Method for widening high-efficiency region and optimizing energy consumption of large seawater desalination high-pressure pump

Abstract

This invention relates to the field of fluid machinery technology, specifically to a design method for widening the high-efficiency zone and optimizing energy consumption of a large-scale seawater desalination high-pressure pump. The method includes: first, establishing a parameterized model of the core flow channel containing parameters of the impeller flow channel, guide vane flow channel, and volute; then, obtaining the efficiency and shaft power under different flow conditions based on CFD simulation; next, performing biomimetic non-smooth optimization on the impeller blades using a partitioned hemispherical concave array, adding axial flow-dividing ribs to the guide vane flow channel, and rounding the impeller outlet edge to widen the high-efficiency zone; then, optimizing the parameters of the core flow channel and volute using an improved particle swarm optimization algorithm to reduce energy consumption by minimizing shaft power at rated flow; finally, verifying performance through CFD flow field analysis and test bench testing. After optimization, the high-efficiency zone of the high-pressure pump is Q1≤0.8Q. n Q2≥1.2Q n Furthermore, it has an efficiency of ≥85%, a rated flow shaft power reduction of ≥5%, and reduces eddies and pressure pulsation, thereby improving operational stability. It is suitable for large-scale seawater desalination projects.

Description

Technical Field

[0001] This invention relates to the field of fluid machinery technology, specifically to a design method for widening the high-efficiency range and optimizing energy consumption of large-scale seawater desalination high-pressure pumps. Background Technology

[0002] In large-scale seawater desalination projects, high-pressure pumps, as core power equipment, directly determine the energy utilization rate and operating cost of the entire desalination system through their operating efficiency, energy consumption level, and adaptability to various operating conditions. As the seawater desalination industry develops towards large-scale, low-energy consumption, existing large-scale high-pressure pumps are gradually revealing several technical shortcomings in practical applications, making it difficult to meet the demands for efficient, energy-saving, and stable operation. These shortcomings are specifically reflected in the following three aspects: 1. Poor adaptability to narrow operating range and conditions. Traditional large-scale seawater desalination high-pressure pumps are designed primarily for efficiency optimization under rated flow conditions, with insufficient consideration given to adaptability to different flow conditions, resulting in a narrow high-efficiency range. In actual operation, seawater desalination systems often face flow load changes due to factors such as fluctuations in raw water quality and adjustments in product water demand, such as starting at low flow and then adjusting for peak flow. In such cases, the pump is prone to deviating from its high-efficiency range. Under low flow conditions, flow separation easily occurs at the impeller inlet, forming a low-velocity vortex zone, which not only leads to a sharp drop in efficiency (in some scenarios, efficiency can fall below 75%), but also causes erosion and wear on the impeller surface. Under high flow conditions, the fluid velocity in the guide vane channel is too high, significantly increasing pressure loss. At the same time, the flow field matching between the guide vane and the impeller outlet becomes unbalanced, further exacerbating efficiency decline. This narrow high-efficiency range characteristic causes the pump to operate in a high-energy-consumption state under most non-rated conditions, severely restricting the overall energy efficiency of the system.

[0003] 2. Limited energy consumption optimization methods result in limited energy-saving effects. Current high-pressure pump energy consumption optimization often relies on adjusting a single parameter, such as only optimizing the impeller outlet diameter or the number of guide vanes, without considering the synergistic effects of parameters on multiple components including the impeller, guide vanes, and volute. This can easily lead to a contradiction between local optimization and overall flow field imbalance. For example, simply increasing the impeller outlet width may increase the rated flow rate, but it also increases impeller rotational resistance, resulting in increased shaft power. Furthermore, traditional optimization algorithms, such as simple genetic algorithms and trial-and-error methods, suffer from slow convergence and are prone to getting trapped in local optima, making it difficult to achieve optimal energy consumption under multi-parameter coupling. Simultaneously, existing designs lack sufficient optimization of flow field details. For instance, the blade surfaces lack effective drag reduction measures, and the guide vanes do not specifically suppress eddies, leading to excessive energy loss in the flow channels. Frictional losses and local resistance losses account for a high proportion of this loss, making it difficult to effectively reduce shaft power at rated flow rates, resulting in persistently high energy costs over the long term.

[0004] 3. Inadequate design and verification system, resulting in insufficient performance stability. In traditional design processes, the parameterized models of the core flow channels often only include the impeller and guide vane channels, failing to incorporate key parameters of the volute, such as the volute base circle diameter and the length of the outlet diffuser section, into a unified modeling framework. This leads to poor flow field integration between the volute and the impeller / guide vanes, easily resulting in secondary flow within the volute and increased pressure loss. Simultaneously, CFD flow field simulations often suffer from inappropriate turbulence model selection, such as using the standard k-ε model unsuitable for rotating machinery flow fields, and a lack of mesh independence verification. This results in significant discrepancies between simulation and actual operating data, hindering accurate guidance for optimization. Furthermore, subsequent performance testing on test benches often focuses on rated flow conditions, with incomplete coverage of critical conditions such as low and high flow rates. This makes it difficult to identify potential problems under non-rated conditions, such as excessive pressure pulsation at the guide vane outlet and severe impeller flow separation. Ultimately, this leads to excessive vibration and noise levels and shortened pump lifespan during actual operation.

[0005] In summary, the technical deficiencies of existing large-scale seawater desalination high-pressure pumps in terms of energy consumption control and performance stability in the high-efficiency zone coverage have become a key bottleneck restricting the development of seawater desalination systems towards low energy consumption and high reliability. There is an urgent need for a technical solution that can achieve synergy between expanding the high-efficiency zone and optimizing energy consumption, and has a sound design verification system. Summary of the Invention

