Invited Paper for ASM 2004


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  • This presentation represents work performed at QinetiQ and its predecessor organisations over a period of years, to meet the requirements of the Ministry of Defence Applied Research customer. I would like to acknowledge the contributions made to this work by my colleagues within the Aerodynamics and Systems Performance and Control groups at QinetiQ, particularly my co-author Steve McParlin.
  • This presentation has an introduction, three main topics and a conclusion. The first, and most important, of the main topics is the assessment process. This ranges from the initial definition of a concept to meet a set of requirements, through the fleshing out of the details of the design, then analysis of the resulting configuration, wind-tunnel testing, followed by comparison of the derived performance with the initial estimates used to define the original concept.
  • The second main topic is a description of the technologies which underpin the assessment capability, and how these have contributed to both reducing the time and cost of the process, but also how these have improved the accuracy and flexibility of the process. My third main topic is a brief description of where the focus of future customer requirements is heading, and why it is increasingly important to understand the impact of aerodynamics on the performance, cost and flexibility of a system as a whole
  • So, without further ado:
  • The capability described here has been developed with funding by the Ministry of Defence Applied Research Programme to meet the needs of stakeholders within the MOD procurement system. MOD procures complex systems, and indeed, systems of systems. Some of these are developmental but there is increasing emphasis on procurement of Commercial-Off-The-Shelf (COTS) items. In all cases, equipment is procured against Operational Requirements. These are defined in a number of ways, but increasingly, the top level drivers are identified, leaving the technical detail to be addressed by the contractor, along with the MOD representatives, in Integrated Project Teams. What MOD requires is an assessment capability that informs them of the impact of technologies, and how these can be traded off against top level drivers, at system level. This is essential to providing MOD with an intelligent customer capability. A key element in the following processes is the extent to which they are driven by the end user requirement. Ideally, the development of any product starts with the definition of the requirements.
  • The most important part of the presentation.
  • It is a legitimate question to ask why MOD is spending money on the development of new assessment processes for air vehicles. Historically, the process has been based on a combination of simple ‘project level’ assessment tools, similar to those in data sheets, followed by extensive (and expensive) wind tunnel testing. Initial phases of this process were potentially inaccurate, particularly for novel concepts. This has acted as a deterrent to the adoption of novel or radical solutions. The time and cost associated with generating extensive wind tunnel data sets for a range of configurations has become increasingly prohibitive. Over the last twelve or so years, we have implemented a number of new technologies to improve the assessment capability. Most of these were radical in the early days of their implementation, but are now mature. The most important individual factor has been the emergence of CFD as an accurate means of calculating lift and drag. Combined with numerical optimisation, and now with Computer-Aided Design capability, this has enabled a much more accurate and flexible assessment process to be implemented.
  • The main tool used within QinetiQ and MOD for assessing air vehicle performance at system level is the Multivariate Optimisation design synthesis method, or MVO, as it is generally known. Although the origins of MVO, and earlier MVA, methods arose from civil transport studies in the late 1960s and early 1970s, the current system has evolved from a manned combat aircraft design synthesis dating from the early 1980s. At it’s simplest, the design synthesis consists of a parametric representation of the outline of a combat aircraft. The parameters are variables under the control of a numerical optimisation process. The performance modelling within MVO is achieved using low-fidelity, data-sheet type, semi-empirical models. These methods are extremely fast, meaning that a typical MVO optimisation, involving many thousands of iterations, takes only a few minutes. In recent years, the MVO method has evolved to reflect the changing nature of MOD requirements. There have been multiple versions of the synthesis, but the current version has been developed largely to represent recent Unmanned Combat Air Vehicle (UCAV) concepts. However, to illustrate the process, we have included illustrations drawn from an earlier exercise involving a manned concept. In recent years, the range of concept classes in which MOD has expressed interest has expanded significantly. It is thus in the best interests of both QinetiQ and MOD to make the MVO tools as flexible and generic as possible. We will describe how we are achieving this later on in the presentation.
  • The starting point of any MVO synthesis is the capture of requirements and their representation in quantitative form. MVO works on the basis of both mission and point performance requirements. The mission requirements consist of the weapons load, and a description of the mission to be flown, as a series of legs, which may include cruise, climb, low-level ingress, dash etc. The point performance requirements are expressed as targets for turn, acceleration or specific excess power at various combinations of mass, altitude and Mach number. The number of performance requirements can go into double figures. This is a key factor in the design of combat aircraft. The requirements are frequently conflicting. Determining which requirements are actually driving the design, and determining the optimal balance between them, is a major part of the rationale for using numerical optimisation.
