Zhiqiang (John) Zhai

On approaches to couple energy simulation and computational fluid dynamics programs

Energy simulation (ES) and computational fluid dynamics (CFD) can play an important role in building design by providing complementary information of the building performance. However, separate applications of ES and CFD usually cannot give an accurate prediction of building thermal and flow behavior due to the assumptions used in the applications. An integration of ES and CFD can eliminate many of these assumptions, since the information provided by ES and CFD is complementary. This paper describes some efficient approaches to integrate ES and CFD, such as static and dynamic coupling strategies, in order to bridge the discontinuities of time-scale, spatial resolution and computing speed between ES and CFD programs. This investigation further demonstrates some of the strategies through two examples by using the EnergyPlus and MIT-CFD programs.

Modeling and measuring the nocturnal drainage flow in a high‐elevation, subalpine forest with complex terrain

[1] The nocturnal drainage flow of air causes significant uncertainty in ecosystem CO2, H2O, and energy budgets determined with the eddy covariance measurement approach. In this study, we examined the magnitude, nature, and dynamics of the nocturnal drainage flow in a subalpine forest ecosystem with complex terrain. We used an experimental approach involving four towers, each with vertical profiling of wind speed to measure the magnitude of drainage flows and dynamics in their occurrence. We developed an analytical drainage flow model, constrained with measurements of canopy structure and SF6 diffusion, to help us interpret the tower profile results. Model predictions were in good agreement with observed profiles of wind speed, leaf area density, and wind drag coefficient. Using theory, we showed that this one-dimensional model is reduced to the widely used exponential wind profile model under conditions where vertical leaf area density and drag coefficient are uniformly distributed. We used the model for stability analysis, which predicted the presence of a very stable layer near the height of maximum leaf area density. This stable layer acts as a flow impediment, minimizing vertical dispersion between the subcanopy air space and the atmosphere above the canopy. The prediction is consistent with the results of SF6 diffusion observations that showed minimal vertical dispersion of nighttime, subcanopy drainage flows. The stable within-canopy air layer coincided with the height of maximum wake-to-shear production ratio. We concluded that nighttime drainage flows are restricted to a relatively shallow layer of air beneath the canopy, with little vertical mixing across a relatively long horizontal fetch. Insight into the horizontal and vertical structure of the drainage flow is crucial for understanding the magnitude and dynamics of the mean advective CO2 flux that becomes significant during stable nighttime conditions and are typically missed during measurement of the turbulent CO2 flux. The model and interpretation provided in this study should lead to research strategies for the measurement of these advective fluxes and their inclusion in the overall mass balance for CO2 at this site with complex terrain.

Solution characters of iterative coupling between energy simulation and CFD programs

Energy simulation (ES) and computational fluid dynamics (CFD) provide important and complementary information for building energy and indoor environment designs. A coupled ES and CFD simulation can eliminate many assumptions employed in the separate ES and CFD computations and thus provide more accurate results. Through theoretical analysis and numerical experiment, this study verified that the solution of a coupled ES and CFD simulation does exist and is unique. The investigation also concluded that a converged and stable simulation can be achieved with three different data coupling methods. The study has further developed an improved iteration and control algorithm for the coupled simulation.

Application of Computational Fluid Dynamics in Building Design: Aspects and Trends

Computational fluid dynamics (CFD), as the most sophisticated airflow modelling method, can simultaneously predict airflow, heat transfer and contaminant transportation in and around buildings. This paper introduces the roles of CFD in building design, demonstrating its typical application in designing a thermally conformable, healthy and energy-efficient building. The paper discusses the primary challenges of using CFD in the building modelling and design practice. Furthermore, it analyses the developing trends in applying CFD to building design, by thoroughly reviewing the literatures in all the proceedings of the International Conference on Building Simulation, one of the most influential symposiums in the building simulation field.

Location identification for indoor instantaneous point contaminant source by probability-based inverse Computational Fluid Dynamics modeling.

UNLABELLED Indoor pollutions jeopardize human health and welfare and may even cause serious morbidity and mortality under extreme conditions. To effectively control and improve indoor environment quality requires immediate interpretation of pollutant sensor readings and accurate identification of indoor pollution history and source characteristics (e.g. source location and release time). This procedure is complicated by non-uniform and dynamic contaminant indoor dispersion behaviors as well as diverse sensor network distributions. This paper introduces a probability concept based inverse modeling method that is able to identify the source location for an instantaneous point source placed in an enclosed environment with known source release time. The study presents the mathematical models that address three different sensing scenarios: sensors without concentration readings, sensors with spatial concentration readings, and sensors with temporal concentration readings. The paper demonstrates the inverse modeling method and algorithm with two case studies: air pollution in an office space and in an aircraft cabin. The predictions were successfully verified against the forward simulation settings, indicating good capability of the method in finding indoor pollutant sources. The research lays a solid ground for further study of the method for more complicated indoor contamination problems. PRACTICAL IMPLICATIONS The method developed can help track indoor contaminant source location with limited sensor outputs. This will ensure an effective and prompt execution of building control strategies and thus achieve a healthy and safe indoor environment. The method can also assist the design of optimal sensor networks.

