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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2010

Q&A with Nobelist George Smoot - 2009 BCCP Cosmology Workshop

July 2009: What happens when dark matter and anti-dark mattter collide? If you were in a gravity free environment, what would happen to time? At the annual Cosmology Workshop at Lawrence Berkeley Lab, Nobelist George Smoot answers these questions and more from high school students and teachers. Dr. Smoot was co-awarded the 2006 Nobel Prize in Physics for the discovery of the blackbody form and anisotropy of the cosmic microwave background radiation.

dark matter, George Smoot↗
DOE OSTI2011

Performance of Charcoal Cookstoves for Haiti Part 1: Results from the Water Boiling Test

In April 2010, a team of scientists and engineers from Lawrence Berkeley National Lab (LBNL) and UC Berkeley, with support from the Darfur Stoves Project (DSP), undertook a fact-finding mission to Haiti in order to assess needs and opportunities for cookstove intervention. Based on data collected from informal interviews with Haitians and NGOs, the team, Scott Sadlon, Robert Cheng, and Kayje Booker, identified and recommended stove testing and comparison as a high priority need that could be filled by LBNL. In response to that recommendation, five charcoal stoves were tested at the LBNL stove testing facility using a modified form of version 3 of the Shell Foundation Household Energy Project Water Boiling Test (WBT). The original protocol is available online. Stoves were tested for time to boil, thermal efficiency, specific fuel consumption, and emissions of CO, CO{sub 2}, and the ratio of CO/CO{sub 2}. In addition, Haitian user feedback and field observations over a subset of the stoves were combined with the experiences of the laboratory testing technicians to evaluate the usability of the stoves and their appropriateness for Haitian cooking. The laboratory results from emissions and efficiency testing and conclusions regarding usability of the stoves are presented in this report.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Quantum Materials at the Nanoscale - Final Report

The central aim of the Quantum Materials at the Nanoscale (QMN) cluster was to understand and control collective behavior involving the interplay of spins, orbitals, and charges, which governs many scientifically interesting and technologically important phenomena in numerous complex materials. Because these phenomena involve various competing interactions, and influence properties on many different length and energy scales in complex materials, tackling this important area of study motivated a collaborative effort that combined the diverse capabilities of QMN cluster experimentalists, the essential theoretical analysis provided by QMN cluster theorists, and the outstanding facilities and staff of the FSMRL. During the funding period 2007-2014, the DOE cluster grant for the Quantum Materials at the Nanoscale (QMN) cluster supported, at various times, 15 different faculty members (14 in Physics and 1 in Materials Science and Engineering), 7 postdoctoral research associates, and 57 physics and materials science PhD students. 41 of these PhD students have since graduated and have gone on to a variety of advanced technical positions at universities, industries, and national labs: 25 obtained postdoctoral positions at universities (14), industrial labs (2 at IBM), DOE national facilities (3 at Argonne National Laboratory, 1 at Brookhaven National Lab, 1 at Lawrence Berkeley National Lab, and 1 at Sandia National Lab), and other federal facilities (2 at NIST); 13 took various industrial positions, including positions at Intel (5), Quantum Design (1), Lasque Industries (1), Amazon (1), Bloomberg (1), and J.P. Morgan (1). Thus, the QMN grant provided the essential support for training a large number of technically advanced personnel who have now entered key national facilities, industries, and institutions. Additionally, during the period 2007-2015, the QMN cluster produced 159 publications (see pages 14-23), including 23 papers published in Physical Review Letters; 16 papers in Nature, Nature Physics, Nature Materials, or Nature Communications; 4 papers in Science, and 8 papers in Applied Physics Letters. In this report, we provide some key highlights of the collaborative projects in which the QMN cluster members have been involved since 2007.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Making the Grid "Smart" Through "Smart" Microgrids: Real-Time Power Management of Microgrids with Multiple Distributed Generation Sources Using Intelligent Control

