A fuzzy comprehensive evaluation method for ship airflow field characteristics based on use requirements

Abstract

The present application proposes a fuzzy comprehensive evaluation method for the characteristics of the ship air flow field based on the use requirements of the ship air flow field of the carrier aircraft and typical carrier equipment. The analytic hierarchy process and the technique for order preference by similarity to ideal solution are extended to the interval type-2 fuzzy group decision-making situation to effectively deal with the uncertainty and difference of expert opinions. To solve the problem that the consistency verification is difficult to pass due to the increase in the number of evaluation indexes, an improved algorithm for geometric consistency of individual judgment matrix is proposed. An improved IT2FAHP-based weighting method for the evaluation indexes of the characteristics of the ship air flow field is established to improve the rationality and operability of the index weight distribution. To solve the problem that it is difficult to scientifically evaluate the satisfaction of the use requirements of the ship air flow field characteristics under the actual atmospheric environment combination working condition, a fuzzy envelope-based performance evaluation method for the ship air flow field indexes is proposed. An improved IT2FTOPSIS-based comprehensive evaluation method for the characteristics of the ship air flow field is established to provide new ideas and methods for solving the evaluation problem of the ship air flow field.

Description

Technical Field

[0001] This invention relates to the field of ship airflow field characteristic analysis technology, and specifically to a fuzzy comprehensive evaluation method for ship airflow field characteristics oriented towards usage requirements. Background Technology

[0002] Ship airflow field design is a crucial aspect of ship-equipment compatibility design, and its quality significantly impacts the safe and efficient operation of carrier-based aircraft and typical shipboard equipment. Therefore, evaluating and analyzing ship airflow field characteristics, accurately identifying weaknesses and shortcomings, and providing decision-making guidance and optimization basis for ship airflow field design are of significant engineering practical importance.

[0003] The evaluation of ship airflow field characteristics is typically based on the acquisition of ship airflow field data. Currently, two main methods are employed for acquiring this data: numerical simulation and experimental research. These methods investigate the airflow fields of different ship types to analyze and understand their characteristics. Numerical simulation studies explore the impact of different grid types, solution methods, and turbulence models on simulation results, establishing numerical calculation strategies for ship airflow fields and providing methodological support for numerical prediction of ship airflow field characteristics. Experimental research involves conducting full-scale ship tests or wind tunnel tests to measure and analyze the physical quantities of ship airflow fields. Full-scale ship tests offer smaller measurement errors and higher reliability, but data acquisition costs are extremely high. Wind tunnel tests, on the other hand, meet accuracy requirements while saving on testing costs, making them more widely used than full-scale ship tests. Currently, a research paradigm for numerical prediction of ship airflow characteristics has been formed that organically combines numerical simulation and experimental verification. This paradigm can reveal the influencing factors and mechanisms of ship airflow characteristics, and propose corresponding control measures to address existing problems in ship airflow, thereby improving the quality of ship airflow, enhancing the safety of carrier-based aircraft takeoff and landing, and accumulating valuable experience for ship airflow design.

[0004] Compared to the acquisition of ship airflow field data, there is very little research on the evaluation of ship airflow field characteristics, and it mainly focuses on the safety assessment of carrier-based aircraft take-off and landing. Based on the acquisition of carrier-based aircraft airflow field data, there are currently two main types of carrier-based aircraft take-off and landing safety assessment methods: one is to establish a carrier-based aircraft dynamic model and combine it with carrier-based aircraft safe take-off and landing criteria to calculate the wind limit diagram for safe take-off and landing of carrier-based aircraft [1, 2]; the other is to address the technical difficulties of numerous influencing factors, complex influencing relationships, and unclear influencing mechanisms of carrier-based aircraft take-off and landing safety by introducing multi-criteria decision analysis technology and applying the knowledge and experience of domain experts to establish a carrier-based aircraft take-off and landing safety assessment method based on a multi-criteria decision model [3-6].

[0005] While existing technologies have provided feasible and effective methods for acquiring ship airflow field data, current approaches to evaluating ship airflow field characteristics only consider the impact of these characteristics on the safety of carrier-based aircraft takeoffs and landings, neglecting their impact on the normal operation of typical shipborne equipment. Due to the lack of analysis of the usage requirements of carrier-based aircraft and typical shipborne equipment regarding ship airflow field characteristics, the systematic approach to solving the ship airflow field characteristic evaluation problem is clearly insufficient. Furthermore, the ship airflow field characteristic evaluation problem is inherently a complex problem involving multiple coupled factors. The complex system characteristics of ship equipment further challenge the evaluation of the ship airflow field characteristics' fulfillment of various usage requirements, especially under actual atmospheric environmental conditions at sea. Although expert systems are an acceptable and relatively reasonable solution, the ambiguity, diversity, and cognitive limitations of expert subjective judgment necessitate targeted improvements in terms of rationality and operability. In conclusion, solving the ship airflow field characteristic evaluation problem requires new thinking and the establishment of new methods.

[0006] [1] Huang Bin. CFD simulation and wind limit diagram calculation of coupled flow field of helicopter / ship [D]. Nanjing University of Aeronautics and Astronautics, 2017;

[0007] [2] Huang Bin, Hao Tong, Zhao Qibing, et al. Research on wind limit diagram of shipborne helicopter take-off and landing theory based on CFD [J]. Ship Engineering, 2020, 42(05): 5-10;

[0008] [3] Li Xiang, Huang Sheng, Zhang Xiuyuan. Evaluation of ship airflow field schemes using the improved TOPSIS method [J]. Journal of Harbin Institute of Technology, 2016, 48(04): 133-138;

[0009] [4] Li Xiang, Sun Peng, Zhang Jiajia. Mass analysis of ship deck flow field based on numerical simulation and TOPSIS [J]. Ship Science and Technology, 2020, 42(01): 68-74;

[0010] [5] Zhang Xiuyuan, Chang Xin, Li Xiang, et al. Evaluation method of ship airflow field scheme based on improved ELECTRE method [J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(11): 2507-2515;

[0011] [6] Chen Tianyou, Sun Peng, Ding Xiaojuan, et al. Mass ranking of deck and carrier-based aircraft hangar flow field based on AHP and TOPSIS methods [J]. Journal of Dalian Maritime University, 2020, 46(01): 47-56. Summary of the Invention

[0012] The purpose of this invention is to establish a fuzzy comprehensive evaluation method for ship airflow field characteristics based on usage requirements, thereby improving the systematicness, rationality, and operability of solving the ship airflow field characteristic evaluation problem to a certain extent.

