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VPIC I/O Utilities provide scalable input and output code for writing and reading HDF5 data by the Vector Particle-in-cell (VPIC) simulation codes. The I/O utility writes particle, field, and hydro data from VPIC to HDF5 files. The filed and hydro codes also write XDMF files, which can be used by visualization tools such as ParaView to read and visualize the HDF5 data written.
As the frequency and duration of grid outages increase, backup power systems are becoming more important for ensuring that critical infrastructure continues to provide essential services. Most facilities rely on diesel generators, which may be ineffective during long outages owing to limited fuel supplies and high generator failure rates. Distributed energy resources such as solar, storage, and combined-heat-and-power systems, coupled with on-site biofuel production, offer an alternative source of on-site generation that can provide both cost savings and resilience (i.e., the ability to respond to catastrophic events with longer-term consequences). A mixed-integer linear program minimizes costs and maximizes resilience at a wastewater treatment plant in Wilmington, North Carolina. We find that the plant can reduce life-cycle energy costs by 3.1% through the installation of a hybrid combined-heat-and-power, photovoltaic, and storage system. When paired with existing diesel generators, this system can sustain full load for seven days while saving $664,000 over 25 years and reducing diesel fuel use by 48% compared with the diesel-only solution. This analysis informed a decision by the Cape Fear Public Utility Authority to allocate funds for the implementation of a combined-heat-and-power system at the wastewater treatment plant in fiscal year 2023. Finally, the benefits of deploying hybrid combined-heat-and-power technologies and the utilization of on-site biofuel production extend, on a national scale, to thousands of wastewater treatment facilities and other types of critical infrastructure.
The Waste to Wisdom project was part of the Biomass Research and Development Initiative (BRDI) and funded by the Department of Energy (DE-EE0006297) at an amount of $5.8 million. Our interdisciplinary research team, consisting of academics, business professionals, and land managers, worked together for about four years (September 2013 to December 2017) to: 1) conduct field-based experiments to develop innovative tools and systems that improve the economics, accessibility, and production of quality feedstocks from forest residues, 2) develop and test stand-alone in-woods or near-the-forest biomass conversion technologies (BCTs) for the production of biochar, torrefied wood, and briquettes, and 3) perform analyses to evaluate the economic feasibility of commercial deployment of BCTs and to quantify the life cycle economic and environmental benefits of utilizing forest residues with BCTs for the production of bioenergy and bioproducts. The research papers presented in this Special Issue cover key aspects of the research efforts and findings made by the project team. We encourage the audience to visit the project web site (http://wastetowisdom.com/) to learn more about the team’s research on feedstock development, biomass conversion technologies, and the financial and environmental benefits of utilizing forest residues for production of bioenergy and biobased products. Keywords: Air quality, Biomass conversion technology, Forest harvesting, Woody biomass.
Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU: RUR dataset is the job scheduler traces collected from the Titan supercomputer from 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected using resource Utilization Report (RUR), a Cray-developed resource-usage data collection and reporting system. It contains the usage information of its critical resources (CPU, Memory, GPU, and I/O) of each running job on Titan during that period (https://ieeexplore.ieee.org/abstract/document/8891001). It includes ProjectAreas as additional information, every job is associated with a project ID. TheProjectAreas.csv dataset provides a mapping of the project ID to its domain science. GPU dataset has information regarding GPU failure on Titan. There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has seven attributes, we provided a short description of these attributes in the ReadMe file. To learn more about this dataset, please refer to the git repository https://github.com/olcf/TitanGPULife and the related publication (https://ieeexplore.ieee.org/abstract/document/9355319).
