Sensitivity analysis and evolutionary optimization for building design

In order to achieve global carbon reduction targets, buildings must be designed to be energy efficient. Building performance simulation methods, together with sensitivity analysis and evolutionary optimization methods, can be used to generate design solution and performance information that can be used in identifying energy and cost efficient design solutions. Sensitivity analysis is used to identify the design variables that have the greatest impacts on the design objectives and constraints. Multi-objective evolutionary optimization is used to find a Pareto set of design solutions that optimize the conflicting design objectives while satisfying the design constraints; building design being an inherently multi-objective process. For instance, there is commonly a desire to minimise both the building energy demand and capital cost while maintaining thermal comfort. Sensitivity analysis has previously been coupled with a model-based optimization in order to reduce the computational effort of running a robust optimization and in order to provide an insight into the solution sensitivities in the neighbourhood of each optimum solution. However, there has been little research conducted to explore the extent to which the solutions found from a building design optimization can be used for a global or local sensitivity analysis, or the extent to which the local sensitivities differ from the global sensitivities. It has also been common for the sensitivity analysis to be conducted using continuous variables, whereas building optimization problems are more typically formulated using a mixture of discretized-continuous variables (with physical meaning) and categorical variables (without physical meaning). This thesis investigates three main questions; the form of global sensitivity analysis most appropriate for use with problems having mixed discretised-continuous and categorical variables; the extent to which samples taken from an optimization run can be used in a global sensitivity analysis, the optimization process causing these solutions to be biased; and the extent to which global and local sensitivities are different. The experiments conducted in this research are based on the mid-floor of a commercial office building having 5 zones, and which is located in Birmingham, UK. The optimization and sensitivity analysis problems are formulated with 16 design variables, including orientation, heating and cooling setpoints, window-to-wall ratios, start and stop time, and construction types. The design objectives are the minimisation of both energy demand and capital cost, with solution infeasibility being a function of occupant thermal comfort. It is concluded that a robust global sensitivity analysis can be achieved using stepwise regression with the use of bidirectional elimination, rank transformation of the variables and BIC (Bayesian information criterion). It is concluded that, when the optimization is based on a genetic algorithm, that solutions taken from the start of the optimization process can be reliably used in a global sensitivity analysis, and therefore, there is no need to generate a separate set of random samples for use in the sensitivity analysis. The extent to which the convergence of the variables during the optimization can be used as a proxy for the variable sensitivities has also been investigated. It is concluded that it is not possible to identify the relative importance of variables through the optimization, even though the most important variable exhibited fast and stable convergence. Finally, it is concluded that differences exist in the variable rankings resulting from the global and local sensitivity methods, although the top-ranked solutions from each approach tend to be the same. It also concluded that the sensitivity of the objectives and constraints to all variables is obtainable through a local sensitivity analysis, but that a global sensitivity analysis is only likely to identify the most important variables. The repeatability of these conclusions has been investigated and confirmed by applying the methods to the example design problem with the building being located in four different climates (Birmingham, UK; San Francisco, US; and Chicago, US)

Revisiting lepton-specific 2HDM in light of muon g-2 anomaly

We examine the lepton-specific 2HDM as a solution of muon $g-2$ anomaly under various theoretical and experimental constraints, especially the direct search limits from the LHC and the requirement of a strong first-order phase transition in the early universe. We find that the muon g-2 anomaly can be explained in the region of 32 $<\tan\beta<$ 80, 10 GeV $<m_A<$ 65 GeV, 260 GeV $<m_H<$ 620 GeV and 180 GeV $<m_{H^\pm}<$ 620 GeV after imposing the joint constraints from the theory, the precision electroweak data, the 125 GeV Higgs data, the leptonic/semi-hadronic $\tau$ decays, the leptonic $Z$ decays and Br$(B_s \to \mu^+ \mu^-)$. The direct searches from the $h\to AA$ channels can impose stringent upper limits on Br$(h\to AA)$ and the multi-lepton event searches can sizably reduce the allowed region of $m_A$ and $\tan\beta$ (10 GeV $<m_A<$ 44 GeV and 32 $<\tan\beta<$ 60). Finally, we find that the model can produce a strong first-order phase transition in the region of 14 GeV $<m_A<$ 25 GeV, 310 GeV $<m_H<$ 355 GeV and 250 GeV $<m_{H^\pm}<$ 295 GeV, allowed by the explanation of the muon $g-2$ anomaly.Comment: 24 pages, 8 figures, 3 Tables, matches published versio

750 GeV Diphoton Resonance, 125 GeV Higgs and Muon g-2 Anomaly in Deflected Anomaly Mediation SUSY Breaking Scenario

We propose to interpret the 750 GeV diphoton excess in deflected anomaly mediation supersymmetry breaking scenarios, which can naturally predict couplings between a singlet field and vector-like messengers. The CP-even scalar component (S) of the singlet field can serve as the 750 GeV resonance. The messenger scale, which is of order the gravitino scale, can be as light as F_\phi \sim {\cal O}(10) TeV when the messenger species N_F and the deflection parameter d are moderately large. Such messengers can induce the large loop decay process S \to \gamma\gamma. Our results show that such a scenario can successfully accommodate the 125 GeV Higgs boson, the 750 GeV diphoton excess and the muon g-2 without conflicting with the LHC constraints. We also comment on the possible explanations in the gauge mediation supersymmetry breaking scenario.Comment: Published version in PLB; 15 pages,2 figure

Explaining 750 GeV diphoton excess from top/bottom partner cascade decay in two-Higgs-doublet model extension

In this paper, we interpret the 750 GeV diphoton excess in the Zee-Babu extension of the two-Higgs-doublet model by introducing a top partner ($T$)/bottom partner ($B$). In the alignment limit, the 750 GeV resonance is identified as the heavy CP-even Higgs boson ($H$), which can be sizably produced via the QCD process $pp \to T\bar{T}$ or $pp \to B\bar{B}$ followed by the decay $T\to Ht$ or $B \to Hb$. The diphoton decay rate of $H$ is greatly enhanced by the charged singlet scalars predicted in the Zee-Babu extension and the total width of $H$ can be as large as 7 GeV. Under the current LHC constraints, we scan the parameter space and find that such an extension can account for the observed diphoton excess.Comment: 19 pages, 4 figures; some discussions and references adde

A comparison of approaches to stepwise regression on variables sensitivities in building simulation and analysis

Developing sensitivity analysis (SA) that reliably and consistently identify sensitive variables can improve building performance design. In global SA, a linear regression model is normally applied to sampled-based solutions by stepwise manners, and the relative importance of variables is examined by sensitivity indexes. However, the robustness of stepwise regression is related to the choice of procedure options, and therefore influence the indication of variables&rsquo; sensitivities. This paper investigates the extent to which the procedure options of a stepwise regression for design objectives or constraints can affect variables global sensitivities, determined by three sensitivity indexes. Given that SA and optimization are often conducted in parallel, desiring for a combined method, the paper also investigates SA using both randomly generated samples and the biased solutions obtained from an optimization run. Main contribution is that, for each design objective or constraint, it is better to conclude the categories of variables importance, rather than ordering their sensitivities by a particular index. Importantly, the overall stepwise approach (with the use of bidirectional elimination,BIC, rank transformation and 100 sample size) is robust for global SA: the most important variables are always ranked on the top irrespective of the procedure options