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At least 1,081 records · Page 60

Cluster Expansion and Resurgence in the Polyakov Model

In the Polyakov model, a nonperturbative mass gap is formed at leading-order semiclassics by instanton effects. By using the notions of critical points at infinity, cluster expansion, and Lefschetz thimbles, we show that a third-order effect in semiclassics gives an imaginary ambiguous contribution to the mass gap, which is supposed to be real and unambiguous. This is troublesome for the original analysis, and it is difficult to resolve this issue directly in quantum field theory (QFT). However, we find a new compactification of the Polyakov model to quantum mechanics, by using a background ’t Hooft flux. The compactification has the merit of remembering the monopole instantons of the full QFT within Born-Oppenheimer approximation, while the periodic compactification does not. In the quantum mechanical limit, we prove the resurgent cancellation of the ambiguity in three-instanton sector against ambiguity in the Borel resummation of the perturbation theory around one instanton. Assuming that this result holds in QFT, we provide a large-order asymptotics of perturbation theory around perturbative vacuum and instanton.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

A Time-Series Distribution Test System Based on Real Utility Data

In this paper, we provide a time-series distribution test system. This test system is a fully observable distribution grid in Midwest U.S. with smart meters (SM) installed at all end users. Our goal is to share a real U.S. distribution grid model without modification. This grid model is comprehensive and representative since it consists of both overhead lines and underground cables, and it has standard distribution grid components such as capacitor banks, line switches, substation transformers with load tap changer and secondary distribution transformers. An important uniqueness of this grid model is it has one-year smart meter measurements at all nodes, thus bridging the gap between existing test feeders and quasi-static time-series based distribution system analysis.

Bu, Fankun↗

TH-E-BRF-01: Exploiting Tumor Shrinkage in Split-Course Radiotherapy

Purpose: In split-course radiotherapy, a patient is treated in several stages separated by weeks or months. This regimen has been motivated by radiobiological considerations. However, using modern image-guidance, it also provides an approach to reduce normal tissue dose by exploiting tumor shrinkage. In this work, we consider the optimal design of split-course treatments, motivated by the clinical management of large liver tumors for which normal liver dose constraints prohibit the administration of an ablative radiation dose in a single treatment. Methods: We introduce a dynamic tumor model that incorporates three factors: radiation induced cell kill, tumor shrinkage, and tumor cell repopulation. The design of splitcourse radiotherapy is formulated as a mathematical optimization problem in which the total dose to the liver is minimized, subject to delivering the prescribed dose to the tumor. Based on the model, we gain insight into the optimal administration of radiation over time, i.e. the optimal treatment gaps and dose levels. Results: We analyze treatments consisting of two stages in detail. The analysis confirms the intuition that the second stage should be delivered just before the tumor size reaches a minimum and repopulation overcompensates shrinking. Furthermore, it was found that, for a large range of model parameters, approximately one third of the dose should be delivered in the first stage. The projected benefit of split-course treatments in terms of liver sparing depends on model assumptions. However, the model predicts large liver dose reductions by more than a factor of two for plausible model parameters. Conclusion: The analysis of the tumor model suggests that substantial reduction in normal tissue dose can be achieved by exploiting tumor shrinkage via an optimal design of multi-stage treatments. This suggests taking a fresh look at split-course radiotherapy for selected disease sites where substantial tumor regression translates into reduced target volumes.

60 APPLIED LIFE SCIENCES↗

Redirector: Designing Cell Factories by Reconstructing the Metabolic Objective

Advances in computational metabolic optimization are required to realize the full potential of new in vivo metabolic engineering technologies by bridging the gap between computational design and strain development. We present Redirector, a new Flux Balance Analysis-based framework for identifying engineering targets to optimize metabolite production in complex pathways. Previous optimization frameworks have modeled metabolic alterations as directly controlling fluxes by setting particular flux bounds. Redirector develops a more biologically relevant approach, modeling metabolic alterations as changes in the balance of metabolic objectives in the system. This framework iteratively selects enzyme targets, adds the associated reaction fluxes to the metabolic objective, thereby incentivizing flux towards the production of a metabolite of interest. These adjustments to the objective act in competition with cellular growth and represent up-regulation and down-regulation of enzyme mediated reactions. Using the iAF1260 E. coli metabolic network model for optimization of fatty acid production as a test case, Redirector generates designs with as many as 39 simultaneous and 111 unique engineering targets. These designs discover proven in vivo targets, novel supporting pathways and relevant interdependencies, many of which cannot be predicted by other methods. Redirector is available as open and free software, scalable to computational resources, and powerful enough to find all known enzyme targets for fatty acid production.

