IAC-02-IAA.1.1.07 OPTIMIZATION OF A FUTURE RLV BUSINESS

IAC-02-IAA.1.1.07 OPTIMIZATION OF A FUTURE RLV BUSINESS CASE USING MULTIPLE STRATEGIC MARKET PRICES A. Charania J. Olds SpaceWorks Engineering, Inc. (SEI) Atlanta, GA U.S.A.

53rd International Astronautical Congress

The World Space Congress - 2002 10-19 Oct 2002/Houston, Texas

For permission to copy or to republish, contact the International Astronautical Federation 3-5 Rue Mario-Nikis, 75015 Paris, France

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OPTIMIZATION OF A FUTURE RLV BUSINESS CASE USING MULTIPLE STRATEGIC MARKET PRICES

A.C. Charania*and John R. Olds†

SpaceWorks Engineering, Inc. (SEI) Atlanta, GA U.S.A.

www.sei.aero

ABSTRACT There is a lack of depth in the current paradigm of conceptual level economic models used to assess the value and viability of future capital projects such as a commercial reusable launch vehicle (RLV). An RLV can potentially address multiple payload markets; each market having a different price elasticity of demand for both the commercial and government customer. Thus, a more resilient examination of the economic landscape requires optimization of multiple prices in which each price affects a different demand curve. Such an examination is performed here using the Cost and Business Analysis Module (CABAM), a MS-Excel spreadsheet-based model that attempts to couple both the demand and supply for space transportation services in the future, specifically optimization for a 3rd Generation RLV program. The economic metric being optimized (maximized) is Net Present Value (NPV) based upon a given financial structure and cost of capital assumptions. Domain spanning/evolutionary algorithms are used to obtain the optimized solution in the design space. These capabilities generally increase model calculation time but incorporate realistic pricing portfolios. This analysis is conducted with CABAM running in Phoenix Integration’s ModelCenter© collaborative design environment using the SpaceWorks Engineering, Inc. (SEI) OptWorks suite of optimization components.

NOMENCLATURE AATE Architecture Assessment Tool

Enhanced AF Airframe AST Administrator for Space

Transportation CABAM Cost and Business Analysis

Module CAPM Capital Asset Pricing Model CER Cost Estimating Relationship COMSTAC Commercial Space Transportation

Advisory Committee CPS Coordinate Pattern Search CSTS Commercial Space Transportation

Study D/E Debt-to-Equity DDT&E Design, Development, Testing, and

Evaluation DSM Design Structure Matrix EB Expendable Booster EBITDA Earning Before Interest, Taxes,

Depreciation, and Amortization ETO Earth-to-Orbit FCF Free Cash Flow FAA Federal Aviation Administration FY Fiscal Year GA Genetic Algorithm GEO Geostationary Earth Orbit GS Grid Search GTO Geostationary Transfer Orbit HEDM High Energy Density Matter HRST Highly Reusable Space

Transportation HS-PTP High Speed-Point to Point IOC Initial Operating Capability IR Incentive Return IRR Internal Rate of Return ISS International Space Station LEO Low-Earth-Orbit LCC Life Cycle Cost LOV Loss of Vehicle

______________________________________________ * - Senior Futurist, Member AIAA. † - President, Associate Member AIAA. Copyright ©2002 by SpaceWorks Engineering, Inc. (SEI). Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Released to IAF/IAA/AIAA to publish in all forms.

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LRU Line Replacement Units MFBF Mean Flights Between Failures MMM Mission Market Model MIRR Modified IRR NAFCOM NASA-Air Force Cost Model NPV Net-Present-Value OEC Overall Evaluation Criteria PLTO Payload to Orbit PTP Point-to-Point RBCC Rocket Based Combined-Cycle RLV Reusable Launch Vehicle ROI Return on Investment ROSETTA Reduced Order Simulation for

Evaluation of Technologies and Transportation Architectures

SG&A Selling, General, and Administrative

SSA Space Station Assembly SSP Space Solar Power SSPATE Space Solar Power Abbreviated

Transportation Economics SSTO Single Stage to Orbit TAT Turn-Around-Time TFU Theoretical First Unit TIF Time-In-Flight TRL Technology Readiness Level TSTO Two Stage To Orbit VTHL Vertical Take-off Horizontal

Landing WACC Weighted Average Cost of Capital WBS Weight Breakdown Structure

INTRODUCTION Over the last few decades outer space has gained an amplified importance to the normal functioning of societies given the increased interconnectivity enabled by telecommunications. The ever increasing ratio of commercial space launches to government-sponsored space launches is an indication of the importance of outer space as both a corridor of transport and location of resources. Recent few years have seen a rather curious interest in the perception of outer space. Whereas in the early years of outer space exploration, governments were the primary driver, a slow transformation has been occurring. In the modern era both payload developers and launch vehicle providers are becoming more commercialized. The current expendable launch

vehicle market is one such example as is the recent phenomena and popularity of space tourism. Even with a lower number of total launches today as compared with time periods of the 1950s-1960s, the modern launch services market is more commercialized. Various vendors are available to launch payloads; however the linchpin to a more expanded market is launch price. Current launch prices of over several thousands of dollars per pound of payload for commercial launch vehicles may be hindering potential untapped space markets. Any envisioned future with ubiquitous space transportation systems will rely on such markets to generate high levels of sustained flights rates. These would encourage development of future launch systems such as the next generation of Reusable Launch Vehicles (RLVs). NASA defines various staggered levels or generations of operation for future RLVs. The current NASA Shuttle (Space Transportation System or STS) is a first generation RLV. Beyond the second generation RLV of 2015 will be a third generation RLV around 2025 whose stated goal is to reach that plateau where space flight will be as routine as modern air travel. In particular, the goals encompass: Improve the expected safety of launch so that the

probability of losing a crew is no worse than 1 in 1,000,000 missions, about the same as today's airliners

