The Fidelity of Measurement-Based Quantum Computation under a Boson Environment

We investigate the fidelity of the measurement-based quantum computation (MBQC) when it is coupled with boson environment, by measuring cluster state fidelity and gate fidelity. Two different schemes of cluster state preparation are studied. In the Controlled-Z (CZ) creation scheme, cluster states are prepared by entangling all qubits in $|+\rangle$ state with CZ gates on all neighboring sites. The fidelity shows an oscillation pattern over time evolution. The influence of environment temperature is evaluated, and suggestions are given to enhance the performance of MBQC realized in this way. In the Hamiltonian creation scheme, cluster states are made by cooling a system with cluster Hamiltonians, of which ground states are cluster states. The fidelity sudden drop phenomenon is discovered. When the coupling is below a threshold, MBQC systems are highly robust against the noise. Our main environment model is the one with a single collective bosonic mode.Comment: 13 pages, 16 figure

Fourier neural operator for real-time simulation of 3D dynamic urban microclimate

Global urbanization has underscored the significance of urban microclimates for human comfort, health, and building/urban energy efficiency. They profoundly influence building design and urban planning as major environmental impacts. Understanding local microclimates is essential for cities to prepare for climate change and effectively implement resilience measures. However, analyzing urban microclimates requires considering a complex array of outdoor parameters within computational domains at the city scale over a longer period than indoors. As a result, numerical methods like Computational Fluid Dynamics (CFD) become computationally expensive when evaluating the impact of urban microclimates. The rise of deep learning techniques has opened new opportunities for accelerating the modeling of complex non-linear interactions and system dynamics. Recently, the Fourier Neural Operator (FNO) has been shown to be very promising in accelerating solving the Partial Differential Equations (PDEs) and modeling fluid dynamic systems. In this work, we apply the FNO network for real-time three-dimensional (3D) urban wind field simulation. The training and testing data are generated from CFD simulation of the urban area, based on the semi-Lagrangian approach and fractional stepping method to simulate urban microclimate features for modeling large-scale urban problems. Numerical experiments show that the FNO model can accurately reconstruct the instantaneous spatial velocity field. We further evaluate the trained FNO model on unseen data with different wind directions, and the results show that the FNO model can generalize well on different wind directions. More importantly, the FNO approach can make predictions within milliseconds on the graphics processing unit, making real-time simulation of 3D dynamic urban microclimate possible

Metamodel Development for Predicting Hygrothermal Performance of Wood-Frame Wall under Rain Leakage

In recent years, stochastic modeling has been increasingly applied to investigate the uncertainties of input parameters in hygrothermal simulation and the moisture damage risks of building envelopes. Generally, stochastic modeling requires hundreds or even thousands of simulations to take into account the uncertainties of input parameters, which is computationally intensive and timeconsuming. This paper aims to apply polynomial and neural network metamodel as a substitute for the traditional hygrothermal model, to predict the hygrothermal performance of building envelopes. In the previous study carried out by the authors, stochastic simulations have been performed based on the traditional hygrothermal model, to investigate the hygrothermal performance of wood-frame walls under different rain leakage levels. The material properties and rain deposition factors were considered as stochastic variables, and stochastic simulations were performed under three rain leakage scenarios: 1%, 0.5% and 0.1% of wind-driven rain. In this paper, the stochastic inputs (the hygric material properties and rain deposition factor) and outputs (the maximum moisture content and mold growth index over a 5-year period of the simulation) of a conventional 2×6 wood-frame wall are used to develop the metamodels through polynomial regression and neural network methods. The metamodels are developed for each rain leakage scenario, and the stochastic data of the three rain leakage scenarios are aggregated together to train another metamodel. It is found that the metamodels generally perform well to predict the maximum moisture content and mold growth index. The metamodels for low rain leakage scenarios are better than those for high rain leakage scenarios and the neural network metamodel is more accurate than polynomial metamodel for high rain leakage scenarios, i.e. 1% of rain leakage

Evaluating Wind-driven Natural Ventilation Potential for Early Building Design

Natural ventilation is widely applied in buildings considering its potential of improving indoor air quality and saving building energy costs. However, to evaluate its viability and determine the ventilation rates quickly and relatively accurately during early design stage is challenging. This paper explores a fast and accurate evaluation approach in the form of empirical equations to estimate the ventilation rate and potential of wind-driven natural ventilation. By using computational fluid dynamics (CFD) with results validated for both cross and single natural ventilation strategies, this study conducted a series of simulations to determine critical ventilation coefficients for the empirical equations as functions of wind direction, speed and building height. The proposed evaluation approach could help architects and engineers to evaluate the viability of natural ventilation during early building design. This approach was also demonstrated to evaluate the potential of natural ventilation in 65 cities of North America so a series of natural ventilation potential maps were generated for a better understanding of natural ventilation potential in different climates and for the climate-conscious design of buildings in North America

Parametric study of air curtain door aerodynamics performance based on experiments and numerical simulations

