A method for predicting performance of a propeller of a real ship

Abstract

The application discloses a real ship propeller performance prediction method, comprising the following steps: processing propeller model test data, performing Fourier transform to obtain pressure amplitude at the first five orders of blade frequency, and obtaining pressure amplitude theoretical baseline at each order under each state; real ship monitoring data preprocessing, eliminating abnormal data, and retaining data under normal weather; real ship fluctuating pressure monitoring value processing, performing cavitation judgment and real ship propeller performance comparison. The application can collect data of the real ship propeller under different load conditions and different weather conditions, accurately evaluate the performance change of the propeller, and provide performance decline early warning.

Description

Technical Field

[0001] This invention belongs to the field of propeller monitoring, and in particular relates to a method for predicting the performance of a ship's propeller. Background Technology

[0002] With the development of ship informatization and digitalization, real-time prediction using propeller data collected from ships has become possible. Low-vibration, high-speed, and large-tonnage ships have become the development trend of the shipbuilding industry, and the main engine power required by ships is also rapidly increasing. As the propulsion device of ships, the propeller generates severe cavitation when passing through a high wake region due to the non-uniformity of the stern wake. The generation, development, and collapse of cavitation bubbles cause drastic pressure changes in the area around the propeller, exhibiting very obvious blade frequency characteristics. If this couples with the natural frequency of the hull structure, it will cause severe vibration of the ship, affecting navigation safety.

[0003] Existing technologies disclose several methods for detecting or suppressing propeller cavitation. For example, Chinese patent document CN110458976A discloses a propeller cavitation detection method based on wavelet and principal component analysis, providing information on both time-frequency and frequency principal components to express the characteristics of the original propeller noise signal, thereby identifying and judging the cavitation stage of the propeller. Chinese patent document CN116933433A discloses a ship propeller cavitation noise suppression method based on distributed aperture groups. This method arranges aperture groups in the severely cavitated area of ​​the propeller blade and selects the optimal aperture arrangement scheme based on the change in blade cavitation volume and the loss of propulsion efficiency to delay or suppress blade cavitation.

[0004] During actual operation, ships encounter varying sea conditions. When the propeller draft changes, the excitation force induced by propeller cavitation also changes. Therefore, there is an urgent need to design an accurate and effective method to assess the performance changes of the excitation force induced by the propeller and provide early warning of performance degradation. Summary of the Invention

[0005] This invention provides a method for predicting the performance of a ship propeller, which can accurately assess the changes in propeller performance based on data collected from ship propellers under different load conditions and weather conditions, and provide early warning of performance degradation.

[0006] A method for predicting the performance of a ship's propeller includes the following steps:

[0007] (1) Collect propeller model test data and obtain the rotational speed and cavitation number σ at the start of propeller cavitation during the test. 空 ;

[0008] (2) Collect time-history curve data of propeller pulsation pressure in cavitation and non-cavitation states under the two working conditions of full load and ballast.

[0009] (3) Perform Fourier transform on the obtained multiple time-history curves respectively, and fit the pulse pressure data at the first 5 blade frequencies after transformation to obtain the theoretical baseline of the pulse pressure amplitude at each level under different working conditions.

[0010] (4) Acquire data collected from the actual ship and perform preprocessing. The data collected from the actual ship includes: ship speed, ship main engine speed, ship bow draft, ship stern draft, ship propeller pulsation pressure, and wind speed and direction encountered by the ship.

[0011] (5) Perform Fourier transform on the ship propeller pulsating pressure time history curve data obtained in step (4) to obtain the actual pulsating pressure amplitudes P1, P2, P3, P4, and P5 at the first 5 blade frequencies.

[0012] (6) Calculate the average propeller speed and the average actual draft at the stern of the ship within 10 minutes. Calculate the actual ship's rotational speed cavitation number σ using the same method as in step (1). 实 Next, determine σ. 实 Is it greater than σ? 空 If it is greater than , then this state is a non-vacuolated state; otherwise, it is a vacuolated state.

