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At least 955 records · Page 53

The effective field theory of dark matter direct detection

We extend and explore the general non-relativistic effective theory of dark matter (DM) direct detection. We describe the basic non-relativistic building blocks of operators and discuss their symmetry properties, writing down all Galilean-invariant operators up to quadratic order in momentum transfer arising from exchange of particles of spin 1 or less. Any DM particle theory can be translated into the coefficients of an effective operator and any effective operator can be simply related to most general description of the nuclear response. We find several operators which lead to novel nuclear responses. These responses differ significantly from the standard minimal WIMP cases in their relative coupling strengths to various elements, changing how the results from different experiments should be compared against each other. Response functions are evaluated for common DM targets — F, Na, Ge, I, and Xe — using standard shell model techniques. We point out that each of the nuclear responses is familiar from past studies of semi-leptonic electroweak interactions, and thus potentially testable in weak interaction studies. Here, we provide tables of the full set of required matrix elements at finite momentum transfer for a range of common elements, making a careful and fully model-independent analysis possible. Finally, we discuss embedding non-relativistic effective theory operators into UV models of dark matter.

dark matter detectors↗

Large-scale terrestrial solar cell power generation cost: A preliminary assessment

A cost study was made to assess the potential of the large-scale use of solar cell power for terrestrial applications. The incentive is the attraction of a zero-pollution source of power for wide-scale use. Unlike many other concepts for low-pollution power generation, even thermal pollution is avoided since only the incident solar flux is utilized. To provide a basis for comparison and a perspective for evaluation, the pertinent technology was treated in two categories: current and optimistic. Factors considered were solar cells, array assembly, power conditioning, site preparation, buildings, maintenance, and operation. The capital investment was assumed to be amortized over 30 years. The useful life of the solar cell array was assumed to be 10 years, and the cases of zero and 50-percent performance deg-radation were considered. Land costs, taxes, and profits were not included in this study because it was found too difficult to provide good generalized estimates of these items. On the basis of the factors considered, it is shown that even for optimistic projections of technology, electric power from large-sclae terrestrial use of solar cells is approximately two to three orders of magnitude more costly than current electric power generation from either fossil or nuclear fuel powerplants. For solar cell power generation to be a viable competitor on a cost basis, technological breakthroughs would be required in both solar cell and array fabrication and in site preparation.

Spakowski, A. E.↗

A Quantum-Assisted Algorithm for Sampling Applications in Machine Learning

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact in deep learning and other machine learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggests it will do so with an instance-dependent effective temperature, different from the physical temperature of the device. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this talk, we present a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a kind of restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep learning architectures. We also provide a comparison to k-step contrastive divergence (CD-k) with k up to 100. Although assuming a suitable fixed effective temperature also allows to outperform one step contrastive divergence (CD-1), only when using an instance-dependent effective temperature we find a performance close to that of CD-100 for the case studied here. We discuss generalizations of the algorithm to other more expressive generative models, beyond restricted Boltzmann machines.

Perdomo-Ortiz, Alejandro↗

Network Optimization of the Electrosynthesis of Chemicals from CO2

Carbon dioxide electroreduction (ECO2R) is gaining attention due to its capacity to mitigate CO2 emissions while using electricity that would otherwise be curtailed. Its foreseeable industrial implementation requires of holistic methods to assess the technological and economic performance of ECO2R processes and integrate them in current chemical supply chains and power systems. Here, we combine techno-economic assessment and mathematical programming to find the optimal paths to electroreduce CO2 into valuable chemicals under variable electricity prices. The proposed approach is tested with a case study addressing the CO2 capture from flue gas or direct air and its electricity-powered reduction into carbon monoxide, formic acid or multi-carbon compounds. The results obtained demonstrate the ability of the framework to build ECO2R networks and provide operation profiles that respond to fluctuating electricity prices.

carbon dioxide↗

Neural network potential for Zr–Rh system by machine learning

Zr–Rh metallic glass has enabled its many applications in vehicle parts, sports equipment and so on due to its outstanding performance in mechanical property, but the knowledge of the microstructure determining the superb mechanical property remains yet insufficient. Here, we develop a deep neural network potential of Zr–Rh system by using machine learning, which breaks the dilemma between the accuracy and efficiency in molecular dynamics simulations, and greatly improves the simulation scale in both space and time. Here, the results show that the structural features obtained from the neural network method are in good agreement with the cases in ab initio molecular dynamics simulations. Furthermore, we build a large model of 5400 atoms to explore the influences of simulated size and cooling rate on the melt-quenching process of Zr 77 Rh 23 . Our study lays a foundation for exploring the complex structures in amorphous Zr 77 Rh 23 , which is of great significance for the design and practical application.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Optimization of Well Configuration for a Sedimentary Enhanced Geothermal Reservoir

