MULTI-DISCIPLINARY DESIGN OPTIMIZATION STRATEGY IN MULTI-STAGE LAUNCH VEHICLE CONCEPTUAL DESIGN

Transcription

1 MULTI-DISCIPLINARY DESIGN OPTIMIZATION STRATEGY IN MULTI-STAGE LAUNCH VEHICLE CONCEPTUAL DESIGN 1 ST Progress Seminar Report Submitted towards partial fulfillment of the requirement for the award of degree of Doctor of Philosophy (Aerospace Engineering) by C.Geethaikrishnan (Roll No ) Under the Guidance of Prof. P.M. Mujumdar Prof. K. Sudhakar Department of Aerospace Engineering Indian Institute of Technology, Bombay August, 2003

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3 CONTENTS Page.No. Table of contents Abbreviations Nomenclature List of Table List of Figures 1. Introduction 1 2. Launch Vehicle Conceptual Design Process 3 3. Literature review on MDO works related to Launch Vehicle Design 7 4. Motivation for present research effort Preliminary work on MDO strategy in conceptual design Conclusions References 21 i ii ii iii iii i

4 Abbreviation LV MDO POST T/W GLOW FASTPASS SWORD AMLS RLV - Launch Vehicle - Multidisciplinary Design Optimisation - Program to Optimise Simulated Trajectories - Thrust to Weight Ratio - Gross Lift Off Weight - Flexible analysis for synthesis trajectory and performance for advanced space systems - Strategic Weapon Optimisation for rapid Design - Advanced Manned Launch System) - Reusable Launch Vehicle Nomenclature S REF V loss V V 1 I sp1 Isp2 σ1 σ 2 m s1 m s2 m p1 m p2 m pl Reference Area Velocity loss Velocity requirement First stage velocity increment Specific impulse of first stage Specific impulse of second stage Structural factor of first stage Structural factor of second stage Structural mass of first stage Structural mass of second stage Propellant mass of first stage Propellant mass of second stage Mass of payload ii

5 List of Tables Table No. Caption Page. No 1. Comparison of three MDO strategies 10 List of Figures Fig. No. Caption Page. No. 2.1 Launch Vehicle conceptual design process Coupling among disciplines in Launch Vehicle Design process Vehicle sizing / performance cycle Lift-off T/W trade for AMLS Vehicle Iterative-loop solution strategy Sequential compatibility constraint strategy Collaborate optimization architecture for launch vehicle design Multistep sequential procedure Schematic Diagram of decomposition of segments Decomposition formulation for two stage to orbit manuvere Liftoff weight Vs first stage velocity Payload fraction Vs first stage velocity Flow diagram of preliminary study on conceptual design of launch vehicle 18 iii

6 Chapter 1 Introduction The design of large, complex system such as launch vehicle requires making appropriate compromises to achieve balance among many coupled objectives such as high performance, safety, simple operation and low cost. The earlier in the design process that these compromises can be understood, the greater the potential for reduction of technical, schedule and cost risks. The conceptual design is intended to reveal trends and allow relative comparisons among alternatives early in the process while design flexibility exists and before a large percentage of life cost are committed. The launch vehicle conceptual design process produces a configuration, usually driven toward high performance (often translated as low weight) for a specified mission requirement. Configuration specification includes definition of number of stages external geometry and internal layout, technology selection, mass properties, performance estimates, operational scenario, and, perhaps, cost estimates. The difficulties associated with conceptual design are (i) conceptual design is characterized by a low level system and (ii)the relationships among design objectives and the conceptual design parameters are often not well modeled or understood. This results in probably inefficient final design, leaving room for significant improvements in performance and reduction in life costs. To improve results during the conceptual design phase at least two emphases must be pursued: (i)improvement of disciplinary analysis, modeling and tools that capture, with sufficient fidelity, the major relationships among design variables and system objectives and (ii) the development of methods for coordinating the engineering analyses and optimizing the total launch vehicle system [1]. The second objective can be achieved by the application of multidisciplinary design optimization(mdo) in conceptual design level. A complex interrelation exists between mission requirements and constraints, trajectory shaping, propulsion, weights and loads with conflicting goals which has to be matched by an appropriate optimization strategy. MDO involves the coordination of multidisciplinary analyses to realize more effective solutions during the design and optimization of complex systems. It will allow system engineers to systematically explore the vast trade space in an intelligent manner and consider many more architectures during the conceptual design phase before converging on the final design. 1

