QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.

A Self-consistent Calculation and an Anisotropic Wavelength Cut off Energy of Spin-wave Spectrumin Magnetic Tunnel Junctions

Temperature dependence of tunnel magnetoresistance (TMR) ratio, resistance, and coercivity from 4.2 K to room temperature (RT), applied de bias voltage dependence of the TMR ratio and resistances at 4.2 K and RT, tunnel current I and dynamic conductance dI/dV as functions of the de bias voltage at 4.2 K, and inelastic electron tunneling (IET) spectroscopy, d(2)I/dV(2) versus V, at 4.2 K for a tunnel junction of Ta(5 nm)/Ni79Fe21(25 nm)/Ir22Mn78(12 nm)/Co75Fe25(4 nm)/Al(0.8 nm)-oxide/Co75Fe25(4 nm)/Ni79Fe21(25 nm)/Ta(5 nm) were systematically investigated. High TMR ratio of 59.2% at 4.2 K and 41.3% at RT were observed for this junction after annealing at 275 degreesC for an hour. The temperature dependence of TMR ratio and resistances from 4.2 to 300 K at 1.0 mV bias and the de bias voltage dependence of TMR ratio at 4.2 K from 0 to 80 mV can be evaluated by a comparison of self-consistent calculations with the experimental data based on the magnon-assisted inelastic excitation model and theory. An anisotropic wavelength cutoff energy of spin-wave spectrum in magnetic tunnel junctions (MTJs) was suggested, which is necessary for self-consistent calculations, based on a series of IET spectra observed in the MTJs.

Photoelectron angular distributions of H ionization in low energy regime:Comparison between different potentials

We theoretically investigate the low energy part of the photoelectron spectra in the tunneling ionization regime by numerically solving the time-dependent Schrdinger equation for different atomic potentials at various wavelengths.We find that the shift of the first above-threshold ionization（ATI） peak is closely related to the interferences between electron wave packets,which are controlled by the laser field and largely independent of the potential.By gradually changing the short-range potential to the long-range Coulomb potential,we show that the long-range potential＇s effect is mainly to focus the electrons along the laser＇s polarization and to generate the spider structure by enhancing the rescattering process with the parent ion.In addition,we find that the intermediate transitions and the Rydberg states have important influences on the number and the shape of the lobes near the threshold.

Influence of temperature on tunneling-enhanced recombination in Si based p–i–n photodiodes

We investigate the dominant dark current transport mechanism in Si based p-i-n photodiodes, namely, BPW 21R, SFH 205FA and BPX 61 photodiodes in the temperature range of 350 to 139 K. The forward current- voltage characteristics of these photodiodes are explained via the tunneling enhanced recombination model, which gives a quantitative description of the electronic mechanism in the p-i-n junction photodiodes. The observed tem- perature dependence of the saturation current and the diode ideality factor of these devices agree well with theo- retical predictions; the analysis also indicates the importance of doping for enhancement of tunneling. The present study will be helpful in applying the devices at low temperature ambience.

Energy tunnels:concept and design aspects

Geotechnical structures are increasingly employed as energy geostructures in Europe and worldwide.Besides being constructed for their primary structural role,they are equipped to exchange heat with the ground and supply thermal energy for heating and cooling of buildings and de-icing of infrastructures.This technology can play a fundamental role in the current challenge of addressing the increasing need for clean and renewable sources of energy.This study investigates the possibility of thermal activation of tunnel linings.Particularly,attention will be paid on a new energy segment,which can be used together with tunnel boring machine tunneling to create so-called energy tunnels.Thermal and mechanical designs need to be developed by making effective use of computational methods to quantify the exploitable heat and assess the possible consequences on the surrounding ground and the structure itself.Guidance on how to proceed in this direction will be provided in this study,showing how thermo-hydro and thermo-mechanical numerical analyses can be used to achieve a proper and effective design of energy tunnels.Two examples of possible applications will also be presented.

Optimization of pipe circuits in energy tunnels

Geothermal energy is a kind of green and renewable energy.Conventionally,ground source heat pumps can be used to harvest geothermal energy from the subsurface.To reduce the initial investment,a good solution is to use tunnel linings as heat exchangers to extract/dump heat.This special infrastructure is called an energy tunnel.In addition to the thermal performance,the impact of pipe network configuration on thermal efficiency is still challenging in the design of energy tunnels.To solve this problem,this study makes the first attempt to carry out research on the optimization of pipe circuits in energy tunnels by a series of numerical analyses.A fully coupled thermo-hydraulic 3D finite element model is established to investigate the response of tunnel-soil interaction under cyclical thermal loading(initial soil temperature varies from 8C to 18C),as well as the thermal transient interactions among air,absorber pipe,tunnel linings and ground,to quantify the amount of useful heat that can be extracted from the tunnel and the ground.On the other hand,the influence of 3 various heat-carrying pipes layout is also investigated.It is found that higher heat transfer efficiency can be obtained when the entrance and exit of pipelines are located below the tunnel in the study.The spatial location of pipelines will also affect the exchanged heat output.