In this Project we collaborated with two DOE National Laboratories, Pacific Northwest National Lab (PNNL) and Lawrence Berkeley National Lab (LBL). Dr. Hammerstrom of PNNL initially supported our project and was on the graduate committee of one of the Ph.D. students (graduated in 2014) who was supported by this project. He is also a committee member of a current graduate student of the PI who was supported by this project in the last two years (August 2014-July 2016). The graduate student is now supported be the Electrical and Computer Engineering (ECE) Department at Montana State University (MSU). Dr. Chris Marney of LBL provided actual load data, and the software WEBOPT developed at LBL for microgrid (MG) design for our project. NEC-Labs America, a private industry, also supported our project, providing expert support and modest financial support. We also used the software “HOMER,” originally developed at the National Renewable Energy Laboratory (NREL) and the most recent version made available to us by HOMER Energy, Inc., for MG (hybrid energy system) unit sizing. We compared the findings from WebOpt and HOMER and designed appropriately sized hybrid systems for our case studies. The objective of the project was to investigate real-time power management strategies for MGs using intelligent control, considering maximum feasible energy sustainability, reliability and efficiency while, minimizing cost and undesired environmental impact (emissions). Through analytic and simulation studies, we evaluated the suitability of several heuristic and artificial-intelligence (AI)-based optimization techniques that had potential for real-time MG power management, including genetic algorithms (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and multi-agent systems (MAS), which is based on the negotiation of smart software-based agents. We found that PSO and MAS, in particular, distributed MAS, were more efficient and better suited for our work. We investigated the following: • Intelligent load control - demand response (DR) - for frequency stabilization in islanded MGs (partially supported by PNNL). • The impact of high penetration of solar photovoltaic (PV)-generated power at the distribution level (partially supported by PNNL). • The application of AI approaches to renewable (wind, PV) power forecasting (proposed by the reviewers of our proposal). • Application of AI approaches and DR for real-time MG power management (partially supported by NEC Labs-America) • Application of DR in dealing with the variability of wind power • Real-time MG power management using DR and storage (partially supported by NEC Labs-America) • Application of DR in enhancing the performance of load-frequency controller • MAS-based whole-sale and retail power market design for smart grid A

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Nyx

Nyx is a highly parallel, adaptive mesh, finite-volume N-body compressible hydrodynamics solver for cosmological simulations. Nyx code solves equations of compressible hydrodynamics on an adaptive grid hierarchy coupled with an N-body treatment of dark matter. The gas dynamics in Nyx uses a finite volume methodology on a set of 3-D Eulerian grids; dark matter is represented as discrete particles moving under the influence of gravity. Particles are evolved via a particle-mesh method, using Cloud-in-Cell deposition/interpolation scheme. Both baryonic and dark matter contribute to the gravitational field. In addition, Nyx includes physics needed to accurately model the intergalactic medium: in optically thin limit and assuming ionization equilibrium, the code calculates heating and cooling processes of the primordial-composition gas in an ionizing ultraviolet background radiation field. Additional physics capabilities are under development.

Sexton, Jean [Lawrence Berkeley National Lab. (LBN↗
DOE OSTI2013

Alquimia

Alquimia provides a generic interface between flow and transport simulators (drivers) and biogeochemical reaction capabilities (engines). Alquimia consists of two parts: an application programming interface, API, and a wrapper library. The API describes in detail how information is exchanged between the driver and the engine. This includes the function call signatures and data structures required for implementing alquimia in a mixed language (C/C++/Fortran) programming environment. The library is an implementation of the documented API, providing an interface into the biogeochemistry routines supplied by other codes: Alquimia does not do any geochemical calculations. Version 1.0 provides interfaces to the open source codes (BSD) CrunchFlow/CrunchTope and (LGPL) PFLOTRAN. Version 1.0 meets the policies set forth by (and is part of) the Extreme-scale Scientific Software Development Kit, xSDK version 0.6.0.

