Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “schedules”

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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Developing Flexible, Networked Lighting Control Systems That Reliably Save Energy in California Buildings

An important strategy to meet California's ambitious energy efficiency goals is to use innovative wireless communications, embedded sensors, data analytics and controls to significantly reduce lighting energy use in commercial buildings. This project developed a suite of networked lighting solutions to further this goal. The technologies include a platform for low-cost sensing, distributed intelligence and communications, the “PermaMote,” which is a self-powered sensor and controller for lighting applications. The project team also developed a task ambient daylighting system that integrates sensors with data-driven daylighting control using an open communication interface, called the “Readings-At-Desk” (RAD) system. To address the problem of building occupants being confused about how to operate traditional lighting control systems, the research team created content that could be the basis for a user interface standard for lighting controls. Finally, to address the difficulty of ensuring that advanced lighting control systems actually deliver their promised energy savings, the project team developed a new method for evaluating and specifying lighting systems’ performance. The research team validated these technologies in the laboratory, showing significant lighting energy savings, up to 73% for the PermaMote sensor system from occupancy control and daylight dimming features, compared to the same light source (LED replacement lamps) operated via simple on/off scheduling. The project team also developed a proposed standard lighting data model and user interface elements, which were contributed to the ANSI Lighting Systems Committee (C137) for standardization. Existing data models are incomplete and inconsistent, whereas the lighting-specific data model developed here is clear and comprehensive, to serve as a starting point for creating common, universally agreed upon semantic definitions of key lighting parameters, to promote interoperability. For the task on verifiable performance of lighting systems, the project team developed a more effective metric for capturing the actual energy impact of a lighting system over time — the energy usage intensity (kWh/ft2/year). Three commercial lighting systems were tested in FLEXLAB® using this new metric, and the tests show a wide range in the accuracy of the self-reported energy-use metric, from 0.5% to 28% error compared to direct measurement of lighting energy using dedicated submeters. Overall, the project team estimates that these advanced technologies can reduce California office lighting energy use by 20% (above and beyond normal advanced lighting controls mandated by Title 24), resulting in about 1,600 GWh/year in savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

`SkyPy`: A package for modelling the Universe

SkyPy is an open-source Python package for simulating the astrophysical sky. It comprises a library of physical and empirical models across a range of observables and a command-line script to run end-to-end simulations. The library provides functions that sample realisations of sources and their associated properties from probability distributions. Simulation pipelines are constructed from these models using a YAML-based configuration syntax, while task scheduling and data dependencies are handled internally and the modular design allows users to interface with external software. SkyPy is developed and maintained by a diverse community of domain experts with a focus on software sustainability and interoperability. By fostering development, it provides a framework for correlated simulations of a range of cosmological probes including galaxy populations, large scale structure, the cosmic microwave background, supernovae and gravitational waves. Version 0.4 implements functions that model various properties of galaxies including luminosity functions, redshift distributions and optical photometry from spectral energy distribution templates. Future releases will provide additional modules, for example, to simulate populations of dark matter halos and model the galaxy-halo connection, making use of existing software packages from the astrophysics community where appropriate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

plutus: An R package to calculate electricity investments and stranded assets from the Global Change Analysis Model (GCAM)

plutus is an R package that post-processes outputs from the Global Change Analysis Model (GCAM) to calculate the power sector capital investment costs and stranded asset costs associated with GCAM projections of electricity generation by technology. GCAM is a market equilibrium model used to examine the dynamics of the coupled human-Earth system and the response of this system to global change, including socioeconomics, technology, climate, and policy. GCAM tracks electricity generation by technology and vintage over 32 geopolitical regions throughout the lifetime of each generating technology. plutus extends GCAM functionality by (1) estimating the foregone economic value of prematurely retired power plants as a result of economic-induced retirements compared to scheduled lifetimes; and (2) estimating the new installations and capital investments driven by future changes in economic, energy, agriculture, and land-use systems in GCAM.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗
DOE OSTI2021

Application of Sequential Design of Experiments (SDoE) to Large Pilot-Scale Solvent-Based CO2 Capture Process at Technology Centre Mongstad (TCM)