[0013] To achieve the above-mentioned objectives, the technical solution of this invention provides a fuzzy comprehensive evaluation method for ship airflow field characteristics based on usage requirements, comprising the following steps:

[0014] Obtain expert information from relevant experts used to evaluate ship airflow field schemes, and preset expert weights based on the expert information;

[0015] The system analyzes the impact of the airflow field characteristics of the ship under test on the safe take-off and landing of carrier-based aircraft and the normal operation of typical ship-based equipment. It establishes a table of various usage requirements of the ship under test and its typical ship-based equipment based on the airflow field of the ship under test, and constructs a comprehensive evaluation index system for the airflow field characteristics of the ship under test in response to various usage requirements.

[0016] A 9-level linguistic scale was used to compare and judge the relative importance of each indicator in the comprehensive evaluation index system of the airflow field characteristics of the ship under test, and multiple individual fuzzy pairwise comparison matrices were constructed.

[0017] Defuzzification is performed on multiple individual fuzzy pairwise comparison matrices to construct corresponding defuzzified individual pairwise comparison matrices;

[0018] The row geometric mean method is used to calculate the index weights for multiple defuzzified individual pairwise comparison matrices, and multiple geometric consistency indices corresponding to the multiple defuzzified individual pairwise comparison matrices are calculated.

[0019] Based on a preset geometric consistency index threshold, determine whether multiple geometric consistency indices are less than or equal to the geometric consistency index threshold in order to perform consistency verification;

[0020] When the geometric consistency index is greater than the geometric consistency index threshold, the consistency verification fails. The defuzzified individual pairwise comparison matrix corresponding to the failure to pass the consistency verification is used as the original individual pairwise comparison matrix and input into the improved algorithm for geometric consistency of the individual judgment matrix. By setting a small tolerance coefficient, the modified preference value is restricted to a smaller neighborhood of the original preference value. The modified individual fuzzy pairwise comparison matrix and the corresponding modified geometric consistency index are output. Using a weighted geometric average operator combined with preset expert weights, the corresponding individual fuzzy pairwise comparison matrix is ​​matched according to the defuzzified individual pairwise comparison matrix corresponding to the success of the consistency verification, and integrated with the modified individual fuzzy pairwise comparison matrix to construct the group fuzzy pairwise comparison matrix.

[0021] Alternatively, if the geometric consistency index is less than or equal to the geometric consistency index threshold, then the consistency verification is passed. Based on the multiple defuzzified individual pairwise comparison matrices corresponding to the consistency verification, the corresponding individual fuzzy pairwise comparison matrices are matched and integrated to obtain the group fuzzy judgment matrix.

[0022] The row geometric mean method is used to calculate the final index fuzzy weights and defuzzification weights for the group fuzzy pairwise comparison matrix or the group fuzzy judgment matrix.

[0023] The final ship airflow field numerical simulation method is adopted to conduct numerical simulation of the airflow field scheme of the ship under test under different atmospheric environment combination conditions, and obtain the ship airflow field data under the corresponding conditions.

[0024] A 7-level linguistic scale was used to quantitatively evaluate the ship's operational requirements by assessing the ship's airflow field characteristics under actual atmospheric environmental conditions, and multiple individual fuzzy decision matrices were constructed.

[0025] By combining preset expert weights and using a weighted arithmetic mean operator, multiple individual fuzzy decision matrices are integrated to construct a group fuzzy decision matrix;

[0026] Under the actual atmospheric environment wind speed and direction combination conditions, the index performance of each combination condition is integrated to calculate the fuzzy envelope of the ship's airflow field performance. The fuzzy envelope is used to describe the degree to which the ship's airflow field characteristics meet the ship's usage requirements under the actual atmospheric environment combination conditions.

[0027] The weighted normalized group fuzzy decision matrix is ​​calculated based on the final index fuzzy weights and fuzzy envelope, and then defuzzification is performed to construct the defuzzified weighted normalized group decision matrix.

[0028] Calculate the distance between the quantitative evaluation of the performance of each index of the ship's airflow field scheme and the determined fuzzy positive ideal solution and fuzzy negative ideal solution respectively;

[0029] The relative proximity coefficients of the quantitative evaluation of the performance of each index of the ship's airflow field scheme and the fuzzy positive ideal solution are calculated respectively, and the solution relative proximity coefficients are obtained by defuzzification.

[0030] The ship airflow field schemes are evaluated and ranked in descending order of the relative proximity coefficients to obtain the fuzzy comprehensive evaluation results of the ship airflow field characteristics.

[0031] Preferably, the comprehensive evaluation index system for the airflow field characteristics of the ship under test includes: the safety of take-off and landing of the ship under test and the operating temperature adaptability of typical shipborne equipment of the ship under test. The safety of take-off and landing of the ship under test includes forward speed, lateral speed, vertical speed, standard deviation of vertical speed, turbulent kinetic energy distribution, streamline distribution and pressure distribution. The operating temperature adaptability of typical shipborne equipment of the ship under test includes the maximum temperature and temperature distribution.

[0032] Preferably, the output of the improved algorithm for geometric consistency of the individual judgment matrix further includes suggestions for prioritizing review judgments, suggestions for prioritizing adjustment directions, and recommended correction values.

[0033] Preferably, the establishment of the final numerical simulation method for ship airflow field includes the following steps:

[0034] Obtain a 3D model of the ship under test and perform a model quality check;

[0035] The blocking rate is calculated based on the blocking rate formula, and the computational domain is divided under the premise that the blocking rate is less than 3%.

[0036] The computational mesh is divided according to the computational conditions and model size, and the mesh on the model surface and key parts is refined according to the Reynolds number of the flow field and the key areas of interest.

[0037] Select appropriate turbulence models and incompressible air physics models, and set boundary conditions according to working conditions to define the model;

[0038] Different grid schemes were obtained by adjusting the grid size, and the grid convergence was verified by trial calculations to establish an initial numerical calculation strategy for ship airflow field.

[0039] A numerical calculation strategy for the initial ship airflow field was adopted to calculate similar ship airflow field schemes based on existing experimental data. The calculation results were compared with the experimental results to verify the accuracy of the initial ship airflow field numerical calculation strategy and to establish a final ship airflow field numerical simulation method.

[0040] Preferably, the turbulence model is selected from... The model employs a pressure-based coupled solution of the governing equations. The convection term is discretized using a second-order upwind scheme, and the dissipation term is discretized using a second-order central difference scheme.

