Optimized design of smart toilet spray bar flow channel based on CFD

Currently, there are significant issues with the intelligent toilets available on the market, such as a large swing of the spray bar when spraying water column and eccentricity. This paper presents four optimization schemes for the intelligent toilet spray bar channel by analysing and redesigning a specific model. Computational Fluid Dynamics (CFD) technology was employed to analyse the water flow conditions inside the spray bar of the intelligent toilet. The calculation results indicate that for the same water inlet flow rate, the optimal structure in optimization scheme 1 is the block with a length (L) of 1.5 mm. In optimization scheme 2, the optimal structure is the block located at position (3). In optimization scheme 3, the optimal structure is a 15° rotation, which exhibits the least swirls and pressure loss while maintaining the prototype structure. When the prototype structure is modified, the optimal structure in optimization scheme 4 is the excision bend pipe. This structure exhibits the lowest swirl number and pressure loss among all the optimization schemes. The determined structure offers valuable insights for enhancing the flow characteristics of the improved product.


Introduction
As the standard of living improves, a healthy and comfortable daily living environment is especially important.Smart toilet as an effective product to improve toilet hygiene environment is increasingly favored by society [1].Smart toilets feature automatic commode flushing, water washing and warm airdrying technology, among the washing technology, the cleaning spray bar is the top priority of the smart toilet, and the performance of the cleaning spray bar has a key impact on the smart toilet experience.
The cleaning spray bar is one of the core components of a smart toilet, it is also a major water and energy consuming component.However, most of the domestic sanitary ware manufacturers are still in the initial stage of research on intelligent toilets [2], more smart toilets on the market have problems such as large swing when spraying water from the cleaning spray bar, eccentricity of the water column and premature breakage of the water column, affecting the performance of smart toilets and user experience.
In order to improve the user experience and working performance of smart toilets, companies and scholars have carried out a lot of research.Domestic scholars Zhou [3] and other research on the styling and structural design of the integrated testing machine for smart toilet seat, optimizing the styling and structure of the existing products and improving their use performance.Han [4] used CFD technology to calculate and analyze, and combined with experimental testing to verify the reproduction of the complex, microscopic flow state of the water flow inside the smart toilet cover, which provides guidance for the optimization and improvement of the product.Overseas COWAY Co et al. patented bidet including spray assembly; TOTO Co., Ltd.applied for patents related to sanitary washing devices for smart toilets; Touru iyo et al. collaborated with TOTO Co., Ltd. on a study of the effect of smart toilet spray cleanliness on the spray and the effect of neutral electrolyzed water on smart toilet sprays and spray bars [5][6].However, there are fewer studies on smart toilet spray bars, and no attention has been paid to the impact of smart toilet cleaning spray bars on the working performance of smart toilets and user experience.It is difficult to measure the flow complexity in the actual project.Physical modeling experiments have limitations and often only give parameters of the total flow, while numerical simulations give specific information about the associated flow field.Therefore, in this paper, CFD technique is used to analyze and study the smart toilet cleaning spray bar computationally.
In this paper, by redesigning a certain model of intelligent toilet cleaning spray bar in China to improve the cleaning spray bar spray water outflow rotation and other issues, the CFD technique was used to simulate and analyze the number of swirls, water velocity, and the flow state in the flow channel for each model.In this paper, the deconvolution study of the smart toilet cleaning spray bar can not only be used for the development and application of new smart toilet, but also has the same reference value and application for the similar runner structure of fire water cannons [7][8][9] and pump station inlet runners [10][11][12] and other scenarios that need to do deconvolution treatment.1. Usually, the smart toilet cleaning spray bar has 1~3 spray holes with the same number of runners corresponding to them, and these runners do not interfere with each other.The cleaning spray bar studied in this paper is a common two-hole design.The design of the dual jet runner makes full use of the spray bar space and provides a more hygienic water discharge, while the two spray holes serve different purposes.The left spray hole of the two holes in figure 1 corresponds to the female cleaning runners and the right spray hole corresponds to the buttock cleaning runners.The bubbling water stream is more delicate and gentler to take care of women's health when washing women, and the warm water stream is utilized to wash the buttocks instead of traditional paper wipes, so that double cleansing can solve the needs of the whole family.

