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.

Hydrogen and ammonia are primary carbon-free fuels that have massive production potential. In regard to their flame properties, these two fuels largely represent the two extremes among all fuels. The extremely fast flame speed of hydrogen can lead to an easy deflagration-to-detonation transition and cause detonation-type engine knock that limits the global equivalence ratio, and consequently the engine power. The very low flame speed and reactivity of ammonia can lead to a low heat release rate and cause difficulty in ignition and ammonia slip. Adding ammonia into hydrogen can effectively modulate flame speed and hence the heat release rate, which in turn mitigates engine knock and retains the zero-carbon nature of the system. However, a key issue that remains unclear is the blending ratio of NH3 that provides the desired heat release rate, emission level, and engine power. In the present work, a 3D computational combustion study is conducted to search for the optimal hydrogen/ammonia mixture that is knock-free and meanwhile allows sufficient power in a typical spark-ignition engine configuration. Parametric studies with varying global equivalence ratios and hydrogen/ammonia blends are conducted. The results show that with added ammonia, engine knock can be avoided, even under stoichiometric operating conditions. Due to the increased global equivalence ratio and added ammonia, the energy content of trapped charge as well as work output per cycle is increased. About 90% of the work output of a pure gasoline engine under the same conditions can be reached by hydrogen/ammonia blends. The work shows great potential of blended fuel or hydrogen/ammonia dual fuel in high-speed SI engines.

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.

<div class="section abstract"><div class="htmlview paragraph">There is a growing interest in ammonia as a potential carbon-free fuel due to the current trend of decarbonization in ground transportation. Benefits of ammonia as a fuel include its high volumetric energy density, ease of storage and transportation, and mature manufacturing infrastructure. On the other hand, ammonia suffers from a low flame speed, long ignition delay times and NOx formation. In this work, a computational investigation of ammonia and hydrogen blends in a 0-D homogeneous charge compression ignition reactor is conducted using different blends under a range of engine-relevant conditions. Iso-contours of the crank angle corresponding to 50% of total heat release (CA50) are developed to assess the reactivity of the different blends under different engine speeds and equivalence ratios. The results show that ammonia requires a high inlet temperature to achieve a CA50 close to top dead center (TDC). An increase in hydrogen concentration resulted in a lower inlet temperature required to achieve a CA50 close to TDC. The gradients of iso-contour can easily show the sensitivity of CA50, as well as NO and H₂ formation, to operating temperature and pressure in a wide range of conditions. A sensitivity analysis of the ignition delay showed that combustion phasing is highly promoted through hydrogen oxidation and the chain-branching reactions of the intermediate species. In terms of emissions, H₂ and NO possess the highest concentrations, which increase further with increasing hydrogen concentration in the fuel blend. A chemical flux analysis is conducted to understand the role of the reactions and species in H₂ and NO formation and consumption. This work provides useful insights into the chemical and thermal role of hydrogen in promoting the combustion of ammonia for future engine applications.</div></div>

With the growing trend of decarbonization in ground transportation, low and zero-carbon fuels have attracted extensive research interest. Liquid ammonia is a promising alternative fuel due to its relatively high volumetric energy density, mature production and distribution infrastructure, convenience of storage, and zero carbon emissions. However, ammonia combustion also suffers from low flame speed and weak chemical reactivity. In this work, we computationally investigate the suitable engine-relevant thermochemical conditions for auto-ignition of constant volume ammonia spray, as well as its spray dynamics, vaporization, flash boiling effects, and emissions. The simulation is first validated by comparing it against available experimental data from a vaporizing ammonia spray and is then extended to chemically reactive conditions. Results show that ammonia sprays under engine-relevant conditions (60 bar and 1200 K) can only successfully auto-ignite for cases with ambient hydrogen addition, through enhancement of thermal condition and chemical reactivity. A chemical flux analysis is conducted to further understand the important species and reactions that promote ammonia auto-ignition from hydrogen, which potentially can be introduced via H2 solubility, exhaust gas recirculation, and onboard ammonia thermal decomposition. Furthermore, results have indicated that charge cooling effects can further decrease the temperature in the flow field and make auto-ignition more difficult. This study provided useful insights for the application of ammonia as a zero-carbon diesel fuel for ground transportation.