The United States Department of Energy’s Carbon Capture Simulation for Industry Impact (CCSI2) program has developed a framework for sequential design of experiments (SDoE) that aims to maximize knowledge gained from budget- and schedule-limited pilot scale testing. SDoE was applied to the planning and execution of campaigns for testing CO2 capture systems at pilot-scale in order to optimally allocate resources available for the testing. In this methodology, a stochastic process model is developed by quantifying the parametric uncertainty in submodels of interest; for a solvent-based CO2 capture system, these may include physical properties and equipment performance submodels (e.g., mass transfer, interfacial area). This uncertainty is propagated through the full process model, over variable operating conditions, for estimating the resulting uncertainty in key model outputs (e.g., percentage of CO2 capture, solvent regeneration energy requirement). In developing a data collection plan, the predicted output uncertainty is incorporated into an algorithm that seeks simultaneously to select process operating conditions for which the predicted uncertainty is relatively high and to ensure that the entire space of operation is well represented. This test plan is then used to guide operation of the pilot plant at varying steady-state conditions, with resulting process data incorporated into the existing model using Bayesian inference to refine parameter distributions. The updated stochastic model, with reduced parametric uncertainty from data collected, is then used to guide additional data collection, thus the sequential nature of the experimental design. The SDoE process was implemented at the pilot test unit (12 MWe in scale) at Norway’s Technology Centre Mongstad (TCM) in a summer 2018 test campaign with aqueous monoethanolamine (MEA). During the test campaign, the varied operating conditions included the flowrates of circulated solvent, flue gas, and reboiler steam and the CO2 concentration in the flue gas. The process data were used to update probability distributions of mass transfer and interfacial area parameters of a stochastic process model developed by the CCSI2 team. Two iterations of the SDoE process were executed, resulting in the uncertainty in model predicted CO2 capture percentage decreasing by an average of 58.0 ± 4.7% over the full input space of interest. This work demonstrates the potential of the SDoE process for model refinement through reduction in process model parametric uncertainty, and ultimately risk in scale-up, in CO2 capture technology performance.

carbon capture↗
DOE OSTI2025

Design considerations for a Digital Twin built to improve nitrification performance at a water resource recovery facility

A Digital Twin built around Activated Sludge Model No. 1 was deployed at a full-scale water resource recovery facility. Its design included a waste rate recommender system based on automatic scenario analyses, where influent loads and waste rates are varied to determine their impact on nitrification. At the same frequency as these scenario analyses, scheduled auto-calibrations allow for nitrifier maximum specific growth rate (μmax-NITO) soft sensing, the only kinetic parameter shown to require adjustment if the objective is aeration tank effluent ammonia forecasting accuracy. By integrating temperature forecasting over the next three sludge ages, this Digital Twin approach creates opportunities for advancing waste rate decisions in anticipation of seasonal temperature changes, optimizing ammonia control authority under varying influent loads, and furnishing valuable insights for future capital projects requiring nitrifier kinetic understanding and modelling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Utility Sector Impacts of Reduced Electricity Demand: Updates to Methodology and Results

This report presents revisions to the methodology for estimating the utility sector impacts associated with electricity demand reductions, first published in our 2014 study. The earlier report described how to calculate utility impact factors based on data from the Energy Information Agency's Annual Energy Outlook (AEO) projections of electricity supply and demand. The 2014 methodology relied on data from the AEO Reference case, and side cases implementing demand-side policies with a substantial impact on total demand. Beginning in 2015, the AEO publication schedule was revised to limit the number of side cases published with each AEO, and no suitable demand-focused side cases have been released since 2014. Hence, this paper presents a generalization of the methodology that allows a uniform approach irrespective of the nature of the side cases published with the AEO. The impact factors we calculate represent coeffcients of variation between a unit reduction to site electricity demand and corresponding supply-side changes, including reductions to electric power generation and primary fuel consumption by fuel type, emissions of criteria pollutants (Hg, NO x and SO 2 ) and greenhouse gases (CO 2 , N 2 O and CH 4 ), and total installed capacity by plant type. The impact factors are time-dependent, and also depend on the demand sector and end use. In this report, we apply the methodology to all AEO publications from 2014-2019, and all scenarios. We quantify the variation in factors across different side cases within each AEO, and across different AEO publications, and compare this to the variation due to changes in methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Demand Driven Cycamore Archetypes

