• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文OA文献 >Exergy Based SI Engine Model Optimisation. Exergy Based Simulation and Modelling of Bi-fuel SI Engine for Optimisation of Equivalence Ratio and Ignition Time Using Artificial Neural Network (ANN) Emulation and Particle Swarm Optimisation (PSO).
【2h】

Exergy Based SI Engine Model Optimisation. Exergy Based Simulation and Modelling of Bi-fuel SI Engine for Optimisation of Equivalence Ratio and Ignition Time Using Artificial Neural Network (ANN) Emulation and Particle Swarm Optimisation (PSO).

机译：基于火用的SI发动机模型优化。基于火用的双燃料SI发动机仿真和建模，用于使用人工神经网络（ANN）仿真和粒子群优化（PSO）优化当量比和点火时间。

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising ¿total availability¿. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints.ududThe engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. udThe exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) ududIn this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction.ududThe constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis.udIn this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising ¿total availability¿. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints.ududThe engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. udThe exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) ududIn this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction.ududThe constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis.
机译：本文研究和评估了基于火用的SI发动机模型优化（EBSIEMO）。基于火用分析对四冲程双燃料火花点火（SI）发动机进行建模，以优化发动机性能。人工神经网络（ANN）用作仿真器以加快优化过程。约束粒子群优化（CPSO）用于基于最大化“总可用性”来识别诸如当量比和点火时间之类的参数，以优化发动机性能。在优化过程中，应用了发动机废气标准排放，其中包括制动特定的CO（BSCO）和制动特定的NOx（BSNOx）作为约束。 ud ud在考虑化学反应性的同时，在两区域模型中开发了发动机模型。合成燃料，包括10种化学物质。在MATLAB软件中开发了一个计算机代码，用于求解方程式，以预测各阶段混合物的温度和压力（压缩冲程，燃烧过程和膨胀冲程）。此外，进气和排气过程是使用近似方法计算的。该模型具有模拟湍流燃烧的能力，与计算流体力学（CFD）模型相比，它的计算速度更快且效率更高。选择性输出包括气缸温度和压力，传热，制动功，制动热效率和容积效率，制动扭矩，制动功率（BP），制动比油耗（BSFC），制动平均有效压力（BMEP），CO2浓度，制动比CO（BSCO）和制动比NOx（BSNOx）。在该模型中，分析了发动机转速，当量比和点火时间对使用汽油和压缩天然气的性能参数的影响。另外，该模型通过使用双燃料发动机测试获得的结果通过实验数据进行验证。因此，该发动机模型能够预测，分析并有助于优化发动机性能参数。 此处基于火用的四冲程双燃料（CNG和汽油）火花点火（SI）发动机模型（EBSIEM）用于分析双燃料SI发动机。因为，仅靠热力学第一定律（FLT）无法充分理解发动机的运行情况。因此，本文重点研究了利用热力学第二定律（SLT）或火用分析前景（火用基于SI发动机模型（EBSIEM））的发达发动机模型对SI发动机的运行情况进行调查的方法。使用基于基于火力的SI发动机的人工神经网络（ANN）模型，介绍了双燃料发动机（CNG和汽油）的总可用性，制动比CO（BSCO），制动比NOx（BSNOx）和制动扭矩的预测（EBSIEM）（ANN-EBSIEM）作为模拟器来加速优化过程。换句话说，使用训练有素的人工神经网络通常要比数学模型或常规仿真程序快得多。 ud ud受约束的粒子群优化（CPSO）-EBSIEM（EBSIEMO）能够优化模型参数发动机性能。与FLT分析相比，通过分析可用性项（尤其是可用性破坏（测得的发动机不可逆性））而基于可用性分析（SLT）的优化结果被认为具有更高的优先级。 ud在本文中，基于火用的SI发动机模型优化（ EBSIEMO）进行了研究和评估。基于火用分析对四冲程双燃料火花点火（SI）发动机进行建模，以优化发动机性能。人工神经网络（ANN）用作仿真器以加快优化过程。约束粒子群优化（CPSO）用于基于最大化“总可用性”来识别诸如当量比和点火时间之类的参数，以优化发动机性能。在优化过程中，应用了发动机废气标准排放，其中包括制动特定的CO（BSCO）和制动特定的NOx（BSNOx）作为约束。 ud ud在考虑化学反应性的同时，在两区域模型中开发了发动机模型。合成燃料，包括10种化学物质。在MATLAB软件中开发了一个计算机代码，用于求解方程式，以预测各阶段混合物的温度和压力（压缩冲程，燃烧过程和膨胀冲程）。此外，进气和排气过程是使用近似方法计算的。该模型具有模拟湍流燃烧的能力，与计算流体力学（CFD）模型相比，它的计算速度更快且效率更高。选择性输出为气缸温度和压力，传热，制动功，制动热效率和容积效率，制动扭矩，制动功率（BP），制动比油耗（BSFC），制动平均有效压力（BMEP），CO2浓度，刹车特定的CO（BSCO）和刹车特定的NOx（BSNOx）。在该模型中，分析了发动机转速，当量比和点火时间对使用汽油和压缩天然气的性能参数的影响。另外，该模型通过使用双燃料发动机测试获得的结果通过实验数据进行验证。因此，该发动机模型能够预测，分析并有助于优化发动机性能参数。 此处基于火用的四冲程双燃料（CNG和汽油）火花点火（SI）发动机模型（EBSIEM）用于分析双燃料SI发动机。因为，仅靠热力学第一定律（FLT）无法充分理解发动机的运行情况。因此，本文重点研究了利用热力学第二定律（SLT）或火用分析前景（火用基于SI发动机模型（EBSIEM））的发达发动机模型对SI发动机的运行情况进行调查的方法。使用基于基于火力的SI发动机的人工神经网络（ANN）模型，介绍了双燃料发动机（CNG和汽油）的总可用性，制动比CO（BSCO），制动比NOx（BSNOx）和制动扭矩的预测（EBSIEM）（ANN-EBSIEM）作为模拟器来加速优化过程。换句话说，使用训练有素的人工神经网络通常要比数学模型或常规仿真程序快得多。 ud ud受约束的粒子群优化（CPSO）-EBSIEM（EBSIEMO）能够优化模型参数发动机性能。与FLT分析相比，由于分析可用性项（尤其是可用性破坏）（基于测得的发动机不可逆性）而导致的基于可用性分析（SLT）的优化结果被认为具有更高的优先级。

著录项

  • 作者

    Rezapour Kambiz;

  • 作者单位
  • 年度 2011
  • 总页数
  • 原文格式 PDF
  • 正文语种 en
  • 中图分类

相似文献

  • 外文文献
  • 中文文献
  • 专利

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号