  • This diagram represents the process by which MVO generates a solution concept for a given requirement. Note that the performance requirements are an input to the process, rather than a fall-out. The synthesis generates a parametric representation of the air vehicle concept. The mass, aerodynamic characteristics and performance are calculated. These outputs are then compared with the performance requirements, which are set as constraints. If these are not met, or the concept is not feasible, the optimiser changes the parameters in the synthesis. The optimisation loop continues until the performance requirements are met. The objective function, which the optimisation algorithm seeks to minimise, is usually Basic Mass Empty, although life-cycle cost models are available, and these are undergoing further development. At the end of the process, the final versions of the parameters are output.
  • As an example of the use of MVO we can look at a manned aircraft design which was completed several years ago. The operational requirement was for a deep strike aircraft. A mission profile was defined as input to the MVO process, together with payload, range and a series of point performance targets. The MVO process optimises the concept layout and sizing to minimise basic mass empty, whilst meeting the imposed constraints. The resulting aircraft design is shown. Details of internal packaging arrangement, engine installation and control surfaces can be seen. These pictures show the typical level of output from MVO. It should be noted that this output is effectively a 2D outline of the concept.
  • During the MVO design it is possible to identify which constraints have the largest impact upon the design, and which most limit further improvements from being obtained. Hence the key design drivers can be identified. For the manned aircraft design, it was noted that there was a clear, key design driver for the concept. This corresponded to a requirement to accelerate from Mach 0.85 to Mach 1.4 within a fixed elapse time. The figure shows the MVO predictions of net thrust and total drag for the design over this Mach number range. The difference between the net thrust and drag gives the available specific excess power. Hence the time for acceleration is derived as a time integral over the Mach number range. Hence it is essential that the levels of drag predicted by the semi-empirical, data-sheet type performance models within MVO are valid. I will come back to this acceleration requirement later in the presentation.
  • As highlighted the MVO method uses data-sheet type, semi-empirical models for estimating aerodynamic performance. In order to validate the MVO design, we wish to validate these performance estimates using higher fidelity analysis methods, such as CFD and wind-tunnel testing. However, the geometry output from MVO is essentially a 2D representation of the concept. To use the higher fidelity analysis methods a corresponding higher fidelity, 3D representation of the vehicle must be generated. The creation of a 3D geometry, using a computer aided design or CAD tool can be very time-consuming. An important improvement which has been completed during the development of the overall assessment process is the adoption of parametric CAD technology. In particular the CATIA V5 rules-based CAD software is used extensively. This now allows the generation of an initial 3D CAD model automatically within a few minutes following an MVO run.
  • This rules-based CAD creation is shown for the manned aircraft example. The MVO description, as we can see, is fairly basic, but it does include details of the concept layout, interior packaging and possible control surface arrangements.
  • More importantly the rules-based CAD process allows the rapid generation of good quality external surfaces. The aerodynamicist can input additional information at this stage. Effectively the beginnings of aerodynamic design can be added. For example, initial aerofoil sections can be chosen, or local changes made to the shape to accommodate, for example, a radome installation. For the manned aircraft concept the fuselage shaping was modified, compared to the simple MVO representation, in order to give a more aerodynamically smooth initial CAD shape. However in this case, this aerodynamic shaping lead to a violation of the original MVO constraints. Analysis of the starting CAD model for the manned aircraft indicated that the fuel volume was now too small - in fact it was 25% less than assumed within the MVO model. This fuel volume constraint would need to be addressed later in the assessment process, as described later.
  • The initial CAD model is still not representative of a realistic geometry. For example the wing is uncambered and untwisted, and as indicated, the fuel volume is too low. We are effectively trying to estimate the performance that would be obtained if the concept were to be fully designed, manufactured and flown. Hence to predict this realistic level of performance, a realistic level of detailed design must first be incorporated, and the resulting shape then analysed. This detailed design should satisfy the same constraints that were used within the MVO process, if the resulting performance predictions are to be used to validate the MVO performance levels. In order to perform a detailed aerodynamic design of the configuration, subject to numerous constraints, within a short timescale, we use the QinetiQ CODAS CFD shape optimisation method. CODAS is a mature design method, that has been used extensively over the last 12 years. A single CODAS run may require a few hundred CFD analyses, typically taking 1-2 days.