59 BASIC BIOLOGICAL SCIENCES↗

Renewable Energy Potential Model: Geothermal Supply Curves

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. The included paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results provided here should be considered with care due to the high uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for three scenarios: two hydrothermal (3.5km depth, USGS heat flow & SMU temperatures respectively) and one EGS (4.5km depth, SMU temperatures). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

15 GEOTHERMAL ENERGY↗

Advanced Turbomachinery Components for Supercritical CO 2 Power Cycles

Six indirectly heated supercritical CO 2 (SCO 2 ) Brayton cycles with turbine inlet conditions of 1300°F and 4000 psia with varying plant capacities from 10MWe to 550MWe were analyzed. 550 MWe plant capacity directly heated SCO 2 Brayton cycles with turbine inlet conditions of 2500°F and 4000 psia were also analyzed. Turbomachinery configurations and conceptual designs for both indirectly and directly heated cycles were developed. Optimum turbomachinery and generator configurations were selected and the resulting analysis provides validation that the turbomachinery conceptual designs meet efficiency performance targets. Previously identified technology gaps were updated based on these conceptual designs. Material compatibility testing was conducted for materials typically used in turbomachinery housings, turbine disks and blades. Testing was completed for samples in unstressed and stressed conditions. All samples exposed to SCO 2 showed some oxidation, the extent of which varied considerably between the alloys tested. Examination of cross sections of the stressed samples found no evidence of cracking due to SCO 2 exposure.

33 ADVANCED PROPULSION SYSTEMS↗

Engineering Spin-Orbit Interaction in Silicon

There has been much interest in leveraging the topological order of materials for quantum information processing. Among the various solid-state systems, one-dimensional topological superconductors made out of strongly spin-orbit-coupled nanowires have been shown to be the most promising material platform. In this project, we investigated the feasibility of turning silicon, which is a non-topological semiconductor and has weak spin-orbit coupling, into a one-dimensional topological superconductor. Our theoretical analysis showed that it is indeed possible to create a sizable effective spin-orbit gap in the energy spectrum of a ballistic one-dimensional electron channel in silicon with the help of nano-magnet arrays. Experimentally, we developed magnetic materials needed for fabricating such nano-magnets, characterized the magnetic behavior at low temperatures, and successfully demonstrated the required magnetization configuration for opening the spin-orbit gap. Our results pave the way toward a practical topological quantum computing platform using silicon, one of the most technologically mature electronic materials.

36 MATERIALS SCIENCE↗

Improving landscape-level environmental impact evaluations.

New spatial data and advancements in GIS tools allow much more comprehensive and quantitative analyses of the large datasets required when making programmatic evaluations of the ecological effects of proposed activities that cover a large area or region. Understanding the environmental impacts of proposed human developments is critical to making appropriate siting decisions and designing mitigation strategies to reduce impacts on important resources. Impact analyses conducted under the National Environmental Policy Act (NEPA) in Environmental Impact Statements (EISs) or Environmental Assessments (EAs) are intended to determine the resource-specific impacts of proposed activities of federal agencies and their alternatives using the best available information. Impacts to ecological resources are often a primary focus of these analyses. Information used in NEPA analyses include some measure of the known or probable presence of plants and wildlife in the project area, with special emphasis placed on threatened, endangered, and other special-status species. Site-specific information pertaining to ecological resources is usually easier to obtain for small-scale activities such as a local facility, road, or transmission upgrade project, where the ability to conduct fieldwork is more often feasible. However, site-specific data is more difficult-and sometimes impossible-to obtain for proposed activities that could affect a large area or region. These types of analyses often are considered in programmatic NEPA documents, in which a federal agency evaluates the implementation of a broad program or plan. Under these programmatic evaluations, the exact location and size of developments are often not known. Because obtaining quantitative information for ecological resources at such large spatial scales is difficult, programmatic impact evaluations typically rely on sketchy or partial information such as recorded species occurrences, species ranges, and general habitat descriptions. However, new spatial data and improved GIS tools allow much more comprehensive and quantitative analyses using large, readily available datasets. The availability of large-scale regional data such as GAP land-cover models or species habitat suitability models, combined with more robust spatial analysis procedures available through ArcGIS for Desktop software, allowed the analysis of multiple datasets at large spatial scales. This enabled researchers to surpass previous qualitative evaluations by developing a more accurate and quantitative approach for determining the environmental impacts of human activities at larger spatial scales. These approaches, combined with the utility of ModelBuilder and operability of Python scripts in ArcGIS, allow a more timely and cost-effective synthesis of available spatial data for programmatic evaluations and add a quantitative basis to environmental decision making.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Development of a Geothermal Module in reV: Quantifying the Geothermal Potential while Accounting for the Geospatial Intersection of the Grid Infrastructure and Land Use Characteristics