[0041] Preferably, the formula for calculating the geometric consistency index is:

[0042]

[0043]

[0044] In the formula, For individuals in the original pairwise comparison matrix, and The original indicator weights corresponding to the individuals.

[0045] Preferably, the formula for calculating the ensemble fuzzy decision matrix is:

[0046]

[0047] In the formula, To preset expert weights, Experts The proposed airflow field scheme for ships Regarding indicators wind speed wind direction Evaluation values ​​under combined operating conditions.

[0048] Preferably, the formula for calculating the fuzzy positive ideal solution is:

[0049]

[0050] In the formula, B is the set of benefit-type indicators, and C is the set of cost-type indicators. To weighted normalize the fuzzy decision matrix of the group, To defuzzify the weighted normalized group decision matrix.

[0051] Preferably, the calculation formula for the fuzzy negative ideal solution is:

[0052] .

[0053] Preferably, the formula for calculating the relative proximity coefficient is:

[0054]

[0055] In the formula, The distance between the proposed ship airflow field scheme and the fuzzy positive ideal solution is given. The distance between the ship's airflow field scheme and the fuzzy negative ideal solution is given.

[0056] This invention addresses the various usage requirements of shipborne aircraft and typical shipborne equipment on ship airflow fields. It proposes a fuzzy comprehensive evaluation method for ship airflow field characteristics based on these requirements. The Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) are extended to the interval type-II fuzzy group decision-making scenario, effectively addressing the uncertainty and differences in expert opinions. To address the difficulty in achieving consistency verification due to the increased number of evaluation indicators after considering ship usage requirements, an improved algorithm for geometric consistency of individual judgment matrices is proposed. A weighting method for ship airflow field characteristic evaluation indicators based on the improved IT2FAHP is established, improving the rationality and operability of indicator weight allocation. Furthermore, to address the difficulty in scientifically evaluating the satisfaction of ship airflow field characteristics with ship usage requirements under actual atmospheric environmental conditions, a performance evaluation method for ship airflow field indicators based on fuzzy envelopes is proposed, along with a comprehensive evaluation method for ship airflow field characteristics based on the improved IT2FTOPSIS. These findings provide new ideas and methods for solving the ship airflow field evaluation problem. Attached Figure Description

[0057] Figure 1 This invention provides a comprehensive evaluation index system for ship airflow field characteristics based on usage requirements, as exemplified in this invention.

[0058] Figure 2 A flowchart of the improved IT2FAHP method provided in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram illustrating the performance evaluation language under combined operating conditions provided in an embodiment of the present invention.

[0060] Figure 4 Geometric model and area of ​​interest diagram of the "America" ​​class amphibious assault ship provided for embodiments of the present invention;

[0061] Figure 5 This is a computational domain grid partitioning diagram provided in an embodiment of the present invention;

[0062] Figure 6 A comparison of PIV test data and CFD calculation results provided in the embodiments of the present invention;

[0063] Figure 7 The vorticity distribution provided in this embodiment of the invention under the condition of wind speed of 10 m / s and direction of 30°.

[0064] Figure 8 A global weight ranking graph provided for embodiments of the present invention;

[0065] Figure 9 The D1 forward velocity diagram provided in this embodiment of the invention;

[0066] Figure 10 The D2 lateral velocity diagram provided in the embodiment of the present invention;

[0067] Figure 11 The D3 vertical velocity diagram provided in this embodiment of the invention;

[0068] Figure 12 The highest temperature diagram D8 is provided for an embodiment of the present invention;

[0069] Figure 13 The D9 temperature distribution diagram provided for an embodiment of the present invention;

[0070] Figure 14 The airflow field performance and positive ideal solution of the "America" ​​class amphibious assault ship provided in the embodiments of the present invention Distance map;

[0071] Figure 15 The airflow field performance and negative ideal solution of the "America" ​​class amphibious assault ship provided in the embodiments of the present invention Distance map. Detailed Implementation

[0072] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0073] like Figures 1-3 As shown in the figure, the fuzzy comprehensive evaluation method for ship airflow field characteristics based on usage requirements proposed in this embodiment of the invention includes the following steps:

[0074] By systematically analyzing the impact of ship airflow field on the safe take-off and landing of carrier-based aircraft and the normal operation of typical shipborne equipment, this study comprehensively captures the various operational requirements of carrier-based aircraft and typical shipborne equipment on the ship airflow field (see Table 1). Based on this, a comprehensive evaluation index system for ship airflow field characteristics oriented towards operational requirements is constructed (see Table 1). Figure 1 ).

[0075] Table 1. Requirements of Carrier-based Aircraft and Typical Carrier-based Equipment for Ship Airflow Field

[0076]

[0077] The influence of ship airflow on the safe take-off and landing of carrier-based aircraft is mainly located above and around the take-off and landing position of the carrier-based aircraft, and the influencing factors are mainly physical quantities related to flow field velocity; while the influence of ship airflow on the normal operation of typical shipborne equipment is mainly located in the superstructure, and the influencing factors are mainly physical quantities related to flow field temperature.

[0078] This paper proposes a weighting method for ship airflow field characteristic evaluation indicators based on the improved Interval Type-2 Fuzzy AHP (IT2FAHP) method. It extends the classical AHP method to the interval type-2 fuzzy group decision-making scenario to effectively address the fuzziness and discrepancies in expert opinions. Furthermore, it proposes an improved algorithm for the geometric consistency of individual judgment matrices, providing crucial information support for experts to revise their original opinions when the individual judgment matrix fails the consistency verification. Figure 2 As shown, the specific steps are as follows:

[0079] Step 0: Define the evaluation object and decision-making purpose, and invite experts in related fields of ship airflow field schemes to form an expert group, denoted as... , Indicates the first Experts Expert weights are assigned based on their professional knowledge, work experience, education level, and other expert information, and are denoted as follows: ,have and .

[0080] Step 1: Using the 9-level linguistic scale "EMI-VE-VSMI-SV-SMI-MS-MMI-EM-EI" shown in Table 2, the relative importance of the evaluation indicators is compared and judged, and an individual fuzzy pairwise comparison matrix (FPCM) is constructed, denoted as... .

[0081] (1)

[0082] (2)

[0083] Table 2. Relative Importance of Evaluation Indicators on a 9-Level Language Scale

[0084]

[0085] Step 2: Verify the individual FPCM Consistency.

[0086] Step 2.1: Apply the area centroid method to individual FPCMs Defuzzification is performed, and a pairwise comparison matrix (PCM) is constructed, denoted as... .

