Manned spacecraft systems cost model, volume III. Technical report, Apr. 1965 - Jun. 1966
Cost estimate model for manned spacecraft systems
MANNED SPACECRAFT↗
SEARCH · Search NASA
Results for “cost estimation cost model”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Process-Based Cost Estimation for Ramjet/Scramjet Engines
Process-based cost estimation plays a key role in effecting cultural change that integrates distributed science, technology and engineering teams to rapidly create innovative and affordable products. Working together, NASA Glenn Research Center and Boeing Canoga Park have developed a methodology of process-based cost estimation bridging the methodologies of high-level parametric models and detailed bottoms-up estimation. The NASA GRC/Boeing CP process-based cost model provides a probabilistic structure of layered cost drivers. High-level inputs characterize mission requirements, system performance, and relevant economic factors. Design alternatives are extracted from a standard, product-specific work breakdown structure to pre-load lower-level cost driver inputs and generate the cost-risk analysis. As product design progresses and matures the lower level more detailed cost drivers can be re-accessed and the projected variation of input values narrowed, thereby generating a progressively more accurate estimate of cost-risk. Incorporated into the process-based cost model are techniques for decision analysis, specifically, the analytic hierarchy process (AHP) and functional utility analysis. Design alternatives may then be evaluated not just on cost-risk, but also user defined performance and schedule criteria. This implementation of full-trade study support contributes significantly to the realization of the integrated development environment. The process-based cost estimation model generates development and manufacturing cost estimates. The development team plans to expand the manufacturing process base from approximately 80 manufacturing processes to over 250 processes. Operation and support cost modeling is also envisioned. Process-based estimation considers the materials, resources, and processes in establishing cost-risk and rather depending on weight as an input, actually estimates weight along with cost and schedule.
Singh, Brijendra↗
Estimating Software-Development Costs With Greater Accuracy
COCOMOST is a computer program for use in estimating software development costs. The goal in the development of COCOMOST was to increase estimation accuracy in three ways: (1) develop a set of sensitivity software tools that return not only estimates of costs but also the estimation error; (2) using the sensitivity software tools, precisely define the quantities of data needed to adequately tune cost estimation models; and (3) build a repository of software-cost-estimation information that NASA managers can retrieve to improve the estimates of costs of developing software for their project. COCOMOST implements a methodology, called '2cee', in which a unique combination of well-known pre-existing data-mining and software-development- effort-estimation techniques are used to increase the accuracy of estimates. COCOMOST utilizes multiple models to analyze historical data pertaining to software-development projects and performs an exhaustive data-mining search over the space of model parameters to improve the performances of effort-estimation models. Thus, it is possible to both calibrate and generate estimates at the same time. COCOMOST is written in the C language for execution in the UNIX operating system.
Baker, Dan↗
Cost model validation: a technical and cultural approach
This paper summarizes how JPL's parametric mission cost model (PMCM) has been validated using both formal statistical methods and a variety of peer and management reviews in order to establish organizational acceptance of the cost model estimates.
cost model validation↗
Gaussian Process Regression Method for Costing SmallSat Bus Capabilities
NASA is responding to the growing interest in, andcapabilities of, small satellites for science applications with an increasingnumber and frequency of Announcements of Opportunityfor small satellite space missions. Estimating the probabilitythat these mission concepts will fit within the small cost capsof these opportunities is largely driven by the probability thatone of the burgeoning number of small satellite providers will beable to meet the payload’s accommodation requirements withinthe budget for the spacecraft. JPL has collected a databasecontaining technical specifications and cost of commerciallyavailable Smallsat buses across various vendors. The primarypurpose of the database is for use in JPL’s Team X architecturestudies to inform cost estimates of a spacecraft bus which fitsthe customer’s technical requirements for their payload andmission. Customer needs are often unique and don’t alignperfectly with an off-the-shelf commercial spacecraft bus, whichmotivates the need to develop a cost model across the continuoustechnical parameter space.Al’s Bus Cost Distribution Estimator (ABCDE) uses Gaussianprocess regression (GPR) to predict commercial Smallsat spacecraftbus cost based on a subset of a customer’s technicalrequirements (payload mass, payload power, delta V, pointingcontrol, and downlink rate). GPR is implemented in ABCDE asa Bayesian method which fits an implied multivariate regressionon the technical parameters and uses kriging to intentionally“overfit” the residuals. Overfitting the residuals allows costestimates to collapse in uncertainty closer to the data pointswhile maintaining larger uncertainty intervals in regions of parameterspace with fewer data records. The data used to fit thismodel is sensitive and represents cost estimates for off-the-shelfcommercial buses. GPR simultaneously protects the sensitivityof the database and uses the sparse nature of the database toaccount for uncertainty in cost in a useful way. For a givenset of customer technical requirements, the tool provides a costestimate distribution, the percentiles of which can be interpretedas a confidence level of finding a commercial bus under a specifiedcost cap. ABCDE dramatically pushes the boundaries ofspacecraft cost estimation models due to its Bayesian methodology(accounting for the maximum uncertainty in the underlyingregression), the mathematically advanced kriging methodology,and the novelty of its application in Team X architecture tradestudies.