[0013] (7) Calculate the toe clearance ratio (TCR) of the actual ship. 实 According to step (6), the current cavitation state or non-cavitation state is obtained, and the first, second, third, fourth and fifth order pulsating pressure amplitude data of the actual ship under the corresponding state with different tip clearance ratios under full load and ballast are extracted and fitted; finally, the theoretical pulsating pressure values ​​X1, X2, X3, X4 and X5 under the actual draft are calculated.

[0014] (8) Compare the actual pulsating pressure amplitudes P1, P2, P3, P4, P5 with the theoretical pulsating pressure amplitudes X1, X2, X3, X4, X5, calculate the differences α1, α2, α3, α4, α5 for each order, and judge the propeller performance based on the obtained differences α1, α2, α3, α4, α5 for each order.

[0015] Furthermore, in step (1), the rotational speed and cavitation number σ at the start of propeller cavitation during the experiment are calculated. 空 The formula is:

[0016]

[0017] In the formula, σ represents the cavitation number at rotational speed; P0 is the background pressure of the water tank, determined by the stern draft of the ship; P eρ is the vaporization pressure of water; n is the density of water; D is the propeller speed; and D is the propeller diameter.

[0018] In step (2), the formula for calculating the tip gap ratio (TCR) is as follows:

[0019]

[0020] In the formula, L represents the distance from the propeller shaft centerline to the bottom of the ship, and D is the propeller diameter.

[0021] In step (3), Fourier transform is performed on the multiple time-history curves to obtain the pulsating pressure amplitude at the first, second, third, fourth, and fifth order leaf frequencies of each time-history curve; the data is divided into 20 data groups according to the cavitation state under full load, the cavitation state under ballast, the non-cavitation state under full load, and the non-cavitation state under ballast.

[0022] Fitting was performed on each of the 20 data sets, using y = ax 2 The fitting process is performed using +bx+c, where y is the pulsating pressure value, x is the tip-to-gap ratio (TCR), and a, b, and c are coefficients. This yields 20 fitting formulas, which serve as the theoretical baseline for the pulsating pressure amplitude at various stages under different operating conditions.

[0023] In step (4), preprocessing includes removing abnormal data collected from the actual ship and removing data under abnormal weather and motion conditions;

[0024] Abnormal data collected from actual ships were excluded, including: ship speed >30 knots or <0 knots; ship main engine speed >100 rpm or <0 rpm; ship bow draft <0 m or 30 m; and ship stern draft <0 m or 30 m.

[0025] Specifically, data removed under abnormal weather and motion conditions includes: using a 10-minute period, determining whether any of the following conditions exist within 600 data sets over a 10-minute period: 1. The wind speed encountered by the ship is >40 knots; 2. The difference between the maximum and minimum draft at the bow or stern of the ship is greater than 2 meters. If any of these conditions exist, the 10-minute data set is removed and not used for subsequent calculations.

[0026] In step (7), the theoretical pulsating pressure values ​​X1, X2, X3, X4, and X5 at the actual draft are calculated. The specific process is as follows:

[0027] The first, second, third, fourth, and fifth order pulsating pressure amplitudes under different clearance ratios under full load and ballast conditions were extracted and fitted. The formulas for each order under full load are f. t1 (x), f t2 (x), f t3 (x), f t4 (x), f t5(x); the formulas for each stage under ballast are f b1 (x), f b2 (x), f b3 (x), f b4 (x), f b5 (x);

[0028] TCR 实 Substituting the values ​​into the formulas for each stage under full load and ballast, we finally obtain x = TCR. 实 The predicted values ​​of the pulsating pressure for the first 5 stages under full load and ballast are P1t, P2t, P3t, P4t, and P5t, respectively. The predicted values ​​of the pulsating pressure for the first 5 stages under full load are P1b, P2b, P3b, P4b, and P5b, respectively.