[0087] (3)

[0088] Step 2.2: Calculate the index weights using the Row Geometric Mean (RGM) method.

[0089] , (4)

[0090] Step 2.3: Calculate individual PCM The geometric consistency index (GCI).

[0091] , (5)

[0092] Step 2.4: Based on the GCI threshold shown in Table 3 (denoted as...), For individuals PCM Perform consistency verification. If , believe that individuals PCM It exhibits acceptable consistency. If individuals... PCM It has acceptable consistency, and considers individuals FPCM It also exhibits acceptable consistency.

[0093] Table 3. Thresholds for GCI-based conformance verification

[0094]

[0095] Step 2.5: When the individual judgment matrix fails the consistency verification, it is necessary to organize experts to review and revise the original opinions. To this end, an improved algorithm for the geometric consistency of the individual judgment matrix was established to provide experts with key information support, such as the judgments to be reviewed first, the directions to be adjusted first, and the specific values ​​to be revised.

[0096] Specifically, the improved algorithm for geometric consistency of individual judgment matrices takes the following input: the original individuals. PCM tolerance coefficient The output is a modified individual. FPCM The corresponding geometric consistency index The following are the specific algorithm steps, including:

[0097] Step 0: Set ,in , These are the row and column indices of the individual judgment matrix, respectively.

[0098] Step 1: If ,make Otherwise, remain unchanged.

[0099] Step 2: For all ,calculate ;

[0100] Step 3: Find , subscript ,make ;

[0101] Step 4: Calculation ;

[0102] a) If , ;

[0103] b) If , ;

[0104] make , Update the individual judgment matrix Recorded as and update ;

[0105] Step 5: Calculation

[0106] a) If ,calculate , and update the individual FPCM , obtain the corrected individual FPCM ;

[0107] Output correction individual FPCM and the corresponding geometric consistency index ;

[0108] b) If and Repeat steps 1-5.

[0109] The improved algorithm for geometric consistency of the individual judgment matrix is ​​achieved by setting a smaller tolerance coefficient. This will correct the preference value. Limit to the original preference value smaller neighborhood This approach preserves as much of the original expert preference information as possible, which is beneficial for experts' acceptance of the weighting results. Furthermore, the improved algorithm for geometric consistency of the individual judgment matrix can iteratively provide key information support for experts to correct their preference information, including judgments suggesting priority reviews. Recommended directions for priority adjustment And the specific values ​​to be suggested for correction. This enables automatic correction of expert judgments. Compared with traditional methods, it avoids the blindness of expert self-correction, reduces the mental burden on experts, improves the operability of the weighting process, and reduces potential secondary errors, further ensuring the rationality of the weighting results.

[0110] Step 3: Integrate individuals using the weighted geometric averaging (WGA) operator. FPCM Construct a population FPCM, denoted as .

[0111] (6)

[0112] When the group fuzzy pairwise comparison matrix description fails the consistency verification, after the individual judgment matrix geometric consistency improvement algorithm is used to correct it, the corresponding individual fuzzy pairwise comparison matrix is ​​matched with the defuzzified individual pairwise comparison matrix corresponding to the one that passed the consistency verification, and then integrated with the corrected individual fuzzy pairwise comparison matrix to form a set. In this case, the group FPCM is the group fuzzy pairwise comparison matrix. When the group fuzzy judgment matrix description passes the consistency verification, the corresponding individual fuzzy pairwise comparison matrix is ​​matched with the multiple defuzzified individual pairwise comparison matrices corresponding to the one that passed the consistency verification, and then integrated to form a set. In this case, the group FPCM is the group fuzzy judgment matrix.

[0113] Existing research has shown that population PCM using WGA operator ensembles The consistency of the individual PCM is better than or equal to that of the individual with the worst consistency. Therefore, if all individual PCM All passed the consistency verification, so the population PCM using WGA operator ensemble... It must possess acceptable consistency. Due to individual PCM With individual FPCM Approximately equivalent, therefore, if all individual FPCM If all pass the consistency verification, then the population FPCM using WGA operator ensemble can be considered valid. It must have acceptable consistency.

[0114] (7)

[0115] Step 4: Calculate the fuzzy weights of the indicators, denoted as . .

[0116] , (8)

[0117] (9)

[0118] The numerical simulation method of ship airflow field based on CFD (Computational Fluid Dynamics) uses computational fluid dynamics to numerically simulate the ship airflow field scheme to obtain ship airflow field data, providing input for the evaluation of ship airflow field characteristics.

[0119] The specific steps of the CFD-based numerical simulation method for ship airflow fields are as follows:

[0120] Step 1: Use engineering modeling software to create a 3D model of the ship's airflow field, ensuring the model is closed during the modeling process.

[0121] Step 2: Import the 3D model into the fluid calculation software and check the model quality to ensure that the model's triangular faces are not repeated or intersecting.

[0122] Step 3: Divide the computation domain to ensure that the computation blocking rate is less than 3%.

[0123] (10)

[0124] Step 4: Divide the computational mesh according to the calculation conditions and model size, and refine the mesh on the model surface and key parts according to the Reynolds number of the flow field and the key areas of interest.

[0125] Step 5: Define the calculation model, select a suitable turbulence model and incompressible air physics model, and set boundary conditions according to the working conditions.

[0126] Step 6: By adjusting the grid size, different grid schemes are obtained. The convergence of the grid is verified through trial calculations, and a preliminary numerical calculation strategy for the ship's airflow field is established.

[0127] Step 7: Using the initially established numerical calculation strategy for ship airflow field, calculate similar ship airflow field schemes based on existing experimental data, compare the calculation results with the experimental results, verify the accuracy of the initially established numerical calculation strategy for ship airflow field, and establish the final numerical simulation method for ship airflow field.

[0128] Step 8: Using the established numerical simulation method for ship airflow field, numerical simulations are performed on the airflow field schemes of the target ship under different operating conditions to obtain ship airflow field data, providing input for the evaluation of ship airflow field characteristics.

[0129] A comprehensive evaluation method for ship airflow field characteristics based on an improved interval type II fuzzy hierarchical analysis method is proposed. This method extends the classic TOPSIS method to the interval type II fuzzy group decision-making scenario to effectively address the fuzziness and differences in expert opinions. At the same time, a performance evaluation method for ship airflow field indicators based on fuzzy envelopes is proposed to quantitatively evaluate the degree to which ship airflow field characteristics meet the ship's operational requirements under actual atmospheric environmental conditions.

