KR20090037524A - Robust design method and very light jet optimum design using thereof - Google Patents

Abstract

A robust design method and design method for very light jet aircraft using thereof are provided to design more efficiently and objectively by excluding a determination of being subjective of a designer and to reduce a repeated calculation and a turnaround time of the design process. Necessary elements for performing a task based on a performance and a shape of a new system are drawn. A maximum value expected from the system and a minimum value which is survived in a market is set up by using the drawn elements. An associative relationship between the design variables including the Affinity Diagram, the Nested Column Diagram, and the QFD(Quality Function Deployment) is analyzed. By using analyzed result, the alternative shape is settled around the major design variable and shape factor. The alternative shape is selected through the Morphological Matrix and Pugh Concept Selection Matrix. The design possible area of the alternative shape selection item is analyzed. The tentative design is selected by using the deterministic optimization technique in the design possible area. A change of a user demand about the tentative design and the influence from an error generated in a development stage are reduced.

Description

Robust Optimal Design Method and Optimal Design Method of Micro Jet Aircraft Using the Same {ROBUST DESIGN METHOD AND VERY LIGHT JET OPTIMUM DESIGN USING THEREOF}

The present invention relates to a robust design method, more specifically, through the decision-making tools, statistical methods and quality management techniques, etc., to exclude the subjective judgment of the designer as much as possible, more objective and efficient design is made, On the basis of this, the present invention relates to a robust optimal design method for achieving optimal design of a micro jet aircraft (VLJ) and an optimal design method for a micro jet aircraft using the same.

In general, robust design is an industrial methodology for improving productivity at the research and development stage, and means a technique that can produce a high quality product quickly and inexpensively. Research in the early 1960s developed the basis for robust design. The basic principle of robust design is to improve the quality of the product by minimizing the effect of the cause, without eliminating the cause of the change, and to optimize the product and process design to obtain the performance that is least affected by the various change factors. It is done through the process.

The robust design established the basic principles of statistical design and analysis of analysis (ANOVA) in order to obtain reliable information on the factors involved in engineering decision making. Important means are (1) the S / N (signal-to-noise) ratio, which measures quality, and (2) orthogonal arrays, to study several design factors simultaneously. This robust design can be applied throughout the industry, such as quality control, experimental design, applied chemicals, mechanical engineering, production line design, and product development.

The basic principle of the construction industry is to optimize the product and process design so that the quality of the product is minimized by minimizing the effects of the adverse effects of the product, that is, the performance is least affected by various variance factors. This is called parameter design and has the advantage that quality can compensate for the additional cost by controlling the variable for better quality improvement. For example, a series of total nets for designing, developing, acquiring, and operating an aircraft begins with conceptual studies of consumers, and goes through the design stages of conceptual design, basic design, and detailed design, and proceeds to the development stage, production stage, and operation maintenance and disposal stage. It is classified as the concept research and concept design stage that has the greatest impact on all these periods. That is, the future design will be based on the key factors determined at this stage, which will depend on the cost, development time, and performance of the final product.

Therefore, sufficient research and review should be conducted at the conceptual and conceptual design stages to reduce the redesign due to errors occurring at the later stages of the aircraft's design, production and operation. However, the concept research and concept design stages are relatively low in cost and effort compared to their importance.

In the early aircraft initial design method, the designer establishes the design requirements according to the market research or the demand of the consumer and prepares several conceptual sketches to satisfy the requirements. At this time, some candidate shapes among the many conceptual shapes created are selected and compared with each other to be used as reference shapes. Based on the formation design determined through the shape comparison study, the analysis is performed for each field such as aerodynamic analysis, propulsion engine, and detailed system to predict its performance.

However, the aircraft designed in this way generally do not meet or exceed the design requirements in many cases, so that iterative calculations satisfy the user's needs. In addition, the existing shape-setting process has a large influence by the subjective judgment of the designer and cannot be said to be an optimal configuration concept in which all determined shapes satisfy the design requirements. Consumed.

Therefore, the design result obtained through this process improves the performance by applying the optimal design technique. However, these optimal design results generally exist at the boundary of the design area, which may cause them to deviate from the design constraints due to the uncertainty of the design variables or to the designers' unintended noise during manufacturing and operation in the real environment. The problem arises that the target value may not be met.