Computational domain extraction and meshing.
Through figure 1, it can be found that the smart toilet cleaning spray bar has dual jet runners, but due to its symmetry and noninterference, so when this paper in the study of dual-jet spray bar runners, only one of the runners need to be studied, and the conclusions obtained from the study are also applicable to the other runners.In this paper, the lower side of the flow channel in figure 1 is chosen as more typical for the study because the straight section of the channel is shorter and the flow is more unstable.
The model flow channel was meshed using Fluent Meshing software.The mesh is encrypted at the more drastic flow changes such as bends.figure 2 shows a portion of the mesh in the computational domain of the model flow channel.Encrypting from 600,000 to 1.5 million to verify the grid irrelevance, the number of grids is finally selected to be no less than 1 million, taking into account the computational accuracy and efficiency.

Computational model setup.
During the numerical simulation of the bend, the k-ε model [13] is selected considering that the fluid inside the bend may generate shunt, secondary flow and cyclone, etc., and the SIMPLE algorithm is also utilized to solve the problem.The standard k-ε model is somewhat empirical, while the Realizable k-ε model improves on it.On the one hand it introduces a more rational formulation of the turbulent viscosity, while at the same time it uses a new transport equation for the energy dissipation rate which is no longer based on assumptions but is derived from an accurate transport equation for eddy pulsation.The model can be expressed as: Where:  represents turbulent kinetic energy;  represents turbulent kinetic energy dissipation rate;   and   are the coordinates;  1 is the laminar vortex viscosity coefficient;   represents the molecular viscosity and turbulent viscosity;   represents the turbulent kinetic energy generated by the velocity gradient;   represents the turbulent kinetic energy due to buoyancy;   is the fluctuation due to diffusion;   and   are parameters;  1 ,  2 and  3 are model constants.
The standard wall function is used to solve for the near-wall region, numerical simulations use the control volume method to discretize the control equations.The diffusion term of the program is in center-difference format, the convection term is in second-order windward format, and a coupled algorithm is used for the pressure-velocity solution.
(1) Boundary conditions Mass flow inlet is used for the inlet boundary and pressure outlet is used for the outlet boundary.
The working condition of a model of smart toilet spray bar in the market is selected as a comparison for analysis.In this case, the inlet mass flow is 0.01kg/s and the outlet pressure is set to standard atmospheric pressure.
(2) Convergence criterion Numerical simulation calculates residuals below 10 -5 , the mass difference between imports and exports is below 0.5%, and the monitored maximum velocity of exports reaches a smooth considered to satisfy the convergence requirements.

2.1.4.
Validation of computational schemes.The validity of the numerical simulation is verified by simulating the simulation experiment of Chen et al [14] for the bend flow channel, and the simulation results are within 5% of the results of Chen et al.The computational scheme is validated and can be used for the optimized design of spray bar runners for smart toilets.

Runner performance
The outlet swirl number is defined as [15]: Where: u z denotes the average value of axial velocity on the cross-section; u θ is the average value of tangential velocity on the cross-section; R is the cross-section diameter; r is the cross-section radius.
The swirls number S is a dimensionless number, a parameter that describes the degree of swirling, which is determined by a combination of factors such as the density, velocity, radius of swirling and viscosity of the fluid.The larger the swirls number, the stronger the degree of rotation of the swirl.
Pressure loss is defined as: ) Where: P 1 indicates the total inlet pressure; P 2 is the total outlet pressure.The flow characteristics of the flow path of the prototype cleaning spray bar were numerically simulated using CFD technology calculations, and the combination of the above equations resulted in an outlet swirl number S of 0.116 and an inlet and outlet pressure loss ∆P of 26,250 Pa for the prototype cleaning spray bar.