Future nuclear fuel cycle options may present advantages over today’s once-through fuel cycle. Nuclear fuel cycle simulation tools assess the performance of those fuel cycles as well as the dynamics of long-term technology transitions. In many nuclear fuel cycle simulation tools, it has historically been the responsibility of the user to manually define facility deployment schemes and all facility parameters. While this is straightforward in simple fuel cycles, transitions from one fuel cycle to another can be more complex. In particular, deployment schemes for supportive fuel cycle facilities beyond the reactor become complex if the analyst desires to avoid gaps in the nuclear fuel and power supply chain during those transition scenarios. As nuclear fuel cycle analysis approaches questions regarding the feasibility and performance of the deployment schemes and technology choices during technology transition, automation of this historically manual process is necessary. The main objective of this work was to develop and demonstrate Cyclus automation capabilities toward key nuclear fuel cycle transition scenarios. While deploying reactors to meet power demand is trivial, and existed in the earliest versions of CYCLUS, automated, predictive deployment and decommissioning of other facilities is more complex. These include mining, milling, enrichment, fuel fabrication, reprocessing, and others. For example, a balanced closed fuel cycle may require ensuring that there is enough fast reactor fuel for their operation and may drive deployment of a fleet of light water reactors. This concern comprises the main challenge that drove the project effort. The Demand-Driven Cycamore Archetype project (NEUP-FY16-10512) aimed to develop CYCAMORE demand-driven deployment capabilities and thereby automate transition scenario definition. The developed software package, d3ploy, in the form of a CYCLUS Institution agent, deploys Facilities to meet the front-end and back-end demands of the fuel cycle. The University of South Carolina and the University of Illinois applied multiple algorithmic approaches to this challenge. This project developed an in situ demand-driven development schedule calculation through non-optimizing, deterministic-optimizing, and stochastic-optimizing algorithms as CYCLUS archetypes and demonstrated these new archetypes in program-supporting fuel cycle transition scenarios. Both objectives were achieved. This report documents the results and deliverables obtained toward these achievements in detail.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Quarterly Management Document – FY20, 1st Quarter, Multi-pass Hybrid Laser Arc Welding of Alloy 740H

During the first quarter of FY20, work focused on implementing a subcontract with Penn State University to collaborate on the project. Significant delays prevented the subcontract from being placed until the end of December. As a result only limited progress was made on the project. Required equipment was researched and ordered during the first quarter but will not be delivered until the middle of the second quarter of FY20. Delays are not expected to adversely impact the schedule of the project.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Project Plan: Salt in Situ Heater Test

This project plan gives a high-level description of the US Department of Energy Office of Nuclear Energy (DOE-NE) Spent Fuel and Waste Disposition (SFWD) campaign in situ borehole heater test project planned for the Waste Isolation Pilot Plant (WIPP) site, titled the Brine Availability Test in Salt (BATS). BATS is the first stage in a planned sequence of tests to bolster the technical basis for disposal of heat generating waste in salt. This plan provides an overview of the schedule and responsibilities of the parties involved. This project is a collaborative effort by Sandia, Los Alamos, and Lawrence Berkeley National Laboratories to execute a series of small-diameter borehole heater tests in salt for the DOE-NE SFWD campaign. Design of a heater test in salt at WIPP has evolved over several years. The experiment has begun in January 2020 and the first phase will continue for several months with the possibility for follow on testing. BATS comprises a suite of modular tests, which consist of a group of adjacent horizontal boreholes in the wall of drifts at WIPP. Each test is centered around a packer-isolated heated borehole (12.2 cm [4.81 diameter) containing equipment for water-vapor collection and borehole closure monitoring, surrounded by smaller-diameter (up to 5.3 cm [2.11 diameter) satellite observation boreholes. Observation boreholes contain grouted-in temperature sensors, electrical resistivity tomography (ERT) sensors, and fiber optics; packer-isolated tracer release and sampling intervals; and acoustic emission (AE) piezoelectric sensors. A larger-diameter (12.2 cm [4.81) satellite borehole includes sorel and salt cement plugs, as part of an engineered barrier sealing test. The first two tests, to be implemented in parallel, are heated (target borehole wall temperature of 120 °C) and unheated, with similar arrays of observation borehole monitoring changes. Follow-on tests will be designed using information gathered from the first two tests, and may be conducted at other borehole wall temperatures, use multiple observation boreholes, and may include different measurement types and test designs. This 2020 update of the original 2018 project plan satisfies DOE-NE Spent Fuel and Waste Science and Technology (SFWST) milestone M3SF-205N010303034, as part of the SNL "Salt Disposal R&D" work package.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Requirements Document for a Replacement Data Acquisition System