7 The progress made with respect to research efforts on MDO strategy in concurrent optimization of multi-stage launch vehicle configuration and trajectory is presented in this report. Launch vehicle conceptual design process is explained in Chapter 2. The works, related to the topic of interest, available in literature is highlighted in Chapter 3. The next chapter briefs about the motivation for the present research effort. Chapter 5 presents preliminary efforts made in the proposed work. The conclusions are given in Chapter 6. 2

8 Chapter 2 Launch vehicle conceptual design process The conceptual design of launch vehicle is highly coupled and significant data exchange and iterations are often required among discipline and disciplinary tools as shown in Fig 2.1 Mission Requirements Vehicle sizing Propulsion Options & design Layout & Surface geometry Structural, Control & Thermal Analyses Weight & C.G Vehicle Configuration Dimens ions Steering rate history Trajectory Analyses Aerodynamic Analysis Fig 2.1 Launch vehicle conceptual design process The process includes: i) specification of the mission requirements (e.g., payload size, mass, destination, environmental constraints, on-orbit operations); ii) selection of a vehicle approach (e.g., single or multiple stages, rocket or airbreather, expendable or reusable, etc.,); iii) selection of associated operational scenarios (including assembly, launch site, recovery ); iv) selection of technologies (e.g., structural materials, thermal protection system, avionics, propulsion), v) creation of a physical layout and surface geometry that will contain the payload, subsystems, and support equipment; vi) estimation of aerodynamics (subsonic, supersonic, hypersonic); vii) calculation of trajectory and flight environment; viii) execution of structural, heating, and controls analyses based on the flight environment; ix) estimation of the vehicle weights, dimensions and center of gravity based on mission, layout, environment and chosen technologies and x) feedback of these results for modification and optimization of the overall system to meet mission requirements and design objectives. 3

9 The conceptual design process is highly coupled and non-hierarchical and significant data exchange and iteration are often required among disciplines and disciplinary tools. Fig 2.2 depicts the coupling among various disciplines including cost estimation in launch vehicle design process [1]. Vehicle Concept Mission Requirements Propulsion option Technology options Operational option Layout &Surface geometry Aerodynamic analysis Trajectory analysis Configuration, Weights and sizing Operational analysis Structural, Control, thermal Propulsion analyses Cost Analysis Rethink/modify requirements and options Fig 2.2 Coupling among disciplines in Launch vehicle design process Determining the optimal configuration of a launch vehicle requires the evaluation of the interactions between the vehicle systems and the impact of these systems upon the vehicle s ability to perform the desired mission. This interaction, as shown in fig 2.3, leads to vehicle sizing/performance evaluations cycle. Resize vehicle Vehicle Sizing Vehicle performance Fig 2.3 Determine Performanc e Vehicle Sizing/ Performance cycle 4

10 The evaluation of the sizing/performance cycle was a manual process. This manual process has two problems:i) The vehicle must be repeatedly sized and performance evaluated and ii) once sized, the vehicle may not be optimal [2]. To obtain the optimum values of sizing parameters, vehicle performance will be carried out to examine the value of each parameter by fixing the values of remaining parameters. This is referred as one variable at a time approach. As an example, the conceptual design of fully reusable manned launch system is briefed here [3]. The conceptual design of a rocket-powered, two stage fully reusuable launch vehicle has been performed as a part of advanced manned launch system(amls) study by NASA. The reference geometry was chosen, the vehicle aerodynamics were evaluated, a propulsion system was selected, ascent and entry trajectories were analyzed, a centerline heating analysis was performed, baseline structural concepts and thermal protection system materials were selected, and a weight and sizing analysis was performed. After finalizing the reference vehicle, a series of parametric trade studies were also performed on the reference vehicle to determine the effect of varying major vehicle parameters. For example the Liftoff thrust-to-weight ratio(t/w) was chosen in the following manner: Throughout the initial design of the two stage AMLS fully reusable vehicle, a value of 1.3 was assumed for the liftoff T/W. This was judged to be an optimal value based on the results of previous studies; however, since such optimal parameters tend to be vehicle dependent, a trade study was performed using a variety of T/W values. The results of this parametric trade are presented in Fig

