Conceptual design using executable architectures for a planned mission to mars

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IEEE SYSTEMS JOURNAL 1

Conceptual Design Using Executable Architecturesfor a Manned Mission to Mars

John M. Colombi, Member, IEEE, Michael E. Miller, James S. Bohren, and John K. Howard

Abstract—Solar radiation events present a significant threatto future manned missions to Mars. However, little architecturalanalysis has been documented on how to best protect these in-terplanetary manned missions. With renewed interest in mannedmissions to Mars, there is a clear need to develop and analyzeradiation protection architectures. The Mars scenario was selecteddue to its relatively high likelihood, the robust body of backgrounddata available, and the adaptability of the analytic methods, con-cepts, and trade spaces to other planetary missions. An executablemodel was developed to assess the cost and effectiveness of 14candidate solar warning architectures. Candidates were evaluatedand compared based upon two performance metrics: warning timeand solar coverage. The cost of each architecture was assessed byestimating the total dry mass of all required components. Relationof the performance metric of each architecture to its estimated costenabled construction of a Pareto frontier of design options usingmultiattribute utility theory to combine performance metrics intoan overall “best value” solution. This analysis resulted in a mannedMars spacecraft with onboard sensing/processing.

Index Terms—Manned space flight, Pareto efficient frontier,performance analysis, solar warning, systems analysis, valuemodeling.

I. INTRODUCTION

IN the 151 years since Richard Carrington’s first solar flareobservation on September 1, 1859, mankind’s knowledge

of the Sun, solar weather, the solar cycle, and their combinedimpacts on human activities has grown significantly. Solarstorms represent a particularly significant hazard to mannedspace missions in the form of potentially lethal high-dosageexposure to radiation [4].

Based on increasing scientific and public discussions ofmanned missions to Mars and the Moon and the veracityof near-Earth asteroids, a deep-space solar weather warningrequirement is expected to exist within the next few decades.More specifically, a manned mission to Mars has been partof the official United States Space Policy during the last twopresidential administrations [17], [25]. The National Aeronau-tics and Space Administration (NASA) has also developed and

Manuscript received March 28, 2013; revised February 14, 2014; acceptedFebruary 16, 2014.

J. M. Colombi and M. E. Miller are with the Department of SystemsEngineering and Management, Air Force Institute of Technology, Wright-Patterson Air Force Base, OH 45433 USA (e-mail: [email protected]).

J. S. Bohren is with Booz Allen Hamilton, McLean, VA 22102 USA.J. K. Howard is with the Air Force Center for Systems Engineering, Air Force

Institute of Technology, Wright-Patterson Air Force Base, OH 45433 USA.Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/JSYST.2014.2314793

regularly updated a Mars mission reference design for overa decade [14]. More recently, the U.S. Human Space FlightPlans Committee has identified Mars as the best opportunityfor meaningful manned space exploration [20].

Despite recent efforts to expand coverage of the Sun, Earth’ssolar monitoring architecture is highly Earth-centric and limitedto detecting and tracking solar storms aimed in the vicinityof Earth. Simply stated, manned interplanetary missions willrequire more robust solar surveillance and warning systemsthan those that currently exist. The selection of a robust archi-tecture is necessary to provide accurate forecasting, coverage,and timely warnings beyond the “Sun-Earth corridor.”

Modern system design techniques of complex systems in-creasingly make use of integrated design, definition, analy-sis, modeling, and simulation tools. The systems engineeringcommunity continues to emphasize model-based systems en-gineering (MBSE) methodologies that facilitate the ability tomodel and assess early design concepts for performance andvalue analysis. These models would evolve throughout thesystem life cycle. A similar concept has been termed executablearchitecture by Wagenhals et al. [32]. Recent studies haveexamined novel methods for integrating architectural frame-works and a variety of system modeling languages to theexecutable simulation [34], [35]. Such simulations may includeagent-based models, discrete-event simulation, or colored Petrinetworks [33].

This paper extends the executable architecture literature us-ing a discrete-event simulation to generate performance metricsfor candidate designs, then integrating those with a multiat-tribute value model, and assessing each candidate design withrespect to alert value, which is obtained from the attributes ofsolar coverage and warning time, and dry mass. There has beenvery little published on developing and analyzing solar stormwarning architectures to manned missions to Mars. Turner andLevine [22] analyzed 63 transit missions and presented thenumber of monitoring satellites required by each, with thegoal of minimizing cost. Turner and Levine provided a briefoverview of potential options for positioning warning sensorsatellites and acknowledged that there will be communicationsconcerns across the vast distances, but they did not model thisaspect in detail.

In Section II, a brief review on the problem domain (so-lar radiation) is provided, with an introduction to the NASAMars reference mission. Section III describes the five-modelintegrated approach, including an architectural representation(design model), the executable (mission) model, performancemodels, cost/weight models, and, finally, a value model. Anal-ysis of the approach is provided in Section IV, to include

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estimated dry mass budgets; alert, radiation, and warning timesover the entire mission; and the final Pareto frontier for the14 candidate architectures (CAs). Conclusions and recom-mended extensions are provided in Section V.

