Bio: Zheng Xu is an academic researcher from Beihang University. The author has contributed to research in topics: Two-stroke engine & Piston. The author has an hindex of 4, co-authored 13 publications receiving 37 citations.
TL;DR: In this paper, a simulation model of a range-extended electric vehicle (REEV) based on vehicle dynamic is constructed to evaluate the contribution of MGTRE to REEV performance, especially for driving range based on the New European Driving Cycle.
TL;DR: The paper proposes a digital twin (DT)-driven optimization method with several DT modules for the system to virtually simulate and optimize the parameters, performance and manufacturing with data interaction and recorded.
TL;DR: In this article, a reformative experimental platform and one-dimensional simulation model are proposed to accurately predict the high-altitude performance and explore the improvement methods of a PV2S aircraft diesel engine with a combined supercharging system.
TL;DR: The U-type loop scavenging of a two-stroke diesel engine with two poppet valves is generally unsatisfactory as mentioned in this paper. But, the scavenging is becoming one of the determinants of twostroke engine performance.
Abstract: Scavenging is becoming one of the determinants of two-stroke engine performance. Efficiency of U-type loop scavenging of two-stroke diesel engine with two poppet valves is generally unsatisfactory ...
TL;DR: In this article , the development history and characteristics of gas exchange types, as well as the current state of theory and the validation methods of Gas Exchange technology, while also discussing the trends of cutting-edge technologies in the field.
TL;DR: This paper provides a comprehensive review of different types of EV range extending technologies, including internal combustion engines, free-piston linear generators, fuel cells, micro gas turbines, and zinc-air batteries, outlining their definitions, working mechanisms, and some recent developments of each range extending technology.
Abstract: Emissions from the transportation sector are significant contributors to climate change and health problems because of the common use of gasoline vehicles Countries in the world are attempting to transition away from gasoline vehicles and to electric vehicles (EVs), in order to reduce emissions However, there are several practical limitations with EVs, one of which is the “range anxiety” issue, due to the lack of charging infrastructure, the high cost of long-ranged EVs, and the limited range of affordable EVs One potential solution to the range anxiety problem is the use of range extenders, to extend the driving range of EVs while optimizing the costs and performance of the vehicles This paper provides a comprehensive review of different types of EV range extending technologies, including internal combustion engines, free-piston linear generators, fuel cells, micro gas turbines, and zinc-air batteries, outlining their definitions, working mechanisms, and some recent developments of each range extending technology A comparison between the different technologies, highlighting the advantages and disadvantages of each, is also presented to help address future research needs Since EVs will be a significant part of the automotive industry future, range extenders will be an important concept to be explored to provide a cost-effective, reliable, efficient, and dynamic solution to combat the range anxiety issue that consumers currently have
TL;DR: This paper breaks traditional procedures and presents a DT-based optimization strategy on the consideration of both machining efficiency and aerodynamic performance, as well as builds a reified 5-dimensional DT model.
TL;DR: A solution to existing challenging issues is proposed by introducing the digital twin (DT) technology into the OWT support structures and this new DT framework will enable real-time monitoring, fault diagnosis and operation optimization of the O WT support structures, which may provide a useful application prospect in the reliability analysis in the future.
TL;DR: A Digital Twin-driven thin-walled part manufacturing framework to allow the machine operator to manage the product changes, make the start-up phases faster and more accurate, and serve as a guideline for establishing the machine tool and workpiece Digital Twin and integrating them into the machining process.
TL;DR: A random forest machine learning model is proposed as a cost-effective tool for optimizing engine performance and was shown to be able to predict the combustion-related feedback information with good accuracy and accurately reproduced the effect of control variables on IMEP.
Abstract: Engine calibration requires detailed feedback information that can reflect the combustion process as the optimized objective. Indicated mean effective pressure (IMEP) is such an indicator describing an engine’s capacity to do work under different combinations of control variables. In this context, it is of interest to find cost-effective solutions that will reduce the number of experimental tests. This paper proposes a random forest machine learning model as a cost-effective tool for optimizing engine performance. Specifically, the model estimated IMEP for a natural gas spark ignited engine obtained from a converted diesel engine. The goal was to develop an economical and robust tool that can help reduce the large number of experiments usually required throughout the design and development of internal combustion engines. The data used for building such correlative model came from engine experiments that varied the spark advance, fuel-air ratio, and engine speed. The inlet conditions and the coolant/oil temperature were maintained constant. As a result, the model inputs were the key engine operation variables that affect engine performance. The trained model was shown to be able to predict the combustion-related feedback information with good accuracy (R2 ≈ 0.9 and MSE ≈ 0). In addition, the model accurately reproduced the effect of control variables on IMEP, which would help narrow the choice of operating conditions for future designs of experiment. Overall, the machine learning approach presented here can provide new chances for cost-efficient engine analysis and diagnostics work.