Here, thorium has great potential to stretch nuclear fuel reserves because of its natural abundance and because it is possible to breed the 232 Th isotope into a fissile fuel ( 233 U). Various scenarios exist for utilization of thorium in the nuclear fuel cycle, including use in different nuclear reactor types (e.g., light water, high-temperature gas-cooled, fast spectrum sodium, and molten salt reactors), along with use in advanced accelerator-driven systems and even in fission-fusion hybrid systems. The most likely near-term application of thorium in the United States is in currently operating light water reactors (LWRs). This use is primarily based on concepts that mix thorium with uranium (UO 2 + ThO 2 ) or that add fertile thorium (ThO 2 ) fuel pins to typical LWR fuel assemblies. Utilization of mixed fuel assemblies (PuO 2 + ThO 2 ) is also possible. The addition of thorium to currently operating LWRs would result in a number of different phenomenological impacts to the nuclear fuel. Thorium and its irradiation products have different nuclear characteristics from those of uranium and its irradiation products. ThO 2 , alone or mixed with UO 2 fuel, leads to different chemical and physical properties of the fuel. These key reactor safety–related issues have been studied at Oak Ridge National Laboratory and documented in “Safety and Regulatory Issues of the Thorium Fuel Cycle” (NUREG/CR-7176, U.S. Nuclear Regulatory Commission, 2014). Various reactor analyses were performed using the SCALE code system for comparison of key performance parameters of both ThO 2 + UO 2 and ThO 2 + PuO 2 against those of UO 2 and typical UO 2 + PuO 2 mixed oxide fuels, including reactivity coefficients and power sharing between surrounding UO 2 assemblies and the assembly of interest. The decay heat and radiological source terms for spent fuel after its discharge from the reactor are also presented. Based on this evaluation, potential impacts on safety requirements and identification of knowledge gaps that require additional analysis or research to develop a technical basis for the licensing of thorium fuel are identified.
The efficient use of hemicellulose in the plant cell wall is critical for the economic conversion of plant biomass to renewable fuels and chemicals. Previously, the yeast Saccharomyces cerevisiae has been engineered to convert the hemicellulose-derived pentose sugars xylose and arabinose to D-xylulose-5-phosphate for conversion via the pentose phosphate pathway (PPP). However, efficient pentose utilization requires PPP optimization and may interfere with its roles in NADPH and pentose production. Here, we developed an alternative xylose utilization pathway that largely bypasses the PPP. In the new pathway, D-xylulose is converted to D-xylulose-1-phosphate, a novel metabolite to S. cerevisiae, which is then cleaved to glycolaldehyde and dihydroxyacetone phosphate. This synthetic pathway served as a platform for the biosynthesis of ethanol and ethylene glycol. The use of D-xylulose-1-phosphate as an entry point for xylose metabolism opens the way for optimizing chemical conversion of pentose sugars in S. cerevisiae in a modular fashion.
The economic production of cellulosic biofuel requires efficient and full utilization of all abundant carbohydrates naturally released from plant biomass by enzyme cocktails. Recently, we reconstituted the Neurospora crassa xylodextrin transport and consumption system in Saccharomyces cerevisiae, enabling growth of yeast on xylodextrins aerobically. However, the consumption rate of xylodextrin requires improvement for industrial applications, including consumption in anaerobic conditions. As a first step in this improvement, we report analysis of orthologues of the N. crassa transporters CDT-1 and CDT-2. Transporter ST16 from Trichoderma virens enables faster aerobic growth of S. cerevisiae on xylodextrins compared to CDT-2. ST16 is a xylodextrin-specific transporter, and the xylobiose transport activity of ST16 is not inhibited by cellobiose. Other transporters identified in the screen also enable growth on xylodextrins including xylotriose. Taken together, these results indicate that multiple transporters might prove useful to improve xylodextrin utilization in S. cerevisiae. Efforts to use directed evolution to improve ST16 from a chromosomally-integrated copy were not successful, due to background growth of yeast on other carbon sources present in the selection medium. Future experiments will require increasing the baseline growth rate of the yeast population on xylodextrins, to ensure that the selective pressure exerted on xylodextrin transport can lead to isolation of improved xylodextrin transporters.