11 This trade was performed for a thrust split of 60% of the liftoff thrust of the SSME-derivative engines on the booster and 40% on the orbiter. The curves presented in Fig. 2.4 indicates that the minimum total gross weight occurs for a liftoff T/W of 1.5, and the minimum total dry weight occurs for a T/W of about However, the minimum non propulsion dry weight occurs for a liftoff T/W of 1.3. The dry weight increases for higher T/W values because of the additional propulsion weight needed to achieve the required high thrust values. The gross weight increases for lower T /W values because of the additional time and propellant required to accelerate to orbital velocities. However, the slope of these curves is quite small. Choosing a liftoff T/W of 1.3 allows a healthy thrust margin, minimizes nonpropulsion dry weight, and causes less than a 1 % increase In total dry weight over the minimum value. Similarly all other parameters such as staging Mach number are also chosen through parametric studies keeping other parameters constant. In this One variable at a time approach, the relationships among the design variables are not considered in choosing optimum parameters. This may result in nearoptimum configuration. Instead, if all at the same time approach will bring out more optimum configuration. This can be achieved by application of MDO methods in conceptual design process. A good amount of work related to MDO methods in launch vehicle system and trajectory optimization. The highlights of the works available in literature is presented in next chapter. 6

12 Chapter 3 Literature review on MDO works related to launch vehicle design As stated earlier, choosing the optimal configuration requires launch vehicle performance optimization. The performance optimization of launch vehicles implies the tasks of system design and trajectory optimization. System design provides parameters like the number of the stages and engine sizing. Trajectory optimization gives the control vector that optimizes the performance for the chosen configuration. Ideally, design of the vehicle and propulsion system and trajectory shaping should be iteratively refined together by a coupled multidisciplinary optimization scheme to obtain optimum solution. One approach to optimize vehicle performance is to collect all elements of the trajectory control vector and system design variables in one vector of optimization parameters to be manipulated by an appropriate non-linear programming algorithm. This approach has been applied successfully to ascent mission of rocket powered single-stageto-orbit vehicle in multidisciplinary design environment. [4] [5]. These studies focus on development of rapid multidisciplinary analysis and optimization capability for launch vehicle design. To simplify the analysis, several disciplines were decoupled and propulsion, performance and weights and sizing are considered for the study. For propulsion system, the parameters supplied by Pratt & Whitney is used after regression analysis. Program to optimize simulated trajectories(post) is used for trajectory optimization. An existing vehicle geometry, aerodynamic database were used and data from aerodynamics, structures, heating and other subsystems were fixed or scaled appropriately. Two architecture referred as Iterative loop solution strategy and sequential compatibility constraint solution are addressed in [4] with 40 design variables and 13 constants. Iterative loop method is depicted in Fig 3.1. Here an iterative loop is set up between the trajectory and weights and sizing disciplines. Values of GLOW, S REF, the base diameter and the landed weight are used as loop convergence criteria. This formulation may be referred multidisciplinary feasible since for each set of design variables the looped analyses return a design candidate that is consistent across disciplinary boundaries. In the sequential compatibility constraint method approach, the iterative loop is replaced by use of auxiliary variables and compatibility constraints As shown in Fig.3.2, 7

