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This review brings rigorous life cycle cost (LCC) analysis into discussions about COTS program costs. We gather publicly available cost data, review the data for credibility, check for consistency among sources, and rigorously define and analyze specific cost metrics.
Traditionally, space missions have relied on relatively large and monolithic satellites, but in the past few years, under a changing technological and economic environment, including instrument and spacecraft miniaturization, scalable launchers, secondary launches as well as hosted payloads, there is growing interest in implementing future NASA missions as Distributed Spacecraft Missions (DSM). The objective of our project is to provide a framework that facilitates DSM Pre-Phase A investigations and optimizes DSM designs with respect to a-priori Science goals. In this first version of our Trade-space Analysis Tool for Constellations (TAT-C), we are investigating questions such as: How many spacecraft should be included in the constellation? Which design has the best costrisk value? The main goals of TAT-C are to: Handle multiple spacecraft sharing a mission objective, from SmallSats up through flagships, Explore the variables trade space for pre-defined science, cost and risk goals, and pre-defined metrics Optimize cost and performance across multiple instruments and platforms vs. one at a time.This paper describes the overall architecture of TAT-C including: a User Interface (UI) interacting with multiple users - scientists, missions designers or program managers; an Executive Driver gathering requirements from UI, then formulating Trade-space Search Requests for the Trade-space Search Iterator first with inputs from the Knowledge Base, then, in collaboration with the Orbit Coverage, Reduction Metrics, and Cost Risk modules, generating multiple potential architectures and their associated characteristics. TAT-C leverages the use of the Goddard Mission Analysis Tool (GMAT) to compute coverage and ancillary data, streamlining the computations by modeling orbits in a way that balances accuracy and performance.TAT-C current version includes uniform Walker constellations as well as Ad-Hoc constellations, and its cost model represents an aggregate model consisting of Cost Estimating Relationships (CERs) from widely accepted models. The Knowledge Base supports both analysis and exploration, and the current GUI prototype automatically generates graphics representing metrics such as average revisit time or coverage as a function of cost.
Many space applications such as sensor networks, on-board satellite-based platforms, on-board vehicle monitoring systems, etc. handle large amounts of data and analysis of such data is often critical for the scientific mission. Transmitting such large amounts of data to the remote control station for analysis is usually too expensive for time-critical applications. Instead, modern space applications are increasingly relying on autonomous on-board data analysis. All these applications face many resource constraints. A key requirement is to minimize energy consumption. Several approaches have been developed for estimating the energy consumption of such applications (e.g. [3, 1]) based on measuring actual consumption at run-time for large sets of random inputs. However, this approach has the limitation that it is in general not possible to cover all possible inputs. Using formal techniques offers the potential for inferring safe energy consumption bounds, thus being specially interesting for space exploration and safety-critical systems. We have proposed and implemented a general frame- work for resource usage analysis of Java bytecode [2]. The user defines a set of resource(s) of interest to be tracked and some annotations that describe the cost of some elementary elements of the program for those resources. These values can be constants or, more generally, functions of the input data sizes. The analysis then statically derives an upper bound on the amount of those resources that the program as a whole will consume or provide, also as functions of the input data sizes. This article develops a novel application of the analysis of [2] to inferring safe upper bounds on the energy consumption of Java bytecode applications. We first use a resource model that describes the cost of each bytecode instruction in terms of the joules it consumes. With this resource model, we then generate energy consumption cost relations, which are then used to infer safe upper bounds. How energy consumption for each bytecode instruction is measured is beyond the scope of this paper. Instead, this paper is about how to infer safe energy consumption estimations assuming that those energy consumption costs are provided. For concreteness, we use a simplified version of an existing resource model [1] in which an energy consumption cost for individual Java opcodes is defined.
