CN110309573A - It is a kind of that based on coupling, adjoint submarine navigation device is multidisciplinary to act on behalf of optimization method - Google Patents
It is a kind of that based on coupling, adjoint submarine navigation device is multidisciplinary to act on behalf of optimization method Download PDFInfo
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Abstract
Based on coupling, adjoint submarine navigation device is multidisciplinary to act on behalf of optimization method the present invention provides a kind of, the gradient of sub- subject of submarine navigation device is combined with the gradient of multidisciplinary system using coupling adjoint equation, adjoint equation is coupled by solving, the gradient of aircraft systems is calculated, substantially increases gradient computational efficiency.Furthermore, the grad enhancement Kriging agent model of entire AUV multidisciplinary system is established using gradient information, the model analysis number of each subject valuableness of aircraft can be effectively reduced in sequence optimisation based on this model, and significantly improves the searching ability of system globally optimal solution.Therefore, the present invention can reduce the time cost of AUV master-plan significantly, improve design efficiency, have very high engineering practicability.
Description
Technical field
The invention belongs to submarine navigation device master-plan fields, and in particular to a kind of submarine navigation device is in conceptual phase
The multidisciplinary of progress acts on behalf of optimization method.
Background technique
With the development of human society, land resources day is becoming tight, and the mankind gradually pay attention to exploitation and benefit to marine resources
With, and Autonomous Underwater Vehicle (Autonomous Underwater Vehicle, AUV) is as capableing of the underwater of autonomous navigation
Robot, each tasks such as the Yu Haiyang that has been widely used investigation, seabed resources detection, maritime search and rescue and marine water quality monitoring
In, play increasingly important role.
The master-plan of AUV is a complicated Design of Engineering Systems problem, contain shape and fluid dynamic, power with
Many multi-disciplinary designs such as the energy, quality layout are analyzed, and there is also the relationships for interacting, intercoupling between these subjects.It passes
The design method of system usually ignore in the design process or weaken it is interdisciplinary connect each other, the designs of different subjects analysis is each other
The problems such as independence, generally requires multiple cyclic design, and there are the lead time is long, development cost is high, overall performance is not good enough.It is multidisciplinary
Design optimization (Multidisciplinary design optimization, MDO) method is made to solve this complication system
Design problem is set by each subject analysis of reasonable arrangement and data transitive relation, the totality that can more efficiently complete AUV
Meter.
The multidisciplinary design optimization of AUV is still related to the multiple analysis of every subjects, and subject analysis is often very time-consuming
(such as CFD emulation is carried out when Fluid Dynamical Analysis), therefore it is still higher to calculate cost.
Summary of the invention
For overcome the deficiencies in the prior art, the present invention provides a kind of submarine navigation device multidisciplinary generation adjoint based on coupling
Optimization method is managed, coupling adjoint equation and agent model sequence optimisation method is used in combination, reduces being designed to for submarine navigation device
This, improves design efficiency.