The extraction of geothermal energy in the form of hot water from sedimentary rock formations could expand the current geothermal energy resources toward new regions. From previous work, we observed that sedimentary geothermal reservoirs with relatively low permeability would require the application of enhancement techniques (e.g., well hydraulic stimulation) to achieve commercial production/injection rates. In this paper we extend our previous work to develop a methodology to determine the optimum well configuration that maximizes the hydraulic performance of the geothermal system. The geothermal systems considered consist of one vertical well doublet system with hydraulic fractures, and three horizontal well configurations with open-hole completion, longitudinal fractures and transverse fractures, respectively. A commercial thermal reservoir simulation is used to evaluate the geothermal reservoir performance using as design parameters the well spacing and the length of the horizontal wells. The results obtained from the numerical simulations are used to build a response surface model based on the multiple linear regression method. The optimum configuration of the sedimentary geothermal systems is obtained from the analysis of the response surface model. The proposed methodology is applied to a case study based on a reservoir model of the Lyons sandstone formation, located in the Wattenberg field, Denver-Julesburg basin, Colorado.

15 GEOTHERMAL ENERGY↗

Work Smarter, Not Harder: Improving Energy Efficiency and Safety through Smarter Ventilation: Preprint

Ventilation is a key component to maintaining healthy, safe indoor air quality. Especially important in laboratories, ventilation is the first line of defense against airborne hazards produced during research activities. Though a vital component, laboratory ventilation systems are often victim to ineffective operation, posing a risk to the most important asset - the researchers. Furthermore, system inefficiencies can lead to up to 50% wasted energy. To improve both energy efficiency and safety in laboratories, we present the Smart Labs Toolkit - a resource developed by the U.S. Department of Energy Federal Energy Management Program and the International Institute for Sustainable Laboratories that guides laboratory stakeholders through a straight-forward, holistic approach to achieve dynamic, high-performance laboratories. Smart Labs enable safe and efficient world class science to occur in laboratories through high-performance methods. A Smart Labs program employs a combination of physical, administrative, and management techniques to assess, optimize, and manage high performance laboratories. We will focus on a central component of the Smart Labs approach - the Laboratory Ventilation Risk Assessment, a systematic process for identifying risk due to airborne hazards to inform the operation of dynamic ventilation that optimizes safety and efficiency. Case studies of organizations who have successfully implemented Smart Labs ventilation management programs will also be shared. In learning ventilation strategies successful in critical laboratory environments, learn the tools and resources needed to successfully manage energy in any building through smarter, safer ventilation.

buildings↗

First Steps Toward a Quality of Climate Finance Scorecard (QUODA-CF): Creating a Comparative Index to Assess International Climate Finance Contributions

Are climate finance contributor countries, multilateral aid agencies and specialized funds using widely accepted best practices in foreign assistance? How is it possible to measure and compare international climate finance contributions when there are as yet no established metrics or agreed definitions of the quality of climate finance? As a subjective metric, quality can mean different things to different stakeholders, while of donor countries, recipients and institutional actors may place quality across a broad spectrum of objectives. This subjectivity makes the assessment of the quality of climate finance contributions a useful and necessary exercise, but one that has many challenges. This work seeks to enhance the development of common definitions and metrics of the quality of climate finance, to understand what we can about those areas where climate finance information is available and shine a light on the areas where there is a severe dearth of data. Allowing for comparisons of the use of best practices across funding institutions in the climate sector could begin a process of benchmarking performance, fostering learning across institutions and driving improvements when incorporated in internal evaluation protocols of those institutions. In the medium term, this kind of benchmarking and transparency could support fundraising in contributor countries and help build trust with recipient countries. As a feasibility study, this paper attempts to outline the importance of assessing international climate finance contributions while describing the difficulties in arriving at universally agreed measurements and indicators for assessment. In many cases, data are neither readily available nor complete, and there is no consensus on what should be included. A number of indicators are proposed in this study as a starting point with which to analyze voluntary contributions, but in some cases their methodologies are not complete, and further research is required for a robust measurement tool to be created.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Design And Control of Thermal Storage for Ventilative Cooling in Multifamily Buildings (Final Report)

