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At least 343 records · Page 19

Development of Engine Valve Materials for Next Generation Higher Efficiency Engines

The growing demand to increase the performance and efficiency of light-, medium-, and heavy-duty engines continues to drive increases in combustion intensities and cylinder pressures, which result in higher exhaust gas temperatures. Thus, there is a critical need for new materials that can meet the performance and cost targets for components such as exhaust valves which are exposed to these higher exhaust gas temperatures. Oak Ridge National Laboratory (ORNL) has developed several lower-cost, high-strength alloys that have the potential to be adopted into intake and/or exhaust valves in the next generation, high-efficiency engines and other high temperature applications. These alloys are covered by two issued patents.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

UHT-CAMANCHE: Ultra-High Temperature Ceramic Additively Manufactured Compact Heat Exchangers

The conceptual basis for this project is the convergence of advanced ultra-high temperature ceramic materials and additive manufacturing technologies to produce compact ceramic heat exchangers with complex internal flow path geometries. Task areas were broadly divided into materials and manufacturing development, heat exchanger design, component testing, and techno-economic analysis. Technical challenges included the design and commissioning of new test facilities, improving feature resolution and deposition rate of ceramic additive manufacturing techniques, establishing process-structure-property relationships in additively manufactured ultra-high temperature ceramics, and assessing high temperature materials compatibility in CO 2 environments. The primary candidate material evaluated in this work is a composite comprising zirconium diboride (ZrB2) with 30 vol. % silicon carbide (SiC) which was selected based on its desirable combination of high temperature mechanical properties, high thermal and electrical conductivities, and oxidation resistance. High solids loaded ZrB2-SiC pastes suitable for extrusion-based additive manufacturing were developed for the first time as part of this work. Materials compatibility studies indicate this material oxidizes in CO 2 to form a protective borosilicate scale which transforms to pure silica above 1000°C. Parts made by additive manufacturing displayed enlarged grain sizes produced by pressureless sintering as compared to hot-press sintering. Increases in microstructural coarseness have outsized effect on oxidation performance up to 1400°C due to incomplete oxidation of coarse large diameter SiC particles resulting in lower amounts of silica that apparently inhibit protective scale formation. Additive manufacturing as a forming technique did not appear to significantly affect thermal conductivity, hardness, or elastic modulus, though flexural strength was reduced by half or more as compared to traditionally hot-pressed materials. This effect was attributed to the presence of strength-limiting flaws (ca. 40 microns in size) originating from extrudate inhomogeneities that could potentially be eliminated with further process improvements. Attempts to attain economies of scale for production of multi-kilowatt scale heat exchangers by ceramic additive manufacturing proved difficult. Lack of automation and a modest extrusion rate while retaining fine feature resolution made the overall process labor intensive and limited experimental throughput. A number of full-scale components were taken through post-process heat treatments including drying, binder burnout, and sintering; however, none survived without significant flaws or cracks. Therefore, no operational data from a newly installed heat exchanger test loop were able to be obtained during the performance period. Continued research and development is recommended to improve economic feasibility of ceramic additive manufacturing by standardizing the use of advanced sensors, artificial intelligence, and automation tools to reduce associated labor costs and accelerate production rates. The materials and manufacturing techniques demonstrated in this work are likely to find applications in defense and energy applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Demand Response in Industrial Facilities: Peak Electric Demand

The US Department of Energy’s (DOE’s) Better Buildings, Better Plants Program (Better Plants) is a voluntary energy efficiency leadership initiative for US manufacturers and water/wastewater entities. The program encourages organizations to commit to reducing the energy intensity of their US operations over a 10-year period, typically by 25%. Companies joining Better Plants are recognized by DOE for their leadership in implementing energy efficiency practices and for reducing their energy intensity. Better Plants Partners are assigned to a Technical Account Manager, who can help companies establish energy intensity baselines, develop energy management plans, and identify key resources and incentives from DOE, other federal agencies, states, utilities, and other organizations that can enable them to reach their goals. Better Plants Partners are expected to report their progress to DOE once a year. This involves establishing an energy intensity baseline upon joining the program and then tracking their progress over time. Demand Response in Industrial Facilities: Peak Electrical Demand is intended to help companies understand peak demand response programs offering by their local utility. Manufacturing industries can learn about time-varying rates and smart technologies they can use to help them reduce their energy bills. This guidance document is applicable to companies participating at either the program or challenge level. Although this guide is intended primarily to assist companies participating in Better Plants, the methodologies and guidance within the document are applicable to any organization interested in understanding peak demand response programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Sand-Based Thermal Storage for Building Heating Applications: A District Energy Case Study