Reduce the cost of delivering a pound of payload to low Earth orbit from today's $10,000 down to hundreds of dollars

Reduce vehicle turnaround time with minimal ground crew flying several hundred times a year

MOTIVATION There is a lack of depth in the current paradigm of conceptual level economic models used to evaluate the value and viability of future capital projects such as a commercial reusable launch vehicle (RLV). Current modeling methods assume a single price is charged to all customers, public or private, in order to optimize the economic metrics of interest. This assumption may not be valid given the different utility functions for space services of public and private entities. The government’s requirements are generally more inflexible than its commercial

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counterparts. A government’s launch schedules are much more rigid, choices of international launch services restricted, and launch specifications generally more stringent as well as numerous. These requirements generally make the government’s demand curve more inelastic. Subsequently, a launch vehicle provider will charge a higher price (launch price per lb) to the government and may obtain a higher level of financial profit compared to an equivalent a commercial payload. This profit is not a sufficient condition to enable RLV development by itself but can help in making the financial situation slightly better. An RLV can potentially address multiple payload markets; each market has a different price elasticity of demand for both the commercial and government customer. A resilient examination of the economic landscape requires optimization of multiple prices in which each price affects a different demand curve. An optimization to the business case (maximize financial profit) for such ventures is performed here is for a 3rd Generation RLV program. The economic metric being optimized (maximized) is Net Present Value (NPV) based upon a given company financial structure and cost of capital assumptions. Such an optimization process demands more sophisticated optimizers and can result in non-unique solutions/local minimums if using traditional gradient-based optimization. Domain spanning/evolutionary algorithms are used to obtain the optimized solution in the design space. These capabilities generally increase model calculation time but incorporate realistic pricing portfolios than just assuming one unified price for all launch markets. This analysis is conducted with CABAM running in Phoenix Integration’s ModelCenter© collaborative design environment using the SpaceWorks Engineering, Inc. (SEI) OptWorks suite of optimization components.

PROCESS OVERVIEW This examination will present an overview of the CABAM model and some associated functionalities. Details will be presented in regards to model operation and calculation. Subsequent application of the model will be presented to a 3rd Generation Two-Stage-To-Orbit (TSTO) RLV. The application will consist of optimizing the business case using several

SpaceWorks Engineering, Inc. (SEI) developed optimizers in the Phoenix Integration ModelCenter© collaborative engineering framework. In twelve case studies, four optimizers were used for three different classes of problems. The number of design variables used by the optimizer to maximize the objective function distinguished each class. The four optimizers included a gradient-based DOT optimizer, Coordinate Pattern Search (CPS), genetic algorithm (GA), and grid search (GS). The latter three all originated from the SEI OptWorks suite of collaborative design optimization components. The first class consisted of using one market price to maximize the financial objective, the second class used two prices, and the final class used four prices (see Table 1 for price ranges). Specific classes include: Class I: One price for both commercial and

government cargo to LEO, static price to both commercial and government passenger markets

Class II: Two separate prices charged for cargo in the marketplace, one for commercial cargo and one for government cargo, static price to both commercial and government passenger markets

Class III: Four separate prices charged for both cargo and passengers; this includes commercial cargo, government cargo, commercial passenger, and government passenger markets

Table 1. Ranges of Input Prices

Item Value Commercial Cargo Price Per lb Starting Minimum Maximum

$2,000/lb $500/lb

$40,000/lb Government Cargo Price Per lb Starting Minimum Maximum

$2,000/lb $500/lb

$40,000/lb Commercial Price Per Passenger Starting Minimum Maximum

$2 M/passenger

$0.5 M/passenger $40 M/passenger

Government Price Per Passenger Starting Minimum Maximum

$10 M/passenger $0.5 M/passenger $40 M/passenger

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MODEL DESCRIPTION The Cost and Business Analysis Module (CABAM) is an MS-Excel spreadsheet-based model that attempts to model both the demand and supply for space transportation services in the future. The demand takes the form of market assumptions (both near term and far-term) and the supply comes from user-defined vehicles that are placed into the model. CABAM takes inputs from various other disciplinary models to generate Life-Cycle-Cost (LCC) and economic metrics. One of the major assumptions inherent in CABAM is that the project is modeled as a commercial endeavor with the possibility to model the effects of government contribution, tax-breaks, loan guarantees, etc. CABAM can model the economics of multi-stage launch vehicles (expendable or reusable). CABAM is arranged to offer wide flexibility as far as modeling various types of RLV architectures. Given the constraints of the spreadsheet environment however, CABAM cannot model all RLV variants. The actual type of ETO RLV transportation architecture that can be modeled can include any combination of the following: Up to a three stage launch vehicle architecture

with the following conditions can be modeled: o First stage (referred to as the Booster)

airframe and propulsion units are either reusable or expendable

o Second stage (referred to as the LEO module) airframe and propulsion units are either reusable or expendable

o Third stage (referred to as the GTO module) airframe and propulsion units can only be expendable

Architecture can contain reusable or expendable containers (cargo pods or crew transfer vehicles)

Architecture can handle modeling strap-on expendable boosters (EBs)

Architecture can handle modeling multiple similar Booster stages (i.e. Bimese)

Architecture can handle separate DDT&E, TFU, learning curves, turn-around-time (TAT), time-in-flight (TIF), and operational costs for each portion of the configuration (booster, LEO module, containers, etc.) excluding the EBs

