Study of Current Sheath Velocity and Its Distribution Using Tridimensional Magnetic Probe in Sahand Plasma Focus

The current sheath velocity in 0.25 Torr gas pressure of Filippov type plasma focus is studied experimentally. By using two tridimensional magnetic probes on top of the anode surface, the current sheath velocity is measured for argon, oxygen and nitrogen. Additionally, the effect of charging voltage on the current sheath velocity is studied in both axial and radial phases. We found that, the maximum current sheath velocities at both radial and axial phases are respectively 4.33 ± 0.28 （cm/μs） and 3.92 ± 0.75 （cm/μs） with argon as the working gas at 17 kV. Also, the minimum values of current sheath velocity are 1.48 ± 0.15 （cm/μs） at the radial phase and 1.14 ± 0.09 （cm/μs） at the axial phase with oxygen at 12 kV. The current sheath velocity at the radial phase is higher than that at the axial phase for all gases and voltages. In this study, variation of the full width half maximum （FWHM） of magnetic probe signals with voltage is investigated for different gases at radial and axial phases.

A new biomimicry marine current turbine: Study of hydrodynamic performance and wake using software OpenFOAM

Inspired by Dryobalanops aromatica seed, a new biomimicry marine current turbine is proposed. Hydrodynamic performance and wake properties are two key factors determining whether a new marine current turbine design is practical or not. Thus, a study of hydrodynamic performance and wake of the proposed biomimicry turbine is conducted. The computational fluid dynamics（CFD） software, Open FOAM is used to generate the required results for the mentioned study. The hydrodynamic performance and wake properties of the proposed biomimicry turbine is compared to two conventional turbines of Bahaj et al. and Pinon et al. respectively. The simulation results showed that the proposed biomimicry marine current turbine gives optimum power output with its power coefficient, 0.376 PC ≈ at the tip speed ratio（TSR） of 1.5. Under the same boundary conditions, the maximum torque produced by the proposed biomimicry turbine at zero rotational speed is 38.71 Nm which is 1110% greater than the torque generated by the turbine of Bahaj et al.. The recovery distance for the wake of the biomimicry turbine is predicted to be 10.6% shorter than that of IFREMER-LOMC turbine. The above-mentioned results confirm the potential application of the proposed biomimicry marine current turbine in the renewable energy industry.

Importance of initial buoyancy field on evolution of mantle thermal structure:Implications of surface boundary conditions

Although there has been significant progress in the seismic imaging of mantle heterogeneity, the outstanding issue that remains to be resolved is the unknown distribution of mantle temperature anomalies in the distant geological past that give rise to the present-day anomalies inferred by global tomography models. To address this question, we present 3-D convection models in compressible and self-gravitating mantle initialised by different hypothetical temperature patterns. A notable feature of our forward convection modelling is the use of self-consistent coupling of the motion of surface tectonic plates to the underlying mantle flow, without imposing prescribed surface velocities （i.e., p/ate-like boundary condition）. As an approximation for the surface mechanical conditions before plate tectonics began to operate we employ the no-slip （rigid） boundary condition. A rigid boundary condition dem- onstrates that the initial thermally-dominated structure is preserved, and its geographical location is fixed during the evolution of mantle flow. Considering the impact of different assumed surface boundary conditions （rigid and plate-like） on the evolution of thermal heterogeneity in the mantle we suggest that the intrinsic buoyancy of seven superplumes is most-likely resolved in the tomographic images of present-day mantle thermal structure. Our convection simulations with a plate-like boundary condition reveal that the evolution of an initial cold anomaly beneath the Java-lndonesian trench system yields a long-term, stable pattern of thermal heterogeneity in the lowermost mantle that resembles the present- day Large Low Shear Velocity Provinces （LLSVPs）, especially below the Pacific. The evolution of sub- duction zones may be, however, influenced by the mantle-wide flow driven by deeply-rooted and long- lived superplumes since Archean times. These convection models also detect the intrinsic buoyancy of the Perm Anomaly that has been identified as a unique slow feature distinct from the two principal LLSVPs. We find there is no need for dense chemical ＇piles＇ in the lower mantle to generate a stable distribution of temperature anomalies that are correlated to the LLSVPs and the Perm Anomaly. Our tomography-based convection simulations also demonstrate that intraplate volcanism in the south-east Pacific may be interpreted in terms of shallow small-scale convection triggered by a superplume beneath the East Pacific Rise.

Computing Open-Loop Optimal Control of the q-Profile in Ramp-Up Tokamak Plasmas Using the Minimal-Surface Theory

The q-profile control problem in the ramp-up phase of plasma discharges is consid- ered in this work. The magnetic diffusion partial differential equation （PDE） models the dynamics of the poloidal magnetic flux profile, which is used in this work to formulate a PDE-constrained op-timization problem under a quasi-static assumption. The minimum surface theory and constrained numeric optimization are then applied to achieve suboptimal solutions. Since the transient dy- namics is pre-given by the minimum surface theory, then this method can dramatically accelerate the solution process. In order to be robust under external uncertainties in real implementations, PID （proportional-integral-derivative） controllers are used to force the actuators to follow the computational input trajectories. It has the potential to implement in real-time for long time discharges by combining this method with the magnetic equilibrium update.

Effect of collector bias current on the linearity of common-emitter BJT amplifiers

Using a Volterra series, an explicit formula is derived for the connection between input 3rd-order intercept point and collector bias current （IcQ） in a common-emitter bipolar junction transistor amplifier. The analysis indicates that the larger/CQ is, the more linear the amplifier is. Furthermore, this has been verified by experiment. This study also integrates a method called dynamic bias current for expanding the dynamic range of an LNA （low noise amplifier） as an application of the analysis result obtained above. IMR3 （3rd-order intermodulation rate） is applied to evaluate the LNA＇s performance with and without adopting this method in this study.

Physical-based training data collection approach for data-driven lithium-ion battery state-of-charge prediction

Data-Driven approaches for State of Charge(SOC)prediction have been developed considerably in recent years.However,determining the appropriate training dataset is still a challenge for model development and validation due to the considerably varieties of lithium-ion batteries in terms of material,types of battery cells,and operation conditions.This work focuses on optimization of the training data set by using simple measurable data sets,which is important for the accuracy of predictions,reduction of training time,and application to online esti-mation.It is found that a randomly generated data set can be effectively used for the training data set,which is not necessarily the same format as conventional predefined battery testing protocols,such as constant current cycling,Highway Fuel Economy Cycle,and Urban Dynamometer Driving Schedule.The randomly generated data can be successfully applied to various dynamic battery operating conditions.For the ML algorithm,XGBoost is used,along with Random Forest,Artificial Neural Network,and a reduced-order physical battery model for comparison.The XGBoost method with the optimal training data set shows excellent performance for SOC prediction with the fastest learning time within 1 s,a short running time of 0.03 s,and accurate results with a 0.358%Mean Absolute Percentage Error,which is outstanding compared to other Data-Driven approaches and the physics-based model.