[0130] The specific steps for improving the IT2FTOPSIS method are as follows:

[0131] Step 1: The performance evaluation method of ship airflow field index based on fuzzy envelope is used to quantitatively evaluate the degree to which the ship airflow field characteristics meet the ship's usage requirements under actual atmospheric conditions.

[0132] Step 1.1: Organize experts to quantitatively evaluate the degree to which the ship's airflow field characteristics meet the ship's operational requirements under actual atmospheric environmental combinations, using the "VL-L-ML-M-MH-H-VH" 7-level linguistic scale shown in Table 4. Figure 3 As shown, construct the individual fuzzy decision matrix, denoted as... .

[0133] , (11)

[0134] In the formula, Experts The given indicators Ship airflow field scheme The evaluation value, Experts The proposed airflow field scheme for ships Regarding indicators wind speed wind direction Evaluation values ​​under combined operating conditions.

[0135] Table 4 Evaluation Indicators: 7-Level Language Scale

[0136]

[0137] Step 1.2: Integrate individual fuzzy decision matrices using the weighted arithmetic mean (WAM) operator. Construct a fuzzy decision matrix for the population, denoted as . .

[0138] (12)

[0139] Step 1.3: Under the actual atmospheric environment with combined wind speed and direction conditions, integrate the performance indicators for each combination condition to calculate the fuzzy envelope of the ship's airflow field performance indicators, denoted as... .

[0140] (13)

[0141] The fuzzy envelope reflects the degree to which the ship's airflow field characteristics meet the ship's operational requirements under actual atmospheric environmental conditions.

[0142] Step 2: Calculate the weighted normalized ensemble fuzzy decision matrix using the WAM operator, denoted as... .

[0143] (14)

[0144] Step 3: Use the area centroid method to defuzzify the weighted normalized fuzzy decision matrix, and construct the defuzzified weighted normalized fuzzy decision matrix, denoted as . .

[0145] Step 4: Determine the fuzzy positive ideal solution (FPIS), denoted as Determine the fuzzy negative ideal solution (FNIS), denoted as .

[0146] , (15)

[0147] In the formula, This represents a set of benefit-oriented indicators. This represents a set of cost-related indicators.

[0148] Step 5: Calculate the airflow field scheme for each ship. With fuzzy positive ideal solution and fuzzy negative ideal solution The distances between them are denoted as follows: and .

[0149] , (16)

[0150] Step 6: Calculate the airflow field scheme for each ship With fuzzy positive ideal solution The relative proximity coefficient is denoted as .

[0151] (17)

[0152] Step 7: [Regarding...] Defuzzification is performed to obtain the defuzzified relative proximity coefficient, denoted as . .

[0153] Step 8: Based on the relative proximity coefficient Descending order of ship airflow field scheme To evaluate and rank.

[0154] Example

[0155] Using the comprehensive evaluation of the airflow field characteristics of the "America"-class amphibious assault ship as an example, the feasibility and effectiveness of the proposed method are verified. In this example, three experts in the field of shipbuilding and marine engineering, designated P1, P2, and P3, were invited to conduct the work of allocating index weights and evaluating index performance. The expert weights were set as follows: .

[0156] This study systematically analyzes the impact of the airflow characteristics of the "America"-class amphibious assault ship on the safe take-off and landing of carrier-based aircraft and the normal operation of typical shipborne equipment. It establishes a table of various usage requirements of the "America"-class amphibious assault ship and its typical shipborne equipment on the airflow characteristics of the "America"-class amphibious assault ship, and constructs a comprehensive evaluation index system for the airflow characteristics of the "America"-class amphibious assault ship based on usage requirements.

[0157] The feasibility and effectiveness of the ship airflow field characteristic evaluation index weighting method based on the improved IT2FAHP established above are verified by taking the weighting node of carrier-based aircraft take-off and landing safety as an example.

[0158] Three experts used a 9-level linguistic scale (EMI-VE-VSMI-SV-SMI-MS-MMI-EM-EI) as shown in Table 2 to compare and judge the relative importance of the evaluation indicators, and gave initial individual results. FPCM As shown below:

[0159]

[0160] Calculate the corresponding consistency index As shown in Table 5, it can be seen from the table that experts , , None of them passed the consistency verification. Therefore, a tolerance coefficient was set. The improved algorithm for geometric consistency of the individual judgment matrix is ​​invoked to process the individuals respectively. PCM , , After making corrections, we obtain the corrected individual judgment matrix. , , and the corresponding consistency index , , See Table 5. The results show that, through subtle adjustments to expert judgments, the experts... , , All passed the individual consistency verification.

[0161] Table 5. Individual weighting results for weighting node 2

[0162]

[0163] The following are expert opinions. Individuals before and after the revision of opinions PCM and The italicized matrix elements have been corrected by the algorithm. It can be seen that by setting a smaller tolerance coefficient... The corrected preference value Limit to the original preference value smaller neighborhood This approach preserves as much of the original expert preference information as possible, thus facilitating expert acceptance of the weighting results. Under this condition, the proposed improved algorithm for geometric consistency of the individual judgment matrix can iteratively provide key information support for experts to correct their preference information, including judgments that need to be corrected first. Directions that need to be corrected And recommended correction values This enables automatic correction of expert judgments. Compared with traditional methods, it avoids the blindness of expert self-correction, reduces the mental burden on experts, improves the operability of the weighting process, and reduces potential secondary errors, further ensuring the rationality of the weighting results.

[0164]

[0165] ↓

[0166]

[0167] Then calculate And feed this ratio back to the individual. FPCM In the middle, the corrected individual was obtained FPCM Then, the WGA operator is used to integrate individuals. FPCM , gain the group FPCM Because each individual FPCM All passed the consistency verification, so the group FPCM It must have an acceptable level of consistency. Therefore, the RGM method is used to calculate the fuzzy weights and defuzzification weights of the indicators, as shown in Table 6.

[0168] Table 6. Weighting Results of Safety Indicators for Carrier-based Aircraft Takeoff and Landing

[0169]

[0170] Figure 8 The global weights of the ship's airflow field evaluation indicators are given. For ease of analysis, the evaluation indicators are rearranged in descending order of their weights in the figure. From Figure 8 As you can see, Vertical speed has the largest weight, accounting for 32.5% of the total, and its impact on the safety of carrier-based aircraft takeoffs and landings reaches 39.9%; followed by... The highest temperature at the superstructure location accounts for 20.2% of the single indicator, and its impact on the operating temperature adaptability of typical shipborne equipment reaches 82.3%. The sum of the global weights of these two indicators even exceeds 50%. Therefore, designers need to focus on these two indicators when carrying out subsequent design schemes.