The present invention was created to solve such a problem, and an object of the present invention is to use a decision tool, a statistical method, a quality management technique, and the like in the concept establishment step to reduce the iteration calculation and the time required in the existing design process. It is to provide a robust optimal design method that can be designed to be more objective and efficient without excluding the subjective judgment of the designer.

Another object of the present invention is to provide a robust optimal design method that can impart robustness to a design result so as to cope with an uncertain factor such as a change in a design variable or a change in user demand.

Another object of the present invention is to complete the aircraft design by being less affected by errors that may occur in operation and production that are not considered in the design of the micro jet aircraft (VLJ) based on the robust optimal design technique applied in the present invention. It is to provide an optimal design method of a small jet aircraft using a robust optimal design method that can increase.

The robust optimal design method according to the first aspect of the present invention for achieving the above object, in the robust optimal design method, a) to derive the necessary elements for performing the task based on the performance and shape that the new system will have, A user needs analysis step of setting a maximum value that can be expected for the system and a minimum value that can survive in the market; b) Analyze the relationship between design variables through Affinity Diagram, Nested Column Diagram, QFD (Quality Function Deployment), establish alternative shapes centering on major design variables and shape factors, and use Morphological Matrix and Pugh Concept Selection Matrix. Alternative shape selection step of selecting alternative shapes; c) an alternative shape analysis step of performing a designable area analysis on the alternative shape selection item; And d) finding a tentative design using the deterministic optimization technique in the designable area, and the alternative evaluation and steel construction stages for reducing the influence of user demand and errors that may occur in the actual development stage. do.

To achieve the above object, an optimal design method of a micro jet aircraft using the robust optimal design method according to the second aspect of the present invention is based on a very small jet (Very Light Jet (VLJ)) that has a conceptual design based on the robust optimal design method. A robust optimal design method for a method, comprising: a) selecting an object function, control variables, and noise variables, the object function setting a maximum range; b) extracting an experimental point using a Latin-Hyper cube method and setting a response plane approximation formula considering noise and control variables; c) optimizing the objective function such that the mean value and variation of the response are close to the target value; And d) comparing the cumulative density function, CDF, probability density function, and PDF of the cruising distance to derive a robust optimal design result by applying a noise factor to the general optimization result of the microjet aircraft shape and the steel construction result. It is characterized by.

The robust optimal design method and the optimal design method of a small jet aircraft using the present invention make a combination of alternative shapes to present alternative shapes of the system and select the shapes that are expected to have the best performance through comparison with the basic shapes. By doing so, there is an effect that it is possible to select more objectively and logically the alternative shape of the aerospace system that can meet the needs of users.

In addition, the present invention has the effect of providing a robust design result having a higher probability than the existing deterministic optimization results and less dispersion of results by minimizing the effects of factors that designers cannot adjust through the introduction of the steel construction system .

In addition, the present invention can be applied to other designs other than aerospace systems, and if applied to more fields, the present invention not only reduces the waste of time and effort due to design repetition in the initial design process, but also shortens the development period and development costs. There is an effect that can be achieved.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

1 is a flowchart showing the overall process of robust design in accordance with the present invention. As shown, it consists of four processes: ① user needs analysis, ② alternative shape selection, ③ alternative shape analysis, ④ alternative evaluation, and steel construction. The shape selection process proceeds based on user requirements, and the design requirements and alternative shapes are further refined through each step.

First, in the above ① user demand analysis process, the engineering result should be derived based on the user's demand given by an unambiguous and unquantified concept. Through this, the tasks that the user wants are analyzed and the tasks to be performed in order to satisfy the requirements of the user are selected.

Investigate aircraft that perform similar missions and benchmark them to gather the data to characterize the performance and shape of similar aircraft. The performance and characteristics identified here are used to calculate the metrics of performance desired by the user. Based on this, the performance and shape of the new system will be analyzed, and then, the factors necessary for performing the user's desired task are derived and the range is selected.

At this time, the maximum value that can be expected of the system and the minimum value that can survive in the market are determined. This provides basic operational requirements that specify the consumer's mission needs and operational concepts. Design requirements are established based on these operational requirements, which are the minimum conditions that can be designed by the actual aircraft while satisfying the operational requirements presented by the consumer.

After that, look at the above-mentioned ② alternative shape selection process. This establishes an alternative that can further refine and satisfy user requirements through the decision model as shown in the alternative configuration process of FIG. 2.

