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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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Focal Area(s) The proposed white paper focuses on focal areas 2 and 3, and intends to build a pipeline to automate the detection of unresolved processes based on model errors and assist subsequent modeling solutions.
Focus Area: This white paper addresses DOE AI4ESP focus area #2 by providing a sketch blueprint for a next generation, hybrid AI/process-based global Land Surface Modeling (LSM) framework to improve projections of climate change impacts on the land surface system including agriculture and the hydrological cycle. Science Challenge: Climate change impacts on agriculture are highly uncertain: -50% to +150% global production changes for major grains under high-end climate change, for example. The core challenge in land surface system predictability is the large gap between the scale at which the relevant biological processes act and the scale at which the risks need to be assessed. Conventional empirical and process-based modeling approaches are insufficient. A new multi-scale modeling paradigm which employs AI/ML to learn from new streams of remote sensing data and targeted ’gene-to-global’ simulations is needed.
This white paper responds to Focal Area 2. We seek to build predictive models of leaf and surface conductance of water by implementing deep learning (DL) data assimilation techniques. These new models would then be implemented in existing Land Surface Models (LSMs) and Earth System models (ESMs), generating novel water cycle feedbacks. In doing so, we would improve predictability of expected changes in land precipitation, soil moisture, and vegetation dynamics in the long-term, and the role of land cover on the impacts and feedbacks of extreme weather events in the short-term
Soil water content is a function of inputs from precipitation and outputs via evaporation, transpiration, lateral flow, and vertical percolation, and is sensitive to biogeochemical processes. As such, soils serve as an ideal integrator of atmospheric, hydrological, and biogeochemical processes affecting the water cycle. In addition, soil water retention capacity, infiltration rates, and hydraulic conductivity can buffer or exacerbate the effects of extreme precipitation events (e.g., flooding, runoff, subsurface transport, erosion, greenhouse gas emissions) and mitigate the impact of droughts and heat waves on land systems (e.g., fire, crop failure). However, integrating water cycle measurements spanning different land atmosphere compartments across scales is a fundamental barrier for numerical model predictability. A significant challenge is that each domain (soil, hydrology, biology, and atmosphere) typically collects different sets of data at different temporal and spatial frequencies/scales, and even different dimensionalities (2D vs 3D). To implement soil as an integrator of the water cycle in land models, we suggest that novel machine learning (ML) tools can be developed to effectively simulate complex landscapes across various domains and scales, extended to regions with sparse or no data. The ultimate goals are to improve predictive understanding of land-atmosphere interactions and to extend the predictability of current Earth System Models (ESMs) through better integration of hydrological and biogeochemical data. We envision a framework in which: (1) ML-aided data reconstructions enable the merger of data sources into a unified geospatial product; (2) automated detection techniques are used to improve the knowledge of complex soil processes and interactions; and (3) this knowledge is leveraged and incorporated into models through AI-based emulators to distinctly connect the land and atmospheric compartments of the water cycle in models.
A 6-month fellowship program, a joint initiative of the University of California and Los Alamos, supports early career scientists as they explore applications and evaluate market opportunities for their technologies.
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Abstract not provided.
The percentage integration of photovoltaic (PV) inverters in the field has increased significantly in the past 5 years. Regardless of the size of the PV plants and the inverters (residential vs. commercial), it is becoming crucial that these devices have the capability to communicate with peers (other smart devices) and with components that are at a hierarchy above the inverters (e.g., supervisory control and data acquisition (SCADA) systems, distributed controllers, and data managers). This project aims to develop a standard SCADA software code for inverters’ embedded controllers that will enable interoperability with other components in the system. To achieve this, the code will be developed using two different protocols: Distributed Network Protocol 3 and International Electrotechnical Commission 61850. The developed code is aimed to be deployed in simple embedded controllers. It will be tested in the National Renewable Energy Laboratory’s (NREL’s) Energy Systems Integration Facility. The tested code will then be made available through Triangle MicroWorks’s (TMW’s) software platform. The primary objectives of this project include training the NREL team with TMW’s embedded controller libraries, developing an interoperable communication code for embedded controllers, successfully testing and deploying the code, and demonstrating the newly developed code in a conference.