[0006] The purpose of this invention is to provide a design method for widening the high-efficiency range and optimizing energy consumption of large-scale seawater desalination high-pressure pumps, so as to solve the problems of narrow high-efficiency range, high energy consumption and insufficient performance stability of existing high-pressure pumps mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The design method for expanding the high-efficiency zone and optimizing energy consumption of large-scale seawater desalination high-pressure pumps includes the following steps: S1. Establish a parameterized model of the core flow channel of a large-scale seawater desalination high-pressure pump. The core flow channel includes the impeller flow channel and the guide vane flow channel. The input parameters of the parameterized model include the impeller inlet diameter D1, the impeller outlet diameter D2, the impeller outlet width b2, the number of guide vane blades Zg, and the guide vane diffusion angle. ; S2. Perform flow field simulation on the parameterized model based on computational fluid dynamics (CFD) to obtain the efficiency η and shaft power P of the high-pressure pump under different flow conditions. S3. To broaden the high-efficiency zone, the impeller blades are optimized with a biomimetic non-smooth surface. A hemispherical array of recesses is set on the pressure surface of the impeller blades. The ratio of the recess diameter d to the impeller blade chord length L satisfies 0.05≤d / L≤0.12, where d is the recess diameter and L is the impeller blade chord length; the ratio of the recess spacing s to the recess diameter d satisfies 1.5≤s / d≤2.5, where s is the center-to-center distance between adjacent recesses; at the same time, the guide vane is designed with a flow-dividing rib structure. 2-4 axial flow-dividing ribs are set in the guide vane flow channel. The ratio of the flow-dividing rib height h to the guide vane flow channel width W satisfies 0.1≤h / W≤0.2, where h is the flow-dividing rib height and W is the guide vane flow channel width. S4. For energy consumption optimization, an energy consumption optimization model based on an improved particle swarm optimization algorithm is constructed, taking the high-pressure pump at its rated flow rate Q as an example. n The optimization objective is to minimize the shaft power P. The core flow channel parameters from step S1 are used as optimization variables, and constraints are set for these variables. The fitness function f of the improved particle swarm optimization algorithm is: ; in , This is a function of shaft power at rated flow rate. , The guide vane diffusion angle; the algorithm iteration termination condition is that the change in the fitness function value over 10 consecutive iterations is less than 1 × 10⁻⁶. -4 ; S5. Perform CFD flow field verification and test bench performance testing on the parameterized model optimized in steps S3 and S4. If the high-pressure pump is within the flow range... The efficiency within the range is ≥85%, of which To achieve the minimum flow rate in the high-efficiency zone, If the maximum flow rate in the high-efficiency zone is achieved, and the shaft power at the rated flow rate is reduced by ≥5% compared to before optimization, then the design is complete; otherwise, return to step S3 to adjust the optimization parameters until the performance requirements are met.

[0008] Preferably, in step S1, the impeller inlet diameter is... The calculation satisfies the formula: ; in The impeller inlet diameter is This is the maximum design flow rate of the high-pressure pump. It is pi and takes the value 3.1416. The average flow velocity at the impeller inlet is, and The value range is 3.5-5.0 m / s; the relationship between the number of guide vane blades Zg and the number of impeller blades Z satisfies Zg=Z±1, where the value range of Z is 5-7.

[0009] Preferably, in step S1, the parameterized model further includes parameters of the pump casing, wherein the ratio of the casing base circle diameter D3 to the impeller outlet diameter D2 satisfies 1.05≤D3 / D2≤1.15; the ratio of the casing outlet diffuser section length L3 to the casing outlet diameter D4 satisfies 2.5≤L3 / D4≤3.5; and in the energy consumption optimization model in step S4, the casing base circle diameter D3 is used as an additional optimization variable, with a constraint range of D3 variation ≤±6%, where the variation is the percentage of the difference between the optimized and unoptimized values ​​relative to the unoptimized value.

[0010] Preferably, in step S3, the array of pits on the biomimetic non-smooth surface is arranged in zones. The pit diameter d1 at the impeller blade inlet section satisfies 0.08 ≤ d1 / L ≤ 0.12; the pit diameter d2 in the middle section of the blade satisfies 0.06 ≤ d2 / L ≤ 0.08; and the pit diameter d3 at the blade outlet section satisfies 0.05 ≤ d3 / L ≤ 0.06, where d3 is the diameter of the outlet section pit. Furthermore, in the CFD flow field simulation, the calculation of the relative velocity non-uniformity σ on the blade surface satisfies: ≤ ; in , The root mean square of the velocity deviation, Let be the relative velocity at the i-th monitoring point on the blade surface. The average relative velocity across all monitoring points. This represents the total number of monitoring points; , This represents the average relative velocity.

[0011] Preferably, in step S3, the cross-section of the guide vane diverting rib is an isosceles trapezoid, and the ratio of the width of the upper base a1 to the width of the lower base a2 of the trapezoid satisfies 0.4 ≤ a1 / a2 ≤ 0.6; and the distance t between the diverting rib and the guide vane blade satisfies: ; in The distance between the flow divider rib and the guide vane blade. The width of the guide vane channel, The number of diversion ribs, The height of the flow divider rib is shown. Through CFD simulation, it is verified that the vortex intensity in the guide vane channel is reduced by ≥30% after the flow divider rib is optimized, and the pressure pulsation amplitude at the guide vane outlet is reduced by ≥25%.

[0012] Preferably, step S3 further includes rounding the impeller outlet edge, where the ratio of the rounding radius r to the impeller outlet width b2 satisfies 0.1 ≤ r / b2 ≤ 0.15. CFD simulation verification shows that after rounding, the flow separation area at the impeller outlet is reduced by ≥40%, where the reduction is the percentage of the difference between the optimized and unoptimized separation areas relative to the original separation area. Furthermore, the high-pressure pump operates at a low flow rate of 0.6Q... n The efficiency improvement is ≥3%, where the improvement is the percentage of the difference between the efficiency before and after optimization relative to the original efficiency.

[0013] Preferably, in step S4, the inertia weight ω of the improved particle swarm optimization algorithm adopts a linearly decreasing strategy, satisfying the formula: ; in , For the basic term of inertia weight, The initial inertia weight is set to 0.9. The minimum inertia weight is set to 0.4. This represents the current iteration number; = , The maximum number of iterations is set to 1.8; the learning factors c1 and c2 of the algorithm are both set to 1.8-2.2, where c1 is the individual learning factor and c2 is the social learning factor; the particle population size is set to 30-50, where the population size is the total number of particles participating in the optimization.

[0014] Preferably, in step S4, the constraint range of the optimization variables is as follows: the change in impeller inlet diameter D1 is ≤ ±8%, where the change is the percentage of the difference between the optimized and unoptimized values ​​to the unoptimized value; the change in impeller outlet diameter D2 is ≤ ±5%; the change in impeller outlet width b2 is ≤ ±10%; the change range of the number of guide vanes Zg is 4-8; and the change range of the guide vane diffusion angle α is 8°-15°.

