The authors thank Mr. Wu, MS Sun, Mr. Wang, Mr. Mu, and Mr. Li for their help. This study was supported by the China Postdoctoral Science Foundation (2022M721604) and the Wenzhou Key Science and Technology Tackling Programmer (ZG2023015).

1. Model
NOTE: Due to the complex structure of the ring main unit (Figure 1A), an online design software was chosen to simplify the operation of the ring main unit.
<ol>
	<li>Modelling simplification
	<ol>
		<li>Partially simplify the model, preserving the air box section of the RMU while removing or simplifying other components such as insulating shafts, fastening bolts, nuts, sealing components, and pressure support brackets. The simplified version is shown in (Figure 1B).
		<ol>
			<li>In the process of simplifying the 630A type ring main unit, remove the insulated shaft connecting the circuit breaker room with the instrument box and many fixed bolts and nuts. Take out the sealing parts and pressure-retaining bracket and connect the static contacts of the isolated static beam with the lower branch busbar under the premise of ensuring that the whole setup has the same conductive current and only the vacuum circuit breaker, the circuit breaker fixing plate, and the static contacts and the vacuum circuit breaker are retained.</li>
			<li>Retain only the vacuum circuit breaker, circuit breaker fixing plate, static contact, and vacuum circuit breaker blocking plate. Overall, remove bolts and gaskets from the model, fill the holes after removing the bolts with solids, reduce the number of mesh parts, and optimize the irregular shapes of the parts. Remove instruments for panel operation, mounting plates, brackets, and other operating parts, such as instrument boxes, that have no effect on the temperature rise simulation process.</li>
			<li>Remove the insulated housings of some components can be disregarded under simulation as they have little effect on the simulation results. In addition, grounding switches that have no effect on the use of the equipment during normal operation remove them, and retain the circuit breaker room for simulation.</li>
		</ol>
		</li>
		<li>To delete any section, simply select it and click on the Delete option.</li>
	</ol>
	</li>
</ol>
2. Eddy field solution
<ol>
	<li>Pre-processing settings 
	NOTE: The Eddy current field emulation is the basis for performing the temperature field solution, which requires the subsequent analysis of the solved heat source as a load on the temperature field.
	<ol>
		<li>Refer to the equipment documentation for the ring main unit and the relevant manuals to gather information on the physical properties and parameters of each component of the ring main unit. Set the physical attributes and parameters of the ring main unit components in Maxwell based on the information obtained, as detailed in Table 1.</li>
		<li>Set the eddy current field load current at 630 A with a frequency of 50 Hz. In the Maxwell software, select One Side of the Upper and Lower Outgoing Arms, enter the excitation module, and set the current magnitude to 630 A. In the solution settings section, choose a frequency of 50 Hz. 
		NOTE: In a ring main unit&#39;s conductive circuit, the pathway formed by all the components from the upper outlet arm to the lower outlet arm is known as the phase sequence. Therefore, in this paper, the phases A, B, and C are arranged from left to right.</li>
		<li>The material parameters of the components of the ring main unit are shown in Table 2.</li>
		<li>Direct the current through the outgoing line arms, flexible connections, busbars, circuit breakers, static contact support busbars, and branch busbars for each phase. The aim is to realize a current path that allows the components to complete the load.</li>
		<li>Utilize Maxwell&#39;s adaptive meshing to complete grid control for the model. Use the Maxwell adaptive mesh partitioning method for larger components and the local mesh refinement for smaller internal components. 
		NOTE: Maxwell can continuously enhance grid precision during the solving process, eliminating the need to click Mesh Operations for additional mesh partitioning.</li>
		<li>Set the solution step size. Click on Analysis in the model tree, open the Solve Step settings, and set the Maximum Number of Passes to 10. Keep other settings at their default values without making any changes.</li>
	</ol>
	</li>
	<li>Principle of Eddy current field calculation15,16.
	<ol>
		<li>Use Maxwell&#39;s first equation, which describes the action of charge on the generation of an electric field17. 
		<img alt="Equation 1" src="/files/ftp_upload/66643/66643eq01.jpg" style="margin: auto;" />&#160; &#160; &#160;(1) 
		where&#160;&#961; represents the charge density; &#949;0 represents the vacuum dielectric constant.</li>
		<li>Use Maxwell&#39;s second equation, which describes the relationship between a changing magnetic field and an electric field and the effect of a magnetic field on the motion of a charge. 
		<img alt="Equation 2" src="/files/ftp_upload/66643/66643eq02.jpg" style="margin: auto;" />&#160; &#160; &#160;(2) 
		where <img alt="Equation 3" src="/files/ftp_upload/66643/66643eq03.jpg" style="margin: auto;" />&#160;represents the magnetic field strength. This equation describes that a varying magnetic field produces a vortex electric field, i.e., the spin of the vortex electric field is equal to the negative of the rate of change of the magnetic field with time.</li>
		<li>Use Maxwell&#39;s third equation, which describes the effect of magnetic charge on the production of a magnetic field. 
		<img alt="Equation 4" src="/files/ftp_upload/66643/66643eq04.jpg" style="margin: auto;" />&#160; &#160; &#160;(3) 
		This equation describes the magnetic field produced by a magnetic charge as being passive, i.e., there are no monopoles in the magnetic field.</li>
		<li>Use Maxwell&#39;s fourth equation, which describes the relationship between a varying electric field and a magnetic field and the effect of an electric current on a magnetic field. 
		<img alt="Equation 5" src="/files/ftp_upload/66643/66643eq05.jpg" style="margin: auto;" />&#160; &#160; &#160;(4) 
		where <img alt="Equation 6" src="/files/ftp_upload/66643/66643eq06.jpg" style="margin: auto;" /> represents the current density and&#160;&#956;0 represents the vacuum permeability. This equation describes that a varying electric field produces a vortex magnetic field, i.e., the spin of the vortex magnetic field is equal to the sum of the current density and the rate of change of the electric field with time.</li>
		<li>Based on the above equations, use the Maxwell 3D using Eddy current solver module to solve the ohmic loss generated by the conductive circuit in the RMU, which provides a heat source for the subsequent thermal simulation analysis. Its mathematical expression is given as18 
		<img alt="Equation 7" src="/files/ftp_upload/66643/66643eq07.jpg" style="margin: auto;" />&#160; &#160; &#160;(5) 
		where&#160;&#963; denotes the conductivity of the conducting loop material; J is the current density in the loop.</li>
	</ol>
	</li>
	<li>Calculation results
	<ol>
		<li>Click on the Maxwell 3D option in the interface and open the validation check to review all settings for errors. If there are no errors, proceed to click Analyze All to initiate the solving process.</li>
		<li>Utilize Maxwell&#39;s post-processing Calculator to compute and plot the ohmic losses in the Eddy current field of the ring main unit, as shown in Table 3.</li>
	</ol>
	</li>
</ol>
3. Temperature field solution
NOTE: For comparative purposes, divide the temperature field into Icepak and steady state thermal. Set up and solve each separately to achieve a comparative analysis.
<ol>
	<li>Icepak model setup
	<ol>
		<li>Set the material properties as follows: designate all circuit solid materials as Cu-Pure, with surfaces using Cu-polished-surface. For the panel components, select Aluminum6061-T6 material, with a surface coating of Paint-AL surface with an emissivity of 0.35. See Table 4 for details. Right-click on the Selected Component, click Edit and then go to Properties to set the material for both the surface and solid materials.</li>
		<li>Select the model and click on Set in the Edit menu, then choose Multilevel Meshing Level to adjust the mesh settings. Set the external cabinet to a mesh level of 2, and all boundaries to a mesh level of 2. For all other components, set the mesh level to 3. Finally, open Mesh control and click Generate to create the mesh.</li>
		<li>To ensure the accuracy and efficiency of the simulation regardless of the grid size, validation of grid independence is necessary. Import the geometric model of the temperature field enclosure, established using the design software for meshing.</li>
		<li>As depicted in Figure 2, the polarization curves of the four grid sets are well-aligned. At a working voltage of 0.5 V, the current densities for the four grid sets are 2.357 A/cm2, 2.358 A/cm2, 2.356 A/cm2, and 2.454 A/cm2, respectively, with the error between the maximum and current densities being less than 1%. To balance efficiency and accuracy, determine the grid size which comes to be 987924.</li>
	</ol>
	</li>
	<li>Solution setup
	<ol>
		<li>Set the directions of the solution domain Cabinet to Opening.</li>
		<li>In the software, select Problem Step. Under Basic Parameters, check the Surface-to-Surface Radiation Model, choose Zero Equation for Turbulent Flow Regime, select the Gravity option for Natural Convection, and set the ambient temperature to 20 &#176;C.</li>
		<li>In the File settings, choose Volumetric Heat Losses for EM Mapping and select All Objects Shown to complete the loss settings.</li>
	</ol>
	</li>
	<li>Temperature field calculation
	<ol>
		<li>In icepak, apply three main conservation equations for energy: mass conservation equation, momentum conservation equation, and energy conservation equation. Specifically, use the momentum conservation equation, which is as follows19: 
		<img alt="Equation 8" src="/files/ftp_upload/66643/66643eq08.jpg" style="margin: auto;" />&#160; &#160; &#160;(6) 
		Energy Conservation Equation: 
		<img alt="Equation 9" src="/files/ftp_upload/66643/66643eq09.jpg" style="margin: auto;" />&#160; &#160; &#160;(7) 
		Mass conservation equations: 
		<img alt="Equation 10" src="/files/ftp_upload/66643/66643eq10.jpg" style="margin: auto;" />&#160; &#160; &#160;(8) 
		Energy transfer equation for heat transfer from a solid heat source: 
		<img alt="Equation 11" src="/files/ftp_upload/66643/66643eq11.jpg" style="margin: auto;" />&#160; &#160; &#160;(9) 
		&#961;&#160;represents the density of the fluid; v represents the flow velocity vector; T represents the temperature; p is the pressure; &#964; is the viscous force on the surface of the micro metabolite;&#160;&#954; is the heat transfer coefficient; Sh is the body heat source; h is the specific enthalpy of the fluid and F is the body force of the micro metabolite. 
		NOTE: The results of the temperature field calculations are shown in Figure 3A and Figure 4A.</li>
	</ol>
	</li>
	<li>Steady-state thermal model setup
	<ol>
		<li>Maintain the material properties as per Table 3 in the material settings. Generate the ohmic losses resulting from the eddy current field simulation analysis in the Steady State Thermal module by clicking on Thermal Load generation.</li>
		<li>Click on the Convective Temperature Value and set it to 20 &#176;C, with a convective coefficient of 5 (W/m&#178;&#176;C) applied to the inner walls of the cabinet, components, and external cabinet. Apply the settings and generate. Set the output to solve for temperature by clicking on Solve &#62; Output Results. 
		NOTE: The main temperature field governing equations in the steady-state thermal temperature field calculation20,21,22 principle is usually derived from the law of heat conduction (Fourier&#39;s law of heat conduction). In the one-dimensional case, the temperature field heat transfer equation can be expressed as20: 
		<img alt="Equation 12" src="/files/ftp_upload/66643/66643eq12.jpg" style="margin: auto;" />&#160; &#160; &#160;(10) 
		In this equation, T represents the temperature inside the object, t is time, x&#160;is spatial coordinates, and&#160;&#945; is the thermal diffusivity. This equation describes the variation of temperature with respect to time and space, where the right side expresses the relationship between heat conduction rate and temperature gradient. In a more general three-dimensional scenario, the heat conduction equation for the temperature field can be expressed in the following form: 
		<img alt="Equation 13" src="/files/ftp_upload/66643/66643eq13.jpg" style="margin: auto;" />&#160; &#160; &#160;(11) 
		&#961;&#160;represents the density of the object, c is the specific heat capacity, K is the thermal conductivity and Q is the heat source term within the volume. This equation describes the variation of the temperature field, influenced by heat conduction, heat sources, and thermal capacitance.</li>
		<li>The temperature field calculation results are shown in Figure 3. Compare the temperature values summarized in Table 5 and Table 6.</li>
	</ol>
	</li>
</ol>

