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Transmittance of optical materials between 1050 and 3000 angstroms, noting effect of simulated high energy space environment
Increasing envelope facet albedos considerably reduces solar heat gain, thus yielding building cooling energy savings. Few studies have explored the potential benefits of utilizing cool coatings on building envelopes (“cool-coated buildings”) based on life-cycle cost analysis. A holistic approach integrating the field testing, building energy simulation, and a 20-year life-cycle-based optimization was developed to explore cool-coated building performance and the maximum net savings of optimal building envelope retrofit and design. Experimental results showed that applying cool coatings to a west wall of an office building in Chongqing, China reduced its exterior surface temperature by up to 9.3 °C in summer. Additionally, simulation results showed that in Chongqing, making the roof and walls cool could reduce annual HVAC electricity use by up to 11.9% in old buildings (with poorly insulated envelopes) and up to 5.9% in new buildings. Retrofitting old buildings with a cool roof provided the net savings per modified area with present values up to 42.8 CNY/m2; retrofitting a new building with a cool roof or cool walls was not cost-effective. Optimizing both envelope insulation and envelope albedo can achieve 5.6 times the net savings of optimizing the insulation only, and 1.6 times that of optimizing albedo only.
Heat transfer to slab foundations has remained an area of building science with poor understanding over the last three decades of energy efficiency research. This is somewhat surprising since the area of floors in single family homes is generally equal to wall, or windows or attics which have been extensively evaluated. Research that has been done has focused in the impact of slab on grade foundations and insulation schemes on heat losses associated with heating in predominantly heating dominated climates. Slab on grade construction is very popular in cooling-dominated southern states where it accounts for 77 percent of new home floors according to U.S. Census data in 2014. There is a widespread conception that tile flooring, as opposed to carpet, makes for a cooler home interior in warm climates. Empirical research is needed as building energy simulations such as DOE-2 and EnergyPlus rely on simplified models to evaluate these influences. BA-PIRC performed experiments over an entire year from 2014-2015 in FSEC's Flexible Residential Test Facilities (FRTF) intended to assess for the first time 1) slab on grade influence in a cooling dominated climate, and 2) how the difference in a carpeted vs. uncarpeted building might influence heating and cooling. Two identical side by side residential buildings were evaluated, the East with pad and carpet and the west with a bare slab floor. A highly detailed grid of temperature measurements were taken on the slab surface at various locations as well as at depths of 1, 2.5, 5, 10 and 20 feet.
The article gives results of structure formation in metallic materials after different types of deformation: tungsten after rolling, nickel after thermal cycling and nickel single crystal after rolling, aluminum single crystal after cyclic tension and aluminum alloys (D16, 2024-T3) after impulsive load. We used optical and electron microscopy methods for investigation of deformation structure formation and molecular dynamics method for computer modeling of the processes of plastic deformation (LAMMPS software). The following quantitative parameters were calculated during the simulation: energy of pair interaction, temperature, total cumulative pressure, stress tensor and pressure tensor. The study shows that the plastic flow of metallic materials with any initial structure at certain loading stage can be carried out by homogeneous mass transfer through channels with a liquid-like structure, from which follows that the dislocation mechanism of plastic deformation of crystals has a limited field of application. The main role has a synergistic structure formation in the conditions of distance from thermodynamic equilibrium. The relaxation processes proceed intensively after and during the process of destruction. The results of modeling have shown the possibility of forming channels of hydrodynamic flow without the participation of defects in the initial material.
This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.
The radial and local diffusion processes induced by various plasma waves govern the highly energetic electron dynamics in the Earth's radiation belts, causing distinct characteristics in electron distributions at various energies. In this study, we present our simulation results of the energetic electron evolution during a geomagnetic storm using the University of California, Los Angeles 3-D diffusion code. Following the plasma sheet electron injections, the electrons at different energy bands detected by the Magnetic Electron Ion Spectrometer (MagEIS) and Relativistic Electron Proton Telescope (REPT) instruments on board the Van Allen Probes exhibit a rapid enhancement followed by a slow diffusive movement in differential energy fluxes, and the radial extent to which electrons can penetrate into depends on energy with closer penetration toward the Earth at lower energies than higher energies. We incorporate radial diffusion, local acceleration, and loss processes due to whistler mode wave observations to perform a 3-D diffusion simulation. Here, our simulation results demonstrate that chorus waves cause electron flux increase by more than 1 order of magnitude during the first 18 h, and the subsequent radial extents of the energetic electrons during the storm recovery phase are determined by the coupled radial diffusion and the pitch angle scattering by EMIC waves and plasmaspheric hiss. The radial diffusion caused by ULF waves and local plasma wave scattering are energy dependent, which lead to the observed electron flux variations with energy dependences. Lastly, this study suggests that plasma wave distributions in the inner magnetosphere are crucial for the energy-dependent intrusions of several hundred keV to several MeV electrons.