Flow analysis
The 3D streamline distribution of the cleaning spray bar flow path of the smart toilet prototype with pressure, velocity and turbulent kinetic energy cloud is shown in figure 3, where (a) is the 3D flow map, (b) is the pressure cloud of the spray rod cross-section, (c) is the velocity cloud of the spray rod cross-section, and (d) is the turbulent kinetic energy cloud of the spray rod cross-section.
From figure 3(a), it can be seen that when the water flow enters the spray bar from the diversion pipe, due to the existence of a 90° right-angle bend in the spray bar, the water flow will form a spiraling upward whirlpool at the outlet end of the spray bar.From figure 3(b), (c) and (d), it can be concluded that the velocity and pressure of the water flow at the two walls of the spray bar bends are more varied, and at the bends, there is a significant difference between the pressure of the water flow at the outer wall and the pressure and velocity of the water flow at the inner wall, and the pressure and velocity of the water flow at the outer wall are significantly higher than that of the water flow at the inner wall.Difference in wall pressure reflects the difference in the speed of water flow through the elbow, the elbow wall pressure is higher at the water flow rate is faster.
According to figure 3, due to the existence of 90° nozzle bend, when the water flow through the bend, the pressure and speed of the water flow in the two walls of the bend will have a significant difference between the two different pressures and speeds of the water flow out of the bend will be in the spray nozzle exit friction and collision with each other, the pressure and speed of the larger water flow will impact on the other pressure and speed of the smaller water flow, which explains why the spinning is not in the center of the spray nozzle export, but rather partial spray nozzle export water flow pressure is less, slower flow rate of the side.

Optimization programming
From the above analysis, it can be seen that under the prototype scheme, due to the existence of 90° bend pipes in the spray bar of the smart toilet, the water flow in the flow channel will form a high-intensity swirling flow.Therefore, in order to eliminate the swirling flow, the following four runner structure optimization schemes are done.As shown in figure 4.
(1) Scheme 1 is to place a small block at the outlet end of the runner, the position of the block is located in the (1) position in figure 4 (a), the width of the block D is 0.5 mm, and the length of the block L is set to 0.5, 1, 1.5, and 2 mm respectively for the simulation of the four kinds of calculations.(2) Scheme 2 is based on the results of optimization scheme 1, selecting the optimal length of the block, and then placing the block of this length at the four positions ( 2), ( 3), ( 4) and ( 5