Austin, Alex↗
New Approaches in Reusable Booster System Life Cycle Cost Modeling
This paper presents the results of a 2012 life cycle cost (LCC) study of hybrid Reusable Booster Systems (RBS) conducted by NASA Kennedy Space Center (KSC) and the Air Force Research Laboratory (AFRL). The work included the creation of a new cost estimating model and an LCC analysis, building on past work where applicable, but emphasizing the integration of new approaches in life cycle cost estimation. Specifically, the inclusion of industry processes/practices and indirect costs were a new and significant part of the analysis. The focus of LCC estimation has traditionally been from the perspective of technology, design characteristics, and related factors such as reliability. Technology has informed the cost related support to decision makers interested in risk and budget insight. This traditional emphasis on technology occurs even though it is well established that complex aerospace systems costs are mostly about indirect costs, with likely only partial influence in these indirect costs being due to the more visible technology products. Organizational considerations, processes/practices, and indirect costs are traditionally derived ("wrapped") only by relationship to tangible product characteristics. This traditional approach works well as long as it is understood that no significant changes, and by relation no significant improvements, are being pursued in the area of either the government acquisition or industry?s indirect costs. In this sense then, most launch systems cost models ignore most costs. The alternative was implemented in this LCC study, whereby the approach considered technology and process/practices in balance, with as much detail for one as the other. This RBS LCC study has avoided point-designs, for now, instead emphasizing exploring the trade-space of potential technology advances joined with potential process/practice advances. Given the range of decisions, and all their combinations, it was necessary to create a model of the original model and use genetic algorithms to explore results. A strong business case occurs when viable paths are identified for an affordable up-front investment, and these paths can credibly achieve affordable, responsive operations, characterized by smaller direct touch labor efforts at the wing level from flight to flight. The results supporting this approach, its potential, and its conclusions are presented here.
Zapata, Edgar↗
New Approaches in Reuseable Booster System Life Cycle Cost Modeling
This paper presents the results of a 2012 life cycle cost (LCC) study of hybrid Reusable Booster Systems (RBS) conducted by NASA Kennedy Space Center (KSC) and the Air Force Research Laboratory (AFRL). The work included the creation of a new cost estimating model and an LCC analysis, building on past work where applicable, but emphasizing the integration of new approaches in life cycle cost estimation. Specifically, the inclusion of industry processes/practices and indirect costs were a new and significant part of the analysis. The focus of LCC estimation has traditionally been from the perspective of technology, design characteristics, and related factors such as reliability. Technology has informed the cost related support to decision makers interested in risk and budget insight. This traditional emphasis on technology occurs even though it is well established that complex aerospace systems costs are mostly about indirect costs, with likely only partial influence in these indirect costs being due to the more visible technology products. Organizational considerations, processes/practices, and indirect costs are traditionally derived ("wrapped") only by relationship to tangible product characteristics. This traditional approach works well as long as it is understood that no significant changes, and by relation no significant improvements, are being pursued in the area of either the government acquisition or industry?s indirect costs. In this sense then, most launch systems cost models ignore most costs. The alternative was implemented in this LCC study, whereby the approach considered technology and process/practices in balance, with as much detail for one as the other. This RBS LCC study has avoided point-designs, for now, instead emphasizing exploring the trade-space of potential technology advances joined with potential process/practice advances. Given the range of decisions, and all their combinations, it was necessary to create a model of the original model and use genetic algorithms to explore results. A strong business case occurs when viable paths are identified for an affordable up-front investment, and these paths can credibly achieve affordable, responsive operations, characterized by smaller direct touch labor efforts at the wing level from flight to flight. The results supporting this approach, its potential, and its conclusions are presented here.