Moisture buffering of building materials has a significant impact on the building's indoor humidity, and building energy simulations need to model this buffering to accurately predict the humidity. Researchers requiring a simple moisture-buffering approach typically rely on the effective-capacitance model, which has been shown to be a poor predictor of actual indoor humidity. This paper describes an alternative two-layer effective moisture penetration depth (EMPD) model and its inputs. While this model has been used previously, there is a need to understand the sensitivity of this model to uncertain inputs. In this paper, we use the moisture-adsorbent materials exposed to the interior air: drywall, wood, and carpet. We use a global sensitivity analysis to determine which inputs are most influential and how the model's prediction capability degrades due to uncertainty in these inputs. We then compare the model's humidity prediction with measured data from five houses, which shows that this model, and a set of simple inputs, can give reasonable prediction of the indoor humidity.
Geothermal deep direct use (DDU) has potential across a wide swath of the United States but is underutilized due to challenging project economics associated with developing a deep geothermal resource for a large-scale and variable heat demand. The National Renewable Energy Laboratory (NREL) and University of Oklahoma evaluated the feasibility of a geothermal district heating (GDH) and cooling system in two schools and 250 houses by utilizing existing oil and gas (O&G) wells in Tuttle, Oklahoma. Heating and cooling demand in the two schools and a typical single-family house were modeled using EnergyPlus building energy simulation software. The modeling results indicated that annual heating demand in two schools and 250 houses is approximately 2.61 GWhth, and cooling demand in the two schools is approximately 2.65 GWhth. In this scope, the techno-economic analysis (TEA) was conducted using the GEOPHIRES tool combined with the TOUGH2 reservoir simulator. The reservoir performance, including geothermal heat production capacity, was modeled by the reservoir simulator TOUGH2. Then, levelized cost of heat (LCOH) was calculated using GEOPHIRES version 3.0, which includes new features such as hourly heat load optimization and peak performance evaluation. Geothermal reservoir temperature was estimated as 90.5 degrees C at a total depth of 3.3 km by the regional average temperature gradient of 22.8 degrees C/km and validated by cation geothermometer calculations. Four production scenarios with two different well configurations and two different heat load profiles have been developed for well flow rates ranging between 3.1 kg/s and 9.3 kg/s. The LCOH of the district heating and cooling system was calculated between $95 and $210/MWh ($28/MMBtu to $62/MMBtu) for four different production scenarios. Typical natural gas prices for residential customers in Oklahoma have ranged from 9 to 19 $/MMBtu over the past decade, which indicates a challenge for deployment of such a GDH system.
Geothermal deep direct use (DDU) has potential across a wide swath of the United States but is underutilized due to challenging project economics associated with developing a deep geothermal resource for a large-scale and variable heat demand. The National Renewable Energy Laboratory (NREL) and University of Oklahoma evaluated the feasibility of a geothermal district heating (GDH) and cooling system in two schools and 250 houses by utilizing existing oil and gas (O&G) wells in Tuttle, Oklahoma. Heating and cooling demand in the two schools and a typical single-family house were modeled using EnergyPlus building energy simulation software. The modeling results indicated that annual heating demand in two schools and 250 houses is approximately 2.61 GWhth, and cooling demand in the two schools is approximately 2.65 GWhth. In this scope, the techno-economic analysis (TEA) was conducted using the GEOPHIRES tool combined with the TOUGH2 reservoir simulator. The reservoir performance, including geothermal heat production capacity, was modeled by the reservoir simulator TOUGH2. Then, levelized cost of heat (LCOH) was calculated using GEOPHIRES version 3.0, which includes new features such as hourly heat load optimization and peak performance evaluation. Geothermal reservoir temperature was estimated as 90.5 degrees C at a total depth of 3.3 km by the regional average temperature gradient of 22.8 degrees C/km and validated by cation geothermometer calculations. Four production scenarios with two different well configurations and two different heat load profiles have been developed for well flow rates ranging between 3.1 kg/s and 9.3 kg/s. The LCOH of the district heating and cooling system was calculated between $95 and $210/MWh ($28/MMBtu to $62/MMBtu) for four different production scenarios. Typical natural gas prices for residential customers in Oklahoma have ranged from 9 to 19 $/MMBtu over the past decade, which indicates a challenge for deployment of such a GDH system.