In general, a user defines a particular program that consists of a certain set of economic and schedule

assumptions. The performance, cost, production, and operational properties of the vehicle are then defined. The user can then manipulate the price charged per market to maximize the economic return. The model takes a corporate finance mentality as far as economic modeling. Thus various input financial ratios and rates (debt-to-equity, discount rates, etc.) are need for calculation of final economic metrics. CABAM uses both short-term and long-term market forecasts. The short-term market forecasts are based upon the a RLV market analysis model (known hereafter as the NASA Mission Market Model or MMM) as developed by Andy Prince of NASA Marshall Space Flight Center’s Cost Engineering group. The MMM takes short terms (less than 10 year forecasts) such as those examined by the Federal Aviation Administration’s (FAA) Administrator for Space Transportation (AST) Commercial Space Transportation Advisory Committee (COMSTAC), Futron Corporation, Teal Group, or Euroconsult. The market projections consist of the number of available payloads (not flights) that are available to be captured in the marketplace. Later, duopoly-based manifesting algorithms are used that take into account the specific launch price and number of competitors. The duopoly market modeling does not apply to ISS servicing or Exploration class payloads since those are assumed to be non-elastic markets. These markets are then most appropriate for what are termed 2nd Gen RLVs wherein the initial operating capability (IOC) is around 2010-2015. Even though the input actual market forecasts go out only to 30 years from the base year, the market forecasts can be extrapolated (with a yearly growth rate) to finish in the actual program end year. These forecasts are sub-divided into various payload categories for various markets (ISS servicing, NASA, DoD, current commercial, and exploration). The user has the option of changing the years and the payloads available to be captured. The long-term market forecasts are based upon the Commercial Space Transportation Study (CSTS) from the early 1990s1. In March, 1993 six aerospace companies met at Langley Research Center (LaRC) and determined “that a new, state-of-the-art launch system can provide an order of magnitude reduction in launch costs and that a reduction of that magnitude will cause the equivalent of a space industrial revolution” and “that to become economically viable, a new launch system must generate new commercial markets.” This group of aerospace companies went

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on to perform market exploration studies, leading to the Commercial Space Transportation Study (CSTS). Data from the CSTS report is the basis for the long term, elastic market forecasts in CABAM. These are elastic curve fits for commercial and government markets based upon markets that will be enabled by next generation launch services (see Figures 1 and 2). In this model they are broken out into four sub-markets from these two higher-level markets. The sub-markets include: LEO cargo delivery market (includes CSTS Government, DoD, Space Science, & Business Park missions), LEO passenger market (includes ISS assembly, crew rotation, and space tourism), GTO cargo delivery market (includes GTO communications and escape-bound scientific exploration payloads), ultra-high-speed global delivery market (includes extremely high priority small payloads). Each market is represented by a set of tables of price versus captured market. A maximum market size is listed versus end-customer launch prices. A percent market capture multiplier is used as an attempt to approximate launch competition. As the charged price drops, a new vehicle will capture more of a given market. The captured market data is the product of available market.

Annual LEO Cargo Traffic

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Figure 1a. CSTS Commercial Payload Market

Elasticity: Market Capture for Price Charged (FY2002)

Annual Passengers to LEO

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Figure 1b. CSTS Commercial Passenger Market


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Figure 2a. CSTS Government Payload Market


Annual Passengers to LEO

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Figure 2b. CSTS Government Passenger Market


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HISTORICAL DEVELOPMENT The original CABAM model was developed in the School of Aerospace Engineering at the Georgia Institute of Technology (Atlanta, GA U.S.A.) in 1996-1997. Michael Hosung Lee, a graduate student at the time, created the model, under the advisement of Dr. John R. Olds (professor in the School of Aerospace Engineering). This was the initial version of CABAM cited in AIAA 97-39112. This initial work was partially supported by cooperative agreement NCC1-229 (entitled “Improving Conceptual Design for Launch Vehicles”) between NASA Langley Research Center (Vehicle Analysis Branch) and the Georgia Institute of Technology (School of Aerospace Engineering). This original model consisted of only the CSTS markets to represent demand and multiple static inputs (such as program years) as well as approximations of learning curve effects and static acquisition and production formulas3,4. The model was used for analysis of 3rd Generation RLVs (which have an IOC around 2025). Work progressed on the model, mainly in applications to probabilistic molding, at the Space Systems Design Lab (SSDL) under graduate student Jeff Whitfield and cited in AIAA 98-51795. Eventually another graduate student at SSDL, A.C. Charania (one of the authors here), provided the first major modifications to the model. These modifications kept the basic format of the model (multiple sheets for various aspects of the program (recurring, non-recurring costs, equity calculations, operations, facilities, and financial statements). However, many of the formulas and logic were replaced and enhanced with higher fidelity algorithms. Eventually, some of the fundamental aspects of the CABAM model were used to develop other space economic models including the in-space economics (INSINC, IN-Space INCorporated) model, Space Solar Power Abbreviated Transportation Economics (SSPATE) model, and the space tourism focused Launch Marketing for Normal People (LMNoP) model6. In addition, the core of CABAM was used in the development of the economic component of the ROSETTA RLV model. From these models, SEI has incorporated many advancements into the SSDL derived CABAM model. The current version of the CABAM model, as

enhanced by SEI, stands at revision 8.73. The advancements in the model include: General refinement of model organization and

calculations Incorporation of cargo and crew containers into

modeling capability Incorporation of expendable boosters as vehicle

option Ability to model Bimese (multiple same reusable

configurations) Price ceilings on input prices (use during

optimization) Ability to apply government contribution

assumptions across the entire fleet production cost (for both airframe and propulsion) rather than just upon the TFU cost

Weighted Average Cost of Capital (WACC) calculations to determine discount rates based upon comparable industry Betas

Generalization (removal of CERs) on DDT&E and TFU cost sheets to take higher level inputs from other cost models

Incorporation of MSFC’s Mission Market Model (MMM) launch projections

Development of manifesting algorithms that use MMM demand to allocate payloads based upon expert logic

Development of a grid search optimization sheet to maximize NPV based upon multiple input prices (built internal to the spreadsheet, separate from external gird search routines to be discussed later)

Multiple return metrics based upon various configurations of financial cash flows (before/after taxes, before/after income expense).

