Abstract Stirling engines are constructed with different mechanical configurations namely Alpha, Beta, Gamma and all of them can be powered by any source of renewable energies including biomass, solar energy or even waste heat from industrial sectors. The question that arises is: how can we choose the right type of Stirling engine that best matches our renewable energy source? In the existing bibliography, there is a great lack of an in-depth approach that establishes an objective comparison between the different types of Stirling engine, which leads researchers to make an inappropriate choice of a given configuration for a specific application. Consequently, a decrease in engine output performance can be noted while increasing the engine operating price. In this context, this paper develops a solution to Stirling engine users that will help them choose the suitable configuration for a given energy source. To do this, a non-ideal adiabatic model which takes several thermal and mechanical losses into consideration has been proposed using MATLAB software. The results of the current model have been compared with experimental findings of NASA Lewis Research Center and previous models and the comparison results demonstrate the high accuracy of the present model. The same physical and geometrical parameters were used to analyze the different configurations of Stirling engine by using crank drive mechanism. The results of this work demonstrate that each Stirling engine type is more suitable for a particular application where it provides high performances. The findings show the ability of Alpha configuration to operate with high temperature difference which correspond to waste heat recovery in industrial sectors where the gas temperature reaches high values. Alpha engine can produce high power-volume ratio and efficiency thanks to its low dead volume in working spaces and the separation that exists between its hot and cold sources. Under the same operating conditions, the output power and efficiency of Alpha, Beta and Gamma were 4120 W, 2280 W, 2500 W and 40.19%, 32.13%, 32.95% respectively. Beta and Gamma types have been found to be more suitable for low and medium temperature difference such as biomass and solar energy. Within these temperature ranges, Beta and Gamma types can produce almost the same output power as Alpha machine with less engine pressure requirement since they contain only one power piston. Beta and Gamma types have efficiencies of 17.57% and 18.40% with only mean pressure of 2.6 MPa and 2.7 MPa respectively, while Alpha arrangement provides an efficiency of 20.95% at 3.5 MPa mean pressure. This contributes to a main advantage of Beta and Gamma Stirling engines in terms of sealing process which will decrease the engine construction cost of these types. In addition, the code developed in this work indicates, at its output, the appropriate type for any application based on the user’s input parameters including the temperature of the hot source. This contributes to a new decision support approach allowing the choice of the suitable configuration for a given energy source.

中文翻译：

---

斯特林发动机类型选择决策支持方法的新方法，以更好地利用可再生能源

摘要 斯特林发动机由不同的机械配置构成，即阿尔法、贝塔、伽马，所有这些发动机都可以由任何可再生能源提供动力，包括生物质能、太阳能甚至工业部门的废热。出现的问题是：我们如何才能选择最适合我们可再生能源的正确类型的斯特林发动机？在现有的参考书目中，非常缺乏在不同类型斯特林发动机之间建立客观比较的深入方法，这导致研究人员针对特定应用对给定配置做出不适当的选择。因此，在增加发动机运行价格的同时可以注意到发动机输出性能的降低。在这种情况下，本文为斯特林发动机用户开发了一个解决方案，帮助他们为给定的能源选择合适的配置。为此，使用 MATLAB 软件提出了一种考虑了多种热和机械损失的非理想绝热模型。将当前模型的结果与 NASA 刘易斯研究中心的实验结果和以前的模型进行了比较，比较结果证明了当前模型的高精度。使用相同的物理和几何参数来分析使用曲柄驱动机构的斯特林发动机的不同配置。这项工作的结果表明，每种斯特林发动机类型都更适合提供高性能的特定应用。研究结果显示了 Alpha 配置在高温差下运行的能力，这对应于气体温度达到高值的工业部门的废热回收。Alpha 发动机由于其工作空间的死体积很小，并且其冷热源之间存在隔离，因此可以产生高功率体积比和效率。在相同的工作条件下，Alpha、Beta和Gamma的输出功率和效率分别为4120 W、2280 W、2500 W和40.19%、32.13%、32.95%。Beta 型和 Gamma 型已被发现更适用于中低温差，例如生物质能和太阳能。在这些温度范围内，Beta 和 Gamma 类型可以产生与 Alpha 机器几乎相同的输出功率，但发动机压力要求更低，因为它们仅包含一个动力活塞。Beta 型和 Gamma 型的效率分别为 17.57% 和 18.40%，平均压力仅为 2.6 MPa 和 2.7 MPa，而 Alpha 型布置在平均压力为 3.5 MPa 时提供了 20.95% 的效率。这有助于 Beta 和 Gamma 斯特林发动机在密封过程方面的主要优势，这将降低这些类型的发动机制造成本。此外，这项工作中开发的代码在其输出中根据用户的输入参数（包括热源的温度）指示任何应用程序的适当类型。这有助于采用新的决策支持方法，允许为给定的能源选择合适的配置。