The use of robots is important, to help prevent people from being exposed to hazardous materials within a glovebox. The research proposal for the summer was to make a pick and place demo for a seven degrees-of-freedom Motoman Sia5d robot. The Robot Operating System (ROS) was used and the knowledge of C++ and Linux had to be applied. The robot was used to pick up and sort four objects (cylinder, sphere, rectangular prism, and cone) within a virtual environment.
Process Automation and Control (E-3) is a part of the Weapons Associate Laboratory Directorate (DDW) which includes Weapons Engineering Directorate (WE) and the Engineering Technology and Design (E) Division. There are three teams: Fabrication and Assembly, Process Controls, and Automation and Robotics. After an overview of the group, information is provided about an internship and how to apply.
The hydrogen permeation system at LANL has been modified and improved to perform high temperature testing of metals and metal/ceramic samples. This will allow us to demonstrate the ability to reduce hydrogen permeation for microreactor materials. For this program, two metals have been chosen for analysis, stainless steel as a base metal and TZM based on its high temperature properties and anticipated higher H permeation resistance. Samples have been fabricated and coated with Al 2 O 3 via atomic layer deposition in order to evaluate the potential to reduce hydrogen permeation with a thin layer ceramic coating. Current efforts include literature review of systems and sample fabrication to demonstrate feasibility, future work will focus on collecting hydrogen permeation data.
Abstract not provided.
The objective of this Phase 1 project is to demonstrate the feasibility of structures and components for advanced fossil energy power cycle by fusion welding powder metallurgy (PM) based near net shape (NNS) hot isostatic pressing (HIP) Nickel superalloy Inconel 740H (IN740H) components to wrought IN740H components. The outcome of the project includes a prototype pipe elbow demonstration of the NNS HIP process using IN740H powder, an initial assessment of IN740H PM/wrought weldability, and a manufacturing and capital cost analysis of IN740H NNS HIP components for advanced ultra-super critical steam (AUSC) power plants. The anticipated value proposition of NNS HIP manufacturing is ~50% cost reduction of large complex components in AUSC or super-critical carbon dioxide (sCO2) power cycles. This document is a final technical report for the project, covering efforts conducted from October 2019 to June 2021.
C16 Biosciences will partner with NTESS (Sandia National Laboratory) and Pacific Northwest National Laboratory in order to develop microbial technologies for producing sustainable alternatives to palm oil and palm oil derivatives.
Develop a high-yield and cost-efficient bioprocess to produce an AMP (anti-microbial peptide) in a current ABF (Agile BioFoundry) host or readily onboarded hosts. Scale up the process in stirred tank reactors and produce at least 100 grams of AMP at 1-5g/L.
The US nuclear industry is on the cusp of significant growth in the areas of advanced reactors, SMRs, and microreactors. The existing commercial nuclear reactor fleet is considering expanding the life of the reactor cores by slightly increasing enrichment of their fuel. The common thread is the demand for High Assay Low Enriched Uranium (HALEU). A technical challenge exists with a blend down of Highly Enriched Uranium (HEU) from spent nuclear fuel (SNF) to meet the American Society for Testing and Materials (ASTM) and the “Y12” specifications. U-236 impacts reactor performance and is not desired in fresh fuel cores. In order to meet tight specifications, U-236 must be diluted by addition of fresh HEU. Fresh HEU is highly sought after and not plentiful in the US. A solution to this issue lies in the application of a technology to selectively remove the U-236 with non-hazardous microorganisms, which will be evaluated in this project by experimentally separating different isotopes of uranium.
Platooning has become a focus area for heavy-duty vehicle fuel savings during highway driving. Often, the platoon formation is controlled by a system called Coordinated Adaptive Cruise Control (CACC). CACC platooning seeks aerodynamic fuel economy benefits while simultaneously decreasing driver strain by setting and maintaining a desired headway from preceding vehicles. Under close following conditions, the controller must exhibit robust characteristics during testing to be considered safe. Specific to this study, real-time vehicle to vehicle (V2V) communication shares vehicle state information among members of the same platoon, enhancing control response as the platoon members adjust to surrounding vehicles. Trucks not on the leading or trailing edge of the platoon exhibit benefits stemming from a push effect from the truck behind and a pull effect from the truck preceding it.