Air curtains have been widely used to reduce infiltration through door openings and save heating/cooling energy in different types of buildings. Previous studies have found that there exist three aerodynamics conditions: optimum condition (OC), inflow break-through (IB), and outflow break-through (OB) conditions, which are important for categorizing air curtain performance subject to such key parameters including supply speed and angle, and presence of a person during an actual operation. However, few studies have focused on the effects of these parameters on air curtain performance in terms of resisting infiltration and reducing exfiltration. This research presents a parametric study of air curtain performance based on reduced-scale experiments and full-scale numerical simulations. It was found that increasing air curtain supply angle improves air curtain performance when it is operated under the OC and IB conditions but creates excessive exfiltration under the OB condition. Increasing supply speed of air curtain generally improves the air curtain performance whereas this improvement deteriorates with the increase of supply angle under the OB condition. The presence of person, either directly under or below the air curtain, almost has no effect on the infiltration/exfiltration during the OC condition. Moreover, the person in the doorway can block airflow from both directions, contributing to less infiltration under the IB condition and less exfiltration under the OB condition than without the person. This study provides valuable insights into air curtain aerodynamics performance under different operational conditions and key contributing parameters

A Review on the Impact of Building Design and Operation on Buildings Cooling Loads

Energy consumption in buildings is considerably high in areas of hot and humid climates due to its association with high cooling loads. Electricity grids are highly affected by the consumption of cooling systems like air-conditioning and large refrigeration facilities, which significantly impact the economic and environmental sectors. As building design and operating parameters influence the cooling demand in the building, it is believed the root cause of the problem may be detected at an early building design stage. Thus, this review identifies the building design parameters that impact the cooling loads in buildings that are geographically restricted to countries with hot and humid climates. The building’s design characteristics are classified into four main categories: glass characteristics, wall characteristics, building orientation and dimensions (BO & D), and building cooling system. The review was conducted over high-rise and low-rise buildings. Annual energy requirements (in some cases overlapping with electricity consumption), annual cooling loads, and peak cooling loads are the three forms in which energy demand reductions in buildings are represented. It is found that maximum annual cooling load savings are obtained through cooling systems, followed by wall characteristics, then glass characteristics, with the least for BO & D, with maximum reductions of up to 61%, 59%, 55%, and 21%, respectively. As for the peak cooling load reductions, wall characteristics, cooling systems, and glass characteristics had almost the same average values of 18.7%, 15.2%, and 17.2%, respectively, while BO & D are not reported due to the incomparable number of case studies. The parameters that have the most influence on reductions in peak cooling loads are wall and glass characteristics. In general, savings that are associated with wall characteristics are more significant for low-rise buildings than for high-rise buildings, while the latter is more influenced by glass characteristics. This is a reasonable conclusion since high-rise buildings, in general, acquire higher window-to-wall ratios than the former. In general, most studies considered glass characteristics, while fewer studies considered BO & D. This review has shown various aspects that are vital in studying building cooling load demand and its related energy performance.This paper was made possible by an National Priorities Research Programs (NPRP) award (#NPRP11S-1208-170073) from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors

Effects of pool size and spacing on burning rate and flame height of two square heptane pool fires

The interaction of multiple pool fires might lead to higher burning rate and flame height than single pool fire, raising the possibility of fire ignition and flame spread and increasing the risks to people, buildings and environment. To quantify the burning rate and flame height of multiple pool fires from the view of physical mechanism, this paper presents an experimental study on two identical square pool fires. Heptane was used as fuel. The pool size and spacing were varied. Results showed that both the burning rate and flame height change non-monotonically with spacing. From the view of air entrainment, a correlation for the flame height of two pool fires is developed involving pool size, spacing and the flame height of zero spacing. The comparison with experimental results shows that the developed correlation is suitable for two heptane or propane fires. A theoretical study based on energy balance at one of the pool surfaces is performed to evaluate the burning rate of two fires, which is finally expressed as a function of pool size, spacing, burning rate and the flame height of single fire. The proposed model is validated using the experimental and literature data, which presents a reasonable reliability

Orthogonal printing of uniform nanocomposite monolayer and oriented organic semiconductor crystals for high-performance nano-crystal floating gate memory

Inkjet printing is of great interest in the preparation of optoelectronic and microelectronic devices due to its low cost, low process temperature, versatile material compatibility, and ability to precisely manufacture multi-layer devices on demand. However, interlayer solvent erosion is a typical problem that limits the printing of organic semiconductor devices with multi-layer structures. In this study, we proposed a solution to address this erosion problem by designing polystyrene-block-poly(4-vinyl pyridine)-grafted Au nanoparticles (Au@PS-b-P4VP NPs). With a colloidal ink containing the Au@PS-b-P4VP NPs, we obtained a uniform monolayer of Au nano-crystal floating gates (NCFGs) embedded in the PS-b-P4VP tunneling dielectric (TD) layer using direct-ink-writing (DIW). Significantly, PS-b-P4VP has high erosion resistance against the semiconductor ink solvent, which enables multi-layer printing. An active layer of semiconductor crystals with high crystallinity and well-orientation was obtained by DIW. Moreover, we developed a strategy to improve the quality of the TD/semiconductor interface by introducing a polystyrene intermediate layer. We show that the NCFG memory devices exhibit a low threshold voltage (100 cycles), and long-term retention (>10 years). This study provides universal guidance for printing functional coatings and multi-layer devices