CN105975712A - Design optimization method for spacecraft passive thermal control parameters - Google Patents

Abstract

The invention discloses a design optimization method for spacecraft passive thermal control parameters. The design optimization method comprises the following steps that firstly, statistics is carried out on a thermal control parameter set to be designed and the design scope of the thermal control parameter set, and objective temperatures and the optimum value and the limit range of the objective temperatures are determined; secondly, an objective function is established, the sensitivity of the objective function on the thermal control parameters is analyzed, and the parameters are classified according to sensitivity; then, a sampled optimal sample serves as an initial value, and step-by-step optimization is carried out on the thermal control parameters through a quasi-linear algorithm according to the sensitivity classification; finally, whether an optimized value meets the temperature limitation or not under various thermal working conditions is detected, and if not, a suboptimum sample is selected again to serve as an initial value for optimization until the optimized value meeting the temperature limitation is acquired. The establishing method for the objective function with a weight mean square error, a sensitivity analysis classifying method for multiple working condition mean square errors on the thermal control parameters, the thermal control parameter step-by-step optimizing method and the like are put forward, and the effective optimization design method is provided for improving the cost-effectiveness and reliability of a spacecraft thermal control system to meet the harsh requirements of a current spacecraft.

Description

A kind of spacecraft passive thermal control parameter design optimization method

Technical field:

The present invention relates to a kind of spacecraft passive thermal control parameter design optimization method, it belongs to spacecraft thermal control technology neck Territory.

Background technology:

The main task of heat control system is to ensure the temperature environment that spacecraft the most normally works, and is described as " spacecraft Life line ".Therefore, the quality of heat control system performance, the height of reliability directly determine the development success or failure of spacecraft and work the longevity Life.For the spacecraft that single-piece is developed, heat control system designing technique all plays extremely in spacecraft development each time Close important effect, core technology that it is also considered as spacecraft thermal control always and be concerned with widely studied.

Heat control system design generally uses the mentality of designing of " passive thermal control be main, Active thermal control be auxiliary ", this be due to Passive thermal control mode (such as: heat conduction/heat insulation, thermal control coating etc.) reliability is high, idle, and Active thermal control mode is (such as: actively add Heat, active refrigeration etc.) reliability is low and consumes the energy.Therefore, passive thermal control performance how is given full play to reduce Active thermal control Application scale and energy consumption, just become heat control system design vital task.Current heat control system design uses Dan Yin more The forward optimization method that element is analyzed, is optimized passive thermal control parameter.Although this design optimization method is obtained in that meets temperature The thermal control design scheme of degree demand, but owing to impact point temperature can be existed close coupling impact, so it is not by design parameter There is the performance potential playing passive thermal control completely.

In recent years, along with spacecraft field is integrated and the going deep into of miniaturization technology, and formation networking, survey of deep space, The prominent development of Space Attack technology, spacecraft functional density ratio is substantially improved, device backup redundancy and can arrange electric energy surplus Decline to a great extent.This proposes the most harsh requirement to reliability and the economy of energy of On-Star system.The present invention is based at random The inverting optimization method of approximation proposes a kind of spacecraft passive thermal control parameter design optimization method, plays passive heat prosecutor further Formula potentiality, improve spacecraft thermal control system efficiency-cost ratio and reliability, to adapt to current spacecraft development new model demand.

Summary of the invention:

The present invention provides a kind of spacecraft passive thermal control parameter design optimization method, the method inverting based on stochastic approximation Method, with temperature design value and the minimum target of optimum error, com bined-sampling and inverting by many thermal controls parameter are taken into account many The couple variations of parameter, gives full play to passive thermal control mode potentiality.

The present invention adopts the following technical scheme that a kind of spacecraft passive thermal control parameter design optimization method, including walking as follows Rapid:

Step 1, add up thermal control parameter sets to be designed and can scope of design；

Step 2, determine impact point and optimum temperature value thereof and restriction scope；

Step 3, build impact point temperature design value and optimum error target function under multi-state, to heat transfer parameter can Uniform sampling, and the target function value under statistics difference sampling sample on the basis of heat emulates is carried out in scope of design；

Step 4, build object function to thermal control Parameter sensitivity function, and thermal control parameter is carried out sensitivity analysis and by Sensitivity is classified；

Step 5, with Latin Hypercube Sampling optimum sample as initial value, sensitive thermal control parameter is used pseudo-linear algorithm (BFGS) step-by-step optimization is carried out by sensitivity classification；

Under step 6, inspection thermal control parameter optimization value, whether impact point meets temperature limiting, if be unsatisfactory for, again chooses Initial value is optimized, until obtaining the optimal value meeting temperature limiting.