[0029] Calculate the theoretical pulsating pressure values ​​for each order at the actual draft depth h:

[0030] First-order theoretical pulsating pressure value: X1=(h-Ba)*(P1b-P1t) / (Ta-Ba)+P1t

[0031] Second-order theoretical pulsating pressure value: X2=(h-Ba)*(P2b-P2t) / (Ta-Ba)+P2t

[0032] Third-order theoretical pulsating pressure value: X3=(h-Ba)*(P3b-P3t) / (Ta-Ba)+P3t

[0033] Fourth-order theoretical pulsating pressure value: X4=(h-Ba)*(P4b-P4t) / (Ta-Ba)+P4t

[0034] Fifth-order theoretical pulsating pressure value: X5=(h-Ba)*(P5b-P5t) / (Ta-Ba)+P5t

[0035] In the formula, Ba represents the stern draft of the ship under ballast conditions, and Ta represents the stern draft of the ship under full load conditions. In step (8), the differences α1, α2, α3, α4, and α5 of each order are calculated using the following formula:

[0036] α1=|P1-X1| / X1

[0037] α2=|P2-X2| / X2

[0038] α3=|P3-X3| / X3

[0039] α4=|P4-X4| / X4

[0040] α5 = |P5 - X5| / X5.

[0041] In step (8), the propeller performance is judged based on the differences α1, α2, α3, α4, and α5 obtained for each order, specifically as follows:

[0042] If α1 ≥ 10%, the output conclusion is that the propeller performance deteriorates; if α1 < 10% and α2 > 10%, the output conclusion is that the propeller performance has a downward trend; if α1, α2, α3, α4, and α5 < 10%, the output conclusion is that the propeller performance is good; otherwise, the output conclusion is that the propeller performance is within the normal range.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The propeller cavitation starting speed and cavitation number provided in step (1) can determine whether there is cavitation in the actual ship under different operating conditions, so as to identify the propeller operating status more quickly.

[0045] 2. Based on the theoretical baseline of pulse pressure amplitude at each stage under different operating conditions obtained from model tests, this baseline serves as the boundary value of propeller pulse pressure performance, and can be quickly interpolated to obtain the theoretical prediction value of different operating conditions of the actual ship.

[0046] 3. This invention proposes a method for judging the health status of propeller performance. It can compare and analyze the propeller pulse pressure amplitude value obtained from actual ship monitoring with the theoretical prediction value, and quickly determine whether there is any abnormality in propeller performance. This improves the monitoring capability of ship propeller performance, avoids ship navigation failures due to propeller performance degradation, and improves the ability to predict propeller performance degradation. Attached Figure Description

[0047] Figure 1 This is a flowchart of a method for predicting the performance of a ship propeller according to the present invention. Detailed Implementation

[0048] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be noted that the embodiments described below are intended to facilitate the understanding of the present invention and do not constitute any limitation thereof.

[0049] like Figure 1 As shown, a method for predicting the performance of a ship's propeller includes the following steps:

[0050] S01, Fitting the theoretical performance baseline of the propeller

[0051] ① Processing propeller model test data

[0052] a. Based on the propeller test phenomena, determine the rotational speed at which cavitation occurs, and then determine the value of cavitation during the test, i.e., the rotational speed and cavitation number σ at the start of cavitation, according to formula (1). 空 :

[0053]

[0054] In formula (1), σ represents the cavitation number at rotational speed, P0 is the background pressure of the water tank, Pe is the vaporization pressure of water, ρ is the density of water, n is the propeller speed, and D is the propeller diameter.

[0055] b. Collect the time-history curve data of propeller pulsation pressure in cavitation and non-cavitation states during the propeller model test under two working conditions: full load and ballast. The propeller has multiple tip clearance ratios (TCR) (calculated as shown in formula (2)).

[0056]

[0057] In formula (2), L represents the distance from the propeller shaft centerline to the bottom of the ship, and D is the propeller diameter.

[0058] Assuming that during the theoretical test, the non-cavitation and cavitation data of five clearance ratios (0.35, 0.31, 0.27, 0.23, 0.19) under two working conditions of full load and ballast were determined, it is possible to collect time-history curve data of 20 propeller pulsating pressures.