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. This paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results should be considered with care due to the highly uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for five scenarios: four hydrothermal (3.5km depth) and one EGS (4.5km depth). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

exclusions↗

Development of a Geothermal Module in reV: Quantifying the Geothermal Potential While Accounting for the Geospatial Intersection of the Grid Infrastructure and Land Use Characteristics: Preprint

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. This paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results should be considered with care due to the highly uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for five scenarios: four hydrothermal (3.5km depth) and one EGS (4.5km depth). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

exclusions↗

Parameter extraction approaches for compact modeling of thermoelectric modules

Thermoelectric (TE) cooling has experienced rapid advancements with the foundational understanding of TE materials. TE modules, compact and lightweight devices, have become the prevalent approach for implementing TE technologies. Accurately quantifying TE physical parameters (Seebeck coefficient α, thermal conductivity κ, and thermal resistance ρ) is challenging due to the dynamic temperature changes in operation. Furthermore, extracting lumped property parameters is crucial for designing energy systems using TE modules. Existing research has several limitations, such as lack of comparative analysis between prevalent formulae, reliance on potentially inaccurate vendor-supplied data, disregard for fundamental assumptions, and absence of empirical measurements. Further, this study addresses these gaps by conducting TE material characterization, comparing three existing formulae using vendor datasheets, designing a laboratory test facility for model validation and refinement, and outlining a structured data extraction procedure. The study's novelty lies in multiple key contributions: (1) a detailed comparative analysis of existing formulae for extracting TE property parameter; (2) executing experimental work in a laboratory setting to validate the model and elucidate its limitations; (3) highlighting potential risks; (4) clarifying possible assumptions from both material and engineering perspectives; and (5) considering temperature differential impacts. This comprehensive approach addresses the current research gaps and provides valuable insights into the design and application of TE modules in various energy systems.

36 MATERIALS SCIENCE↗

Positive and negative effects of dielectric breakdown in transformer oil based magnetic fluids

The transformer oil based magnetic fluids can be considered as the next-generation insulation fluids because they offer exciting new possibilities to enhance dielectric breakdown voltage as well as heat transfer performance compared to pure transformer oils. In this study, we have investigated the dielectric breakdown strength of the fluids with the various volume concentrations of nanoparticles in accordance with IEC 156 standard and have tried to find the reason for changing the dielectric breakdown voltage of the fluids from the magnetic field analysis. It was found that the dielectric breakdown voltage of pure transformer oil is around 12 kV with the gap distance of 1.5 mm. In the case of our transformer oil-based magnetic fluids with 0.08% < Φ < 0.6% (Φ means the volume concentration of magnetic nanoparticles), the dielectric breakdown voltage shows above 40 kV, which is 3.3 times higher positively than that of pure transformer oil. Negatively in the case when the volume concentration of magnetic nanoparticles is above 0.65%, the dielectric breakdown voltage decreases reversely. From the magnetic field analysis, the reason might be considered as two situations: the positive is for the conductive nanoparticles dispersed well near the electrodes, which play an important role in converting fast electrons to slow negatively charged particles, and the negative is for the agglomeration of the particles near the electrodes, which leads to the breakdown initiation.

