller was implemented in a microscopic traffic simulation software to test its network-wide performance. The test results from an arterial corridor with three signalized intersections demonstrate that the proposed system can effectively reduce stop-and-go traffic in the vicinity of signalized intersections producing savings of 7.4% in energy consumption, 5.8% in traffic delay and 23% vehicle stops, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗
DOE OSTI2017

Security-informed safety: integrating security within the safety demonstration of a smart device - 157

Safety and security engineering have, over the years, developed their own regulations, standards, cultures, and practices. However, there's a growing realisation that security is closely connected to safety. Safety must be security-informed: if a safety-critical system isn't secure, it isn't safe. A safety demonstration is incomplete and unconvincing unless it considers security. In our work for government and industry, we have used the Claims, Arguments, Evidence (CAE) framework to analyse the impact of security on a safety justification or safety case and identified the significant changes needed to address security explicitly. This will impact the design and implementation process as well as the assurance and V and V approach. In this paper we discuss the impact of integrating security when developing a safety demonstration of a smart device. A smart device is an instrument, device or component that contains a microprocessor (and therefore contains both hardware and software) and is programmed to provide specialised capabilities, often measuring or controlling a process variable. Examples of smart devices include radiation monitors, relays, turbine governors, uninterruptible power supplies and heating ventilation, and air conditioning controllers. (authors)

42 ENGINEERING↗
DOE OSTI2021

Machine Learning Based Software Package for Reproducible Data Analysis for Electron Microscopy

Advances in hardware and computation technologies are enabling large hyperspectral imaging during Electron Microscopy (EM), resulting in datasets being produced at rapidly increasing spatial and temporal resolutions. This presents exciting opportunities for microstructure analysis, but also poses great challenges in data analysis. Under this STTR program, QuesTek Innovations LLC, a leader in the field of integrated computational materials engineering (ICME), collaborated with Lawrence Livermore National Laboratory and Argonne National Laboratory. The team developed an open-source machine learning (ML) powered tool for EM data analysis of multiple systems and data types, and collected EM data for ML model development using the cutting-edge PicoProbe equipment at Argonne National Laboratory.

Lin, Yu↗
DOE OSTI2020

Prescient

Prescient is a software package for simulating electricity grid (power systems) operations, under both nominal and extreme event conditions. Core functionality includes discrete event simulation, commitment and dispatch optimization, probabilistic scenario construction, and reporting/visualization. SAND2020-0169 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Watson, Jean Paul↗
DOE OSTI2021

Cubit SDK

SAND2021-3052 O CUBIT SDK is a software development kit for customizing the CUBIT software. The SDK provides the ability to customize the CUBIT GUI and to program custom commands to add to the CUBIT application. The SDK also provides the ability to integrate CUBIT into another application. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Stimpson, Clinton↗
DOE OSTI2024

recon3d

SAND2025-00533O recon3d is a software tool that provides automated 3D reconstruction and meshing capabilities. It processes labeled 3D image data from various sources, starting from image stacks, and calculates 3D feature distributions like size, shape, and location. The software also has tools for downscaling rectilinear grid data and creating tetrahedral meshes directly from image data. recon3d can be used by novice users via the command line with a properly formatted configuration file. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Emery, John↗
DOE OSTI2023

Aerial Captured Data and Processed Models in Beaumont-Port Arthur Region in Feb and Oct, 2023

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu.We used a DJI Mavic 2 Pro to capture aerial photos in Beaumont-Port Arthur, TX, in February 2023, including:I. Beaumont Soccer ClubII. Corps’ Port Arthur Resident OfficeIII. Halbouty Pump Station comprises its vicinityIV. Lamar University (Including Exxon Power Plants close to Lamar Univ.)V. MLK Boulevard for aerial images of the industry and the ship channelVI. Salt Water Barrier (include some aerial images about the Big Thicket)Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each location.The processed data package including 3D models, geospatial data, mappings, point clouds, and the animation video of Halbouty Pump Station has various file types:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.In October 2023, we had our follow-up data collection, including:I. Beaumont Soccer ClubII. Shipping and Receiving Center at Lamar UniversityAfter the aerial collection, we obtained aerial photos of those two locations mentioned above, as well as processed data (such as point clouds and models).