13 Iterative-loop solution strategy Optimizer Minimize J=dry weight Design variables(40) Subject to inflight and terminal constraints Initial guess at GLOW, Sref Base diameter Landed weight Trajectory Weights & Sizing propulsion GLOW=GLOWc Sref=Sref c Landed wt =Landed wt c base diameter =base diameter c Delta =(GLOWc-GLOW) 2 +(Sref c -Sref) 2 +(Landed wt c -Landed wt) 2 +(base diameter c - base diameter) 2 Done N0 Is Delta small Yes Fig 3.1 Iterative Loop MDO strategy Sequential compatibility-constraint solution Optimizer Minimize J=dry weight Design variables(40) Subject to inflight and terminal constraints Trajectory propulsion Inflight & terminal constraints Weights & Sizing Compatibility constraints GLOWc-GLOW =0 Sref c -Sref = 0 Landed wt c -Landed wt= 0 base diameter c - base diameter= 0 GLOWc Sref c Landed wt c base diameter c Dry weight Fig 3.2 Sequential compatibility constraint solution 8

14 an auxiliary variable and a compatibility constraint are added to the optimization-problem statement for each variable that is required as input to one discipline but is computed by another discipline later in the analysis sequence. Hence, S ref, GLOW, the base diameter, and the landed weight are added as design variables. In this manner, the iterative loop is removed, and configuration control becomes an additional task of the optimizer. By satisfying these four compatibility constraints, consistent vehicle model is guaranteed. However, as opposed to the iterative loop approach, compatibility is required at the solution only. This type of approach may be referred to as "simultaneous analysis and design," since both a consistent and an optimum set of design variables converged upon simultaneously. This study indicates that use of the sequential compatibility constraint approach has several advantages relative to the iterative-loop approach. These advantages include i) being 3-4 times more computationally efficient ii) providing greater flexibility in the way in which consistency is maintained across disciplinary boundaries, and iii) a smoother design space. The only disadvantage of the compatibility constraint approach is in situations when the optimizer terminates without reaching the solution on account of poor scaling or model non-smoothness. Because multidisciplinary feasibility is only guaranteed at a solution in this approach, the design information could be invalid. A new design architecture collaboration optimization, with 95 design variables(23 interdisciplinary) and 16 constraints, is studied in [5]. Collaborative optimization is a new design architecture whose characteristics are well suited to largescale, distributed design. The fundamental concept behind the development of this architecture is the belief that disciplinary experts should be able to contribute to the design process while not having to fully address local changes imposed by other groups of the system. To facilitate this decentralized design approach, a problem is decomposed into subproblems along domain-specific boundaries. Through subspace optimization, each group is given control over its own set of local design variables and is charged with satisfying its own domain-specific constraints. The objective of each subproblem is to reach agreement with the other groups on values of the interdisciplinary variables. A system-level optimizer is employed to orchestrate this interdisciplinary compatibility process while minimizing the overall objective. This decomposition strategy allows for the use of existing disciplinary analyses without major modification and is also well suited to parallel execution across a network of heterogeneous computers. 9

15 Fig 3.3 Collaborative optimization architecture for launch vehicle design MDO Architecture Function Evaluation Modification time, month Communication Requirements Iterative method Combatiblity Constraint Collaborative Table 3.1 Comparisons of MDO strategies in launch vehicle design 10

16 Advantages of this collaborative architecture are that it i) may not require either modification of codes or explicit integration into an automated computing framework, ii) allows subproblems to be optimized by the best-suited method, iii) allows for the addition or modification of subproblems and iv) can efficiently accommodate a large number of variables. Table 3.1 shows the performance comparison between the above three methods for this design problem. Communication requirements are minimal because knowledge of the other groups' constraints or local design variables is not required. Optimisation of system and trajectory together is applied to Reusable launch Vehicle (RLV) by Tsuchiya [6]. In this study MDO method is applied to choose best among seven typical concepts of RLV. The design variables representing geometry and shape of vehicles, flight performance of flight trajectories are considered as design variables. The MDO architecture used in this study is similar to sequential compatibility constant solution. The study concludes that the proposed MDO optimization method is effective for the design problem considered. Though these MDO architectures has been applied successfully to the ascent mission of single stage vehicle, it has shown poor convergence properties even for less complex mission examples of an expendable multistage rocket launches, when major system design parameters such as the mass split of stages or engine sizing were included to optimize trajectory control and vehicle parameters simultaneously [7]. Another approach that overcome this difficulty is a multistep sequential optimization procedure [8]. In this multistep sequential procedure, outlined in Fig.3.4, consists of a performance optimization cycle (inner loop) and a vehicle design cycle (outer loop). The first loop uses the data of the latter to determine the control functions and major system parameters yielding the optimum performance. This automatic inner loop responds to varying vehicle size needs as long as the departure from the preset design (outer loop) remains small. Otherwise, a vehicle redesign including system modifications and reevaluation of the aerodynamic coefficients (which are held constant in the inner optimization cycle) is performed in separate computations in the outer iteration loop. The latter requires manual interaction and is supported by graphic interface tools. This scheme outlined above is applied to enhance the performance of a reusable rocket launcher which is part of Ariane X family [9]. 11