The energy performance of residential buildings significantly depends on the building occupants’ behavior, which can be highly variable. When the heating, ventilation and air conditioning (HVAC) system is controlled based on the presence or absence of occupants in a building, occupant behavior is of even further importance to its energy performance. In current practice, building energy simulation tools generally use a single occupancy profile to represent the building’s occupancy schedule, the schedule of which is considered to be the same, regardless of the type of household being modeled. Thus, there is significant potential for improvement to allow for more flexibility and accuracy in calculation of occupancy. The objective of this study is to assess the variations in the typical types of occupancy schedules followed by the U.S. population using cluster analysis. American Time Use Survey data, which statically represents the overall U.S. population’s activities, across 12 years (2006–2017), is used. The ATUS data is segregated into smaller groups based on age and weekday/weekend, then divided into activities that are considered “at home” and “away from home”, which are mapped to the presence or non-presence of occupants in the home. Cluster analysis is then used to identify common types of occupancy schedule patterns for each age group. Three main types of patterns are obtained from cluster analysis for each age group, which together represent approximately 88% of people in the United States. The output of the cluster analysis is further analyzed to evaluate the variation in characteristics, including the number of times leaving home, time of day when leaving the home, and the timespan of absence from the home. The results of this study provide detailed insights on how typical occupants in the United States spend their time in residential spaces which can be used to create occupancy profiles for residential buildings. Finally, these occupancy profiles could be utilized inform an assessment of the energy use impact of occupancy-based controls of energy consuming systems and technologies.
The study investigates the economic and technical performance of a novel dual-source heat pump (DSHP) compared with that of air-source heat pumps (ASHPs) and ground-source heat pumps (GSHPs). The DSHP can use both ambient air and the ground as a heat source or heat sink. It uses ambient air when its temperature is favorable for efficient heat pump operation. When the ambient temperature is too hot or cold, the ground source is used to retain high-efficiency heat pump operation. Since the DSHP can alternately use either the ground heat exchanger (GHE) or ambient air to meet the thermal load, the required size of GHE can be smaller than those of GSHPs. This study models the DSHP using a whole building energy simulation tool (EnergyPlus) coupled with a Python plug-in and Heat Pump Design Model (HPDM) to simulate its heating and cooling performance for a typical single-family home in 15 US climate zones. The required GHE size of the DSHP system is determined through simulations and compared with that of GSHPs. DSHP deployment can reduce electricity use compared to ASHPs, especially in cold climates where it shows a reduction of around 50%. When compared to GSHPs, DSHPs use 20%–40% more electricity in warm climates but consume around the same amount in moderate and colder climates. Since the DSHP can use air source when the ambient temperature is mild, the GHE size needed for the DSHP is about 40% less than that needed for GSHPs in hot climates and about 25% less in cold climates. In conclusion, the life cycle cost analysis shows that the DSHP is economically more feasible than ASHPs in colder regions and economically more feasible than GSHPs in hot and cold regions.
Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.
High-resolution imaging using Transmission Electron Microscopy (TEM) is essential for applications such as grain boundary analysis, microchip defect characterization, and biological imaging. However, TEM images are often compromised by electron energy spread and other factors. In TEM mode, where the objective and projector lenses are positioned downstream of the sample, electron–sample interactions cause energy loss, which adversely impacts image quality and resolution. This study introduces a simulation tool to estimate the electron energy loss spectrum (EELS) as a function of sample thickness, covering electron beam energies from 300 keV to 3 MeV. Leveraging recent advances in MeV-TEM/STEM technology, which includes a state-of-the-art electron source with 2-picometer emittance, an energy spread of 3 × 10 -5 , and optimized beam characteristics, we aim to minimize energy spread. By integrating EELS capabilities into the BNL Monte Carlo (MC) simulation code for thicker samples, we evaluate electron beam parameters to mitigate energy spread resulting from electron–sample interactions. Based on our simulations, we propose an experimental procedure for quantitively distinguishing between elastic and inelastic scattering. The findings will guide the selection of optimal beam settings, thereby enhancing resolution for nanoimaging of thick biological samples and microchips.
The increasing interest in retrofitting of existing buildings is motivated by the need to make a major contribution to enhancing building energy efficiency and reducing energy consumption and CO2 emission by the built environment. This paper examines the relevance of calibration in model-based analysis to support decision-making for energy and carbon efficiency retrofits of individual buildings and portfolios of buildings. The authors formulate a set of real retrofit decision-making situations and evaluate the role of calibration by using a case study that compares predictions and decisions from an uncalibrated model with those of a calibrated model. The case study illustrates both the mechanics and outcomes of a practical alternative to the expert- and time-intense application of dynamic energy simulation models for large-scale retrofit decision-making under uncertainty.