II. BACKGROUND

A. Space Radiation and Solar Weather

There are two primary sources of high-energy radiation(HER) in deep space: galactic cosmic rays (GCRs) and solarcosmic radiation (SCR). GCRs are high-energy particles tran-siting free space in all directions, having originated from starsin other solar systems and galaxies. GCR is considered back-ground radiation because it only accounts for about 5%–10% ofthe total radiation dose astronauts receive on any given missionand find very difficult to shield against due to the high energiesinvolved [21].

SCR is attributed to solar flares, solar proton events (SPEs),and coronal mass ejections (CMEs). A solar flare is a “highlyconcentrated, explosive release of energy within the solar atmo-sphere which appears as a sudden, short-lived brightening of alocalized area in the chromosphere” [21]. SPEs are a specialcategory of solar flares that involve the release and accelerationof large quantities of highly energetic protons, which can reachEarth in as little as 30–90 min [21]. These radiation stormstypically travel at 75 000 km/s, about one fourth the speed oflight [18].

CMEs are relatively slow-moving eruptions of enormousquantities of solar material into space. CMEs are much slowerthan flares, only moving at 14–1800 km/s, with CMEs movingfaster than 400 km/s possibly causing interplanetary shocks.The actual CME material is not a fast-moving radiation threatand tends to largely dissipate by the time it reaches the radius ofthe Earth’s orbit. During SCR, heavy particles are accelerated tohigh energies (107–109 eV) during large solar flares and CMEs[21]. These particle storms can represent a 1000 times increasein radiation dosage, making them highly lethal. For this reason,astronauts transiting deep space will require the means to detectand track solar radiation storms, determine the threat they pose,and have sufficient time to enable any countermeasures that willensure their wellness and survival.

Earth observations of CMEs during high solar activity can betwo or three per day, with low periods of only one per week. Thelikelihood of some solar event during a three-year mission isnontrivial. Correspondingly, the severity of high dose exposurewould jeopardize the success of the mission.

B. Radiation Effects on Humans

The scientific and medical communities have conducted ex-tensive research on radiation and its effects on living organ-isms. HER is extremely harmful to humans and most livingorganisms and causes two primary categories of damage at thecellular level: cell death and genetic damage.

Researchers refer to the symptoms associated with cell deathas “deterministic effects” because there are direct quantifiablerelationships between HER dose and the onset of symptoms.

Deterministic effects are commonly known as radiation sick-ness or acute radiation syndrome, with the exact symptomsand severity determined by total dose, how quickly the doseis received, and the dosage distribution within the body [27].Deterministic effects begin 30 min to 2 h after a high dose ofradiation is received within a short span of time, which is theexposure profile an astronaut will likely face in the path of asolar storm of high-energy particles. Even more alarming froma space mission standpoint is that the initial radiation dose ispainless and undetectable to human senses [27], posing dangerto mission success.

Genetic damage occurs whenever an HER particle happens tohit a cell, harming or altering the cell’s deoxyribonucleic acid[9]. Genetic damage is collectively referred to as “stochasticeffects” because they are characterized by probability, givenhealth problems will manifest over a period of time, typicallyyears or decades [7].

C. Earth’s Solar Warning Architecture

During the past four decades, NASA, the National Oceanicand Atmospheric Administration (NOAA), and the U.S. AirForce have all played significant roles in establishing the solarmonitoring architecture relied upon today [18]. Earth’s modernsolar monitoring architecture consists of an extensive networkof sensors, communications, and command and control nodes,which operate around the world to provide near-real-time moni-toring of the Sun, 24 h per day and seven days per week. Sensornodes include various ground observatories and solar observa-tion satellites in the Earth’s orbit positioned at Sun-Earth L1and in deep space. The premier organization responsible forworldwide solar forecasting is the Space Weather PredictionCenter (SWPC) located in Boulder, Colorado. The SWPC issupported by NASA, NOAA, the U.S. Air Force, the Jet Propul-sion Laboratory, and dozens of other solar-monitoring organiza-tions around the world. The SWPC conducts around-the-clockmonitoring of solar weather and conditions, concerning bothperiodic forecasts and near-real-time alerts during hazardousspace weather conditions.

Primary satellite missions that are currently monitoring theSun are relied upon daily by solar weather forecasters. Theseinclude the Geostationary Operational Environmental Satelliteconstellation, the Solar and Heliospheric Observatory, the Ad-vanced Composition Explorer, the Wind Spacecraft, and theSolar Terrestrial Relations Observatory (STEREO). The newestis the Solar Dynamics Observatory. Thus, this paper examinesextensions to this current capability.

D. Mars Mission Planning and NASA DRA 5.0

Planning for manned missions to Mars has been underway,both officially and unofficially, within the space communitysince the beginning of the space age. Encyclopedia Astronau-tica provides descriptions of 61 various Manned Mars Missionproposals [23]. The United States and Russia have producedmost of the Mars Mission studies in existence. However, manyother nations [11], private organizations [1], [12], [26], andindustry [28] have produced studies of their own. All of these


COLOMBI et al.: CONCEPTUAL DESIGN USING EXECUTABLE ARCHITECTURES FOR A MANNED MISSION TO MARS 3

Fig. 1. Mars mission profile from DRA 5.0 [13].

mission planning efforts can be divided into two distinct cat-egories of mission profile: conjunction class and oppositionclass.