A flight vehicle is in the developmental stages. While in development flight testing is required by both the manufacturer and customer to rate design performance. The performance of the vehicle must fulfill the agreed upon requirements. Testing of a flight vehicle during operation requires various sensors in order to record and monitor performance for evaluation. A Data Acquisition System (DAQ) is needed in order to control and capture data from various sensors placed throughout the vehicle during flight. This DAQ will receive the analog data from the suite of sensors and convert them to a digital signal that can be transmitted to a recording station where the data will later be analyzed by test experts. Some of the key features to be included in the DAQ will be a self-check operation to verify the health of all sensors including the DAQ. The DAQ will also have the capability to store sensor calibration data files loaded during assembly and transmit those files to the recording station during flight. Breakthroughs in new technologies has allowed for higher resolution recording systems and faster sampling rates. Improved sensor data using a new Data Acquisition System (DAQ) can provide important answers to questions discovered during the last few flight tests. These questions arose when the flight system expressed unexpected behaviors. Having the capability of the new DAQ in flight tests would allow for more high-fidelity data to be captured from the system during flight tests and ultimately reducing the number of required flight test. Reducing the number of flight tests also reduce developmental cost and schedule.

42 ENGINEERING↗
DOE OSTI2020

Production of Biocrude in an Advanced Photobioreactor-Based Biorefinery

Algenol Biotech, the National Renewable Energy Laboratory (NREL), Georgia Institute of Technology, Arizona State University, and Reliance Industries formed a team to advance the state of the art in algal biomass production and downstream processing technologies, with the end goal of a sustainable, economically viable biofuel intermediate (BFI, biocrude) product. The project included examination of high value co-product production as a market entry strategy and for enhancing the economics of a biorefinery for BFI production. The project targeted innovations in biology, operations, and engineering. The goals of the project were: BFI productivity greater than 4,000 gal-BFI/acre-yr on an annualized basis; energy efficient innovations in downstream operations resulting in an energy expenditure less than 10% of the BFI energy content and a carbon footprint reduction of more than 60% compared to fossil alternatives; and a comprehensive Techno-Economic Analysis (TEA) that identifies limiting factors for commercial viability of a photobioreactor (PBR)-based biofuel product. The project achieved the overall objectives. Strain development efforts led to the identification of a strain (Cyanobacterium sp. AB1166) that, relative to the previous best strain (Cyanobacterium sp. AB1), exhibited a ~10% increase in productivity under commercially-relevant cultivation conditions and also resulted in cultures with a >50% reduction in viscosity such that harvesting efficiency was improved; these results represent achievement of key project milestones. Progress was also made at NREL in altering the biochemical composition of algal biomass to improve the yield of BFI produced via HTL. These strain enhancements coupled with improved outdoor cultivation practices, including semi-continuous operation, increased areal biomass productivity by nearly 80% over the established baseline productivity. The annualized productivity achieved (26.8 g/m2-d), paired with HTL conversion yields realized at NREL and RIL (38% ± 2% BFI), translates to 4,100 gal-BFI/acre-yr, exceeding the FY20 BETO goal of 3,700 gal-BFI/acre-yr. Significant progress was also demonstrated in large scale PBR-based production system design, operability, and cost reduction. A 24,000-L production module comprised of 240 interlinked PBRs was constructed and successfully operated outdoors for over one year in Fort Myers, Florida. Aided by a state-of-the-art productivity model, the productivities achieved convincingly demonstrated scalability of laboratory results determined at the mL to L scale to large-scale outdoor operations exceeding 20,000 L. The system was used to cultivate Arthrospira platensis (Spirulina), an industrially-relevant cyanobacterium and source for phycocyanin, an approved blue food colorant that Algenol is developing as a risk reduction strategy for future biofuel projects and as a potential business opportunity. A key project milestone to develop phycocyanin extraction and purification technologies was achieved ahead of schedule, and product samples received positive feedback from potential customers. The production and downstream operations data generated in this project were used to conduct and refine Techno-Economic and Life Cycle Assessments to provide research guidance for reducing the costs and environmental footprint of algal biofuel and co-product manufacturing plants. Several CO 2 supply scenarios for an algal biorefinery were identified as being capable of providing a large (>60%) reduction in carbon footprint in comparison to gasoline. The TEA assessments incorporated detailed comparisons of PBR versus open pond production systems, yielding a 3-fold higher areal productivity for PBRs and suggesting overall production cost parity for the two systems. The progress in this ABY2 project addressed many of the the barriers identified for the Advanced Algal Systems R&D Program and are directly relevant to achieving the established BETO goals associated with large scale biofuel production and cost reduction.