Further, object function construction method in step 3, account for spacecraft according to the operation time of different heat condition operating modes The ratio definition weights of entire life, utilize target temperature design load and optimum mean square deviation and the structure mesh of Weighted Coefficients under multi-state Scalar functions, minimum with optimum error in the hope of the design load that object function minima is optimization aim, i.e. temperature, specific practice is such as Under:

$D^j=\sqrt{\frac{1}{m}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{({T_{si}}^j-{T_{pi}}^j)}^2}$

$\begin{array}{cc}{\mathrm{\η}}^j=\frac{t_j}{t_P}& t_P=\underset{j=1}{\overset{K}{\mathrm{\Σ}}}{t}_j\end{array}$

$f\left(X\right)=S=\underset{j=1}{\overset{K}{\mathrm{\Σ}}}{\mathrm{\η}}^j{D}^j$

OBJ:min(f(X))

X=(x₁,x₂,x₃…x_n)

In formula: D^jFor operating mode j all impact points temperature design value and optimum Minimum Mean Square Error (K)；T_siFor impact point i temperature Degree design load (K)；T_piFor impact point i temperature optima (K)；t_jThe operation time shared by operating mode j in spacecraft in-orbit life-span (h)；t_PFor the spacecraft life-span (h) in-orbit；η^jFor by the weights running time scale；S is the band of K operating mode altogether in the life-span in-orbit Weights error and；F (X) is object function；OBJ is the thermal control parameter sample vector X, x asking S minima and correspondence thereof₁,x₂,x₃… x_nIt is respectively different thermal control parameter sample values.

Further, sensitivity analysis method in step 4, define each working temperature mean square deviation pair based on coefficient of rank correlation Thermal control Parameter sensitivity, is considered as sensitivity virtual value by the coefficient of rank correlation maximum under different operating modes, has according to sensitivity Valid value carries out the classification of thermal control parameter, during to avoid utilizing normalization object function direct analysis sensitivity, quick to single operating mode Sensible heat control parameter flood phenomenon, being defined as follows of sensitivity:

$r_{x_k}^j=1-6\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\[O\left(x_{ki}\right)-O\left(D_i^j\right)\]}^2/\[n(n^2-1)\]$

In formula:For impact point temperature design value under j operating mode with optimum mean square deviation to thermal control parameter x_kSensitivity；N is Latin Hypercube Sampling number of times, is set to 200 times herein, and parameter is by being uniformly distributed sampling；x_kiFor thermal control parameter x_kI & lt sampling sample This value；For impact point temperature design value and optimum mean square deviation under the j operating mode that i & lt sampling sample obtains；O(x_ki) it is right N sampling x_kWhen value carries out ascending order arrangement, the arrangement sequence number of i & lt sampling；For n sampling mean square deviation is carried out ascending order Arrangement, corresponding i & lt sampling arrangement sequence number.

Further, in step 5, step-by-step optimization method specifically includes:

In Latin Hypercube Sampling sample, optimal value is as initial value, first keeps little sensitivity thermal control parameter constant, uses BFGS algorithm carries out big sensitivity parameter optimization, it is thus achieved that meet the big sensitivity parameter near-optimal value of object function；

Then with the near-optimal value of big sensitivity parameter and little sensitivity parameter value as starting point, BFGS algorithm is used to carry out Optimize further, it is thus achieved that the optimal value of all sensitive parameters.

Further, in step 6, the optimal value method of inspection specifically includes with the initial value reselecting method being unsatisfactory for when limiting:

Optimal value is substituted into thermal model and carries out simulation analysis, each impact point of each operating mode under inspection thermal control parameter optimization value Whether meet temperature limiting；

If be unsatisfactory for, again choosing Latin Hypercube Sampling suboptimum sample is that initial value is optimized, and meets until obtaining The optimal value of temperature limiting.