The technical solution adopted by the present invention to solve the technical problems the following steps are included:
Step 1, AUV subject models, comprising:
1.1 steps, establish AUV shape and fluid dynamic subject model, and input parameter is geometric shape input parameter and fluid
Power output parameter；
1.2 steps, establish AUV power and energy subject model, input parameter be geometric shape input parameter, resistance coefficient,
Battery parameter and the voyage speed of a ship or plane, output parameter are navigation energy consumption, battery length；
1.3 steps, establish AUV quality layout subject model, input parameter be each bay section quality of AUV comprising battery bay section,
Centroid position, output parameter be entire AUV mass center centre of buoyancy away from；
Step 2, the multi- disciplinary integrated Optimized model of AUV system is established, comprising:
2.1 steps, choosing design variable x is that geometric shape inputs parameter, sets optimization aim f as the energy consumption navigated by water, constrains c
For mass center centre of buoyancy away from；
2.2 steps establish the multi- disciplinary integrated Optimized model of AUV system:
minimize f(x,y(x))
with respect to x
subject to c(x,y(x))
Y (x) is battery length in formula, is to need to iteratively solve coupling variable；Optimizing each step, fixed design is being become
X is measured, circulation carries out analytical calculation to shape and fluid dynamic subject model, power and energy subject model, obtains convergent coupling
Variable y (x) is closed to stop；
Step 3, based on coupling, adjoint submarine navigation device is multidisciplinary to act on behalf of optimization method, comprising:
3.1 steps determine the bound of the geometric shape parameter of AUV, choose number of samples, use optimization Latin hypercube method
Sample input point is taken in design variable space, establishes the sample input space；
3.2 steps, to each sample input point, calculating target function f and constraint c；
3.3 steps, to each sample input point, using coupling adjoint equation calculating target function and constraint to design variable
Gradient df/dx and dc/dx；
3.4 steps by 3.2 steps and the objective function of 3.3 steps calculating, constraint and its supplement as sample the gradient of design variable
All samples are added sample database, and find the current optimal design of AUV in sample database by the output of input point；
3.5 steps, judge whether the current optimal design of AUV meets the optimization termination condition of setting, if being unsatisfactory for terminating item
Part then optimizes continuation, into 3.6 steps；Otherwise optimization stops, into 3.10 steps；
3.6 steps establish the grad enhancement Kriging generation of AUV multidisciplinary system using sample all in sample database
Manage modelWith
3.7 steps are acted on behalf of using grad enhancement Kriging of a lot of point sequence double optimization methods to AUV multidisciplinary system
Model optimizes, and obtains approximate optimal design parameters value, i.e. design variable value；
3.8 steps using approximate optimal design parameters value as new sample input value, and are calculated using 3.2 steps and 3.3 steps
The output valve of new samples；
3.9 step: new sample is supplemented in sample database, and find in new sample database AUV it is current most
Then excellent design returns to 3.5 steps；
3.10 steps export the current optimal design of AUV, are exactly the optimal solution of AUV totality multidisciplinary design optimization.
1.1 steps include the following contents:
1.1.1 the geometric shape parameter for passing through AUV, generates the geometrical model of AUV；
1.1.2 AUV geometry file is imported into ICEM software and divides flow field grid；
1.1.3, AUV grid file is carried out to the resistance coefficient of CFD simulation calculation AUV；
1.1.4 parameter is inputted using geometric shape and fluid dynamic output parameter establishes AUV shape and fluid dynamic subject
Model.
3.2 steps include the following contents:
3.2.1 under the design variable of current sample input, shape and fluid dynamic subject model to coupling is recycled, is moved
Power is analyzed with energy subject model, is obtained convergent coupling variable y (x) and is stopped；
3.2.2 it according to design variable x and convergent coupling variable y (x), calculates shape and is obtained with fluid dynamic subject model
Resistance coefficient y1；
3.2.3 it calculates power and energy subject model obtains total energy consumption and the battery length y of AUV navigation2；
3.2.4 it calculates quality layout's subject model and obtains the mass center centre of buoyancy of AUV away from y3；
3.2.5 calculated value is distributed into objective function f and constraint c according to AUV multi- disciplinary integrated Optimized model.
3.3 steps include the following contents:
3.3.1 shape and fluid dynamic subject, power and energy subject, quality layout are calculated separately using finite difference calculus
The gradient of these three sub- subjects of subject, i.e., output parameter is to the partial derivative of input parameter in subject model, including to design variable
DerivativeWith the derivative exported to other models
3.3.2 coupling adjoint equation combines the gradient of tri- sub- subjects of AUV with the gradient of AUV multidisciplinary system,
The gradient that AUV multidisciplinary system is calculated by solving coupling adjoint equation, coupling adjoint equation are as follows:
Wherein ΨiIt is coupling association factor, contains the gradient df/dx, dc/dx of target and constraint in dF/dx.
3.3.3 calculated value is respectively allocated to objective function and constraint to design according to AUV multi- disciplinary integrated Optimized model
The gradient df/dx and dc/dx of variable.
The optimization termination condition is the relative different of current optimal design and last optimal design less than 1%.