Buildings account for 40% of global energy consumption and contribute to 30% of global carbon emissions. As energy from renewable sources increases in availability and building designers push for increased electrification, thermal energy storage (TES) systems will play a crucial role in extending the usable time horizon of renewable energy. While water, molten salt, and phase change materials are typically used for building TES heating applications, silica-sand has emerged as an alternative medium for concentrated solar power applications due to its low cost, wide availability, and comparable system efficiency. This paper proposes a new silica-sand particle-based TES system for building heating applications. In this work, a novel steam plant for district heating applications is first designed to utilize silica-sand TES, which can be used for different district energy systems. To demonstrate the silica-sand TES plant performance, the design is modelled in Modelica based on a case study on the University of Colorado Boulder’s campus. The simulation results show that the sand TES plant is more costly to operate than a gas-boiler based plant due to the low cost of natural gas, while the site EUI and carbon intensity can be improved. This novel system shows initial promise as a low-carbon alternative to conventional natural gas steam boilers but will require further modelling and follow-up research to improve its energy efficiency. An eventual rise in natural gas prices, and reduction of electricity prices, could improve the economic viability of this system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Bay Area Regional Energy: Network Integrated Commercial Retrofits (BRICR) Project. Final Report

The BRICR project applied large-scale building energy modeling concepts with the aim of reducing the cost of energy efficiency targeting, design, and project development, and measurement of energy savings for energy efficiency programs implemented by local governments that serve small and medium commercial buildings (SMB). The project leveraged the services and resources of existing local government energy programs serving disadvantaged and hard-to-reach SMB customers. In contrast to programs run by utilities, local government programs generally do not have direct access to energy billing records for an entire class of customers in a geographic area, which prior research demonstrated useful for large-scale building energy model baseline development and calibration. , However, local governments are rich in public records that offer important clues about physical attributes and uses that, along with behavior, determine energy use. Relying only on public records, BRICR demonstrated development of credible baseline energy models for 3,792 office, retail, and hotel buildings. Publicly disclosed annual energy use data from a local energy benchmarking program and anonymized data from the Building Performance Database, the nation’s largest dataset about energy-related characteristics of buildings, were utilized to validate and calibrate energy models via an innovative method comparing distributions of energy intensity by fuel type for portfolios of buildings of similar size, vintage, and use. Portfolio calibration does not provide certainty that an energy model fits an individual building; the method is useful when billing data is not accessible – a common situation for researchers, energy service providers and ESCOs, local governments, and any party other than a utility. A software component was developed, the BRICR gem, which automates simulation when relevant data is added or edited by the user to a file saved in the standardized BuildingSync XML schema for energy audit data. The component was demonstrated as a simplified means to generate a mass of energy models corresponding to public records containing basic attributes such as building scale, location, use, year built, and aspect ratio in combination with building energy code prototype data corresponding to use and vintage. The component was also demonstrated as a simplified means to automate energy simulation when attributes are revised; the intention was to enable iterative improvement of the baseline model and energy savings estimates for common energy conservation measures as users revise relevant attributes based on their observations. In the context of institutional change and uncertainty for the participating local government energy programs, 13 whole building retrofits were completed. Impacts were measured by applying the CalTRACK2.0 methods to standardize measurement of normalized metered energy consumption. The GRIDMeter methods of stratified sampling and individual load shape analysis were applied to adjust for impacts of the effect of COVID-19 on retrofitted buildings in the context of all local buildings of similar size and use. Excluding impacts of the pandemic, retrofitted buildings demonstrated between 1.6% and 25.1% reduction in energy use. The project contributed use cases and feedback that helped inform evolution of the software tools and data formats that were combined for the first time in the BRICR project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Efficient screening of rare large pit anomalies on polished surfaces using a minimalist sampling scheme