There is advantages that the present invention proposes mean square deviation and the object function structure of band time scale weights Construction method, meets the real work situation of spacecraft.Propose the classification carrying out thermal control parameter according to sensitivity virtual value, it is to avoid Single operating mode sensitivity thermal control parameter flood phenomenon.Propose by the step-by-step optimization method of sensitivity, it is proposed that optimal value is examined Proved recipe method and the optimization initial value reselecting method being unsatisfactory for when limiting, improve optimization precision and optimize efficiency.The present invention proposes A kind of spacecraft passive thermal control parameter design optimization method, has played passive thermal control mode potentiality further, has improve spacecraft Heat control system efficiency-cost ratio and reliability.

Accompanying drawing illustrates:

Fig. 1 is spacecraft passive thermal control parameter design optimization flow chart.

Fig. 2 is the change of step-by-step optimization temperature residual error.

Fig. 3 (a) and 3 (b) are prioritization scheme flight worst hot case temperature curve in-orbit.

Fig. 4 (a) and 4 (b) are prioritization scheme flight worst cold case temperature curve in-orbit.

Fig. 5 (a) and 5 (b) are that prioritization scheme flies stealthy working temperature curve in-orbit.

Detailed description of the invention:

The present invention proposes a kind of spacecraft passive thermal control parameter design optimization method, and the method is based on stochastic approximation anti- Drilling method, with temperature design value and the minimum target of optimum error, com bined-sampling and inverting by many thermal controls parameter are taken into account The couple variations of multiparameter, gives full play to passive thermal control mode potentiality.It is concrete as it is shown in figure 1, a kind of spacecraft passive thermal control is joined Number design optimization method, comprises the steps:

Step 1, add up thermal control parameter sets to be designed and can scope of design；

Step 2, determine impact point and optimum temperature value thereof and restriction scope；

Step 3, build impact point temperature design value and optimum error target function under multi-state, to heat transfer parameter can Uniform sampling, and the target function value under statistics difference sampling sample on the basis of heat emulates is carried out in scope of design；

Step 4, build object function to thermal control Parameter sensitivity function, and thermal control parameter is carried out sensitivity analysis and by Sensitivity is classified；

Step 5, with Latin Hypercube Sampling optimum sample as initial value, sensitive thermal control parameter is used pseudo-linear algorithm (BFGS) step-by-step optimization is carried out by sensitivity classification；

Under step 6, inspection thermal control parameter optimization value, whether impact point meets temperature limiting, if be unsatisfactory for, again chooses Initial value is optimized, until obtaining the optimal value meeting temperature limiting.

In step 3, object function construction method specifically includes: according to spacecraft real work situation, the optimization of thermal control parameter Degree should be more prone to ensure the temperature optima under long period operating mode, is just more beneficial for reducing Active thermal control energy consumption and improves system Efficiency-cost ratio.Accordingly, account for the ratio definition weights of spacecraft entire life according to the operation time of different heat condition operating modes, utilize multiplexing Under condition, the target temperature design load of Weighted Coefficients and optimum mean square deviation and structure object function, be excellent in the hope of object function minima The design load changing target, i.e. temperature is minimum with optimum error.Specific practice is as follows:

$D^j=\sqrt{\frac{1}{m}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{({T_{si}}^j-{T_{pi}}^j)}^2}$

$\begin{array}{cc}{\mathrm{\η}}^j=\frac{t_j}{t_P}& t_P=\underset{j=1}{\overset{K}{\mathrm{\Σ}}}{t}_j\end{array}$

$f\left(X\right)=S=\underset{j=1}{\overset{K}{\mathrm{\Σ}}}{\mathrm{\η}}^j{D}^j$

OBJ:min(f(X))

X=(x₁,x₂,x₃...x_n)

In formula: D^jFor operating mode j all impact points temperature design value and optimum Minimum Mean Square Error (K)；T_siFor impact point i temperature Degree design load (K)；T_piFor impact point i temperature optima (K)；t_jThe operation time shared by operating mode j in spacecraft in-orbit life-span (h)；t_PFor the spacecraft life-span (h) in-orbit；η^jFor by the weights running time scale；S is the band of K operating mode altogether in the life-span in-orbit Weights error and；F (X) is object function；OBJ is the thermal control parameter sample vector X, x asking S minima and correspondence thereof₁,x₂,x₃… x_nIt is respectively different thermal control parameter sample values.