17 Mass Estimate Opt Loop System scaling Flight simulation Verification And valuation Design cycle Aerodynamic Aerothermodynamic Model definition Fig.3.4 : Multistep sequential procedure Two design software FASTPASS (Flexible analysis for synthesis trajectory and performance for advanced space systems) [2] developed by Lockheed Martin Astronautics and SWORD (Strategic Weapon Optimisation for rapid Design) [10] developed by Lockheed Missile design and space Co. for solid motor missile are based on the schemes similar to multistage sequential optimization process. Though this scheme was able to solve the optimization problem of a two-stage, winged rocket launch vehicle designed tor vertical takeoff, severe convergence problems were encountered when it was applied to the more complex mission of an airbreathing Sanger-type STS [7]. These difficulties were attributed in part to different performance sensitivities of the various flight phases, controls, and major system design parameters, and to scaling problems. A decomposition approach has been taken in the present study to solve the overall optimization problem of a Sanger-type launch system. Decomposition of a mission means partitioning the trajectory into subarcs such that each mission segment can be optimized independently. These subproblems constitute the first level of optimization. A second-level controller is then used to optimize the entire mission. Hence, a two-level optimization procedure results, with. the master-level algorithm optimally coordinating the solution of the subproblems. The schematic diagram of decomposition of segments and the decomposition formulation for the two stage to which stage missions is shown in Fig3.5 and Fig3.6 respectively 12

18 Schematic diagram Decomposition of segments Segment 3 h Segment 1 Segment 2 Fig 3.5 Schematic Diagram of decomposition of segments Master Problem: Maximize upper-stage payload mass Independent variables: Staging Mach number Longitude at staging Load factor at pull-up Time interval for pull-up Subproblem 1: Minimize: Booster stage ascent propellant Subproblem 1: Minimize: Booster stage flyback propellant Subproblem 1: Minimize: Orbiter ascent propellant Subject to: Staging Mach no. (master contr.) Staging longitude(master contr.) Latitude at staging heading staging Independent variables: flight heading after take-off supersonic cruis e flight length bank angle control parameter determines the length of the turn flight Subject to: Max flight acceleration Max dynamic pressure End head towards landing site Independent variables: Angle of attack control Bank angle control Parameter determines the length of the turn flight. Subject to: Max long. Flight acceleration Perigee velocity Perigee altitude Perigee path angle Independent variables: Angle of attack control Fig.3.6 Decomposition formulation for two stage to orbit mission 13

19 This algorithm is applied to determine the optimal ascent trajectory of an airbreathing launch vehicle of Sanger type that delivers a maximum load to desired orbit while staging condition and mass distribution of the two vehicles are unknown and to be determined. This study demonstrates the capability of the decomposition method to successfully optimize the entire mission and major design variables. MDO methods may be divided into three groups: i)parameters methods based on design of experiments (DOE) techniques ii)gradient or Calculus based methods and iii)stochastic methods such as geometric algorithm and simulated annealing. Parametric methods as well as gradient based methods are applicable at conceptual design phase[11]. The above mentioned studies are all based on gradient based optimization methods. Launch vehicle conceptual design studies have been carried out using parametric optimization method. Stanley [12] uses parametric optimization study which employs Taguchi design method to determine the proper levels of a variety of engine and vehicle parameter for single-stage-to-orbit vehicle. This study considers five design parameters. The configuration selection for rocket powered single stage vehicle configuration using response surface methodology is presented in [13]. Five configuration parameters that greatly affect the entry vehicle flying qualities and vehicle weight considered for study. RSM was used to determine the minimum dry weight entry vehicle to meet constraints on performance. Olds has applied Taguchi s method to conceptual design of a conical (wingedcone) single-stage-to-orbit launch vehicle [14]. Taguchi method was used to evaluate the effects of changing 8 design variables (2 of which were discrete) in an "all at the same time" approach. Design variables pertained to both the vehicle geometry (cone halfangle, engine cowl wrap around angle) and trajectory parameters (dynamic pressure limits, heating rate limits, and airbreathing mode to rocket mode transition Mach number). The vehicle payload was fixed at 10,000lbs to 100Nmi circular polar orbit. Vehicle dry weight and gross weight were determined for each of the 27 point designs performed. Anderson et al., have investigated the potential of using a multidisciplinary genetic algorithm approach to the design of a solid rocket motor propulsion system as a component within overall missile system [15]. Aerodynamics and trajectory performance disciplines were considered in this study. 14