Calculation results and analysis of the optimization scheme 4.2.1. Optimize solution performance.
Through the analysis of the original spray bar structure above, the number of swirls and total differential pressure of the simulation results of the optimization scheme are compared with the prototype, As shown in figure 5.For the optimization schemes 1 and 2, it can be seen from figure 5(a) that the optimal length of the stopper is 1.5 mm with constant position, at which time the number of swirls accounts for 37.6% of the number of swirls of the prototype.From figure 5(b), it can be seen that when the block lengths are all 1.5 mm, the optimal position of the block is at position (3) in figure 4(a), and the number of swirls at this time accounts for 29.7% of the number of swirls of the prototype.For optimization scheme 3, it can be seen from figure 5(c) that when the four blocks are rotated clockwise by 15°, the number of swirls at this point is optimal and the number of swirls is 24.1% of the number of swirls of the prototype.For optimization scheme 4, it can be seen from figure 5(d) that the number of swirls of optimization scheme 4 is optimal, which accounts for 12.7% of the number of swirls of the prototype.
The simulation results show that the optimization of the nozzle runner structure has a significant reduction for the number of swirls of the water flow.The optimized spray bar flow path has a better flow stabilization effect, reducing the spinning effect brought by the flow path bends, thus reducing the energy loss, forming a more uniformly distributed velocity field at the spray bar outlet, and also significantly optimizing the overall use of the spray bar.By comparing the flow diagrams of the spray bar prototype figure 3 (a), the optimization scheme 1 figure 6 (a) and the optimization scheme 7 (a), it can be clearly seen that the spray bar outlet in figure 5 (a) has the rotational intensity of the rotational intensity of the spray bar and the rotational scope of the rotational flow, Signs of cyclonic flow are also present in figure 6(a), but the intensity and extent of cyclonic flow in figure 6(a) is significantly weaker than that in figure 3(a), while no significant cyclonic flow can be seen in the model of figure 7(a).Signs of cyclonic flow are also present in figure 6(a), but the intensity and extent of swirling is significantly weaker in Figure 6(a) than in figure 3(a), and no significant swirling can be seen in the model of figure 7(a).The model in figure 8(a) has no swirls occurring in the flow diagram of this model due to the fact that its elbow portion has been removed directly from the nature, and the flow of water through this model's spray bar is not restricted by the bends.
From figure 6(b), (c) and (d), it can be concluded that when the water flows through the bend, there will still be a significant difference in pressure and velocity between the water flow on the outer wall and the inner wall of the bend.However, due to the presence of the block, the block consumes the vast majority of the energy of the outer wall flow, thereby reducing the pressure and velocity of the outer wall flow and reducing the difference in pressure and velocity between the inner and outer wall flows.Due to the restrictive nature of the block, the block is unable to keep the pressure and velocity of the water flow on the inner and outer walls in the same state, and there is still a gap between the two water flows in terms of pressure and velocity, so the two water flows will still have a cyclonic flow when they collide and friction.c) and (d) then it can be found that the optimization model under the inner and outer wall of the water flow after flowing through the elbow will meet the blocking of the baffle, the outer wall of the water flow and the inner wall of the water flow in the two sides of the baffle block will consume a part of the energy, thus reducing the pressure and speed, and the two streams of water cancel each other out, so as to achieve the effect of the dissipation of the swirl.

Conclusion
(1) Under the condition of the same water inlet flow rate and without changing the structure of the prototype, the optimal length of the block is 1.5mm when the width of the block of optimization scheme 1 is 0.5mm, and the number of swirls at this time is 37.6% of the number of swirls of the prototype, and the pressure loss is 68.9% of the prototype.The optimal position among the five placements of the block in optimization scheme 2 is position (3), where the number of swirls is 29.7% of the number of swirls of the prototype and the pressure loss is 68.1% of the prototype.The optimal position among the six placement positions of four blocks in optimization scheme 3 is 15° clockwise rotation of four blocks, at which the number of swirls is 24.1% of the number of swirls of the prototype, and the compression loss is 68.9% of the prototype.(2) In the same water inlet flow and keep the water outlet area unchanged, the optimization plan 4 to excise the bends, the use of shrinking structure connected to the optimal structure, the number of swirls at this time for the lowest only 12.7% of the prototype, the pressure loss for the prototype of 56.3%.(3) Although the optimization scheme 4 has the best effect of suppressing swirling, the scheme causes certain energy loss and water column eccentricity, and it is necessary to continue to study the scheme in depth, and to strive to design a scheme that improves the water column eccentricity, etc. as much as possible with the same degree of effect of suppressing swirling.(4) The simulation calculations and results of this paper can not only be used to improve the structural performance of the smart toilet cleaning spray bar and enhance the competitiveness of the product, but also can be applied to the structure of fire water cannons and pumping station intake runners and other structures with similar flow paths by applying the optimized structure and simulation calculations of this paper, so as to improve the cyclonic strength and jet eccentricity of these structures.

2. 1 .
Smart toilet spray bar flow channel modelling 2.1.1.Spray bar structure and working principle.The overall structure and internal flow path of the smart toilet cleaning spray bar are shown in figure

Figure 1 .
Figure 1.Schematic diagram of smart toilet spray bar model.

Figure 2 .
Figure 2. Mesh of the computational domain of the model flow channel.

Figure 3 .
Figure 3. Spray bar prototype streamlines and flow field details.

