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To validate a novel, lightweight coating material for reducing electric propulsion thruster plume erosion of solar array interconnects with the goals of increasing reliability and decreasing cost.
Space ROS is a matured version of ROS 2 using flight software engineering practices to facilitate mission- and safety-critical applications. Our goal is to reduce the barrier to maturation and infusion of software developed using the popular ROS framework.
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Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases.1 Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission.2 Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an Advanced Design Methods (ADM) based approach. This approach applies the concepts of Design of Experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development e ort. In order to t a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of Ground Rules and Assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated \what if" questions. The example problem will then illustrate the setup and execution of a DOE through the automated ACO tools. This process is accomplished more efficiently in this work by splitting the tools into two separate environments. The first environment encompasses the structural optimization and mass estimation tools, while the second is focused on trajectory optimization. Surrogate models are t to the outputs of each environment and are integrated via connection of the surrogate equations. Throughout this process, checks are implemented to compare the output of the surrogates to the output of manually run cases to ensure that the error of the final surrogates is at an acceptable level. The conclusion of the example problem demonstrates the utility of the ADM based approach. Using surrogate models gives the ACO team the ability to visualize vehicle sensitivities to various design parameters and identify regions of interest within the design space. The ADM approach can thus be used to inform concept down selection and isolate promising vehicle configurations to be explored in more detail through the manual design process. In addition it provides the customer with an almost instantaneous turnaround on any \what if" questions that may arise within the bounds of the surrogate model. This approach ultimately expands the ability of the ACO team to provide its customer with broad and rapid turnaround trade studies for launch vehicle conceptual design. The ability to identify a selection of designs which can meet the customer requirements will have the potential to lower LCC of launch vehicle designs originating from ACO.
Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases. Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission. Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an advanced design methods (ADM) based approach. This approach applies the concepts of design of experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development effort. In order to fit a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of ground rules and assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated "what if" questions. The example problem will then illustrate the setup and execution of a DOE through the automated ACO tools. This process is accomplished more efficiently in this work by splitting the tools into two separate environments. The first environment encompasses the structural optimization and mass estimation tools, while the second is focused on trajectory optimization. Surrogate models are fit to the outputs of each environment and are "integrated" via connection of the surrogate equations. Throughout this process, checks are implemented to compare the output of the surrogates to the output of manually run cases to ensure that the error of the final surrogates is at an acceptable level. The conclusion of the example problem demonstrates the utility of the ADM based approach. Using surrogate models gives the ACO team the ability to visualize vehicle sensitivities to various design parameters and identify regions of interest within the design space. The ADM approach can thus be used to inform concept down selection and isolate promising vehicle configurations to be explored in more detail through the manual design process. In addition it provides the customer with an almost instantaneous turnaround on any ''what if" questions that may arise within the bounds of the surrogate model. This approach ultimately expands the ability of the ACO team to provide its customer with broad and rapid turnaround trade studies for launch vehicle conceptual design. The ability to identify a selection of designs which can meet the customer requirements will help ensure lower LCC of launch vehicle designs originating from ACO.
The Advanced Concepts Office (ACO) at NASA, Marshall Space Flight Center is expanding its current technology assessment methodologies. ACO is developing a framework called TAPP that uses a variety of methods, such as association mining and rule learning from data mining, structure development using a Technological Innovation System (TIS), and social network modeling to measure structural relationships. The role of ACO is to 1) produce a broad spectrum of ideas and alternatives for a variety of NASA's missions, 2) determine mission architecture feasibility and appropriateness to NASA's strategic plans, and 3) define a project in enough detail to establish an initial baseline capable of meeting mission objectives ACO's role supports the decision-making process associated with the maturation of concepts for traveling through, living in, and understanding space. ACO performs concept studies and technology assessments to determine the degree of alignment between mission objectives and new technologies. The first step in technology assessment is to identify the current technology maturity in terms of a technology readiness level (TRL). The second step is to determine the difficulty associated with advancing a technology from one state to the next state. NASA has used TRLs since 1970 and ACO formalized them in 1995. The DoD, ESA, Oil & Gas, and DoE have adopted TRLs as a means to assess technology maturity. However, "with the emergence of more complex systems and system of systems, it has been increasingly recognized that TRL assessments have limitations, especially when considering [the] integration of complex systems." When performing the second step in a technology assessment, NASA requires that an Advancement Degree of Difficulty (AD2) method be utilized. NASA has used and developed or used a variety of methods to perform this step: Expert Opinion or Delphi Approach, Value Engineering or Value Stream, Analytical Hierarchy Process (AHP), Technique for the Order of Prioritization by Similarity to Ideal Solution (TOPSIS), and other multi‐criteria decision-making methods. These methods can be labor-intensive, often contain cognitive or parochial bias, and do not consider the competing prioritization between mission architectures. Strategic Decision-Making (SDM) processes cannot be properly understood unless the context of the technology is understood. This makes assessing technological change particularly challenging due to the relationships "between incumbent technology and the incumbent (innovation) system in relation to the emerging technology and the emerging innovation system." The central idea in technology dynamics is to consider all activities that contribute to the development, diffusion, and use of innovations as system functions. Bergek defines system functions within a TIS to address what is actually happening and has a direct influence on the ultimate performance of the system and technology development. ACO uses similar metrics and is expanding these metrics to account for the structure and context of the technology. At NASA technology and strategy is strongly interrelated. NASA's Strategic Space Technology Investment Plan (SSTIP) prioritizes those technologies essential to the pursuit of NASA's missions and national interests. The SSTIP is strongly coupled with NASA's Technology Roadmaps to provide investment guidance during the next four years, within a twenty-year horizon. This paper discusses the methods ACO is currently developing to better perform technology assessments while taking into consideration Strategic Alignment, Technology Forecasting, and Long Term Planning.
Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases [1]. Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission [2]. Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an advanced design methods (ADM) based approach. This approach applies the concepts of design of experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development effort. In order to fit a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of ground rules and assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated "what if" questions.
The NASA has determined that a multi-functional exercise device will be developed for use as an exercise device during exploration missions. The device will allow for full body resistance and metabolic exercise necessary to minimize physiological losses during space flight and to maintain fitness necessary to perform critical mission tasks. Prior to implementation as an exercise device on an Exploration vehicle, there will be verification and validation testing completed to determine device efficacy at providing the necessary training stimuli to achieve desired goals. Because the exploration device will be new device that has yet be specified, specific Verification and Validation (V&V) protocols have yet to be developed. Upon delivery of an exploration exercise device training unit, stakeholders throughout NASA will develop V&V plans that include ground-based testing and testing on the International Space Station (ISS). Stakeholders will develop test protocols that include success criterion for the device. Ground tests will occur at NASA Johnson Space Station prior to flight testing. The intents of the ground tests are to allow crew, spaceflight medicine, science, engineering, Astronaut Strength, Conditioning, and Reconditioning staff, and others to gain experience in the best utilization of the device. The goal is to obtain an evidence base for recommending use of the device on the ISS. The developed protocol will be created to achieve multiple objectives, including determining if the device provides an adequate training stimulus for 5th - 95th percentile males and females, allows for exercise modalities that protect functional capability, and is robust and can withstand extensive human use. Although protocols are yet to be determined, current expectations include use of the device by test subjects and current crew in order to obtain quantitative and qualitative feedback. Information obtained during the ground tests may be used to influence device modifications during design iterations. Assuming successful ground tests, the device will be installed on the ISS for testing during space flight. Spaceflight testing is envisioned to include an activation and checkout (ACO) phase and a V&V phase. During the ACO phase, 1-2 crewmembers will exercise with the device to ensure proper function. ACO is expected to last multiple months because of the many modes and methods of exercise that need to be assessed. However, the goal is to complete the ACO as quickly as possible. Once successful ACO occurs, the crew will be free to use the device for normal exercise pending concurrence from stakeholders. V&V tests on the ISS will ideally consist of crew using the device for all of their exercise for an entire mission. Exercise prescriptions will be supplied that replicate expected prescriptions during exploration missions. Crew that are not enrolled in the V&V studies would be also free to use the device as their schedule permits. As experience is gained by users, exercise protocols could change. The intent of all V&V testing is to ensure that all have thorough understanding of experience at optimizing device capability
Since 2002 the National Aeronautics and Space Administration (NASA) has carried out an optical survey of the debris environment in the geosynchronous Earth-orbit (GEO) region with the Michigan Orbital Debris Survey Telescope (MODEST) in Chile. The survey coverage has been similar for 4 of the 5 years allowing us to follow the orbital evolution of Correlated Targets (CTs), both controlled and un-controlled objects, and Un-Correlated Targets (UCTs). Under gravitational perturbations the distributions of uncontrolled objects, both CTs and UCTs, in GEO orbits will evolve in predictable patterns, particularly evident in the inclination and right ascension of the ascending node (RAAN) distributions. There are several clusters (others have used a "cloud" nomenclature) in observed distributions that show evolution from year to year in their inclination and ascending node elements. However, when MODEST is in survey mode (field-of-view approx.1.3deg) it provides only short 5-8 minute orbital arcs which can only be fit under the assumption of a circular orbit approximation (ACO) to determine the orbital parameters. These ACO elements are useful only in a statistical sense as dedicated