The beneficial effects of the present invention are: using coupling adjoint equation by the gradient of the sub- subject of submarine navigation device with it is multidisciplinary
The gradient of system combines, and couples adjoint equation by solving, the gradient of aircraft systems is calculated, substantially increases ladder
Spend computational efficiency.In addition, establishing the grad enhancement Kriging agent model of entire AUV multidisciplinary system, base using gradient information
The model analysis number of each subject valuableness of aircraft can be effectively reduced in the sequence optimisation of this model, and it is complete to significantly improve system
The searching ability of office's optimal solution.Therefore, the present invention can reduce the time cost of AUV master-plan significantly, improve design efficiency, tool
There is very high engineering practicability.
Detailed description of the invention
Fig. 1 is Autonomous Underwater Vehicle geometric shape parameter schematic diagram of the invention；
Fig. 2 is the multidisciplinary broad flow diagram for acting on behalf of optimization method of Autonomous Underwater Vehicle of the invention.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and examples, and the present invention includes but are not limited to following implementations
Example.
The present invention uses the thought of agent model sequence optimisation, constructs the agent model of entire AUV multidisciplinary system, and adopt
Gradient information needed for efficiently calculating building agent model with coupling adjoint equation has studied a kind of adjoint based on coupling
AUV is multidisciplinary to act on behalf of optimization method, to reduce the calculating cost in conceptual phase, improves design efficiency.
Specific technical solution is as follows:
The sub- subject modeling of step 1:AUV, including following several steps:
1.1 steps: AUV shape is established based on UG software, ICEM software and Fluid Dynamical Analysis software FLUENT and fluid is dynamic
Mechanics section model.Basic process is as follows:
1.1.1 AUV geometric shape program is write using the secondary development function of UG software, is joined by the geometric shape of AUV
Number, generates the geometrical model of AUV；
1.1.2 AUV geometry file is imported into ICEM software and divides flow field grid；
1.1.3, AUV grid file is imported in FLUENT software to the resistance coefficient for carrying out CFD simulation calculation AUV；
1.1.4 parameter is inputted using geometric shape and fluid dynamic output parameter establishes AUV shape and fluid dynamic subject
Model.
1.2 steps: AUV power and energy subject model are established using Visual Studio software, input parameter is outside geometry
Shape inputs parameter, resistance coefficient, battery parameter and the voyage speed of a ship or plane, and output parameter is navigation energy consumption, battery length.
1.3 step: establishing AUV quality layout subject model using Visual Studio software, input parameter is comprising battery
Each bay section quality of the AUV of bay section, centroid position, output parameter be entire AUV mass center centre of buoyancy away from.
Step 2: establishing the multi- disciplinary integrated Optimized model of AUV system.Including following several steps:
2.1 steps: AUV design object is that navigation energy consumption is minimum, and choosing design variable x is that geometric shape inputs parameter, setting
Optimization aim f be navigation energy consumption, constraint c be mass center centre of buoyancy away from；
2.2 steps: the multi- disciplinary integrated Optimized model of AUV system is established:
Y (x) is battery length in formula, is to need to iteratively solve coupling variable.Optimizing each step, fixed design is being become
Measure x (i.e. geometric shape input parameter), due to the resistance coefficient that shape and fluid dynamic subject model calculate be output to power with
Energy subject, and power and energy subject model calculating output battery length can change AUV overall length, reversely be input to shape and stream
There is the coupled relation of interaction between two subjects in body dynamics section.Circulation is to shape and fluid dynamic subject model, power and the energy
Subject model carries out analytical calculation, obtains convergent coupling variable y (x) and stops.
Step 3: based on coupling, adjoint submarine navigation device is multidisciplinary to act on behalf of optimization method.Including following several steps:
3.1 steps: determining the bound of these design variables of the geometric shape parameter of AUV, chooses number of samples, uses optimization
Latin hypercube method takes sample input point in design variable space, establishes the sample input space.