In step 4, sensitivity analysis method specifically includes: define each working temperature mean square deviation pair based on coefficient of rank correlation Thermal control Parameter sensitivity, is considered as sensitivity virtual value by the coefficient of rank correlation maximum under different operating modes, has according to sensitivity Valid value carries out the classification of thermal control parameter, during to avoid utilizing normalization object function direct analysis sensitivity, quick to single operating mode Sensible heat control parameter flood phenomenon.Being specifically defined of sensitivity is as follows by the sorting technique of sensitivity with parameter:

$r_{x_k}^j=1-6\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\[O\left(x_{ki}\right)-O\left(D_i^j\right)\]}^2/\[n(n^2-1)\]$

In formula:For impact point temperature design value under j operating mode with optimum mean square deviation to thermal control parameter x_kSensitivity；N is Latin Hypercube Sampling number of times, is set to 200 times herein, and parameter is by being uniformly distributed sampling；x_kiFor thermal control parameter x_kI & lt sampling sample This value；For impact point temperature design value and optimum mean square deviation under the j operating mode that i & lt sampling sample obtains；O(x_ki) it is To n sampling x_kWhen value carries out ascending order arrangement, the arrangement sequence number of i & lt sampling；For n sampling mean square deviation is risen Sequence arranges, corresponding i & lt sampling arrangement sequence number.

Therefore, impact point temperature design value and optimum mean square deviation are to thermal control parameter x_kThe virtual value of sensitivity be all The maximum of sensitivity under operating mode, is:

$r_{x_k}^E=\max (r_{x_k}^1,r_{x_k}^2,r_{x_k}^3\mathrm{...}{r}_{x_k}^K)$

By sensitivity virtual value defining classification principle it is:

Time, parameter x_kFor big sensitivity parameter；

Time, parameter x_kFor little sensitivity parameter；

Time, parameter x_kFor unwise sensitivity parameter.

In step 5, step-by-step optimization method specifically includes: in Latin Hypercube Sampling sample, optimal value is as initial value, first protects Hold little sensitivity thermal control parameter constant, use BFGS algorithm to carry out big sensitivity parameter optimization, it is thus achieved that to meet the big of object function Sensitivity parameter near-optimal value；Then with the near-optimal value of big sensitivity parameter and little sensitivity parameter value as starting point, adopt Optimize further with BFGS algorithm, it is thus achieved that the optimal value of all sensitive parameters.

In step 6, the optimal value method of inspection specifically includes with the initial value reselecting method being unsatisfactory for when limiting: by optimal value generation Entering thermal model and carry out simulation analysis, under inspection thermal control parameter optimization value, whether each impact point of each operating mode meets temperature limiting； If be unsatisfactory for, again choosing Latin Hypercube Sampling suboptimum sample is that initial value is optimized, and meets temperature limiting until obtaining Optimal value.

Below by utilizing passive thermal control parameter design optimization process and the result of certain type microsatellite, illustrate this Invention spacecraft passive thermal control parameter design optimization method.

1, thermal control parameter sets and can scope of design.The passive thermal control parameter of this satellite and can scope of design such as table 1 institute Show.

Table 1 can design parameter set

2, this design of satellites impact point and optimum temperature value thereof and temperature limited region are as shown in table 2.