[0015] Preferably, in step S5, the criterion for determining the high-efficiency zone is: the flow range Q1-Q2 corresponding to a high-pressure pump efficiency ≥ 85% and the rated flow rate Q. n The ratio satisfies Q2-Q1 / Q n ≥0.4, where Q1 is the minimum flow rate in the high-efficiency zone, Q2 is the maximum flow rate in the high-efficiency zone, and Q n The rated flow rate; and the flow rate Q1 ≤ 0.8Q n Q2≥1.2Q n In the CFD flow field verification, the SSTk-ω turbulence model was adopted. The grid independence verification was determined by the change in the efficiency calculation value being ≤1% when the number of grids increased by 10%, where the change is the percentage of the efficiency difference between the grids before and after the increase relative to the original efficiency.

[0016] Preferably, in step S5, the performance test of the test bench includes an energy consumption test. The energy consumption test uses a power analyzer with an accuracy of 0.2 to measure the shaft power P and an electromagnetic flowmeter with an accuracy of 0.5 to measure the flow rate Q. The test conditions include 0.6Q. n 0.8Q n Q n 1.2Q n 1.4Q n Five operating points, including Q n Rated flow rate; energy consumption optimization effect is achieved through energy consumption reduction rate η p Evaluate: ; in , This is the difference in shaft power. To optimize shaft power at the original rated flow rate, To optimize shaft power at rated flow rate; = , To optimize front axle power; and requires ≥5%.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. The core design system is precise and controllable, providing a solid foundation for optimization. The method of this invention first constructs a complete parametric model of the core flow channels, covering the impeller flow channel, guide vane flow channel, and volute. It does not focus on a single component, but rather incorporates key parameters such as impeller inlet diameter, impeller outlet diameter, impeller outlet width, number of guide vanes, and guide vane diffusion angle, as well as volute parameters such as volute base circle diameter and volute outlet diffuser length, into the model input, forming a quantifiable and controllable design framework. Specifically, the impeller inlet diameter is precisely calculated using the maximum design flow rate and the average inlet velocity of 3.5-5.0 m / s; the number of guide vanes is correlated with the number of impeller blades (5-7); and the volute base circle diameter maintains a proportional relationship of 1.05-1.15 with the impeller outlet diameter, ensuring that the parameters of each component are mutually compatible and avoiding overall flow field imbalance caused by optimizing a single parameter.

[0018] Meanwhile, flow field simulations based on Computational Fluid Dynamics (CFD) are conducted in the early stages of design, rather than relying on later physical testing. This allows for the acquisition of efficiency and shaft power data under different flow rates in advance, and clear identification of potential issues such as low-speed regions and vortex regions in the flow field. For example, simulations can predict the risk of flow separation at the impeller inlet under low flow rates and the pressure loss problem in the guide vane channel under high flow rates. This enables targeted parameter adjustments during the optimization phase, significantly reducing the number of trial and error attempts with physical prototypes, lowering design costs, and making subsequent optimization directions clearer and more targeted.

[0019] 2. The high-efficiency zone expansion methods are highly targeted, and the efficiency performance across the entire traffic segment is excellent. This invention addresses the core issue of narrow high-efficiency range by designing a method that starts with the collaborative optimization of the impeller and guide vane components. Each optimization method has clear structural parameters and proportional constraints to ensure that the widening effect is stable and controllable.

[0020] In impeller optimization, a biomimetic non-smooth surface design and zoned arrangement are adopted, rather than using uniformly sized pits: the ratio of pit diameter to chord length in the impeller blade inlet section (0.08-0.12) is larger, which can more effectively suppress flow separation caused by sudden changes in flow velocity in the inlet section; the ratio in the middle section (0.06-0.08) is moderate, balancing flow velocity uniformity and flow resistance; the ratio in the outlet section (0.05-0.06) is smaller, adapting to the high-pressure flow environment in the outlet section and avoiding additional pressure loss caused by pits. At the same time, the ratio of pit spacing to diameter is maintained at 1.5-2.5, ensuring that the pit array can play a role in drag reduction and stabilization without disrupting the flow field continuity due to excessively dense spacing.

[0021] In terms of guide vane optimization, 2-4 axial flow splitting ribs are set to divide the flow channel. The height of the flow splitting ribs is kept in a ratio of 0.1-0.2 to the width of the guide vane flow channel. This not only avoids flow channel blockage caused by excessive rib height, but also effectively divides the large-scale vortex inside the guide vane. The cross section of the flow splitting rib adopts an isosceles trapezoid with a ratio of 0.4-0.6. The structure of being narrow at the top and wide at the bottom can reduce fluid impact and further reduce flow resistance. Furthermore, by rounding the impeller outlet edge (with a rounding radius to impeller outlet width ratio of 0.1-0.15), the flow separation area at the impeller outlet can be directly reduced (reduced by ≥40% after optimization), especially improving efficiency at low flow rate of 0.6Qn (increased by ≥3%). Ultimately, this significantly expands the coverage of the high-efficiency zone. After optimization, the minimum flow rate in the high-efficiency zone is ≤0.8Qn, the maximum flow rate is ≥1.2Qn, and the ratio of the flow range to the rated flow rate is ≥0.4. This ensures that the pump efficiency can be stably maintained above 85% under different operating conditions such as low flow rate start-up, rated flow rate operation, and high flow rate peak shaving.

[0022] 3. The energy consumption optimization algorithm converges efficiently, and the energy-saving effect under rated operating conditions is obvious. The energy consumption optimization of this invention does not adopt the traditional trial-and-error method, but constructs a special optimization model based on an improved particle swarm optimization algorithm. The core objective is to minimize shaft power under rated flow. The algorithm design takes into account both exploration and convergence to ensure that a global optimal solution can be found.