3.2 steps: to each sample input point, calculating target function f and constraint c.Basic process is as follows:
3.2.1 under the design variable of current sample input, shape and fluid dynamic subject model to coupling is recycled, is moved
Power is analyzed with energy subject model, is obtained convergent coupling variable y (x) and is stopped；
3.2.2 it according to design variable x and convergent coupling variable y (x), calculates shape and is obtained with fluid dynamic subject model
Resistance coefficient y1；
3.2.3 it calculates power and energy subject model obtains total energy consumption and the battery length y of AUV navigation2；
3.2.4 it calculates quality layout's subject model and obtains the mass center centre of buoyancy of AUV away from y3；
3.2.5 calculated value is distributed into objective function f and constraint c according to AUV multi- disciplinary integrated Optimized model.
3.3 steps: to each sample input point, using coupling adjoint equation calculating target function and constraint to design variable
Gradient df/dx and dc/dx.Basic process is as follows:
3.3.1 shape and fluid dynamic subject, power and energy subject, quality layout are calculated separately using finite difference calculus
The gradient of these three sub- subjects of subject, i.e., output parameter is to the partial derivative of input parameter in subject model, including to design variable
DerivativeWith the derivative exported to other models
3.3.2 coupling adjoint equation combines the gradient of tri- sub- subjects of AUV with the gradient of AUV multidisciplinary system,
The gradient that AUV multidisciplinary system is calculated by solving coupling adjoint equation, coupling adjoint equation are as follows:
Wherein ΨiIt is coupling association factor, contains the gradient df/dx, dc/dx of target and constraint in dF/dx.
3.3.3 calculated value is respectively allocated to objective function and constraint to design according to AUV multi- disciplinary integrated Optimized model
The gradient df/dx and dc/dx of variable.
3.4 steps: by objective function that 3.2 steps and 3.3 steps calculate, constraint and they be to the gradient supplement of design variable
Sample database is added in all samples by the output of sample input point, and is found in sample database the current of AUV and optimal set
Meter.
3.5 steps: judging whether the current optimal design of AUV meets optimization termination condition, and optimization termination condition is and upper one
The relative different of suboptimum design is specific as follows less than 1%:
Wherein fnFor current optimal value, fn-1For last optimal value.If being unsatisfactory for termination condition, optimization continues, and enters
3.6 step；Otherwise optimization stops, into 3.10 steps.
3.6 steps: the grad enhancement Kriging generation of AUV multidisciplinary system is established using sample all in sample database
Manage modelWith
3.7 steps: it is acted on behalf of using grad enhancement Kriging of a lot of point sequence double optimization methods to AUV multidisciplinary system
Model optimizes, and obtains approximate optimal design parameters value, i.e. design variable value；
3.8 steps: it using approximate optimal design parameters value as new sample input value, and is calculated using 3.2 steps and 3.3 steps
The output valve of new samples；
3.9 steps: being supplemented in sample database for new sample, and find in new sample database AUV it is current most
Then excellent design returns to 3.5 steps.
3.10 steps: exporting the current optimal design of AUV, is exactly the optimal solution of AUV totality multidisciplinary design optimization.
3.11 steps: terminate.
Shown in referring to Fig.1, the specific implementation step of the embodiment of the present invention are as follows:
The sub- subject modeling of step 1:AUV, including following several steps:
1.1 steps: AUV shape is established based on UG software, ICEM software and Fluid Dynamical Analysis software FLUENT and fluid is dynamic
Mechanics section model.Basic process is as follows:
1.1.1 AUV geometric shape program is write using the secondary development function of UG software, is joined by the geometric shape of AUV
Number (as shown in Figure 1), generates the geometrical model of AUV；
1.1.2 AUV geometry file is imported into ICEM software and divides flow field grid；
1.1.3, AUV grid file is imported in FLUENT software to the resistance coefficient C for carrying out CFD simulation calculation AUVxs；
1.1.4 parameter is inputted using geometric shape and fluid dynamic output parameter establishes AUV shape and fluid dynamic subject
Model:
y1(Cxs)=f1(L,D,qh1,qh2,qt1,qt2,DF,DE,LH,LC,LTC,LTE,b0,b1,bs,a) (4)
L is AUV overall length in formula, and D is parallel-segment diameter, qh1,qh2,qt1,qt2For line style parameter end to end, DFFor head front end
Diameter, DEFor tail portion rear end diameter, LHFor head segment length, LCFor parallel segment length, LTCFor tail portion curve segment length, LTEFor tail
Bore segment length, b0,b1,bs, a is all movable rudder parameter.A part of geometric shape parameter is definite value parameter in formula, and a part is design
Variable, after see step 2.