This dataset contains input data, code, ReadMe files, output data, and figures that summarize the results of a stochastic analysis of geothermal reservoir production from two potential geothermal reservoirs that were evaluated for the Cornell University Deep Direct-Use project. These potential reservoirs are the Trenton-Black River (TBR) from 2.27-2.3 km depth, and basement rocks from 3.0-3.5 km depth and 3.5-4.0 km depth. Several utilization scenarios consisting of different injection fluid temperatures and flow rates were evaluated for each reservoir. Uncertainty in geologic properties, thermal properties, economic costs, and utilization efficiencies were evaluated using a Monte Carlo analysis of the reservoir simulations. Some reservoir simulations of the TBR were completed using the TOUGH2 software, as implemented in PetraSIM. The PetraSIM run files and associated data are provided with this submission. All other reservoir simulations were completed using the GEOPHIRES software, with some modifications to complete the uncertainty analyses. ReadMe files that describe additions to GEOPHIRES, the GEOPHIRES input data, and the output data are all provided, and references are provided to the code repository. Figures that summarize the reservoir heat production, temperature drawdown, and the probability of meeting targeted building heating demands with the produced heat and fluid temperatures are provided.
In thist study, we develop a procedure to utilize error estimates for samples of a surrogate model to compute robust upper and lower bounds on estimates of probabilities of events. We show that these error estimates can also be used in an adaptive algorithm to simultaneously reduce the computational cost and increase the accuracy in estimating probabilities of events using computationally expensive high-fidelity models. Specifically, we introduce the notion of reliability of a sample of a surrogate model, and we prove that utilizing the surrogate model for the reliable samples and the high-fidelity model for the unreliable samples gives precisely the same estimate of the probability of the output event as would be obtained by evaluation of the original model for each sample. The adaptive algorithm uses the additional evaluations of the high-fidelity model for the unreliable samples to locally improve the surrogate model near the limit state, which significantly reduces the number of high-fidelity model evaluations as the limit state is resolved. Numerical results based on a recently developed adjoint-based approach for estimating the error in samples of a surrogate are provided to demonstrate (1) the robustness of the bounds on the probability of an event, and (2) that the adaptive enhancement algorithm provides a more accurate estimate of the probability of the QoI event than standard response surface approximation methods at a lower computational cost.
Fermilab is undertaking the development of a new 800 MeV superconducting RF linac to replace its present normal conducting 400 MeV linac. The PIP-II linac consists of a warm front-end generating 2 mA of 2.1 MeV H⁻ followed immediately by a series of superconducting RF cryomodules to 800 MeV. To limit the potential damage to the superconducting RF cavities, PIP-II will utilize laser-based monitors to obtain beam profiles via photoionization. This paper will present the results of transverse and longitudinal beam profile measurements using a prototype profile monitor that was tested with 2.1 MeV H⁻ beam at the PIP-II Injector Test (PIP2IT) accelerator. This prototype profile monitor utilizes a high repetition rate fiber laser and fiber optic transport into the PIP2IT enclosure. In addition, results will be shown of narrow-band electron detection from amplitude modulated laser pulses.
The Great Basin Center for Geothermal Energy (GBCGE or the Center) was established at the University of Nevada, Reno (UNR) in May 2000 to promote research and utilization of geothermal resources. The Center received funding through this grant to promote increased geothermal development in the Great Basin, with most of the funding used for peer-reviewed research. Funding to the Center and work under the contract were initiated in March 2002, with supplemental funding in subsequent years. The Center monitored the research projects that were competitively awarded in a series of proposal calls between 2002 and 2007. Peer-reviewed research promoted identification and utilization of geothermal resources in Nevada. Projects used geology, geochemistry, geophysics, remote sensing, and the synthesis of multi-disciplinary information to produce new models of geothermal systems in the Western U.S. and worldwide. Funds were also used to support graduate student research and training. Part of the grant was used to support public outreach activities, including webpages, online maps and data resources, and informational workshops for stakeholders.
Argonne is assisting SHINE Medical Technologies (SHINE) in their efforts to develop SHINE, an accelerator-driven process that will utilize a uranyl-sulfate solution for the production of fission product Mo-99. An integral part of the process is the development of a column for the separation and recovery of Mo-99, followed by a concentration column to reduce the product volume from 15-25 L to <1 L. Argonne has collected data from batch studies and breakthrough column experiments to utilize the VERSE (Versatile Reaction Separation) simulation program (Purdue University) to design plant-scale product recovery and concentration processes.