Experimental verification of tracking algorithm for dynamically-releasing single indoor contaminant

Identifying contaminant sources in a precise and rapid manner is critical to indoor air quality (IAQ) management as disclosed source information can facilitate proper and effective IAQ controls in environments with airborne infection, fire smoke and chemical pollutant release etc. Probability-based inverse modeling method was shown feasible for locating single instantaneous source in IAQ events. To tackle more realistic sources of continuous release, this paper advances the method to identify continuously releasing single contaminant source. The study formulates a suite of inverse modeling algorithms that can promptly locate dynamic source with known release time for IAQ events. Two field experiments are employed to verify the prediction: one in a multi-room apartment and the other in a hospital ward which was involved in a SARS outbreak in Hong Kong in 2003. The developed algorithms promptly and accurately identify the source locations in both cases.

Performance evaluation of network airflow models for natural ventilation

The potential for natural ventilation in building design to reduce associated building cooling and fan energy consumption makes it an area of great interest. However, its increased use is dependent on the availability of accurate modeling tools to predict its impact on a given building design. Network airflow models have been developed to predict airflows within buildings and between indoor and outdoor, which usually require user-supplied thermal conditions in a space. Because of the dependency between airflow and space temperature in naturally ventilated buildings, network airflow models need to be incorporated into whole building energy simulation models. This study reviews existing network models that may be used for natural ventilation design and investigates the capability and accuracy of four prevalent network airflow models to simulate a wide range of natural ventilation scenarios, with varying combinations of geometry and natural ventilation driving forces. Comparison of predictions with measurements obtained from literature shows that the tools are generally able to predict natural ventilation rates within 35% except for the wind-driven single-sided ventilation case. The article indicates some significant needs for improvement of the network airflow model for natural ventilation applications.

State-of-the-art methods for inverse design of an enclosed environment

Abstract The conventional design of enclosed environments uses a trial-and-error approach that is time consuming and may not meet the design objective. Inverse design concept uses the desired enclosed environment as the design objective and inversely determines the systems required to achieve the objective. This paper discusses a number of backward and forward methods for inverse design. Backward methods, such as the quasi-reversibility method, pseudo-reversibility method, and regularized inverse matrix method, can be used to identify contaminant sources in an enclosed environment. However, these methods cannot be used to inversely design a desired indoor environment. Forward methods, such as the CFD-based adjoint method, CFD-based genetic algorithm method, and proper orthogonal decomposition method, show the promise in the inverse design of airflow and heat transfer in an enclosed environment. The CFD-based adjoint method is accurate and can handle many design parameters without increasing computing costs, but the method may find a locally optimal design that could meet the design objective with constrains. The CFD-based genetic algorithm method, on the other hand, can provide the global optimal design that can meet the design objective without constraints, but the computing cost can increase dramatically with the number of design parameters. The proper orthogonal decomposition method is a reduced-order method that can significantly lower computing costs, but at the expense of reduced accuracy. This paper also discusses the possibility to reduce the computing costs of CFD-based design methods.

Identification of Appropriate CFD Models for Simulating Aerosol Particle and Droplet Indoor Transport

Computational fluid dynamics (CFD) has been widely used to predict indoor particle and droplet transport and dispersion. CFD solves simplified conservative equations that describe the major characteristics of particle and droplet indoor movement, along with the flow governing equations. This paper reviews the principles of three prevalent CFD models for indoor particle and droplet simulation: the lazy particle model, isothermal particle model and vaporizing droplet model, with a focus on the disparities between these models. The study verifies that different particle and droplet models provide distinct simulation results in which size of particle and droplet is a critical factor. To justify proper application of these models for particles and droplets with different sizes, the paper theoretically analyzes the Lagrangian transport equations for particle and droplet and identifies two crucial time numbers — particle momentum response time and evaporation lifetime. Upon these numbers, two new indices have been introduced — Stokes number and evaporation effectiveness number, which can be used as simple criteria to guide the model selection. The case studies confirm the value of the indices and provide the rules of thumb for determining appropriate CFD models for particle and droplet indoor transport under typical room conditions.

Application of coarse-grid computational fluid dynamics on indoor environment modeling: Optimizing the trade-off between grid resolution and simulation accuracy

Computational fluid dynamics has been playing an important role in building design and indoor environment study for decades. However, the computing cost of grid-independent computational fluid dynamics prevents its application from real-time simulation in many areas, such as building emergency evacuation and hourly-based energy simulation. Although coarse-grid computational fluid dynamics has the potential of being as fast as or faster than real time and can provide valuable information for decision making, the credibility of coarse-grid computational fluid dynamics results is questioned due to the unknown error scale it brings. This study investigates the grid-induced error, evaluates the potential computing cost saving by using a coarse grid, and provides a guideline for optimizing the trade-off between grid resolution and computing cost. The numerical error caused by coarse grid can be minimized by appropriately adapting the distribution of grid size. Following the guideline of coarse-grid specifications, coarse-grid computational fluid dynamics can provide informative prediction that is comparable to a grid-independent result on building environment modeling. The computing cost of computational fluid dynamics with an optimized coarse grid is usually orders of magnitude less than that with uniform fine grid.