MODELING THE MARS-EARTH LOGISTICS AND TRANSFER ARCHITECTURE: FOCUS ON CONDUCTING INEXPENSIVE EXPLORATION THROUGH VARIANCE OF KEY MISSION REQUIREMENTS

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1 Reynerson_1999 MODELING THE MARS-EARTH LOGISTICS AND TRANSFER ARCHITECTURE: FOCUS ON CONDUCTING INEXPENSIVE EXPLORATION THROUGH VARIANCE OF KEY MISSION REQUIREMENTS Dr. Charles M. Reynerson 1

2 ABSTRACT This paper addresses a onept-level model that produes tehnial design parameters and eonomi feasibility information addressing future Mars Exploration platforms. In this ontext, the platforms onsidered inlude those urrently hosen in the NASA Mars Design Referene Mission. This paper uses a design methodology and analytial tools to reate feasible onept design information for these spae platforms. The design tool has been validated against a number of atual faility designs, and appropriate modal variables are adjusted to ensure that statistial approximations are valid for subsequent analyses. The tool is then employed in the examination of the impat of various payloads on the power, size (volume), and mass of the platform proposed. The development of the analytial tool employed an approah that aommodated possible payloads haraterized as simplified parameters suh as power, weight, volume, rew size, and endurane. In reating the approah, basi priniples are employed and ombined with parametri estimates as neessary. Key system parameters are identified in onjuntion with overall system design. Typial ranges for these key parameters are provided based on empirial data extrated from atual human spaeflight systems. In order to provide a redible basis for a valid engineering model, an extensive survey of existing manned spae platforms was onduted. This survey yielded key engineering speifiations that were inorporated in the engineering model. Data from this survey is also used to reate parametri equations and graphial representations in order to establish a realisti range of engineering quantities used in the design of manned spae platforms. Using this tool a sample Mars exploration arhiteture is formulated with emphasis on ost minimization through variane of key mission requirements. This paper is based on work Dr. Reynerson reently ompleted at George Washington University in fulfillment for the degree of Dotor of Siene in Astronautis. Dr. Mike Griffin, former head of NASA s Human Mars Mission, was a member of the dissertation ommittee. INTRODUCTION The design of human spaeflight systems is not a field that has an overabundane of design examples. But from over 40 years of experiene there exists enough quantitative data on whih to attain initial estimates for future designs. On this premise the following paper is written. Prior work has been done in this area relative to spae station designs. In the dotoral dissertation work performed by Dr. Charles Reynerson an engineering model was reated for preliminary spae station design. The model is general enough in that it an be used for any spaeraft, regardless of it s purpose. Typial spaeraft with only sensors for payloads would be a

3 speialized ase of the model. The generalized model inorporates the addition of humans and their assoiated life support and habitation systems. It is the intent of this paper to show that the same model an be used for elements needed for human transportation and settlement on extraterrestrial bodies. This model an be easily adapted to have appliability to elements suh as transportation vehiles, non-earth orbiting spae stations, and surfae habitations. The model also provides a rough ost estimate for future manned missions. As an example, the NASA Mars Design Referene Mission (DRM) will be used as a baseline arhiteture whih will be perturbed by altering the desired payload amount during transfers and landings. This variation will show the impat of entire system mass required as well as system ost. HUMAN SPACE SYSTEM MODEL A human spae system model was reated that is omposed of both an engineering model and a ost model. This model treats two basi types of payloads: humans and spae hardware (i.e. sensors, ommuniations, manufaturing, ). The model flow is shown in Figure 1. Five inputs are put through the engineering model and spaeraft power, mass, and volume are output. The ost model uses spaeraft weight as an input sine it tends to drive about 80% of ost on typial spae systems. Inputs: User Payload: Size, Power, Weight Crew Number, Endurane Cost Model Expenditures Engineering Model Revenues Power Weight Volume Faility Power Faility Weight Faility Volume Eonomi Parameters Fig 1. Modeling Flow Diagram