1.2 steps: AUV power and energy subject model are established using Visual Studio software:
y2(LE, E) and=f2(Cxs,S,ηE,d,v,ρ) (5)
S is cross-sectional area, the η of AUV in formulaEThe efficiency of aircraft kinetic energy is converted into for battery power, d is voyage, v is boat
Speed, ρ are density of sea water, and E is that AUV navigates by water the energy needed in total, LEIt is the length of battery.
1.3 steps: AUV quality layout subject model is established using Visual Studio software:
y3(rc)=f3(mi,ri) (6)
M in formulai, riIt is each bay section quality of AUV comprising battery bay section, centroid position, r respectivelyc=(xc,yc,zc) it is AUV
Mass center centre of buoyancy away from.
Step 2: establishing the multi- disciplinary integrated Optimized model of AUV system.Including following several steps:
2.1 steps: AUV design object is that navigation energy consumption is minimum, chooses design variable x=qh1,qh2,qt1,qt2,b0,b1,
bs, a (line style parameter and all movable rudder parameter end to end), setting optimization aim (navigation energy consumption) f=E and constraint c=rc=(xc,yc,
zc) (mass center centre of buoyancy away from)；
2.2 steps: the multi- disciplinary integrated Optimized model of AUV system is established:
L in formulaEIt is battery length, is to need to iteratively solve.Optimizing each step, design variable x be it is fixed, due to outer
The resistance coefficient C that shape and fluid dynamic subject model calculatexsIt is output to power and energy subject, and power and energy subject model
Calculate output battery length LEAUV overall length can be changed, shape and fluid dynamic subject is reversely input to, there is interaction between two subjects
Coupled relation.Circulation carries out analytical calculation to shape and fluid dynamic subject model, power and energy subject model, is received
The coupling variable L held backE, this process, which can be regarded as, solves following equation group:
R(LE)=LE-f2(f1(LE))=0 (8)
Step 3: based on coupling, adjoint submarine navigation device is multidisciplinary to act on behalf of optimization method.As shown in Fig. 2, including following several
Step:
3.1 steps: determining the bound of these design variables of the geometric shape parameter of AUV, chooses number of samples, uses optimization
Latin hypercube method takes sample input point in design variable space, establishes the sample input space.
3.2 steps: to each sample input point, calculating target function f and constraint c.Basic process is as follows:
3.2.1 under the design variable of current sample input, equation (8) is solved and obtain convergent coupling variable LE(x), i.e.,
Circulation analyzes shape and fluid dynamic subject model, power and energy subject model, obtains convergent coupling variable LE
(x) stop when；
3.2.2 according to design variable x and convergence coupling variable LE(x), shape is calculated to obtain with fluid dynamic subject model
Resistance coefficient y1=Cxs；
3.2.3 it calculates power and energy subject model obtains total energy consumption y of AUV navigation2=(E, LE)；
3.2.4 it calculates quality layout's subject model and obtains the mass center centre of buoyancy of AUV away from y3=rc；
3.2.5 calculated value is distributed into objective function f and constraint c according to AUV multi- disciplinary integrated Optimized model.
3.3 steps: to each sample input point, using coupling adjoint equation calculating target function and constraint to design variable
Gradient df/dx and dc/dx.Basic process is as follows:
3.3.1 shape and fluid dynamic subject, power and energy subject, quality layout are calculated separately using finite difference calculus
The gradient of these three sub- subjects of subject, i.e., output parameter is to the partial derivative of input parameter in subject model, including to design variable
DerivativeWith the derivative exported to other models
3.3.2 coupling adjoint equation combines the gradient of tri- sub- subjects of AUV with the gradient of AUV multidisciplinary system,
The gradient that AUV multidisciplinary system is calculated by solving coupling adjoint equation, coupling adjoint equation are as follows:
Wherein ΨiIt is coupling association factor, contains the gradient df/dx, dc/dx of target and constraint in dF/dx.
