In the present paper, an efficient optimization method based on Bayesian updating strategy is developed for the design of a spark-ignition engine equipped with pre-chamber. 3D computational fluid dynamics (CFD) simulation coupled with strategies including design of experiment, genetic algorithm, and machine learning methods is used to optimize the pre-chamber with desired combustion phasing. The optimization process starts from a design of experiment matrix of 11 design parameters, which are used to analytically characterize the pre-chamber geometry and set up the 3D combustion CFD. Taking CA50 as the single objective, the CFD results are then used to train the machine learning models. Different machine learning models are evaluated based on their Root Mean Square Error. Five machine learning models from five different categories are selected for second round evaluation. The trained machine learning model is used in the genetic algorithm optimization, which yields the optimized configuration and is again justified by CFD. The new CFD results based on the optimized design are added into the database to further refine the machine learning model. After 24 iterations for each selected machine learning models, the medium Gaussian support vector machine model is identified as the best method for the present application. Iterations using the medium Gaussian support vector machine model continue until a satisfactory result is achieved. Detailed combustion analysis is conducted to investigate the physical mechanism about how the design of pre-chamber influences the engine's performance. It is found that larger volume of the upper part of the pre-chamber results in stronger jet flow and turbulent intensity which further accelerates the flame propagation inside the pre-chamber, dominating the contrary effects from reduced pressure and temperature. Regression analysis shows that the radius of the pre-chamber is the most influential design parameter. The current work not only sheds light on the optimization of engine design, but also has demonstrated a general strategy applicable to the purpose of arbitrary engine optimization and mechanical system design.

People are on the move for many reasons such as war and civil war, human rights, violation, economic, social, climate, environmental, political, and individual reasons that create these changing aspects. In such complex situations, the need to ee (forcibly displaced) versus the choice to leave (migration)can be difficult to determine. The issue of refugee resettlement is complex and includes many factors to consider. Factors being considered for their impact on resettlement include budget and cost issues, federal law and policies, administration challenges, security screening process, education and training, health and housing, crime rate, socioeconomic issues and many others. The objective of this article is to discuss how the aforementioned factors relates and interact with one another using Interpretive Structural Modeling (ISM). ISM methodology implementation against this problem was consisted of a group of 25 undergraduate students in senior design class, all pursuing Mechanical Engineering degree at Texas Tech University, two Ph.D. students, one faculty member in design, four research engineers from different companies. This group recognized significant difficulties and challenges in carrying out successful refugee resettlement and sought to identify the main factors affecting the problem and how they were interrelated, with the goal of improving the rate of success for these displaced individuals.

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