  • This diagram describes the CODAS optimisation design method. Essentially, the process starts, as with MVO, with a parametric representation of the geometry. The performance of the shape is analysed at a range of conditions, using CFD. We have used multiple design points, variable geometry, and even different analysis methods for different design points, depending on the spread of required performance points. For each iteration of the geometry, a whole range of constraints are evaluated, to establish if particular requirements have been met. As experience in the use of CODAS has developed, as with MVO, the choice of constraints has become a more important aspect of the process than the choice of objective function. The optimisation algorithm cycles through the process until all of the constraints have been met, or the conflicts between requirements have been defined. Determining the balance between design requirements is a fundamental part of the process, and it is usual for the process to be repeated, yielding useful information on the detailed design requirements. The output from CODAS is a full 3D CAD model of the concept, incorporating detailed aerodynamic design shaping, and satisfying the numerous imposed constraints.
  • The geometry output from MVO is essentially a series of 2D sections and wireframe representations of the internal and external lines. To determine the actual performance of the vehicle requires representative lines to be generated. Initially, these are constructed using 3D CAD from the initial MVO representations. The performance of the vehicle is sensitive to the detail of the external lines, thus it is necessary to perform a detailed aerodynamic design of the configuration. For this we use the QinetiQ CODAS design method, to produce a multi-point optimised aerodynamic design of the configuration. A single CODAS run involves approximately 25k individual CFD analyses, whereas MVO runs typically take 5-10 minutes.
  • This is a the final version of the external lines generated by CODAS. The sharper-eyed among you (and I’ll forgive the rest of you at this late stage of the afternoon) will notice a number of subtle changes from the earlier image of the original shape. The wing is now cambered and twisted, to meet a range of performance requirements. Much of the fuel volume from the inner portion of the wing has migrated into the fuselage. The wing has moved slightly forward, while the tailplane has increased in area. To meet trim requirements, CODAS has added camber to the tailplanes and fins, while the boat-tail angle has been changed. It should be noted that the changes made to the external lines are subtle, yet they result in an enormous improvement to the aerodynamic performance of the configuration. This is then representative of a real vehicle, whereas the earlier shape would not be.
  • The red line on this plot indicates the level of drag predicted by MVO, as shown in the earlier plot. This green effectively indicated the level of drag that should be achievable based on concept level, semi-empirical performance predictions. The purple line shows the CFD predicted drag for the initial CAD shape. As indicated previously this initial shape was too low on fuel volume, perhaps explaining why lower level of supersonic drag are obtained. Also this starting shape has an undesigned wing, explaining the higher levels of transonic drag predicted. The red curve shows the drag obtained for the final CODAS design. It should be noted that this final CAD shape has quite a lot more overall volume than the starting CAD shape, due to the requirement to satisfy the fuel volume requirement. It can seen that CODAS has achieved a very effective design over a wide region of the Mach number range. In fact the time for acceleration has been undercut by 20% compared to the MVO assumed level. The success of this CODAS indicates a limitation in the MVO performance prediction method. In particular this would indicate that the transonic drag prediction within MVO is too high. Determining that the performance of the vehicle is likely to be better for this critical design driver might allow an improved trade-off against other performance requirements. For example the lower level of drag could be used to reduce engine thrust, and hence engine mass, and hence overall vehicle mass.
  • Having generated a set of external lines, the time taken to manufacture and test a wind tunnel model can be significant. In many cases, it is desirable to know whether it is worth proceeding to the stage of experimental testing. As a consequence, it is now usual to perform series of CFD analyses between definition of external lines and the availability of a wind tunnel model. These analyses are also used to make first estimates of which elements of a model to build for high speed test, including choice of flap settings and tailplane angles to trim. This part of the process is entirely predicated on the assumption that CFD can be used to give reliable predictions of overall aerodynamic loads, particularly drag. Thus far, this has effectively limited the applicability of this approach to attached flow conditions. I will make some further comments about the scope of CFD versus experiment at a later stage. It is likely that in future, a greater proportion of air vehicle concepts will reach this level of maturity than before. It is also likely that a smaller proportion will go on to experimental testing.
  • Despite the time and cost taken to manufacture and test wind tunnel models, where large numbers of data points are required, wind-tunnel testing remains the most cost-effective means of generating these. The repeatability of wind tunnel balance measurements is still an order of magnitude less than the best accuracy in loads achievable with current CFD. Wind tunnel data can be gathered at conditions beyond the capability of CFD methods. Indeed, wind tunnel models can also gather data at or beyond the limits of the flight envelope for most aircraft. Low speed wind tunnel testing is significantly cheaper than transonic or supersonic testing. The range of configuration variables which can be tested at low speed is significantly greater than can be afforded at high speed. The role of high speed testing is increasingly to validate the predictions made by CFD, and to provide data for conditions beyond the capability of CFD methods. Surface oil flow visualisation is an important tool for understanding how fluid mechanics impact on the performance, stability and control of air vehicles. We use CFD results to help put together our test programmes. In this particular case, the choice of trailing edge flap settings was fundamentally driven by RANS analyses.