This paper presents an evaluation of the potential energy savings from adding advanced control to existing packaged air conditioners. Advanced control options include air-side economizer, multi-speed fan control, demand control ventilation and staged cooling. The energy and cost savings from the different control strategies individually and in combination are estimated using the EnergyPlus detailed energy simulation program for four building types, namely, a small office building, a stand-alone retail building, a strip mall building and a supermarket building. For each of the four building types, the simulation was run for 16 locations covering all 15 climate zones in the U.S. The maximum installed cost of a replacement controller that provides acceptable payback periods to owners is estimated.
Community-scale building retrofits are not merely scaled-up versions of single-building retrofits. They involve complex challenges, such as reconciling individual interests with collective goals and managing the dynamic interplay between buildings through mechanisms like power grids and social connections. Internet of Things (IoT) connectivity holds the potential to leverage these interplays to balance individual and collective interests effectively in smart communities. One critical aspect of this interplay is information diffusion, which shapes how retrofit decisions spread among neighbors, influencing individual choices and ultimately impacting community-level retrofit outcomes. In other words, IoT-based smart devices automatically push tailored retrofit notifications to homeowners, which completely changes the format of information diffusion in the future. To investigate this influence by such information diffusion, the study used CityBES to simulate energy performance for different retrofits and applied an information diffusion model to analyze how decisions spread in a networked community of 192 buildings. The diffusion process was modeled on a weighted, directed network, capturing the dynamics of information flow and decision-making across 16 scenarios. Individual retrofit benefits were evaluated through payback years, while community-level retrofit outcomes were assessed using greenhouse gas (GHG) emission reductions. The results demonstrate that easier information diffusion among neighbors encourages households to prioritize retrofit measures that align with the majority’s optimal choices, even at the expense of individual financial benefits. In this case, such collective prioritization enhanced community-level retrofit performance, increasing GHG emission reductions by up to 29.4 %. However, this improvement came with trade-offs, as the average payback period for households extended by approximately 1.74 years. These findings highlight the potential of IoT-based information diffusion in future smart communities to coordinate individual interests with collective goals, ultimately accelerating community-level building retrofits.
Ionic liquids (ILs) and porous frameworks are commonly employed in gas separation, but rarely together. To leverage their combined advantages, we designed a novel system comprising an ionic liquid inside a charged porous-polymer framework. A positively charged ionic porous aromatic framework was impregnated with an ionic liquid to confine the cations and anions inside the polymeric framework via electrostatic interactions. Molecular dynamics and free energy simulations were performed to determine the solubility, diffusivity, and hence permeability of CO 2 and CH 4 through the composite material. We found that the polymeric framework introduces nanoscale space in the confined IL, improving gas solubility, while the IL promotes gas transport in the porous structure. Here, the confinement impacts CO 2 and CH 4 very differently regarding their solubility and diffusivity. Through tuning of the ionic-liquid loading, both high CO 2 permeability and high CO 2 /CH 4 selectivity could be achieved, overcoming the Robeson upper bound. This work highlights the advantages of confined ionic liquids in an ionic porous-polymer framework in enhancing selective gas separation.
Here, the emergence and dissemination of bacterial resistance to β-lactam antibiotics via β-lactamase enzymes is a serious problem in clinical settings, often leaving few treatment options for infections resulting from multidrug-resistant superbugs. Understanding the catalytic mechanism of β-lactamases is important for developing strategies to overcome resistance. Binding of a substrate in the active site of an enzyme can alter the conformations and p K a s of catalytic residues, thereby contributing to enzyme catalysis. Here we report X-ray and neutron crystal structures of the class A Toho-1 β-lactamase in the apo form and an X-ray structure of a Michaelis-like complex with the cephalosporin antibiotic cefotaxime in the active site. Comparison of these structures reveals that substrate binding induces a series of changes. The side chains of conserved residues important in catalysis, Lys73 and Tyr105, and the main chain of Ser130 alter their conformations, with Nζ of Lys73 moving closer to the position of the conserved catalytic nucleophile Ser70. This movement of Lys73 closer to Ser70 is consistent with proton transfer between the two residues prior to acylation. In combination with the tightly bound catalytic water molecule located between Glu166 and the position of Ser70, the enzyme is primed for catalysis when Ser70 is activated for nucleophilic attack of the β-lactam ring. Quantum mechanical/molecular mechanical (QM/MM) free energy simulations of models of the wild-type enzyme show that proton transfer from the Nζ of Lys73 to the Oε2 atom of Glu166 is more thermodynamically favorable than when it is absent. Taken together, our findings indicate that substrate binding enhances the favorability of the initial proton transfer steps that precede the formation of the acyl-enzyme intermediate.