SELECTED MODEL ALGORITHMS Financing and Cost of Capital The purpose of these calculations is to help determine the method of financing for this project and to determine an appropriate rate to discount the project’s cash flows in order to obtain the Net-Present-Value (NPV). The user is also given the option to define their own discount rate. Generally two methods of financing are available in any given year where yearly payables are greater than yearly receivables: debt or equity. In CABAM the user specifies how equity financing will be managed. The

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two methods in which equity financing can be handled include: Predetermine the amount and frequency of

equity financing, thus equity will remain constant throughout the program but debt will vary

Predetermine a debt-to-equity ratio and let the amount of equity and debt be adjusted accordingly per year

The Weighted Average Cost of Capital (WACC) that can be defined as the calculation of a firm's cost of capital by weighting each category of capital proportionately (shareholder's equity, bank loans, bonds, etc.). This is the average expected return on a firm's investments. The WACC is determined from weighting the cost of equity and the after-tax cost of debt (calculated form the tax rate and average annual real interest rate on debt). The weighting for debt and equity come from the debt-to-equity ratio (as input or determined from the projects cash flows when a static equity amount is entered). The cost of equity comes from the calculation of the equity risk premium and the risk free rate (typically the return of a “near” risk free investment like a U.S. government Treasury bill). The equity risk premium is derived from the market risk premium times the project’s equity beta. The beta is a measure of risk of this project versus the overall market. The beta used here is associated with the debt-to-equity ratio. The beta of this project is determined from examination of three different industries gathered from a list of comparable companies7. The industries for the nominal model include aerospace, air transport, and e-commerce. The first two industries have much lower betas than third. The model has the flexibility to substitute these for other user-defined industries and betas. NASA MMM Manifesting Algorithm The NASA Mission Market Model (MMM) has a list of possible payloads that can be captured in various payload categories ranging from the bantam class (<500lbs to LEO) to super heavy (>12,000lbs to GTO). These would not be the best representatives of flights since a 60klb vehicle would not carry a nominal 500lb payload by itself. Thus a manifesting algorithm is present in CABAM that takes the payloads captured from a given input payload capability and price and manifests together selected payloads together onto the same flights. This is not

an optimization routine to minimize the net excess payloads per flight per year (very time consuming in a spreadsheet based environment) but a heuristic based VBA macro that examines the payloads and applies a certain set of rules to the manifesting problem. The current manifesting algorithm does not manifest all flights from all markets. The particular rules imbedded in the current algorithm include: NASA payloads are manifested first with

commercial payloads, then any remaining commercial payloads are manifested with DoD payloads

Smallest NASA or DoD payload is manifested with largest available commercial payload

Only single or dual manifesting options will be used

Average LEO Equivalent Payloads are taken as value of payloads to be manifested

No NASA payloads will be manifested with other NASA or DoD payloads

No DoD payloads will be manifested with other DoD or NASA payloads

Manifesting will try to maximize the packaging efficiency

ISS Servicing, Exploration, Emerging Market payloads will fly as unique flights

Vehicle Acquisition Algorithm To accurately model reality one needs to know both the number and the years of production of the entire vehicle fleet. The number of vehicles depends upon characteristics of the architecture whereas the acquisition timeline depends upon assumptions about vehicle production. CABAM uses a VBA macro algorithm for various architecture components (Booster, LEO Module, Booster propulsion, LEO module propulsion, cargo container, and crew container) to determine a production / acquisition schedule. Several assumptions are made with regards to the acquisition schedule in CABAM, these include: The production line will start before IOC The production line will be continuous and not

take any yearly break once it starts The number of vehicles produced will at least

meet the flight rate requirement for the next flight year

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The determination of the required fleet (and inputs to the macro) depends upon the following parameters: Flight rate per year for each years in the program Turn-Around-Time (TAT) [days] Time-In-Flight (TIF) [days] Vehicle Lifetime [flights] Maximum number of years that a production line

can stay open Minimum number of vehicles that have to be

produced per year in the production line The algorithm tracks the actual number of flights for each vehicle acquired. In addition, the algorithm attempts to smooth out the production such that there are as few spikes in production as possible. Vehicle Learning Curve Algorithm Once the determination is made as to the actual number of fleet and their production, learning effects are applied. Throughout the model the term “learning curve effect” is used but this is a proxy for the combination of learning, rate, and production effects which are all aggregated into the learning curve percentage input parameter. The learning curve calculation is used per year for various architecture components (Booster, LEO Module, Booster propulsion, LEO module propulsion, cargo container, and crew container) needing the number of units produced so far, the number of units to be produced in a given year, and the learning curve effect percentage.

DOMAIN OF EVALUATION: XCALIBUR-10 3rd GENERATION TSTO RLV

Xcalibur is a 3rd Generation (first flight 2025) Reusable Launch Vehicle (RLV) developed at SEI. It consists of a fully reusable, Two-Stage-To-Orbit (TSTO) system with vertical takeoff, horizontal landing (VTHL) capable (see Figures 3, 4, and 5). The orbiter is carried inside at the rear of the booster and separates out the back. The main propulsion system on the booster is Rocket Based Combined-Cycle (RBCC) with advanced LOX/LH2 rocket engines using High Energy Density Matter (HEDM) propellants on the orbiter. The vehicle is designed for autonomous, un-piloted flight with a powered supersonic fly-back for the booster. Xcalibur was designed with advanced systems engineering tools

and processes used for conceptual design (see Figures 6, 7, 8, and 9). The baseline vehicle was designed to deliver 20K lbs. to Low-Earth-Orbit (LEO) from a KSC-type (28.5 degree inclination) launch site. For this particular examination a lower payload (10,000 lbs to the same orbit) capable vehicle, referred to as Xcalibur-10, will be used.