[0059] ② Perform Fourier transform on the propeller model test data

[0060] According to the requirements of GB / T36580-2018, the Fourier formula is used to perform Fourier transform on the multiple time-history curves obtained in the previous step to obtain the pulsating pressure results at the first 5 leaf frequencies of each curve.

[0061] ③ Fit the transformed data to obtain the theoretical baseline of the pulsating pressure amplitude at each stage under different working conditions.

[0062] After the transformation in the previous step, each time-history curve yields first-order, second-order, third-order, fourth-order, and fifth-order pulsating pressure values. The full-load, ballast, cavitation, and non-cavitation curves are used to distinguish the data, resulting in the following 20 data sets:

[0063] 1. First-order data set for different shoot gap ratios under full-load cavitation conditions; 2. Second-order data set for different shoot gap ratios under full-load cavitation conditions; 3. Third-order data set for different shoot gap ratios under full-load cavitation conditions; 4. Fourth-order data set for different shoot gap ratios under full-load cavitation conditions; 5. Fifth-order data set for different shoot gap ratios under full-load cavitation conditions; 6. First-order data set for different shoot gap ratios under full-load non-cavitation conditions; 7. Second-order data set for different shoot gap ratios under full-load non-cavitation conditions; 8. Third-order data set for different shoot gap ratios under full-load non-cavitation conditions; 9. Fourth-order data set for different shoot gap ratios under full-load non-cavitation conditions; 10. Fifth-order data set for different shoot gap ratios under full-load non-cavitation conditions; 11. Pressure 11. First-order data set of different tip gap ratios under cavitation conditions under ballast conditions; 12. Second-order data set of different tip gap ratios under cavitation conditions under ballast conditions; 13. Third-order data set of different tip gap ratios under cavitation conditions under ballast conditions; 14. Fourth-order data set of different tip gap ratios under cavitation conditions under ballast conditions; 15. Fifth-order data set of different tip gap ratios under cavitation conditions under ballast conditions; 16. First-order data set of different tip gap ratios under non-cavitation conditions under ballast conditions; 17. Second-order data set of different tip gap ratios under non-cavitation conditions under ballast conditions; 18. Third-order data set of different tip gap ratios under non-cavitation conditions under ballast conditions; 19. Fourth-order data set of different tip gap ratios under non-cavitation conditions under ballast conditions; 20. Fifth-order data set of different tip gap ratios under non-cavitation conditions under ballast conditions.

[0064] Fit the above 20 sets of data respectively, using y = ax 2 The fitting process is performed using +bx+c, where y is the pulsating pressure value, x is the TCR, and a, b, and c are coefficients. This yields 20 fitting formulas.

[0065] 1. Fitting formula for first-order pulsating pressure with different shoot gap ratios under full-load cavitation conditions; 2. Fitting formula for second-order pulsating pressure with different shoot gap ratios under full-load cavitation conditions; 3. Fitting formula for third-order pulsating pressure with different shoot gap ratios under full-load cavitation conditions; 4. Fitting formula for fourth-order pulsating pressure with different shoot gap ratios under full-load cavitation conditions; 5. Fitting formula for fifth-order pulsating pressure with different shoot gap ratios under full-load cavitation conditions; 6. Fitting formula for first-order pulsating pressure with different shoot gap ratios under full-load non-cavitation conditions; 7. Fitting formula for second-order pulsating pressure with different shoot gap ratios under full-load non-cavitation conditions; 8. Fitting formula for third-order pulsating pressure with different shoot gap ratios under full-load non-cavitation conditions; 9. Fitting formula for fourth-order pulsating pressure with different shoot gap ratios under full-load non-cavitation conditions; 10. Fitting formula for fifth-order pulsating pressure with different shoot gap ratios under full-load non-cavitation conditions; 11. Pressure 12. Fitting formula for first-order pulsating pressure with different gap ratios under ballast cavitation conditions; 13. Fitting formula for second-order pulsating pressure with different gap ratios under ballast cavitation conditions; 14. Fitting formula for third-order pulsating pressure with different gap ratios under ballast cavitation conditions; 15. Fitting formula for fourth-order pulsating pressure with different gap ratios under ballast cavitation conditions; 16. Fitting formula for fifth-order pulsating pressure with different gap ratios under ballast cavitation conditions; 17. Fitting formula for first-order pulsating pressure with different gap ratios under ballast non-cavitation conditions; 18. Fitting formula for second-order pulsating pressure with different gap ratios under ballast non-cavitation conditions; 19. Fitting formula for third-order pulsating pressure with different gap ratios under ballast non-cavitation conditions; 20. Fitting formula for fifth-order pulsating pressure with different gap ratios under ballast non-cavitation conditions.

