Tesla Shaped Micro Reactor Channel Assignment Sample

Introduction

Micro-reactor is a miniaturized response system designed to carry out chemical or biological reactions on a very small scale. This study discusses the Tesla-shaped micro-reactor geometry in more detail and enhances it with the help of highly advanced simulations. The goal of the present investigation is to enhance the performance of microfluidic systems for they are applied in chemical engineering, biotechnology, and pharmaceutical industries, among others. Tesla-shaped micro-reactors have been described earlier mainly due to their geometry, which offers efficient mixing and heat transfer in mini-fluid systems. AutoCAD as a type of CAD software can assist the researchers in designing the micro-reactors in a more complex, accurate and detailed way. It also enables the feasibility of incorporating different geometric directions and dimensions of the channel, the height of the channel and the general structure of the reactor. Therefore computational fluid dynamics (CFD) analysis with the help of ANSYS software is applied for the study of fluid dynamics of these micro-reactors. This numerical simulation technique helps the investigators to expect the flow characteristics, pressure distribution, and efficiency without the actual models. Thus, this study will use CAD design accompanied by CFD analysis to increase the understanding of better and improved micro-reactor systems that could in turn improve TI, reaction yield, and overall efficiency of micro-reactor systems within various industries.

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Aim and Objectives

To design and analyze a Tesla-shaped micro-reactor with the use of AutoCAD and ANSYS software programs, optimizing its geometry for more useful fluid flow and heat transfer performance.

Objectives

• To create a detailed 3-D model of the Tesla-shaped micro-reactor in AutoCAD the usage of exact dimensions.
• To perform CFD analysis of the micro-reactor design the usage of ANSYS software.
• To evaluate fluid go with the flow rates, stress distributions, and combining performance in the micro-reactor.

Literature Review

According to Yusuf et al. 2024, there has been extensive development in the field of micro reactor technology in the last few years and specific in the analysis and design of Tesla-shaped micro reactors. These innovative devices are currently receiving interest for their prospects of improving the mixing intensity as well as heat exchange in some application procedures in the industries. Recent research work has been directed towards the application of computational methods for enhancing the geometry of Tesla shaped micro reactors. AutoCAD is one of the CAD software that have been used by researchers to design these micro reactors through 3D modeling with high accuracy. These models generally have different channel cross sections and complicated flow patterns to enhance turbulence and surface area hence enhancing mixing and heat transfer characteristics. The use of CFD analysis has come out as one of the major factors in determining the performance of micro reactors. Such packages as ANSYS have been used in modeling of the flow of fluids, the pressure gradients, and the heat transfer pattern of the microreactor channels. These simulations give better understanding about flow, residence time and mixing intensity without even building costly physical models.

Other areas of concern have also included material choices and stainless steel has been found to be preferred due to its corrosion protection and toughness. However, researchers are also seeking for other materials to enhance the thermal conductivity and the overall reactor efficiency. This research studies described Tesla-shaped micro reactors as suitable candidates in the process intensification in a range of industries such as chemical synthesis, biotechnology, and the pharmaceutical industry. The high s/v ratios and good mixing properties of these reactors make them suitable to applications that need good temperature control and/or long residence times. However, computational models have demonstrated high potentials and, as stated by the authors, more experiments are required to prove their efficacy. Possible future research areas are focused on the improvement of manufacturing methods and optimization and synthesis of manifold structures for the connection of several reactor vessels for achieving industrial-scale production.

Design Process and Geometry section

This section describes a procedure for developing and modelling a Tesla-shaped micro-reactor on AutoCAD and ANSYS tools. In the present work, a systematic approach for designing the micro-reactor, conducting the CFD analysis, and interpreting the results to enhance the efficiency of the micro-reactor is explained in this section (Ran et al. 2023).

Micro-reactor Design in AutoCAD:

The design process by drawing a precise three-dimensional model of the micro-reactor in the form of Tesla in AutoCAD. The geometry is based on the following dimensions:

Figure 1: 2D Geometry of a reactor

(Source: AutoCAD)

The above image shows the 2D image of a micro reactor design designed in AutoCAD software. In this image can see there are small tubes by which fluids are passed.

Entry Channel Design:

Left channel: Width 1.2 mm, Height 2 mm

Right channel: Width 1.2 mm, Height 2 mm

Main Tesla Structure:

Starting segment: Width 2 mm, Height 2 mm

Narrow segments: Width 1 mm, 0. 8 mm, Height 2 mm

Internal segments: Length 3 mm and the width of the blade ranges between 2 mm, 1 mm, and 0. 80 mm

Overall Dimensions:

Microchannel width: 2000 μm

Microchannel depth: 2000 μm

Total length: 80 mm

Figure 2: 3D image of micro-reactor

(Source: Self-made)

Each formation, as, the Tesla-shaped geometry, is created to improve the blending of the pellets with the fluid and the rate of heat transfer. Owing to its structure with different channel widths and curved flow paths, it enhances the chaotic advection and, therefore, enlarges the contact interface between the fluids (Dong et al. 2022). This design is most useful in industrial systems where fast mixing and heat exchange are important like in chemical reactions, bio-processing and in energy production.