36 MATERIALS SCIENCE↗

Advanced multimaterial shape optimization methods as applied to advanced manufacturing of wind turbine generators

Abstract Currently, many utility‐scale wind turbine generator original equipment manufacturers are dependent on imported rare earth permanent magnets, which are susceptible to market risks from cost instability. To lower the production costs of these generators and stay competitive in the market, several small wind manufacturers are pursuing continuous improvements to both generator design and manufacturing. However, traditional design and manufacturing methods have yielded marginal improvements in wind power performance. This work presents novel methods to redesign a baseline 15‐kW wind turbine generator with reduced rare‐earth permanent magnets by leveraging cutting‐edge three‐dimensional (3D) printed polymer‐bonded permanent magnets and steel. Symmetric, asymmetric, and multimaterial‐magnet parametrization methods are introduced for shape optimization. We extend the symmetric and asymmetric methods to the back iron in the stator to further investigate the impact and opportunities for performance improvements with lesser active materials. We employ a design‐of‐experiments approach with parametric computer‐aided design for shape generation and evaluate different designs by magneto‐thermal modeling and finite‐element analysis. We use adaptive sampling technique to identify better performing designs with lesser magnet mass, higher efficiency, and lower cogging torque when compared with the baseline generator. Asymmetric pole designs resulted in a magnet mass in the range of 4.77–5.37 kg, which was 27%–35% lighter than the baseline generator, suggesting that a new design freedom exists that can be enabled by advanced manufacturing, such as 3D printing. Shaping the back iron in the stator resulted in material savings in electrical steel of up to 14.62 kg, which was 20% lighter than the baseline stator. We conducted a structural analysis to evaluate an optimized asymmetric rotor design from the point of view of mechanical integrity and air‐gap stiffness. The magnetically optimal shape profile was shown as having a positive impact on the radial stiffness, and an optimal solution was discovered to reduce the structural mass by nearly 30 kg, which was 29% lighter than the baseline.

17 WIND ENERGY↗

Uranium from Seawater Cost Analysis: Recent Updates

While it is not meant to be a direct competitor with conventionally mined uranium, seawater uranium has significant potential to sway policy makers and the public toward the long term viability of nuclear power. Given the large mass of uranium contained within the ocean and its continual replenishment by freshwater run-off and the ocean floor, seawater uranium can be considered for all practical purposes a renewable resource. In addition to this assurance of supply security placing nuclear power on equal footing with other renewable energy generation technologies, seawater uranium removes a key uncertainty by establishing a cost ceiling on uranium based fuels. Given the evolving nature of this technology, this paper aims to provide an update of the economic model to reflect recent progress. This analysis will reference a passive collection system currently under development by Oak Ridge and Pacific Northwest National Labs. This system consists of a polymer modified to contain amidoxime functional groups, a ligand with high uranium affinity. The adsorbing material is then braided into long segments so it may be moored to the bottom of the ocean where it passively collects uranium for a period on the order of weeks to months. Upon completion of its soaking campaign adsorbent is winched to the surface by work boats so the uranium may be eluted off the braids. After a chemical rinse the braids may then be redeployed for another soaking campaign and uranium elution. The adsorbent can be redeployed as many times as is economically feasible, which is a function of the degradation the material suffers upon each reuse. Noteworthy experimental progress has tightened the gap separating estimates of the optimistic and pessimistic case uranium production cost relative to the most recent analysis. It has been demonstrated before that significant uncertainty in adsorbent performance, and thus recovery cost, arises primarily from variability in adsorbent degradation upon reuse and the effects of marine biofouling on uptake. While the underlying recovery mechanism remains, significant changes have been made regarding the method of adsorbent synthesis, propagating to improvements in adsorbent durability. Additionally, further experimentation regarding the area of marine biofouling has tightened the range of expected adsorbent performance when placed in true marine conditions.