  • This model was tested on a sting rig in the transonic tunnel at ARA in the UK. This provides the bulk of the aerodynamic data set for this configuration, although another tunnel test entry took place to investigate supersonic performance. This model is representative of those used for the assessment process. The model is fitted with a strain gauge balance, The wings are pressure tapped,. The intakes are faired over, and separate processes are used to determine the effect of propulsion installation. In this particular case, motorised tailplanes were installed.
  • Wind tunnel testing is not the end of the process of generating a performance data set. Some of the CFD results will be trimmed, but these are less accurate than experimental measurements. They may also be at model-scale , full-scale or infinite Reynolds number. The tunnel data will, in general, be at sub-scale, and untrimmed. We have developed a technique for deriving trimmed data from an untrimmed wind tunnel data set, using response surfaces and, yet again, numerical optimisation. The process also generates optimal device schedules as a by-product. There still remains the issue of correcting tunnel measurements from model to full-scale. We tend to estimate a correction to zero-lift drag by using flat-plate skin friction estimates applied to Euler solutions, or by comparing RANS results at model and full scale.
  • This plot shows a comparison of the original MVO performance predictions with trimmed data derived from wind tunnel tests using the response surface method. In this case, each curve corresponds to a particular setting for both leading and trailing edge flaps. The optimum trimmed drag polar will follow a curve which cuts each of these as angle of incidence, and hence lift coefficient, varies. It should be noted that the experimental data has not been corrected for Reynolds number, while the MVO points are for a range of altitude and mass conditions at the same Mach number.
  • Because MVO is the only tool that can illustrate the impact of technologies at system level, it is important that it is accurate. To ensure confidence in MVO results, it is desirable that MVO performance predictions match those from CFD and experiment. Where consistent or systematic differences are found, these can be rectified. It is also important that CFD methods continue to be validated against experimental data. An interesting example follows later.
  • This figure summarises the assessment process that has been described. The aim of the assessment process is effectively to obtain a prediction of achievable performance. However this prediction process requires a significant element of design in order to obtain an accurate performance prediction. In fact the process is possibly more easily seen as a multi-level optimisation process. The higher fidelity information generated within the aerodynamics area is used to validate the MVO modelling. This information is also used to improve the data-sheet methods used within MVO. In particular the adoption of Design of Experiments and Response Surface Modelling techniques at this higher fidelity level, is leading to significant improvements in accuracy and flexibility for the performance modelling used within MVO.
  • What are we using to improve the process ?
  • We started to analyse and design complex configurations in the early 1990s. It became immediately apparent that CAD techniques were needed to describe the geometry, let alone modify it. After initial experience of working through a drawing office, it was decided to train aerodynamicists to use 3D CAD systems for surface generation and modification. Roughly simultaneously, it became apparent that the most time-consuming part of the CFD analysis process was not mesh generation, or flow solution, but geometry pre-processing. Over the last twelve or so years, these capabilities have evolved and expanded. We have two main CAD based tools. The first is the CATIA V5 parametric, rules-based CAD system. This now forms the basis for our multi-disciplinary assessment and optimisation process. The other CAD tool is the GEMS CAD-CFD front-end. This is a tailored capability that we have had developed to meet our requirements over the last 8 years. This is now the common front end for all of our CFD tools.
  • As an example of the flexibility of our rules-based CAD processes, this slide shows five different configurations which were created from the same parametric model, using different sets of parameters.
  • The majority of methods used in MVO for prediction of aerodynamic characteristics are reasonably similar in capability and vintage to data sheet methods. These are limited in applicability to classical configurations, and are largely based on ARC and NACA sources from the 1940s and 50s. We need to be able to produce methods which are more accurate and flexible, without adversely impacting on the runtime or complexity of MVO. We have identified a set of technologies to replace the existing MVO methods with response surfaces. This involves the generation of parametric data sets using CFD analysis, and their conversion into algebraic expressions. Commercial software is available to determine optimal sample points for accuracy, and automatically generate fits to the data. Initial experience with simple swept-tapered wings has been very encouraging.
  • We now have a wide-ranging CFD capability: Euler methods are a mature tool, with both mesh generation and flow solution being routine. They are also a fast and accurate means of predicting aerodynamic loads in attached flows. We are using both panel methods and Euler methods as a means of mass production of parametric databases, for populating response surfaces and generating stability and control models. We make extensive use of RANS, but aspects of this capability are not yet fully mature and require improvement. We have some remaining issues with the robustness, accuracy and particularly the consistency of these methods. We see the primary rationale for RANS being the ability to predict the onset of flow separation and to extend analysis capability to unsteady, separated flows.