observing runs would be required to obtain sufficient orbital coverage to determine a set of accurate orbital elements and then to follow their evolution. Identification of the source(s) for these "clusters of UCTs" would be advantageous to the overall definition of the GEO orbital debris environment. This paper will set out to determine if the ACO elements can be used to in a statistical sense to identify the source of the "clustering of UCTs" roughly centered on an inclination of 12deg and a RAAN of 345deg. The breakup of the Titan 3C-4 transtage on February 21, 1992 has been modeled using NASA s LEGEND (LEO-to-GEO Environment Debris) code to generate a GEO debris cloud. Breakup fragments are created based on the NASA Standard Breakup Model (including fragment size, area-to-mass (A/M), and delta-V distributions). Once fragments are created, they are propagated forward in time with a subroutine GEOPROP. Perturbations included in GEOPROP are those due to solar/lunar gravity, radiation pressure, and major geopotential terms. The question to be addressed: are the UCTs detected by MODEST in this inclination/RAAN region related to the Titan 3C-4 breakup? Discussion will include the observational biases in attempting to detect a specific, uncontrolled target during given observing session. These restrictions include: (1) the length of the observing session which is 8 hours or less at any given date or declination; (2) the assumption of ACO elements for detected object when the breakup model predicts debris with non-zero eccentricities; (3) the size and illumination or brightness of the debris predicted by the model and the telescope/sky limiting magnitude.
The Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC)has been performing conceptual design of launch vehicles since the Space Transportation System Program. As part of its duties ACO has been tasked with investigating the effects of known and potential threats and opportunities to a myriad of launch vehicle concepts, launch sites, and mission profiles known as the launch space. As the launch space becomes more crowded and busy with launch providers and their vehicles, the legacy method of prescribing each design discipline to a single human Subject Matter Expert (SME) has become infeasible. Rapidly changing launch, economical, and political constraints require rapid evaluation of their effects. While moving into this future of rapid response does require streamlining of data and processes, it does not however lead directly to reinventing the wheel. Flight-validated analysis tools, procedures, and lessons learned can be effectively leveraged to bring the experience from decades of government and commercial flight programs to the new paradigm. In this publication we describe the aggregation of all previous launch vehicle design efforts of ACO into an automated and integrated toolchain called menagerie. The toolchain utilizes a suite of NASA, Air Force Research Lab (AFRL), and ACO-developed tools and processes to evaluate launch vehicle concepts, mission profiles, and trades in a fraction of the time previously required.
The Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center has analyzed over 2000 Ares V and other heavy lift concepts in the last 3 years. These concepts were analyzed for Lunar Exploration Missions, heavy lift capability to Low Earth Orbit (LEO) as well as exploratory missions to other near earth objects in our solar system. With the pending retirement of the Shuttle fleet, our nation will be without a civil heavy lift launch capability, so the future development of a new heavy lift capability is imperative for the exploration and large science missions our Agency has been tasked to deliver. The majority of the heavy lift concepts analyzed by ACO during the last 3 years have been based on liquid oxygen / liquid hydrogen (LOX/LH2) core stage and solids booster stage propulsion technologies (Ares V / Shuttle Derived and their variants). These concepts were driven by the decisions made from the results of the Exploration Systems Architecture Study (ESAS), which in turn, led to the Ares V launch vehicle that has been baselined in the Constellation Program. Now that the decision has been made at the Agency level to cancel Constellation, other propulsion options such as liquid hydrocarbon fuels are back in the exploration trade space. NASA is still planning exploration missions with the eventual destination of Mars and a new heavy lift launch vehicle is still required and will serve as the centerpiece of our nation s next exploration architecture s infrastructure. With an extensive launch vehicle database already developed on LOX/LH2 based heavy lift launch vehicles, ACO initiated a study to look at using a new high thrust (> 1.0 Mlb vacuum thrust) hydrocarbon engine as the primary main stage propulsion in such a launch vehicle.