Figure 3. Xcalibur-10 Take-off

Figure 4. Xcalibur-10 Stage Separation

Figure 5. Xcalibur-10 Upperstage

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Figure 6. Xcalibur-10 Upperstage Internal View

Figure 7. Xcalibur-10 Booster Internal View

Figure 8. Xcalibur-10 Booster Three-View

Figure 9. Xcalibur-10 Upperstage Three-View

COST MODELING Non-recurring costs include Design, Development, Testing, and Evaluation (DDT&E) and Theoretical First Unit (TFU) costs. The non-recurring cost tool entitled NASA Air Force Cost Model (NAFCOM) was used to determine development acquisition costs for the concept (see Table 2). This data was coupled to the CABAM economic model. This MS Excel based model uses subsystem weight-based cost estimation relationships (CER’s) sourced in part from data within NASA’s unrestricted release version of NASCOM database II8. DDTE & TFU is organized around a common WBS similar to that found in the mass properties calculations. Currently, CER’s for the level 1 breakdown are used. The propulsion system is treated separately since it is commonly acquired separately from the airframe subsystems. Programmatic costs present in the model include: system test hardware; integration, assembly, & checkout; system test operations; ground support equipment; systems engineering & integration; and program management. Near full-scale prototyping and testing for

booster airframe, half-scale prototyping and testing for orbiter airframe

Full-scale prototyping and testing of propulsion systems for both booster and orbiter

Higher development complexity for orbiter HEDM propulsion system due to current, very low Technology Readiness Level (TRL)

Utilizing advancements in these areas (avionics, environmental control, and power subsystems) from other industries

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Cost margin of 20% in place of weight margin Costs based only upon vehicle dry weight

exclusive of margin

Table 2. Xcalibur-10 Non-Recurring and First Vehicle Acquisition Cost Summary†

Cost Item Booster [$M]

Orbiter [$M]

Total [$M]

DDT&E (Airframe) $4,378 $1,404 $5,782 DDT&E Engines (RBCC) $1,411 DDT&E Engines (Rocket) $22 DDT&E (All Engines) $1,411 $22 $1,483 Total DDT&E $5,789 $1,426 $7,265 TFU (Airframe) $980 $265 $1,245 TFU Per Engine (RBCC)‡ $226 TFU Per Engine (Rocket)‡ $17 Acquisition Engines (RBCC)¥ $756 Acquisition Engines (Rocket)¥ $46 Total Acquisition (All Engines) $756 $46 $802 Total Acquisition (First Vehicle) $1,736 $311 $2,047 Cost to First Vehicle $9,312 † - Rounded FY2002 US$, assuming a 2.1% inflation rate ‡ - Per engine with no learning/production rate effects ¥ - For all engines (with 4 RBCC engines on booster, 3 rocket engines on orbiter) with 85% learning/production rate effect percentage

OPERATIONS AND SAFETY MODELING The Architectural Assessment Tool–enhanced (AATe) is a tool for assessing a space transportation system for its operational impacts, mainly costs and cycle times9. It is capable of providing both qualitative and quantitative insights into systems still being conceptualized. The tool is based on the work of both the national Space Propulsion Synergy Team (SPST) and of the joint NASA, Industry & Academia Vision Spaceport project. This model requires both quantitative inputs and qualitative order of magnitude comparisons of the concept vehicle to the Space Shuttle. Inputs include: overall vehicle reliability, airframe life, payload weight, dry weight, vehicle length, and payload demand per year. Outputs include: ground turnaround time, facilities cost, labor cost per flight, line replaceable unit (LRU) cost per flight, and operating expenses per flight. GTSafetyII is a top-level MS Excel based spreadsheet for determining various safety and reliability metrics for reusable launch vehicles (RLVs). The model requires both quantitative inputs from other RLV disciplinary tools (referred to as coupling variables) as well as specific qualitative user inputs as to the architecture being examined

(including safety adjustment factors). The coupling variables consist of variables that describe the physical dimensions of the vehicle (wetted area, length, height, etc.), the configuration (number of propulsion systems, etc.), and the use of the vehicle in the program (flights per year, passengers per flight, etc.). The other types of variables are used for qualitative comparisons of the vehicle in question with the Space Shuttle. The additional safety calculations are separated into the following areas: public/collateral safety, ground personnel safety, flight crew/passenger safety, TPS reliability, engine reliability, and overall mission/vehicle reliability calculations. Output metrics are then determined that relate to both vehicle reliability and program safety. The reliability metrics include terms for both loss of mission and loss of vehicle. The safety metrics include for casualty rate and loss of crew events. Output metrics are listed at the top of worksheet and are shown in terms of both flights and years between incidents. Both models utilized the Weight Breakdown Structure (WBS) of the Xcalibur-10 vehicle, along with assumptions as to flights rates, to obtain operations cost and Loss of Vehicle (LOV) parameters that are input into CABAM (see Table 3). The LOV metric is required to calculate the hull replacement insurance for each flight. Currently CABAM only models such vehicle-specific insurance and not payload or public launch liability insurance.