Lawrence Livermore National Laboratory (LLNL) has made significant strides in generating clean energy through its inertial confinement fusion (ICF) experiments. These experiments rely on high-density carbon (HDC) coated shells to encapsulate the fusion fuel. The success of these experiments is heavily dependent on the surface quality of these shells, as even minor imperfections, such as deep pits, can negatively impact fusion yield. Ensuring the required smoothness involves an extensive surface-finishing process that spans approximately 20 stages, making it both time-intensive and resource-demanding. A critical challenge in this process is the need for high-resolution scans to detect rare deep pits, which can be costly and impractical if performed on every shell. This highlights the necessity of developing more efficient scanning methods to optimize time and cost without compromising accuracy. To address these challenges, we introduce a novel approach that employs the multivariate Dvoretzky–Kiefer–Wolfowitz (DKW) inequality to provide a probabilistic upper bound on the error in estimating pit distribution characteristics via a Kernel Density Estimator (KDE). This error bound enables efficient and reliable estimation of pit distribution characteristics at a specified statistical confidence level using a minimal number of surface scans. The integrated DKW-KDE approach was validated through surface-finishing experiments across two batches of HDC-coated shells, demonstrating consistent and robust performance across multiple stages of the surface-finishing experiments. The validation studies suggest that the integrated DKW-KDE approach achieves comparable accuracy in estimating the risk of deleterious large pits with six scans, thus conserving time and resources. Further evaluations show that performance remains consistent across batches and over multiple polishing stages. In conclusion, based on these findings, one can leverage the minimal-scan insights to strategically improve the bottleneck inspection process, thus enhancing the productivity and quality of shell polishing and similar challenging manufacturing processes.

Inertial confinement fusion↗

GreenThrift: Optimizing Carbon and Cost for Flexible Residential Loads

Reducing buildings’ carbon emissions is an important sustainability challenge. While scheduling flexible building loads has been previously used for a variety of grid and energy optimizations, carbon footprint reduction using such flexible loads poses new challenges since such methods need to balance both energy and carbon costs while also reducing user inconvenience from delaying such loads. This paper highlights the potential conflict between electricity prices and carbon emissions and the resulting trade-offs in carbon-aware and cost-aware load scheduling. To address this trade-off, we propose GreenThrift, a home automation system that leverages the scheduling capabilities of smart appliances and knowledge of future carbon intensity and cost to reduce both the carbon emissions and costs of flexible energy loads. At the heart of GreenThrift is an optimization technique that automatically computes schedules based on user configurations and preferences. We evaluate the effectiveness of GreenThrift using real-world carbon intensity data, electricity prices, and load traces from multiple locations and across different scenarios and objectives. Our results show that GreenThrift can replicate the offline optimal and retains 97% of the savings when optimizing the carbon emissions. Moreover, we show how GreenThrift can balance the conflict between carbon and cost and retain 95.3% and 85.5% of the potential carbon and cost savings, respectively.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Impact of installation faults in air conditioners and heat pumps in single-family homes on U.S. energy usage

To ensure that new residential air conditioners and heat pumps operate optimally, care must be taken during the initial system design and installation to avoid missteps that immediately degrade a system’s performance. Laboratory and simulation studies have quantified the impact that faults can have on capacity and efficiency. Field studies have demonstrated that installation-related faults are commonplace. However, the national impact of installation-related faults cannot be accurately estimated by only simulating a limited number of homes at several fault levels because of the variety of home characteristics and climates involved. In our analysis, we use an improved residential building stock simulation tool to predict the annual energy increase and additional utility costs resulting from two common installation faults: indoor airflow rate and refrigerant charge level. Our method considers the wide range of building characteristics and climate zones of the U.S. housing stock. We use existing field data to develop fault intensity probability distributions to inform our analysis. The study shows that these two faults result in approximately 20.7 TWh/y of additional energy use for central air conditioners and air-source heat pumps in U.S. single-family detached homes, which is a 9% increase over baseline (no-fault) usage, costing homeowners approximately $2.5 billion annually on utility bills. Air-source heat pumps are responsible for a disproportionate fraction of this energy use increase because of the larger number of operating hours compared to central air conditioners and the sensitivity of heating mode performance to the faults analyzed compared to cooling mode.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Application Potential of a Dew-Point Cooling Tower in Selected Energy Intensive Applications in Temperate Climate