The Advanced Concepts Office (ACO) Launch Vehicle Team at the NASA Marshall Space Flight Center (MSFC) is recognized throughout NASA for launch vehicle conceptual definition and pre-phase A concept design evaluation. The Launch Vehicle Team has been instrumental in defining the vehicle trade space for many of NASA s high level launch system studies from the Exploration Systems Architecture Study (ESAS) through the Augustine Report, Constellation, and now Space Launch System (SLS). The Launch Vehicle Team s approach to rapid turn-around and comparative analysis of multiple launch vehicle architectures has played a large role in narrowing the design options for future vehicle development. Recently the Launch Vehicle Team has been developing versions of their vetted tools used on large launch vehicles and repackaged the process and capability to apply to smaller more responsive launch vehicles. Along this development path the LV Team has evaluated trajectory tools and assumptions against sounding rocket trajectories and air launch systems, begun altering subsystem mass estimating relationships to handle smaller vehicle components, and as an additional development driver, have begun an in-house small launch vehicle study. With the recent interest in small responsive launch systems and the known capability and response time of the ACO LV Team, ACO s launch vehicle assessment capability can be utilized to rapidly evaluate the vast and opportune trade space that small launch vehicles currently encompass. This would provide a great benefit to the customer in order to reduce that large trade space to a select few alternatives that should best fit the customer s payload needs.
The Earth to Orbit (ETO) Team of the Advanced Concepts Office (ACO) at NASA Marshal Space Flight Center (MSFC) is considered the preeminent group to go to for prephase A and phase A concept definition. The ACO team has been at the forefront of a multitude of launch vehicle studies determining the future direction of the Agency as a whole due, in part, to their rapid turnaround time in analyzing concepts and their ability to cover broad trade spaces of vehicles in that limited timeframe. Each completed vehicle concept includes a full mass breakdown of each vehicle to tertiary subsystem components, along with a vehicle trajectory analysis to determine optimized payload delivery to specified orbital parameters, flight environments, and delta v capability. Additionally, a structural analysis of the vehicle based on material properties and geometries is performed as well as an analysis to determine the flight loads based on the trajectory outputs. As mentioned, the ACO Earth to Orbit Team prides themselves on their rapid turnaround time and often need to fulfill customer requests within limited schedule or little advanced notice. Due to working in this fast paced environment, the ETO team has developed some finely honed skills and methods to maximize the delivery capability to meet their customer needs. This paper will describe the interfaces between the 3 primary disciplines used in the design process; weights and sizing, trajectory, and structural analysis, as well as the approach each discipline employs to streamline their particular piece of the design process.
Welcome to this third edition of the Flight Opportunities program annual report. In this edition, we continue our story of pathfinding NASA's role in the partnership with the U.S. commercial space and space technology R&D communities to advance national space interests and develop technologies critical to NASA's future missions. 2015 was the year in which a planned change to our payload solicitation strategy saw its first tangible result. As you might remember from our 2014 annual report, in 2015 we set out to facilitate a more direct interaction between flight providers and technology developers by providing fixed funding awards to researchers to directly purchase the flight service(s) that best meet their needs. The selection and award of the first six REDDI-F1 flight grants to non-U.S. government researchers was an important milestone in this regard. From now on, using the REDDI-F1 solicitation appendix, the program will enable non-U.S. government researchers to directly purchase flight services on the emerging suborbital market. The same (or similar) commercial flight services will be available to NASA and other U.S. government agencies (OGA) through commercial contracts that NASA has established through our program. For the latter, our program is available to provide campaign management services, similar to the role we play(ed) for technology payloads remaining in our pool from earlier selections. The full impact of this broader strategic change will likely become more visible in the years ahead as our legacy pool gets depleted and we have implemented a new NASA- and OGA-specific call for proposals. One observation that can already be made after two rounds of REDDI-F1 solicitations is that through this change, the list of commercial flight service providers of interest to non-U.S. government researchers has grown from five in 2014 to nine in 2015. On the industry development front, our Announcement of Collaborative Opportunities (ACO) solicitation was promoted to an STMD-wide solicitation and released in 2015 in combination with the Tipping Point solicitation. A total of 22 awards was announced in November 2015, 12 of which are ACO awards, and six of these are funded by Flight Opportunities. Through these ACO awards, the program funds NASA technical expertise and NASA test facilities to aid industry partners in maturing key space technologies, in our case focusing on small launch vehicle technology development. Flight test activity in 2015 saw a steady 13 campaigns with 31 payload-flights (29 unique payloads). Thirteen new payloads were selected into the program in FY2015, and 14 payloads completed flight testing, bringing the total number of completed technology demonstration payloads to 69. Overall, we are pleased with the evolution and growth of the Flight Opportunities program and look forward to continued success in our partnership with the technology R&D community and the commercial space sector.