3.3.3 calculated value is respectively allocated to objective function and constraint to design according to AUV multi- disciplinary integrated Optimized model
The gradient df/dx and dc/dx of variable.
3.4 steps: by objective function that 3.2 steps and 3.3 steps calculate, constraint and they be to the gradient supplement of design variable
Sample database is added in all samples by the output of sample input point, and is found in sample database the current of AUV and optimal set
Meter.
3.5 steps: judging whether the current optimal design of AUV meets optimization termination condition, and optimization termination condition is and upper one
The relative different of suboptimum design is specific as follows less than 1%:
Wherein fnFor current optimal value, fn-1For last optimal value.If being unsatisfactory for termination condition, optimization continues, and enters
3.6 step；Otherwise optimization stops, into 3.10 steps.
3.6 steps: the grad enhancement Kriging generation of AUV multidisciplinary system is established using sample all in sample database
Manage modelWith
3.7 steps: it is acted on behalf of using grad enhancement Kriging of a lot of point sequence double optimization methods to AUV multidisciplinary system
Model optimizes, and obtains approximate optimal design parameters value, i.e. design variable value；
3.8 steps: it using approximate optimal design parameters value as new sample input value, and is calculated using 3.2 steps and 3.3 steps
The output valve of new samples；
3.9 steps: being supplemented in sample database for new sample, and find in new sample database AUV it is current most
Then excellent design returns to 3.5 steps.
3.10 steps: exporting the current optimal design of AUV, is exactly the optimal solution of AUV totality multidisciplinary design optimization.
3.11 steps: terminate.
Claims (5)
1. a kind of, based on coupling, adjoint submarine navigation device is multidisciplinary to act on behalf of optimization method, it is characterised in that the following steps are included:
Step 1, AUV subject models, comprising:
1.1 steps, establish AUV shape and fluid dynamic subject model, and input parameter is geometric shape input parameter and fluid dynamic
Output parameter；
1.2 steps, establish AUV power and energy subject model, and input parameter is geometric shape input parameter, resistance coefficient, battery
Parameter and the voyage speed of a ship or plane, output parameter are navigation energy consumption, battery length；
1.3 steps, establish AUV quality layout subject model, and input parameter is each bay section quality of AUV comprising battery bay section, mass center
Position, output parameter be entire AUV mass center centre of buoyancy away from；
Step 2, the multi- disciplinary integrated Optimized model of AUV system is established, comprising:
2.1 steps, choosing design variable x is that geometric shape inputs parameter, sets optimization aim f as the energy consumption navigated by water, constraint c is matter
The flighty and impatient heart away from；
2.2 steps establish the multi- disciplinary integrated Optimized model of AUV system:
minimize f(x,y(x))
with respect to x
subject to c(x,y(x))
Y (x) is battery length in formula, is to need to iteratively solve coupling variable；Optimizing each step, to fixed design variable x,
Circulation carries out analytical calculation to shape and fluid dynamic subject model, power and energy subject model, obtains convergent coupling and becomes
Y (x) is measured to stop；
Step 3, based on coupling, adjoint submarine navigation device is multidisciplinary to act on behalf of optimization method, comprising:
3.1 steps determine the bound of the geometric shape parameter of AUV, choose number of samples, are being set using optimization Latin hypercube method
The meter variable space takes sample input point, establishes the sample input space；
3.2 steps, to each sample input point, calculating target function f and constraint c；
3.3 steps using coupling adjoint equation calculating target function and constrain the gradient to design variable to each sample input point
Df/dx and dc/dx；
3.4 steps by 3.2 steps and the objective function of 3.3 steps calculating, constraint and its supplement as sample input the gradient of design variable
All samples are added sample database, and find the current optimal design of AUV in sample database by the output of point；
3.5 steps, judge whether the current optimal design of AUV meets the optimization termination condition of setting, if being unsatisfactory for termination condition
Optimization continues, into 3.6 steps；Otherwise optimization stops, into 3.10 steps；
3.6 steps act on behalf of mould using the grad enhancement Kriging that sample all in sample database establishes AUV multidisciplinary system
TypeWith
3.7 steps, using a lot of point sequence double optimization methods to the grad enhancement Kriging agent model of AUV multidisciplinary system
It optimizes, obtains approximate optimal design parameters value, i.e. design variable value；
3.8 steps using approximate optimal design parameters value as new sample input value, and calculate new sample using 3.2 steps and 3.3 steps
This output valve；
3.9 steps: being supplemented in sample database for new sample, and finds in new sample database the current of AUV and optimal set
Meter, then returns to 3.5 steps；
3.10 steps export the current optimal design of AUV, are exactly the optimal solution of AUV totality multidisciplinary design optimization.