The terms “conjunction” and “opposition” refer to the rela-tive position of Mars compared with Earth and the Sun at themidpoint of the mission. In a conjunction class mission, Marshas moved behind the Sun (i.e., Mars and the Sun are conjoinedas viewed from Earth). In an opposition class mission, Mars ison the opposite side of Earth from the Sun (i.e., in opposition)[19]. Fig. 1 illustrates the flight profile of the currently preferredconjunction class mission.

It is interesting to note that the preferred mission type hasshifted over time. Although the work of Von Braun in the late1950s used a conjunction class mission [19], and with only afew exceptions, the rest of the studies conducted up until the1980s focused on opposition class missions. The ascendance ofthe conjunction class missions is due to three primary reasons:reduced fuel (Δv) requirements, longer stay times on thesurface of Mars, and reduced radiation risks. Despite the factthat conjunction class missions have longer overall durations,all opposition class missions have one transit leg, which issignificantly longer than the other and which passes inside theorbit of Venus [28], placing the craft and its crew closer to theSun, where higher levels of radiation can be expected during asolar event.

Human Exploration of Mars: Design Reference Architecture5.0 (DRA 5.0) [8] is the culmination of NASA efforts for plan-ning Manned Mars Missions that began with the publication ofthe “Mars Design Reference Mission (DRM)” in 1993. DRA5.0 covers the full set of considerations for a Mars mission,including scientific objectives, potential methods, vehicle andhabitat design, communication considerations, propulsion op-tions, radiation risks, and interplanetary trajectories.

DRA 5.0 discusses space weather both in terms of being asubject of continued scientific study and a threat to the successof the mission itself. Within its Key Challenges section, DRA5.0 goes as far as identifying “. . .establishment of a spaceweather forecasting system and implementation of sufficient‘storm shelters’ to warn crews against the transitory, but ex-treme, levels of radiation that would be encountered duringsolar flares” [13], [15]. DRA 5.0 calls for a system to providesufficient time for the crew to take mitigating actions such asreturning from an extravehicular activity and entering a stormshelter and notes that warning times may be as short as 30 min.

Fig. 2. Libration points.

However, it does not define this warning system’s architectureor components. Furthermore, DRA 5.0 does not identify wherethe “30 minute” requirement was derived from, nor does itprovide any indication on what minimum warning time wouldbe required to successfully implement mitigating strategies. Itdoes, however, clearly state that a “. . .specific warning systemwill need to be developed.” [13], [15]. Identifying potential op-tions for such a system and the application of MBSE principlesto recommend a potential solution was the primary objective ofthis research.

Another key aspect of DRA 5.0 is that NASA planners intendto leave the Mars Transfer Vehicle (MTV) in orbit above Mars,while the crew is on the surface, and then reuse it for thereturn leg. This decision directly impacts the cost (in termsof dry mass) of any candidate warning architectures, whichinclude placing a solar monitoring sensor suite on board theMTV. Likewise, this decision provides a potential platform fora sensor in orbit at Mars throughout the stay, which could relaywarnings to the surface.

III. METHOD

This section describes a method using five interrelated mod-els. These models address identification of the 14 candidate so-lar warning architectures (which includes the current baseline),the Mars mission simulation, the two performance metrics, acost estimate proxy, and, finally, the weighted value model.

A. CA Development

Functionally, any CA needs to monitor the Sun’s surface forindications of an erupting solar radiation storm and to providetimely warning to a spacecraft potentially threatened by thestorm.

Analysis of these functional requirements indicates that anygiven CA requires a combination of three basic componentcategories, to include sensors, communications, and processing.Another more detailed design aspect could have included datastorage, which is not addressed in this paper. The processingwe refer is that analysis that is currently accomplished in the



TABLE ICANDIDATE ARCHITECTURE NOMENCLATURE

SWPC. This analysis, like Earth weather forecasting, is notentirely automated. Additionally, the protected asset itself (thespacecraft), the source of the threat (the Sun), and Earth (apotential baseline processing and communications node) arecommon to all CAs. The first step is to define the alternativesfor each of the three main architecture components.

To position sensors within the solar system, three optionswere considered: 1) in orbit around a given planetary body;2) aboard the manned spacecraft itself; or 3) at one or moreLagrange (or Libration) points. Lagrange points representpoints in space about two large masses at which the gravita-tional pull from the two masses are balanced along at least oneaxis. As a result, if an object is placed at one of these five points,it will maintain its position relative to the two masses withlittle or no additional energy expenditure (see Fig. 2). Theselibration points are simply referred to as L1 through L5 and arefrequently used to describe points of neutral gravity relative toEarth and the Sun.

The sensor satellite location options are the following: Sun-Earth L1, Sun-Mars L1, and a pair of sensors at Sun-Earth L4and L5. Sun-Mars L4 and L5 are ruled out because they are sofar away from both the Sun and Mars that any alert they providewill come after the arrival of the radiation storm. Processingoptions are limited to the current Earth-bound solar forecastingarchitecture and on board the spacecraft itself. Of note, onboardprocessing would require advanced signal processing or havelarge human factors/human safety implications that are beyondthe scope of this current research, such as the crew’s situationalawareness, astronaut training on Sun activity/analysis, crewsize, and workload.