4 ENGINEERING MATH MODEL GOVERNING EQUATIONS The inputs to the model are the following variables: W p = reate V p = P p = N = payload power (payload being defined as spae rated hardware and equipment used to revenue for the spae business park) payload volume payload user power (most ommonly referred to as user power on spae stations) number of rew members E = designed endurane limit for the rew. This time fator will also be the assumed resupply interval for onsumables alulations. Assume the outputs to our model are the following variables: W f = faility weight V f = faility volume P f = faility power Assume that the output variables are some linear ombination of the input variables. In reality the input variables may be raised to some arbitrary power as follows. a b d W = f ( W, V, P, N, E ) = α W + β V + χ P + δ N + ε E f p p p p f g h i V = g( W, V, P, N, E ) = φ W + ϕ V + γ P + η N + ι E f p p p p k l m n P = h( W, V, P, N, E ) = κ W + λ V + µ P + ν N + ο E f p p p p p p p p p p j o e (1-3) If a linearized form of equations 1 through 3 are used then the following approximations an be made:

5 W α W + β V + χ P + δ N + ε E f p p p V φ W + ϕ V + γ P + η N + ι E f p p p P κ W + λ V + µ P + ν N + ο E f p p p (4-6) In matrix form: f = Α p where f Wp W f α β χ δ ε Vp = V f, Α = φ ϕ γ η ι, and p = P p P f κ λ µ ν ο N E The above equations form the basis for the engineering onept model. (7) In reality the omponents of matrix A will vary depending on the payload input values in vetor p. Thus the system is not truly linear; nonetheless, we will approximate it as suh. Eah omponent of A will be disussed in the following setions. When fousing on the meaning of eah omponent in relation with the physial system some of the A matrix oeffiients an be negleted. For the faility volume alulation, the payload weight term does not affet the payload volume term if they are assumed to be independent. Similarly, in the faility weight alulation the payload volume term is independent of the payload weight term. For the faility power alulation, only the payload power and rew number terms are assumed to have an effet on payload power. Therefore, the A matrix beomes, α 0 χ δ ε A = 0 ϕ γ η ι. (8) 0 0 µ ν 0 Through areful aounting of typial spaeraft weight, volume, and power budgets the above assumptions an be shown to have validity. 2 FACILITY WEIGHT The weight of the faility an be onsidered in terms of its basi segments. The most general division is that of payload and bus. The payload in this ase is onsidered to be both spae hardware and people. The spaeraft bus must be designed to support both the hardware and the rew. Sine the effet of varying the amount of spae hardware and rew on the faility is to be onsidered, the bus is broken into two portions: that whih supports the spae

6 hardware, and that whih supports the rew. The third major weight ategory is onsumables. This an also be subdivided between onsumables for the spaeraft bus and onsumables for the rew. The faility weight equation is given by: W α W + β V + χ P + δ N + ε E. (9) f p p p The orresponding terms are defined as followed: α W p = payload weight + payload bus support weight - power system weight required to support payload β V p = 0 χ P p = power system weight required to support the payload δ N = spaeraft subsystem weight required to support the rew (inludes power) and rew weight ε E = weight of onsumables for the rew and the faility. The seond term is assumed to be zero sine the payload effet is inluded in the first term. This might be useful if the assumed payload density is not omparable to urrent systems. The above equation oeffiients an be shown to relate to atual spaeraft weight budgets. FACILITY VOLUME For the volume equation, the terms are analogous to those of the weight equation exept for the fourth term. For the rew members, there is an additional volume alloation for habitability. This was negleted in the weight equation sine the weight of the air within that volume is small relative to the other terms. The faility volume equation is given by: V φ W + ϕ V + γ P + η N + ι E (10) f p p p where the orresponding terms are defined as followed: φ W p = 0 ϕ V p = payload volume + payload bus support volume - power system volume required to support payload