Figure 5 .
Figure 5. Optimization scheme performance.4.2.2.Optimization program flow analysis.For the spray bar runner optimization scheme described above in this paper, figure6corresponds to the data in figure5(a) and (b) when the length of the block is 1.5 and the block position is 3, figure 7 corresponds to the data in figure 5(c) when the angle is 15, and figure 8 corresponds to the data in figure 5(d) when the deformation is 2.By comparing the flow diagrams of the spray bar prototype figure3(a), the optimization scheme 1 figure6(a) and the optimization scheme 7 (a), it can be clearly seen that the spray bar outlet in figure5(a) has the rotational intensity of the rotational intensity of the spray bar and the rotational scope of the rotational flow, Signs of cyclonic flow are also present in figure6(a), but the intensity and extent of cyclonic flow in figure6(a) is significantly weaker than that in figure3(a), while no significant cyclonic flow can be seen in the model of figure7(a).Signs of cyclonic flow are also present in figure6(a), but the intensity and extent of swirling is significantly weaker in Figure6(a) than in figure3(a), and no significant swirling can be seen in the model of figure7(a).The model in figure8(a) has no swirls occurring in the flow diagram of this model due to the fact that its elbow portion has been removed directly from the nature, and the flow of water through this model's spray bar is not restricted by the bends.From figure6(b), (c) and (d), it can be concluded that when the water flows through the bend, there will still be a significant difference in pressure and velocity between the water flow on the outer wall and the inner wall of the bend.However, due to the presence of the block, the block consumes the vast majority of the energy of the outer wall flow, thereby reducing the pressure and velocity of the outer wall flow and reducing the difference in pressure and velocity between the inner and outer wall flows.Due to the restrictive nature of the block, the block is unable to keep the pressure and velocity of the water flow on the inner and outer walls in the same state, and there is still a gap between the two water flows in terms of pressure and velocity, so the two water flows will still have a cyclonic flow when they collide and friction.

Figure 7 (
Figure 7(b), (c) and (d) then it can be found that the optimization model under the inner and outer wall of the water flow after flowing through the elbow will meet the blocking of the baffle, the outer wall of the water flow and the inner wall of the water flow in the two sides of the baffle block will consume a part of the energy, thus reducing the pressure and speed, and the two streams of water cancel each other out, so as to achieve the effect of the dissipation of the swirl.Figure8Due to the partial removal of the original elbow, the water flow inlet and outlet are directly connected by tapering.With this optimization model the flow pressure, velocity and turbulent kinetic energy only change in the final upward bend section due to the absence of the original bend section.Although this model is the optimized model for suppressing the of cyclone, it can be seen through figure8(b), (c) and (d) that under this model, due to the narrow outlet of the final upward bend portion and the large degree of curvature of the inner side of the pipeline, the water flow is unable to fill the entire upward bend portion of the pipeline, which may result in a certain amount of energy loss and the eccentricity of the water droplets at the outlet of the cleaning spray bar.

Figure 8 Figure 6 .Figure 7 .Figure 8 .
Figure 7(b), (c) and (d) then it can be found that the optimization model under the inner and outer wall of the water flow after flowing through the elbow will meet the blocking of the baffle, the outer wall of the water flow and the inner wall of the water flow in the two sides of the baffle block will consume a part of the energy, thus reducing the pressure and speed, and the two streams of water cancel each other out, so as to achieve the effect of the dissipation of the swirl.Figure8Due to the partial removal of the original elbow, the water flow inlet and outlet are directly connected by tapering.With this optimization model the flow pressure, velocity and turbulent kinetic energy only change in the final upward bend section due to the absence of the original bend section.Although this model is the optimized model for suppressing the of cyclone, it can be seen through figure8(b), (c) and (d) that under this model, due to the narrow outlet of the final upward bend portion and the large degree of curvature of the inner side of the pipeline, the water flow is unable to fill the entire upward bend portion of the pipeline, which may result in a certain amount of energy loss and the eccentricity of the water droplets at the outlet of the cleaning spray bar.