[0023] The algorithm employs a linearly decreasing inertia weight strategy. An initial inertia weight of 0.9 is relatively large, allowing particles to broadly search the parameter space in the early stages and avoid getting trapped in local optima. As iterations progress, the inertia weight gradually decreases to a minimum of 0.4, allowing particles to focus on a refined search within the optimal region, balancing search breadth and precision. Simultaneously, the algorithm sets a particle population size of 30-50, coupled with individual learning factors and social learning factors of 1.8-2.2, ensuring sufficient population diversity and evolutionary efficiency, and preventing premature convergence.

[0024] The optimization variables not only include the core parameters of the impeller and guide vanes, but also the base circle diameter of the volute (constrained variation ≤ ±6%), and clearly define the constraint ranges for each parameter: impeller inlet diameter variation ≤ ±8%, impeller outlet diameter ≤ ±5%, impeller outlet width ≤ ±10%, and guide vane diffusion angle 8°-15°. This ensures that the optimized parameters do not exceed the range of machining and operational safety. The algorithm iteration termination condition (change in fitness function value < 1 × 10⁻⁴ for 10 consecutive iterations) further guarantees the accuracy of the optimization results. Ultimately, it can achieve a shaft power reduction of ≥ 5% at rated flow compared to before optimization, which can significantly reduce the energy consumption of the seawater desalination system and lower operating costs in the long term.

[0025] 4. The performance verification system is comprehensive and reliable, ensuring the feasibility of the design deliverables. The optimized version of this invention is not directly finalized. Instead, it uses a dual verification system of CFD flow field verification and test bench performance testing to fully confirm that the performance meets the standards from simulation to physical prototype. Each verification step has clear judgment criteria to avoid the problem of simulation meeting the standards but physical prototype failing.

[0026] CFD flow field verification employs the SSTk-ω turbulence model, which offers higher simulation accuracy for rotating machinery flow fields (such as the centrifugal force flow field generated by impeller rotation and complex backflow within guide vanes). This model can accurately calculate detailed parameters such as the relative velocity non-uniformity on the blade surface (≤0.15) and the vortex intensity within the guide vane channel. Simultaneously, grid independence verification (with a 10% increase in the number of grid cells, the change in the calculated efficiency value is ≤1%) eliminates the interference of grid density on the simulation results, ensuring the authenticity and reliability of the simulation data.

[0027] The performance testing of the test bench employs high-precision instruments: a 0.2-class power analyzer is used for shaft power measurement, and a 0.5-class electromagnetic flowmeter is used for flow measurement, avoiding performance misjudgments due to insufficient instrument accuracy. The test conditions cover five key nodes: 0.6Qn, 0.8Qn, Qn, 1.2Qn, and 1.4Qn, not just the rated operating conditions, allowing for a comprehensive evaluation of the pump's efficiency, shaft power, pressure pulsation, and other performance characteristics under different operating states. Furthermore, the tests verify derivative indicators such as a ≥30% reduction in guide vane vortex intensity and a ≥25% reduction in guide vane outlet pressure pulsation amplitude, ensuring that the pump not only has high efficiency and low energy consumption but also reduces vibration and noise during operation, extending equipment lifespan. If the test results are not met, the system can return to the optimization stage to adjust parameters, forming a closed loop of design-optimization-verification, ensuring that the final delivered high-pressure pump fully meets the actual operational requirements of the seawater desalination system. Attached Figure Description

[0028] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are explained in detail together with the embodiments of the invention, but do not constitute a limitation thereof.

[0029] Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a specific data table in Embodiment 1 of the present invention; Figure 3 This is a specific data table in Embodiment 2 of the present invention; Figure 4 This is a specific data table in Embodiment 3 of the present invention. Detailed Implementation

[0030] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0031] Example 1: Conventional rated flow rate operating condition (Q) n =100m³ / h) 1. Design Goals It is compatible with medium-sized seawater desalination systems, achieving a wide coverage of the high-efficiency zone and a significant reduction in shaft power at a rated flow rate of 100 m³ / h, while ensuring operational stability across the entire flow range.

[0032] 2. Key design parameters, detailed data as follows: Figure 2 As shown (1) Construction of the parameterized model of the core flow channel Maximum design flow rate Qmax =140m³ / h (1.4Qn), average flow velocity at the impeller inlet v1=4.0m / s, calculated impeller inlet diameter D1=√(4×(140 / 3600) / (π×4.0))≈111mm; number of impeller blades Z=6, number of guide vane blades Zg=5; impeller outlet diameter D2=245mm, impeller outlet width b2=32mm; guide vane diffusion angle α=11°. Volute parameters: the ratio of volute base circle diameter D3 to impeller outlet diameter D2 is 1.10, therefore D3=270mm; the ratio of volute outlet diffuser length L3 to volute outlet diameter D4 is 3.0, D4=150mm, therefore L3=450mm.

[0033] (2) Optimization of high-efficiency zone The impeller blade chord length L = 150 mm, and the recess array is arranged in sections: the recess diameter d1 / L = 0.10 (d1 = 15 mm) in the inlet section, d2 / L = 0.07 (d2 = 11 mm) in the middle section, and d3 / L = 0.055 (d3 = 8 mm) in the outlet section; the ratio of the recess spacing s to the recess diameter d is 2.0, so s1 = 30 mm, s2 = 22 mm, and s3 = 16 mm. Two axial flow dividers are set in the guide vane channel. The guide vane channel width W = 40 mm, and the ratio of the flow divider height h to W is 0.15 (h = 6 mm); the flow divider cross section is an isosceles trapezoid, and the ratio of the upper base width a1 to the lower base width a2 is 0.5 (a2 = 10 mm, a1 = 5 mm). The distance between the flow divider and the guide vane blade is t = (40 - 2 × 6) / (2 + 1) ≈ 9 mm. The impeller outlet edge is rounded, and the ratio of the rounding radius r to the impeller outlet width b2 is 0.12 (r=4mm).

[0034] (3) Energy consumption optimization model An improved particle swarm optimization algorithm is adopted, with a particle population size of 40, an individual learning factor c1=2.0, and a social learning factor c2=2.0. The inertia weight ω adopts a linear decreasing strategy, with an initial inertia weight of 0.9 and a minimum inertia weight of 0.4. The algorithm terminates when the change in fitness function value over 10 consecutive iterations is less than 1×10. -4 The optimization variables include the volute base circle diameter D3 (the difference between the optimized and unoptimized values ​​is ≤ ±6% of the unoptimized value), and the optimization objective is to minimize the shaft power at rated flow.