In the article, the application potential of the dew-point cooling tower (DPCT) in selected energy-intensive applications in temperate climates was analyzed and discussed. The applications selected for analysis are power generation with natural gas turbines and chilled water air conditioning systems. The study is based on a mathematical model derived from a modified ε-NTU model. The model was validated against experimental results and showed satisfactory agreement with the experimental data. DPCT was compared with a typical cooling tower limited by the wet-bulb temperature (wet-bulb cooling tower, WBCT). The simulation results showed that DPCT is able to provide significant energy savings in energy-intensive applications; therefore, its application potential in temperate climates can be considered justified. In the case of gas turbines, DPCT was able to generate 2 to 10 percentage points more capacity than operating on outdoor air and 1.8 to 5 percentage points more than operating with WBCT. In the case of air conditioning systems, the system equipped with DPCT achieved EERs (energy efficiency ratios) higher by 1 to 7.2 compared to dry cooling and by 0.3 to 5.1 compared to systems equipped with WBCT. The annual energy savings obtained by the system with DPCT were 14.7 MWh compared to WBCT and 30 MWh compared to dry cooling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

RapidClave Technology Demonstrations-II Kevlar Composite Vehicle Floor

This Demonstration’s objective was to develop enhanced methods to produce a complex preform comprised of Kevlar fabric and thermoplastic adhesive and rapidly consolidate the preform into the desired functional shape of a vehicular floor protection system. The approach and outcome goals were to reduce the tooling cost by 50% and shorten the processing time by 50% using the RapidClave® to consolidate the part rather than an autoclave. UDRI partnered with O’Gara Armoring who currently produces one piece Kevlar floor composite laminates that offer enhanced impact resistance at lighter weights as compared to steel. These laminates are constructed by hand layup of 3000 denier K29 fiber in a plain weave architecture with a thermoplastic film adhesive layer manufactured by Barrday and will be referred to as semi-preg throughout this paper. The current process is labor intensive and costly. Current production rates are only about one per month but an improved process is expected to enable product growth. A 50% reduction in tooling cost was achieved through the use of additive tooling, and the RapidClave® process enabled a 60% reduction in cycle time. Further cost savings were realized through the implementation of a preforming process that resulted in a 65% reduction in labor hours. This new approach for manufacturing reinforced vehicle floors has been partially implemented by O’Gara with plans to continue work on additional vehicle models. The cost savings has potential for increasing O’Gara’s production to reach new markets, and lead to job creation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Demand Response in Industrial Facilities: Peak Electric Demand

The US Department of Energy’s (DOE’s) Better Buildings, Better Plants Program (Better Plants) is a voluntary energy efficiency leadership initiative for US manufacturers and water/wastewater entities. The program encourages organizations to commit to reducing the energy intensity of their US operations over a 10-year period, typically by 25%. Companies joining Better Plants are recognized by DOE for their leadership in implementing energy efficiency practices and for reducing their energy intensity. Better Plants Partners are assigned to a Technical Account Manager, who can help companies establish energy intensity baselines, develop energy management plans, and identify key resources and incentives from DOE, other federal agencies, states, utilities, and other organizations that can enable them to reach their goals. Better Plants Partners are expected to report their progress to DOE once a year. This involves establishing an energy intensity baseline upon joining the program and then tracking their progress over time. Demand Response in Industrial Facilities: Peak Electrical Demand is intended to help companies understand peak demand response programs offering by their local utility. Manufacturing industries can learn about time-varying rates and smart technologies they can use to help them reduce their energy bills. This guidance document is applicable to companies participating at either the program or challenge level. Although this guide is intended primarily to assist companies participating in Better Plants, the methodologies and guidance within the document are applicable to any organization interested in understanding peak demand response programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Decarbonization pathways for the residential sector in the United States