Utility customer-supported financing programs are receiving increased attention as a strategy for achieving energy saving goals. Rationales for using utility customer funds to support financing initiatives
In India, there is growing interest among policymakers, planners, and regulators for aggressive electrification of passenger vehicles. For example, Piyush Goyal, the Minister of State for India’s Ministry of Coal, Power, New and Renewable Energy, announced an aspirational goal of converting all vehicle sales in India to battery electric vehicles (BEVs) by 2030 (Economic Times, 2016). In 2012, India has already announced the National Mission on Electric Mobility (NMEM) sets a countrywide goal of deploying 6 to 7 million hybrid and electric vehicles (EVs) by 2020 (DHI, 2012). A major policy motivation for transport electrification is to reduce India’s oil import dependency. The objective of this paper is to assess the effect of full electrification of vehicle sales in India by 2030 on the key stakeholders such as BEV owners, electric utilities, and the government. Specifically, we attempt to answer the following questions: (a) How does the total vehicle ownership cost of BEVs compare with the conventional vehicles? (b) What is the additional load due BEV charging? (c) What is the impact on the power sector investments, costs, and utility revenue? (d) How can smart BEV charging help renewable energy grid integration? (e) What is the impact on the crude oil imports? (f) What is the impact on the greenhouse gas (GHG) emissions?
Integrated resource planning (IRP) is a process used by many vertically-integrated U.S. electric utilities to determine least-cost/risk supply and demand-side resources that meet government policy objectives and future obligations to customers and, in many cases, shareholders. Forecasts of energy and peak demand are a critical component of the IRP process. There have been few, if any, quantitative studies of IRP long-run (planning horizons of two decades) load forecast performance and its relationship to resource planning and actual procurement decisions. In this paper, we evaluate load forecasting methods, assumptions, and outcomes for 12 Western U.S. utilities by examining and comparing plans filed in the early 2000s against recent plans, up to year 2014. We find a convergence in the methods and data sources used. We also find that forecasts in more recent IRPs generally took account of new information, but that there continued to be a systematic over-estimation of load growth rates during the period studied. We compare planned and procured resource expansion against customer load and year-to-year load growth rates, but do not find a direct relationship. Load sensitivities performed in resource plans do not appear to be related to later procurement strategies even in the presence of large forecast errors. These findings suggest that resource procurement decisions may be driven by other factors than customer load growth. Our results have important implications for the integrated resource planning process, namely that load forecast accuracy may not be as important for resource procurement as is generally believed, that load forecast sensitivities could be used to improve the procurement process, and that management of load uncertainty should be prioritized over more complex forecasting techniques.
The Nuclear Security Enterprise, managed by the National Nuclear Security Administration - a semiautonomous agency within the Department of Energy - has been associated with numerous assessments with respect to the estimating, management capabilities, and practices pertaining to nuclear weapon modernization efforts. This report identifies challenges in estimating and analyzing the Nuclear Security Enterprise through an analysis of analogous timeframe conditions utilizing two types of nuclear weapon data - (1) a measure of effort and (2) a function of time. The analysis of analogous timeframe conditions that utilizes only two types of nuclear weapon data yields four summary observations that estimators and analysts of the Nuclear Security Enterprise will find useful.
