Simulation of the temperature distribution during the Pulse Electrochemical Machining (PECM) process provides information on system design and guidelines for practical use. The pulses that are applied to the PECM system have to be described on a time scale that can be orders of magnitude smaller than the time scale on which the thermal effects evolve. If the full detail of the applied pulses has to be taken into account, the time accurate calculation of the temperature distribution in PECM can become a computationally very expensive procedure. A new approach is introduced by time averaging the heat sources of the system. Performing this, the time steps used during the calculations are no longer dictated by the pulse characteristics. Using this approach, computationally very cheap, yet satisfactory results can be obtained. In this part of the work, the hybrid calculation and the Quasi Steady State ShortCut (QSSSC) are introduced. The hybrid calculation is a method, by which averaged and pulsed heat sources are combined in one calculation. The QSSSC is a method for quickly calculating the Quasi Steady State (QSS) in numerical calculations with time stepping. Analytical solutions of simplified cases are studied to provide useful insights into the more general case. It is shown that the averaging technique adopted in this work does not always deliver perfect results. However, using a technique of shifting the pulses in time, the results can become very satisfactory yet still extremely cheap. The more general case, which will be solved numerically, can be found in part II [Smets et al. J Appl Electrochem (Submitted)] of this work. Notation A Electrode surface (m 2 ) Bi Biot Number ð¼ hH k Þ C p Heat capacity (J kg À1 K À1 ) Fo Fourier Number ð¼ a 0 t H 2 Þ h Heat transfer coefficient (W m À2 K À1 ) H Characteristic size electrode (m) j Current density (A m À2 ) k Thermal conductivity (W m À1 K À1 ) L Electrode length (m) P dl Heat density produced in the double layer (W m À2 ) P bulk Heat density produced in the bulk (W m À3 ) r General location vector (m) St Strouhal Number ð¼ L vT Þ t Time (s) t 0 Time (s) k Time (s) T Pulse period (s) v Scalar velocity (m s À1 ) v Velocity vector (m s À1 ) V Volume (m 3 ) x Distance (m) a Duty cycle a 0Thermal diffusivity (m 2 s À1
Stem cells derived from midguts of the caterpillar, Spodoptera littoralis, can be induced to multiply and differentiate in vitro. Ecdysone (E) and 20-hydroxyecdysone (20E) had a concentration-dependent effect: E was more active in cell proliferation and 20E in differentiation. Ecdysteroid receptors in midgut stem cell nuclei were stained with the antibody 9B9. In addition, alpha-arylphorin and four midgut differentiation factors (MDF) specifically stimulated proliferation and differentiation of stem cells, respectively. The activity of a panel of peptide growth factors and hormones on growth and metamorphosis of the insect midgut is discussed.
Simulation of the temperature distribution and evolution during pulse electrochemical machining can be a computationally very expensive procedure. In a previous part of the work [Smets et al. J Appl Electrochem 37(11):1345 a new approach to calculate the temperature evolution was introduced: the hybrid method, which combines averaged and pulsed calculations. The averaged calculations are performed by time averaging the boundary conditions and the bulk heat sources of the system. The timesteps used during the averaged calculations are then no longer dictated by the pulse characteristics. Using this approach, computationally very cheap, yet satisfactory results can be obtained. The analysis in the previous part of the work was obtained from analytical solutions on simplified models. In this part, the more general case is solved numerically. Multiple geometries are simulated and analyzed and methods are compared. Very satisfactory, yet cheap results are obtained.
Pulse Electrochemical Machining (PECM) is a manufacturing process which provides an economical and effective method for machining hard materials into complex shapes. One important drawback of ECM is the lack of quantitative simulation software to predict the tool shape and machining parameters necessary to produce a given work-piece profile. Calculating temperature distributions in the system allows more accurate simulations, as well as the determination of the thermal limits of the system. In this paper temperature transients over multiple pulses are calculated. It is found that the way the system is modeled has a great impact on the temperature evolution in the thermal boundary layer. The presence of massive electrodes introduces extra time scales which may not be negligible. It is advantageous to identify the thermal time scales in the system, to see whether the heat produced during separate pulses will accumulate or not during the process. The occurring thermal time scales in the system are discussed in detail. List of Symbolsa Polarization parameter 1/S m )2 A Electrode surface/m 2 b Polarization parameter 2/A m )2 Bi Biot number/) C p Heat capacity/J kg )1 K )1 D 0,i Diffusion coefficients at infinite dilution/ m 2 s )1 E 0 Equilibrium potential/V Fo Fourier number/) h Heat transfer coefficient/W m )2 K )1 H Characteristic size electrode/m I Electrical current/A J Current density distribution/A m )2 k Thermal conductivity/W m )1 K )1 k mol Molecular thermal conductivity/ W m )1 K )1 k turb Turbulent thermal conductivity/ W m )1 K )1 L Electrode length/m P doublelayer Heat produced in the double layer/W m )2 P bulk Heat produced in the bulk/W m )3 Pr t Turbulent Prandtl number/) q Heat/W St 0 Strouhal number/) St 0 Strouhal number for flushing period/) t Time/s t 0 Pulse off-time/s T Pulse period/s Dt c Transient duration due to convection/s U Potential distribution/V m Scalar velocity/m s )1 " m Velocity vector/m s )1 V Volume/m 2 x Distance/m a Duty cycle/) a¢ Thermal diffusivity/m 2 s )1 g
Ion transport models are compared by computing the limiting current density of an electrodeposition on a rotating disk electrode for various hypothetical electrolytes. The first ion transport model is the pseudoideal solution model, on which many commercial electroanalytical simulation tools are built. The second, more rigorous model consists of the linear phenomenological equations for which the activity coefficients and Onsager coefficients are calculated locally with the mean spherical approximation (MSA).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.