20 Chapter 4 Motivation for present research effort A complex interrelation exists between mission requirements and constraints, flight path selection, engine performance and weights, vehicle design and flight loads with conflicting goals, which have to be matched by an appropriate optimization strategy during conceptual design process. Ideally, design of the vehicle and propulsion system and trajectory shaping should be iteratively refined together by a coupled, MDO scheme to obtain the optimum solution. However this was not practical because of high computational expenditure associated with the numerical prediction methods [8]. Therefore a multistep sequential analysis and vehicle design procedure employing parameter optimization methods had been developed Now with availability of various methods, good amount of work related to MDO in launch vehicle design appear in literature. A survey on literature reveals that MDO works related to conceptual design, that is, simultaneous optimization of system and trajectory are limited to enhancement of an existing reference vehicle system or subsystem optimization with respect to vehicle performance. This may be attributed to the focused effort on the Advanced Manned Launch System (AMLS) activity since Two vehicles, single stage and two stages were used for this AMLS mission and all further design studies are to optimize the performance of these configuration. Also, other recently developed vehicles are designed by evolution strategy. An MDO strategy which has zero order sizing capability would be useful in developing a new vehicle. That is, given the range of realizable mass fraction and specific impulse. The scheme should be able to decide number of stages, mass and propellant fraction and iterate this vehicle and propulsion system and trajectory shaping and give optimum configuration and trajectory that meets the specification. This would be useful when no propulsion system or technological constraints are identified and the initial trade space is being defined. This scheme may come up with a design which is non- intuitive and much better than traditional design technique. Development of such scheme is the aim of present research effort. 15

21 Chapter 5 Preliminary work done in MDO strategy in conceptual design As an effort to understand the advantage of MDO based conceptual design of V, the following work has been carried out. The conceptual design of Launch Vehicle starts with the orbit and payload specification. The velocity requirement ( V) can be derived from orbit specifications. Once the V is assessed, certain value of number of stages, velocity loss ( V loss ), structural factors (σ) and specific impulses (I sp ) for each stage are assumed based on the data base available. Based on assumed values, an optimum launch vehicle configuration is arrived, through ideal velocity calculations. In this, V loss is due to gravity and aerodynamic of vehicle. This can be accurately assessed through aerodynamic modeling and trajectory performance. Trajectory performance can be carried out after sizing, geometrical modeling, weight estimation and aerodynamic modeling. Structural factor is the outcome of sizing of propulsion system, tanks and mass estimation of all sub systems. Specific impulse is achieved by propulsive system design. So, after completion of final design, there may be deviations from the values assumed. This may result in non-optimum configuration with room for improvement. These deviations can be reduced if the above mentioned disciplines are considered in conceptual design. It also depends on the number of disciplines brought into the conceptual design loop and the fidelity of discipline models. In this preliminary study, a two stage rocket is developed for deploying 20t in 400km is considered for the study. The V required for 400km circular orbit is 7.7 km/s. A V loss of 1.8 m/s is considered and two stage vehicle configuration was designed with specific impulse values of 435s and 454s which can be well achieved with cryo propellant. Structural values of 0.17 and 0.11 is initially considered for design. These values are based on data base available for similar type of stages. Here, the aim of the optimization is to arrive at a configuration which gives low liftoff weight, in other words high payload fraction. Payload fraction is defined as the ratio of payload to liftoff weight. So, the velocity increment achieved by each stage is to be optimized. Since the final velocity is known, the first stage velocity ( V 1 ) can be independent parameter. Now, the optimization problem is Given the payload and final velocity to be achieved, with assumed V loss, specific impulse (I sp ) and σ, optimize first stage velocity to achieve high payload fraction. 16