7 γ P p = power system volume required to support the payload η N = spaeraft subsystem volume required to support the rew (inludes power) + rew habitable volume alloation ι E = volume of onsumables for the rew and the faility The first term is zero sine the payload effet is aounted for in the seond term. This might be useful if the assumed payload density is not omparable to urrent systems. The above equation oeffiients an be shown to relate to atual spaeraft volume FACILITY POWER The faility power equation is given by: P κ W + λ V + µ P + ν N + ο E (11) f p p p where the orresponding terms are defined as follows: µ P p = power required for supporting the payload and payload support systems ν N = P = power required to support rew related subsystems and faility support systems κ W = λ V = ο E = p p 0. The first (payload weight) and seond (payload volume) terms are not needed sine the third term, represented by the payload power, gives the diret relation for the payload. The last term is appliable if the power subsystem design strategy is to treat power as a ommodity to be resupplied at various time intervals. An example may be fuel ells, non-rehargeable batteries, or short-lived nulear power soures. For this model we will assume onventional photovoltai or solar dynami systems are used and that suffiient design margin is used to ompensate for power system effiieny degradation over time. The above equation oeffiients an be shown to relate to atual spaeraft power budgets. The above equations form the basis for the engineering onept model. Details of this model and its validation are beyond the sope of this paper but an be found in referene [4]. 3 Internal to the model, the oeffiients of the governing equations an be related to 14 key system parameters that are unique to eah system. These parameters are presented in Table 1 along with their typial range for several human spaeflight designs.

8 Internal Model Typial Average Physial Meaning Variable Range Value ν, kw/person 0.5 to Crew Speifi Power ζ 0.19 to Bus to Payload Power Ratio α to Powerless Bus to Payload Weight Ratio δ s, kg/person 1573 to Crew System Speifi Weight χ, kg/kw 75 to Power System Speifi Weight ε a, kg/person-day 2.6 to Crew Member Consumption Rate ε p, kg/day 5.6 to Propellant Consumption Rate ρ p, kg/u. m 69.3 to User Payload Density ρ s, kg/u. m 64.6 to Crew System Density ρ b, kg/u. m 25.8 to Bus Density η h, u. m/person 1.1 to Crew Habitation Speifi Volume η g, u. m/person 0.03 to Crew Gear Speifi Volume ι a, u.m/personday to Crew Member Volumetri Consumption Rate ρ p, kg/u. m 70 to Propellant Density Table 1. Key System Parameters Internal to Engineering Model COST MODEL A ost model was reated to omplement the engineering model. For a ommerial development ost tends to be the bottom line and therefore should be addressed. Various ost fators that result from spae faility designs and an estimation of rough order of magnitude ost are inluded in this ost model. The model onsists of two main setions: required investments and revenues. The required investment areas addressed inlude the spae segment, launh

9 vehiles, operations, and logistis. The revenues onsidered inlude rew and user payload related revenues. Spae Segment Cost The spae segment is modeled using the produt of four variables. The spae segment ost fator (Sf) is the prie per kg of faility on orbit. This value typially varies from 58 to 148 $K/kg for unmanned spaeraft. 4 For manned spae programs (Note that these values are for government run programs) the range is 38 to 157 $K/kg and the mean is 104 $K/kg. The program ost is normalized over the number of manned vehiles produed. The low number in the Skylab program is likely due to less researh and development required sine it was derived from the Apollo program. The researh, test, development, and engineering (RTD&E) ost fator (Rf) is used to ompensate for new development ost. RTD&E ost tends to be about three times that of the theoretial first unit (TFU) ost. For manned systems this would make the Sf range from 22 to 52 $K/kg for the TFU if you assume all of the programs were pure RTD&E ost (not inluding Skylab). Assuming this range for the TFU, then the Rf should be 3 for new development programs, 1 for a program based on existing hardware, or somewhere in between if there is partial development required. The spae segment ost (S) an now be defined by the following equation: S = Sf Pf Rf Wf (12) Launh Vehile Cost The launh ost fator (Lf) an be estimated using historial data and planned ost goals for future developments. Launh vehile osts 5 for several ompeting launh systems range from 4.4 to 57.4 $K/kg. The average ost is around 15.2 $K/kg. An insurane ost fator (If) is used to aount for insurane ost related to launh. Typially for ommerial launhes, insurane runs about one third of the launh ost. The If would therefore be a value of around The launh ost (L) for delivering the faility to orbit an now be defined by the following equation: L = Lf I f Wf (13)