A Comprehensive Approach to Investigate RMU Temperature Rise Problem

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The authors do not have any conflicting interests.

This paper is a comparative simulation analysis of the temperature rise of the ring cabinet based on engineering modeling software and finite element software, and the most suitable solution for the actual temperature rise situation is analyzed by two finite element temperature field solution modules. Thermal management is also described in Icoz23 as a critical and essential component in maintaining the high efficiency and reliability of electronic components. The significance of conducting a comparative analysis is summarized by drawing on the work in Steiner24: a comparative analysis has been carried out using COMSOL and ANSYS Mechanical. Therefore, in the process of manufacturing the actual RMU, its temperature distribution can be analyzed by finite simulation software, which can greatly save manpower and production costs.
The three-dimensional model of the RMU can be created using SolidWorks, as shown in Figure 1A. In Step 1.1, model simplification is emphasized as a crucial step in finite element analysis23. Given the numerous components of the RMU, which may impact solution accuracy and computational results, unnecessary parts are removed after considering only the conductive components. The main working parts are retained, as illustrated in Figure 1B.
In the preprocessing stage of the Eddy current field, a critical aspect involves the addition and verification of load excitation. As described in Step 2.1, it is essential to accurately add the load current to the upper and lower outgoing arms, ensuring that the entire conductive circuit forms a path for the current. This facilitates the calculation of the distribution cloud map of the eddy current field. The difficulty lies in the need to check the circuit before the solution calculation, as an incomplete current path in the circuit can lead to non-convergence during solving or situations where Maxwell cannot compute. Subsequent temperature field analysis relies entirely on the losses obtained from solving this eddy current field, making the inspection of the current path a necessary step.
In the temperature field solution, Step 3 presents two different modules for solving the model. However, ensuring identical initial conditions during the solution process poses a challenge. Since the modules have different emphases in their solutions, it is necessary to approximate or set identical environmental temperatures, convective coefficients, and other solving conditions to ensure consistency under the unique heat source provided by Maxwell. Furthermore, controlling the intervals for temperature field solution in both models to be consistent allows for a direct comparison of temperature displays for the same components in the same temperature range, highlighting differences in the solutions and facilitating intuitive conclusions.
This paper presents an innovative method for accurately measuring and analyzing the temperature distribution of electrical equipment during operation. Addressing the challenge of precisely measuring ohmic losses with traditional methods, this study utilizes the Maxwell eddy current solver for accurate calculations, which then serves as the basis for solving the temperature field. Subsequently, the temperature field-solving module is used to visually display the temperature distribution of each component, significantly enhancing the efficiency and accuracy of engineering tests. The novelty of this paper lies not only in providing an efficient method for solving the temperature field but also in demonstrating how to use these results for structural optimization and heat dissipation analysis. This offers new technical means for the design and optimization of electrical equipment. However, there were limitations in the research process, and in future work, further research will be conducted in the following areas. Firstly, model refinement and multi-field coupling. The model will be further refined to accurately reflect the thermal characteristics of the ring network cabinet during actual operation. Secondly, studying transient temperature fields. In-depth research will be carried out on the temperature field changes of the ring network cabinet under varying load conditions to provide additional support for the stable operation of the ring network cabinet in complex operating conditions.

The ring main unit is a group of high-voltage switchgear mounted in a steel metal cabinet or made of assembled spaced ring network power supply unit of electrical equipment. The overall structure of the load switch and conductive circuit consists of the conductive circuit, which includes a number of components comprising the main core of the ring unit. However, due to its compact internal structure, the ring main unit faces challenges in heat dissipation. This can lead to thermal deformation and aging when operating for extended periods in high-temperature environments. These issues not only affect the service life of the unit but also impact its insulating properties, posing safety risks. In particular, equipment damage and electrical accidents become more likely, posing significant safety hazards.
Within different research areas, scholars have conducted a series of studies on the temperature rise of overhead line switchgear and analyzed various factors affecting the temperature distribution1. In Polykrati et al.2, a mathematical model for the estimation of the temperature rise of components installed on the distribution network during a short-circuit fault is presented. The model was applied to the common disconnecting switches of the network, and the characteristics of the results were plotted according to the different forms of the asymmetrical part of the short-circuit current waveform and the initial value of the short-circuit DC current component. Guan et al., on the other hand, have taken into account the contact resistance and electromagnetic repulsion by building an equivalent contact bridge to simulate the contact interface and further analyzed the electromagnetic-thermal coupling field and temperature rise experiment3. In addition, the researchers investigated the temperature field and thermal stress distribution of the dynamic and static contacts inside the ring main unit by finite element simulation, which provided a basis for the study of circuit breaker life4. Finally, Mueller et al. have focused on the geometrical characteristics of heat sinks and evaluated the effects of material selection, total surface area, temperature uniformity, and maximum surface temperature on thermal performance5. These studies provide valuable insights and methods to improve switchgear performance and reliability, reduce temperature rise, and extend equipment life. Wang et al. proposed a MiNET Deep Learning Model (MDLM) in the UPIOT environment with the purpose of detecting fault diagnosis of electrical ring cabinets, which was validated to have an identification accuracy of 99.1%, which is significantly higher than that of other methods6. Lei et al. studied the thermal performance of a GIS busbar in a steady state using the magneto-fluid-thermal coupling analysis method, thereby optimizing the conductor and tank diameter based on the temperature rise simulation results7. Ouerdani et al. used the RMU temperature rise simulation model to determine the temperature rise at critical locations inside it, thereby fixing the duration of the maximum overload for the components inside the RMU accordingly8. Zheng et al. described a conventional rectangular busbar in a model of high-current switchgear by building a two-dimensional model and applying the finite element method (FEM) for electromagnetic field calculations. It enabled them to obtain the distribution of bus conductor current density and power loss. An irregular busbar was designed after considering the effects of proximity effect and skin effect. This irregular busbar design improved the performance of conventional rectangular busbar9.
As for the aspect of using the icepak simulation, Wang et al. carried out a temperature rise simulation through vortex field, airflow field, and temperature field theories and found that the temperature rise of the ring main unit was more serious under natural convection. They successfully reduced the temperature rise level by adding forced air cooling and making improvements to the internal contact structure10. Zhu et al.11 used the icepak to simulate a thermal model in order to compare the effect of the presence of thermal vias on the PCB and the presence of heat sinks on the temperature of the power devices. Finally, the theoretical analysis is compared with the simulation results to verify the correctness of the theoretical analysis. Mao et al.12 studied the temperature and internal airflow distribution under summer operating conditions by thermal simulation based on the CAE software in the icepak simulation. The problem of how to improve the cooling efficiency and control the temperature rise of multiple silver-plated contacts is given, and the temperature and internal airflow contours captured in the simulation will lay the foundation for the design of the cooling scheme for the six silver-plated contacts mounted in the sealing unit. Conversely, in the use of a steady-state thermal module, Zhang13 Modeling methods are discussed for solving the thermal network of a high-pressure bushing using an alternative transient procedure. Test and simulation results are in good agreement with the thermal steady state and transient states of the bushing. The transient results are then used to evaluate the bushing overload capacity. Vaimann et al.14 developed and analyzed an analytical thermal model of a synchronous reluctance motor for predicting the temperature of its different components and the set total parameter thermal network.
With the continuous advancement of research on electrical equipment such as ring main units, conventional temperature rise tests, and production methods are relatively inefficient. Therefore, by utilizing finite element technology combined with offline tests, not only the design cost issues are addressed, but adjustments and optimizations can be promptly made to real-world problems based on simulations. Based on the research progress mentioned above, the use of ANSYS Icepak and Steady-state thermal coupling for comparative analysis is rarely mentioned. Therefore, the protocol describes the mechanism research of finite elements, uses numerical and morphological combinations to establish a finite element temperature rise simulation model for the enclosure, and discusses the finite element temperature rise simulation model based on the results of the two analytical modules by comparing the results of the two simulation modules. Through the comparison between the two simulation modules, we will get the characteristics of the temperature rise trend of the ring main unit and find the most applicable method so as to provide the necessary basis and research ideas for a strategy to mitigate the temperature rise of the ring main unit.