Andre, Benjamin [Lawrence Berkeley National Lab. (↗
DOE OSTI2019

PVMI Bay Area Photovoltaic Consortium (Final Report)

The Bay Area Photovoltaic Consortium (BAPVC) was formed to fund university research on technologies industry will use and thereby investigate new ways for the photovoltaic (PV) industry and university scientists to identify and explore research priorities. Our thesis is that great innovation is more likely stimulated by the collective efforts of manufacturing and academic experts working together rather than direction from either community alone. The consortium is quite effective in promoting the interactions needed to form collaborative teams, often engaging industry members as research participants. All similar university research consortia employ their industry members in advisory capacity. In BAPVC, industry members deliver far more than guidance and advice. They are part of the management team. They review and down select proposals, review projects, and guide the university researchers is setting their research directions. The BAPVC Industry Board holds executive authority over the strategy and operations of the consortium. BAPVC is a consortium led by Stanford University (SU) and University of California at Berkeley (UCB) joined by three leading DOE labs—Lawrence Berkeley National Laboratory, SLAC National Laboratory, and the National Renewable Energy Lab. BAPVC’s success is attributed to its three pillars of support. Three Pillars: (1) Industry sets research agenda, assures relevance, provides path to commercial impact, and contributes funding. (2) Universities and National Labs conduct innovative research and provide workforce development. (3) US Department of Energy provides leadership, support and guidance. Since inception in 2011, BAPVC funded 72 projects involving 26 organizations. Bringing together the differing backgrounds of industry and university participants stimulates innovation. BAPVC’s cultivation of larger collaborative projects among university investigators has also produced notable results. This report will highlight BAPVC contributions in several exciting PV technologies including perovskite/silicon tandem cells, CdTe, reliability and adhesion metrology, ultra-thin silicon cells and application of carrier selective contacts in silicon PV that all owe a portion of their success to cross-consortium collaboration. More importantly, many of the BAPVC-fostered teams have successfully bid for additional support from other sources and continue to expand their productivity. In one case, we report a recent Nature Energy paper published by a team including both university members and two industry members (separate companies) in a collaborative analysis of thin-film tandem cells. Our original proposal set ambitious goals – “BAPVC is composed of the industry members and prestigious universities needed to add substantial value to the open and competitive process of providing universities all over the nation with funding to perform industry-relevant R&D…Our consortium will accelerate the coordination of stakeholders across the solar industry and academic sectors into a cohesive research force developing innovative solutions to the key industrial issues.” While the PV landscape has undergone vast change during the period of this award, BAPVC and its research and industry members have adapted, delivering the promised cohesive research force for the benefit of DOE and the public.

14 SOLAR ENERGY↗
DOE OSTI2012

Novel wide band gap materials for highly efficient thin film tandem solar cells. Final report