To this end, we analyze and refine the relationship between design variables through Affinity Diagram, Nested Column Diagram, and QFD (Quality Function Deployment). In addition, alternative shapes are established around the main design variables and shape factors, and alternative shapes are selected through the Morphological Matrix and the Pugh Concept Selection Matrix.

By analyzing the user demands, design factors that can satisfy this are classified from the user's point of view. In this case, the user may be a consumer who is a direct user of the system, a producer who produces the system, an operator who operates the system, and social considerations. The items required by the system are sorted and grouped by each category. This is called voice of customer. The voice of customer may be classified into performance, structure, propulsion, aerodynamics, control, and the like as shown in FIG.

In addition, it is called voice of engineer to classify parts that should be considered in engineering to satisfy the items of voice of customer by each field. These voice of engineers can organize the concept of matters to consider in design through classification of Manufacturer, Customer, and Society.

Afterwards, the classification of the affinity diagram can be arranged for each field of each design factor through nested column diagram, and the relationship between each variable can be identified. By subdividing and stratifying design variables, the relationship between design variables can be identified so that they can be referred to later in the creation and design stage of QFD. And, using the quality function deployment (QFD) shown in Figure 4 to analyze and specify the relationship between the design variables and to select the main items that have a lot of influence on the user's needs and the shape of the system through the weight of each design factor. do.

From the main design factors obtained in the QFD, each alternative shape for each shape factor and the alternative shape of the aerospace system combining them are prepared using the morphological matrix. In the Pugh concept selection matrix, the performance and improvement are judged relative to the baseline to select the alternative shape with the highest score. At this time, if only the same criteria for each item are judged, it is not effective in selecting the alternative shape. For this reason, the weights are assigned to scores between 1 and 5, with the scores focused on the items with high scores in the QFD, thereby making it possible to select a better alternative shape.

Then, on the basis of the above-described process (3) An alternative shape analysis process is as follows.

5 is a view showing an analysis process for an alternative shape obtained based on the above-described process. First, the analysis tool that can interpret this through the task analysis is selected. Design space model is created by setting input variable range of analysis program, and prediction profile which shows the relationship between design result according to sensitivity analysis and design variable is used. Also, contour plot for designable area analysis is made to check designable area and constraints and to derive tentative design with optimum value in design area.

Select the analysis tool that can analyze the result of the user's desired task and set the design variables for the analysis of the alternative shape and the basic shape and determine the range. Next, considering the number of design variables, select a sufficient number of design points and organize the results of independent changes within the range of each design variable. You can use this to create an approximation equation and also create an environment in which prediction profiles and contour plots can be created. This is called design space model.

Using the information of the design space model, a prediction profile representing the relationship between the results of the sensitivity analysis and the design results is prepared. Prediction profiles can be used to determine how design results change when design variables change in real time using information on design spaces created through design space models. FIG. 6 is a diagram illustrating an example of creating a prediction profile, in which items on a horizontal axis are input variables, and items on a vertical axis are design result values.

Next, create a contour plot for designable area analysis. In the contour plot, the highly sensitive input variables are plotted horizontally and vertically, and the results are displayed in one plane, and the constraints are displayed in their own colors so that the design items can be visually identified.

In addition, it is possible to immediately grasp the change in the design area according to the change of design variables and the change of constraints so that the design area can be expanded and reduced together with users and producers. Through this, we find the tentative design result with the best value in the design area. If you can't find a designable area on the contour plot, first find out if any of the user requirements can be changed.

If there is a user requirement that can be changed among the conditions that constrain the designable area, it is changed to find the designable area. In general, however, this is more likely not to be satisfied. In this case, we estimate the performance improvement by applying new technology that can improve the performance that does not satisfy the user's requirements. This finds a designable area in the contour plot while satisfying user requirements. FIG. 7 illustrates the above-described contour plot. As an example, a contour plot of an alternative aircraft shape is shown.

Then, in the designable area obtained from the contour plot, a method for finding a tentative design using a deterministic optimization technique and reducing the influence of changes in user requirements and errors that may occur in the actual development stage may be used. Explain the process of steel construction.

First, Figure 8 shows a flow chart for alternative evaluation and robust design, the first step is to select Noise Variables, which is selected as the user requirements and design variables expected changes or errors in the design process and production process.