<table><tbody><tr><td>Air</td><td>/</td><td>/</td><td>Conventional gases</td></tr><tr><td>Aluminum</td><td>/</td><td>/</td><td>Alloy Materials</td></tr><tr><td>Copper</td><td>/</td><td>/</td><td>Alloy Materials</td></tr><tr><td>Icepak</td><td>ANSYS company</td><td>ANSYS 2021R1</td><td>A CFD thermal simulation software</td></tr><tr><td>PC hosting</td><td>/</td><td>12th Generation Intel(R) Core(TM) i5-13500F CPU</td><td>Host computer equipment</td></tr><tr><td>SolidWorks</td><td>Subsidiary of Dassault Systemes</td><td>SolidWorks2021</td><td>An engineering software drawing tool</td></tr><tr><td>Steady-state thermal</td><td>ANSYS company</td><td>ANSYS 2021R1</td><td>A thermal simulation solution tool</td></tr></tbody></table>

comparative study of simulation of temperature rise in ring main unit

The Ring Main Unit (RMU) is a critical device in power distribution systems used for connecting and distributing electricity. However, due to its compact internal structure and high current load, heat dissipation issues are particularly prominent. To address this problem, this study innovatively proposes a simplified RMU model, employing finite element simulation methods to accurately solve for the ohmic losses of conductors under actual operating conditions and obtain ohmic loss data for various components. This is the first in-depth investigation of the RMU's temperature rise problem using such a comprehensive approach. Subsequently, the temperature field was solved using two different temperature field analysis modules, with a detailed comparison and analysis of the simulation results to identify similarities, differences, and trends in temperature distribution. The results indicate that the temperature field solution model, which considers convective heat transfer, is more accurate and aligns with actual operating conditions. This research provides an innovative approach and practical solutions for the design and optimization of RMUs. Future research can further explore multiphysics coupling analysis methods to address structural design and mandatory validation issues for high and ultra-high voltage RMUs and other electrical equipment, thereby providing important insights for engineering design.