10 Ground Operations and Support The ost for the ground equipment is typially muh smaller than the ost needed for the spae segment and launh. But the operations for the ground stations beomes signifiant over time and should be onsidered in the ash flow alulations to ounter the yearly revenues. Operations, mission, and program support osts for the Skylab program 6 average (over 4 years) $31.6 M in 1970 s dollars. This ost is roughly $83 M in The International Spae Station program has $13 B in its operations budget over 10 years for an average of $1.3 B per year. 7 This figure likely inludes logistis osts for delivery of onsumables and maintenane osts to upkeep the faility over its ten-year design life. It may also inlude the RDT&E for future payloads, experiments, and support. For the purposes of this ROM ost model, a figure of $80M per year is used for yearly operations and support osts (Yos). A ten-year operational period (Ny) is assumed for life yle osting purposes. Logistis: To aount for the delivery of people, payloads, onsumables, and produts to and from the faility, a yearly logistis ost is alulated based on weight delivered and launh ost. A logistis rew speifi weight (δ s ) is defined as the equipment weight needed for rew support during the trip to and from orbit. This value should be no more than the rew speifi weight for spae failities due to muh shorter duration on orbit. The value for rew speifi weight an be estimated from previous manned missions (1500 kg/person for Merury to kg/person for Apollo). The Apollo rew speifi weight is high due to the stressing requirements to go to the moon. The Spae Shuttle rew speifi weight is high due to its design to aommodate a heavy lift payload. For this ost model a nominal value of 2000 kg/person is assumed for logistis rew speifi weight, whih is just higher than a Gemini apsule. Equation 14 defines the yearly rew logistis weight (Wl) inluding onsumables. This model assumes resupply intervals to be that of the endurane interval, E. W N = 365 ( δ + δ + δ ) + ε (14) E l s rew g Here δ rew and δ g are the rew system speifi weights for the rew itself and their gear, respetively. ε is the onsumable onsumption rate for the entire faility. For user payload logistis, a yearly turnover fration (Tf) is defined as that fration of total payload weight that is replaed during the year. This term is more useful for permanent failities. If the payload requires a ertain amount of prodution materials delivered, then a materials weight fration (Mf) is used. The Mf is defined as that fration of equivalent payload

11 weight is required per year for payload prodution needs. The value for Mf is highly dependent upon the payload mission. For a tourism mission it might be zero and for a materials proessing faility it ould be more than 100%. The value for Mf is also mission dependent and very muh market driven. If payloads have a nominal life of 5 years, then the turnover rate would be 100% in ten years. Therefore the yearly turnover rate would average 10%. The yearly user payloads logistis weight (Wupl) is then defined by the following: ( ) W = W M + T (15) upl p f f To aount for maintenane materials required for the faility, a maintenane materials weight fration (Mmf) is established. This term like the previous term is also more useful for permanent failities. The Mmf is that fration of the faility weight that is required to be replaed eah year. A nominal value of Mmf = 0.01 is assumed for this model. The maintenane materials yearly delivery weight (Wmm) is therefore: Wmm = Wf M mf (16) The total yearly logistis weight is the sum of equations 14, 15, and 16 as follows: Wl = Wl + Wupl + Wmm (17) The yearly logistis ost is similar to equation 13 but based on logistis weight: Lg = Lf I f Wl (18) then: The total life yle operations and support ost (Os) inluding ground and logistis is ( ) O = N Y + L (19) s y os g The total investment required over the faility life is then: TI = S + L + Os (20)