Table 3. Xcalibur-10 Operations Cost and Safety Summary

Item Value Fixed Operations Cost Per Year Variable Operations Cost Per Flight Site Fee Cost Per Flight Average Recurring Cost Per Flight Facilities / GSE Acquisition (one time) Booster Ground Cycle Time [days] Orbiter Ground Cycle Time [days]

$49.3 M $3.8 M $0.50 M

$5.725 M $494.8 M 4.4 Days 3.8 Days

Booster Loss of Vehicle (LOV) [MFBF] Orbiter Loss of Vehicle (LOV) [MFBF]

2,628 Flights 5,685 Flights

† - rounded FY2002 US$, 2.1% inflation rate, flights rates going into GTSafetyII used flight rate assumptions of 25-26 flights per year.

ECONOMIC MODELING ASSUMPTIONS Assumptions were composed in CABAM in regards to the type of concept being developed. These included parameters to define both the timeline and

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the economic environment of the program. Additional assumptions regard the magnitude of government offsets, recurring costs, and lifetime of the vehicle airframe and propulsion units. Specific assumptions broken out by category include: Program and Economic Environment Initial Operating Capability (IOC): 2025,

Technology freeze date: 2020, Program life: 28 years (21 flight years)

Payload based upon LEO equivalent capability (15% inefficiency factor)

Inflation rate: 2.1%, Tax rate: 30%, Debt-to-equity ratio: 3, Average annual nominal interest rate: 7.5%

Market risk premium: 9.6% + 2.5% required incentive return, Insurance premium (over estimated loss): 5%

Markets Commercial Space Transportation Study (CSTS)

market curve fits Market elastic commercial and government

cargo and passenger markets Government contribution and Production 25% towards airframe, 100% towards propulsion

(DDT&E cost), 100% towards facilities development cost

Government purchases first vehicle off production line

Learning curve on airframes and boosters: 85%, 2 years to of fleet production before IOC

Recurring Cost (Operations and Total) Operations (parts + labor + recurring facility)

and Total (operations + propellant + insurance + site fee)

Vehicle hull insurance cost per flight: based upon acquisition value of airframe + engines, reliability, and risk premium

Specific insurance cost nominally between $1-3M cost per flight and nominal site fee of $0.50 M per flight

System lifetime Booster airframe / propulsion: 1,000 / 500

flights; orbiter airframe / propulsion: 1,000 / 500 flights

COUPLED DISCIPLINARY MODELS Modeling helps to determine the properties of a technically feasible design. In the conceptual design stage, modeling can include the use of monolithic synthesis/sizing codes or integrated disciplines in a multi-disciplinary environment (see Figure 10). These models are representations of the real world based on processes in terms of physics, human operations, financials, etc. Only the most relevant tools that impact the final output metrics in a substantial manner are detailed here. Previous analyses have yielded the performance and weight summary for the Xcalibur concept. Data is incorporated from that process into cost, operations, and safety models to yield input data for the economic model CABAM.

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Figure 10. Type II Design Structure Matrix (DSM) of Cost, Operations, Economics and Safety Processes

Within ModelCenter© Many types of design processes exist for the coupling between the disciplines of cost, operations, safety, and economics. The more coupled the disciplines the more difficult it becomes to converge the design process. Specific types include: Type I No coupling between Economics, Operations

and Safety input/output variables All models run in parallel Positive: Fast system model response time Negative: Loss of real world fidelity

Type II (see Figure 11) Limited coupling between Economics,

Operations and Safety input/output variables

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Approximations and assumptions of some input parameters

Positive: Semi-fast response time, more accurate Negative: Average level of fidelity with more

optimization overhead Type III Complete coupling between Economics,

Operations and Safety input / output variables Use of multi-sublevel optimizers and/or MDO

techniques Positive: Complete inclusion of model-to-model

feedbacks to accurately simulate relationships Negative: Converger/optimizer reliance

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Coupling: Operations feeds Safety a headcount but that is dependent upon a loss of vehicle reliability coming from Safety

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Figure 11. Type II Design Structure Matrix (DSM) of Cost, Operations, Economics and Safety Processes

Within ModelCenter© The full, feed-forward, feedback models (Type III) consisting of all disciplines have problems of convergence with gradient-based optimizers. For Type II couplings, the operations, safety, and economics disciplines are also tightly coupled and problematic to converge (see Figure 11). Implementation can be eased with removal of the coupling between operations and the other disciplines. Operations costs are then determined from the average of various input flights rate (see Figure 11). Fixed and variable operations costs and turn-around-time then feed into the economics disciplines as average values. Optimization Based Decomposition (OBD) can be used to help decouple some of the links to a top-level optimizer.

COLLABORATIVE DESIGN ENVIRONMENT Launch vehicle conceptual design is performed using disciplinary engineering models in a collaborative engineering framework, namely Phoenix Integration’s ModelCenter© and Analysis Server© products. These tools allow the designer to join disparate models and simulations together in a unified environment wherein each discipline can interact with any other discipline10. This is performed through a visual interface of an engineering workflow of events where inputs and outputs from various models can be linked together (called a ModelCenter© “model”, see Figure 12). This interface allows the engineering process to be more automated and flexible with regards to computing platforms since ModelCenter© and Analysis Server© are relatively platform independent. In addition, these products allow disciplinary models (or ModelCenter© “components”) to be located at diverse geographical locations since data exchange can be performed seamlessly in the environment through the Internet. Driver components besides the models and simulation themselves can be added to the environment. These can include optimizers, trade studies, Design of Experiments (DOE), as well as Monte Carlo components.