07 ISOTOPE AND RADIATION SOURCES↗

Bench Scale Process for Low Cost CO 2 Capture Using a PhaseChanging Absorbent: Techno-Economic Analysis Topical Report

The objective of this project is to design and build a bench-scale process for a novel phase-changing CO 2 capture solvent. The project will establish scalability and technical and economic feasibility of using a phase-changing CO 2 capture absorbent for post-combustion capture of CO 2 from coal-fired power plants with 90% capture efficiency and 95% CO 2 purity at a cost of $40/tonne of CO 2 captured by 2025 and a cost of <$10/tonne of CO 2 captured by 2035. This report presents system and economic analysis for a process that uses a phase changing aminosilicone solvent to remove CO 2 from pulverized coal (PC) power plant flue gas. The aminosilicone solvent is a pure 1,3-bis(3-aminopropyl)-1,1,3,3-tetramethyldisiloxane (GAP-0). Performance of the phase-changing aminosilicone technology is compared to that of a conventional carbon capture system using aqueous monoethanolamine (MEA). This analysis demonstrates that the aminosilicone process has significant advantages relative to an MEA-based system. The first-year CO 2 removal cost for the phase-changing CO 2 capture process is $52.1/tonne, compared to $66.4/tonne for the aqueous amine process. The phase-changing CO 2 capture process is less costly than MEA because of advantageous solvent properties that include higher working capacity, lower corrosivity, lower vapor pressure, and lower heat capacity. The phase-changing aminosilicone process has approximately 32% lower equipment capital cost compared to that of the aqueous amine process. However, this solvent is susceptible to thermal degradation at CSTR desorber operating temperatures, which could add as much as $88/tonne to the CO 2 capture cost associated with solvent makeup. Future work is focused on mitigating this critical risk by developing an advanced low-temperature desorber that can deliver comparable desorption performance and significantly reduced thermal degradation rate.

20 FOSSIL-FUELED POWER PLANTS↗

Floating Wind Array Ontology and Modeling Framework

While there are many tools for designing and modeling a single floating turbine, array level design and modeling has much more to consider. Designing floating wind arrays requires a coupled approach considering many variables, from bathymetry to installation and maintenance to failure and risk analysis. With all of these considerations, an array-level modeling tool is needed to quickly evaluate array designs. The Floating Array Model (FAModel) tool developed at the National Renewable Energy Laboratory was created to fill this gap in low-fidelity array modeling. FAModel is a python framework created to streamline holistic low-fidelity floating wind modeling for array-level analysis. FAModel integrates site data and models with a variety of open-source modeling tools developed by NREL, including FLORIS, RAFT, MoorPy, and anchor capacity models. The integration of these tools allows users to quickly and holistically design an array by considering forces, area analysis, visualization, annual energy production, failure modeling, and component costs.

17 WIND ENERGY↗

An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations and splice variants identified in cancer cells are translated. Herein we therefore describe a proteogenomic data integration tool (QUILTS) and illustrate its application to whole genome, transcriptome and global MS peptide sequence datasets generated from a pair of luminal and basal-like breast cancer patient derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS process replicates. Despite over thirty sample replicates, only about 10% of all SNV (somatic and germline) were detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNV without a detectable mRNA transcript were also observed demonstrating the transcriptome coverage was also incomplete (~80%). In contrast to germ-line variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than the luminal tumor raising the possibility of differential translation or protein degradation effects. In conclusion, the QUILTS program integrates DNA, RNA and peptide sequencing to assess the degree to which somatic mutations are translated and therefore biologically active. By identifying gaps in sequence coverage QUILTS benchmarks current technology and assesses progress towards whole cancer proteome and transcriptome analysis.

Environmental Molecular Sciences Laboratory↗

Data Crosscutting Requirements Review

In April 2013, a diverse group of researchers from the U.S. Department of Energy (DOE) scientific community assembled to assess data requirements associated with DOE-sponsored scientific facilities and large-scale experiments. Participants in the review included facilities staff, program managers, and scientific experts from the offices of Basic Energy Sciences, Biological and Environmental Research, High Energy Physics, and Advanced Scientific Computing Research. As part of the meeting, review participants discussed key issues associated with three distinct aspects of the data challenge: 1) processing, 2) management, and 3) analysis. These discussions identified commonalities and differences among the needs of varied scientific communities. They also helped to articulate gaps between current approaches and future needs, as well as the research advances that will be required to close these gaps. Moreover, the review provided a rare opportunity for experts from across the Office of Science to learn about their collective expertise, challenges, and opportunities. The "Data Crosscutting Requirements Review" generated specific findings and recommendations for addressing large-scale data crosscutting requirements.

ASCR↗