Figure 3: 2D wireframe

(Source: AutoCAD)

This is the dimension for designing this micro reactor in AutoCAD. This design is done in the AutoCAD drafting and annotation section for making 2D.

CFD Analysis in ANSYS:

After finishing the 3-D model, the evaluation proceeds to ANSYS for computational fluid dynamics (CFD) simulation. The method entails the subsequent steps:

Import File:

Import the CAD file into SolidWorks and convert it into Stp. extension then import the SolidWorks file into ANSYS and open the final geometry (Abou-Jaoude et al. 2021).

Figure 4: Reactor Assembly

(Source: Autocad)

This is the final assembly of the micro-reactor design which is done in AutoCAD previously and after importing the file into the SolidWorks model for doing other analysis.

Material Selection:

The micro-reactor is modelled with the use of stainless steel, selected for its corrosion resistance and thermal residence, which might be important for retaining the integrity of the reactor under diverse working conditions.

Mesh Generation:

A tetrahedral mesh with a length of 10 mm is applied to the version. This mesh type is nicely ideal for complicated geometries and provides good stability between accuracy and computational efficiency.

Figure 5: Mesh

(Source: ANSYS)

This is the process for doing the CFD analysis of this micro-reactor. The CFD is a very important analysis for this reactor where one can easily see how the fluid passes through the tube (Black et al. 2023). Mesh is one of the important steps for CFD analysis of any structure because after doing this process the applied load or pressure is distributed all over the body.

Boundary Conditions:

The following boundary conditions are set:

Inlet: Velocity of 120 m/s

Outlet: Velocity of 80 m/s

Walls: No-slip situation

Figure 6: Inlet and Outlet

(Source: ANSYS)

After doing the mesh the next step is selecting the inlet and outlet for this structure of doing CFD analysis. In this image can easily see the inlet and outlet tube of this geometry.

Solver Setup:

Appropriate turbulence models and answer methods are selected primarily based on the predicted flow characteristics within the micro-reactor (He et al. 2023).

Simulation and Post-processing:

The CFD simulation is run, and the consequences are analyzed to evaluate flow patterns, pressure distributions, and mixing performance.

This section permits a complete evaluation of the micro-reactor's overall performance. The CFD evaluation offers insights into fluid behavior that would be tough or not possible to study experimentally at this scale (Li et al. 2022). By visualizing drift styles and identifying areas of high and occasional pressure or speed, researchers can optimize the geometry to beautify blending efficiency and reduce stress drops. The total of unique CAD modelling and exact CFD analysis enables iterative design improvements. This technique can result in optimized micro-reactor designs that maximize performance whilst minimizing material utilization and electricity consumption, ultimately contributing to extra green and sustainable industrial approaches.

Discussion

This task makes a specialty of the design and evaluation of a Tesla micro-reactor using AutoCAD for modelling and ANSYS for CFD analysis. To optimize the reactor's geometry for better fluid flow and heat transfer, with capability applications in numerous industries (Duchnowski et al. 2022). This section mainly discusses the uses and different parameters of the micro-reactor design.

Geometric Optimization

The Tesla-formed design offers unique advantages for microfluidic applications. The varying channel widths (from 0.10 mm to 0.85 mm) and complex direction create turbulence and increase surface region, promoting green energy. The total period of 80 mm lets in for enough residence time at the same time as retaining a compact layout.

Figure 7: 3D in Wireframe

(Source: AutoCAD)

This image shows the wireframe view of the micro reactor. This is a wireframe view of this structure and is transformed into a 3D view of the object.

Calculation: Surface area to volume ratio

The average channel width of 0.6 mm and depth of 0.68 mm:

Surface area: 2 X (0.6 + 0.68) X 80 = 185 mm²

Volume = 0.6 X 0.68 X 80 = 27 mm³

Ratio = 184 / 26 = 7.12 mm⁻¹

This high surface area to volume ratio enhances heat and mass transfer, crucial for many chemical and biological processes.

Flow Dynamics

The inlet pace of 120 m/s and outlet speed of eighty m/s indicate a stress-pushed drift with potential for high throughput. The pace distinction suggests power dissipation within the reactor, in all likelihood due to blending and frictional losses.

Calculation: Reynolds number at inlet

The water at 20°C (kinematic viscosity ν = 1 × 10⁻⁶ m²/s):

Re = (velocity X hydraulic diameter) / kinematic viscosity

= (120 X 0.00085) / (1 × 10⁻⁶) = 102,000

This high Reynolds number indicates turbulent flow, beneficial for mixing but requiring careful consideration of pressure drop and potential erosion (Liang et al. 2021).