Figure 12. Conceptual RLV Design Process Within ModelCenter© Collaborative Design Environment

OPTIMIZERS UTILIZED Once the inputs to CABAM from the cost, operations, and safety disciplines have been established, optimization of the business case can be

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determined. ModelCenter© has a gradient-based optimizer standard in the installation. For this examination additional and alternate optimizers were required given previous hardships encountered when optimizing a business case with multiple design variables (i.e. market prices). Subsequently, an eight component optimization suite for the ModelCenter© collaborative design environment was utilized. Specifically this refers to the SpaceWorks Engineering, Inc. (SEI) newly developed suite of optimization tools entitled OptWorks. This suite initially consists of eight non-gradient based optimizers each implemented as Java-based components which can function on any platform running Phoenix Integration’s ModelCenter© or Analysis Server©. The specific types of optimizers include: OptWorks_GeneticAlgorithm:

o Utilizes properties of natural selection found in biological evolution

OptWorks_AutoGA o Same as above but with pre-defined setup

parameters for novice users OptWorks_SimulatedAnnealing

o Domain spanning search method based upon metallurgical processes

OptWorks_AutoSA o Same as above but with pre-defined setup

parameters for novice users OptWorks_RandomWalk

o Search along randomly determined direction for next movement

OptWorks_CoordinatePatternSearch o N-orthogonal search able to handle

discontinuities but not multiple local minima OptWorks_RandomSearch

o Domain spanning method with random determination of analysis point within design space

OptWorks_GridSearch o Exhaustive search of design space with auto

resolution refinement These tools enhance the current gradient based optimization tools in ModelCenter© to allow solution of previously intractable problems. Characteristics of these sets of applications include the capability to handle problems with high dimensionality, discrete or mixed variables (continuous and discrete), and multi-modal solutions spaces.

This package is currently available for purchase through individual/group site licenses. The full product suite includes optimizers in Java byte code, documentation with case study examples, and selected online support. For this examination only three of the optimizers in the OptWorks suite were utilized. These included: Coordinate Pattern Search (CPS), genetic algorithm (GA), and grid search (GS). The optimizers were then linked up with CABAM in ModelCenter© (see Figure 13). Additionally the default gradient-based optimizer within ModelCenter© was compared with these others.

Figure 13. CABAM Model and OptWorks CPS Optimizer Within

ModelCenter© Collaborative Design Environment The objective function for all the cases was to maximize the Net Present Value (NPV) of the project based upon a WACC. The total number of iterations was predetermined to a limit of fifty in order to compare the results of the optimizers over an equivalent number of attempts.

NPV OPTIMIZATION RESULTS Tables 4, 5, 6, and 7 and Figures 14, 15, 16, and 17 show the results for the various optimization runs for the three classes of problems. Various optimizer settings are also given along with the values of the final design variables and objective functions. For most of the examples shown, the Net Present Value (NPV) of the project is below zero. This would indicate that from a DCF analysis, these projects

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should not be undertaken. This particular assessment will be held back until more examination can be performed on how each optimizer performed. Importance is given here to determining the better optimization methods for complex financial models such as CABAM. It is also important to keep in mind the subtleties with regards to these outputs (in terms of parameters such as units produced and effects of government acquisition). Initial testing with the optimizers yielded estimates for individual optimization settings (such as a high Genetic Algorithm mutation rate). A final optimization case using four market prices was performed using the Grid Search component to examine a wide area of the design space.

Table 4. CABAM NPV Optimization: Gradient-Based (ModelCenter©)†

Class Value Class I Commercial Cargo Price Per lb Government Cargo Price Per lb NPV Number of Function Calls (best iteration at)

$1,987.81/lb $1,987.81/lb -$13.46 M

5 (5) Class II Commercial Cargo Price Per lb Government Cargo Price Per lb NPV Number of Function Calls (best iteration at)

$1,947.38/lb $2,007.88/lb

-$8.98 M 7 (7)

Class III Commercial Cargo Price Per lb Commercial Passenger Price Per Flight Government Cargo Price Per lb Government Passenger Price Per Flight NPV Number of Function Calls (best iteration at)

$1,996.06/lb

$2.17 M $2,001.25/lb

$10.91 M -$29.54 M

6 (6) † - conjugate gradient method, relative convergence = 0.01, finite difference method, maximum number of function calls = 10000.

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Figure 14. CABAM NPV Optimization: Gradient-Based Progress

Table 5. CABAM NPV Optimization: Coordinate Pattern Search (CPS)†

Class Value Class I Commercial Cargo Price Per lb Government Cargo Price Per lb NPV Number of Function Calls (best iteration at)

$2,493.75/lb $2,493.75/lb $350.34 M

13 (12) Class II Commercial Cargo Price Per lb Government Cargo Price Per lb NPV Number of Function Calls (best iteration at)

$2,493.75/lb $2,493.75/lb $350.34 M

33 (29) Class III Commercial Cargo Price Per lb Commercial Passenger Price Per Flight Government Cargo Price Per lb Government Passenger Price Per Flight NPV Number of Function Calls (best iteration at)

$2,987.50/lb

$2.00 M $2,493.75/lb

$9.01 M $364.36 M

69 (60) † - initial step size = 0.2, minimum step size = 0.01, no compass search, maximum number of function calls-limit not used.

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Table 6. CABAM NPV Optimization: Genetic Algorithm (GA)†

Class Value Class I Bit size Population Size Commercial Cargo Price Per lb Government Cargo Price Per lb NPV Number of Function Calls (best iteration at)

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$2,477.01/lb $2,477.01/lb $347.19 M

220 (2) Class II Bit size Population Size Commercial Cargo Price Per lb Government Cargo Price Per lb NPV Number of Function Calls (best iteration at)

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$38.854.07/lb $2,893.83/lb -$528.66 M 581 (365)

Class III Bit size Population Size Commercial Cargo Price Per lb Commercial Passenger Price Per Flight Government Cargo Price Per lb Government Passenger Price Per Flight NPV Number of Function Calls (best iteration at)

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$3,358.79/lb $25.82 M

$2,723.07/lb $8.76 M

$220.38 M 620 (404)

† - tournament selection, two-point cross-over, design value mutation type, random seed = 0, tournament participants = 2, crossover probability = 0.6, mutation probability = 0.6, maximum number of generations = 5000, convergence iterations = 10, maximum number of function calls-limit not used.