We report residential GHG emissions in the United States are driven in part by a housing stock where onsite fossil combustion is common, home sizes are large by international standards, energy efficiency potential is large and electricity generation in many regions is GHG intensive. In this analysis, we assess decarbonization pathways for the US residential sector to 2060, through 108 scenarios describing housing stock evolution, new housing characteristics, renovation levels and clean electricity. The lowest emission pathways involve very rapid decarbonization of electricity supply alongside extensive renovations to existing homes, including improving thermal envelopes and heat pump electrification of heating. Reducing the size and increasing the electrification of new homes provide further emission cuts and combining all strategies enables reductions of 91% between 2020 and 2050. The potential of individual mitigation strategies shows great regional variation. Reaching zero emissions will require simultaneous deployment of multiple strategies and greater reduction of embodied emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Optimizing enzymes for plastic upcycling using machine learning design and high throughput experiments

Plastic use is ubiquitous in the modern world, and polyethylene terephthalate (PET) is one of the most abundantly produced plastics (and the most highly produced polyester), with ~65 million metric tons manufactured annually. To the consumer, PET is likely most recognizable as the plastic used to make beverage bottles. Like many plastics, traditional mechanical or chemical means of PET deconstruction and upcycling are costly and inefficient. Because of these challenges, recycled plastic is generally of lower quality and is more expensive to produce than virgin plastic derived from petroleum. Ultimately, this results in most plastic ending up as waste. We view plastic waste as an underutilized resource which, with the development of more efficient and high-quality recycling processes, could (1) generate significant economic value while (2) decreasing petroleum usage and greenhouse gas emissions, as well as (3) minimizing its negative environmental and health impacts. Biocatalytic recycling, or biomanufacturing the basic building blocks of new plastic from plastic waste, is a promising approach to plastic reuse that complements existing recycling technologies. Recently, biological enzymes capable of breaking down PET have garnered significant attention as an attractive means of dealing with the plastic problem. These enzymes are currently undergoing pilot studies for implementation in industrial-scale enzyme-based recycling. However, there are significant limitations to current enzymes, including the need to perform costly pre-processing of the plastic waste before the enzymes are able to work. Further optimization of these enzymes is necessary to make these technologies competitive, and ultimately incentivise industry-wide adoption of this biology-based green recycling technology. n this work we demonstrate a means to design and generate performant biological enzymes, capable of efficiently deconstructing plastic waste. Specifically, we applied recent advances in artificial intelligence, machine learning, and statistical analysis to design new versions and discover natural enzymes capable of breaking down PET. We focused on optimizing key properties that are important for industrial-scale enzymatic recycling such as pH and thermotolerance. Normal testing of enzymatic plastic-deconstruction is extremely labor intensive and so through this work we also developed a robotic-assisted experimental pipeline capable of characterizing thousands of candidate enzymes. The results of this iterative, AI-guided, multi-discipline approach have led to increases in enzymatic breakdown of over 150X over starting enzymes. This work supports the rapidly developing and transformative field of biocatalytic solutions to environmental problems beyond the discovery and predictive understanding of enzymes for polymer recycling, and has wide implications for tackling numerous energy problems such as carbon capture and fixation (e.g., engineering carbon monoxide dehydrogenase and the rubisco-pathway), biomining (e.g., design of lanthanide-binding proteins) and biomanufacturing (e.g., lignin-deconstruction enzymes).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Using Earth Observations to Analyze Shoreline Changes and Understand the Effects of Sea Level Rise in Southern Puerto Rico