  • This comparison shows an interesting case. It should be noted that both the Euler and RANS analyses were performed several months before the wind tunnel test. These results are for a transonic case, with a deployed wing leading edge flap. The Euler analysis predicts a high suction peak on the leading edge flap knuckle, with a strong shock developing outboard. The upstream Mach number normal to the shock at the outboard pressure station would normally be considered more than adequate to produce a shock-induced flow separation. The RANS analysis indicates that the flow behind the flap knuckle suction peak separates, forming into a vortical flow over the outboard part of the wing. Based on both CFD analyses, we expected the flow over the wing to be separated at this condition. The experimental pressures, confirmed by surface oil flows, showed that the flow behind the knuckle suction peak remained attached, even with a strong shock. Flow separation eventually developed at higher alpha, with a myriad of small shock-induced bubble separations developing along the line of the flap knuckle. This indicates some of the issues arising from use of RANS to predict flow separations from designed wings, rather than wings for which the location of flow separation is known a priori. It also underlines the reason why experiment is, and will remain, necessary.
  • So, where do we go from here ?
  • MOD is showing increasing interest in top-level driving requirements, with less emphasis on the underlying details of how these translate into equipment roles and capabilities. There is more emphasis on outcomes and effects of using ‘systems of systems’, rather than on pursuing individual technologies for their own sake. Given that the impact of aerodynamics, and other technologies, can only really be assessed at system level, the importance of MVO remains fundamental, particularly when looking at balance of investments to achieve a given outcome. There is a need for tools like MVO to cover a full spread of air vehicle options, from surveillance and communications platforms to high speed weapons.
  • Many of the technologies that we have demonstrated have their origins outside aerodynamics. Similarly they have applications beyond aerodynamics. The combination of parametric CAD, automated analysis processes and response surface generation is completely generic, and the data structures we have produced can be enriched to take into account other issues related to system performance. These technologies make much more accurate and flexible synthesis methods feasible. They also expedite the processes for detailed analysis of specific concepts. Building design rules and engineering knowledge into the CAD model offers a more detailed level of understanding than is possible with the current MVO tool, this understanding can then be used to improve the fidelity of the MVO process.
  • So, to conclude:
  • The requirements of MOD and other customers for future systems have been evolving rapidly over the last few years, and are likely to continue to do so. To provide intelligent customer capability for a range of systems requires more flexible assessment tools, particularly for novel concepts. We have described the process used to assess the aerodynamic performance of new concepts, and its impact at system level. New technologies, from a wide range of sources, have been adopted to reduce the time and cost associated with the assessment of concepts. New aerodynamic technologies, particularly those related to CFD, have been matured, validated and harnessed into the assessment process. Although there are significant areas where aerodynamics technology can be improved, particularly with respect to increasing the range of applicability of CFD, the customer focus on system-level issues means that multidisciplinary analysis and trade-offs will be a main area of interest. Thank you.
  • The requirements of MOD and other customers for future systems have been evolving rapidly over the last few years, and are likely to continue to do so. To provide intelligent customer capability for a range of systems requires more flexible assessment tools, particularly for novel concepts. We have described the process used to assess the aerodynamic performance of new concepts, and its impact at system level. New technologies, from a wide range of sources, have been adopted to reduce the time and cost associated with the assessment of concepts. New aerodynamic technologies, particularly those related to CFD, have been matured, validated and harnessed into the assessment process. Although there are significant areas where aerodynamics technology can be improved, particularly with respect to increasing the range of applicability of CFD, the customer focus on system-level issues means that multidisciplinary analysis and trade-offs will be a main area of interest. Thank you.
