Enhancing the efficiency of cabin heaters in emergency shelters after earthquakes through an optimized fuzzy controller

In just one and half minutes,more than fifty thousand died due to the 7.7 and 7.6 magnitude earthquakes that struck Turkey’s southeast on February 6,2023;thousands of families who barely escaped struggled to survive in the freezing weather.A warm shelter was the most basic requirement of these families.Container buildings are a rapid and easy solution to this issue.However,there is a need for a more effective and safe heating option than a wood fire for these buildings.In this study,cabin heaters,which allow truck drivers to warm up when they park their vehicles to sleep,are specially optimized for emergency shelters after an earthquake.An optimized fuzzy controller was developed to use in such buildings,which allows an air–fuel ratio in the combustion chamber of the cabin heater to be controlled adaptively based on system dynamics to get lower carbon emissions and fuel consumption.The TRNSYS software was used to establish the transient simulation model of a cabin heater with a capacity of 4 kW for a typical 21 m^(2) shelter building in Turkey’s cold regions.The developed fuzzy controller carried out the heating process of this shelter from the 15th of November to the 15th of March.Instead of using expert knowledge,the Gray Wolf Optimization(GWO)method was applied to optimize the fuzzy controller parameters developed for the cabin heater.With the optimized fuzzy controller,the fuel consumption at the end of the heating season was reduced by an average of 0.2 L/h,and the cabin heater’s efficiency increased by more than 13%.Our simulation results show that the intelligent controller we developed could improve diesel fuel combustion efficiency by up to 85%.

Multi-criteria Satisfaction Assessment of the Spatial Distribution of Urban Emergency Shelters Based on High-Precision Population Estimation

This article introduces a framework for the multi-criteria satisfaction assessment of the spatial distribution of urban emergency shelters.A GIS-based analytic hierarchy process approach was utilized to conduct the assessment based on selected criteria layers for daytime and nighttime scenarios,respectively.The layers were generated from high-precision land use data based on highresolution aerial images and census data.Considering the uncertainty in criteria weighting,a spatial sensitivity analysis was undertaken for deriving more accurate results.The feasibility of the framework was tested on a case study in Jing'an District,Shanghai,China.The assessment results show that both at nighttime and during daytime,even if all potentially available shelters are open,the demand in large areas can only be marginally satisfied or not satisfied,especially in the northern,eastern,and central parts of Jing'an District.The quantitative analysis of the satisfaction conditions of the buildings or land parcels and the affected people,especially children and the elderly,shows a low satisfaction level of shelter services in these areas.The satisfaction assessment of emergency shelters can help government decision makers find low satisfaction areas of sheltering services and support further locationallocation optimization of urban emergency shelters.

Supply-Demand Analysis of Urban Emergency Shelters Based on Spatiotemporal Population Estimation

Supply–demand analysis is an important part of the planning of urban emergency shelters.Using Pudong New Area,Shanghai,China as an example,this study estimated daytime and nighttime population of the study area based on fine-scale land use data,census data,statistical yearbook information,and Tencent user-density big data.An exponential function-based,probability density estimation method was used to analyze the spatial supply of and demand for shelters under an earthquake scenario.The results show that even if all potential available shelters are considered,they still cannot satisfy the demand of the existing population for evacuation and sheltering,especially in the northern region of Pudong,under both the daytime and the nighttime scenarios.The proposed method can reveal the spatiotemporal imbalance between shelter supply and demand.We also conducted a preliminary location selection analysis of shelters based on the supply–demand analysis results.The location selection results demonstrate the advantage of the proposed method.It can be applied to identify the areas where the supply of shelters is seriously inadequate,and provide effective decision support for the planning of urban emergency shelters.

An Emergency Shelter Location Model Based on the Sense of Security and the Reliability Level

Based on the value function of the prospect theory,this paper constructs a security function,which is used to describe the victims’feelings about the distance in emergency evacuation.Since different paths between the demand points and the emergency shelters are generally of different importance degrees,they are divided into main paths and auxiliary paths.The security function values and the reliability levels of main paths and auxiliary paths are given different weights.The weighted sum of the security function values and the weighted sum of the reliability level function values of all demand points are maximized to determine the location and the number of the emergency shelters,the transfer paths,the reinforced edges and the incremental reliability level of the selected edge.In order to solve the model,a two-stage simulated annealing-particle swarm optimization algorithm is proposed.In this algorithm,the particle swarm optimization(PSO)algorithm is embedded into the simulated annealing(SA)algorithm.The cumulative probability operator and the cost probability operator are formed to determine the evolution of the particles.Considering the budget constraint,the algorithm eliminates the shelter combinations that do not meet the constraint,which greatly saves the calculation time and improves the efficiency.The proposed algorithm is applied to a case,which verifies its feasibility and stability.The model and the algorithm of this paper provide a basis for emergency management departments to make the earthquake emergency planning.