[0171] The established CFD-based numerical simulation method for ship airflow fields was adopted to develop a numerical calculation strategy for the airflow field of the "America"-class amphibious assault ship. CATIA software was used to create a 3D model of the "America"-class amphibious assault ship, ensuring the model's closure. Figure 4 As shown. The CATIA 3D model was imported into STAR-CCM+ software, ensuring that the model's triangular faces were non-repeating and non-intersecting. Real-scale calculations were performed, and considering both computational accuracy and mesh quantity, the principal scale of the computational domain was set to 7L×10D×6H, ensuring a computational blocking rate of less than 3%. Based on the computational conditions and model dimensions, the "America" ​​class amphibious assault ship was meshed at a real scale, and the mesh on the model's surface and in key areas was appropriately refined, such as... Figure 5 As shown. The turbulence model selected is... The model employs a pressure-based coupled solution for the governing equations. The convection term is discretized using a second-order upwind scheme, and the dissipation term is discretized using a second-order central difference scheme. The Peng-Robinson gas model is selected to define the computational model. The calculation is based on the number of grid cells. Different grid schemes were generated incrementally. After comprehensive analysis of data convergence and computational efficiency, the optimal grid size was determined to be 30 million, thus establishing a preliminary numerical calculation strategy for the airflow field of the "America"-class amphibious assault ship, as shown in Table 7. Further, using this preliminary numerical calculation strategy, the airflow field of the "America"-class amphibious assault ship was numerically calculated. The accuracy of the preliminary numerical calculation strategy was verified using PIV experimental data from a large 1:48 scale model, as shown in Table 7. Figure 6 As shown in the diagram, the comparison of the cloud images reveals that the CFD calculation results and the PIV experimental results show a consistent trend with minimal deviation. Therefore, it can be concluded that the preliminarily established numerical calculation strategy for the airflow field of the "America"-class amphibious assault ship can provide reliable flow field data for the comprehensive evaluation of the airflow field characteristics of the "America"-class amphibious assault ship.

[0172] Table 7 Numerical Calculation Strategy for Ship Airflow Field

[0173]

[0174] Based on this, the established numerical simulation method for the airflow field of the "America"-class amphibious assault ship was used to numerically simulate the airflow field scheme of the "America"-class amphibious assault ship under different operating conditions. In terms of operating condition settings, one operating condition was set every 5 m / s at wind speeds of 0-25 m / s and every 30° in the 360° wind direction, for a total of 61 operating conditions. Some CFD calculation results are shown below. Figure 7 As shown. Based on the constructed ship airflow field evaluation index system oriented towards operational needs, and considering the operational characteristics of the "America"-class amphibious assault ship, three carrier-based aircraft take-off and landing positions and three areas—fore, mid, and aft—of the superstructure were designated as areas of interest (see...). Figure 4 The CFD calculation results at these locations are extracted and used, together with the criteria for safe take-off and landing of carrier-based aircraft and the criteria for normal operation of typical shipborne equipment, as inputs for evaluating the performance of ship airflow field indicators.

[0175] The improved IT2FTOPSIS method was used to comprehensively evaluate the airflow field of the "America" ​​class amphibious assault ship to verify the feasibility and effectiveness of the proposed method.

[0176] A fuzzy envelope-based method for evaluating the performance of ship airflow field indicators is used to quantitatively assess the degree to which ship airflow field characteristics meet the ship's operational requirements under actual atmospheric conditions.

[0177] Experts were organized to give a linguistic evaluation of the performance of the index under each combined working condition using a 7-level linguistic scale as shown in Table 4. Then, the WAM operator was used to fuse the expert opinions to obtain the linguistic evaluation of the performance of the index under each combined working condition by the expert group, and an envelope diagram was drawn accordingly.

[0178] This invention innovatively employs fuzzy envelope volume to evaluate the overall performance of indicators. By integrating the envelope diagram across all wind speeds and directions, the fuzzy envelope volume of various performance indicators for the airflow field scheme of the "America"-class amphibious assault ship is obtained, as shown in the table. The table shows that the indicator with the largest fuzzy envelope volume under the condition of aircraft takeoff and landing safety is... Lateral velocity at position #2, and the minimum index is The positive velocity at position #1 is also the worst performing among all indicators; the largest fuzzy envelope volume under typical shipborne equipment operating temperature adaptability is... Temperature distribution at position #4 is also the best performing among all indicators, while the smallest indicator is... The highest temperature is at position #6.

[0179] Table 8. Fuzzy envelope volume of performance indicators

[0180]

[0181] Figures 9-13 Further details were provided Forward velocity, Lateral velocity, highest temperature and The envelope diagram of the temperature distribution at three locations shows that the lower the gray value, the better the performance of the indicator, and the more beneficial it is to the safety of carrier-based aircraft take-off and landing and the temperature adaptability of typical carrier-based equipment.

[0182] Among them, from Figure 9 As can be seen, position #1 basically meets the forward speed requirements for safe take-off and landing of carrier-based aircraft only within the wind speed range of 300° to 60° with a wind direction of less than 10 m / s. However, it cannot meet the requirements under other wind speed and direction combinations. Positions #2 and #3 are relatively better.

[0183] from Figure 10 As can be seen, position #2 can basically meet the lateral speed requirements for safe take-off and landing of carrier-based aircraft within a wind speed range of 10 m / s and 180° wind direction, and within a wind speed range of approximately 25 m / s at both 0° and 180° wind directions. Positions #1 and #3 are slightly worse, which is due to the obstruction of the superstructure on the starboard lateral speed of position #2.

[0184] from Figure 12 As can be seen, position #6 cannot meet the maximum temperature requirements for normal operation of typical shipboard equipment on the superstructure within a wind speed range of 0~25 m / s and a wind direction range of 240°~90°. Position #5 is slightly better, while position #4 is significantly better. This is because position #4 is located on a small platform in front of the superstructure and is least affected by the smoke emitted from the chimney.

[0185] from Figure 13 As can be seen, position #4 basically meets the temperature distribution requirements for the normal operation of typical shipborne equipment on the superstructure, except that it meets the requirements under other wind speed and direction combinations within the wind speed range of 10~25 m / s and 180°~240°. Positions #5 and #6 are relatively worse. This is because position #4 is located on a small platform in front of the superstructure and is least affected by the smoke exhaust from the chimney. Position #5 is located in front of the chimney, and when the wind comes from the rear or side rear, it will blow the high-temperature airflow and soot particles discharged from the chimney directly onto the special equipment, which will have an adverse effect on the shipborne equipment. Position #6 is the opposite of position #5.