  • Invited Paper for ASM 2004

    1. 1. Generic process for air vehicle concept design and assessment (Paper AIAA-2004-0895) John J Doherty and Stephen C McParlin Aerodynamics, QinetiQ Ltd, Farnborough AIAA Aerospace Sciences Conference, 5-8 January 2004
    2. 2. Contents (1 of 2) <ul><li>1 Introduction </li></ul><ul><ul><li>MOD customer requirements </li></ul></ul><ul><li>2 The assessment process </li></ul><ul><ul><li>Conceptual design </li></ul></ul><ul><ul><li>Detailed configuration design </li></ul></ul><ul><ul><li>Analysis and WT testing </li></ul></ul><ul><ul><li>Synthesis of performance </li></ul></ul>
    3. 3. Contents (2 of 2) <ul><li>3 Underlying technologies </li></ul><ul><ul><li>CAD Integration </li></ul></ul><ul><ul><li>DoE and RSM </li></ul></ul><ul><ul><li>CFD methods </li></ul></ul><ul><li>4 Future directions </li></ul><ul><ul><li>Systems of systems </li></ul></ul><ul><ul><li>Multidisciplinary aspects </li></ul></ul><ul><li>5 Conclusions </li></ul>
    4. 4. Introduction Section 1
    5. 5. UK MOD customer requirements <ul><li>Capability developed to meet MOD needs in the procurement of future air vehicles. </li></ul><ul><li>Complex systems need to be assessed against Operational Requirements considering a range of attributes: </li></ul><ul><ul><li>Capability </li></ul></ul><ul><ul><li>Affordability </li></ul></ul><ul><ul><li>Flexibility </li></ul></ul><ul><li>Intelligent Customer status implies an ability to understand trade-offs and their implications at system level. </li></ul>Introduction
    6. 6. The assessment process Section 2
    7. 7. Rationale for improved process <ul><li>Limitations of the historical assessment process. </li></ul><ul><ul><li>Data sheet methods fast, cheap but often inaccurate. </li></ul></ul><ul><ul><li>Wind tunnel tests accurate, but slow and expensive. </li></ul></ul><ul><li>New processes, using new technologies, have been matured: </li></ul><ul><ul><li>Adoption of CFD-based methods. </li></ul></ul><ul><ul><li>Increasing power and fidelity of design tools. </li></ul></ul><ul><li>New processes improve accuracy, speed and flexibility. </li></ul><ul><ul><li>Particularly for novel concepts. </li></ul></ul>The assessment process
    8. 8. Conceptual design synthesis <ul><li>Multi-Variate Optimisation (MVO) has a long pedigree. </li></ul><ul><ul><li>Civil transport MVO from late ‘60s. </li></ul></ul><ul><ul><li>Combat aircraft MVO from early ‘80s. </li></ul></ul><ul><li>A parametric concept model is evolved to meet performance requirements at minimum mass or cost. </li></ul><ul><ul><li>Performance modelling via data-sheet type methods </li></ul></ul><ul><ul><li>MVO run-time typically 5 minutes CPU </li></ul></ul><ul><li>MVO has evolved to meet changing MOD requirements. </li></ul><ul><ul><li>Wider range of air-vehicle concept types </li></ul></ul>The assessment process
    9. 9. MVO synthesis - requirements capture Requirements capture process including formulation of design constraints E.g. mission profile E.g. point performance The assessment process Opt climb / cruise climb FLOT Opt Alt & A/S Release A/A Weapons Penetration CV to FLOT O/H Fuel SUTTO No Credit Combat No Distance Credit
    10. 10. Performance requirements. Design constants. Engine data. Start point for design variables. The assessment process MVO synthesis - optimisation process Synthesise geometry, mass, aerodynamics, performance Meets performance? & Sensible design? & Minimum mass? NO Change values of design variables Optimisation loop Solution Aircraft YES
    11. 11. MVO conceptual design example (1 of 2) <ul><li>Manned aircraft concept requirements </li></ul><ul><ul><li>Deep strike/penetration role </li></ul></ul><ul><ul><li>Defined mission profile </li></ul></ul><ul><ul><li>Defined payload/range </li></ul></ul><ul><ul><li>Point performance requirements </li></ul></ul><ul><li>MVO optimises concept layout/sizing </li></ul><ul><ul><li>Final design meets requirements </li></ul></ul><ul><li>MVO output provides 2D definition of concept </li></ul>The assessment process
    12. 12. <ul><li>For manned aircraft example, the key performance requirement driving concept sizing was the transonic to supersonic acceleration time. </li></ul>MVO conceptual design example (2 of 2) The assessment process Acceleration time derived from (thrust - drag) integration
    13. 13. High-fidelity concept assessment <ul><li>MVO performance levels for designed concept are based on semi-empirical estimates. </li></ul><ul><li>Wish to validate MVO performance levels using higher fidelity analysis methods ( e.g. CFD, Wind-tunnel). </li></ul><ul><ul><li>MVO output is limited to 2D geometry representation. </li></ul></ul><ul><ul><li>A corresponding high-fidelity 3D geometry is required. </li></ul></ul><ul><li>Initial 3D model produced automatically from MVO output. </li></ul><ul><ul><li>Process based on parametric, rules-based CAD (CATIA V5) </li></ul></ul>The assessment process
