To address the issues of subjectivity in the selection of design schemes for pocket parks in public building environments, as well as the lack of scientific and objective decision making, a comprehensive TOPSIS method-based optimization decision model for pocket park design schemes was developed. Firstly, an evaluation index system for the pocket park design schemes was established through a literature review and expert interviews. Secondly, the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) were employed to determine the weight coefficients of various evaluation criteria, combining qualitative and quantitative approaches. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm was applied, constructing a weighted decision matrix for the design schemes based on each criterion. By calculating the distance between each evaluation scheme and the ideal solution, the final ranking was determined, thus completing the comprehensive evaluation and optimization of the design schemes. This model was used to assist designers in the decision-making process for scheme optimization, achieving a priority ranking of five pocket park design schemes in a district of Xi’an, and validating the objectivity, feasibility, and scientific nature of this method.

BACKGROUND: The cockpit of an aircraft is the main place where the pilot controls the aircraft on a mission. An excellent cockpit environment not only ensures the pilot’s basic survival needs but also improves the comfort level and alleviates fatigue when performing missions. OBJECTIVE: On the basis of domestic and international airworthiness standards, a top-down refinement method is deployed to determine the initial goal, and the environmental criteria are fully discussed and balanced in a dynamic process to build a comprehensive evaluation system for environmental factors in the aircraft cockpit. METHODS: Based on the fuzzy comprehensive evaluation theory, an evaluation model for environmental factors is constructed by combining analytic hierarchical analysis (AHP) and particle swarm optimization (PSO). Then the feasibility of the evaluation model is verified by an illustrative example. RESULTS: The results suggest that the light environment gains the highest score among the 4 environmental criteria followed by the thermal environment, while both sound environment and microenvironment have relatively low scores. CONCLUSION: As for the 27 environmental sub-criteria, temperature, illumination, lighting clarity, light-color coordination, noise duration and pressure score the highest. The evaluation findings can provide important environmental control criteria for the subsequent environmental control system in the cockpit of the aircraft.

The assessment of indoor thermal environments is crucial to achieving thermal comfort and energy efficiency. However, the inaccurate evaluation and strong nonlinear variations of thermal comfort parameters limit engineering designs. Therefore, a coupled heat-transfer model was developed in this study, and large eddy simulations were performed to verify the influence of inertia and buoyancy—which are mutually exclusive forces but coexist in large-space building environments—on inhomogeneous thermal environments. Furthermore, an artificial neural network (ANN) model was designed to overcome the limitations of the nonlinear relationships between thermal parameters and predicted mean vote (PMV) values. PMV indexes can be predicted using the ANN model when thermal parameters are used as input data. Subsequently, a genetic algorithm, harmony search algorithm, gravitational search algorithm, and whale optimization algorithm were adopted to optimize the neural network structure to prevent its confinement in a local optimum. Finally, with 5000 data sets, the minimum-error neural network structure 6-22-23-1 of the ANN-GA neural network model had high prediction accuracy, mean relative error < 1.38, root mean square error < 1.34, and a regression coefficient of ~1. The proposed ANN model can help improve the assessment of the thermal environment and thermal comfort of buildings.

In-flight airflow and temperature distributions are key parameters for the thermal design of aircraft cockpit. In real working conditions, the effect of gravitational acceleration on flow and heat transfer characteristics is crucial but has long been ignored in the cockpit study. Gravitational acceleration also induces buoyant force and inhomogeneous thermal transport, which gives rise to thermal challenges of aircraft cockpit. To address this challenge, an SST k-ω turbulence model was well validated with 94% prediction accuracy to evaluate the inhomogeneous characteristics. The results indicated that the buoyancy-driven temperature distribution became progressively more inhomogeneous as gravitational acceleration increased. It was also found that the nonlinear variations in temperature were observed in the vertical direction from 0 to 1.5 m. Besides, a dimensionless parameter Gr* (∝10⁷) was used to quantitatively assess the buoyant force. The results showed that the buoyancy force was considerably similar at 2.0 g and 4.0 g, and largely different from the trend at 1.0 g. Indeed, a common characteristic of these three conditions is the presence of a large inhomogeneity coefficient R in the top region of the cockpit. Therefore, an important limitation when designing the thermal structure of cockpit is the inhomogeneous feature of which the follow-up study needs to be carried out.