There are two communications strategies identified, eachassociated with the location of the processing node: 1) allmonitoring data are routed back to Earth, processed, and anyrequired warning data transmitted from Earth to the spacecraft(Earth relay); or 2) all monitored sensor data are also directlysent to the spacecraft and directly processed on board (di-rect). Via permutation, all possible combinations of the sensor,

processing, and communication options are identified, and allnonsensical or impractical configuration options are eliminated.This process results in a total of 14 potentially effective CAs,which can be considered competing designs.

Table I summarizes the CA nomenclatures and descriptions.The letter (A through H) identifies which combination of sen-sor locations is used, whereas the number (1 or 2) identifieswhether the architecture sends data to Mars Traveler (MT) viaEarth or also transmits directly to the spacecraft.

B. BCA

From the set of 14 CAs, Candidate A1 is identified as thebaseline CA (BCA) due to its high degree of similarity tothe Earth’s current solar warning architecture. The BCA isdefined as the “do nothing” architecture, which means that littleto no additional resources will be required to implement ormaintain it beyond what current solar forecasting organizationsand governments are currently investing. In fact, if a mannedmission to Mars were to take place in the very near future,Candidate A1 provides the most accurate representation of thesolar warning architecture.

C. Performance Metrics

With a total of 14 potentially viable CAs, further analysisis required to find a set of metrics by which the CAs can bemeasured and compared. Examination of the functional require-ments yields performance metrics of Warning Time and SolarCoverage. Warning Time is assessed to be twice as importantas Solar Coverage because the warning metric pertains to theprimary functional requirement of the architecture. Althoughimportant, Solar Coverage is not weighted equally, since anyarchitecture with at least one solar sensor must provide someminimum amount of solar coverage. Once defined, each ofthe performance metrics is further specified in quantifiablemeasurable terms.



1) Warning Time: Warning Time is defined as the amountof time a crew has to react to an impending radiation storm andtake protective measures. A key concept with Warning Timeis that it exists as a requirement, independent of the protectivecountermeasures themselves. It will likely be the case thatvarious countermeasures have a warning time requirement, butthose requirements are unknown at present. Therefore, WarningTime is constrained by the Radiation Time, which is simplydefined as

Warning(t) = Radiation(t)− Alert(t).

Radiation Time is defined as the amount of time it takesa radiation storm originating from the surface of the Sun toreach a spacecraft. Alert Time is defined as the total path timerequired for an electromagnetic indication of a radiation stormto travel from the Sun, be detected, processed/analyzed, andthen communicated to the crew. Radiation Time is calculatedby taking the distance of the spacecraft from the surface of theSun, at any given point in time, and dividing by the worst casespeed of HER, i.e., 75 000 km/s [18].

Alert Time is calculated by summing the total path timerequired for a radiation storm signature to travel from the Sunto one or more sensor nodes, on to the processing node foranalysis and warning determination, and then to the spacecraftcrew. Thus

Alert(t) = Sense(t) + Comm2Process(t)

+ Processing(t) + Comm2Crew(t).

The solar electromagnetic signature indicating HER andall communications links are defined as operating at c, i.e.,the speed of light. Furthermore, constant node delays are as-sessed and added for each sensor or processing node tran-sited. Sensor nodes are assigned a 4-min delay, based oncurrent solar monitor satellite refresh rates, and Earth pro-cessing is assigned a 10-min delay based on current solarwarning timelines involving the International Space Station.Processing aboard the spacecraft is assessed as 0–30 s and notfactored into the overall delay. This subminute delay time isderived from the Solar Environment Center warning timeline,which launches its first solar weather warning within 1 minafter the solar event signature data first reach a Sun-Earth L1satellite [18].

2) Solar Coverage: Solar coverage is defined as the totalpercentage of the Sun’s surface in view of a given CA’s setof solar sensors at a given point in time. Although the Sun is a3-D roughly spherical object, any sensor with an unobstructedfield of view and a sufficient observation distance sees theSun as a flat disc and can view approximately 50% of theSun’s surface at any time. If the solar coverage concept istranslated into two dimensions with a top-down perspective,then each sensor can be considered to cover 180◦ of the Sun’s360◦ circumference from any given angle. Adding one or moresensors at fixed angles relative to a baseline sensor expands theangular coverage by a fixed number of degrees equal to theangular separation of the sensors.

TABLE IISTEREO/MRO FACT SHEET [10], [16]

D. Cost Model

Relative cost of each CA is required to make informedcost–benefit analyses and comparisons. Because cost is iden-tified as the primary constraint metric, it must be estimated.An examination of the Space Mission Analysis and Designhandbook [24] reveals that Δv, in units of meters per second,is a measure of the effort to change orbital trajectories andis a common proxy used for early cost comparison of spacemission alternatives. However, Δv is derived from the timing,duration, and trajectory of asset maneuvers. Because it focuseson quantifying “how” objects are positioned, Δv has too manyopen-ended variable assumptions to be of objective use in thecurrent evaluation. Further analysis of the constraints results inadoption of total dry mass as a quantifiable proxy cost metric foreach CA. Total dry mass is defined as the total unfueled (dry)mass of all the architecture components that must be launchedinto space to form the architecture. Launch mass costs are aprimary driver in most space acquisition costs and serve asan early standard benchmark during concept development forcomparing competing space solutions [24].