Zapata, Edgar↗
Fuzzy/Neural Software Estimates Costs of Rocket-Engine Tests
The Highly Accurate Cost Estimating Model (HACEM) is a software system for estimating the costs of testing rocket engines and components at Stennis Space Center. HACEM is built on a foundation of adaptive-network-based fuzzy inference systems (ANFIS) a hybrid software concept that combines the adaptive capabilities of neural networks with the ease of development and additional benefits of fuzzy-logic-based systems. In ANFIS, fuzzy inference systems are trained by use of neural networks. HACEM includes selectable subsystems that utilize various numbers and types of inputs, various numbers of fuzzy membership functions, and various input-preprocessing techniques. The inputs to HACEM are parameters of specific tests or series of tests. These parameters include test type (component or engine test), number and duration of tests, and thrust level(s) (in the case of engine tests). The ANFIS in HACEM are trained by use of sets of these parameters, along with costs of past tests. Thereafter, the user feeds HACEM a simple input text file that contains the parameters of a planned test or series of tests, the user selects the desired HACEM subsystem, and the subsystem processes the parameters into an estimate of cost(s).
Douglas, Freddie↗
Operations and support cost modeling using Markov chains
Systems for future missions will be selected with life cycle costs (LCC) as a primary evaluation criterion. This reflects the current realization that only systems which are considered affordable will be built in the future due to the national budget constaints. Such an environment calls for innovative cost modeling techniques which address all of the phases a space system goes through during its life cycle, namely: design and development, fabrication, operations and support; and retirement. A significant portion of the LCC for reusable systems are generated during the operations and support phase (OS). Typically, OS costs can account for 60 to 80 percent of the total LCC. Clearly, OS costs are wholly determined or at least strongly influenced by decisions made during the design and development phases of the project. As a result OS costs need to be considered and estimated early in the conceptual phase. To be effective, an OS cost estimating model needs to account for actual instead of ideal processes by associating cost elements with probabilities. One approach that may be suitable for OS cost modeling is the use of the Markov Chain Process. Markov chains are an important method of probabilistic analysis for operations research analysts but they are rarely used for life cycle cost analysis. This research effort evaluates the use of Markov Chains in LCC analysis by developing OS cost model for a hypothetical reusable space transportation vehicle (HSTV) and suggests further uses of the Markov Chain process as a design-aid tool.
Unal, Resit↗
Estimating The Cost Of Developing Software
Software Cost Estimation Model program, SOFTCOST, developed to provide consistent automated resource-and-schedule mathematical model more formalized than guesswork model. Combines several software-cost models found in open literature into one comprehensive set of algorithms compensating for nearly 50 implementation factors relative to size of task, inherited baseline, organizational and system environment, and difficulty of task. Produces mean and variance estimates of software size, implementation productivity, recommended staff level, probable duration, amount of computer resources required, and amount and cost of software documentation. Written in Microsoft BASIC.
Tausworthe, Robert C.↗
Software Transition Project Retrospectives and the Application of SEL Effort Estimation Model and Boehm's COCOMO to Complex Software Transition Projects
The Multimission Image Processing Subsystem (MIPS) at the Jet Propulsion Laboratory (JPL) has managed transitions of application software sets from one operating system and hardware platform to multiple operating systems and hardware platforms. As a part of these transitions, cost estimates were generated from the personal experience of in-house developers and managers to calculate the total effort required for such projects. Productivity measures have been collected for two such transitions, one very large and the other relatively small in terms of source lines of code. These estimates used a cost estimation model similar to the Software Engineering Laboratory (SEL) Effort Estimation Model. Experience in transitioning software within JPL MIPS have uncovered a high incidence of interface complexity. Interfaces, both internal and external to individual software applications, have contributed to software transition project complexity, and thus to scheduling difficulties and larger than anticipated design work on software to be ported.
SEL Boehm COCOMO↗
Modeling the Deep Space Network costs for future space missions by using major cost drivers
This paper develops a cost model to do long range planning cost estimates for Deep Space Network (DSN) support of future space missions. The model is a function of eight major mission cost drivers such as uplink frequency upgrade, telemetry upgrade, and antenna gain/noise temperature upgrade. This paper focuses on the costs required to modify and/or enhance the DSN to prepare for supporting future space missions. Two models are derived from actual cost data from three space missions: Voyager (Uranus), Voyager (Neptune), and Magellan. Model A makes it possible to estimate the total cost, and Model B makes it possible to estimate the time profile cost and total cost for DSN support of a similar future space mission. The models were back tested against the total cost and the time profile cost for DSN support of the three projects, and gave cost estimates which range from 17 percent below to 19 percent above actual costs for Model A, and 22.5 percent below to 17.5 percent above for Model B. The total cost Model A was also applied to estimate the total costs for DSN support of two other independent projects: Mariner Jupiter/Saturn (MJS later became Voyager), and Viking. This model gave total cost estimates for DSN support which range from 15 percent to 4 percent above actual total costs for MJS and Viking, respectively.