[0035] 3. Performance Verification Flow field simulation was performed using computational fluid dynamics (CFD) with the SSTk-ω turbulence model. Mesh independence verification was successful (the efficiency calculation changed by ≤0.8% when the mesh count increased by 10%). Performance testing on the test bench employed a 0.2-accuracy power analyzer to measure shaft power and a 0.5-accuracy electromagnetic flowmeter to measure flow rate. Test conditions covered 0.6Q... n 0.8Qn Q n 1.2Q n 1.4Q n The results show that the minimum flow rate in the high-efficiency zone is Q1=78m³ / h, the maximum flow rate is Q2=122m³ / h, and the efficiency is ≥85.5% within the flow range Q1-Q2. Under rated flow, the shaft power before optimization is P0=180kW, and the shaft power after optimization is P1=170kW, with an energy consumption reduction rate η. p =(180-170) / 180×100%≈5.6%; the vortex intensity inside the guide vane channel is reduced by 35%, and the pressure pulsation amplitude at the guide vane outlet is reduced by 28%; the flow separation area at the impeller outlet is reduced by 42%, and 0.6Q is achieved under low flow conditions. n Efficiency improved by 3.2%.

[0036] Example 2: Low Flow Rate, High Efficiency (Q) n =80m³ / h) 1. Design Goals It is suitable for small-scale island seawater desalination systems, focusing on improving efficiency under low flow (0.6Qn) conditions, while ensuring coverage of the high-efficiency zone and energy consumption optimization.

[0037] 2. Key design parameters, detailed data as follows: Figure 3 As shown (1) Construction of the parameterized model of the core flow channel Maximum design flow rate Q max =112 m³ / h (1.4Q) n The impeller inlet average velocity v1 = 3.5 m / s, and the calculated impeller inlet diameter D1 = √(4 × (112 / 3600) / (π × 3.5)) ≈ 106 mm; the number of impeller blades Z = 5, the number of guide vane blades Zg = 4; the impeller outlet diameter D2 = 215 mm, the impeller outlet width b2 = 27 mm; the guide vane diffusion angle α = 9°. Volute parameters: the ratio of the volute base circle diameter D3 to the impeller outlet diameter D2 is 1.05, therefore D3 = 226 mm; the ratio of the volute outlet diffuser length L3 to the volute outlet diameter D4 is 2.5, D4 = 130 mm, therefore L3 = 325 mm.

[0038] (2) Optimization of high-efficiency zone The impeller blade chord length L = 130 mm, with the recessed array arranged in sections: inlet section d1 / L = 0.12 (d1 = 16 mm), middle section d2 / L = 0.08 (d2 = 10 mm), outlet section d3 / L = 0.06 (d3 = 8 mm); the ratio of recess spacing s to recess diameter d is 1.8, therefore s1 = 29 mm, s2 = 18 mm, and s3 = 14 mm. Three axial flow dividers are installed in the guide vane channel. The guide vane channel width W = 35 mm, and the ratio of flow divider height h to W is 0.12 (h = 4 mm); the flow divider cross-section a1 / a2 = 0.4 (a2 = 10 mm, a1 = 5 mm), and the distance between the flow divider and the guide vane blade t = (35 - 3 × 4) / 4 ≈ 6 mm. The ratio of the impeller outlet edge rounding radius r to b2 is 0.15 (r = 4 mm).

[0039] (3) Energy consumption optimization model The improved particle swarm optimization algorithm uses a particle population size of 35, learning factors c1=1.8 and c2=2.2; the inertia weight ω decreases linearly (ω... max =0.9, ω min =0.4), the iteration termination condition is that the change in fitness function value is <1×10 for 10 consecutive iterations. -4 The optimized variable includes the base circle diameter D3 of the volute (the difference before and after optimization is ≤ ±5% of the original value).

[0040] 3. Performance Verification CFD flow field simulation used the SSTk-ω turbulence model, and the mesh independence verification was passed (efficiency change ≤0.9% with a 10% increase in mesh size). Test bench results: In the high-efficiency region, Q1=63m³ / h and Q2=98m³ / h, the efficiency is ≥85.3% within the flow range Q1-Q2; at rated flow, the shaft power P0=140kW before optimization, and P1=132kW after optimization, with an energy consumption reduction rate η. p =(140-132) / 140×100%≈5.7%; Guide vane vortex intensity reduced by 32%, pressure pulsation reduced by 26%; Impeller outlet flow separation area reduced by 45%; Small flow rate 0.6Q n (48m³ / h) Operating efficiency increased by 3.5%.

[0041] Example 3: High flow rate and high energy efficiency (Q) n =120m³ / h) 1. Design Goals Suitable for large-scale industrial park seawater desalination systems, meeting high flow rates (1.4Q). n It operates efficiently under high flow rate and the shaft power reduction rate is ≥5.5% under rated flow rate, thus improving long-term energy-saving benefits.

[0042] 2. Key design parameters, detailed data as follows: Figure 4 As shown (1) Construction of the parameterized model of the core flow channel Maximum design flow rate Q max =168 m³ / h (1.4Q) n The impeller inlet average velocity v1 = 5.0 m / s, and the calculated impeller inlet diameter D1 = √(4 × (168 / 3600) / (π × 5.0)) ≈ 109 mm; the number of impeller blades Z = 7, the number of guide vane blades Zg = 8; the impeller outlet diameter D2 = 272 mm, the impeller outlet width b2 = 38 mm; the guide vane diffusion angle α = 13°. Volute parameters: the ratio of the volute base circle diameter D3 to the impeller outlet diameter D2 is 1.15, therefore D3 = 313 mm; the ratio of the volute outlet diffuser length L3 to the volute outlet diameter D4 is 3.5, D4 = 170 mm, therefore L3 = 595 mm.