09 BIOMASS FUELS↗
DOE OSTI2020

SunDial – An Integrated SHINES System to Enable High-penetration Feeder-level PV

The Project Team of Fraunhofer USA, National Grid, and IPKeys developed and conducted a pilot deployment of the SunDial system, a virtual power plant platform that enables high-penetrations of solar PV to be integrated into the distribution grid. The pilot was conducted over a 15-month period from August 2018 through October 2019 on a National Grid distribution feeder in Shirley, MA. A vendor-agnostic control platform (the “Global Scheduler”) optimally shaped the net load for a virtual portfolio of non-co-located DERs based on user-defined policy objectives. The goal of the SunDial project was to simplify and reduce the risk associated with the deployment of solar in high-penetration environments by: (1) Developing an open-source, vendor-agnostic dispatch platform that can be readily adapted to optimize control of DERs over a variety of use cases; (2) Developing auto-calibrating load and solar prediction methodologies that can be readily implemented and scaled to new deployments; (3) Developing a methodology to use demand-side management with traditional electrochemical energy storage to provide “load shaping” services in high solar penetration environments; (4) Using grid-scale storage to minimize short-term intermittency association with PV production; and (5) Deploying on the National Grid distribution system to gain experience on the potential for (and limits of) integrated storage with demand-side management.

14 SOLAR ENERGY↗
DOE OSTI2020

TA-16-0303 Crystal Grow Lab Renovation

The LANL High Explosives (HE) Crystal Lab at TA-40 Building 12 is a unique, and nearly the sole, national resource for the growth and sample preparation of large HE single crystals and related materials. This work has outgrown is current facility and is in dire need of both additional space and modern lab technology. The goal of this project is to renovate the 1950’s era building TA-16-303 to accommodate these operations and successfully relocate them from TA-40-12. Phase 1 of this project, completed in May of 2018, was to de-energize the facility and perform cleanup / remediation activities. Phase 2, the actual renovation of the facility, is currently underway and scheduled to achieve Occupancy by September of 2020.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Eligibility Assessment of TA-52-1 and TA-52-11: The Ultra-High Temperature Reactor Experiment (UHTREX) Complex