Based on the data in Table 3, the following conclusions can be drawn: The overall losses for Phases A, B, and C are relatively similar. Specifically, the total losses for Phase A are 16.063 W/m&#179;, Phase B is 16.12 W/m&#179;, and Phase C is 19.57 W/m&#179;. The locations with higher losses may be at the connections of various components. This is mainly because contact resistance and conductor resistance typically exist at these connection points. When current passes through these connections, significant heat is generated, leading to an increase in temperature and higher losses in those areas.
The upper and lower outgoing arms of the ring main unit bear some losses, especially when carrying the main load. The losses in the outgoing arms of the three phases are approximately the same. This is because this part of the current is relatively concentrated, and due to the regular shape, the resistance value is small. Therefore, the losses in these parts are approximately equal.
The losses in the branch busbar are relatively high, primarily due to its numerous bends and the presence of angled sections. Most of the current is concentrated in the bend area and near the corners. Apart from the vacuum circuit breaker section, the losses in the copper tube part of the busbar are also relatively high, totaling 22.32 W/m&#179;, accounting for 40% of the overall ohmic losses of the three phases. The losses in the static contact part are relatively small compared to the overall losses.
Other components, such as the circuit breaker fixed plate, circuit breaker baffle, and the outer shell of the ring main unit, have smaller losses. Since they do not directly participate in the load, their losses are mainly generated by the internal components through conduction, resulting in smaller loss values. In post-processing calculations, the losses of these components are not particularly detailed. In summary, the main characteristics of the loss distribution have been outlined, providing valuable insights for further design optimization.
Combining the results from Figure 3 and Figure 4, along with Table 5 and Table 6, a thorough comparison of the temperature field solution results between the Icepak and Steady-state Thermal modules was conducted. In the analysis process, differences and similarities were observed between the two modules.
In the temperature range of 20-31.24 &#176;C, as shown in Figure 2A, the higher temperature areas are concentrated in the regions where the RMU enclosure contacts the internal components. The main reason is that these contact areas typically serve as critical paths for heat conduction. During operation, the internal components generate heat, which is conducted to the enclosure through the contact surfaces with good thermal conductivity, thereby causing an increase in temperature in these regions. These areas include the contact points of the upper outgoing line arm and the portions of the lower outgoing line arm in contact with its nearby shell, with a temperature range of approximately 24-27 &#176;C. Compared with Figure 3B, the warming range of the shells in both cases is roughly the same, and the temperature changes propagate from the hotspots to the cooler areas. Examining Figure 4A,B, the overall temperature trends in both cases are also approximately similar. The primary source of temperature rise is the heat conduction through the lower outgoing line arm, which passes through the internal conductors and dissipates from the upper outgoing line arm. The warming trend is concentrated in areas close to the lower outgoing line arm, including the branch busbars of each phase and the connected supporting busbars. Furthermore, from the observation of the temperature distribution in the internal circuits in Figure 4, whether using Icepak or the Steady-state Thermal module for solving, the overall temperature of Phase B is consistently higher than the other two phases. This mainly suggests that Phase B, during the load process, not only bears the heat generated by the current of that phase but also, due to the overly compact internal structure of the RMU, the heat produced by each phase cannot dissipate promptly. Due to the exchange of heat, the temperature of Phase B remains higher than the other two phases regardless of the solver used. The overall temperature trends also exhibit a high degree of similarity, with the main temperature rise stemming from the heat conduction through the lower outgoing line arm and the dissipation of heat from the upper outgoing line arm. This trend appears highly consistent between the two modules. Moreover, the positions of the hotspots in the temperature distribution maps of both modules are highly consistent. Particularly, in the branch busbar section of Phase C, both solvers show the same highest temperature and the differences are almost negligible. This implies that regardless of the solver used, accurate identification of the hottest temperature positions is crucial for the thermal management of the RMU.
Secondly, it can be seen from the table that the same current produces the same loss, but the temperature value of each phase component in Table 6 is generally lower than that in Table 5; for example, the temperature value of the branch bus in the hot spot area of the entire RMU is the highest temperature in Table 6 at 30.91 &#176;C, and on the other hand, the highest temperature of the branch bus in Table 5 is 31.24 &#176;C. The reason is the solution logic of Icepak: the RMU as a heat source will continue to exchange heat and dissipate heat, and when a fluid is set as a medium for heat transfer, the temperature in the solution domain will gradually be emitted through the medium, which results in a decrease in temperature. The Steady-state Thermal module places less emphasis on heat convection with the surrounding air and instead focuses on a heat conduction-based solution. Compared to models that account for convection, this approach results in a less comprehensive temperature field solution. Consequently, in areas where the temperature is higher, hot spots become more pronounced. In the process of the actual temperature rise test, in addition to considering the characteristics of the model itself, heat transfer at the same time also considers the influence of heat dissipation medium, such as air, etc., on the overall temperature. Combined with the practical needs and experimental operation considerations, the RMU as a heat source continues to carry out heat convection and heat dissipation; the temperature simulation of icepak is more in line with the actual needs. In the case where the ring main unit is used as a heat source with continuous heat convection and dissipation, the temperature simulation of icepak is more in line with the practical needs. On the contrary, the steady-state thermal module mainly focuses on heat conduction, which may not be able to meet the actual needs in some cases.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66643/66643fig01.jpg" /> 