    14. 14. Initial 3D CAD definition (1 of 2) <ul><li>CAD created from MVO output using rules-based CAD </li></ul><ul><ul><li>3-D layout </li></ul></ul><ul><ul><li>packaging </li></ul></ul><ul><ul><li>control surfaces </li></ul></ul>The assessment process
    15. 15. Initial 3D CAD definition (2 of 2) <ul><li>External CAD surfaces also created using rules-based CAD </li></ul>The assessment process <ul><li>Process allows initial aerodynamic design choices to be made: </li></ul><ul><ul><li>aerofoil sections </li></ul></ul><ul><ul><li>radome shaping </li></ul></ul><ul><ul><li>fuselage shaping </li></ul></ul><ul><li>For manned aircraft example, initial CAD model was 25% short on fuel volume </li></ul>
    16. 16. Detailed 3D concept design <ul><li>To predict the realistic performance achievable for a concept, a realistic level of detailed design must be incorporated. </li></ul><ul><li>The 3D geometry must first be designed, and then the performance assessed. </li></ul><ul><li>This detailed design should aim to satisfy the same constraints as the original MVO design. </li></ul><ul><ul><li>Or, identify that the performance cannot be realised. </li></ul></ul><ul><li>The CODAS aerodynamic shape optimisation process is used to achieve this detailed design. </li></ul>The assessment process
    17. 17. Design conditions. Performance objective. Aerodynamic/geometric constraints. Initial values. The assessment process CODAS shape optimisation process Optimised Geometry YES Surface geometry creation (Parametric CAD) Satisfies constraints & no further improvement? NO Change values of design variables Performance analysis (CFD & Empirical Methods) Optimisation loop
    18. 18. CODAS detailed design example (1 of 3) <ul><li>Manned aircraft designed using CODAS, with Euler CFD. </li></ul><ul><li>MVO identified acceleration time as a key driver. </li></ul><ul><ul><li>Acceleration time used as optimisation objective. </li></ul></ul><ul><ul><li>Multi-point transonic/supersonic design. </li></ul></ul><ul><li>Numerous constraints within shape optimisation. </li></ul><ul><ul><li>Packaging constraints. </li></ul></ul><ul><ul><li>Fuel volume (starting CAD shape is 25% too low). </li></ul></ul><ul><ul><li>Control hinge lines. </li></ul></ul><ul><li>Concept trimmed throughout. </li></ul>The assessment process
    19. 19. <ul><li>Extensive design of external surfaces. </li></ul>CODAS detailed design example (2 of 3) The assessment process <ul><ul><li>Full wing design. </li></ul></ul><ul><ul><li>Control deflections. </li></ul></ul><ul><ul><li>Tailplane & deflection. </li></ul></ul><ul><ul><li>Fin setting angle. </li></ul></ul><ul><ul><li>Fuselage upper surface. </li></ul></ul><ul><li>Final design satisfies constraints. </li></ul><ul><ul><li>Corresponds to a detailed representation of MVO concept. </li></ul></ul>
    20. 20. <ul><li>Acceleration time bettered by 20% compared to MVO. </li></ul>CODAS detailed design example (3 of 3) The assessment process
    21. 21. Post design CFD analysis The assessment process Euler and RANS methods are used to predict the off-design performance.
    22. 22. Wind tunnel testing <ul><li>Wind tunnel testing is the most cost-effective way of generating bulk aerodynamic data. </li></ul><ul><li>More accurate than CFD. </li></ul><ul><li>Generates data up to and beyond limits of flight envelope. </li></ul><ul><li>Low speed tests are much cheaper than high speed tests. </li></ul><ul><li>Generates force and pressure measurements for validation. </li></ul><ul><li>Flow visualisation is important for understanding physics. </li></ul><ul><li>CFD informs testing process. </li></ul>The assessment process
    23. 23. CODAS design wind tunnel tested <ul><li>Low-speed, transonic & supersonic testing completed. </li></ul>The assessment process
    24. 24. Synthesis of performance <ul><li>Neither wind tunnel or CFD data is fully representative </li></ul><ul><li>CFD can provide some points, but not all </li></ul><ul><li>Wind tunnel data is usually untrimmed and sub-scale. </li></ul><ul><li>Response surface techniques can generate trimmed drag. </li></ul><ul><li>Same methods can also generate device schedules. </li></ul><ul><li>CFD can correct zero-lift drag from model to full scale. </li></ul>The assessment process
    25. 25. MVO predicted vs. experimental trimmed drag The assessment process C L C D - (C L 2 / (   AR )) Wing LE -5, TE 0, Trimmed Wing LE 0, TE 0, Trimmed Wing LE 5, TE 0, Trimmed Wing LE 5, TE 5, Trimmed Mission performance targets (MVO) Point performance targets (MVO)  C D = 0.01