The U.S. Department of Energy (DOE), National Nuclear Security Administration, Los Alamos Field Office (NA-LA) requests the State Historic Preservation Officer (SHPO) to concur with the eligibility determinations contained in this report for Buildings 1 and 11 in Technical Area 52 (TA-52) at Los Alamos National Laboratory (LANL or the Laboratory). Triad National Security LLC cultural resources staff have completed the evaluation of two buildings, called the UHTREX Complex, for inclusion in the National Register of Historic Places (Register). This complex includes the UHTREX Reactor Building (TA-52-1), and an associated Mechanical Assembly Building (TA-52-11). As part of LANL’s Footprint Reduction Program, both facilities of the UHTREX Complex are scheduled for characterization and demolition. In addition to evaluating their eligibility in the Register, the properties at TA-52 were assessed for potential adaptive reuse, long-term preservation, and public interpretation. Based on the findings in this assessment report, both TA-52-1 and TA-52-11 have been determined to not be eligible for inclusion in the Register. The history of the UHTREX Complex lacks association with exceptionally significant Cold War events of scientific developments. Both TA-52-1 and TA-52-11 lack the necessary internal historic integrity suitable for long-term preservation or public interpretation. And both facilities contain legacy radioactive contamination, which prohibits their reuse. In addition to its loss of integrity and context, TA-52- 11 has been determined ineligible due to its status as a support building of secondary or minor importance. In compliance with Section 106 and Section 110 of the National Historic Preservation Act of 1966, as amended, and with the Programmatic Agreement among the U.S. Department of Energy, National Nuclear Security Administration, Los Alamos Field Office, the New Mexico State Historic Preservation Office, and the Advisory Council on Historic Preservation Concerning Management of the Historic Properties at Los Alamos National Laboratory, Los Alamos, New Mexico, the SHPO is requested to concur with the eligibility determinations contained in this report for the UHTREX Complex in TA-52.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Usable Data Abstractions for Next-Generation Scientific Workflows

Data- and computationally-intensive scientific research, such as numerical simulations and inversions or the training of large neural networks in machine learning applications, that are well suited for HPC environments also often require expert insight and evaluation throughout the computation which can be greatly facilitated with the use of interactive computing tools, such as those in the Jupyter ecosystem. HPC workflows and interactive workflows are typically treated as orthogonal, however, the next generation of research will require both. The first challenge we face in this project is thus designing the right level of abstractions to allow interactive capabilities in the JupyterLab environment to allow the working scientist to flexibly explore and query their data at multiple levels, with a minimal amount of customization required of the underlying optimized codes. In addition to these questions regarding the high-level representation of data for interactive use in HPC, we tackled two additional issues that are part of the entire lifecycle of research and that become particularly acute in HPC contexts: how to improve the experience of interfacing with the HPC system's scheduling environment for a scientist focused on exploratory questions, and how can that scientist then best share the results of their work with others in a self-contained, reproducible manner.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Wood-to-Fuel for California's Transportation Sector using Autothermal Pyrolysis (Progress Report for FRD-17-002)

In the month of April, we planned to have the Site License Agreement with SPI signed and fully executed, make further progress on the market assessment for the Wood-to-Bio-oil Market Report, and hoped to have the subcontracts in place with Iowa State University and Frontline BioEnergy, LLC once we have approval from the CAM. We also plan to have the updated budget from Iowa State in order to send both updated sub-recipient budget sheets to the CAM for approval.We continue to make more progress towards getting our subcontracts in place and received the requested adjustments to ISU’s budget sheets. These were sent to the CAM on 4/30/2020. As the COVID-19 situation is country-wide, we anticipate slower responses from our partners due to similar delays in their organizations to what we are experiencing internally. Although their response times will be slower, both ISU and Frontline are continuing to make good progress on their work for the demonstration in Iowa for this summer, which is assisting with keeping us on schedule for the overall effort. We are continuing to work on the required documents for our subcontracts – though neither will be placed before we receive official approval from our CAM – so that once we have approval, we can place them very quickly.

09 BIOMASS FUELS↗
DOE OSTI2020

TA-55 Forensic Support Operations Cross-Training Exercise

As part of the annual FBI/LANL training schedule, a joint training exercise was conducted during the week of 10FEB2020 with FBI HEAT members and LANL Fissile Material Handlers (FMHs). The joint training exercise was conducted in a non-radiological area to reduce cost and ensure the test object would not be contaminated. The exercise test object was fabricated by LANL using surrogate materials to represent an object containing Special Nuclear Material (SNM). Working with Jim Blankenship and Kevin Swearingen, functional requirements of the test object included the following: unclassified, disassembly (to include screws), and moderately heavy. To meet the objectives, the test object was fabricated by welding a custom aluminum box (8” x 8” x 12”) with a lid which was secured by four (4) screws. The custom box housed a track and field shot put (16 lbs.) that was anchored by a hose clamp. The intent of the training was to disassemble the test object and conduct traditional forensic determinations on the parts. This lessons learned report documents all of the associated aspects related to the joint training effort.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