Low-Power Thermoelectric Generator Using Waste Heat...

Jo Jun 10, 2025

Electricity production is an important issue for our life. At present, power generation depends on fossil fuels. Excessive fossil fuel consumption by human activities has led to serious atmospheric and environmental problems including global warming, greenhouse gas emission, climate change, ozone layer depletion and acid rain. One of the energy generation methods to reduce the impact of natural disasters is thermoelectric conversion technology.

A thermoelectric generator produces electricity by using the thermoelectric effect. Thermoelectric generators consist of several sets of thermoelectric modules inserted between two heat exchangers. Each module is then composed of several tens to hundreds of pairs of thermoelectric couples connected together electrically in series and thermally in parallel, which directly convert some of the thermal energy that passes through them into electricity. However, the defects of thermoelectric technology are low energy-conversion efficiency and high material cost.

So far, different plans to get a higher degree of its efficiency have been suggested. Most of them, however, based on the main dimension of heat exchangers, focus on the influences of factors including temperature, velocity and flow direction in fluid, and the type of its internals, but not on the effect of the array of thermoelectric devices.

Kim Kwang Jin, a researcher at the Semiconductor Institute, has carried out a coupled fluid-solid numerical simulation of the heat exchanger internal flow field of a low-power thermoelectric generator using the waste heat from stove.

He carried out thermal field simulations in the heat exchanger with fins and without fins and an analysis of the shape of the inner fins, which were in good agreement with the results of the previous studies. Then, he determined the design parameters of the thermoelectric generator through the simulations according to the main dimensions, the number of inner fins and the arrangement of thermoelectric elements of the heat exchanger. He found that the performance of the heat exchanger is better, when the main dimensions of the heat exchanger is 250×200×30mm, the array of thermoelectric devices is linear and the internal topology of the heat exchanger is straight with 9 fins.

Design of Oxygen Sensor Digital Filter using Improv...

Jo Jun 9, 2025

The environment of virtual motion chambers must be designed to be arbitrarily controlled according to the purpose. Main environmental parameters include temperature, humidity, CO₂ concentration, and oxygen concentration. In order to measure and control these parameters, environmental sensors should be installed in various parts of the chamber for real-time monitoring control.

Existing sensors usually consist of a transducer that maps physical quantities to electrical signals, an analog-to-digital converter (ADC), a microprocessor, and an output interface that maps them to standard analog or digital signals. To measure physical quantities, the systematic error component can be modified by calibrating the sensors.

For noise filtering, hardware methods (e.g., installing analog active and analog passive reference frequency filters at the input of ADC or installing discrete active reference frequency filters at the output) or program filters (average filters, moving average filters, Kalman filters and artificial neural network filters) can be used. The device active reference filter is bulky and expensive, and consumes a large amount of power, and the passive reference frequency filter is less efficient, so it is most reasonable to use a program filter.

Under operating conditions, the input of the sensor has continuous signals, which vary randomly with time, and the rate of change is limited by physical reasons. In this case, a distorted signal appears at the output of the filter. The dynamic distortion of effective signals by the average filter and the moving average filter depends on the maximum rate of change of input signals. Therefore, it is effective to use the average filter or moving average filter when the change rate of input signals is not large.

Han Chol Hak, a researcher at the Faculty of Automation Engineering, has proposed an improved additive-mean algorithm that suits the characteristics of objects to improve the accuracy of oxygen concentration measurements in environmental sensors, and demonstrated the superiority of its filtering effect through comparisons with other algorithms.

The improved average algorithm can be used effectively for oxygen concentration sensing in embedded systems as well as for processing signals with low measurement signal variation and high noise intensity.

Calculation of Graphene Layer Number Prediction Bas...

Jo Jun 8, 2025

Graphene, a two-dimensional (2D) carbon nanomaterial, has attracted worldwide attention owing to its fascinating properties. Graphene, first manufactured by micromechanical exfoliation in 2004, has been regarded as a typical nanomaterial in the 21st century due to its great potential for use in various fields such as information industry, electronics, environment and energy, biotechnology, chemical industry, etc.

Graphene can be fabricated by electrochemical methods such as mechanical exfoliation of graphite, chemical vapor deposition (CVD) on copper film surface, cutting of carbon nanotubes, liquid phase exfoliation and Hummers method.

Since the unique properties of graphene are largely determined by its structure, it is important to study the number of layers that reflect its structural properties.

The methods for determining the number of layers of graphene include Raman spectroscopy, scanning electron microscopy (SEM), specific surface area measurement (BET), atomic force microscopy (AFM), and transmission electron microscopy (TEM). These methods have relatively high accuracy, but they require high cost and high-quality equipment.

Ri Ju Song, a researcher at the Institute of Nano Science and Technology, has proposed a method for determining the number of layers of graphene obtained by liquid phase exfoliation using XRD diffraction and Laue functions.

The results calculated by the Laue function model are in close agreement with the experimental data measured by Raman spectroscopy.

This method is relatively simple, but has the advantage of estimating the number of graphene layers with low cost and simple equipment.