This paper describes the development and testing of the initial version of a common space systems ontology (CoSSO) for use by the Advanced Concepts Office (ACO) at NASA's Marshall Space Flight Center. The ontology provides a shared conceptualization of concepts of interest to the ACO for modeling aerospace systems concepts in a pre-phase A context to aid with the transition to a more model-based paradigm. The ontological concepts and relations, as well as the anticipated use cases, were developed through interactions with the relevant subject matter experts at the ACO and implemented in the Ontological Modeling Language (OML). The ontology builds on the Basic Formal Ontology (BFO) and the Common Core Ontologies (CCO). While most of the ontology is still in the initial stages, an Environmental Control and Life Support System (ECLSS) ontology is being built on top of the main CoSSO and heavily developed as a proof of concept. The ECLSS ontology is designed with different use cases in mind, namely predicting and diagnosing errors in ECLS systems on long-duration missions, with a focus on the Four-Bed CO$_2$ carbon dioxide scrubber currently on board the ISS. The ECLSS ontology is being developed in a similar manner to the CoSSO, and designed to be compatible with it. The current state of both ontologies is presented and discussed, along with plans for future development and testing.
The dominant galaxy of a rich cluster is often an object whose formation and evolution is closely connected to the dynamics of the cluster itself. Hoessel (1980) and Schneider et al. (1983) estimate that 50 percent of the dominant galaxies are either of the dumb-bell type or have companions at projected distances less than 20 kpc, which is far in excess of the number expected from chance projection (see also Rood and Leir 1979). Presently there is no complete sample of these objects, with the exception of the listing of dumb-bell galaxies in BM type I and I-II clusters in the Abell statistical sample of Rood and Leir (1979). Recent dynamical studies of dumb-bell galaxies in clusters (Valentijn and Casertano, 1988) still suffer from inhomogeneity of the sample. The fact that it is a mixture of optically and radio selected objects may have introduced an unknown biases, for instance if the probability of radio emission is enhanced by the presence of close companions (Stocke, 1978, Heckman et al. 1985, Vettolani and Gregorini 1988) a bias could be present in their velocity distribution. However, this situation is bound to improve: a new sample of Abell clusters in the Southern Hemisphere has been constructed (Abell et al., 1988 hereafter ACO), which has several advantages over the original northern catalog. The plate material (IIIaJ plates) is of better quality and reaches fainter magnitudes. This makes it possible to classify the cluster types with a higher degree of accuracy, as well as to fainter magnitudes. The authors therefore decided to reconsider the whole problem constructing a new sample of dumb-bell galaxies homogeneously selected from the ACO survey. Details of the classification criteria are given.
The Marshall Space Flight Center Advanced Concepts Office (ACO) has the leading role for NASA s preliminary conceptual launch vehicle design and performance analysis. Over the past several years the ACO Earth-to-Orbit Team has evaluated thousands of launch vehicle concept variations for a multitude of studies including agency-wide efforts such as the Exploration Systems Architecture Study (ESAS), Constellation, Heavy Lift Launch Vehicle (HLLV), Heavy Lift Propulsion Technology (HLPT), Human Exploration Framework Team (HEFT), and Space Launch System (SLS). NASA plans to continue human space exploration and space station utilization. Launch vehicles used for heavy lift cargo and crew will be needed. One of the current leading concepts for future heavy lift capability is an inline one and a half stage concept using solid rocket boosters (SRB) and based on current Shuttle technology and elements. Potentially, the quickest and most cost-effective path towards an operational vehicle of this configuration is to make use of a demonstrator vehicle fabricated from existing shuttle assets and relying upon the existing STS launch infrastructure. Such a demonstrator would yield valuable proof-of-concept data and would provide a working test platform allowing for validated systems integration. Using shuttle hardware such as existing RS-25D engines and partial MPS, propellant tanks derived from the External Tank (ET) design and tooling, and four-segment SRB s could reduce the associated upfront development costs and schedule when compared to a concept that would rely on new propulsion technology and engine designs. There are potentially several other additional benefits to this demonstrator concept. Since a concept of this type would be based on man-rated flight proven hardware components, this demonstrator has the potential to evolve into the first iteration of heavy lift crew or cargo and serve as a baseline for block upgrades. This vehicle could also serve as a demonstration and test platform for the Orion Program. Critical spacecraft systems, re-entry and recovery systems, and launch abort systems of Orion could also be demonstrated in early test flights of the launch vehicle demo. Furthermore, an early demonstrator of this type would provide a stop-gap for retaining critical human capital and infrastructure while affording the current emerging generation of young engineers opportunity to work with and capture lessons learned from existing STS program offices and personnel, who were integral in the design and development of the Space Shuttle before these resources are no longer available. The objective of this study is to define candidate launch vehicle demonstration concepts that are based on Space Shuttle assets and determine their performance capabilities and how these demonstration vehicles could evolve to a heavy lift capability to low earth orbit.