Table 2 sets of target points

Numbering	Corresponding device	Optimum temperature (K)	Restriction scope (K)
				1	Communication control processor	283	253～313
2	Communication transmitter	283	253～313
				3	Sun sensor	283	263～303
4	Gyro	283	263～303
				5	Gaussmeter	273	253～313
6	GPS	283	253～313
				7	Momenttum wheel	283	263～303
8	Magnetic torquer	283	253～313
				9	Power supply box	283	273～303
10	CCD camera	283	263～283
				11	Aggregation of data	283	263～303
12	USB	283	253～313

3, Latin Hypercube Sampling method is utilized to be sampled by being uniformly distributed probability thermal control parameter sets, frequency in sampling Being defined as 200 times, sampling sample substitutes into thermal model and carries out heat analysis.Then according to object function construction method, calculate target letter Numerical value, this satellite operating mode number K=3 in-orbit, respectively worst hot case, worst cold case and stealthy operating mode, the life-span is 4 years in-orbit, Three operating mode operation times account for 40%, 40%, 20% respectively, thus weights are respectively 0.4,0.4,0.2, utilize this weights meter Calculate target function value；

4, to impact point temperature design value under three operating modes with optimum mean square deviation to 13 thermal control Parameter sensitivities respectively Being analyzed, parameter presses sensitivity classification as shown in table 3.

Table 3 sensitivity is added up

5, the step-by-step optimization of sensitive parameter.Insensitive parameter 4,7,10,12 does not optimizes.With object function in 200 sampling Thermal control parameter sampling sample value corresponding to minima is initial value, uses BFGS algorithm first to carry out parameter 1,2,6,8,11,13 Optimize, be then combined into starting point with its optimum results and 3,5,9 parameter values, then use BFGS algorithm to be optimized, optimization divides The residual error of step changes as shown in Figure 2.In figure, under j operating mode, i-th impact point residual error is defined as follows:

$\mathrm{\Δ}T=\underset{j=1}{\overset{k}{\mathrm{\Σ}}}\sqrt{{({T_{si}}^j-{T_{pi}}^j)}^2}/{\mathrm{KT}}_{pi}\×100\%$

This residual error represents the mean square deviation error of each impact point temperature design value and optimum and accounts for the percentage ratio of optimum, from Figure can be seen that, step-by-step optimization respectively walks impact point residual error and the most effectively declines, this demonstrates the effective of object function and optimization method Property.

6, optimum results is substituted into thermal model and carries out heat analysis, be respectively compared whether impact point temperature under three operating modes surpasses Limit.This suboptimization occurs in that stealthy working temperature overrun condition for the first time, obtains in second time and meet the excellent of temperature limiting Change result.Parameter optimization result is as shown in table 4.Under the thermal control scheme that parameter value after optimization is formed, actively power consumption is 0W, optimizes Before at least need the power consumption of peak value 18.5W, in-orbit dynamic temperature such as Fig. 3 (a) and 3 (b), Fig. 4 (a) and 4 (b), Fig. 5 (a) and 5 Shown in (b), it can be seen that temperature dynamic changes all in the range of limits value under each operating mode, flying quality checking in-orbit The effectiveness of optimization method.

Parameter value after table 4 optimization

Parameter is numbered	Optimal value
		1	0.19/0.05
2	0.66/0.82
		3	0.38/0.65
5	0.28/0.48
		6	8×10-4W/m·K
8	5×10-3W/m·K
		9	500W/m·K
11	1510W/m·K
		13	2W/m·K

The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For Yuan, can also make some improvement under the premise without departing from the principles of the invention, these improvement also should be regarded as the present invention's Protection domain.

Claims (5)

1. a spacecraft passive thermal control parameter design optimization method, it is characterised in that: comprise the steps

Step 1, add up thermal control parameter sets to be designed and can scope of design；

Step 2, determine impact point and optimum temperature value thereof and restriction scope；

Impact point temperature design value and optimum error target function under step 3, structure multi-state, can design heat transfer parameter In the range of carry out uniform sampling, and the target function value under statistics difference sampling sample on the basis of heat emulates；

Step 4, build object function to thermal control Parameter sensitivity function, and thermal control parameter is carried out sensitivity analysis and by sensitivity Degree classification；

Step 5, with Latin Hypercube Sampling optimum sample as initial value, use pseudo-linear algorithm (BFGS) to press sensitive thermal control parameter Sensitivity classification carries out step-by-step optimization；

Under step 6, inspection thermal control parameter optimization value, whether impact point meets temperature limiting, if be unsatisfactory for, again chooses initial value It is optimized, until obtaining the optimal value meeting temperature limiting.