[0066] S02, Preprocessing of Ship Monitoring Data

[0067] This step is mainly used to remove abnormal data collected from actual ships and data under abnormal weather and motion conditions.

[0068] The data collected from the actual ship includes: ship speed, main engine speed, bow draft, stern draft, propeller pulsation pressure, wind speed and direction encountered by the ship, and the above data are collected once per second.

[0069] ① Abnormal data removal

[0070] The exclusion criteria are as follows: speed >30 knots or <0 knots; engine speed >100 rpm or <0 rpm; bow draft <0 m or 30 m; stern draft <0 m or 30 m.

[0071] ② Retain data under normal weather conditions

[0072] Using a 10-minute cycle, determine whether any of the following conditions exist within 600 data sets over 10 minutes: the wind speed encountered by the ship is >40 knots; or the difference between the maximum and minimum draft at the bow or stern is greater than 2 meters.

[0073] If any of the above situations occur, the 10-minute data will be removed and not used for subsequent calculations.

[0074] S03, Propeller Performance Assessment

[0075] Analyze the data collected from the actual ship to determine if there are significant performance fluctuations.

[0076] ① Processing of ship-to-ship pulsating pressure monitoring values

[0077] According to the requirements of GB / T36580-2018, the pulsating pressure monitoring values ​​obtained in step 2 are processed by Fourier transform to obtain the actual pulsating pressure values ​​P1, P2, P3, P4, and P5 of the first, second, third, fourth, and fifth orders.

[0078] ② Determining whether vacuolation exists

[0079] First, calculate the average rotational speed n of the main unit over ten minutes. ms The actual draft h at the stern of the ship is used to calculate the cavitation number σ of the actual ship's rotational speed according to formula (1). 实 ;

[0080] Next, determine σ. 实 Is it greater than σ? 空 If the value is greater than 0, the state is non-cavitation; otherwise, it is cavitation.

[0081] ③ Comparison of actual ship propeller performance

[0082] First, calculate the average actual draft at the stern over 10 minutes, and then calculate the ship's tip clearance ratio (TCR) according to formula (2). 实 .

[0083] Next, based on the current cavitation or non-cavitation state obtained in the previous step, the first, second, third, fourth, and fifth order pulsating pressure amplitude data for different clearance ratios under full load and ballast conditions are extracted and fitted. The formulas for each order under full load are f. t1 (x), f t2 (x), f t3 (x), f t4 (x), f t5 (x); the formulas for each stage under ballast are f b1 (x), f b2 (x), f b3 (x), f b4 (x), f b5 (x).

[0084] Then, calculate the TCR 实 Substituting the values ​​into the various formulas, we finally obtain x = TCR. 实 Five levels of pulsating pressure forecast values ​​under full load and ballast: under full load, the forecast values ​​for each level are P1t, P2t, P3t, P4t, and P5t; under ballast, the forecast values ​​for each level are P1b, P2b, P3b, P4b, and P5b.