The JSC Life Sciences Project Division has been directly supporting NASA Headquarters, Life Sciences Division, in the preparation of data from JSC and ARC to assist in defining the Space Biology Initiative (SBI). GE Government Services and Horizon Aerospace have provided contract support for the development and integration of review data, reports, presentations, and detailed supporting data. An SBI Definition (Non-Advocate) Review at NASA Headquarters, Code B, has been scheduled for the June-July 1989 time period. In a previous NASA Headquarters review, NASA determined that additional supporting data would be beneficial to determine the potential advantages in modifying commercial off-the-shelf (COTS) hardware for some SBI hardware items. In order to meet the demands of program implementation planning with the definition review in late spring of 1989, the definition trade study analysis must be adjusted in scope and schedule to be complete for the SBI Definition (Non-Advocate) Review. The relative costs of modifying existing commercial off-the-shelf (COTS) hardware is compared to fabricating new hardware. An historical basis for new build versus modifying COTS to meet current NMI specifications for manned space flight hardware is surveyed and identified. Selected SBI hardware are identified as potential candidates for off-the-shelf modification and statistical estimates on the relative cost of modifying COTS versus new build are provided.
Abstract not provided.
The NASA Glenn Research Center is supporting the development of a Stirling converter with the Department of Energy (DOE, Germantown, Maryland) for an advanced Stirling Radioisotope Power System (SRPS) to provide spacecraft onboard electric power for NASA space science missions. A key technology assessment completed by Glenn and DOE has led to the SRPS being identified as a high-efficiency power source for such deep space missions as the Europa Orbiter and the Solar Probe. In addition, the Stirling system is now being considered for unmanned Mars rovers, especially where mission profiles may exclude the use of photovoltaic power systems, such as exploration at high Martian latitudes or for missions of long duration. The SRPS efficiency of over 20 percent will reduce the required amount of radioisotope by more than a factor of 3 in comparison to current radioisotope thermoelectric generators. This significantly reduces radioisotope cost, radiological inventory, and system cost, and it provides efficient use of scarce radioisotope resources. In support of this technology assessment, Glenn conducted a series of independent evaluations and tests to determine the technology readiness of a 55-We Stirling converter developed by Stirling Technology Company (Kennewick, Washington) and DOE. Key areas evaluated by Glenn included: 1) Radiation tolerance of materials; 2) Random vibration testing of the Stirling converter in Glenn's Structural Dynamics Lab to simulate operation in the launch environment; 3) Electromagnetic interference and compatibility (EMI/EMC) of the converter operating in Glenn's EMI lab; Independent failure modes, effects, and criticality analysis, and life and reliability 4. Independent failure modes, effects, and criticality analysis, and life and reliability assessment; and 5) SRPS cost estimate. The data from these evaluations were presented to NASA Headquarters and the Jet Propulsion Laboratory mission office by a joint industry/Government team consisting of DOE, Glenn, and Lockheed Martin Astronautics. This team concluded that there are no technical reasons that would rule out using the Stirling converter for deep space missions. As a direct result of the successful testing at Glenn, the DOE/Stirling Technology Company 55-We Stirling converter has been baselined for the SRPS. Glenn is now continuing an in-house project to assist in developing the Stirling converter for readiness for space qualification and mission implementation. As part of this effort, the Stirling converter will be further characterized under launch environment random vibration testing, methods to reduce converter EMI will be developed, and an independent performance verification will be completed. Converter life assessment and permanent magnet aging characterization tasks are also underway. Substitute organic materials for the linear alternator and piston bearing coatings for use in a high-radiation environment have been identified and have now been incorporated in Stirling converters built by Stirling Technology Company for Glenn. Electromagnetic and thermal finite element analyses for the alternator are also being conducted.
The Pacific Northwest Land Resources Inventory Demonstration Project i s an a ttempt to combine a whole spectrum of heterogeneous geographic, institutional and applications elements in a synergistic approach to the evaluation of remote sensing techniques. This diversity is the prime motivating factor behind a theoretical investigation of alternative economic analysis procedures. For a multitude of reasons--simplicity, ease of understanding, financial constraints and credibility, among others--cost-effectiveness emerges as the most practical tool for conducting such evaluation determinatIons in the Pacific Northwest. Preliminary findings in two water resource application areas suggest, in conformity with most published studies, that Lands at-aided data collection methods enjoy substantial cost advantages over alternative techniques. The pntential for sensitivity analysis based on cost/accuracy tradeoffs is considered on a theoretical plane in the absence of current accuracy figures concerning the Landsat-aided approach.