Tandem solar cells (TSCs), which use two or more materials to absorb sunlight, have achieved power conversion efficiencies of >25% versus 11-20% for commercialized single junction solar cell modules. The key to widespread commercialization of TSCs is to develop the wide-band, top solar cell that is both cheap to fabricate and has a high open-circuit voltage (i.e. >1V). Previous work in TSCs has generally focused on using expensive processing techniques with slow growth rates resulting in costs that are two orders of magnitude too expensive to be used in conventional solar cell modules. The objective of the PLANT PV proposal was to investigate the feasibility of using Ag(In,Ga)Se 2 (AIGS) as the wide-bandgap absorber in the top cell of a thin film tandem solar cell (TSC). Despite being studied by very few in the solar community, AIGS solar cells have achieved one of the highest open-circuit voltages within the chalcogenide material family with a Voc of 949 mV when grown with an expensive processing technique (i.e. Molecular Beam Epitaxy). PLANT PV's goal in Phase I of the DOE SBIR was to (1) develop the chemistry to grow AIGS thin films via solution processing techniques to reduce costs and (2) fabricate new device architectures with high open-circuit voltage to produce full tandem solar cells in Phase II. PLANT PV attempted to translate solution processing chemistries that were successful in producing >12% efficient Cu(In,Ga)Se 2 solar cells by replacing copper compounds with silver. The main thrust of the research was to determine if it was possible to make high quality AIGS thin films using solution processing and to fully characterize the materials properties. PLANT PV developed several different types of silver compounds in an attempt to fabricate high quality thin films from solution. We found that silver compounds that were similar to the copper based system did not result in high quality thin films. PLANT PV was able to deposit AIGS thin films using a mixture of solution and physical vapor deposition processing, but these films lacked the p-type doping levels that are required to make decent solar cells. Over the course of the project PLANT PV was able to fabricate efficient CIGS solar cells (8.7%) but could not achieve equivalent performance using AIGS. During the nine-month grant PLANT PV set up a variety of thin film characterization tools (e.g. drive-level capacitance profiling) at the Molecular Foundry, a Department of Energy User Facility, that are now available to both industrial and academic researchers via the grant process. PLANT PV was also able to develop the back end processing of thin film solar cells at Lawrence Berkeley National Labs to achieve 8.7% efficient CIGS solar cells. This processing development will be applied to other types of thin film PV cells at the Lawrence Berkeley National Labs. While PLANT PV was able to study AIGS film growth and optoelectronic properties we concluded that AIGS produced using these methods would have a limited efficiency and would not be commercially feasible. PLANT PV did not apply for the Phase II of this grant.

14 SOLAR ENERGY↗
DOE OSTI2025

eLog analysis for accelerators: status and future outlook

This work demonstrates electronic logbook (eLog) systems leveraging modern AI-driven information retrieval capabilities at the accelerator facilities of Fermilab, Jefferson Lab, Lawrence Berkeley National Laboratory (LBNL), SLAC National Accelerator Laboratory. We evaluate contemporary tools and methodologies for information retrieval with Retrieval Augmented Generation (RAGs), focusing on operational insights and integration with existing accelerator control systems. The study addresses challenges and proposes solutions for state-of-the-art eLog analysis through practical implementations, demonstrating applications and limitations. We present a framework for enhancing accelerator facility operations through improved information accessibility and knowledge management, which could potentially lead to more efficient operations.

Sulc, A. [LBL, Berkeley]↗
DOE OSTI2011

What's Right with Kansas? (LBNL Science at the Theater)

On Monday, Oct. 3 at 7 p.m. in Berkeley's Repertory Theater, the Lab presented "What's Right with Kansas," an evening of conversation with the Kansas-based Climate and Energy Project's founder and board chair, Nancy Jackson, and Berkeley Lab scientist Merrian Fuller, an electricity-market, policy and consumer behavior expert. Berkeley Lab will also debut its video "Common Ground," which showcases how CEP has become a Kansas mainstay and an inspiration to environmental organizations across the country. In a state rife with climate-change skepticism, CEP has changed behavior, and some minds, by employing rural values of thrift, independence, conservation, and friendly competition to promote energy efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Developing a predictive model for the chemical composition of soot nanoparticles

In order to provide the scientific foundation to enable technology breakthroughs in transportation fuel, it is important to develop a combustion modeling capability to optimize the operation and design of evolving fuels in advanced engines for transportation applications. The goal of this proposal is to develop a validated predictive model to describe the chemical composition of soot nanoparticles in premixed and diffusion flames. Atomistic studies in conjunction with state-of-the-art experiments are the distinguishing characteristics of this unique interdisciplinary effort. The modeling effort has been conducted at the University of Michigan by Prof. A. Violi. The experimental work has entailed a series of studies using different techniques to analyze gas-phase soot precursor chemistry and soot particle production in premixed and diffusion flames. Measurements have provided spatial distributions of polycyclic aromatic hydrocarbons and other gas-phase species and size and composition of incipient soot nanoparticles for comparison with model results. The experimental team includes Dr. N. Hansen and H. Michelsen at Sandia National Labs' Combustion Research Facility, and Dr. K. Wilson as collaborator at Lawrence Berkeley National Lab's Advanced Light Source. Our results show that the chemical and physical properties of nanoparticles affect the coagulation behavior in soot formation, and our results on an experimentally validated, predictive model for the chemical composition of soot nanoparticles will not only enhance our understanding of soot formation since but will also allow the prediction of particle size distributions under combustion conditions. These results provide a novel description of soot formation based on physical and chemical properties of the particles for use in the next generation of soot models and an enhanced capability for facilitating the design of alternative fuels and the engines they will power.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Vegetation transect surveys from 2022 and 2023 within the Kougarok Fire Complex (KFC) on the Seward Peninsula in Alaska, USA