2D mapping↗
DOE OSTI2025

Discovering methylated DNA motifs in bacterial nanopore sequencing data with MIJAMP

Abstract Bacterial DNA methylation is involved in diverse cellular functions, including modulation of gene expression, DNA repair, and restriction–modification systems for defense against viruses and other foreign DNA. Restriction systems hinder efforts to engineer organisms to produce fuels and chemicals from waste and renewable feedstocks by degrading DNA during transformation. Methylome analysis allows identification of motifs within a bacterial chromosome that may be targeted by native restriction enzymes. Further expression of the corresponding methyltransferases in Escherichia coli allows plasmid DNA to be protected from restriction in the target organism, thereby drastically enhancing transformation efficiency. Nanopore sequencing can detect methylated bases, but software is needed to transform modified base coordinates into methylated motifs. Here, we develop MIJAMP (MIJAMP Is Just A MethylBED Parser), a software package that was developed to discover methylated motifs from the output of ONT’s Modkit or other data in the methylBED format. MIJAMP employs a human-driven refinement strategy that empirically validates all motifs against genome-wide methylation data, thus eliminating incorrect motifs. MIJAMP also reports methylation data on specific, user-defined motifs. Using MIJAMP, we determined the methylated motifs both in a control strain (wild-type E. coli) and in Synecococcus sp. strain PCC7002, laying the foundation for improved transformation in this organism. MIJAMP is available at https://code.ornl.gov/alexander-public/mijamp/. One Sentence Summary: Here we describe software written to discover DNA methylation motifs from nanopore sequencing data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Integral Full Core Multi-Physics PWR Benchmark with Measured Data

In recent years, the importance of modeling and simulation has been highlighted extensively in the DOE research portfolio with concrete examples in nuclear engineering with the CASL and NEAMS programs. These research efforts and similar efforts worldwide aim at the development of high-fidelity multi-physics analysis tools for the simulation of current and next-generation nuclear power reactors. Like all analysis tools, verification and validation is essential to guarantee proper functioning of the software and methods employed. The current approach relies mainly on the validation of single physic phenomena (e.g. critical experiment, flow loops, etc.) and there is a lack of relevant multiphysics benchmark measurements that are necessary to validate high-fidelity methods being developed today. This work introduces a new multi-cycle full-core Pressurized Water Reactor (PWR) depletion benchmark based on two operational cycles of a commercial nuclear power plant that provides a detailed description of fuel assemblies, burnable absorbers, in-core fission detectors, core loading and re-loading patterns. This benchmark enables analysts to develop extremely detailed reactor core models that can be used for testing and validation of coupled neutron transport, thermal-hydraulics, and fuel isotopic depletion. The benchmark also provides measured reactor data for Hot Zero Power (HZP) physics tests, boron letdown curves, and three-dimensional in-core flux maps from 58 instrumented assemblies. The benchmark description is now available online and has been used by many groups. However, much work remains to be done on the quantification of uncertainties and modeling sensitivities. This work aims to address these deficiencies and make this benchmark a true non-proprietary international benchmark for the validation of high-fidelity tools. This report details the BEAVRS uncertainty quantification for the first two cycle of operations and serves as the final report of the project.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Sensitivity of Viscosity on Molten Ti Infusion into a B{sub 4}C-Packed Bed at the Microscale

The effect of the surface tension–viscosity dissipation driving liquid Ti flow into a B{sub 4}C packed bed was analyzed at 1941 K and 2573 K. The model consisted of randomized size distributions of pores (1 to 10 µm) mimicking a packed bed to determine the depth of infusion. Statistical uncertainty was determined as a function of viscosity and pore geometry. The computations were performed with our in-house software, which incorporates a directed acyclic graph and modified nodal analysis algorithms.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Dakota Graphical User Interface v.6.14

Dakota GUI is a graphical analysis environment for Sandia’s Dakota software for optimization and uncertainty quantification. This version of the Dakota GUI includes new functionality such as plotting capabilities, Eclipse-based resource management, and integration with the SAW-IF Next Gen Workflow editor. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-10629 O

Glickman, Matthew↗