2. the submarine navigation device adjoint based on coupling according to claim 1 is multidisciplinary to act on behalf of optimization method, feature exists
In 1.1 steps include the following contents:
1.1.1 the geometric shape parameter for passing through AUV, generates the geometrical model of AUV；
1.1.2 AUV geometry file is imported into ICEM software and divides flow field grid；
1.1.3, AUV grid file is carried out to the resistance coefficient of CFD simulation calculation AUV；
1.1.4 parameter is inputted using geometric shape and fluid dynamic output parameter establishes AUV shape and fluid dynamic subject model.
3. the submarine navigation device adjoint based on coupling according to claim 1 is multidisciplinary to act on behalf of optimization method, feature exists
In 3.2 steps include the following contents:
3.2.1 under the design variable of current sample input, recycle to the shape of coupling and fluid dynamic subject model, power with
Energy subject model is analyzed, and is obtained convergent coupling variable y (x) and is stopped；
3.2.2 it according to design variable x and convergent coupling variable y (x), calculates shape and fluid dynamic subject model obtains resistance
Coefficient y1；
3.2.3 it calculates power and energy subject model obtains total energy consumption and the battery length y of AUV navigation2；
3.2.4 it calculates quality layout's subject model and obtains the mass center centre of buoyancy of AUV away from y3；
3.2.5 calculated value is distributed into objective function f and constraint c according to AUV multi- disciplinary integrated Optimized model.
4. the submarine navigation device adjoint based on coupling according to claim 1 is multidisciplinary to act on behalf of optimization method, feature exists
In 3.3 steps include the following contents:
3.3.1 shape and fluid dynamic subject, power and energy subject, quality layout's subject are calculated separately using finite difference calculus
The gradient of these three sub- subjects, i.e., output parameter is to the partial derivative for inputting parameter in subject model, including leading to design variable
NumberWith the derivative exported to other models
3.3.2 coupling adjoint equation combines the gradient of tri- sub- subjects of AUV with the gradient of AUV multidisciplinary system, passes through
The gradient that AUV multidisciplinary system is calculated in coupling adjoint equation is solved, coupling adjoint equation is as follows:
Wherein ΨiIt is coupling association factor, contains the gradient df/dx, dc/dx of target and constraint in dF/dx.
3.3.3 calculated value is respectively allocated to objective function and constraint to design variable according to AUV multi- disciplinary integrated Optimized model
Gradient df/dx and dc/dx.