In the robust design, the objective function of the robust optimal design is made so that the mean value and the variance of the given objective function are close to the target values, respectively. At this time, the items and ranges that can act as noise in each aerospace system are selected. The noise factor is given as a normal distribution through monte carlo simulation within that range, and other design variables are insensitive to this noise change and find a value that satisfies the target value.

Compare robustness design results with tentative design results and verify robustness and probability. This steel construction process is shown in FIG. As shown in the figure, the steel construction system is performed by setting a new objective function whose constraints are the probability of the tentative design result applying the noise variable to violate user requirements, and the variance and the mean value of the distribution.

In addition, a new objective function is established to perform robust optimal design, which reflects the mean and variance of the distribution based on user requirements as the objective value. As an optimization technique, genetic algorithm is used to optimize the entire design area. The results obtained in this way are designed so that the distribution and average of the distribution can satisfy the user's needs more than the deterministic optimization technique.

On the other hand, if the robust optimal design according to the present invention is applied to the VLJ shape of the conceptual design is as follows.

First, FIG. 10 is a flowchart illustrating a robust optimal design method for a VLJ according to the present invention. Before explaining the robust optimal design, empirical based analysis program is used for the performance analysis of the aircraft, and the noise factor is selected as the cruise speed and the cruise altitude depending on the environment during the operation of the aircraft. Robust optimal design is performed by approximation formula.

As the first step in the robust optimization design using approximation, we select the object function, control variables and noise variables. Here, the object function maximizes the range of travel and is represented by Equation 1 below.

max,

max,

And control variables and noise variables;

Design parameters Noise parameters Responses Aspect Ratio, Sweep Back Angle, Taper Ratio, Aspect Ratio of Horizontal Tail, Taper Ratio Horizontal Tail, Aspect Ratio of Vertical Tail, Taper Ratio Vertical Tail Cruising Speed Cruising Altitude Range, Approach Speed, Take-off Field Length

Same as

On the other hand, in the second stage of robust optimal design, response surface approximation formula considering noise and control variables is prepared. Here, the Latin-Hyper cube technique can be used to extract experimental points and construct approximations. As a result, it can be seen that the approximation formula created for this shape is reliable as R ² _adj = 0.98.

In the third step, the objective function is optimized so that the mean value and variance of the response are close to the target values. Mean Value of Response and Variance of Response are as shown in Equation 2 below.

If the average value and the variance are adjusted to the target values, the objective function is changed as shown in Equation 3 below to optimize.

Min  Z = ( Mean Value  Adjustment) + ( Variance  control)

In addition, the average value aimed at by this invention is 1800 NM in cruising distance, and 400 in dispersion. 10 is a diagram showing an application example of the robust optimal design.

On the other hand, the noise factor is applied to the general optimization result of the selected aircraft shape and the steel construction result, and the cumulative density function, CDF, probability density function, and PDF of the range are compared. Here, the noise factor was created by the normal distribution with the mean value of the altitude and velocity created by the designer. Monte Carlor simulation was used to apply the noise, and the result of each 1,000,000 simulations was performed. Accordingly, as shown in FIG. 11, when the steel construction system is applied, the mean value increases by about 2%, the variance decreases by 1/40, and the probability increases.

As described above, the present invention starts with a concept study for the consumer, and the required cost for the final result based on the design process, development process, production process, operation / maintenance and disposal process such as concept design, basic design, detailed design, etc. It is expected that there will be sufficient contribution value to the aviation industry by maximizing utilization value throughout the industry by efficiently designing the development period, development period, etc. and using it to efficiently design the micro jet aircraft. .

1 is a flowchart showing the overall process of robust design in accordance with the present invention.

2 is a flowchart illustrating an alternative configuration process according to the present invention.

3 is a view for explaining the consumer requirements and producer requirements described in the embodiment of the present invention.

4 is a matrix structure showing the quality function deployment (QFD) described in the present invention.

5 is a flowchart illustrating an alternative shape analysis process according to the present invention.

6 is a graph showing an example of creating a prediction profile according to the present invention.

7 is a view showing a contour plot for the alternative aircraft shape described in the present invention.

8 is a flowchart illustrating an alternative evaluation and steel construction system procedure of the present invention.

9 is a view for explaining a steel construction process according to the present invention.