The concept of natural ventilation is to provide a heat sink for cooling the building during the occupancy periods at night and improve indoor thermal comfort during the daytime without or with minimal need for mechanical cooling. Commonly, natural ventilation through the window or mechanical circulation of the air through the ventilation ducts are two key methods used to achieve night ventilation. Due to the increasing electric need for space heating and cooling, renewable energies combined with energy storage systems are receiving attention worldwide nowadays. When designed and controlled properly, this strategy can reduce energy use for cooling, reduce the size of mechanical cooling equipment, reduce peak electrical demand for cooling, and better enable demand flexibility for mechanical cooling equipment. In some cases, ventilative cooling can eliminate the need for mechanical cooling altogether. For these reasons, California’s Building Energy Efficiency Standards have recently added prescriptive requirements that all new single-family residences (in most California climates) must include ventilative cooling systems (aka: “whole house fans”). However, the current standards do not address multifamily buildings because market-available ventilative cooling products are designed for single-family residences and are physically incompatible with many multifamily building archetypes. The results from the study show that the combination of thermal energy storage on building walls along with nighttime ventilation cooling assisted with nighttime ventilation can save total building energy usage by up to 9% and peak load by 16% in moderate climate zones. The controlled nighttime ventilation cooling (activate ventilation only when outdoor conditions are favorable) can also provide total electricity savings of up to 5% and 7% in hot and cold climates, respectively. In the case of moderate climates, kitchen exhaust-assisted nighttime ventilation cooling can eliminate the need for cooling during overnight hours. This can also significantly assist in reducing carbon emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Comparison of MPC Formulations for Building Control under Commercial Time-of-Use Tariffs

© 2019 IEEE. Most medium and large commercial buildings in the U.S. are subject to complex electricity tariffs that combine both Time-of-Use (TOU) energy and demand charges. This study analyses the performances of different economic Model Predictive Control (MPC) formulations, from the standpoints of monthly bill reduction, load shifting, and peak demand reduction. Simulations are performed on many simplified commercial building models, with multiple TOU demand charges, and under various summer conditions. Results show that compared to energy-only MPC, the traditional method for dealing with demand charges significantly reduces peak demand and owner bill, however, highlight a lack of load shifting capability. A proposed incremental approach is presented, which better balances the bill components in the objective function. In the case study presented, this method can improve monthly bill savings and increase load shifting during demand response events, while keeping a similarly low peak demand, compared to traditional MPC methods taking into account demand charges.

Cutsem, Olivier Van↗

National Renewable Energy Laboratory/Habitat for Humanity Zero Energy Home: A Cold Climate Case Study for Affordable Zero Energy Homes

The design of this 1,280-square-foot, three-bedroom Habitat for Humanity of Metro Denver zero energy home carefully combines envelope efficiency, efficient equipment, appliances and lighting, and passive and active solar features to reach the zero energy goal. The home was designed with an early version (July 22, 2004) of the BEOpt building optimization software; DOE2 and TRNSYS were used to perform additional analysis. This engineering approach was tempered by regular discussions with Habitat construction staff and volunteers. These discussions weighed the applicability of the optimized solutions to the special needs and economics of a Habitat house, moving the design toward simple, easily maintained mechanical systems and volunteer-friendly construction techniques. A data acquisition system was installed in the completed home to monitor its performance. The home's energy performance was monitored for 10 years. A comprehensive report on the home was done after the first 2 years of monitoring and a follow-up ACEEE paper on the project including 10 years of performance data was presented in 2016.

Affordable Housing↗

Assimilation of NASA’s Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System

he NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF-Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.

Hydrologic modeling↗

Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System

The NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF-Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.

Timothy M. Lahmers↗

Rebuilding for Sustainability: Case Studies in the Making (Presentation)

NREL has made significant contributions to communities suffering from natural disasters since 2007 in terms of technical assistance regarding energy efficiency and renewable energy options. NREL's work has covered all aspects of energy, including energy opportunities in community planning, policy design, new program design, and specific project design and implementation for energy related to electricity generation, building energy use, and transportation. This presentation highlights work done in New Orleans following Hurricane Katrina; Greensburg, Kansas, following a devastating tornado; and New York and New Jersey following Hurricane Sandy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Technology Solutions Case Study: Southern Energy Homes, First DOE Zero Energy Ready Manufactured Home

The country’s first Zero Energy Ready manufactured home that is certified by the U.S. Department of Energy (DOE) is up and running in Russellville, Alabama. The manufactured home was built by a partnership between Southern Energy Homes and the Advanced Residential Integrated Energy Solutions Collaborative (ARIES), which is a DOE Building America team. The effort was part of a three-home study including a standard-code manufactured home and an ENERGY STAR® manufactured home. Cooling-season results showed that the building used half the space-conditioning energy of a manufactured home built to the U.S. Department of Housing and Urban Development’s (HUD’s) Manufactured Home Construction and Safety Standards. These standards are known collectively as the HUD Code, which is the building standard for all U.S. manufactured housing.