12 Table 2. Some Key Parameters for the Transit Surfae Habitat Internal Model Variable Typial Range Average Value Transit Surfae Habitat Physial Meaning ν, kw/person 0.5 to Crew Speifi Power ζ 0.19 to Bus to Payload Power Ratio α to Powerless Bus to Payload Weight Ratio δ s, kg/person 1573 to Crew System Speifi Weight χ, kg/kw 75 to Power System Speifi Weight ε a, kg/person-day 2.6 to Crew Member Consumption Rate APPLICATION TO HUMAN MARS MISSIONS This model an be applied to most vehiles that uses humans as payloads. The initial appliation for this model was spae stations. An evaluation of NASA s X-15 high speed test vehile was onduted using this model. Some minor modifiation was required. The onept model was within 22% of the atual weight, whih is aeptable for onept level designs. For the NASA Mars Design Referene Mission (DRM), this model an be applied to the transfer habitat, surfae habitats, asent/desent vehiles, and the Earth Return Vehile (ERV). By using the weight and power budget data in referene 7, Table 2 shows some of the key system parameters for the Transit Surfae Habitat design. These system parameters were found using the engineering model desribed above. Of the six parameters alulated, two are outside of the typial range for typial human spae system designs. The first, the bus to payload power ratio, is somewhat higher than the typial range. This ould mean that the bus power was overestimated, the power required for the payloads was underestimated, or some of both. The seond, the powerless bus to payload weight ratio is somewhat lower than the typial range for typial human spae system designs. This ould mean that the powerless bus weight was underestimated, the payload weight was overestimated, or some of both. The powerless bus weight is the weight of the bus less that of the power subsystem. Comparing the NASA DRM to Apollo As one looks to the one big past human exploration mission to another elestial body, Apollo, it is enlightening to see the enormity of the mission. Table 3 ompares the net mass of the two missions at various stages. To keep the missions normalized for onsisteny, eah one assumes 12 flights to the destination and bak.

13 Table 3. Mass Comparison between the Mars DRM and Apollo Mass Charaterized (metri tons) NASA Mars DRM Apollo Multipliation Fator Total System (est.) Delivered to LEO Delivery to Body Orbit Delivery to Body Surfae Delivered from Body Surfae Returned to Earth One may ask why there is suh a big differene in these masses when ompared. The return mass to earth is more for the Apollo missions sine there were men returned to earth with eah mission. For the Mars DRM only three of the twelve missions are piloted for a total of 18 people as opposed to Apollo returning 36 people (that is, if we had really done 12 missions). If we were to normalize by number of people then the multipliation fators would be even higher than shown in the table. The mass delivered from the body surfaes mathes but the Apollo missions would have lifted 24 persons from the surfae in 12 missions. The differene in mass per person is likely due to a more effiient paking fator for the Mars DRM. The Apollo LEM was designed to lift two at a time while the Mars Asent Vehile is designed to haul six. The effiieny ours sine the MAV needs less support system mass per person. The mass delivered to the body surfae differs by more than an order of magnitude. Perhaps this an be aounted for by the length of stay required on the surfae and the desired redundany needed for suh a remote loation. On the other hand, a loser look should be performed to see if an unreasonable amount of material is being transported to the surfae. The mass delivered to Mars orbit should be somewhat greater than the Apollo missions sine the transfer vehile and Earth Return Vehile require a larger delta-v for TEI as ompared to a trip from the moon. The total system mass and the mass delivered to LEO have about the same multipliation fator of 3.5. This is just the differene in mass between going to Mars (using the NASA DRM methods and tehnologies) as ompared to the moon (using Apollo s methods and tehnologies). Note that the DRM total system mass is estimated and based upon using a system similar to the Saturn V for delivery to a LEO parking orbit. This is somewhat of a disturbing figure to most aerospae engineers who realize that ost an be linked to around 80% of the system mass. For