[0186] Using the obtained global index fuzzy weights and index performance envelope volume as input, the WAM operator is further used to calculate the weighted normalized fuzzy decision matrix. The ideal solution and negative ideal solution The following settings are used to evaluate how well the airflow field of a typical ship meets the ship's operational requirements.

[0187]

[0188] For each index, calculate the performance of typical ship airflow field indices and the positive ideal solution. and negative ideal solution The distances are shown in Tables 9 and 10, respectively. The typical ship airflow field scheme and the ideal solution are calculated again. The relative proximity coefficient was calculated, and then defuzzified. The results are shown in Table 11. As can be seen from Table 11, the comprehensive evaluation result of the airflow field characteristics of the "America" ​​class amphibious assault ship is 0.2192, which is basically consistent with the evaluation result obtained by calculating the area of ​​the traditional wind limit diagram.

[0189] Table 9 Performance of each indicator and positive ideal solution distance

[0190]

[0191] Table 10 Performance of each indicator and negative ideal solution distance

[0192]

[0193] Table 11 Typical Ship Airflow Fields and Positive Ideal Solutions and negative ideal solution distance

[0194]

[0195] Figure 14The performance indicators and positive ideal solutions of typical ship airflow fields are given. The distance. As can be seen from the diagram, positions #1, #2, and #3... Performance of vertical velocity and positive ideal solution The distance is the largest, and the performance is relatively poor, especially at positions #1 and #2, where it reaches 0.106. Positions #1, #2, and #3... Lateral velocity performance and positions #4, #5, and #6 Performance of temperature distribution and positive ideal solution The distance is the smallest, and the performance is superior, especially at position #4. The temperature distribution is only 0.007; in a horizontal comparison, the maximum value is more than 15 times that of the minimum value (15.143). It can be seen that the performance of each indicator is unbalanced and varies greatly.

[0196] Figure 15 The performance indexes and negative ideal solutions of the airflow field of typical ships are given. The distance. As can be seen from the diagram, positions #1, #2, and #3... Flow distribution and Performance of pressure distribution and negative ideal solution The distance is the smallest, resulting in poor performance, almost zero (0.0004); while position #4... Performance at maximum temperature and negative ideal solution The value with the largest distance is 0.034, indicating superior performance. In a horizontal comparison, the maximum value is 85 times the minimum value. This demonstrates an extreme imbalance and significant differences in the performance of each indicator.

[0197] In addition, from Figure 14 and Figure 15 You can also see positions #1, #2, and #3. The performance in vertical velocity is poor, and targeted improvements are needed in these areas. According to the table... The fuzzy envelope volume of the vertical velocity revealed that the performance index at position #3 was worse than that at positions #1 and #2. Further analysis... Figure 11 of The performance envelope diagram of vertical speed shows that position #3 cannot meet the vertical speed requirements for safe take-off and landing of carrier-based aircraft in the wind direction range of 270° to 90° with wind speeds above 5 m / s. Positions #1 and #2 perform relatively better under these conditions, allowing wind speeds of around 10 m / s for safe take-off and landing of carrier-based aircraft.

[0198] The fuzzy comprehensive evaluation method for ship airflow field characteristics proposed in this invention, oriented towards usage requirements, can provide decision-making guidance and optimization basis for ship airflow field scheme design, and has significant engineering practical value. Specifically, it includes the following beneficial effects:

[0199] 1. The constructed comprehensive evaluation index system for ship airflow field characteristics based on usage requirements fully reflects the various usage requirements of shipborne aircraft and typical shipborne equipment on ship airflow field, effectively improving the systematic nature of solving the problem of ship airflow field characteristic evaluation.

[0200] 2. The established weighting method for ship airflow field characteristic evaluation index based on the improved IT2FAHP extends the classic AHP method to the interval type II fuzzy group decision-making scenario, which can effectively deal with the fuzziness and differences of expert opinions and effectively improve the rationality of index weighting results.

[0201] 3. The established improved algorithm for geometric consistency of individual judgment matrix can provide key information support for experts to correct their preferences, such as the judgments that need to be corrected first, the direction of correction, and the recommended correction value, while preserving the original expert preference information as much as possible. This enables automatic correction of individual expert judgments, avoids the blindness of expert self-correction, reduces the mental burden on experts, and reduces the secondary errors that may be caused by it, ensuring the rationality of the weighting results and the operability of the weighting process.

[0202] 4. The established comprehensive evaluation method for ship airflow field characteristics based on the improved IT2FTOPSIS extends the classic TOPSIS method to the interval type II fuzzy group decision-making scenario, which can effectively deal with the fuzziness and differences of expert opinions and effectively improve the rationality of the comprehensive evaluation results.