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Figure 16. CABAM NPV Optimization: Genetic Algorithm (GA) Progress

Table 7. CABAM NPV Optimization: Grid Search (GS)

Class Value Class I Grid-points per design variable Sub-searches Commercial Cargo Price Per lb Government Cargo Price Per lb NPV Number of Function Calls

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$2,663.37/lb $2,663.37/lb $368.95 M 100 (60)

Class II Grid-points per design variable Sub-searches Commercial Cargo Price Per lb Government Cargo Price Per lb NPV Number of Function Calls

25 1

$2,214.41/lb $3,037.33/lb $399.81 M 1250 (664)

Class III Grid-points per design variable Sub-searches Commercial Cargo Price Per lb Commercial Passenger Price Per Flight Government Cargo Price Per lb Government Passenger Price Per Flight NPV Number of Function Calls (best iteration at)

5 1

$5,437.50/lb $5.44 M

$5,437.50/lb $10.38 M

-$1,686.49 M 1,250 (876)

Class III (Additional case) Grid-points per design variable Sub-searches Commercial Cargo Price Per lb Commercial Passenger Price Per Flight Government Cargo Price Per lb Government Passenger Price Per Flight NPV Number of Function Calls (best iteration at)

10 1

$2,694.44/lb $2.69 M

$2,694.44/lb $9.03 M

$394.50 M 20,000 (14,001)

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Figure 17. CABAM NPV Optimization: Grid Search (GS) Progress

The gradient-based optimizer had the worst performance of any of the optimization cases, stopping after only a few iterations. This is reflective of the uneven design space. Generally, the CPS could

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find the most optimum case using the fewest number of function calls. The GA also was successful most of the time in finding the optimum, though not as successful as CPS. The Grid Search could find the most optimum but at the expense of more function calls. Using data from all the cases, the highest NPV achieved was $399.81 M for prices of $2,214.41/lb, $2.00 M, $3,037.33/lb, and $10.00 M for the following types: commercial cargo, commercial passengers, government cargo, and government passengers (using Grid Search). Cargo (commercial and government) prices were generally in the two to three thousand dollar range for most of the optimized cases. For these cases, the price of cargo was generally the same for commercial and government customers. Commercial passenger prices were generally in the low millions of dollars and government passenger prices were in the eight to ten million dollar range for most of the optimized cases.

CONCLUSIONS CABAM is a financial model tailored for the analysis of RLV concepts that can help define landscapes of economic value (see Figure 18). Optimization of the RLV business case using higher fidelity analysis than in previous tools is accomplished through this model. Maximization of the NPV case was performed using various types of optimizers in the OptWorks suite. Coordinate Pattern Search (CPS) and genetic algorithm (GA) rated as better than standard gradient-based optimizers. RLVs designers need to pay particular attention to the financial aspects of their concepts. These aspects are some of the most important drivers when it comes to making future concepts viable. In many cases the economic parameters of the concept are more important than any advanced technology being applied to the concept. As can be seen for this examination, the price per lb was relatively high. Future vehicles will need to have lower initial fixed costs in order to reach the goal of ubiquitous space travel. Figure 19 displays scenarios that can be played with financial models such as CABAM in which initial development and recurring operations cost parameters are reduced (in this case for another sample 3rd Generation RLV). Substantial reduction in the baseline concept’s DDT&E, TFU, and operations costs need to be performed in order to achieve low

market prices and high flight rates that meet expected financial returns.

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Effects Upon Launch Price ($/lb)

REFERENCES 1. Anon. “Commercial Space Transportation Study

(CSTS) - Executive Summary.” NASA -

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Langley Research Center, Hampton, VA, April 1994.

2. Lee, H. and Olds, J. R., “Integration of Cost

Modeling and Business Simulation into Conceptual Launch Vehicle Design” presented at the 1997 AIAA Defense & Space Programs Conference and Exhibit, AIAA 97-3911, September 1997.

3. Lee, Michael Hosung, “Cost and Business

Analysis Module (CABAM): Final Report and User’s Manual,” School of Aerospace Engineering, Georgia Institute of technology, October 2, 1997 (rev. A.).

4. Olds, J. R. and Lee, H., “Application of a New

Economic Analysis Tool to a Two-Stage-To-Orbit RBCC launch Vehicle.” presented at the 1996 6th AIAA/NASA/ISSMO Symposium on Multi-disciplinary Analysis and Optimization, AIAA 96-4092, September 1996.

5. Whitfield, J. A. and Olds, J. R., "Economic

Uncertainty of Weight and Market Parameters for Advanced Space Launch Vehicles," AIAA 98-5179, 1998 Defense and Civil Space Programs Conference and Exhibit, Huntsville, AL, October 28-30, 1998.

6. Charania, A., Olds, J., "A Unified Economic,

View Of Space Solar Power (SSP)," IAF-00-R.1.06, 51st International Astronautical Congress, Rio de Janeiro, Brazil, October 2-6, 2000.

7. Value Line Database of 5671 firms, Jan. 2001. 8. Anon. “The Marshall Space Flight Center’s

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10. Charania, A.C., Bradford, J. E., Olds, J., Graham, M., "System Level Uncertainty Assessment for Collaborative RLV Design," JANNAF-2002-2B-4-MSS, 2002 JANNAF 38th Combustion Subcommittee/26th Airbreathing Propulsion Subcommittee/20th Propulsion Systems Hazards Subcommittee/2nd Modeling and Simulation Subcommittee Joint Meeting, Destin, Florida, April 8-12, 2002.

IAC-02-IAA.1.1.07 OPTIMIZATION OF A FUTURE RLV BUSINESS

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