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Guo Li

Bio: Guo Li is an academic researcher from Beihang University. The author has contributed to research in topics: Turbine & Waste heat recovery unit. The author has an hindex of 7, co-authored 13 publications receiving 113 citations.

Papers
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Journal ArticleDOI
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.

51 citations

Journal ArticleDOI
TL;DR: In this paper, the authors formulated a systematic design methodology to seek optimal parameters and geometric model of the Tesla turbine which is applied to a coolant waste heat recovery system of an automobile engine.

44 citations

Journal ArticleDOI
Fenzhu Ji1, Yong Pan1, Yu Zhou1, Farong Du1, Qi Zhang1, Guo Li1 
TL;DR: In this paper, the authors presented an energy recovery method for electric vehicles. But, it is still a challenging issue to maximize the energy recovery and extend the driving range of the vehicle.
Abstract: Energy recovery is a key technology to improve energy efficiency and extend driving range of electric vehicle. It is still a challenging issue to maximise energy recovery. We present an ene...

23 citations

Journal ArticleDOI
TL;DR: This work proposes a method of probabilistic failure risk assessment for aeroengine disks considering a transient process, and the core procedure is zone definition through refinement and further partition of a constant pre-zone based on the time-varying stress in a flight cycle.

15 citations

Journal ArticleDOI
TL;DR: In this article, a noncontact rotational thermal-structure deformation measurement system based on digital image correlation (DIC-2D) technology was proposed and established to measure in-plane thermal structure deformation on a high speed rotating disk accurately.

14 citations


Cited by
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Journal ArticleDOI
23 Sep 2021-Sensors
TL;DR: In this paper, a survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics.
Abstract: Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

54 citations

Journal ArticleDOI
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.

51 citations

Journal ArticleDOI
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.

51 citations

Journal ArticleDOI
TL;DR: Shortening product development cycles and fully customisable products pose major challenges for production systems as mentioned in this paper, and these not only have to cope with an increased product diversity but also enable h.....
Abstract: Shortening product development cycles and fully customisable products pose major challenges for production systems. These not only have to cope with an increased product diversity but also enable h...

44 citations

Journal ArticleDOI
TL;DR: In this paper, a temperature-constrained topology optimization for thermo-mechanical coupled problems under a design-dependent temperature field considering the thermal expansion effect remains an open problem.
Abstract: Temperature-constrained topology optimization for thermo-mechanical coupled problems under a design-dependent temperature field considering the thermal expansion effect remains an open problem. A t...

44 citations