This summer, the Jobos Bay National Estuarine Research Reserve (JBNERR) partnered with NASA DEVELOP to evaluate water quality, mangrove forest extent, sea level rise, and land cover changes in Jobos Bay. DEVELOP is a national capacity building program that connects satellite data with early career scientists and decision makers. Through ten-week feasibility studies, DEVELOP partners with organizations that have the power to enact policy within their communities. Jobos Bay, in the southeast coast of Puerto Rico, has experienced intense hurricane seasons with increased seasonal storm surge, near-shore land use changes, and strong variations in precipitation and runoff. Through proposals, bi-weekly meetings, and frequent email communication, this cross-sector initiative provided valuable results for management decisions within the reserve. Using satellite observations to measure land use change, the team found that 17% of the reserve has shifted from land to water along the coast since 1997. Using Sentinel-2 the team was able to visualize turbidity, chlorophyll-a, and colored dissolved organic matter (CDOM) for Jobos Bay. While some large events such as Hurricane Maria were reflected in the turbidity analysis, chlorophyll-a and CDOM visualizations were limited by the influence of shallow-water bottom reflectance, particularly around reefs and within the Bay. The team also found that Jobos Bay lost 4.9 square kilometers of mangrove habitat over the past decade, calculated using Landsat 7 and 8 within Google Earth Engine. The results from this study will inform JBNERR and its community members on the regional impacts of sea level rise as well as serve as a baseline for future conservation efforts and research in the estuary.

Olivia Spencer↗

Thermally anisotropic building envelope for thermal management: finite element model calibration using field evaluation data

The thermally anisotropic building envelope (TABE) is an active building envelope that redistributes thermal loads in response to weather conditions and building energy demand. Conductive layers throughout the TABE distribute low-grade heat among hydronic loops, altering heat flow direction and intensity. Finite element models of TABE roof and wall panels were developed and calibrated using field evaluation data. The calibration results showed that heat flux differences between the experimental data and finite element models averaged –0.42% and 3.57%, with a maximum mean square error of 1.78 and 3.96 for roof and wall panels, respectively. A reduction in heat flux from the environment to the building living space over the entire testing period (weeks in July/August) was found to be 85% for roof panels and 335% (load reversed) for wall panels. Finally, these results indicate TABE can effectively harness low-grade thermal energy sources to achieve high energy efficiency and promote demand-side management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A Posteriori Study of a DNS Database Describing Super critical Binary-Species Mixing

Currently, the modeling of supercritical-pressure flows through Large Eddy Simulation (LES) uses models derived for atmospheric-pressure flows. Those atmospheric-pressure flows do not exhibit the particularities of high densitygradient magnitude features observed both in experiments and simulations of supercritical-pressure flows in the case of two species mixing. To assess whether the current LES modeling is appropriate and if found not appropriate to propose higher-fidelity models, a LES a posteriori study has been conducted for a mixing layer that initially contains different species in the lower and upper streams, and where the initial pressure is larger than the critical pressure of either species. An initially-imposed vorticity perturbation promotes roll-up and a double pairing of four initial span-wise vortices into an ultimate vortex that reaches a transitional state. The LES equations consist of the differential conservation equations coupled with a real-gas equation of state, and the equation set uses transport properties depending on the thermodynamic variables. Unlike all LES models to date, the differential equations contain, additional to the subgrid scale (SGS) fluxes, a new SGS term that is a pressure correction in the momentum equation. This additional term results from filtering of Direct Numerical Simulation (DNS) equations, and represents the gradient of the difference between the filtered pressure and the pressure computed from the filtered flow field. A previous a priori analysis, using a DNS database for the same configuration, found this term to be of leading order in the momentum equation, a fact traced to the existence of high-densitygradient magnitude regions that populated the entire flow; in the study, models were proposed for the SGS fluxes as well as this new term. In the present study, the previously proposed constantcoefficient SGS-flux models of the a priori investigation are tested a posteriori in LES, devoid of or including, the SGS pressure correction term. The present pressure-correction model is different from, and more accurate as well as less computationally intensive than that of the a priori study. The constant-coefficient SGS-flux models encompass the Smagorinsky (SMC), in conjunction with the Yoshizawa (YO) model for the trace, the Gradient (GRC) and the Scale Similarity (SSC) models, all exercised with the a priori study constant coefficients calibrated at the transitional state. The LES comparison is performed with the filtered- and-coarsened (FC) DNS, which represents an ideal LES solution. Expectably, an LES model devoid of SGS terms is shown to be considerably inferior to models containing SGS effects. Among models containing SGS effects, those including the pressure-correction term are substantially superior to those devoid of it. The sensitivity of the predictions to the initial conditions and grid size are also investigated. Thus, it has been discovered that, additional to the atmospheric-pressure models currently used, a new model is necessary to simulate supercritical-pressure flows. This model depends on the thermodynamic characteristics of the chemical species involved.