    26. 26. Closing the loop <ul><li>MVO results are based on low-fidelity methods. </li></ul><ul><li>MVO predictions should be supported by high-fidelity data. </li></ul><ul><li>CFD methods should agree with experimental data. </li></ul><ul><li>Differences between MVO, CFD and experiment should be accounted for. </li></ul><ul><ul><li>Systematic errors can be identified and reduced. </li></ul></ul>The assessment process
    27. 27. Assessment and optimisation process MVO modelling validated. Improved data-sheet methods derived from CFD and WT data (Response Surface Models). The assessment process Rules-based CAD MVO design synthesis Aerodynamics: CFD, CODAS, WT testing
    28. 28. Underpinning Technologies Section 3
    29. 29. Integration of CAD into assessment <ul><li>CAD methods are essential for handling complex configurations. </li></ul><ul><ul><li>But, geometry generation and input to CFD was previously the slowest step in the process. </li></ul></ul><ul><li>Rules-based CAD now allows automated generation of 3D CAD models. </li></ul><ul><ul><li>Now forms the basis of a multidisciplinary capability. </li></ul></ul><ul><li>Tailored CAD-CFD interface (GEMS) developed to allow rapid meshing of CAD geometry. </li></ul><ul><ul><li>Common front-end for many CFD tools. </li></ul></ul>Underpinning technologies
    30. 30. Parametric, knowledge-based, CAD <ul><li>Wide range of air-vehicle types generated from single parametric, rules-based CAD model. </li></ul>Underpinning technologies
    31. 31. Response surface modelling (RSM) <ul><li>The majority of aerodynamic methods in MVO are similar to those in data sheet methods. </li></ul><ul><ul><li>Limited to “classical” configurations. </li></ul></ul><ul><ul><li>Underlying technology dates from 1940s-1950s. </li></ul></ul><ul><li>There is a need to generate more accurate and flexible data sets, and represent these as algebraic functions. </li></ul><ul><ul><li>Design of Experiments (DoE) for minimum error samples. </li></ul></ul><ul><ul><li>CFD methods to generate data bases. </li></ul></ul><ul><ul><li>RSM software fits relationships to data. </li></ul></ul>Underpinning technologies
    32. 32. CFD methods (1 of 2) <ul><li>Mature CFD mesh generation and flow solution techniques: </li></ul><ul><ul><li>Panel & Euler methods are fast and consistent. </li></ul></ul><ul><ul><li>Accuracy levels and applicability are well understood. </li></ul></ul><ul><ul><li>These methods can mass produce data for RSM. </li></ul></ul><ul><li>Areas for improvement with RANS CFD: </li></ul><ul><ul><li>Robustness, accuracy and especially consistency. </li></ul></ul><ul><ul><li>Prediction of flow separation onset. </li></ul></ul><ul><ul><li>Unsteady, separated flows. </li></ul></ul>Underpinning technologies
    33. 33. <ul><li>Manned aircraft concept at transonic manoeuvre condition. </li></ul>CFD methods (2 of 2) Underpinning technologies Euler effective when flow primarily attached. RANS can give false flow features - in this case flow separation from leading edge control surface. Euler RANS Experiment +
    34. 34. Future directions Section 4
    35. 35. Systems of systems <ul><li>Top level drivers for MOD requirements are clear. </li></ul><ul><li>Roles and capabilities required are less so. </li></ul><ul><li>End user focus on effects, rather than technologies. </li></ul><ul><li>MVO gives the effect of technology on system performance. </li></ul><ul><li>Need for MVO to cover the full range of systems. </li></ul><ul><ul><li>HALE concepts. </li></ul></ul><ul><ul><li>Weapons concepts. </li></ul></ul>Future directions
    36. 36. Multidisciplinary aspects <ul><li>These techniques are suitable for wider application. </li></ul><ul><li>Generic means of inserting analyses into simpler tools. </li></ul><ul><li>More accurate, more flexible synthesis methods are possible. </li></ul><ul><li>Detailed assessment is now much faster and cheaper. </li></ul><ul><li>More understanding of detail than is feasible with MVO. </li></ul><ul><ul><li>Feedback to improve MVO. </li></ul></ul>Future directions
    37. 37. Conclusions Section 5
    38. 38. Conclusions <ul><li>Requirements will change and evolve. </li></ul><ul><li>The process needs to be more flexible to assess novel concepts accurately. </li></ul><ul><li>An improved assessment process has been described. </li></ul><ul><li>New generic technologies reduce time and cost. </li></ul><ul><li>New aerodynamic technologies have been matured. </li></ul><ul><li>There are continuing areas for improvement: </li></ul><ul><ul><li>CFD for the whole flight envelope. </li></ul></ul><ul><ul><li>Multidisciplinary trade-offs. </li></ul></ul>
    39. 39. Thank You