The analysis begins with an entry for each component orcomponent configuration that will have to be deployed toimplement one or more of the CAs. Using NASA Fact Sheetsfor STEREO [16] and the Mars Reconnaissance Orbiter (MRO)(see Table II) and a historical breakdown of past mission massbudgets (not shown), the mass budget of each component iscalculated. Total dry mass is divided into three subcategories:spacecraft, payload (sensor), and antenna (communications),as shown in Table III. Antenna sizes and quantities are thenassigned to each CA based on the link and range requirements.The total mass budget for each overall CA is tallied in Table IV.

The result of the Cost Model is the estimate of the total drymass of each CA, obtained by summing the mass contributionsof each component present in a given CA. For example, Candi-date Architecture D1 includes a Sun-Earth L1 satellite, a Sun-Earth L4 satellite, a Sun-Earth L5 satellite, and a Sun-Mars L1satellite, all of which amount to an estimated total dry mass of2728 kg. The Cost Model is then incorporated into the Paretofrontier analysis, which is discussed in the next section.

E. Analysis Strategy and Value Modeling

Due to the complex time–space dynamics of the scenario andarchitecture component interactions, modeling and simulation(M&S) is the most suitable means to measure CA performance.The M&S strategy is to “play” each CA against the NASADRA 5.0 Mars mission scenario as an objective baseline. Thechallenge of characterizing CA performance is complicatedsince warning time and solar coverage are variable and dy-namic, based on the relative motions and locations of all nodes,planetary bodies, and the manned spacecraft itself. Therefore,



TABLE IIICANDIDATE ARCHITECTURE MASTER COMPONENT LIST

TABLE IVCA DRY MASS BUDGETS (IN KILOGRAMS)

the analysis approach is to profile the performance response ofeach CA over time by executing the CA with false radiationstorms at an artificially high rate of once per day over theentire mission epoch. Warning Time and Solar Coverage arethen measured every 24 h for each CA, and the results arecompiled.

When considered in isolation, the analysis results for eachperformance metric could recommend a different CA as theoptimal solution. This situation requires the development ofa single relative valuation to determine the best and mostdesirable overall design. Since the purpose of this research is toidentify an optimal reference architecture, the two performancemetrics are combined using utility theory (a Value Model) toobtain a single weighted value by which to assess each CA.Several techniques and methods to perform this step couldbe applied. Value-focused thinking (VFT) is a widely usedapproach in the field of discrete multiple criteria decisionmaking. A fundamental tenant of VFT is the focus on valuesprior to the identification of alternatives or CAs. In this paper,the values reflected the two performance criteria of warningtime and solar coverage. Extensions to other criteria could befurther developed.

The mathematical underpinning of VFT is multiattribute util-ity theory that leverages the assumption of mutual preferentialindependence in order to employ an additive value function

[29]. Preferential independence assumes that a decision maker’spreferences over a single attribute are unaffected by the levels ofthe other attributes. This independence assumption is a drivingassumption behind the additive value function. After incor-porating a ranking or weighting of the multiple performancecriteria, one overall multiattribute value function (MAVF) isdefined.

Within the systems engineering community, the use of utilitytheory allows design decisions to be cast in decision anal-ysis. Stakeholders can define single-attribute value functions(SAVFs), using ranges, objectives, and thresholds as points ofinterest to shape these functions. There are four primary shapesof SAVFs for continuous value measures: linear, concave, con-vex, and S-curve. A decision maker’s value preferences maybe different for each objective. The linear function represents aconstant valuation, where each unit of the value measure holdsthe same amount of value for the decision maker. This is oftenutilized for monetary attributes. The concave (convex) functionhas decreasing (increasing) marginal value, where each unitof the value measure holds less (more) value as it approachesthe maximum. Concave and convex functions are typicallyrepresented with an exponential curve fitting operation. TheS-curve function captures both a convex and a concave region.This is typically representative of a value measure with anoptimal midpoint or goal.



Fig. 3. Five-model approach.

For the Solar Coverage SAVF, any solar coverage percentageless that 50% has a value of 0.0, since a CA must have atleast one sensor providing a minimum of 50% coverage. Inthis particular analysis, the Warning Time metric is estimatedto have twice the importance of the Solar Coverage metric andis weighted accordingly. Value models are user-defined tools;thus, these performance metric weights and the SAVFs can bemodified based on the values of future acquisition leadership orfurther adjusted based on new information. Utility of a CA isthe weighted utility of Warning Time (WT) and Solar Coverage(SC), i.e.,

U(CAi)

= wWTfWT(CAi) + wSCfSC(CAi) (1)

Warning Time fWT(CAi)

=

{1.0, Warning Time(CAi)>40 minWarning Time(CAi)

40 , else(2)

Solar Coverage fSC(CAi)

=

⎧⎨⎩0.0, Solar Coverage≤50%1.0, Solar Coverage>100%Solar Coverage(CAi)

100 , else.(3)

This weighted values with respect to cost for each architectureallow construction of a Pareto frontier. The use of Paretofrontier has been applied to early conceptual design problems[30], [31]. The frontier is the set of “efficient allocations ofresources forming a surface in the metric space” [31]. Move-ment along the frontier requires relaxation of one metric toimprove another. For example, with more weight (e.g., cost), wecan achieve better warning time and solar coverage. Thus, thisapproach finds efficient designs from a cost-value perspective,keeping in mind human safety and mission success.