Bellan, Josette↗

Estimating irrigation water use from remotely sensed evapotranspiration data: Accuracy and uncertainties at field, water right, and regional scales

Irrigated agriculture is the dominant user of water globally, but most water withdrawals are not monitored or reported. As a result, it is largely unknown when, where, and how much water is used for irrigation. Here, we evaluated the ability of remotely sensed evapotranspiration (ET) data, integrated with other datasets, to calculate irrigation water withdrawals and applications in an intensively irrigated portion of the United States. We compared irrigation calculations based on an ensemble of satellite-driven ET models from OpenET with reported groundwater withdrawals from hundreds of farmer irrigation application records and a statewide flowmeter database at three spatial scales (field, water right group, and management area). At the field scale, we found that ET-based calculations of irrigation agreed best with reported irrigation when the OpenET ensemble mean was aggregated to the growing season timescale (bias = 1.6–4.9%, R 2 = 0.53–0.74), and agreement between calculated and reported irrigation was better for multi-year averages than for individual years. At the water right group scale, linking pumping wells to specific irrigated fields was the primary source of uncertainty. At the management area scale, calculated irrigation exhibited similar temporal patterns as flowmeter data but tended to be positively biased with more interannual variability. Disagreement between calculated and reported irrigation was strongly correlated with annual precipitation, and calculated and reported irrigation agreed more closely after statistically adjusting for annual precipitation. The selection of an ET model was also an important consideration, as variability across ET models was larger than the potential impacts of conservation measures employed in the region. From these results, we suggest key practices for working with ET-based irrigation data that include accurately accounting for changes in soil moisture, deep percolation, and runoff; careful verification of irrigated area and well-field linkages; and conducting application-specific evaluations of uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗

Energy and Comfort Impacts of High Performance Facades in Office Buildings

Building facades have a major effect on energy use, occupant comfort, and well-being. Yet adoption of high-performance facades is often slowed by limited data and unclear cost benefits. To address this gap, Oak Ridge National Laboratory, in collaboration with the Facade Tectonics Institute, conducted whole-building energy simulations to evaluate fenestration technologies for small and medium office buildings across three weather locations: hot (Tampa, Florida, weather zone 2A), mixed (New York, New York, weather zone 4A), and cold (Rochester, Minnesota, weather zone 6A). The simulations included parametric variations in window-to-wall ratio (30%–70%), U-values (0.1–1 Btu/h∙ft 2 ∙°F), solar heat gain coefficients (0.2–0.8), and solar control strategies and devices (e.g., interior shades and switchable glazing). Performance metrics included annual cooling, heating, and total heating, ventilation and air-conditioning (HVAC) energy use intensity, as well as nonenergy factors such as useful daylight illuminance, glare frequency, and thermal comfort during typical office hours (8 a.m.–6 p.m.). Results indicate that cooling energy consumption is most sensitive to solar heat gain coefficient (SHGC) in hot weather, whereas heating energy consumption is strongly influenced by U-value in cold weather. Total HVAC reflects these trade-offs, showing up to 60% difference for a window in total HVAC energy use intensity between different combinations of U-value and SHGC. Daylight dimming generally reduces cooling loads but can increase heating demands in colder locations. Interior solar shades improve useful daylight and reduce discomfort glare, whereas switchable glazing delivers the largest cooling reductions in hot weather but may increase heating loads in winter by limiting passive solar gains. Thermal comfort improves with lower window-to-wall ratios and lower SHGC in hot locations and with lower U-values in cold locations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