Claims

1. A fuzzy comprehensive evaluation method for ship airflow field characteristics oriented towards usage requirements, characterized in that, Includes the following steps: Obtain expert information from relevant experts used to evaluate ship airflow field schemes, and preset expert weights based on the expert information; The system analyzes the impact of the airflow field characteristics of the ship under test on the safe take-off and landing of carrier-based aircraft and the normal operation of typical ship-based equipment. It establishes a table of various usage requirements of the ship under test and its typical ship-based equipment based on the airflow field of the ship under test, and constructs a comprehensive evaluation index system for the airflow field characteristics of the ship under test in response to various usage requirements. A 9-level linguistic scale was used to compare and judge the relative importance of each indicator in the comprehensive evaluation index system of the airflow field characteristics of the ship under test, and multiple individual fuzzy pairwise comparison matrices were constructed. Defuzzification is performed on multiple individual fuzzy pairwise comparison matrices to construct corresponding defuzzified individual pairwise comparison matrices; The row geometric mean method is used to calculate the index weights for multiple defuzzified individual pairwise comparison matrices, and multiple geometric consistency indices corresponding to the multiple defuzzified individual pairwise comparison matrices are calculated. Based on a preset geometric consistency index threshold, determine whether multiple geometric consistency indices are less than or equal to the geometric consistency index threshold in order to perform consistency verification; When the geometric consistency index is greater than the geometric consistency index threshold, the consistency verification fails. The defuzzified individual pairwise comparison matrix corresponding to the failure to pass the consistency verification is used as the original individual pairwise comparison matrix and input into the individual judgment matrix geometric consistency improvement algorithm. By setting a small tolerance coefficient, the modified preference value is restricted to a smaller neighborhood of the original preference value. The modified individual fuzzy pairwise comparison matrix and the corresponding modified geometric consistency index are output. The weighted geometric mean operator is combined with preset expert weights. Based on the defuzzified individual pairwise comparison matrix that has passed the consistency verification, the corresponding individual fuzzy pairwise comparison matrix is ​​matched and integrated with the corrected individual fuzzy pairwise comparison matrix to construct the group fuzzy pairwise comparison matrix. Alternatively, if the geometric consistency index is less than or equal to the geometric consistency index threshold, then the consistency verification is passed. Based on the multiple defuzzified individual pairwise comparison matrices corresponding to the consistency verification, the corresponding individual fuzzy pairwise comparison matrices are matched and integrated to obtain the group fuzzy judgment matrix. The row geometric mean method is used to calculate the final index fuzzy weights and defuzzification weights for the group fuzzy pairwise comparison matrix or the group fuzzy judgment matrix. The final ship airflow field numerical simulation method is adopted to conduct numerical simulation of the airflow field scheme of the ship under test under different atmospheric environment combination conditions, and obtain the ship airflow field data under the corresponding conditions. A 7-level linguistic scale was used to quantitatively evaluate the ship's operational requirements by assessing the ship's airflow field characteristics under actual atmospheric environmental conditions, and multiple individual fuzzy decision matrices were constructed. By combining preset expert weights and using a weighted arithmetic mean operator, multiple individual fuzzy decision matrices are integrated to construct a group fuzzy decision matrix; Under the actual atmospheric environment wind speed and direction combination conditions, the index performance of each combination condition is integrated to calculate the fuzzy envelope of the ship's airflow field performance. The fuzzy envelope is used to describe the degree to which the ship's airflow field characteristics meet the ship's usage requirements under the actual atmospheric environment combination conditions. The weighted normalized group fuzzy decision matrix is ​​calculated based on the final index fuzzy weights and fuzzy envelope, and then defuzzification is performed to construct the defuzzified weighted normalized group decision matrix. Calculate the distance between the quantitative evaluation of the performance of each index of the ship's airflow field scheme and the determined fuzzy positive ideal solution and fuzzy negative ideal solution respectively; The relative proximity coefficients of the quantitative evaluation of the performance of each index of the ship's airflow field scheme and the fuzzy positive ideal solution are calculated respectively, and the solution relative proximity coefficients are obtained by defuzzification. The ship airflow field schemes are evaluated and ranked in descending order of the relative proximity coefficients to obtain the fuzzy comprehensive evaluation results of the ship airflow field characteristics.

2. The fuzzy comprehensive evaluation method for ship airflow field characteristics oriented towards usage requirements as described in claim 1, characterized in that, The comprehensive evaluation index system for the airflow field characteristics of the ship under test includes: the safety of take-off and landing of the ship under test and the operating temperature adaptability of typical shipborne equipment of the ship under test. The safety of take-off and landing of the ship under test includes forward speed, lateral speed, vertical speed, standard deviation of vertical speed, turbulent kinetic energy distribution, streamline distribution and pressure distribution. The operating temperature adaptability of typical shipborne equipment of the ship under test includes the maximum temperature and temperature distribution.

3. The fuzzy comprehensive evaluation method for ship airflow field characteristics oriented towards usage requirements as described in claim 1, characterized in that, The output of the improved algorithm for geometric consistency of the individual judgment matrix also includes suggestions for prioritizing review judgments, suggestions for prioritizing adjustment directions, and recommended correction values.

4. The fuzzy comprehensive evaluation method for ship airflow field characteristics oriented towards usage requirements as described in claim 1, characterized in that, The establishment of the final numerical simulation method for ship airflow field includes the following steps: Obtain a 3D model of the ship under test and perform a model quality check; The blocking rate is calculated based on the blocking rate formula, and the computational domain is divided under the premise that the blocking rate is less than 3%. The computational mesh is divided according to the computational conditions and model size, and the mesh on the model surface and key parts is refined according to the Reynolds number of the flow field and the key areas of interest. Select the turbulence model and the incompressible air physical model, and set the boundary conditions according to the working conditions to define the model; Different grid schemes were obtained by adjusting the grid size, and the grid convergence was verified by trial calculations to establish an initial numerical calculation strategy for ship airflow field. A numerical calculation strategy for the initial ship airflow field was adopted to calculate similar ship airflow field schemes based on existing experimental data. The calculation results were compared with the experimental results to verify the accuracy of the initial ship airflow field numerical calculation strategy and to establish a final ship airflow field numerical simulation method.

5. The fuzzy comprehensive evaluation method for ship airflow field characteristics oriented towards usage requirements as described in claim 4, characterized in that, The turbulence model selected The model employs a pressure-based coupled solution of the governing equations. The convection term is discretized using a second-order upwind scheme, and the dissipation term is discretized using a second-order central difference scheme.

6. The fuzzy comprehensive evaluation method for ship airflow field characteristics oriented towards usage requirements as described in claim 4, characterized in that, The formula for calculating the geometric consistency index is as follows: In the formula, For individuals in the original pairwise comparison matrix, and The original indicator weights corresponding to the individuals.

7. The fuzzy comprehensive evaluation method for ship airflow field characteristics oriented towards usage requirements as described in claim 1, characterized in that, The formula for calculating the group fuzzy decision matrix is ​​as follows: In the formula, To preset expert weights, Experts The proposed airflow field scheme for ships Regarding indicators wind speed wind direction Evaluation values ​​under combined operating conditions.

8. The fuzzy comprehensive evaluation method for ship airflow field characteristics oriented towards usage requirements as described in claim 1, characterized in that, The formula for calculating the fuzzy positive ideal solution is: In the formula, B is the set of benefit-type indicators, and C is the set of cost-type indicators. To weighted normalize the fuzzy decision matrix of the group, To defuzzify the weighted normalized group decision matrix.

9. The fuzzy comprehensive evaluation method for ship airflow field characteristics oriented towards usage requirements as described in claim 8, characterized in that, The formula for calculating the fuzzy negative ideal solution is: 。 10. The fuzzy comprehensive evaluation method for ship airflow field characteristics oriented towards usage requirements as described in claim 1, characterized in that, The formula for calculating the relative proximity coefficient is: In the formula, The distance between the ship's airflow field scheme and the fuzzy positive ideal solution is given. The distance between the ship's airflow field scheme and the fuzzy negative ideal solution is given.

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