The rapid development of microsatellites is triggering a new round of space revolution. For microsatellites, the distribution and control of the overall temperature of the satellite will be a key indicator that affects the normal operation of the on-board equipment inside the satellite and the overall lifespan of the satellite. However, with the development of microsatellites, traditional thermal control technology can no longer meet the satellite's demand for low-power and high-precision temperature control. Advanced materials have reignited the prospects of thermoelectric devices, making the application of thermoelectric technology in microsatellites a certain possibility. This study explored the possibility of using the thermoelectric devices in the microsatellite, that is, whether the current thermoelectric devices can adapt to the working temperature difference of the microsatellites. The finite element method is used to predict the actual complexity of the satellite operation by taking into account both the internal power consumption and external heat flux. According to the obtained numerical results, the microsatellite which is based on a low-power thermoelectric device (0.225W) can operate within the temperature range from 0°C to 40°C under the limited power. All apparatus of microsatellite can work reliably in this temperature. The research shows that thermoelectric devices have the potential to implement effective active thermal control of microsatellites, which will provide an important reference direction for the integration of satellites. In the follow-up work, the focus will be on high-precision temperature control based on thermoelectric devices, and systematic and reliable test verification of this thermal control method will be carried out.
Abstract Advances in science and technology have increased the demand for greater temperature measurement accuracy. Radiation thermometry is a mature technology that is applicable to various industrial fields. In special environments (e.g. a high ambient temperature), background radiation reflected by the target surface is superimposed with the radiation from the target itself, which affects the measured radiation temperature. In this study, different radiation thermometry methods were evaluated. The errors of single-spectral, colorimetric, and multispectral temperature measurements of targets in a high-temperature environment have been discussed. Based on this, we propose a multi-wavelength temperature measurement method with reflection correction to reduce the impact of high-temperature environment. Experiments show that this method can reduce the uncertainty in the multispectral temperature measurement from 4.16% to 0.26% under the experimental conditions.
Abstract To address the challenge of obtaining key device strain parameters within narrow spaces, this paper presents the design of a small-sized optical imaging probe with an independent optical system based on the basic principle of digital image correlation (DIC). To validate the strain measurement accuracy of the probe, a high-temperature heating system was built under in a laboratory setting, and experimental tests involving room temperature strain measurement and high temperature thermal expansion coefficient measurement were conducted. The findings demonstrate the potential of the developed probe for strain measurement in critical components situated in restricted spaces.
Spectral emissivity is an essential and sensitive parameter to characterize the radiative capacity of the solid surface in scientific and engineering applications, which would be non-negligibly affected by surface morphology. However, there is a lack of assessment of the effect of roughness on emissivity and a straightforward method for estimating the emissivity of rough surfaces. This paper established an estimating method based on constructing random rough surfaces to predict rough surface (Geometric region) emissivity for metal solids. Based on this method, the emissivity of ideal gray and non-gray body surfaces was calculated and analyzed. The calculated and measured spectral emissivities of GH3044, K465, DD6, and TC4 alloys with different roughness were compared. The results show that the emissivity increases with the roughness degree, and the enhancement effect weakens with the increase of roughness or emissivity due to the existing limit (emissivity ε = 1.0). At the same time, the roughness would not change the overall spectral distribution characteristics but may attenuate the local features of the spectral emissivity. The estimated results are in good agreement with the experimental data for the above alloys' rough surfaces. This study provides a new reliable approach to obtaining the spectral emissivity of rough surfaces. This approach is especially beneficial for measuring objects in extreme environments where emissivity is difficult to obtain. Meanwhile, this study promotes an understanding of surface morphology's effect mechanism on emissivity.
Uunknown emissivity of objects is an unignorable obstacle in radiation thermometry, especially for multispectral-band pyrometry. Assumed wavelength-emissivity modules are wildly used for solving the underdefined system of multispectral-band pyrometry while the accuracy is strongly dependent on the similarity between real object emissivity characteristic and the assumed one. Data processing methods based on optimization theory have the capability to solving the underdefined system without the emissivity knowledge, however, the calculation time could not satisfy the need for real-time use. Here, we propose a double-stage neural network for multispectral-band pyrometry in the NIR range independent of advanced emissivity knowledge. The first stage is trained by dataset generated by blackbody source and Plank's law with ideal spectral-band radiation energy output, while the second stage is trained by dataset generated by ideal blackbody spectral-band radiation energy added different type wavelength-emissivity module (graybody, linear, sine, mixed graybody-linear-sine), and network final output is the true temperature. Multispectral-band temperature measurement experiment results of Ti-6A1-4V alloy shows that the relative error of predicted temperature and the true temperature is smaller than 2.5% for the neural network trained by single type wavelength-emissivity module, and the relative error is less than 3.0% for the neural network trained by mixed-type wavelength-emissivity module.
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