aries↗

Understanding coronal mass ejections and associated shocks in the solar corona by merging multiwavelength observations

Using multiwavelength imaging observations, in EUV, white light and radio, and radio spectral data over a large frequency range, we analyzed the triggering and development of a complex eruptive event. This one includes two components, an eruptive jet and a coronal mass ejection (CME), which interact during more than 30 minutes, and can be considered as physically linked. This was an unusual event. The jet is generated above a typical complex magnetic configuration that has been investigated in many former studies related to the build-up of eruptive jets; this configuration includes fan-field lines originating from a corona null point above a parasitic polarity, which is embedded in one polarity region of a large active region. The initiation and development of the CME, observed first in EUV, does not show usual signatures. In this case, the eruptive jet is the main actor of this event. The CME appears first as a simple loop system that becomes destabilized by magnetic reconnection between the outer part of the jet and the ambient medium. The progression of the CME is closely associated with the occurrence of two successive type II bursts from a distinct origin. An important part of this study is the first radio type II burst for which the joint spectral and imaging observations were allowed: (1) to follow, step by step, the evolution of the spectrum and of the trajectory of the radio burst, in relationship with the CME evolution and (2) to obtain, without introducing an electronic density model, the B field and the Alfvén speed.

79 ASTRONOMY AND ASTROPHYSICS↗

Multimodal Defect Imaging of Pure Tungsten Components Fabricated via Electron Beam Powder Bed Fusion

The utilization of additive manufacturing (AM) techniques for refractory materials in high-temperature environments has significantly expanded because of the ability to fabricate geometrically complex components. Electron beam powder bed fusion (EB-PBF), which provides lower residual stress, a cleaner vacuum environment, and better efficiency for high melting point, is one of the best-suited AM methods to produce advanced refractory components. However, the property variation attributed to the heterogeneous microstructure and process-induced defects has hindered the widespread adoption of EB-PBF-produced material like tungsten. While numerous in-situ monitoring and defect detection methods have been demonstrated for EB-PBF, a workflow that compares and evaluates process-induced abnormalities from different imaging perspectives is still limited. This study examines a feature-embedded tungsten component manufactured via the EB-PBF process to demonstrate the defect detection capabilities of a multimodal defect imaging workflow. The predefined and process-induced defects are evaluated by harnessing various imaging techniques, including in-situ electron imaging, layerwise near-infrared (NIR) imaging, post-build high-energy x-ray computed tomography (CT), and conventional destructive metallography. The results highlight the strengths and limitations of distinctive defect imaging techniques concerning specific defect types, sizes, and conditions. It was found that electron imaging can provide more abnormal detection capabilities while maintaining a higher measuring accuracy, against the conventional metallography in this case study, compared with NIR and CT imaging techniques.

36 MATERIALS SCIENCE↗

Large-scale inference of gene function through phylogenetic annotation of Gene Ontology terms: case study of the apoptosis and autophagy cellular processes

We previously reported a paradigm for large-scale phylogenomic analysis of gene families that takes advantage of the large corpus of experimentally supported Gene Ontology (GO) annotations. This ‘GO Phylogenetic Annotation’ approach integrates GO annotations from evolutionarily related genes across 100 different organisms in the context of a gene family tree, in which curators build an explicit model of the evolution of gene functions. GO Phylogenetic Annotation models the gain and loss of functions in a gene family tree, which is used to infer the functions of uncharacterized (or incompletely characterized) gene products, even for human proteins that are relatively well studied. Here, we report our results from applying this paradigm to two well-characterized cellular processes, apoptosis and autophagy. This revealed several important observations with respect to GO annotations and how they can be used for function inference. Notably, we applied only a small fraction of the experimentally supported GO annotations to infer function in other family members. The majority of other annotations describe indirect effects, phenotypes or results from high throughput experiments. In addition, we show here how feedback from phylogenetic annotation leads to significant improvements in the PANTHER trees, the GO annotations and GO itself. Thus GO phylogenetic annotation both increases the quantity and improves the accuracy of the GO annotations provided to the research community. We expect these phylogenetically based annotations to be of broad use in gene enrichment analysis as well as other applications of GO annotations.

59 BASIC BIOLOGICAL SCIENCES↗