2. spacecraft passive thermal control parameter design optimization method as claimed in claim 1, it is characterised in that: target in step 3 Function construction method, accounts for the ratio definition weights of spacecraft entire life according to the operation time of different heat condition operating modes, and utilization is many The target temperature design load of Weighted Coefficients and optimum mean square deviation and structure object function under operating mode, in the hope of object function minima be The design load of optimization aim, i.e. temperature is minimum with optimum error, and specific practice is as follows:

$D^j=\sqrt{\frac{1}{m}\underset{i=1}{\overset{m}{\Σ}}{({T_{si}}^j-{T_{pi}}^j)}^2}$

$\begin{array}{cc}{\mathrm{\η}}^j=\frac{t_j}{t_P}& t_P=\underset{j=1}{\overset{K}{\Σ}}{t}_j\end{array}$

$f\left(X\right)=S=\underset{j=1}{\overset{K}{\Σ}}{\mathrm{\η}}^j{D}^j$

OBJ:min(f(X))

X=(x₁,x₂,x₃…x_n)

In formula: D^jFor operating mode j all impact points temperature design value and optimum Minimum Mean Square Error (K)；T_siSet for impact point i temperature Evaluation (K)；T_piFor impact point i temperature optima (K)；t_jFor operation time (h) shared by operating mode j in spacecraft in-orbit life-span； t_PFor the spacecraft life-span (h) in-orbit；η^jFor by the weights running time scale；S is the Weighted Coefficients of K operating mode altogether in the life-span in-orbit Error and；F (X) is object function；OBJ is the thermal control parameter sample vector X, x asking S minima and correspondence thereof₁,x₂,x₃…x_nPoint Wei different thermal control parameter sample values.

3. spacecraft passive thermal control parameter design optimization method as claimed in claim 1, it is characterised in that: sensitive in step 4 Degree analysis method, based on the coefficient of rank correlation each working temperature mean square deviation of definition to thermal control Parameter sensitivity, by under difference operating mode Coefficient of rank correlation maximum be considered as sensitivity virtual value, carry out the classification of thermal control parameter according to sensitivity virtual value, to keep away When exempting to utilize normalization object function direct analysis sensitivity, single operating mode sensitivity thermal control parameter is flooded phenomenon, sensitivity Be defined as follows:

$r_{x_k}^j=1-6\underset{i=1}{\overset{n}{\Σ}}{\[O\left(x_{ki}\right)-O\left(D_i^j\right)\]}^2/\[n(n^2-1)\]$

In formula:For impact point temperature design value under j operating mode with optimum mean square deviation to thermal control parameter x_kSensitivity；N is Latin Hypercube frequency in sampling, is set to 200 times herein, and parameter is by being uniformly distributed sampling；x_kiFor thermal control parameter x_kI & lt sampling sample Value；For impact point temperature design value and optimum mean square deviation under the j operating mode that i & lt sampling sample obtains；O(x_ki) it is to n Secondary sampling x_kWhen value carries out ascending order arrangement, the arrangement sequence number of i & lt sampling；For n sampling mean square deviation is carried out ascending order Arrangement, corresponding i & lt sampling arrangement sequence number.

4. spacecraft passive thermal control parameter design optimization method as claimed in claim 1, it is characterised in that: substep in step 5 Optimization method specifically includes:

In Latin Hypercube Sampling sample, optimal value is as initial value, first keeps little sensitivity thermal control parameter constant, uses BFGS Algorithm carries out big sensitivity parameter optimization, it is thus achieved that meet the big sensitivity parameter near-optimal value of object function；

Then with the near-optimal value of big sensitivity parameter and little sensitivity parameter value as starting point, BFGS algorithm is used to carry out into one Step optimizes, it is thus achieved that the optimal value of all sensitive parameters.

5. spacecraft passive thermal control parameter design optimization method as claimed in claim 1, it is characterised in that: step 6 optimizes The value method of inspection specifically includes with the initial value reselecting method being unsatisfactory for when limiting:

Optimal value substituting into thermal model and carries out simulation analysis, under inspection thermal control parameter optimization value, whether each impact point of each operating mode Meet temperature limiting；

If be unsatisfactory for, again choosing Latin Hypercube Sampling suboptimum sample is that initial value is optimized, and meets temperature until obtaining The optimal value limited.