[0085] Calculate the theoretical pulsating pressure values ​​at the actual draft depth h:

[0086] First-order theoretical pulsating pressure value: X1=(h-Ba)*(P1b-P1t) / (Ta-Ba)+P1t

[0087] Second-order theoretical pulsating pressure value: X2=(h-Ba)*(P2b-P2t) / (Ta-Ba)+P2t

[0088] Third-order theoretical pulsating pressure value: X3=(h-Ba)*(P3b-P3t) / (Ta-Ba)+P3t

[0089] Fourth-order theoretical pulsating pressure value: X4=(h-Ba)*(P4b-P4t) / (Ta-Ba)+P4t

[0090] Fifth-order theoretical pulsating pressure value: X5=(h-Ba)*(P5b-P5t) / (Ta-Ba)+P5t

[0091] By comparing the actual pulsating pressure values ​​P1, P2, P3, P4, P5 with the theoretical pulsating pressure values ​​X1, X2, X3, X4, X5 respectively, the differences between each order are obtained:

[0092] α1=|P1-X1| / X1

[0093] α2=|P2-X2| / X2

[0094] α3=|P3-X3| / X3

[0095] α4=|P4-X4| / X4

[0096] α5=|P5-X5| / X5

[0097] The performance is judged as follows: if α1>=10%, the output conclusion is "propeller performance deteriorates"; if α1<10% and α2>10%, the output conclusion is "propeller performance shows a downward trend"; if α1, α2, α3, α4, and α5<10%, the output conclusion is "propeller performance is good"; otherwise, the output conclusion is "propeller performance is within the normal range".

[0098] The embodiments described above provide a detailed explanation of the technical solutions and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, additions, and equivalent substitutions made within the scope of the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the performance of a ship's propeller, characterized in that, Includes the following steps: (1) Collect propeller model test data and obtain the rotational speed and cavitation number σ at the start of propeller cavitation during the test. 空 ; (2) Collect time-history curve data of propeller pulsation pressure in cavitation and non-cavitation states under the two working conditions of full load and ballast. (3) Perform Fourier transform on the obtained multiple time-history curves respectively, and fit the data at the first 5 blade frequencies after transformation to obtain the theoretical baseline of the pulsating pressure amplitude at each level under different working conditions. (4) Acquire data collected from the actual ship and perform preprocessing. The data collected from the actual ship includes: ship speed, ship main engine speed, ship bow draft, ship stern draft, ship propeller pulsation pressure, and wind speed and direction encountered by the ship. (5) Perform Fourier transform on the ship propeller pulsating pressure time history curve data obtained in step (4) to obtain the actual pulsating pressure amplitudes P1, P2, P3, P4, and P5 at the first 5 blade frequencies. (6) Calculate the average propeller speed and the average actual draft at the stern of the ship within 10 minutes. Calculate the actual ship's rotational speed cavitation number σ using the same method as in step (1). 实 Next, determine σ. 实 Is it greater than σ? 空 If it is greater than , then this state is a non-cavitation state; otherwise, it is a cavitation state. (7) Calculate the toe clearance ratio (TCR) of the actual ship. 实 Based on step (6), the current cavitation or non-cavitation state is obtained, and the first, second, third, fourth, and fifth order pulsating pressure amplitude data of the actual ship under the corresponding state with different tip clearance ratios under full load and ballast are extracted and fitted; finally, the theoretical pulsating pressure amplitudes X1, X2, X3, X4, and X5 at the actual draft are calculated; the specific process is as follows: The first, second, third, fourth, and fifth order pulsating pressure amplitudes under different clearance ratios under full load and ballast conditions were extracted and fitted. The formulas for each order under full load are f. t1 (x), f t2 (x), f t3 (x), f t4 (x), f t5 (x); the formulas for each stage under ballast are f b1 (x), f b2 (x), f b3 (x), f b4 (x), f b5 (x); TCR 实 Substituting the values ​​into the formulas for each stage under full load and ballast, we finally obtain x = TCR. 实 The predicted values ​​of the pulsating pressure for the first 5 stages under full load and ballast are P1t, P2t, P3t, P4t, and P5t, respectively. The predicted values ​​of the pulsating pressure for the first 5 stages under full load are P1b, P2b, P3b, P4b, and P5b, respectively. Calculate the theoretical pulsating pressure values ​​at the actual draft depth h: First-order theoretical pulsating pressure value: X1=(h-Ba)*(P1b-P1t) / (Ta-Ba)+P1t Second-order theoretical pulsating pressure value: X2=(h-Ba)*(P2b-P2t) / (Ta-Ba)+P2t Third-order theoretical pulsating pressure value: X3=(h-Ba)*(P3b-P3t) / (Ta-Ba)+P3t Fourth-order theoretical pulsating pressure value: X4=(h-Ba)*(P4b-P4t) / (Ta-Ba)+P4t Fifth-order theoretical pulsating pressure value: X5=(h-Ba)*(P5b-P5t) / (Ta-Ba)+P5t In the formula, Ba represents the stern draft of the ship in ballast condition, and Ta represents the stern draft of the ship in full load condition. (8) Compare the actual pulsating pressure amplitudes P1, P2, P3, P4, P5 with the theoretical pulsating pressure amplitudes X1, X2, X3, X4, X5, and calculate the differences α1, α2, α3, α4, α5 for each order. The formula is: α1=|P1-X1| / X1 α2=|P2-X2| / X2 α3=|P3-X3| / X3 α4=|P4-X4| / X4 α5=|P5-X5| / X5 The propeller performance is then judged based on the differences α1, α2, α3, α4, and α5 between each order.