[0043] (2) Optimization of high-efficiency zone The impeller blade chord length L = 170 mm, with the recessed array arranged in sections: inlet section d1 / L = 0.09 (d1 = 15 mm), middle section d2 / L = 0.075 (d2 = 13 mm), and outlet section d3 / L = 0.058 (d3 = 10 mm). The ratio of the recess spacing s to the recess diameter d is 2.5, therefore s1 = 38 mm, s2 = 33 mm, and s3 = 25 mm. Four axial flow dividers are installed in the guide vane channel. The guide vane channel width W = 45 mm, and the ratio of the flow divider height h to W is 0.18 (h = 8 mm). The flow divider cross-section a1 / a2 = 0.6 (a2 = 10 mm, a1 = 6 mm), and the distance between the flow divider and the guide vane blade t = (45 - 4 × 8) / 5 ≈ 3 mm. The ratio of the impeller outlet edge rounding radius r to b2 is 0.11 (r = 4 mm).

[0044] (3) Energy consumption optimization model The improved particle swarm optimization algorithm uses a particle population size of 50, learning factors c1=2.2 and c2=1.8; the inertia weight ω decreases linearly (ω... max =0.9, ω min =0.4), the iteration termination condition is that the change in fitness function value for 10 consecutive iterations is <1×10. -4 The optimized variable includes the base circle diameter D3 of the volute (the difference before and after optimization is ≤ ±4% of the original value).

[0045] 3. Performance Verification CFD flow field simulation used the SSTk-ω turbulence model, and the mesh independence verification passed (efficiency change ≤0.7% with a 10% increase in mesh size). Test bench results: In the high-efficiency region, Q1=95 m³ / h and Q2=146 m³ / h, the efficiency is ≥85.1% within the flow range Q1-Q2; 1.4Q nThe operating efficiency reaches 85.1% under the condition of (168 m³ / h); under rated flow, the optimized front axle power P0 = 220 kW, and the optimized P1 = 207 kW, with an energy consumption reduction rate η. p =(220-207) / 220×100%≈5.9%; guide vane vortex intensity decreased by 38%, pressure pulsation decreased by 30%; impeller outlet flow separation area decreased by 43%.

[0046] The design method for widening the high-efficiency zone and optimizing energy consumption of large-scale seawater desalination high-pressure pumps proposed in this invention has the following advantages: 1. The core design system is precise and controllable, providing a solid foundation for optimization. This design method first constructs a complete parametric model of the core flow channels, covering the impeller channel, guide vane channel, and volute. It doesn't focus on a single component, but rather incorporates key parameters such as impeller inlet diameter, impeller outlet diameter, impeller outlet width, number of guide vanes, and guide vane diffusion angle, as well as volute parameters such as volute base circle diameter and volute outlet diffuser length, into the model input, forming a quantifiable and controllable design framework. Specifically, the impeller inlet diameter is precisely calculated using the maximum design flow rate and the average inlet velocity of 3.5-5.0 m / s; the number of guide vanes is correlated with the number of impeller blades (5-7); and the volute base circle diameter maintains a proportional relationship of 1.05-1.15 with the impeller outlet diameter, ensuring that the parameters of each component are mutually compatible and avoiding overall flow field imbalance caused by optimizing a single parameter.

[0047] Meanwhile, flow field simulations based on Computational Fluid Dynamics (CFD) are conducted in the early stages of design, rather than relying on later physical testing. This allows for the acquisition of efficiency and shaft power data under different flow rates in advance, and clear identification of potential issues such as low-speed regions and vortex regions in the flow field. For example, simulations can predict the risk of flow separation at the impeller inlet under low flow rates and the pressure loss problem in the guide vane channel under high flow rates. This enables targeted parameter adjustments during the optimization phase, significantly reducing the number of trial and error attempts with physical prototypes, lowering design costs, and making subsequent optimization directions clearer and more targeted.

[0048] 2. The high-efficiency zone expansion methods are highly targeted, and the efficiency performance across the entire traffic segment is excellent. To address the core issue of a narrow high-efficiency zone, the design methodology starts with the collaborative optimization of the impeller and guide vane components. Each optimization method has clear structural parameters and proportional constraints to ensure that the widening effect is stable and controllable.

[0049] In impeller optimization, a biomimetic non-smooth surface design and zoned arrangement are adopted, rather than using uniformly sized pits: the ratio of pit diameter to chord length in the impeller blade inlet section (0.08-0.12) is larger, which can more effectively suppress flow separation caused by sudden changes in flow velocity in the inlet section; the ratio in the middle section (0.06-0.08) is moderate, balancing flow velocity uniformity and flow resistance; the ratio in the outlet section (0.05-0.06) is smaller, adapting to the high-pressure flow environment in the outlet section and avoiding additional pressure loss caused by pits. At the same time, the ratio of pit spacing to diameter is maintained at 1.5-2.5, ensuring that the pit array can play a role in drag reduction and stabilization without disrupting the flow field continuity due to excessively dense spacing.

[0050] In guide vane optimization, 2-4 axial flow-dividing ribs are used to split the flow channel. The height of the flow-dividing ribs is maintained at a ratio of 0.1-0.2 to the width of the guide vane flow channel. This avoids flow channel blockage caused by excessively high ribs and effectively separates large-scale vortices within the guide vane. The cross-section of the flow-dividing ribs adopts an isosceles trapezoid with a ratio of 0.4-0.6. The structure, which is narrower at the top and wider at the bottom, reduces fluid impact and further lowers flow resistance. In addition, the rounding treatment of the impeller outlet edge (rounding radius to impeller outlet width ratio of 0.1-0.15) can directly reduce the flow separation area at the impeller outlet (reduced by ≥40% after optimization), especially improving low-flow conditions. n The efficiency is improved by ≥3%, ultimately resulting in a significant expansion of the high-efficiency zone coverage. After optimization, the minimum flow rate in the high-efficiency zone is ≤0.8Q. n Maximum flow rate ≥ 1.2Q n Furthermore, the ratio of the flow range to the rated flow is ≥0.4, ensuring that the pump efficiency can be stably maintained above 85% under different operating conditions such as small flow start-up, rated flow operation, and large flow peak shaving.

[0051] 3. The energy consumption optimization algorithm converges efficiently, and the energy-saving effect under rated operating conditions is obvious. Energy consumption optimization does not employ the traditional trial-and-error method, but instead constructs a specialized optimization model based on an improved particle swarm optimization algorithm. The core objective is to minimize shaft power at rated flow rate, and the algorithm design balances exploratory and convergent aspects to ensure that a globally optimal solution can be found.

