SEARCH · Engineering Papers
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.
This report presents the work performed for welding 17-7PH stainless steel hemispheres, flanges, and accelerometer blocks. This includes weld development, microhardness measurements, calculated strengths, and distortion measurements after welding and heat treatment. The results of this report are utilized to propose a recommended welding procedure which was implemented for this experiment.
This project is developing a multiscale, multi-physics modeling framework for probing the effects of materials microstructure and device architecture on ion transport within 3D ceramic solid-state battery materials, with the goal of enhancing performance and reliability. The project has three primary objectives: (1) integrate multi physics and multiscale model components; (2) understand interface- and microstructure-derived limitations on ion transport; and (3) derive key structure-performance relations for enabling future optimization.
Watersheds play a critical role in our water supply infrastructure and require sustainable management in a changing environment. Sustainable management of watershed systems and their interaction with the built environment rely on understanding the hydrologic and biogeochemical processes that control watershed system dynamics and water availability and quality. The overarching objective of the U.S. Department of Energy’s (DOE’s) Environmental System Science (ESS) program is to advance a robust, predictive understanding of how watersheds function and respond to perturbations as integrated hydrobiogeochemical systems. ESS supports a network of watershed testbeds within the United States where national laboratories and university partners work in interdisciplinary teams to advance watershed system science for energy.
This project builds upon knowledge gained from previous projects on Additive Manufacturing (AM) operation, process control and monitoring, repeatability, and process improvement. This project will exclusively focus on metal powder bed additive manufacturing (PBAM). Objective 1: Advance metal powder bed AM R&D capabilities. Perform enhanced R&D on increased build volumes and throughput, safety and handling controls, process monitoring, and diagnostics with a focus on production end-states. Objective 2: Build upon knowledge gained from previous PDRD projects to develop deeper understanding of process controls, essential for transitioning metal AM processes for future applications. Objective 3: Maintain business development and research advantages through collaborations with external industry and university partners.
We’re happy to report that the full-aperture upgrade project, started in FY18, is now complete and short-pulse target experiments are underway. The table below lists the present performance level of ZPW. Additional laser improvements are in progress to increase the laser energy and pulse contrast along with implementing a correction for achromatic aberrations to reduce the focused spot size and pulse width.
Niowave Inc. produces medical radioisotopes within the US. Lead-bismuth eutectic (LBE) is irradiated using an electron beam to produce neutrons that are used in the production of Molybdenum-99 (Mo-99). The medical community relies on a steady supply of Mo-99 which is primarily used in medical diagnostic imaging. The irradiation of LBE results in high temperatures within the molten metal, this work focusses on the design of ancillary systems associated with the liquid metal system. The LBE is initially at a temperature of 200°C and flows over a steel plate to create a waterfall. The electron beam is then aimed at the waterfall to generate neutrons. The peak temperatures of the LBE post irradiation depend on the beam energy of the LINAC. For a 15 MeV and 20 kW beam, the temperatures can range between 300°C and 350°C. The LBE is cycled through the system to create a closed loop that requires the post-irradiation LBE to be cooled back to 200°C. This work focuses on the heat exchanger and condenser system responsible for the cooling of the post-irradiation LBE.
The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g., height, color and grayscale values; all as functions of a location in a plane projection). A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data, flag significant features, execute Machine Learning (ML) algorithms, output parameters for trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Features can be called out by user-specified thresholds, manual labeling or machine learning algorithms when they have been completed. The ability to rapidly label data is important because of the volume of data required for training machine learning algorithms. The GUI has the flexibility to allow addition of improved ML algorithms, methods for data visualization, and statistical computations. Statistical analyses via the GUI include areas of pits within a defined range of pit depths, correlations between Red-Green-Blue (RGB) or grayscale intensity and relative surface height, covariances between values associated with features, and feature histograms. The development of supervised machine learning algorithms, however, has been hindered by a lack of training data. The machine learning algorithms for crack identification are being refined but require improvements to the true positive rate for crack detection. This shortcoming is an artifact of the limited training data currently available, perhaps more so than the structure of the neural networks. At present, the best results are had from a consensus over an ensemble of randomly generated Deep Neural Network (DNN) or Convolutional Neural Network (CNN) algorithms. Although the consensus accuracy method has yielded optimum true positive and true negative rates in excess of 80%, additional validation testing is necessary. In addition to the suite of LCM data that was initially used, and which represents the majority of the work presented in this report, WAMS image data was also reviewed at a preliminary level. The review included a comparison between image resolution and dynamic range for each method. WAMS (ZON file) image data was found to have a pixel pitch of 3.69μm compared to 1 μm for the LCM (vk4 file) data, which implies a lower resolution for the WAMS images. Conversely, the ratio of dynamic range of the WAMS data to the LCM data was approximately 41:20 for height data, suggesting that information from WAMS should more accurately determine the depth of pits. At present, the significance of the greater dynamic range of the WAMS data relative to the LCM data has not yet been evaluated.