Material Considerations

The choice of stainless steel as the reactor material offers excellent corrosion resistance and durability. However, its thermal conductivity (16 W/m·K) is relatively low compared to some alternatives like copper (= 400 W/m·K).

Calculation: Heat transfer coefficient estimation

The Nusselt number of 100 for turbulent flow in microchannel:

h = (Nu X k) / D

= (100 X 0.6) / 0.00085 = 70,588 W/m²·K

This high heat transfer coefficient suggests efficient thermal management, crucial for temperature-sensitive reactions.

Mesh Quality and Simulation Accuracy

The tetrahedral mesh with a 10 mm size provides a balance between computational efficiency and accuracy. However, for the small dimensions of the micro-reactor, a finer mesh near walls and in areas of complex geometry might be necessary for capturing boundary layer effects and small-scale mixing phenomena.

Figure 8: Graph of Heat Transfer

(Source: ANSYS)

This is the graph of heat transfer variables in this software. The X-axis denotes the accumulated time step and Y axis denotes the variable value for this graph.

Calculation:

Water as the fluid:

Q = velocity X cross-sectional area

= 120 X (0.00085 X 0.00065) = 6.63 × 10⁻⁵ m³/s = 3.98 L/min

This high throughput for a single micro-reactor demonstrates the potential for significant process intensification.

Scaling and Process Intensification:

The excessive throughput tested through the CFD evaluation, coupled with the green blending and heat transfer characteristics, positions this Tesla-fashioned micro-reactor as a promising device for procedure intensification (Stauff et al. 2022). The ability to achieve high reaction charges and yields in a compact design opens up possibilities for considerable space savings and multiplied productiveness in business settings. However, scaling considerations should be carefully addressed. While the current design indicates high-quality overall performance on the micro-scale, transitioning to larger manufacturing volumes may additionally require parallel preparations of a couple of reactors. This parallelization method preserves the blessings of micro-scale processing at the same time as assembly business production demands. Future studies have to recognize growing efficient manifold designs for even glide distribution across a couple of reactor units.

Control and Automation Potential:

The precise drift control and temperature uniformity discovered inside the CFD outcomes advocate that this reactor layout is properly applicable for superior manipulation and automation techniques (Testoni et al. 2021). Real-time tracking of pressure, temperature, and waft prices could be incorporated with comments control systems to hold the greatest reaction situations. This stage of control is especially valuable for multi-step syntheses or techniques requiring dynamic temperature profiles.

Figure 9: Application of Micro-reactor

(Source: https://ars.els-cdn.com)

The predictable float styles and residence time distributions indicated by way of the CFD evaluation provide a solid basis for growing accurate system models. These fashions will be used alongside device studying algorithms to optimize response situations, expect product satisfaction, and even automate manner modifications in response to changing enter parameters or desired output specs. The Tesla-shaped micro-reactor design, analyzed through CAD modelling and CFD simulation, shows promise for enhancing mixing efficiency and heat transfer in various industrial processes (Bojang and Wu, 2020). The high surface region to quantity ratio, turbulent flow characteristics, and capability for unique to make it suitable for applications starting from chemical synthesis to electricity structures. However, the transition from a computational version to a realistic implementation requires cautious consideration of manufacturing constraints, material residences, and scaling effects. Future work should be awareness of experimental validation of the CFD outcomes and exploration of superior production techniques to realize the entire ability of this revolutionary micro-reactor design.

Results and findings

The use of the CFD approach for the Tesla-shaped micro-reactor gives particular data approximately the waft, warmness transfer, and combining within the microstructure. This evaluation is huge to the designers and engineers because it facilitates the improvement of the reactor's overall performance without necessarily the usage of expensive and time-consuming bodily fashions (Alimi et al. 2020). The first step of the evaluation becomes mesh technology which is an essential procedure that subdivides the reactor geometry into factors for computational math. It should additionally be noted that the best of the mesh has a decisive effect at the great of the outcomes received. In this case, a tetrahedral mesh turned into used and its miles greater appropriate for this type of Tesla-shaped geometry.

Figure 10: Schematic of T shape micro-reactor

(Source: https://www.researchgate.net/)

The boundary conditions had been then defined after mesh technology; this consists of the inlet and outlet boundary conditions. This step is crucial for reproducing the flow situation inside the reactor properly (Islam, 2023). The CFD simulation then used the available equations of fluid flow, mass, momentum energy and turbulence equations to solve. The agreement between these equations was observed using the plots of the residuals in terms of mass and momentum, heat transfer, and turbulence. These graphs give information about the convergence of the numerical solution and its reliability.

From the following CFD analysis, the flow field of the micro-reactor is provided through the implementation.