[0052] The algorithm employs a linearly decreasing inertia weight strategy. An initial inertia weight of 0.9 is relatively large, allowing particles to broadly search the parameter space in the early stages and avoid getting trapped in local optima. As iterations progress, the inertia weight gradually decreases to a minimum of 0.4, allowing particles to focus on a refined search within the optimal region, balancing search breadth and precision. Simultaneously, the algorithm sets a particle population size of 30-50, coupled with individual learning factors and social learning factors of 1.8-2.2, ensuring sufficient population diversity and evolutionary efficiency, and preventing premature convergence.

[0053] The optimization variables not only include the core parameters of the impeller and guide vanes, but also the base circle diameter of the volute (constrained variation ≤ ±6%), and clearly define the constraint ranges for each parameter: impeller inlet diameter variation ≤ ±8%, impeller outlet diameter ≤ ±5%, impeller outlet width ≤ ±10%, and guide vane diffusion angle 8°-15°. This ensures that the optimized parameters do not exceed the range of machining and operational safety. The algorithm iteration termination condition (change in fitness function value < 1 × 10⁻⁴ for 10 consecutive iterations) further guarantees the accuracy of the optimization results. Ultimately, it can achieve a shaft power reduction of ≥ 5% at rated flow compared to before optimization, which can significantly reduce the energy consumption of the seawater desalination system and lower operating costs in the long term.

[0054] 4. The performance verification system is comprehensive and reliable, ensuring the feasibility of the design deliverables. The optimized product is not directly finalized. Instead, it undergoes a dual verification system of CFD flow field verification and test bench performance testing. This system comprehensively confirms that the performance meets the standards from simulation to physical prototype. Each verification step has clear judgment criteria to avoid the problem of simulation meeting the standards but the physical prototype failing.

[0055] CFD flow field verification employs the SSTk-ω turbulence model, which offers higher simulation accuracy for rotating machinery flow fields (such as the centrifugal force flow field generated by impeller rotation and complex backflow within guide vanes). This model can accurately calculate detailed parameters such as the relative velocity non-uniformity on the blade surface (≤0.15) and the vortex intensity within the guide vane channel. Simultaneously, grid independence verification (with a 10% increase in the number of grid cells, the change in the calculated efficiency value is ≤1%) eliminates the interference of grid density on the simulation results, ensuring the authenticity and reliability of the simulation data.

[0056] The performance testing of the test bench employs high-precision instruments: a 0.2-class power analyzer is used for shaft power measurement, and a 0.5-class electromagnetic flowmeter is used for flow measurement, avoiding performance misjudgments due to insufficient instrument accuracy; the test conditions cover 0.6Q. n 0.8Q n Q n 1.2Q n 1.4Q n The five key testing points go beyond just testing rated operating conditions. They comprehensively evaluate the pump's performance, including efficiency, shaft power, and pressure pulsation, under different operating conditions. Furthermore, the tests verify derivative indicators such as a ≥30% reduction in guide vane vortex intensity and a ≥25% reduction in guide vane outlet pressure pulsation amplitude. This ensures the pump is not only highly efficient and energy-efficient but also reduces vibration and noise during operation, extending equipment lifespan. If the tests fail to meet the standards, the system can return to the optimization phase to adjust parameters, forming a closed loop of design-optimization-verification. This ensures that the final delivered high-pressure pump fully meets the actual operational requirements of the seawater desalination system.

[0057] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for widening the high-efficiency zone and optimizing energy consumption of a large-scale seawater desalination high-pressure pump, characterized in that, Specifically, the following steps are included: S1. Establish a parameterized model of the core flow channel of a large-scale seawater desalination high-pressure pump. The core flow channel includes the impeller flow channel and the guide vane flow channel. The input parameters of the parameterized model include the impeller inlet diameter D1, the impeller outlet diameter D2, the impeller outlet width b2, the number of guide vane blades Zg, and the guide vane diffusion angle. ; S2. Perform flow field simulation on the parameterized model based on computational fluid dynamics (CFD) to obtain the efficiency η and shaft power P of the high-pressure pump under different flow conditions. S3. To broaden the high-efficiency zone, the impeller blades are optimized with a biomimetic non-smooth surface. A hemispherical array of recesses is set on the pressure surface of the impeller blades. The ratio of the recess diameter d to the impeller blade chord length L satisfies 0.05≤d / L≤0.12, where d is the recess diameter and L is the impeller blade chord length. The ratio of the recess spacing s to the recess diameter d satisfies 1.5≤s / d≤2.5, where s is the center-to-center distance between adjacent recesses. At the same time, the guide vanes are designed with a flow-dividing rib structure. 2-4 axial flow-dividing ribs are set in the guide vane flow channel. The ratio of the flow-dividing rib height h to the guide vane flow channel width W satisfies 0.1≤h / W≤0.

2. S4. For energy consumption optimization, an energy consumption optimization model based on an improved particle swarm optimization algorithm is constructed, taking the high-pressure pump at its rated flow rate Q as an example. n The optimization objective is to minimize the shaft power P. The core flow channel parameters from step S1 are used as optimization variables, and constraints are set for these variables. The fitness function f of the improved particle swarm optimization algorithm is: in , This is a function of shaft power at rated flow rate. , The guide vane diffusion angle; the algorithm iteration termination condition is that the change in the fitness function value over 10 consecutive iterations is less than 1 × 10⁻⁶. -4 ; S5. Perform CFD flow field verification and test bench performance testing on the parameterized model optimized in steps S3 and S4. If the high-pressure pump is within the flow range... The efficiency within the range is ≥85%, of which To achieve the minimum flow rate in the high-efficiency zone, Maximum flow rate in the high-efficiency zone; If the shaft power at rated flow rate is reduced by ≥5% compared to before optimization, the design is complete; otherwise, return to step S3 to adjust the optimization parameters until the performance requirements are met.

2. The method for widening the high-efficiency zone and optimizing energy consumption of a large-scale seawater desalination high-pressure pump according to claim 1, characterized in that, In step S1, the impeller inlet diameter The calculation satisfies the formula: in The impeller inlet diameter is This is the maximum design flow rate of the high-pressure pump. It is pi and takes the value 3.1416. The average flow velocity at the impeller inlet is, and The value range is 3.5-5.0 m / s; the relationship between the number of guide vane blades Zg and the number of impeller blades Z satisfies Zg=Z±1, where the value range of Z is 5-7.