14 the Apollo saled omparison realize that only 6 missions atually landed on the moon and returned to the Earth but 32 units were produed. In estimating mission ost it should be somewhat less than Apollo if we aount for the learning urve fator of human spaeflight knowledge over the last 40 years. Perhaps we would also have less test flights prior to an atual landing mission. For a rough ost estimate it should be more aurate to use a speifi ost number for the International Spae Station (ISS) of $66,000 per kg (Apollo was $157,000 per kg). Using the mass delivered to LEO for the DRM, the estimated ost is $165.5 B (FY 97 dollars). This amount is very lose to the Apollo program ost of $166.8 B in FY97 dollars. So the paramount question is: Can we afford to do another Apollo-sized program? REDUCING THE COST TO GO TO MARS The following modifiations ould be made to the NASA Mars DRM in order to save a signifiant amount of infrastruture ost. A. Use of existing transportation systems. To avoid the exessive ost of reating a new launh vehile system, the approah assumed is to use existing transportation systems to deliver modules into a low Earth orbit (LEO) prior to Trans-Mars injetion (TMI). This approah allows existing doking and maneuvering tehniques to be used as well as the existing ISS infrastruture should there be integration or start-up problems. Suh problems are best dealt with prior to TMI, when there is a better hane to reover from serious problems. During return trips the intermediate destination is in LEO. Payloads an be retrieved in LEO by the Spae Shuttle, then subsequently returned to earth. B. Reusable transfer vehile designs. The transfer vehile is designed suh that it an be used more than one. This lends itself to produing less hardware overall that would be more reliable than a single trip vehile. It also would be a huge step in reating a permanent infrastruture for Earth-Mars transfers. C. Mars Orbiting Station (MOS). A spae station in low Mars orbit (LMO) is proposed to allow for staging materials between the surfae and LMO as well as providing human missions an extra safe haven in ase of atastrophi failures in other systems or destrutive weather on the surfae. As the ISS provides a life support node in LEO, the MOS provides one in LMO. D. Change Basi Requirements. If we examine the impat of varying the payload requirements, this an provide a natural way to hoose payload amount in a ost-onstrained program. The key system parameters for the DRM transfer vehile were plaed in the engineering model desribed above. By varying the amount of payload, both human and spae hardware, the resulting transfer vehile mass is shown in Table 4. For 1800 kg of spae hardware payload removed results in a transfer vehile mass savings of 4230 kg. For eah person removed from the mission results in a mass savings of kg.

15 E. Reuse of Surfae Habitats. As time goes on, it is evident that the surfae habitats on Mars for the DRM grows as more people arrive. The number of inhabitants remains the same. This gives the last rew the most omfortable amount of spae as ompared to the previous rews. If one were to normalize the mass per person over all missions using the most austere environment of the first mission, then the overall amount of mass needed to be delivered to the surfae should derease substantially. F. Use A Commerial Approah. The ost of government programs almost always an be done more heaply through the ommerial setor. The last 40 years of human spaeflight endeavors performed by various governments have ertainly redued the risk to the point that ompanies an start to imagine how human spaeflight ould be performed with no, little, or redued government support. At first, some of these arhiteture hanges may appear ostly, but with more investigation they might atually provide a net ost savings while signifiantly improving overall system operational redundany. Table 4. Modeled Transfer Vehile Mass by Varying Payload Amount Number of Payload Mass, kg Transfer Vehile Mass, kg People THE IMPACT OF CHANGING PAYLOAD REQUIREMENTS To see how payload requirements affet the Mars DRM let s first examine what type of payload drives the arhiteture. For the DRM metri tons of siene equipment are planned for delivery to the Martian surfae. There is about 560 metri tons total planned for delivery to the Mars Surfae. So only about 3% of the mass is siene payload. Some of the mass delivered would be to supply power and onsumables for the siene payloads. But it is safe to say that the Mars DRM is driven by the human payload. The mass savings for the transfer vehile is realized by reduing the number of people on the mission desribed above in Table 4. The impat on the entire mission an be approximated by dividing the total DRM system mass (over 12 launhes) by the number of people delivered