F. Five-Model Approach

Leveraging the requirements, performance, cost, and archi-tectural analysis described above, a series of five models aredeveloped, which ultimately provide the tools and data requiredto assess the CAs and recommend a solution. Fig. 3 depicts thefive model sets and how they interact within the analysis.

The 14 different configurations of sensors, processing nodes,and communications strategies result in a total of seven objects,providing 13 potential links, which are organized into eight rel-evant data paths. Collectively labeled as the Architecture Modelin Fig. 3, these 14 CA models serve as a reference source for thedevelopment of later models. The Mission Model, built usingSatellite Tool Kit (STK), provides the dynamics of each CAcomponent during the entire scenario. Location is representedin terms of 3-D space relative to the Sun, Earth, and MT (themanned spacecraft). We felt that an STK simulation providedthe most direct method to model relative locations of Earth,MT, and collections of sensors across the 14 CAs for each day(every 24-h simulated time step). Mission Model outputs aredirectly used in both the Link Model and the Coverage Model,which are together referred to as the Performance Model inFig. 3 The Link Model uses the list of components identified byeach Architecture Model and the distances calculated at each24-h interval by the Mission Model to quantify the WarningTime for each CA. The Coverage Model uses the relativeangles of each CA sensor set to calculate the total number ofsolar degrees “covered” versus time and converts these valuesinto percentages. The Cost Model parametrically estimates therelative costs of the various CAs by assessing the combineddry mass of the various components. Finally, in Fig. 3, theValue Model combines the Warning Times and Solar Coverageperformance metrics to obtain a single weighted value foreach CA. When graphed against the results of each CA drymass, this method enables a “best value” analysis of the entireset of CAs.



The Mission Model scenario is based on analysis of NASADRA 5.0. DRA 5.0 calls for a conjunction class mission usinga fast transfer (as opposed to a Hohmann transfer) to reduce theamount of time in transit, increase surface exploration time, andminimize radiation risk to the crew during the interplanetaryportion of the mission [14], [25]. For reference, “the Hohmanntransfer ellipse provides orbit transfer between two circular,co-planar orbits” [24]. The NASA DRA 5.0 authors studiedmission window opportunities ranging from 2020 to 2046,singling out the 2037 mission window [8], [13].

In addition to the 2037 example mission, DRA 5.0 providesinformation on the standard Earth departure and Mars arrivalorbits, which have been adopted as their baseline. The mis-sion spacecraft always departs Earth from a circular 407-kmorbit, whereas all Mars arrivals target an elliptical 250 km ×33 793 km orbit, with Earth return being handled by directreentry [13], [15].

To accurately determine warning time for each of the variousCAs, a Mission Model encompassing an Earth-to-Mars transfer,a long-term orbital stay at Mars, and the return trajectory arerequired. However, DRA 5.0 does not include the specificsof the orbital trajectory, delta-V on each axis, or inclination/declination of the departure trajectory. Fortunately, it is possibleto develop an adequate model of the DRA 5.0 mission profilewithout these details.

Building the Mission Model to approximate the DRA 5.0mission profile is accomplished in steps. First, the Earth-to-Mars leg is simulated, targeting arrival at Mars on February 17,2038. Next, a midcourse trajectory correction burn is simu-lated, which targets a specific altitude over Mars (250 km, perDRA 5.0). This places the Traveler at a point close enoughto achieve insertion into orbit around Mars on the targetedday. Finally, an injection burn is simulated to actually put theTraveler into a stable elliptical orbit around Mars.

After Mars insertion, the orbit of the Traveler is propagatedfor 539 days, as specified in DRA 5.0 [13]. While in orbitat Mars, NASA specifies two additional vehicles beyond theMTV for the Mars mission: 1) a landing vehicle for the crewto reach the surface and 2) a Mars Ascent Vehicle for thecrew to return to the MTV for the return leg. For CAs incor-porating an “onboard” sensor option, a sensor (and associatedprocessing and communications) is placed on board the MTV(Traveler). The MTV remains in orbit during the stay at Marsand communicates with the crew on the surface via the MarsRelay Satellite. Although this configuration means Mars mayoccasionally block any sensor aboard MT, the highly ellipticalnature of its Mars orbit (250 km × 33 793 km) suggests thatan orbit can be achieved, which minimizes or eliminates theperiods of sensor blockage. Since DRA 5.0 does not specifythe inclination or any other Mars orbital elements for the MTV,the analysis is simplified by inserting the MTV into an orbitthat maintains an almost continuous view of the Sun, using asconstraints only the perigee and apogee parameters provided.Upon Mars departure at the conclusion of the surface mission,the return flight to Earth is built in the same manner as thetrajectory from Earth to Mars.