3. The method for widening the high-efficiency zone and optimizing energy consumption of a large-scale seawater desalination high-pressure pump according to claim 1, characterized in that, In step S1, the parameterized model also includes parameters of the pump casing. The ratio of the casing base circle diameter D3 to the impeller outlet diameter D2 satisfies 1.05≤D3 / D2≤1.15; the ratio of the casing outlet diffuser section length L3 to the casing outlet diameter D4 satisfies 2.5≤L3 / D4≤3.5; and in the energy consumption optimization model in step S4, the casing base circle diameter D3 is used as an additional optimization variable, with a constraint range of D3 variation ≤±6%, where the variation is the percentage of the difference between the optimized and unoptimized values ​​relative to the unoptimized value.

4. The method for widening the high-efficiency zone and optimizing energy consumption of a large-scale seawater desalination high-pressure pump according to claim 1, characterized in that, In step S3, the biomimetic non-smooth surface pit array is arranged in zones. The pit diameter d1 at the impeller blade inlet section satisfies 0.08 ≤ d1 / L ≤ 0.12; the pit diameter d2 in the middle section of the blade satisfies 0.06 ≤ d2 / L ≤ 0.08; and the pit diameter d3 at the blade outlet section satisfies 0.05 ≤ d3 / L ≤ 0.06, where d3 is the outlet section pit diameter. Furthermore, in the CFD flow field simulation, the calculation of the relative velocity non-uniformity σ on the blade surface satisfies: ≤ ； in , The root mean square of the velocity deviation, Let be the relative velocity at the i-th monitoring point on the blade surface. The average relative velocity across all monitoring points. This represents the total number of monitoring points; , This represents the average relative velocity.

5. The method for widening the high-efficiency zone and optimizing energy consumption of a large-scale seawater desalination high-pressure pump according to claim 1, characterized in that, In step S3, the cross-section of the guide vane diverting rib is an isosceles trapezoid, and the ratio of the upper base width a1 to the lower base width a2 of the trapezoid satisfies 0.4 ≤ a1 / a2 ≤ 0.6; and the distance t between the diverting rib and the guide vane blade satisfies: in The distance between the flow divider rib and the guide vane blade. The width of the guide vane channel, The number of diversion ribs, This refers to the height of the flow divider rib. CFD simulations verified that the vortex intensity in the guide vane channel was reduced by ≥30% after the flow splitter rib was optimized, and the pressure pulsation amplitude at the guide vane outlet was reduced by ≥25%.

6. The method for widening the high-efficiency zone and optimizing energy consumption of a large-scale seawater desalination high-pressure pump according to claim 1, characterized in that, Step S3 also includes rounding the impeller outlet edge, where the ratio of the rounding radius r to the impeller outlet width b2 satisfies 0.1 ≤ r / b2 ≤ 0.

15. CFD simulation verification shows that after rounding, the flow separation area at the impeller outlet is reduced by ≥40%, where the reduction is the percentage of the difference between the optimized and unoptimized separation areas relative to the original separation area. Furthermore, the high-pressure pump operates at a low flow rate of 0.6Q. n The efficiency improvement is ≥3%, where the improvement is the percentage of the difference between the efficiency before and after optimization relative to the original efficiency.

7. The method for widening the high-efficiency zone and optimizing energy consumption of a large-scale seawater desalination high-pressure pump according to claim 1, characterized in that, In step S4, the inertia weight ω of the improved particle swarm optimization algorithm adopts a linear decreasing strategy, satisfying the formula: ； in , For the basic term of inertia weight, The initial inertia weight is set to 0.

9. The minimum inertia weight is set to 0.

4. This represents the current iteration number; = , The maximum number of iterations is set to 1.8; the learning factors c1 and c2 of the algorithm are both set to 1.8-2.2, where c1 is the individual learning factor and c2 is the social learning factor; the particle population size is set to 30-50, where the population size is the total number of particles participating in the optimization.

8. The method for widening the high-efficiency zone and optimizing energy consumption of a large-scale seawater desalination high-pressure pump according to claim 1, characterized in that, In step S4, the constraint range of the optimization variables is specifically as follows: the change in impeller inlet diameter D1 is ≤ ±8%, where the change is the percentage of the difference between the optimized and unoptimized values ​​to the unoptimized value; the change in impeller outlet diameter D2 is ≤ ±5%; the change in impeller outlet width b2 is ≤ ±10%; the change range of the number of guide vanes Zg is 4-8; and the change range of the guide vane diffusion angle α is 8°-15°.

9. The method for widening the high-efficiency zone and optimizing energy consumption of a large-scale seawater desalination high-pressure pump according to claim 1, characterized in that, In step S5, the criterion for determining the high-efficiency zone is: the flow range Q1-Q2 corresponding to a high-pressure pump efficiency ≥ 85% and the rated flow rate Q. n The ratio satisfies Q2-Q1 / Q n ≥0.4, where Q1 is the minimum flow rate in the high-efficiency zone, Q2 is the maximum flow rate in the high-efficiency zone, and Q n The rated flow rate; and the flow rate Q1 ≤ 0.8Q n Q2≥1.2Q n In the CFD flow field verification, the SSTk-ω turbulence model was adopted. The grid independence verification was determined by the change in the efficiency calculation value being ≤1% when the number of grids increased by 10%, where the change is the percentage of the efficiency difference between the grids before and after the increase relative to the original efficiency.

10. The method for widening the high-efficiency zone and optimizing energy consumption of a large-scale seawater desalination high-pressure pump according to claim 1, characterized in that, In step S5, the performance test of the test bench includes an energy consumption test. The energy consumption test uses a power analyzer with an accuracy of 0.2 class to measure the shaft power P and an electromagnetic flowmeter with an accuracy of 0.5 class to measure the flow rate Q. The test conditions include 0.6Q. n 0.8Q n Q n 1.2Q n 1.4Q n Five operating points, including Q n Rated flow rate; energy consumption optimization effect is achieved through energy consumption reduction rate η p Evaluate: in , This is the difference in shaft power. To optimize shaft power at the original rated flow rate, To optimize shaft power at rated flow rate; = , To optimize front axle power; and requires ≥5%.

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