Figure 1: Ring main unit model. (A) Overall model of ring main unit (B) Simplified model of ring main unit. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66643/66643fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66643/66643fig02.jpg" /> 
Figure 2: Polarization curves for four sets of meshes. Maxwell adaptive meshing is used for larger parts, and local mesh refinement is used for smaller parts. In design software, a simplified 3D model of the ring main cabinet is created and imported into Maxwell, and the model is meshed using its Mesh module. The figure represents the verification of the model grids. The current density of the four groups of grids is 2.357 A/cm2, 2.358 A/cm2, 2.356 A/cm2 and 2.354 A/cm2 under the voltage loading, and the relative error between the maximum current density and the minimum current density is less than 1%, and in order to take into account the efficiency and accuracy of the calculations, the final number of the grids is determined to be 1169091. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66643/66643fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66643/66643fig03.jpg" /> 
Figure 3: Outer cabinet temperature field analysis model. (A) Shell temperature distribution with Icepak solution. (B) Shell temperature distribution with Steady-state thermal solution. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66643/66643fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/66643/66643fig04.jpg" /> 
Figure 4: Internal circuit temperature field analysis model. (A) Temperature distribution of the conducting loop with Icepak solution (B) Temperature distribution of the conducting loop with Steady-state thermal solution. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66643/66643fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="3">Material</td>
			<td>Air</td>
			<td>Galvanized steel sheet</td>
			<td>Copper</td>
			<td>Aluminum</td>
		</tr>
		<tr>
			<td colspan="3">Specific heat capacity (J/(kg&#183;K))</td>
			<td>1007</td>
			<td>500</td>
			<td>500</td>
			<td>897</td>
		</tr>
		<tr>
			<td colspan="3">Density (kg/m&#179;)</td>
			<td>1.1614</td>
			<td>8030</td>
			<td>8900</td>
			<td>2689</td>
		</tr>
		<tr>
			<td colspan="3">Relative permeability</td>
			<td>1</td>
			<td>0.3</td>
			<td>0.9999991</td>
			<td>/</td>
		</tr>
		<tr>
			<td colspan="3">Relative magnetic permeability</td>
			<td>1</td>
			<td>2500</td>
			<td>1</td>
			<td>1</td>
		</tr>
		<tr>
			<td colspan="3">Thermal conductivity (W/(m&#183;K))</td>
			<td>0.026</td>
			<td>16</td>
			<td>386</td>
			<td>237</td>
		</tr>
		<tr>
			<td colspan="3">Emissivity</td>
			<td>/</td>
			<td>0.65</td>
			<td>0.3</td>
			<td>0.1</td>
		</tr>
		<tr>
			<td colspan="3">Electrical conductivity (S/m)</td>
			<td>/</td>
			<td>0.8</td>
			<td>5.80E+07</td>
			<td>/</td>
		</tr>
	</tbody>
</table>
Table 1: Physical parameters of some materials in the ring main unit.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Components</td>
			<td>Relative permeability</td>
			<td>Conductivity/s</td>
			<td>Material</td>
		</tr>
		<tr>
			<td>Current-carrying circuits (busbar, branch busbar, etc.)</td>
			<td>0</td>
			<td>3,00,00,000</td>
			<td>Aluminium alloy</td>
		</tr>
		<tr>
			<td>Vacuum circuit breaker</td>
			<td>0.9999</td>
			<td>2,00,00,000</td>
			<td>Copper alloys</td>
		</tr>
		<tr>
			<td>Body</td>
			<td>200</td>
			<td>11,00,000</td>
			<td>Structural steel</td>
		</tr>
	</tbody>
</table>
Table 2: List of materials for each component.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Ohmic loss W/m&#179;</td>
			<td>A</td>
			<td>B</td>
			<td>C</td>
		</tr>
		<tr>
			<td>Out let arm</td>
			<td>3.78</td>
			<td>3.72</td>
			<td>3.73</td>
		</tr>
		<tr>
			<td>Branch busbar</td>
			<td>2.1</td>
			<td>2.09</td>
			<td>2.1</td>
		</tr>
		<tr>
			<td>Flexible connection</td>
			<td>1.3</td>
			<td>1.3</td>
			<td>1.3</td>
		</tr>
		<tr>
			<td>Vacuum circuit breaker</td>
			<td>1.023</td>
			<td>0.95</td>
			<td>0.98</td>
		</tr>
		<tr>
			<td>Static contacts</td>
			<td>0.36</td>
			<td>0.36</td>
			<td>0.36</td>
		</tr>
		<tr>
			<td>feeder busbar</td>
			<td>1.33</td>
			<td>1.35</td>
			<td>1.32</td>
		</tr>
		<tr>
			<td>Copper tubes for branch busbars</td>
			<td>6.19</td>
			<td>6.35</td>
			<td>9.78</td>
		</tr>
	</tbody>
</table>
Table 3: Ohmic loss values for phase A, B, and C components.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Number</td>
			<td>1</td>
			<td>2</td>
		</tr>
		<tr>
			<td>Materia</td>
			<td>Aluminum6061-T6</td>
			<td>Cu-Pure</td>
		</tr>
		<tr>
			<td>Thermal conductivity (W/m&#183;K)</td>
			<td>167</td>
			<td>387.6</td>
		</tr>
		<tr>
			<td>Density (kg/m&#179;)</td>
			<td>8933</td>
			<td>2700</td>
		</tr>
		<tr>
			<td>Specific heat capacity/(kg&#183;K)</td>
			<td>/</td>
			<td>896</td>
		</tr>
		<tr>
			<td>Surface material</td>
			<td>Paint-AL surface</td>
			<td>Cu-Polished-surface</td>
		</tr>
		<tr>
			<td>Emissivity</td>
			<td>0.35</td>
			<td>0.052</td>
		</tr>
	</tbody>
</table>
Table 4: Material parameter settings.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Temperature monitoring point/temperature (&#176;C)</td>
			<td>A</td>
			<td>B</td>
			<td>C</td>
		</tr>
		<tr>
			<td>Upper outlet arm</td>
			<td>25.48</td>
			<td>25.79</td>
			<td>25.63</td>
		</tr>
		<tr>
			<td>Main busbar</td>
			<td>25.93</td>
			<td>26.28</td>
			<td>26.13</td>
		</tr>
		<tr>
			<td>Branch busbar</td>
			<td>29</td>
			<td>29.18</td>
			<td>30.01</td>
		</tr>
		<tr>
			<td>Branch busbar copper tube</td>
			<td>31.04</td>
			<td>31.18</td>
			<td>31.24</td>
		</tr>
		<tr>
			<td>Lower outlet arm</td>
			<td>26.5</td>
			<td>26.98</td>
			<td>26.92</td>
		</tr>
	</tbody>
</table>
Table 5: Temperature values of each phase of the ring main unit under the Steady-state thermal temperature field solving module.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Temperature monitoring point/temperature (&#176;C)</td>
			<td>A</td>
			<td>B</td>
			<td>C</td>
		</tr>
		<tr>
			<td>Upper outlet arm</td>
			<td>23.73</td>
			<td>23.82</td>
			<td>23.81</td>
		</tr>
		<tr>
			<td>Main busbar</td>
			<td>25.15</td>
			<td>25.17</td>
			<td>25.35</td>
		</tr>
		<tr>
			<td>Branch busbar</td>
			<td>27.76</td>
			<td>28.04</td>
			<td>29.07</td>
		</tr>
		<tr>
			<td>Branch busbar copper tube</td>
			<td>28.42</td>
			<td>29.31</td>
			<td>30.91</td>
		</tr>
		<tr>
			<td>Lower outlet arm</td>
			<td>24.95</td>
			<td>24.85</td>
			<td>26.33</td>
		</tr>
	</tbody>
</table>
Table 6: Temperature values at monitoring points of each phase of the ring cabinet under Icepak temperature field solution module

