The convection coefficient is an important thermal property. In this study, using an infrared thermometer, the convection coefficient of still air is estimated. First, the sample is heated in a sintering oven, then placed on a wood table for obtaining an almost adiabatic boundary, finally its temperature is recorded with respect to time using an infrared thermometer. The data is curve-fitted to find the sample temperature as a function of time. Using the sample’s physical dimensions, the specific heat capacity and the mass of the sample, the convection coefficient of still air is estimated.

Resistive sintering is a new method which enables the production of different material types by using mechanical load and electric current. For the required process performance, it is necessary to set the parameters such as current, voltage, dissipated power, electric energy, and production time correctly. The container is an important component of the resistive sintering system. Container electrical resistance is a value that is difficult to calculate in terms of the geometry of the build and to measure due to the smallness of the value. In this study, assuming that the container temperature is constant, the Laplace equation of the electric potential is solved in the cylindrical coordinates considering the axial symmetry by the finite difference method and then the electrical current and resistance of the sintering container was calculated by post-processing of the solved electric potential using Ohm's law in vector form. The volumetric power density is used to calculate the dissipated power in the container and the container current is calculated on the stiff area. It is seen that the current density is almost doubled at the stiff-container edges and the power density at these points reaches its maximum. At points distant from the stiffs, the electrical potential is almost constant and the potential difference is negligible. The main source of heating is shown to be the resistance of the stiffs. The resistance of the structure is found to be about 70 μΩ This method given here can be used in more sophisticated models to size an ECAS system.

Memristör 1971 yılında kuramsal olarak ortaya konmuş ve bundan yaklaşık 40 yıl sonra, 2008 yılında memristör davranışına sahip bir memristif sistem bulunmuştur. Memristör günümüzde yaygın biçimde dördüncü temel devre elemanı olarak kabul edilmektedir. Memristörün analog devreler alanında uygulamaları, üzerine artarak ilgi duyulan önemli bir çalışma alanıdır. Memristör temelli analog süzgeçler ayarlanabilir memristans özelliği sayesinde kazanç, değer katsayısı vb. süzgeç özelliklerinde klasik devre elemanları ile sağlanamayan otomatik ayarlanabilme özelliği getirmektedir. Bu çalışmada literatürde ilk kez alçak-geçiren ve yüksek-geçiren olmak üzere iki farklı tip memristör temelli Sallen-Key süzgeç analizi ve bunlara ilişkin benzetim sonuçları sunulmaktadır. Süzgeçler memristörün TiO₂ doğrusal sürüklenme modeli temel alınarak tasarlanmış ve analiz edilmiştir. Çok düşük frekanslarda doyum durumunun oluştuğu gözlenmiş, daha yüksek frekanslarda ise her iki süzgeçte de başarım için bir sınırlama ortaya çıkmadığı belirlenmiştir. Memristör temelli Sallen-Key süzgeçlerin tasarımında kullanılmak üzere doyma ve buna karşılık bozulmanın ortaya çıkmamasına yönelik tasarım kriterleri verilmiştir. Verilen kriterler ve buna ilişkin uygun memristör parametreleri kullanıldığında düşük frekanslarda da süzgeçlerin gerekli başarım koşullarını yerine getirdiği gösterilmiştir. Analiz sonuçları ve verilen tasarım kriterleri memristör temelli Sallen-Key ve diğer tip süzgeçlerin tasarımlarında kullanılabilir.

Lorenz equations are commonly used in chaos education and studies. Simulation programs can be used to produce solutions of Lorenz equations and to examine its chaotic waveforms. However, sometimes a chaotic signal source can be needed. Such a circuit can be made using either analog or digital circuit components. Recently, a microcontroller-based circuit is suggested to obtain chaotic waveforms of Lorenz equations however only simulations are used to show proof of concept. Such circuit needs experimental verification. In this paper, implementation and experimental verification of the microcontroller-based circuit which solves Lorenz equations in real time and produces its chaotic waveforms are presented. Runge-Kutta method is used to solve the equation system. By using Proteus, microcontroller-based chaotic circuit is simulated and designed. Presented design has been implemented using an Arduino Mega 2560 R3 microcontroller. The microcontroller sends the chaotic signals to the outputs of the circuit using digital-to-analog converters. The waveforms acquired experimentally from the implemented circuit matches well with those obtained from Proteus simulations.