SEARCH · Engineering Papers
Results for “2022”
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.
Supercomputing 2022 ODA BoF Presentation on SNL Monitoring and Analysis.
Abstract not provided.
Extension of Regional Infrasonic Processing Tools for Global Analysis: A Case Study From the January 15 2022 Tonga Hunga Volcanic Eruption.
Abstract not provided.
NLIT 2022 Presentation - Idea Exchange of How Sites Comply With CSSP Policies for the NSN.
Abstract not provided.
Workflows Community Summit 2022: A Roadmap Revolution
Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from the execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing landscape of scientific computing (often referred to as a computing continuum) and the evolving needs of emerging scientific applications, it is paramount that the development of novel scientific workflows and system functionalities seek to increase the efficiency, resilience, and pervasiveness of existing systems and applications. Specifically, the proliferation of machine learning/artificial intelligence (ML/AI) workflows, need for processing large-scale datasets produced by instruments at the edge, intensification of near real-time data processing, support for long-term experiment campaigns, and emergence of quantum computing as an adjunct to HPC, have significantly changed the functional and operational requirements of workflow systems. Workflow systems now need to, for example, support data streams from the edge-to-cloud-to-HPC, enable the management of many small-sized files, allow data reduction while ensuring high accuracy, orchestrate distributed services (workflows, instruments, data movement, provenance, publication, etc.) across computing and user facilities, among others. Further, to accelerate science, it is also necessary that these systems implement specifications/standards and APIs for seamless (horizontal and vertical) integration between systems and applications, as well as enable the publication of workflows and their associated products according to the FAIR principles.
2022 Site Environmental Report (SER)
Brookhaven National Laboratory (BNL) is managed on behalf of the Department of Energy (DOE) by Brookhaven Science Associates (BSA), a partnership between Stony Brook University and Battelle, and six core universities: Columbia, Cornell, Harvard, Massachusetts Institute of Technology, Princeton, and Yale. For over 75 years, the Laboratory has played a lead role in the DOE Science and Technology mission and continues to contribute to the DOE’s missions in energy resources, environmental quality, and national security. BNL manages its world-class scientific research operations with sensitivity to environmental issues and community concerns. The Laboratory’s Environmental, Safety, Security, and Health (ESSH) Policy reflects the commitment of BNL’s management to fully integrate environmental stewardship into all facets of its mission and operations. BNL prepares an annual Site Environmental Report (SER) in accordance with DOE Order 231.1B, Environment, Safety, and Health Reporting. The report is written to inform the public, regulators, employees, and other stakeholders of the Laboratory’s environmental performance during the calendar year in review. Volume I of the SER summarizes environmental data; environmental management performance; compliance with applicable DOE, federal, state, and local regulations; and performance in restoration and surveillance monitoring programs. BNL has prepared annual SERs since 1971 and has documented nearly all its environmental history since the Laboratory’s inception in 1947. Volume II of the SER, the Groundwater Status Report, is also prepared annually to report on the status of groundwater protection and restoration efforts. Volume II includes detailed technical summaries of groundwater data and treatment system operations and is intended for regulators and other technically oriented stakeholders. A summary of the information contained in Volume II is included in Chapter 7, Groundwater Protection, of this volume.
2022 Atomically Precise Nanochemistry Gordon Research Conference and Gordon Research Seminar
Final Conference Program & Registration List
2022 Chemical Separations Gordon Research Conference
Final Conference Program & Registration List
SRS Responses to U.S. Environmental Protection Agency Comments on the Perfromance Evaluation Report for the A-Area Burning/Rubble Pits (731-A, -1A) and Rubble Pit (731-2A) and the Miscellaneous Chemical Basin/Metals Burning Pit (731-A, -5A) Operable Unit -- January through December 2022 (U), SEMS Number: 28 (SRNS-RP-2023-00127, Revision 0, May 2023)
Abstract not provided.
MCNP6 Developments: A 2022-23 Year in Review [Slides]
A separate MCNP6.3 V&V document reports on all the default calculations for all test suites is touched on. This report does not include the subcritical multiplication benchmark suite. After some additional clean-up and finalizing the post-processing and documentation steps, the subcritical multiplication benchmark suite will be released in the next version of our vnvstats repository. We tested the new HDF5 PTRAC feature in MCNP6.3 and found encouraging outcomes. Identical results coming out of the simulation with respect to the legacy PTRAC results. The overall runtime for all simulations is reduced by ~20% with the new HDF5 PTRAC capability. We consider giving the new HDF5 PTRAC features a try and using it for all subcritical multiplication and any other relevant (PTRAC) calculations.
2022 AI Testbed Expeditions Report
By exploiting the coherent properties of a light source, coherent diffraction imaging (CDI) is able to obtain the sample image at a nanoscale resolution using the measured diffraction pattern. Bragg Coherent Diffraction Imaging (BCDI) has become valuable for recovering the displacement and strain field of crystals, providing a valuable tool in material science and solid-state physics. X-ray ptychography is another emerging CDI technique that can produce a high-resolution image of the extended sample and has become popular in many research areas (e.g., materials science, biology, electronics, and optics characterization). CDI including BCDI and ptychography has become an established technique in Synchrotron Facilities including the Advanced Photon Source (APS) and will greatly benefit from the 100x coherent flux increase of the upcoming APS Upgrade (APSU). The current image formation process in CDI employs iterative phase retrieval algorithms, which is a time-consuming and computationally expensive process. Especially after APSU, the traditional iterative methods will not be able to match the experimental data acquisition speed. We employ deep learning (DL) approach to replace the iterative approaches, therefore allowing hundreds of times faster recovery of the object. We developed AutoPhaseNN, a DL-based approach which learns to solve the inverse problem without labeled data. Taking 3D BCDI as a representative technique, AutoPhaseNN has been demonstrated to be one hundred times faster than traditional iterative phase retrieval methods while providing comparable image quality. The current network is trained with 64 x 64 x 64 data size, to achieve higher resolution imaging, we will need to scale the network to input and train/infer 3D arrays of size 256 x 256 x 256 (today) and of size 2560x2560x2560 (APSU). However, the scalability of the network is restricted due to the memory-intensive training process. To perform the training for a 256 x 256 x 256 data size, the required memory exceeds the capacity of the current machine. In this project, we explore using Sambanova system to train the network for the direct data inversion for CDI.
Post-Closure Report for Closed Resource Conservation and Recovery Act Corrective Action Units, Nevada National Security Site, Nevada For Calendar Year 2022
This report serves as the combined annual report for post-closure activities in compliance with the requirements listed in Resource Conservation and Recovery Act (RCRA) Permit Number NEV HW0101 (Rev. 6) associated with the following closed corrective action units (CAUs): CAU 90, Area 2 Bitcutter Containment; CAU 91, Area 3 U-3fi Injection Well; CAU 92, Area 6 Decon Pond Facility; CAU 110, Area 3 WMD U-3ax/bl Crater; CAU 111, Area 5 WMD Retired Mixed Waste Pits; CAU 112, Area 23 Hazardous Waste Trenches. All post-closure inspections and evaluations were performed in accordance with requirements set out in RCRA Permit NEV HW0101, Rev. 6.