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

This paper addresses the temperature rise problem of the ring main unit by establishing a simplified model and conducting a comparative analysis in two temperature field-solving modules.

Comparative Study of Simulation of Temperature Rise in Ring Main Unit

The Ring Main Unit (RMU) is a critical device in power distribution systems used for connecting and distributing electricity. However, due to its compact ...

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1.7K Views.  Wenzhou University. This paper addresses the temperature rise problem of the ring main unit by establishing a simplified model and conducting a comparative analysis in two temperature field-solving modules.

Video: Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Shape Memory Alloys (SMA) using elastocaloric cooling processes have the potential to be an environmentally friendly alternative to the conventional vapor compression based cooling process. Nickel-Titanium (Ni-Ti) based alloy systems, especially, show large elastocaloric effects. Furthermore, exhibit large latent heats which is a necessary material property for the development of an efficient solid-state based cooling process. A scientific test rig has been designed to investigate these processes and the elastocaloric effects in SMAs. The realized test rig enables independent control of an SMA&#39;s mechanical loading and unloading cycles, as well as conductive heat transfer between SMA cooling elements and a heat source/sink. The test rig is equipped with a comprehensive monitoring system capable of synchronized measurements of mechanical and thermal parameters. In addition to determining the process-dependent mechanical work, the system also enables measurement of thermal caloric aspects of the elastocaloric cooling effect through use of a high-performance infrared camera. This combination is of particular interest, because it allows illustrations of localization and rate effects &#8212; both important for efficient heat transfer from the medium to be cooled.
The work presented describes an experimental method to identify elastocaloric material properties in different materials and sample geometries. Furthermore, the test rig is used to investigate different cooling process variations. The introduced analysis methods enable a differentiated consideration of material, process and related boundary condition influences on the process efficiency. The comparison of the experimental data with the simulation results (of a thermomechanically coupled finite element model) allows for better understanding of the underlying physics of the elastocaloric effect. In addition, the experimental results, as well as the findings based on the simulation results, are used to improve the material properties.

Experimental methods for investigation of solid state cooling processes and characterization of elastocaloric material properties of Shape Memory Alloys (SMA) are presented. A custom-built test rig has been designed for controlling and comprehensive monitoring of elastocaloric cooling processes. Furthermore, it provides a validation platform for thermomechanically coupled modeling approaches.

Shape Memory Alloys (SMA) using elastocaloric cooling processes have the potential to be an environmentally friendly alternative to the conventional vapor ...

Uncoupling Coriolis Force and Rotating Buoyancy Effects on Full-Field Heat Transfer Properties of a Rotating Channel

An experimental method for exploring the heat transfer characteristics of an axially rotating channel is proposed. The governing flow parameters that characterize the transport phenomena in a rotating channel are identified via the parametric analysis of the momentum and energy equations referring to a rotating frame of reference. Based on these dimensionless flow equations, an experimental strategy that links the design of the test module, the experimental program and the data analysis is formulated with the attempt to reveal the isolated Coriolis-force and buoyancy effects on heat transfer performances. The effects of Coriolis force and rotating buoyancy are illustrated using the selective results measured from rotating channels with various geometries. While the Coriolis-force and rotating-buoyancy impacts share several common features among the various rotating channels, the unique heat transfer signatures are found in association with the flow direction, the channel shape and the arrangement of heat transfer enhancement devices. Regardless of the flow configurations of the rotating channels, the presented experimental method enables the development of physically consistent heat transfer correlations that permit the evaluation of isolated and interdependent Coriolis-force and rotating-buoyancy effects on the heat transfer properties of rotating channels.

Here, we present an experimental method for decoupling the interdependent Coriolis-force and rotating-buoyancy effects on full-field heat transfer distributions of a rotating channel.

An experimental method for exploring the heat transfer characteristics of an axially rotating channel is proposed. The governing flow parameters that ...