5. the submarine navigation device adjoint based on coupling according to claim 1 is multidisciplinary to act on behalf of optimization method, feature exists
In: the optimization termination condition is the relative different of current optimal design and last optimal design less than 1%.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112199792A (en) * | 2020-09-30 | 2021-01-08 | 哈尔滨工程大学 | Multi-disciplinary optimization design method for micro underwater robot |
| CN113606991A (en) * | 2021-07-21 | 2021-11-05 | 西北工业大学 | High-pressure chamber structure for underwater boat speed launching and design method |
| CN114580085A (en) * | 2022-04-29 | 2022-06-03 | 北京理工大学 | Multi-time underwater vehicle head shape optimization method based on proxy model |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150120271A1 (en) * | 2013-10-28 | 2015-04-30 | The Boeing Company | System and method for visualization and optimization of system of systems |
| CN105205261A (en) * | 2015-09-18 | 2015-12-30 | 中国计量学院 | Autonomous underwater vehicle appearance optimization method |
| US20170240258A1 (en) * | 2011-05-17 | 2017-08-24 | Eni S.P.A | Autonomous underwater system for a 4d environmental monitoring |
| CN107515994A (en) * | 2017-09-11 | 2017-12-26 | 西北工业大学 | A kind of more fidelity design optimization methods of the shell structure of Autonomous Underwater Vehicle |
| CN108319799A (en) * | 2018-03-15 | 2018-07-24 | 西北工业大学 | A kind of more fidelity optimum design methods of the shape of Autonomous Underwater Vehicle |
| CN108446481A (en) * | 2018-03-15 | 2018-08-24 | 西北工业大学 | A kind of portable AUV Exterior Surface Designs of low-resistance low noise based on the optimization of grey wolf colony intelligence |
| CN109002636A (en) * | 2018-08-02 | 2018-12-14 | 哈尔滨工程大学 | A kind of underwater autonomous underwater vehicle lower coupling layer architecture optimization method step by step |
| CN109918809A (en) * | 2019-03-14 | 2019-06-21 | 北京理工大学 | Aircraft near-optimal method based on filter and adaptive Kriging model |
-
2019
- 2019-06-25 CN CN201910554370.2A patent/CN110309573A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170240258A1 (en) * | 2011-05-17 | 2017-08-24 | Eni S.P.A | Autonomous underwater system for a 4d environmental monitoring |
| US20150120271A1 (en) * | 2013-10-28 | 2015-04-30 | The Boeing Company | System and method for visualization and optimization of system of systems |
| CN105205261A (en) * | 2015-09-18 | 2015-12-30 | 中国计量学院 | Autonomous underwater vehicle appearance optimization method |
| CN107515994A (en) * | 2017-09-11 | 2017-12-26 | 西北工业大学 | A kind of more fidelity design optimization methods of the shell structure of Autonomous Underwater Vehicle |
| CN108319799A (en) * | 2018-03-15 | 2018-07-24 | 西北工业大学 | A kind of more fidelity optimum design methods of the shape of Autonomous Underwater Vehicle |
| CN108446481A (en) * | 2018-03-15 | 2018-08-24 | 西北工业大学 | A kind of portable AUV Exterior Surface Designs of low-resistance low noise based on the optimization of grey wolf colony intelligence |
| CN109002636A (en) * | 2018-08-02 | 2018-12-14 | 哈尔滨工程大学 | A kind of underwater autonomous underwater vehicle lower coupling layer architecture optimization method step by step |
| CN109918809A (en) * | 2019-03-14 | 2019-06-21 | 北京理工大学 | Aircraft near-optimal method based on filter and adaptive Kriging model |
Non-Patent Citations (6)
| Title |
|---|
| XUCHEN .ETAL: "Gradient-based multidisciplinary design optimization of an autonomous underwater vehicle", 《APPLIED OCEAN RESEARCH》 * |
| 周铸 等: "民用飞机气动外形数值优化设计面临的挑战与展望", 《航空学报》 * |
| 孙春亚 等: "翼身融合水下滑翔机外形优化设计", 《水下无人系统学报》 * |
| 陈潇凯 编著: "《车辆多学科设计优化方法》", 30 June 2018, 北京理工大学出版社 * |
| 韩少强 等: "基于梯度增强型Kriging模型的气动反设计方法", 《航空学报》 * |
| 黄江涛等: "飞行器气动/结构多学科延迟耦合伴随系统数值研究", 《航空学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112199792A (en) * | 2020-09-30 | 2021-01-08 | 哈尔滨工程大学 | Multi-disciplinary optimization design method for micro underwater robot |
| CN112199792B (en) * | 2020-09-30 | 2021-07-20 | 哈尔滨工程大学 | Multi-disciplinary optimization design method for micro underwater robot |
| CN113606991A (en) * | 2021-07-21 | 2021-11-05 | 西北工业大学 | High-pressure chamber structure for underwater boat speed launching and design method |
| CN113606991B (en) * | 2021-07-21 | 2022-07-26 | 西北工业大学 | High-pressure chamber structure for underwater boat speed launching and design method |
| CN114580085A (en) * | 2022-04-29 | 2022-06-03 | 北京理工大学 | Multi-time underwater vehicle head shape optimization method based on proxy model |
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