The purpose of this research is to develop a new approach to estimate the effective leakage area using the inverse modeling process as an alternative to the blower door test. An actual office building, which is the head quarter of Energy Efficiency Hub, was used as an example case in this study. The main principle of the inverse modeling process is comparing the real monitor boiler gas consumption with the result calculated from the EnergyPlus model with a dynamic infiltration rate input to find the best estimation of the parameter of effective leakage area (ELA). This thesis considers only the feasibility of replacing the blower door test with the calibration approach, so rather than attempting an automated calibration process based on inverse modeling we deal with generating a first estimate and consider the role of model uncertainties that would make the proposed method less feasible. There are five steps of the whole process. First, we need to customize our own actual weather data (AMY) needed by the energy model (EnergyPlus model), which can help increase our quality of the result. Second, create the building energy model in EnergyPlus. Third, create a multi-zone model using CONTAM with different ELA estimation of each facade to calculate the dynamic infiltration rate of each ELA estimate. Fourth, input the dynamic infiltration rate got from the CONTAM model to EnergyPlus model and output the boiler energy consumption. Fifth, compare the boiler gas consumption from the model and the real monitor data and find the best match between the two and the corresponding ELA, which gives the best estimate from the whole inverse modeling process. From the simulation result comparison, the best estimation of the total building ELA from the inverse modeling process is the 23437cm2 at 4pa, while the result from the blower door test is 10483cm2 at 4pa. Because of the insufficient information of the building and also the uncertainty of the input parameters, the study has not led to a definite statement whether the proposed calibration of the ELA with consumption data can replace a blower door test to get an equally valid or even better ELA estimate, but it looks feasible. As this this case study is done in a deterministic context, the full feasibility test should be conducted under uncertainty. A first step towards this will talk be discussed in chapter 4.

abstract: The modeling and simulation of airflow dynamics in buildings has many applications including indoor air quality and ventilation analysis, contaminant dispersion prediction, and the calculation of personal occupant exposure. Multi-zone airflow model software programs provide such capabilities in a manner that is practical for whole building analysis. This research addresses the need for calibration methodologies to improve the prediction accuracy of multi-zone software programs. Of particular interest is accurate modeling of airflow dynamics in response to extraordinary events, i.e. chemical and biological attacks. This research developed and explored a candidate calibration methodology which utilizes tracer gas (e.g., CO2) data. A key concept behind this research was that calibration of airflow models is a highly over-parameterized problem and that some form of model reduction is imperative. Model reduction was achieved by proposing the concept of macro-zones, i.e. groups of rooms that can be combined into one zone for the purposes of predicting or studying dynamic airflow behavior under different types of stimuli. The proposed calibration methodology consists of five steps: (i) develop a "somewhat" realistic or partially calibrated multi-zone model of a building so that the subsequent steps yield meaningful results, (ii) perform an airflow-based sensitivity analysis to determine influential system drivers, (iii) perform a tracer gas-based sensitivity analysis to identify macro-zones for model reduction, (iv) release CO2 in the building and measure tracer gas concentrations in at least one room within each macro-zone (some replication in other rooms is highly desirable) and use these measurements to further calibrate aggregate flow parameters of macro-zone flow elements so as to improve the model fit, and (v) evaluate model adequacy of the updated model based on some metric. The proposed methodology was first evaluated with a synthetic building and subsequently refined using actual measured airflows and CO2 concentrations for a real building. The airflow dynamics of the buildings analyzed were found to be dominated by the HVAC system. In such buildings, rectifying differences between measured and predicted tracer gas behavior should focus on factors impacting room air change rates first and flow parameter assumptions between zones second. / Dissertation/Thesis / M.S. Built Environment 2011

As wind energy technology continues to mature, farm sizes grow and wind farm layout design becomes more difficult, in part due to the number of design variables and constraints. Wind farm layout optimization is typically approached using gradient-free methods because of the highly multi-modal shape of the wind farm layout design space. Gradient-free method performance generally degrades with increasing problem size, making it difficult to find optimal layouts for larger wind farms. However, gradient-based optimization methods can effectively and efficiently solve large-scale problems with many variables and constraints. To pave the way for effective and efficient wind farm layout optimization for large-scale wind farms, we have worked to overcome the primary barriers to applying gradient-based optimization to wind farm layout optimization. To improve model/algorithm compatibility, we adjusted wake and wind farm models, adding more realistic curvature and smoothness to enable optimization algorithms to travel through areas in the design space where they had previously gotten stuck. We reduced the number of function calls required for gradient-based wind farm layout optimization by over three orders of magnitude for large farms by using algorithmic differentiation to compute derivatives. We reduced the multi-modality of the wind farm layout design space using wake expansion continuation (WEC). We developed WEC to work with existing optimization algorithms, enabling them to get out of local optima while remaining fully gradient-based. Across four case studies, WEC found results with lower wake loss, on average, than the other methods we tested. To resolve concerns about optimization algorithms exploiting model inaccuracies, we compared the initial and optimized layouts to large-eddy simulation (LES) results. The simple models predicted an AEP improvement of 7.7% for a low-TI case, and LES predicted 9.3%. For a high-TI case, the simple models predicted a 10.0% improvement in AEP and LES predicted 10.7%. To resolve uncertainty regarding relative solution quality for gradient-based and gradient-free methods, we collaborated with seven organizations to compare eight optimization methods. Each method was managed by researchers experienced with them. All methods found solutions of similar quality, with optimized wake loss between 15.48 % and 15.70 %. WEC with SNOPT was the only purely gradient-based method included and found the third-to-best solution.