39 research outputs found

    Manned Mars mission cost estimate

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    The potential costs of several options of a manned Mars mission are examined. A cost estimating methodology based primarily on existing Marshall Space Flight Center (MSFC) parametric cost models is summarized. These models include the MSFC Space Station Cost Model and the MSFC Launch Vehicle Cost Model as well as other modes and techniques. The ground rules and assumptions of the cost estimating methodology are discussed and cost estimates presented for six potential mission options which were studied. The estimated manned Mars mission costs are compared to the cost of the somewhat analogous Apollo Program cost after normalizing the Apollo cost to the environment and ground rules of the manned Mars missions. It is concluded that a manned Mars mission, as currently defined, could be accomplished for under $30 billion in 1985 dollars excluding launch vehicle development and mission operations

    New Ways Of Doing Business (NWODB) cost quantification analysis

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    The cost of designing, producing, and operating typical aerospace flight hardware is necessarily more expensive than most other human endeavors. Because of the more stringent environment of space, hardware designed to operate there will probably always be more expensive than similar hardware which is designed for less taxing environments. It is the thesis of this study that there are very significant improvements that can be made in the cost of aerospace flight hardware

    Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology:The impact of researchers choices on the selection of treatment targets using the experience sampling methodology

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    OBJECTIVE: One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual’s emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them. METHODS: To evaluate this, we crowdsourced the analysis of one individual patient’s ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment. RESULTS: Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0–16) and nature of selected targets varied widely. CONCLUSION: This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation

    The Faster, Better, Cheaper Approach to Space Missions: An Engineering Management Assessment

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    NASA was chartered as an independent civilian space agency in 1958 following the Soviet Union's dramatic launch of the Sputnik 1 (1957). In his state of the union address in May of 1961, President Kennedy issued to the fledging organization his famous challenge for a manned lunar mission by the end of the decade. The Mercury, Gemini and Apollo programs that followed put the utmost value on high quality, low risk (as low as possible within the context of space flight), quick results, all with little regard for cost. These circumstances essentially melded NASAs culture as an organization capable of great technological achievement but at extremely high cost. The Space Shuttle project, the next major agency endeavor, was put under severe annual budget constraints in the 1970's. NASAs response was to hold to the high quality standards, low risk and annual cost and let schedule suffer. The result was a significant delay in the introduction of the Shuttle as well as overall total cost growth. By the early 1990's, because NASA's budget was declining, the number of projects was also declining. Holding the same cost and schedule productivity levels as before was essentially causing NASA to price itself out of business. In 1992, the helm of NASA was turned over to a new Administrator. Dan Goldin's mantra was "faster, better, cheaper" and his enthusiasm and determination to change the NASA culture was not to be ignored. This research paper documents the various implementations of "faster, better, cheaper" that have been attempted, analyzes their impact and compares the cost performance of these new projects to previous NASA benchmarks. Fundamentally, many elements of "faster, better, cheaper" are found to be working well, especially on smaller projects. Some of the initiatives are found to apply only to smaller or experimental projects however, so that extrapolation to "flagship" projects may be problematic

    Improving Space Project Cost Estimating with Engineering Management Variables

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    Current space project cost models attempt to predict space flight project cost via regression equations, which relate the cost of projects to technical performance metrics (e.g. weight, thrust, power, pointing accuracy, etc.). This paper examines the introduction of engineering management parameters to the set of explanatory variables. A number of specific engineering management variables are considered and exploratory regression analysis is performed to determine if there is statistical evidence for cost effects apart from technical aspects of the projects. It is concluded that there are other non-technical effects at work and that further research is warranted to determine if it can be shown that these cost effects are definitely related to engineering management

    The Launch Systems Operations Cost Model

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    One of NASA's primary missions is to reduce the cost of access to space while simultaneously increasing safety. A key component, and one of the least understood, is the recurring operations and support cost for reusable launch systems. In order to predict these costs, NASA, under the leadership of the Independent Program Assessment Office (IPAO), has commissioned the development of a Launch Systems Operations Cost Model (LSOCM). LSOCM is a tool to predict the operations & support (O&S) cost of new and modified reusable (and partially reusable) launch systems. The requirements are to predict the non-recurring cost for the ground infrastructure and the recurring cost of maintaining that infrastructure, performing vehicle logistics, and performing the O&S actions to return the vehicle to flight. In addition, the model must estimate the time required to cycle the vehicle through all of the ground processing activities. The current version of LSOCM is an amalgamation of existing tools, leveraging our understanding of shuttle operations cost with a means of predicting how the maintenance burden will change as the vehicle becomes more aircraft like. The use of the Conceptual Operations Manpower Estimating Tool/Operations Cost Model (COMET/OCM) provides a solid point of departure based on shuttle and expendable launch vehicle (ELV) experience. The incorporation of the Reliability and Maintainability Analysis Tool (RMAT) as expressed by a set of response surface model equations gives a method for estimating how changing launch system characteristics affects cost and cycle time as compared to today's shuttle system. Plans are being made to improve the model. The development team will be spending the next few months devising a structured methodology that will enable verified and validated algorithms to give accurate cost estimates. To assist in this endeavor the LSOCM team is part of an Agency wide effort to combine resources with other cost and operations professionals to support models, databases, and operations assessments
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