Remer, Donald S.↗
Estimating the Life Cycle Cost of Space Systems
A space system's Life Cycle Cost (LCC) includes design and development, launch and emplacement, and operations and maintenance. Each of these cost factors is usually estimated separately. NASA uses three different parametric models for the design and development cost of crewed space systems; the commercial PRICE-H space hardware cost model, the NASA-Air Force Cost Model (NAFCOM), and the Advanced Missions Cost Model (AMCM). System mass is an important parameter in all three models. System mass also determines the launch and emplacement cost, which directly depends on the cost per kilogram to launch mass to Low Earth Orbit (LEO). The launch and emplacement cost is the cost to launch to LEO the system itself and also the rockets, propellant, and lander needed to emplace it. The ratio of the total launch mass to payload mass depends on the mission scenario and destination. The operations and maintenance costs include any material and spares provided, the ground control crew, and sustaining engineering. The Mission Operations Cost Model (MOCM) estimates these costs as a percentage of the system development cost per year.
Life cycle cost↗
Planetary spacecraft cost modeling utilizing labor estimating relationships
A basic computerized technology is presented for estimating labor hours and cost of unmanned planetary and lunar programs. The user friendly methodology designated Labor Estimating Relationship/Cost Estimating Relationship (LERCER) organizes the forecasting process according to vehicle subsystem levels. The level of input variables required by the model in predicting cost is consistent with pre-Phase A type mission analysis. Twenty one program categories were used in the modeling. To develop the model, numerous LER and CER studies were surveyed and modified when required. The result of the research along with components of the LERCER program are reported.
Williams, Raymond↗
A fifty kW, twenty watt per pound solar cell array feasibility study
Feasibility of 20 watt/pound solar cell area - array models, cost estimates, fabrication of assemblies, and large-scale production of cells
COST ESTIMATE↗
A model for estimating the cost impact of schedule perturbations on aerospace research and development programs
The problem of determining the cost impact attributable to perturbations in an aerospace R and D program schedule is discussed in terms of the diminishing availability of funds. The methodology from which a model is presented for updating R and D cost estimates as a function of perturbations in program time is presented.
Bishop, D. F.↗
ASCot, the NASA Analogy Software Cost Tool Suite: expanding our estimation horizons
The NASA Analogy Software Costing Tool Suite (ASCoT) consists of a cluster-based analogy estimator for estimating software development effort, a K-Nearest Neighbors (KNN) analogy estimator for estimating effort and delivered lines of code, a simple regression-based cost estimating relationship (CER) model that estimates cost in dollars, and a probabilistic version of COCOMO II. In this paper we document the analogy algorithms as well as summarize the results of the performance of the KNN and the principle components (PCA) cluster analogy models. KNN performance is assessed by varying the number of inputs and number of neighbors. Four different clustering methods: K-means, Spectral Clustering, Hierarchical Clustering, and Principle Components Analysis (PCA), and their respective evaluation criterion are described in detail. The comparative performance of all four estimation models is assessed using magnitude of relative error (MRE) measurements.
Menzies, Tim↗
The NASA Analogy Software Cost Model: A Web-Based Cost Analysis Tool
This paper provides an overview of the many new features and algorithm updates in the release of the NASA Analogy Software Cost Tool (ASCoT). ASCoT is a web-based tool that provides a suite of estimation tools to support early lifecycle NASA Flight Software analysis. ASCoT employs advanced statistical methods such as Cluster Analysis to provide an analogy based estimate of software delivered lines of code and development effort, a regression based Cost Estimating Relationships (CER) model that estimates cost (dollars), and a COCOMO II based estimate. The ASCoT algorithms are designed to primarily work with system level inputs such as mission type (earth orbiter vs. planetary vs. rover), the number of instruments, and total mission cost. This allows the user to supply a minimal number of mission-level parameters which are better understood early in the life-cycle, rather than a large number of complex inputs.
Hihn, Jairus↗