The United States currently produces over 100 million tons of coal utilization byproducts (CUB) per year in the form of fly ash, bottom ash, slag, and flue gas (American Coal Ash Association (ACCA), 2015). But this “waste material” also contains potentially useful levels of rare earth elements (REE). Rare earth elements are crucial for many existing and emerging technologies, but the U.S. lacks a domestic, sustainable REE source. Our project explored the possibility of developing a supply of REEs for U.S. technologies by extracting REEs from CUBs. This work offers the potential to reduce our dependence on other countries for supply of these critical elements (NETL, REE 2016 Project Portfolio). Geologic and diagenetic history, industrial preparation methods, and the specific combustion process all play major roles in the composition of CUB. During combustion, inorganic mineral phases of coal particles are fluidized at temperatures higher than 1400oC, so inorganic mineral materials are oxidized, fused, disintegrated, or agglomerated into larger spherical and amorphous (non-crystalline) particles. The original mineralogy of the coal-containing rock and heating/cooling of the material significantly affects the composition and morphology of the particles in the combustion byproduct (Kutchko and Kim, 2006). Thus, different types of coal/refuse/ash must be characterized to better understand mineral evolution during the combustion process. Our research focused on developing a working model to address how REE minerals behave during the combustion process: this research should help determine the most effective engineering methods for extracting REEs from CUBs. We used multimodal imaging and image processing techniques to characterize six rock and ash samples from different coal power plants with respect to morphology, grain size, presence of mineral phases, and elemental composition. The results of these characterization activities provided thresholds for realizing the occurrence of REE mineral phases in CUB and allowed us to calculate structural and volumetric estimates of REE. Collectively, the rock and coal ash samples contained minerals such as quartz, kaolinite, muscovite/illite, iron oxide (as hematite or magnetite), mullite, and clinochlore. Trace minerals included pyrite, zircon, siderite, rutile, diopside, foresterite, gypsum, and barite. We identified REE phosphate minerals monazite (Ce,La,Nd,Th)(PO 4 ,SiO 4 ), xenotime (YPO 4 ,SiO 4 ), and apatite (Ca 5 (PO 4 ) 3 (F,Cl,OH) via SEM and electron microprobe analysis: these materials generally occurred as 1-10 μm-long crystals in the rock and ash samples. As has been shown in other studies, amorphous material-aluminosilicate glass or iron oxyhydroxide-are the major components of coal fly and bottom ash. Trace amounts of amorphous calcium oxide and mixed element (e.g., Al-Si-Ca-Fe) slag are also present. Quartz, mullite, hematite, and magnetite are the crystalline phases present. We found that REEs are present as monomineralic grains dispersed within the ash, as well as fused to or encapsulated by amorphous aluminosilicate glass particles. Monazite and xenotime have relatively high melting points (>1800 °C) compared to typical combustion temperatures; our observations indicate that the REE-phosphates, which presumably contribute a large percentage of REE to the bulk ash REE pool, as measured by mass spectroscopy, are largely unaltered by the combustion. Our study shows that conventional coal combustion processes sequester REE minerals into aluminosilicate glass phases, which presents a new engineering challenge for extracting REE from coal ash. The characterization work summarized in this report provides a semi-quantitative assessments of REE in coal-containing rock and CUB. The data we obtained from 2- and 3-D imaging, elemental mapping, volumetric estimates, and advanced high-resolution pixel classification successfully identified the different mineral phases present in CUB. Further, our characterization results can guide techniques for extracting REEs from CUB, or other geologic and engineered materials. Whilst, interpretations will inform future REE separation and extraction techniques and technologies practical for commercial utilization of combustion byproducts generated by power plants.
We consider the use of a novel weak in- dicator alongside more commonly used weak indicators to help detect anomalous behavior in a large computer network. The data of the network which we are studying in this research paper concerns remote log-in information (Virtual Private Network, or VPN sessions) from the internal network of Los Alamos National Laboratory (LANL). The novel indicator we are utilizing is some- thing which, while novel in its application to data science/cyber security research, is a concept borrowed from the business world. The Her ndahl-Hirschman Index (HHI) is a computationally trivial index which provides a useful heuristic for regulatory agencies to ascertain the relative competitiveness of a particular industry. Using this index as a lagging indicator in the monthly format we have studied could help to detect anomalous behavior by a particular or small set of users on the network. Additionally, we study indicators related to the speed of movement of a user based on the physical location of their current and previous logins. This data can be ascertained from the IP addresses of the users, and is likely very similar to the fraud detection schemes regularly utilized by credit card networks to detect anomalous activity. In future work we would look to nd a way to combine these indicators for use as an internal fraud detection system.