Many research works have intended to enhance fuel economy and decrease emissions during conversion from a diesel engine to a dual fuel engine. However, the majority of these works do not take into account enhancement of homogeneity of the mixture inside the engine and precise control of the air fuel ratio. This deficiency can cause higher emissions, greater brake-specific fuel consumption, and likely knocking. Conversely, there is limited research pertaining to empirical equations for projecting the mixture’s homogeneity. In this study, a new air–fuel mixer was devised, produced and tested. For the air-gaseous fuel mixer, the proposed design was meant to be appropriate for mixing air with hydrogen and CNG. It was also designed in such a way that it would result into extremely homogeneous mixing for the gaseous fuel as it mixes with air and exhibits high uniformity index (UI). Lastly, it is also meant to promote easy connection with an electronic control unit so that the air-gaseous fuel ratio could be accurately controlled for varying engine speeds. To optimise the homogeneity within the new mixer, fifteen varying mixer models having 116 cases were made in order to study how the location, diameter, and number of holes within the mixer affect the mixture’s homogeneity and distribution under ACNGR = 34.15 and AHR = 74.76. Afterwards, the distribution, flow behaviour, and homogeneity of the mixture within the new mixer models were checked using computational fluid dynamics analysis software. Based on the simulation results, it was discovered that the best uniformity index (UI) values were achieved for models 7/ case 48. Based on the simulation results, a fairly simple method was then developed to estimate the mixture’s homogeneity (UI) from the new models of the mixer. The basis of the proposal model (empirical equation) is from the best values determined for the unknown constant F so that the equation for UI estimation could be formulated. The particle swarm optimisation (PSO) algorithm was used to solve an optimisation problem and achieve this outcome. The outcomes indicated that the built model could precisely project the UI values.

许多研究工作旨在提高燃油经济性并减少从柴油发动机转换为双燃料发动机的排放。但是，大多数这些工作都没有考虑到发动机内部混合物均匀性的提高以及空燃比的精确控制。这种缺陷会导致更高的排放，更大的制动专用油耗，并可能导致爆震。相反，关于预测混合物均匀性的经验方程式的研究有限。在这项研究中，设计，生产和测试了一种新型的空气燃料混合器。对于空气燃料混合器，建议的设计适合将空气与氢气和CNG混合。它的设计方式还使其在与空气混合时会产生非常均匀的混合气态燃料，并表现出很高的均匀性指数（UI）。最后，这还意味着促进与电子控制单元的轻松连接，以便可以针对变化的发动机转速精确控制气态燃料比。为了优化新混合器内的均匀性，制作了15种不同的混合器模型（共116个案例），以研究在ACNGR = 34.15和AHR = 74.76的情况下混合器中孔的位置，直径和孔数如何影响混合物的均匀性和分布。然后，使用计算流体动力学分析软件检查新混合器模型中混合物的分布，流动特性和均匀性。根据仿真结果，我们发现，对于模型7 /案例48，获得了最佳的均匀度指数（UI）值。基于模拟结果，然后开发了一种相当简单的方法，用于从混合器的新模型中估计混合物的均匀度（UI）。建议模型（经验方程式）的基础是为未知常数F确定的最佳值，因此可以制定UI估计方程式。粒子群优化（PSO）算法用于解决优化问题并实现此结果。结果表明，构建的模型可以精确地投影UI值。建议模型（经验方程式）的基础是为未知常数F确定的最佳值，因此可以制定UI估计方程式。粒子群优化（PSO）算法用于解决优化问题并实现此结果。结果表明，构建的模型可以精确地投影UI值。建议模型（经验方程式）的基础是为未知常数F确定的最佳值，因此可以制定UI估计方程式。粒子群优化（PSO）算法用于解决优化问题并实现此结果。结果表明，构建的模型可以精确地投影UI值。