The final step in building the Mission Model is addingthe simulated satellites, which comprise the range of sensor

Fig. 4. Architecture paths.

location options available to the family of CAs. Simple sen-sor satellites are instanced at Sun-Earth L1, Sun-Earth L4and L5, and Sun-Mars L1 by using the STK/AstrogatorVector Geometry Tool to define the appropriate coordinatereference systems for each point. Each satellite is createdwith the Sun as its central (orbit) body using the appropri-ate coordinate system to govern its location. Building thisportion of the Mission Model is critical to developing theability to extract data relevant to both of the PerformanceModels.

IV. ANALYSIS AND RESULTS

A. Architecture Performance

Within the 14 CAs, there are eight unique propagation pathsthat can provide an alert to MT of an imminent solar radiationstorm (see Fig. 4). Each propagation path provides widelyvarying amounts of warning time, depending on the currentdate of the mission and relative positions of the CA componentsinvolved.

Each alert propagation path directly affects the warning time(and alert time) performance of the various CAs. The firstfeature of interest is the red (dashed) line (see Fig. 5), whichrepresents the time it takes for a radiation storm to reach MTfrom the Sun. When a given path line is below the radiationline, the amount of warning time provided by a given alert pathis represented by the difference between the two lines. When agiven path line lies above the radiation line (i.e., Sun-SML1-Earth-MT), the warning time is negative, which means thatthe alert arrives after the radiation. Another notable feature inFig. 6 is that several of the path lines show dropouts where thepropagation time goes to zero. These dropouts do not represent



Fig. 5. Alert propagation and radiation times.

Fig. 6. Warning times.

conditions of instantaneous alert time; instead, they indicateblackout periods during which a given path is blocked by theSun and is unable to provide an alert.

The Warning Time, in minutes, provided by each CA acrossthe mission epoch is shown in Fig. 6. Due to the high degree ofoverlapping and the fact that there are only five Warning Time



TABLE VCANDIDATE ARCHITECTURE ANALYSIS SUMMARY

Fig. 7. Solar coverage.

curves for the 14 CAs, this graph clearly shows that certainpropagation paths dominated within CA sets with similarfeatures. Warning Time performance for all architectures issummarized in Table V.

The Solar Coverage, in percentage of solar surface, providedby each CA across the mission epoch is depicted in Fig. 7.As with the Warning Time versus Mission Date graph, there isalso a significant amount of profile overlap among the 14 CAs,particularly during the Mars orbit window. This high degree of

overlap simply indicates performance similarities among CAswith the same sensor sets. Solar Coverage for all architecturesis also summarized in Table V.

B. Value Modeling

To combine two performance metrics, as described in themethodology, a MAVF weights the SAVFs for Warning Timeand Solar Coverage. Recall for this analysis that Warning Time



Fig. 8. Frontier of design values to estimated total architecture dry mass.

was assessed by the authors to be twice as important as SolarCoverage. This method resulted in Overall Values (see Fig. 8)for each CA from data found in Table V.

Inspection of the Pareto frontier indicates that architecture E1appears to provide the best value. While several CAs providemore value, such as the F or H family of architectures, theyrequire four to six times the dry mass. Any of the B, C, or Dfamily of architectures is below the frontier. Thus, for the samedry mass and related cost, other architectures provide morevalue. The baseline architecture, i.e., A1, for the Sun-Earthcorridor provides only half the value of E1. Hence, architectureE1, the “knee in the curve,” places a sensor/processing capa-bility aboard the manned Mars spacecraft itself and representsthe best value in terms of combined Warning Time and SolarCoverage for the estimated cost using dry mass.

C. Additional Insights

If after a detailed human subjects analysis the onboard crewdemands for solar forecasting and warning determination can-not be achieved, then only Earth-relay CAs can be examined.These designs include A1, B1, C1, and D1. All others allowraw sensor data to be forwarded to MT for processing, inconjunction with the baseline Earth-based solar forecastingand warning capability. However, in Fig. 8, the values for B1,C1, and D1 are only slightly better than the baseline A1. Infact, all four Earth-relay designs have the exact warning timeprofile, generally between 7 and 20 min, with a brief blackoutperiod when MT is directly behind the Sun, from the Earthperspective.

While another consideration is protection of the crew onthe Mars planet, this is outside the scope of the warningarchitecture. The DRA 5.0 mission profile has MT orbiting the

Mars planet for 539 days, and Figs. 6 and 7 capture the warningtimes and percent coverage during this orbit and landing for allarchitectures, with an assumed elliptical orbit that maintainedsolar coverage. Warning time requirements may differ betweenMars transit and while on surface; future analysis could con-sider any added constraints.

The final comparison was made possible through an exe-cutable architecture. After identifying architectural alternatives,constrained to Lagrange points as a design heuristic, the DRA5.0 mission could be simulated for each day, with effectsfrom solar radiation measured. After the MAVF is established,a utility function, which captures the decision maker’s riskpreferences, could be constructed. The result would be a singleutility metric for each alternative.

In many instances, preferential independence does not holdfor all attributes. The authors assumed that solar coverage andwarning time were independent attributes. The addition of otherattributes and uncertainty may break this independence assump-tion. Such factors such as human safety, human workload, orother probabilistic factors such as solar cycles could be alsoconsidered.