10 is a flowchart illustrating a robust optimization design procedure for a VLJ shape according to the present invention.

11 is a graph showing an application example of the robust optimal design to the VLJ shape according to the present invention.

12 is a graph showing the distribution of Noise Factors as a result of the present invention, optimization results with noise applied, and robust optimization design results.

Claims (7)

In the robust optimal design method, a) user requirements analysis step of deriving the necessary elements for performing the task based on the performance and the shape that the new system will have, and setting the maximum value that can be expected for the system and the minimum value that can survive in the market; b) Analyze the relationship between design variables through Affinity Diagram, Nested Column Diagram, QFD (Quality Function Deployment), establish alternative shapes centering on major design variables and shape factors, and use Morphological Matrix and Pugh Concept Selection Matrix. Alternative shape selection step of selecting alternative shapes; c) an alternative shape analysis step of performing a designable area analysis on the alternative shape selection item; And d) Finding a tentative design using deterministic optimization techniques within the designable area, and the alternative evaluation and steel construction stages to reduce the effects of user changes and errors that may occur in the actual development stage. Robust Optimal Design Method. The method of claim 1, Step a) presents basic operational requirements that specify the mission needs and operational concepts of the consumer, and establishes design requirements based on the operational requirements. Robust optimal design method characterized in that the setting. The method of claim 1, In step b), the items required by the system are sorted based on the consumer, the producer of the system, the operator who operates the system, the social considerations, and the like. Grouping by voice of engineers; b-2) categorizing the voice of customer into Performance, Structure, Propulsion, Aerodynamics, Control; b-3) categorizing the voice of Engineer as Manufacturer, Customer, Society; b-4) arranging the classification of the affinity diagram for each field of each design factor through a nested column diagram to generate an association diagram between the variables; b-5) selecting key items based on the weight of each design factor based on the analysis of the correlation between design variables using quality function deployment (QFD); b-6) extracting each alternative shape for each shape factor from the main design factors obtained in the QFD and alternative shapes of the system combining them using a morphological matrix; And b-7) Robust optimal design method which consists of selecting the alternative shape that has the highest score by judging the performance and improvement with respect to the baseline using the Pugh concept selection matrix. The method of claim 1, C) step c-1) preparing a design space model by selecting an analysis tool for interpreting the task analysis and setting an input variable range of the analysis program; c-2) generating a prediction profile indicating a relationship between a sensitivity analysis and a design result according to a design variable using the design space model; c-3) A robust optimal design method comprising the step of deriving a tentative design having an optimal value within the designable area by creating a contour plot for designable area analysis. The method of claim 1, In step d), d-1) selecting Noise Variables which are selected as user demands and design variables which are expected to change or error in the design process and production process; d-2) in the robust design, setting the objective function of the robust optimal design such that the mean value and the variance of the given objective function are close to the target values, respectively; d-3) Select items and ranges that can act as noise in the system, but noise factor is provided through monte carlo simulation as a normal distribution, and other design variables are insensitive to noise change and satisfy the target value. Setting to; And d-4) setting up a new objective function whose constraints are the probability that the result of the tentative design with the noise variable violates user requirements, and the variance and the mean value of the distribution. Robust Optimal Design Method. In the robust optimal design method for the shape of a very small jet (Very Light Jet) (VLJ) that is conceptually developed based on the robust optimal design method according to claim 1, a) selecting an object function, control variables, and noise variables, the object function setting a maximum range; b) extracting an experimental point using a Latin-Hyper cube method and setting a response plane approximation formula considering noise and control variables; c) optimizing the objective function such that the mean value and variation of the response are close to the target value; And d) comparing the cumulative density function, CDF and probability density function, and PDF of the cruising distance to derive a robust optimal design result by applying noise factors to the general optimization results of the microjet aircraft shape and the steel construction system results, respectively. Optimal Design Method of Micro Jet Aircraft Using Robust Optimal Design Method The method of claim 6, Design parameters for the control variables and noise variables in step a) are Aspect Ratio, Sweep Back Angle, Taper Ratio, Aspect Ratio of Horizontal Tail, Taper Ratio Horizontal Tail, Aspect Ratio of Vertical Tail, Taper Ratio Vertical Tail Cruising Speed, Cruising Altitude is an optimal design method of a small jet aircraft using a robust optimal design method characterized in that.

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