Simulating Temperature in a Soil Incubation Experiment

The study of warming impact on soils requires a realistic and accurate representation of temperature. In laboratory incubation studies, a widely adopted method has been to render constant temperatures in multiple chambers, and via comparisons of soil responses between low- and high-temperature chambers, to derive the warming impact on soil changes. However, this commonly used method failed to imitate both the magnitude and amplitude of actual temperatures as observed in field conditions, thus potentially undermining the validity of such studies. With sophisticated environmental chambers becoming increasingly available, it is imperative to examine alternative methods of temperature control for soil incubation research. This protocol will introduce a state-of-the-art environmental chamber and demonstrate both conventional and new methods of temperature control to improve the experimental design of soil incubation. The protocol mainly comprises four steps: temperature monitoring and programming, soil collection, laboratory incubation, and warming effect comparison. One example will be presented to demonstrate different methods of temperature control and the resultant contrasting warming scenarios; that is, a constant temperature design referred to as stepwise warming (SW) and simulated in situ temperature design as gradual warming (GW), as well as their effects on soil respiration, microbial biomass, and extracellular enzyme activities. In addition, we present a strategy to diversify temperature change scenarios to meet specific climate change research needs (e.g., extreme heat). The temperature control protocol and the recommended well-tailored and diversified temperature change scenarios will assist researchers in establishing reliable and realistic soil incubation experiments in the laboratory.

Laboratory soil warming experiments usually employ two or more constant temperatures in multiple chambers. By presenting a sophisticated environmental chamber, we provide an accurate temperature control method to imitate the magnitude and amplitude of in situ soil temperature and improve the experimental design of soil incubation studies.

The study of warming impact on soils requires a realistic and accurate representation of temperature. In laboratory incubation studies, a widely adopted ...

Environment

A Rapid Method for Modeling a Variable Cycle Engine

The variable cycle engines (VCE) that combine the advantages of turbofan and turbojet engines, are widely considered to be the next generation aircraft engines. However, developing VCE requires high costs. Thus, it is essential to build a mathematical model when developing an aircraft engine, which may avoid a large number of real tests and reduce the cost dramatically. Modeling is also crucial in control law development. In this article, based on a graphical simulation environment, a rapid method for modeling a double bypass variable cycle engine using object-oriented modeling technology and modular hierarchical architecture is described. Firstly, the mathematical model of each component is built based on the thermodynamic calculation. Then, a hierarchical engine model is built via the combination of each component mathematical model and the N-R solver module. Finally, the static and dynamic simulations are carried out in the model and the simulation results prove the effectiveness of the modeling method. The VCE model built through this method has the advantages of clear structure and real-time observation.

Here, we present a protocol to build a component-level mathematical model for a variable cycle engine.

The variable cycle engines (VCE) that combine the advantages of turbofan and turbojet engines, are widely considered to be the next generation aircraft ...

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Electro-hydrostatic actuators (EHAs) have been considerably researched in academia, and their applications in various industrial fields are expanding. The variable-speed EHA has now taken priority over the variable-displacement EHA, but its driving motor and associated electronics encounter issues when applied in high-power applications: low-dynamics, high thermal dissipation, high price, etc. Therefore, a variable-displacement EHA equipped with an electro-variable displacement pump (EVDP) has been considered. The EVDP itself is a mechatronic system that integrates a piston pump, a ball screw, a gearbox, and a permanent magnet synchronous motor (PMSM). Consequently, the EVDP needs to be investigated to ensure its system-level performance when applied in an EHA. In addition to the previous research on the technical parameters of the EVDP, a dedicated design method is necessary for further reducing the cost of using the EVDP and exploring its performance potential. Here, a simulation based EVDP preliminary design method is selected for designing a 37 kW EVDP. Firstly, a previously proposed multidisciplinary model of the EVDP is extended by improving the parameter generation, including the EVDP life, reliability, control models, etc. Secondly, the proposed model is partially verified using a downsized prototype. Thirdly, the EVDP is simulated at a system level, supported by the proposed model. The EVDP performance is evaluated according to the specified design requirements. The temperature, bandwidth and accuracy, reliability and lifetime, etc., are all predicted for the EVDP. The simulation results demonstrate the EVDP's applicability in variable-displacement EHA. The proposed modeling and simulation method can be used to evaluate diverse EVDP performance and respond to general design requirements. The method can also support the resolution of the preliminary design challenges in terms of limited information and robustness. Therefore, the proposed method is appropriate for the realization of the simulation-based EVDP preliminary design method.

A simulation model specifically supporting the preliminary design of an electro-variable displacement pump (EVDP) is developed and partially verified by experiments. The control performance, life, reliability, etc., can all be evaluated using the proposed model, which covers the main performance requirements under the EVDP preliminary design task.

Electro-hydrostatic actuators (EHAs) have been considerably researched in academia, and their applications in various industrial fields are expanding. The ...

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

The rise of renewable energy sources has underscored the significance of microgrids, particularly DC variants, which are well-suited for integrating photovoltaic panels, battery storage systems, and other DC load solutions. This paper presents the development and experimentation of a DC microgrid with hierarchical control implemented in OPAL RT-Lab, a simulator. The microgrid includes distributed energy resources (DERs) interconnected via power converters, a DC bus, and DC loads. The primary control employs a droop control mechanism and double-loop Proportional-Integral (PI) control to regulate voltage and current, ensuring stable operation and proportional power sharing. The secondary control utilizes a consensus-based strategy to coordinate DERs to restore the bus voltage and ensure accurate power sharing, enhancing system reliability and efficiency. The experimental setup detailed in this paper includes circuit modeling, hardware implementation, and control strategies. The hardware platform's circuitry and controller parameters are specified, and the results can be observed through oscilloscope measurements. Two sets of experiments demonstrating the secondary control response with and without delay are conducted to validate the effectiveness of the control strategy. The outcomes confirm the successful implementation of hierarchical control in the microgrid. This study underscores the significance of a comprehensive experimental platform for advancing microgrid technology, providing valuable insights for future research and development.

This article presents a DC microgrid with hierarchical control implemented in a simulator, OPAL RT-Lab. It details the circuit modeling, primary and secondary control strategies, and experimental validation. The results demonstrate effective control performance, highlighting the importance of a robust experimental platform for microgrid research and development.

The rise of renewable energy sources has underscored the significance of microgrids, particularly DC variants, which are well-suited for integrating ...