LANL has devoted a substantial amount effort toward the MCNP6.3 release over the past year by improving many of their processes, infrastructure, and capabilities. Many new resources in support of the MCNP code and DOE NCSP applications are coming online now. LANL is excited to receive feedback on all of the new and improved capabilities that will be packaged with MCNP6.3.
As Northstar enters a stage of rapid progress in the construction and implementation of their accelerator based Mo99 production facility, LANL has been supporting the effort as requested. This support is often an assistance response to issues as they arise. This report will describe these issues and the LANL contribution in analysis, advice and experimentation.
The Multiphysics Object Oriented Simulation Environment (MOOSE) [1] is a massively parallel finite-element/volume package for multiphysics simulation in science and engineering. The package focuses on providing rapid-development capabilities for engineering applications by leveraging well-built features from libMesh [2] and the Portable Extensible Toolkit for Scientific Computation (PETSc) [3]. Fiscal year 2021 (FY-21) was the first year with funding dedicated to supporting MOOSE-derived applications relevant to the Nuclear Engineering Advanced Modeling and Simulation (NEAMS) program. In this report we outline the work done to support NEAMS applications such as BISON, Griffin, Pronghorn, and System Analysis Module (SAM).
This report summarizes Fiscal Year 2021 accomplishments from Sandia National Laboratories Wind Energy Program. The portfolio consists of funding provided by the DOE EERE Wind Energy Technologies Office (WETO), Advanced Research Projects Agency-Energy (ARPA-E), DOE Small Business Innovation Research (SBIR), and the Sandia Laboratory Directed Research and Development (LDRD) program. These accomplishments were made possible through capabilities investments by WETO, internal Sandia investment, and partnerships between Sandia and other national laboratories, universities, and research institutions around the world.
Abstract not provided.
Accelerating intelligent deployment of solar energy technologies demands accurate system modeling every step of the way. From project development to policy research, grid integration studies to development of novel technologies, industry and researchers alike need validated, transparent, easy-to-use, extensible, cutting-edge, and accurate models of both the performance and financing of solar systems. The System Advisor Model (SAM) and PVWatts tools provide a platform to fill that need. The overarching goal of this set of software tools is to enable accurate PV system modeling across the industry, and our usage metrics indicate that we continue to succeed in that endeavor, with a user starting SAM every 2 minutes globally, and over 17 million PVWatts hits per month. This project leveraged DOEs past investment in the SAM and PVWatts platforms to continue to provide valuable and extensible PV, battery, and financial modeling resources to the larger solar community. We pursued multiple avenues in parallel: software maintenance and technical support that are foundational to the continued usability of the SAM and PVWatts platforms; platform and PV model improvements and stakeholder engagement activities that are core to the continued relevance of the platforms; and open source activities to foster the continued creation of a vibrant open-source community around the SAM and PVWatts tools, which opens up exciting new opportunities for industry interaction.
In total, light-duty vehicles in the United States travel on the order of 3 trillion miles annually, providing tremendous societal and personal benefits. However, the environmental burden is excessive, prompting Co-Optimization of Fuel and Engines (Co-Optima) program efforts to provide the science needed to increase engine efficiency and produce non-fossil fuels with reduced greenhouse gas emissions. Boosted spark-ignition (SI) engines provide high power density by offering high loads and engine speeds, making them light-weight and attractive for light-duty vehicles. Unfortunately, the engine efficiency drops off at lower loads and speeds, where the engine spends most time during typical driving. Multimode SI engines can use a more efficient advanced lean combustion mode at lower loads and speeds, while reverting to boosted SI under high-load conditions. Within Co-Optima, multiple advanced lean combustion modes have been explored; these include stratified-charge SI, pre-chamber lean SI, and advanced compression ignition (ACI) techniques such as spark-assisted compression ignition (SACI). For these combustion modes, focus has been on determining fuel properties that enable higher engine efficiency, clean and stable combustion, and effective exhaust aftertreatment. This report highlights recent efforts funded by the Vehicle Technologies Office at multiple National Laboratories that supported the multimode project in Co-Optima. It also includes a brief summary of biofuel production research funded by the Bioenergy Technologies Office.
All 3 BNL NCSP (ND, AM, TS) tasks are reported as being on time and slightly under budget. Several COVID-19 related impacts are noted. The release of ENDF/B-VIII.1 has been delayed for 1 year to Feb 2024. New ENDF Library Manager appointment announced as Gustavo Nobre.
LANL lists several NJOY priorities for the future, including the maintenance of NJOY2016 for the foreseeable future, as well as the delivery of usable NJOY21 components.
This presentation highlights the activities of Los Alamos National Laboratory (LANL) in fiscal year 2021.These include the celebration of the tenth anniversary of NCERC operations, a large experiment campaign (IER 488 - MUSiC: Measurements of Uranium Subcritical and Critical), preparation for FY22 experiments, the completion of several ongoing experiments, and a control system upgrade project.
This presentation details the impacts of the COVID-19 pandemic on LLNL in fiscal year 2021, as well as budget information and major accomplishments for the lab in IE, analytical methods, nuclear data, training and education, and IPD.