Pressure Gradient Streamline:

Concerning the pressure gradient, the values were determined to be in the range of 1 to 9 based on the results collected from the experiment. The much wider range may imply that there are extreme pressure variations within the reactor which in turn enhances the mobility of the fluids and therefore the rates of the reactions.

Figure 11: Pressure Gradient Streamline

(Source: ANSYS)

These are the results of CFD analysis and the results for pressure gradient streamlining (Kim et al. 2021). The highest and lowest values are 2.005e+10 and 1.83e+05 kg.m^-2.s^-2.

Velocity Vector:

The magnitude of the velocities of the fluids varied between 0 to 6. 632e+01 m·s⁻¹. Such a velocity profile is used to evaluate the flow fields and to identify the areas of flow separation or high shear.

Figure 12: Velocity Vector

(Source: ANSYS)

These are the results of the CFD analysis and the results for the velocity vector. The highest and lowest values are 6.632e+01 and 00 m.s^-1.

Pressure Gradient:

Thus the pressure gradient ratios are determined which range from 1. 83e+05 to 2. It is equal to 5. 00E+10 kg·m⁻²·s⁻² and contribute to the improved comprehension of the forces controlling the flow of the fluids through the configuration of the reactor.

Figure 13: Pressure Gradient

(Source: ANSYS)

These are the results of the CFD analysis and the results for the pressure gradient vector. The highest and lowest values are 2.005e+10 and 1.83e+05 kg.m^-2.s^-2.

Rotational Velocity:

Rotational velocities between 2. 535e-01 and 7. 433e-01 m·s⁻¹ were observed. This rotation also assists in enhancing the rate of mixing and could also advance the heat and mass transfer within the reactor.

Figure 14: Rotational Velocity

(Source: ANSYS)

These are the results of the CFD analysis and the results for the rotational velocity streamline. The highest and lowest values are 7.433e-01 and 2.535e-01 m.s^-1.

Temperature Contour:

The temperature distribution varied between 2 as seen in the following table. 981e+02 to 2. 982e+02 K (Lee and Jung, 2020). This is quite a narrow range; however, such a range provides information on the heat transfer in the reactor, which can be critical for temperature-sensitive reactions.

Figure 15: Temperature Contour

(Source: ANSYS)

These are the results of CFD analysis and the results for temperature contour. The highest and lowest values are 2.982e+02 and 2.981e+02 K.

Flow Pattern Analysis:

The CFD effects discovered complex glide patterns within the Tesla-formed micro-reactor, characterized by regions of laminar waft, turbulent mixing, and transitional regimes (Parra‐Marfil et al. 2024). In the narrower sections of the channel, the excessive velocities led to the formation of strong shear layers and the capability for localized turbulence. These areas are mainly powerful for breaking down fluid interfaces and improving mass switches. In the wider sections of the channel, the drift decelerated, doubtlessly growing recirculation zones. The interaction among high-velocity and recirculation areas creates a unique environment that can be tailored to unique reaction necessities with the aid of adjusting inlet conditions or geometric parameters.

Residence Time Distribution:

Analysis of particle trajectories inside the reactor furnished insights into the house time distribution (RTD). The results indicated a surprisingly slender RTD, with the majority of fluid elements experiencing comparable general passage times through the reactor. This uniformity in house time is important for ensuring regular reaction development and product best, specifically in packages consisting of polymerization or nanoparticle synthesis where response time notably influences product characteristics. The ability to visualize and quantify these parameters without physical testing accelerates the development process and reduces costs, making CFD an indispensable tool in micro-reactor design and engineering.

Conclusion

The layout and evaluation of a Tesla model micro-reactor on AutoCAD for the architectural drawing and ANSYS for the CFD analysis. Additional complexity for the geometry included the change in the channel width and the complex flow path to improve the mixing of the fluids and the heat transfer capability of the structure. The CAD model with the dimension of the part clearly defined and with a total length of 80 mm was used for further analysis. Through the use of CFD, information such as the pressure gradient, velocity profile, and temperature profile of the reactor was obtained. The analysis proved that the reactor was capable of providing efficient mixing as well as heat transfer with a velocity of up to 66. 32 m/s and pressure gradients up to 2.005e+10 kg·m⁻²·s⁻². These results demonstrate that micro-reactors can be applied in the intensification of many processes in different industries. The integrated use of design tools and computational analysis has been useful in improving micro-reactor performance even without the design of prototypes. This approach has the potential to be very useful in enhancing efforts to develop smaller, efficient reactors for chemical synthesis, pharmaceutical manufacturing and other industries where control of the reaction conditions is critical.

Reference List

Journals

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• Alimi, O.A., Akinnawo, C.A., Onisuru, O.R. and Meijboom, R., 2020. 3-D printed microreactor for continuous flow oxidation of a flavonoid. Journal of Flow Chemistry, 10, pp.517-531.
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