Tunnelling performance prediction of cantilever boring machine in sedimentary hard-rock tunnel using deep belief network

Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.

An FFT Performance Model for Optimizing General-Purpose Processor Architecture

General-purpose processor （GPP） is an important platform for fast Fourier transform （FFT）,due to its flexibility,reliability and practicality.FFT is a representative application intensive in both computation and memory access,optimizing the FFT performance of a GPP also benefits the performances of many other applications.To facilitate the analysis of FFT,this paper proposes a theoretical model of the FFT processing.The model gives out a tight lower bound of the runtime of FFT on a GPP,and guides the architecture optimization for GPP as well.Based on the model,two theorems on optimization of architecture parameters are deduced,which refer to the lower bounds of register number and memory bandwidth.Experimental results on different processor architectures （including Intel Core i7 and Godson-3B） validate the performance model.The above investigations were adopted in the development of Godson-3B,which is an industrial GPP.The optimization techniques deduced from our performance model improve the FFT performance by about 40%,while incurring only 0.8% additional area cost.Consequently,Godson-3B solves the 1024-point single-precision complex FFT in 0.368 μs with about 40 Watt power consumption,and has the highest performance-per-watt in complex FFT among processors as far as we know.This work could benefit optimization of other GPPs as well.

Knowledge-driven material design platform based on the whole-process simulation and modeling

In order to realize the agility,collaboration and visualization of alloy material devel-opment process,a product development platform based on simulation and modeling technologies is established in this study.In this platform,the whole-process simulation module builds multi-level simulation models based on metallurgical mechanisms from the production line level,the thermo-mechanical coupling field level and the microstructure evolution level.The design knowledge management module represents the multi-source heterogeneous material design knowledge through ontology model,including customers’requirement knowledge,material component knowledge,process design knowledge and quality inspection knowledge,and utilizes the case-based reasoning approach to reuse the knowledge.The data-driven modeling module applies machine learning algorithms to mine the relationships between product mechanical properties,material components,and process parameters from historical samples,and utilizes multi-objective optimiza-tion algorithms to find the optimal combination of process parameters.Application of the developed platform in actual steel mills shows that the proposed method helps to improve the efficiency of product design process.

Development and application of a throughflow method for high-loaded axial flow compressors

In this paper, a novel engineering platform for throughflow analysis based on streamline curvature approach is developed for the research of a 5-stage compressor. The method includes several types of improved loss and deviation angle models, which are combined with the authors' adjustments for the purpose of reflecting the influences of three-dimensional internal flow in high-loaded multistage compressors with higher accuracy. In order to validate the reliability and robustness of the method, a series of test cases, including a subsonic compressor P&W 3S1, a transonic rotor NASA Rotor 1B and especially an advanced high pressure core compressor GE E^3 HPC, are conducted. Then the computation procedure is applied to the research of a 5-stage compressor which is designed for developing an industrial gas turbine. The overall performance and aerodynamic configuration predicted by the procedure, both at design- and part-speed conditions, are analyzed and compared with experimental results, which show a good agreement. Further discussion regarding the universality of the method compared with CFD is made afterwards. The throughflow method is verified as a reliable and convenient tool for aerodynamic design and performance prediction of modern high-loaded compressors. This method is also qualified for use in the further optimization of the 5-stage compressor.