2. The method for predicting the performance of a ship's propeller according to claim 1, characterized in that, In step (1), the rotational speed and cavitation number σ at the start of propeller cavitation during the experiment are calculated. 空 The formula is: In the formula, σ represents the cavitation number at rotational speed; P0 is the background pressure of the water tank, determined by the stern draft of the ship; P e ρ is the vaporization pressure of water; n is the density of water; D is the propeller speed; and D is the propeller diameter.

3. The method for predicting the performance of a ship's propeller according to claim 1, characterized in that, In step (2), the formula for calculating the tip gap ratio (TCR) is as follows: In the formula, L represents the distance from the propeller shaft centerline to the bottom of the ship, and D is the propeller diameter.

4. The method for predicting the performance of a ship's propeller according to claim 1, characterized in that, In step (3), Fourier transform is performed on the multiple time-history curves to obtain the pulsating pressure amplitude at the first, second, third, fourth, and fifth leaf frequencies of each time-history curve. The data is divided into 20 data groups based on the cavitation state under full load, cavitation state under ballast, non-cavitation state under full load, and non-cavitation state under ballast. Fitting was performed on each of the 20 data sets, using y = ax 2 The fitting process is performed using +bx+c, where y is the pulsating pressure value, x is the tip-to-gap ratio (TCR), and a, b, and c are coefficients. This yields 20 fitting formulas, which serve as the theoretical baseline for the pulsating pressure amplitude at various stages under different operating conditions.

5. The method for predicting the performance of a ship's propeller according to claim 1, characterized in that, In step (4), preprocessing includes removing abnormal data collected from the actual ship and removing data under abnormal weather and motion conditions; Abnormal data collected from actual ships were excluded, including: ship speed >30 knots or <0 knots; ship main engine speed >100 rpm or <0 rpm; ship bow draft <0 m or 30 m; and ship stern draft <0 m or 30 m. Specifically, data removed under abnormal weather and motion conditions includes: using a 10-minute period, determining whether any of the following conditions exist within 600 data sets over a 10-minute period:

1. The wind speed encountered by the ship is >40 knots; 2. The difference between the maximum and minimum draft at the bow or stern of the ship is greater than 2 meters. If any of these conditions exist, the 10-minute data set is removed and not used for subsequent calculations.

6. The method for predicting the performance of a ship's propeller according to claim 1, characterized in that, In step (8), the propeller performance is judged based on the differences α1, α2, α3, α4, and α5 obtained for each order, specifically as follows: If α1 ≥ 10%, the output conclusion is that the propeller performance deteriorates; if α1 < 10% and α2 > 10%, the output conclusion is that the propeller performance has a downward trend; if α1, α2, α3, α4, and α5 < 10%, the output conclusion is that the propeller performance is good; otherwise, the output conclusion is that the propeller performance is within the normal range.

Images