22 For given stage velocity, the mass of structure and propellant can be estimated using the following ideal velocity equations Lift-off weight can be calculated by summing up all masses. The optimum final stage velocity can be obtained for minimum lift-off weight. The variation of lift-off weight and payload fraction with respect to first stage velocity are shown as curve (a) in Fig. 5.1 and Fig. 5.2 respectively. (5.1) (5.2) (5.3) (5.4) (5.5). The first stage velocity of 3.6 m/s gives the minimum lift-off weight of 366t. The optimum configuration thus obtained would be C209 + C85. That is, based on the assumed values, a two stage vehicle with cryo engine on both stages with 209t and 85t propellant loading respectively will give minimum lift-off weight. The corresponding maximum payload fraction is 5.5. Now, further study has been done to take more accurate structural factor into design by bringing sizing and mass estimation into loop. Propellant tanks are sized to accommodate the propellant required for ascent flight along with possible in flight losses and residual fluids depending on the propellant used. The volume of the payload is 17

23 Orbit Specifications Payload Choice of propulsion I sp1, I sp2 DV total Ideal velocity Initialize DV s 1, s 1 2 calculations m s1,m p1 m s2,m p2, m pf LOW Dy. Pressure Load factor Area ratios Fineness ratios Sizing of tanks m s1e,m s2e Assumptions DV loss Structural factors (s 1, s 2 ) Vary s 1, s 2 No Is m s1 = m s1e m s2= m s2e Weight estimation Yes LOW Vary DV 1 No Is LOW minimum Yes Optimum LOW & Configuration ig 5.3 : Flow diagram of preliminary study on conceptual design of launch vehicle 18

24 computed from its density derived from database and nominal density of payload is taken as 0.14 t/m 3. A preliminary mass estimation methodology is adopted. It is based on component built up method including propellant tank, payload bay, thrust structure, thermal protection system, avionics and other auxiliary systems. The methodologies, given in [17] & [18] are adopted. The mass estimated using this procedure is compared with mass obtained from ideal velocity calculation. If the values are different, then the structural factor assumed is iterated until both match well. For each first stage velocity value, this procedure is repeated. The methodology is explained well in Fig.5.3. The results obtained using the procedure is given as curve (b) in Fig. 5.1 & Fig The sizing and weight estimation-in-loop process gives optimum first stage velocity as 4.6km/s. The optimized structural factors are 0.08 and 0.12 against the assumed values of 0.17 and The optimum configuration is C197 + C55. That is, two cryo stages with 210t and 83t of cryo propellant respectively for first and second stage. The payload fraction is 6.7 with lift-off weight of 299t. This study shows that bringing sizing and weight estimation (with empirical model) in loop during conceptual design has increased the payload fraction from 5.5 to 6.7 and the configuration is C197 + C56 instead of C207+C85. Similarly if other vital disciplines like propulsion, aerodynamic and trajectory performance are considered in conceptual design process, it will result in more efficient launch vehicle. In first phase of studies, it is proposed to bring aerodynamic, propulsion and trajectory performance in the loop. Then it will be extended to more number of stages. 19

25 Chapter 6 Conclusions Progress made in research work related to MDO strategy in conceptual design of multi-stage launch vehicle is presented. The conceptual design of launch vehicle involves various disciplines and highly coupled. Considering all disciplines with high fidelity model at conceptual design stage improves the efficiency of launch vehicle designed. Survey of literature reveals that the MDO woks on launch vehicle design are restricted to enhancing the existing design and single stage vehicle. An MDO strategy capable of zero order sizing applicable to multistage vehicles would be beneficial in developing new vehicles. Present research effort is focused in this direction. A preliminary study to demonstrate the effect of bringing in mass estimation discipline in conceptual design indicates the payload fraction is increased from 5.5 to