This dataset contains recorded vegetation classifications from 16 transects within the Kougarok Fire Complex (KFC) area in Alaska. Each transect is 50 meters long, and vertical vegetation profiles were sampled every 50cm, resulting in 101 points recorded for each transect. At each point, a vertical rod was inserted in the ground and any living plant or substrate touching the rod was recorded from top to bottom. For each plant, the plant functional type (PFT) and species name was identified and recorded. The file "flmd.xlsx" provides file level meta data for all other included files. The file (transect_metadata.csv) contains descriptive information about each of the 16 transects (field observation year and date; latitude and longitude GPS measurements of the 0, 25, and 50 meter points; and whether that area burned during the 1971, 1997, 2002, 2015, and/or 2019 fires). The files (PFT_names.csv and species_names.csv) map the short hand labels for the plant function type (PFT) and species used in the transect surveys to their full names. The remaining files are named for each transect, and contain the results of the vegetation survey for each transect. These files contain the plant functional type and the species identified at each point. Points along the transect with more than one species at that point are listed vertically from top to bottom in these files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

AmeriFlux FLUXNET-1F US-Hsm Hill Slough Marsh

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Hsm Hill Slough Marsh. This is the FLUXNET version of the carbon flux data for the site US-Hsm Hill Slough Marsh produced by applying the standard ONEFlux (1F) software. Site Description - The site is currently a diked marsh - upland ecotone located at the northern Suisun Marsh. It will undergo tidal restoration in Fall 2021. Restoration work is done by Ducks Unlimited Inc. and will cover an area of 649 acres. After levee breaches, the site tower will be in a high marsh system. Water level at high tide is estimated to be 0.5-1 m above the soil. Ducks Unlimited, Inc. and the Berkeley Biometeorology lab are working together to evaluate the effects of the proposed tidal marsh restoration project.

Kyle_Delwiche, Kyle_Delwiche [University of Califo↗
DOE OSTI2010

Energy Sciences Network

ESnet is a high-speed network serving thousands of Department of Energy scientists at over 40 institutions, as well as connecting to more than 100 other networks. ESnet is a pioneer in providing high-bandwidth, reliable connections that link researchers at national laboratories, universities and other research institutions, enabling them to collaborate on some of the world's most important scientific research challenges including energy, climate science, and the origins of the universe. Funded by the DOE Office of Science, and managed and operated by the ESnet team at Lawrence Berkeley National Laboratory, ESnet provides scientists access to unique DOE research facilities and computing resources.

100 GBPS↗
DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Data Center Energy Efficiency

Private and public sector efforts are underway to reduce energy use in data centers and the Office of Management and Budget’s Data Center Optimization Initiative outlines the energy efficiency requirements and strategies for federal data centers. The Federal Energy Management Program (FEMP) helps federal agencies meet these requirements. FEMP sponsors the Center of Expertise for Energy Efficiency in Data Centers (CoE), which helps federal agencies and other organizations implement data center energy efficiency projects by supplying technical support, tools, best practices, analyses, and the introduction of technologies. The CoE, located at the Lawrence Berkeley National Lab, partners with key public and private stakeholders to further efficiency efforts

Center of Expertise for Energy Efficiency in Data ↗