16 (18). This number is 3463 metri tons per person. Note that for Apollo, 491 metri tons were needed per person on the 6 landing missions showing seven times the mass is needed to support people on Mars (using the NASA DRM) as opposed to going to the moon for muh shorter periods (3463/491 ~ 7). KEY PARAMETERS FOR SURFACE ARCHITECTURE OVER TIME By examining the differene between two key model parameters for the Mars DRM over time, we an see that there is a drasti hange in the amount of equipment available to the seond and third landing groups as ompared to the first. Looking at the rew system speifi weight, this inreases drastially from kg/person to kg/person between the 1 st and 2 nd rews. Similarly the powerless bus to payload ratio dereases from 1.33 to These both indiate that a large inrease in the amount of equipment available to support the later rews. This may just be the plan for providing redundany over time and the prie paid for expanding the exploration frontier. Another way to look at this differene in key system parameters would be to realize that overall system mass ould be saved by maintaining roughly the same amount of equipment for eah rew. Yes, this is an opportunity to save overall system mass. This would ertainly not be true if we were planning to keep personnel there for even longer stay periods (i.e days) requiring 2 rews to be supported at the same time. Then again, that is not the plan for the DRM. CONCLUSIONS This paper has shown the basi governing equations for both an engineering and ost model that an be used for human-tended spae failities and vehiles. Although the ost model used is rough and not absolute, it an show the relative effet of various payloads and faility designs. The power of ombining both engineering and ost models an effetively show the impat of payload requirements on ost. Alternatively, this ombination an show the amount of payloads you an support given a limit on the initial investment amount. Suh trades are ritial during the onept design phase for spae failities. For a ommerially developed and operated faility suh trades are mandatory in providing a business ase based upon sound engineering and ost data. To learn more about the modeling tehniques involved in this paper, please ontat Dr. Charles Reynerson at reyners@alum.mit.edu. Through the above analysis it was found that the DRM system mass, and therefore ost, ould be redued signifiantly by hanging requirements suh as reduing the number of rewmembers and the amount of surfae siene payloads. The effet of reduing the number of rew members by far outweighs the effet of reduing siene payloads. Other potential ways to redue ost inlude reuse of transportation systems and surfae habitats and taking a ommerial

17 approah rather than an inflated government approah. This may be ruial information in selling the prie tag to all ountries destined to be involved in this endeavor. If the mission is not sellable due to the prie tag, this paper has shown that key mission requirements an be traded with desired ost. BIBLIOGRAPHY Ezell, L. N., NASA Historial Data Book, Vol. III, Programs and Projets, , NASA Sientifi and Tehnial Information Division, NASA, Washington, DC, Foley, T., Spae Business News, February 5, 1997, Phillips Business Information, National Aeronautis and Spae Administration, International Spae Station Fat Book, January 1997, information also found in an internet website: Reynerson, C. M., Conept Design Theory And Model For Multi-Use Spae Failities: Analysis Of Key System Design Parameters Through Variane Of Mission Requirements, D. S. dissertation, George Washington University, Wertz, J. R., Larson, W. J., Spae Mission Analysis and Design (SMAD), 2 nd Edition, Miroosm Press, Willenberg, H. J., Hopkins, J., Rubek, M., Torre, L., Lauer, C., Woodok, G. R.,, Commerial Spae Business Parks, Final Report of Task Order # TOF-021 to Contrat #NAS , Boeing Defense and Spae Group, Final Briefing, 7 April 1997 National Aeronautis and Spae Administration NASA Mars Design Referene Mission, internet website: CITED REFERENCES 1 D.S. in Astronautis at the George Washington University, reyners@alum.mit.edu; Resident in Boulder, CO. reyners@alum.mit.edu 2 Reynerson, Chapter 4 3 Reynerson, Chapter 4. 4 Wertz, SMAD, pp 735, table

18 5 Spae Business News, February 5, 1997, pp 3. 6 Ezell, pp International Spae Station Fat Book.