Temperature-Controlled Assembly and Characterization of a Droplet Interface Bilayer

The droplet interface bilayer (DIB) method for assembling lipid bilayers (i.e., DIBs) between lipid-coated aqueous droplets in oil offers key benefits versus other methods: DIBs are stable and often long-lasting, bilayer area can be reversibly tuned, leaflet asymmetry is readily controlled via droplet compositions, and tissue-like networks of bilayers can be obtained by adjoining many droplets. Forming DIBs requires spontaneous assembly of lipids into high density lipid monolayers at the surfaces of the droplets. While this occurs readily at room temperature for common synthetic lipids, a sufficient monolayer or stable bilayer fails to form at similar conditions for lipids with melting points above room temperature, including some cellular lipid extracts. This behavior has likely limited the compositions&#8212;and perhaps the biological relevance&#8212;of DIBs in model membrane studies. To address this problem, an experimental protocol is presented to carefully heat the oil reservoir hosting DIB droplets and characterize the effects of temperature on the lipid membrane. Specifically, this protocol shows how to use a thermally conductive aluminum fixture and resistive heating elements controlled by a feedback loop to prescribe elevated temperatures, which improves monolayer assembly and bilayer formation for a wider set of lipid types. Structural characteristics of the membrane, as well as the thermotropic phase transitions of the lipids comprising the bilayer, are quantified by measuring the changes in electrical capacitance of the DIB. Together, this procedure can aid in evaluating biophysical phenomena in model membranes over various temperatures, including determining an effective melting temperature (TM) for multi-component lipid mixtures. This capability will thus allow for closer replication of natural phase transitions in model membranes and encourage the formation and use of model membranes from a wider swath of membrane constituents, including those that better capture the heterogeneity of their cellular counterparts.

This protocol details the use of a feedback temperature-controlled heating system to promote lipid monolayer assembly and droplet interface bilayer formation for lipids with elevated melting temperatures, and capacitance measurements to characterize temperature-driven changes in the membrane.

The droplet interface bilayer (DIB) method for assembling lipid bilayers (i.e., DIBs) between lipid-coated aqueous droplets in oil offers key benefits ...

Bioengineering

Experimental Multiscale Methodology for Predicting Material Fouling Resistance

The buildup of fouling deposits in energy systems degrades their ability to transfer heat, reducing efficiency and causing operational issues such as localized corrosion. Each application presents its own challenges. In nuclear power generation, the buildup of CRUD (Chalk River Unidentified Deposits) induces unique, negative effects ranging from reduced heat transfer, to axial power shifting, to CRUD-induced localized corrosion (CILC), the last of which can cause sudden fuel failure. Knowledge of why CRUD forms and how to prevent its adhesion to nuclear fuel rods represents a major step towards eliminating it. This paper demonstrates a methodology to ascertain which materials may resist CRUD adhesion, thereby preventing its initiation and growth. It presents experiments targeted at multiple length scales: pool boiling (macroscale), which simulates CRUD initiation processes in a nuclear reactor, and atomic force microscope (AFM) force spectroscopy (microscale), which yields quantitative measurements of the adhesion between CRUD constituents and candidate CRUD-resistant materials. Early data from both sets of experiments show some correlation, suggesting that AFM force spectroscopy (AFM-FS) may be used to pre-screen materials for fouling resistance. So far, it appears that CRUD constituents adhere well to oxides, while carbides show both greatly reduced adhesion and no adherent CRUD in pool boiling experiments. The relationship between the AFM-FS measurements and the growth of CRUD in the pool boiling experiments may provide insight into the possible factors affecting fouling in nuclear reactors, eventually leading to its elimination.

Fouling, or corrosion product deposition, plagues numerous fields of energy production. At its core is adhesion between dissimilar materials. Understanding and controlling adhesion could reduce or eliminate fouling. This paper demonstrates pool boiling experiments (macroscale) and atomic force microscope force-spectroscopy measurements (microscale), which when in agreement, indicate fouling-resistance.

The buildup of fouling deposits in energy systems degrades their ability to transfer heat, reducing efficiency and causing operational issues such as ...

Biology

Establishing A Mouse Model for Respiratory Tract Extreme Low-temperature Air Inhalation

Currently, constructing a mouse model for cold environmental stimulation employs cold-heat plates and wearable cooling devices. These methods can partially fulfill the requirements for studying the responses and regulatory effects of mouse skin or neural circuits to cold stimulation. Numerous clinical studies have substantiated the correlation between exposure to low-temperature environments and the development of various diseases. Recently, there has been a growing emphasis on the continuous exchange of information between organs and tissues, providing a novel perspective on addressing longstanding issues within the human body. However, existing installations are unable to construct a model for mice inhaling cold air. 
Although placing mice in a cold environment seems attractive, it has considerable limitations. While mice inhale cold air, their skin is also being stimulated by the cold environment, making it unclear whether resulting pathological changes are due to lung stimulation through the interaction of distant organs or due to the skin receptors and neural signal transmission. This creates considerable confusion in related research. This scheme presents a new approach for constructing a mouse model for extreme cold air inhalation stimulation. This device allows mice to inhale extremely low-temperature gases while their bodies remain at a normal temperature. It maximizes the simulation of the stimulating effects of extreme ambient temperatures on mice and meets the research needs for studying the relationship between extreme environmental temperatures and related diseases.

Cold environmental stimulation has been implicated in the development of various chronic diseases. Therefore, establishing animal models for preclinical research is crucial. This system addresses this need by offering a device that creates a stimulus model, meeting the requirements for basic research on pathogenic mechanisms.

Currently, constructing a mouse model for cold environmental stimulation employs cold-heat plates and wearable cooling devices. These methods can ...

Neuroscience

Testing the Heat Transfer Efficiency of a Finned-tube Heat Exchanger

Source: Michael G. Benton and Kerry M. Dooley, Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA
Heat exchangers transfer heat from one fluid to another fluid. Multiple classes of heat exchangers exist to fill different needs. Some of the most common types are shell and tube exchangers and plate exchangers1. Shell and tube heat exchangers use a system of tubes through which fluid flows1. One set of tubes contains the liquid to be cooled or heated, while the second set contains the liquid that will either absorb heat or transmit it1. Plate heat exchangers use a similar concept, in which plates are closely joined together with a small gap between each for liquid to flow1. The fluid flowing between the plates alternates between hot and cold so that heat will move into or out of the necessary streams1. These exchangers have large surface areas, so they are usually more efficient1.
The goal for this experiment is to test the heat transfer efficiency of a finned-tube heat exchanger (Figure 1) and compare it to the theoretical efficiency of a heat exchanger without fins. The experimental data will be measured for three different flow rates of monoethylene glycol (MEG). Two different water flow rates for each MEG flow rate will be used. Using the Wilson plot method the heat transfer coefficients will be determined from the experimental data. Additionally, the Reynold&#39;s number and the amount of heat transferred will be compared for flow with and without the fins to evaluate heat transfer efficiency.
<img alt="Figure 1" src="/files/ftp_upload/10437/10437fig1.jpg" /> 
Figure 1: Finned-tube Heat Exchanger. 1) MEG outlet temperature 2) water inlet temperature 3) MEG inlet temperature 4) water outlet temperature 5) water meter 6) MEG accumulation sight glass/cylinder.

Heat Transfer Efficiency Test of a Finned-tube Heat Exchanger

Source: Michael G. Benton and Kerry M. Dooley, Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA
Heat exchangers transfer ...