Optimal Design and Performance Analysis of Thermoelectric Power Generation Device Based on Multi‐Objective Genetic Algorithm

To explore fire hazards and provide real‐time path navigation services for passenger ships, this study proposes an improved artificial fish swarm algorithm (IAFSA) to give the optimal evacuation route for evacuees when a passenger ship is on fire. First, the PyroSim software is used to simulate the fire scene on the sixth deck of the passenger ship. Through which the real‐time temperature, carbon monoxide (CO) concentration, and visibility of each exit are obtained, and the spread speed of the fire hazard area is determined. Then, the artificial fish swarm algorithm is improved by using the dynamic Visual and step, and the optimal path planning for the evacuation of passenger ships is achieved. The Visualized Evacuation Guidance System (VEGS) is developed to provide the optimal route for evacuees on the passenger ship. Finally, VEGS is applied to the fire evacuation of passenger ships. The results show that VEGS can provide the optimal evacuation path for evacuees and avoid detours in fire hazard areas compared with other algorithms. Seventy‐five percent of the evacuees who used VEGS in the event of a passenger ship can escape in a time shortened by up to 40.8%.

Section: Introductionmentioning

confidence: 99%

“…In recent years, many researchers have explored using artificial intelligence algorithms with promising results. [ 21–24 ] Zhai et al. [ 25 ] proposed a new evacuation route planning method combining genetic and simulated annealing algorithms.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Evacuation Routes Navigation for Passenger Ship Fire Based on Intelligence Algorithms

Liu,

Zhang,

Yin

et al. 2023

Modeling and Performance Optimization of Louvered Fin Radiators in Negative Gauge Pressure Condition

Wan

Liu

et al. 2022

The purpose of this paper is to study the effect of the structural parameters of louvered fin radiators on its thermal‐hydraulic performance, optimize its structural parameters, and improve the performance of louvered fin radiators in negative gauge pressure condition. The thermal calculation model of louvered fin radiator in negative gauge pressure is established. Then, the effects of design variables on thermal‐hydraulic characteristics are investigated. The second generation of nondominated sorting genetic algorithm (NSGA‐II) is applied to optimize the louvered fin heat exchanger in negative gauge pressure environment. Technique for order preference by similarity to an ideal solution is applied to choose the final optimal solution. Numerical simulation of the solution is conducted and the result is compared with literature in order to verify the optimization method. The optimal structural parameters are obtained under constraints. Compared with the original test piece, the heat transfer rate of optimal solution is increased by 7.7%, and total annual cost is reduced by 24.8%. The present paper establishes optimization method of louvered fin radiators in negative gauge pressure condition, the optimization result is superior to the original radiator. The optimization method can be used in the design of radiators operating in negative gauge pressure conditions.

Quantum Analog Annealing of Gain‐Dissipative Ising Machine Driven by Colored Gaussian Noise

Liao

Sarker

et al. 2022

Gain‐dissipative Ising machines (GIMs) are a type of quantum analog equipment that can rapidly determine the optimal solution for combinatorial optimization problems. When the noise intensity is significantly lower than the fixed point of the system, the performance of a GIM is not influenced by the fluctuation of the noise intensity. However, the noise in this study is limited to Gaussian white noise. The influence of prevalent colored noise on GIMs has not been researched. In this study, the influence of common‐colored noise on the performance of GIMs is numerically investigated. The results of a domain clustering dynamics analysis reveal that red noise can better suppress the generation of the noise‐induced irregular temporary domain. Furthermore, several prevalent MAXCUT problem topologies, including the Moebius ladder, random Moebius ladder, and 2D random lattice, are adopted as test benchmarks. The results reveal that GIMs influenced by white, blue, and violet noise perform better at low‐intensity noise condition. In contrast, pink and red noise‐injected GIMs demonstrate higher performances when applied to MAXCUT topologies with both ferromagnetic and antiferromagnetic connections under larger noise intensity conditions. This indicates that the noise dispersion can be used as an additional hyperparameter to optimize the performance of GIMs.