26 Chapter 7 References [1] Lawrence F. R., Braun R. D., Olds J.R., and Unal R, Multidisciplinary Conceptual Design Optimization of Space Transportation Systems Journal of Aircraft, Vol. 36, No. 1, January-February 1999, pp [2] Szedula, J.A., FASTPASS: A Tool For Launch Vehicle Synthesis, AIAA CP, 1996 [3] Stanley, D.O., Talay, A.T., Lepsch, R.A., Morris, W.D., Kathy, E.W. Conceptual Design Of A Fully Reusuable Manned Launch System. Journal of Spacecraft And Rockets, Vol 29, No.4, pp , July-August, 1992 [4] Braun, R. D., Powell, R. W., Lepsch, R. A.. Stanley, D. 0., and Kroo, 1. M., "Comparison of Two Multidisciplinary Optimization Strategies for Launch-Vehicle Design," Journal of Spacecraft and Rockets, Vol. 32, No. 3, 1995,pp [5] Braun, R.D. and Moore., Collaborative approach to launch vehicle design Journal of Spacecraft and Rockets, Vol. 34, No.4, pp , July-August,1997. [6] Tsuchiya, T. and Mori. T. Multidisciplinary Design Optimization to future space transportation vehicle. AIAA [7] Rahn, M. and Schottle, U. M., "Decomposition Algorithm for Performance Optimization of a Launch Vehicle," Journal of Spacecraft and Rockets, Vol. 33, No. 2, 1996, pp [8] Schottle, U. M., AND Hilleshcimer, M.. "Performance Optimization Of An Airbreathing Launch Vehicle By A Sequential Trajectory Optimization And Vehicle Design Scheme; AIAA PAPER , AUG [9] Hillesheimer, M., Schotlle, U. M. and Messerschmid, E., "Optimization of Two-Stage Reusable Space Transportation Systems with Rocket and Airbreathing Propulsion Concepts," International Astronautical Federation Paper 92-O863, Sept [10] Hempel, P. R., Moeller C. P., and Stuntz L. M., Missile Design Optimization Experience And Developments, AIAA ,1994-cp [11] Lawrence, F. R.,. Braun R. D., Olds J.R., and Unal, R. Recent experiences in Multidisciplinary Conceptual Design Optimization of Space Transportation Systems AIAA CP, 1996 [12] Stanley, D. O., Unal, R., and Joyner, C. R., "Application of Taguchi Methods to Dual Mixture Ratio Propulsion System Optimization for SSTO Vehicles," Journal of Spacecraft and Rockets, Vol. 29, No. 4, 1992, pp [13] Stanley, D. 0., Engelund. W. C., Lepsch. R. A., McMillin, M. L.Wt K. E.. Powell. R. W., Guinta. A. A., and Unal, R. "Rocket-Powered Single Stage Vehicle Configuration Selection and Design," Journal of Spacecraft and Rockets, Vol. 31, No. 5, pp ; also AIAA Paper93-Feb [14] Olds, J., and Walberg, G., Multidisciplinary Design of a Rocket-Based Combined-Cycle SSTO Launch Vehicle using Taguchi Methods, AIAA , Feb,

27 [15] Anderson, M., Burkhalter J., AND Jenkins R Multidisciplinary Intelligence Systems Approach To Solid Rocket Motor Design, Part I: Single And Dual Goal Optimization. AIAA , July, [16] Wurster, K.E., Lawrence, R.F. and Hampton, V.A. The Next Generation Manned Launch System A Complex System, AIAA [17] Glatt, C.R., WATTS-A Computer Program For Mass Analysis Of Advanced Transportation System, NASA-CR-2420, September, [18] Harloft, G.J., and Berkoutiz, B.M., HASA-Hypersonic Aerospace Sizing Analysis for Preliminary Design of Aerospace Vehicles, NASA-CR , November,