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A heat engine is a device used to extract heat from a source and then convert it into mechanical work used for various applications. For example, a steam engine on an old-style train can produce the work needed for driving the train.
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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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Related Experiment Video

Updated: Jun 24, 2025

A Rapid Method for Modeling a Variable Cycle Engine
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Gas engine CCHP system optimization: An energy, exergy, economic, and environment analysis and optimization based on

Jiangping Nan1, Qi Xiao2, Milad Teimourian3,4

  • 1Xi'an Traffic Engineering Institute, Xi'an, 710300, Shaanxi, China.

Heliyon
|June 7, 2024
PubMed
Summary

The DNGO algorithm optimizes Combined Cooling, Heating, and Power (CCHP) systems for residential buildings, improving energy efficiency and reducing emissions. This advanced swarm intelligence method surpasses traditional algorithms in performance and speed.

Keywords:
Combined cooling heating and power (CCHP)Developed northern goshawk optimizationEconomic assessmentEnergy analysisEnvironmental impact assessmentExergy analysisGas engine systemSystem sizing

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Area of Science:

  • * Energy Systems Engineering
  • * Optimization Algorithms
  • * Sustainable Energy

Background:

  • * Combined Cooling, Heating, and Power (CCHP) systems are crucial for residential energy efficiency.
  • * Traditional optimization methods for CCHP systems face limitations in performance and speed.
  • * Evaluating CCHP systems requires a comprehensive approach, including Energy, Exergy, Economic, and Environmental (4E) analysis.

Purpose of the Study:

  • * To enhance the design and operation of a gas engine-driven CCHP system for a Chinese residential building.
  • * To evaluate the effectiveness of the novel Dragonfly-based Grey Wolf Optimizer (DNGO) algorithm.
  • * To compare the DNGO algorithm against the Northern Goshawk Optimization (NGO) and Genetic Algorithm (GA).

Main Methods:

  • * Implementation of a 4E analysis framework (Energy, Exergy, Economic, Environmental).
  • * Application and evaluation of the DNGO algorithm, incorporating chaos theory, Lévy flight, and swarm intelligence.
  • * Comparative analysis of DNGO against NGO and GA using a residential CCHP case study.

Main Results:

  • * The DNGO algorithm identified the optimal gas engine size at 130 kW.
  • * DNGO demonstrated superior performance, achieving higher energy efficiency and reduced exergy destruction.
  • * Significant reductions in CO2 emissions and improved economic indicators (shorter payback period, higher profit) were observed.
  • * DNGO exhibited a faster convergence rate and effectively avoided local optima compared to NGO and GA.

Conclusions:

  • * The DNGO algorithm offers a significant advancement in optimizing CCHP systems, surpassing traditional methods.
  • * The optimized CCHP system design leads to enhanced energy efficiency, environmental benefits, and economic viability.
  • * This research provides a novel perspective on swarm-based optimization for complex energy systems.