Understanding the difference between value and utility is acritical concept to decision analysis practitioners. Value definesthe attribute’s importance to the decision maker, which isidentified by decomposing the fundamental objective. Utility isconcerned with a decision maker’s attitude toward risk taking.Manned missions always contain a complex variety of factorswith regard to performance, safety, mission assurance, andcost. The authors propose that early architectural analyses suchas demonstrated in this paper can be performed during theconcept development phase to inform the decision maker onthe relative performance of early concepts across a multiple ofthese considerations.



V. CONCLUSION

The primary objective of this study has been to identify amethod for recommending a solar radiation warning architec-ture for manned missions to Mars. Architectural combinationsof five sensor set locations, two data processing node sites, andtwo communication path strategies (Earth relay and direct),yielded a total of 14 CA configurations. All candidates satisfied,to varying degrees, the functional requirements of monitoringfor solar events and communicating a warning to a threatenedspacecraft. Within STK, these 14 architectures were simulatedagainst the NASA DRA 5.0 mission to Mars scenario, andthe resulting performance data were postprocessed to createa Pareto frontier for evaluation. Analysis infers that the bestvalue architecture was E1: the placement of a solar sensor andprocessing capability directly on board MT in addition to usingthe existing baseline Sun-Earth L1 solar sensors.

A decision to go with the onboard option for a solar warningarchitecture, in turn, suggests new requirements for a mannedMars mission: to pursue either an automated or a human-operated onboard monitoring system. An automated solar mon-itoring system must be intelligent enough to perform solarforecasting, which is considered as both science and art bycurrent human practitioners. Developing such a system will re-quire detailed quantification of the knowledge solar forecastersleverage when they make their assessments. A human-operatedsystem will drive an entire host of human factor concerns andramifications, such as crew size, training, duties and workload,and additional dry mass for supporting facilities and supplies.Although the particulars of the mission and orbital trajectorieswould change based on the timing, window, and destination, theanalysis techniques are equally applicable across a wide rangeof interplanetary scenarios.

Another critical implication of this work directly influencesthe development of solar radiation protection measures. Theresults of this or similar analyses have direct bearing on thetiming requirements for various radiation countermeasures,which may be under consideration. For example, the worstcase scenario for architecture E1 (on board MT) indicated aminimum warning time of 19 min. This performance metriccould be directly translated into a performance requirementfor radiation countermeasures, with some additional margin oferror or safety. With an assumption to perform only offboardEarth-based processing and warning, this minimum warningtime would reduce to several minutes before the deadly radi-ation would reach the crew. These system trades are clearlyillustrated through the application of executable architectureand multiattribute decision analysis.

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John M. Colombi (M’06) received the B.S.E.E. de-gree from the University of Massachusetts, Lowell,MA, USA, and the M.S.E.E and Ph.D. degreesfrom the Air Force Institute of Technology (AFIT),Wright-Patterson Air Force Base, OH, USA, in 1992and 1996, respectively.

He is currently an Assistant Professor of systemsengineering with AFIT. He has 21 years of militaryexperience. His research interests include systemsand enterprise architecture, complex adaptive sys-tems, and human systems integration.

Dr. Colombi is a member of the Institute of Industrial Engineers and theInternational Council on Systems Engineering.

Michael E. Miller received the B.S. and M.S. de-grees in industrial and systems engineering fromOhio University, Athens, OH, USA, and the Ph.D.degree from Virginia Polytechnic Institute and StateUniversity, Blacksburg, VA, USA, in 1993.

He is currently an Assistant Professor of systemsengineering with the Air Force Institute of Technol-ogy, Wright-Patterson Air Force Base, OH. He hasover 18 years of experience in the industry, withexperience at International Business Machines andEastman Kodak Company. His research interest is

human systems integration, including human–machine interface design.Dr. Miller is a member of the International Council on Systems Engineering,

the Human Factors and Ergonomics Society, and the Institute of IndustrialEngineers.

James S. Bohren received the B.A. degree in in-ternational studies from The Ohio State University,Columbus, OH, USA, in 1991, the M.S. degree inspace studies from the University of North Dakota,Grand Forks, ND, USA, in 1996, and the M.S degreein systems engineering from the Air Force Instituteof Technology, Wright-Patterson Air Force Base,OH, in 2011.

He is currently a Consultant supporting clientefforts to operationalize cyberspace operations withBooz Allen Hamilton, McLean, VA, USA. He has

20 years of military experience as an Officer in the U.S. Air Force, from wherehe retired in 2011.

John K. Howard received the B.S.E.E degree fromTennessee Tech University, Cookeville, TN, USA,and the M.S. degree in systems engineering from theAir Force Institute of Technology, Wright-PattersonAir Force Base, OH, USA, in 2011.

He is currently a Systems Engineer with the AirForce Center for Systems Engineering, Air ForceInstitute of Technology. He has 18 years of militaryexperience in the U.S. Air Force with assignmentsin spacelift, intelligence, operations planning, cyber,test and evaluation, program management, and space

architecture.

Conceptual design using executable architectures for a planned mission to mars

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