Switchgrass is a promising bioenergy feedstock due to its high biomass yield potential, adaptability to marginal lands, and low carbon intensity for feedstock production. However, accurate cost estimation and assessment of greenhouse gas (GHG) emissions for the energy-intensive harvesting process are essential for evaluating the sustainability of bioenergy. This study provides a comparative analysis of two harvesting methods: the Stepwise Method, which separates operations into multiple stages, and the Integrated Method, which combines mowing and raking into a single pass. The analysis was conducted under four scenarios based on field sizes and biomass yields. Using three years of field-scale switchgrass harvest data from 125 sites, GHG emissions, energy consumption, and harvesting costs were quantified using the GREET model and techno-economic analysis. Additionally, regression analysis identified key climate and operational factors affecting fuel consumption. The Stepwise method was the most cost-effective for large fields with high biomass yield, achieving the lowest harvesting costs ($37.70 per ton). In contrast, the Integrated Method performed better in small fields and low-yield conditions, reducing GHG emissions by 9 % and energy use by 5 %. Regression analysis confirmed that a larger field size reduced fuel consumption, while higher biomass yield and longer operational time increased fuel use. Maximum temperature also contributed to a slight increase in fuel consumption. Furthermore, these results provide actionable insights for optimizing harvesting strategies based on field-specific conditions and operational goals, contributing to the economic and environmental sustainability of bioenergy production.
The Earth Observing System Data and Information System archives many datasets that are critical to understanding long-term variations in Earth science properties. Thus, some of these are large, multi-decadal datasets. Yet the challenge in long time series analysis comes less from the sheer volume than the data organization, which is typically one (or a small number of) time steps per file. The overhead of opening and inventorying complex, API-driven data formats such as Hierarchical Data Format introduces a small latency at each time step, which nonetheless adds up for datasets with O(10^6) single-timestep files. Several approaches to reorganizing the data can mitigate this overhead by an order of magnitude: pre-aggregating data along the time axis (time-chunking); storing the data in a highly distributed file system; or storing data in distributed columnar databases. Storing a second copy of the data incurs extra costs, so some selection criteria must be employed, which would be driven by expected or actual usage by the end user community, balanced against the extra cost.
An analysis was made of the economic benefits which might be derived from reduced forecasting errors made possible by data obtained from a synchronous satellite system which can collect earth observation and meteorological data continuously and on demand. User costs directly associated with achieving benefits are included. In the analysis, benefits were evaluated which might be obtained as a result of improved thunderstorm forecasting, frost warning, and grain harvest forecasting capabilities. The anticipated system capabilities were used to arrive at realistic estimates of system performance on which to base the benefit analysis. Emphasis was placed on the benefits which result from system forecasting accuracies. Benefits from improved thunderstorm forecasts are indicated for the construction, air transportation, and agricultural industries. The effects of improved frost warning capability on the citrus crop are determined. The benefits from improved grain forecasting capability are evaluated in terms of both U.S. benefits resulting from domestic grain distribution and U.S. benefits from international grain distribution.
Developing credible mass and cost estimates for space exploration and development architectures require multidisciplinary analysis based on physics calculations, and parametric estimates derived from historical systems. Within the National Aeronautics and Space Administration (NASA), concurrent engineering environment (CEE) activities integrate discipline oriented analysis tools through a computer network and accumulate the results of a multidisciplinary analysis team via a centralized database or spreadsheet Each minute of a design and analysis study within a concurrent engineering environment is expensive due the size of the team and supporting equipment The Advanced Technology Lifecycle Analysis System (ATLAS) reduces the cost of architecture analysis by capturing the knowledge of discipline experts into system oriented spreadsheet models. A framework with a user interface presents a library of system models to an architecture analyst. The analyst selects models of launchers, in-space transportation systems, and excursion vehicles, as well as space and surface infrastructure such as propellant depots, habitats, and solar power satellites. After assembling the architecture from the selected models, the analyst can create a campaign comprised of missions spanning several years. The ATLAS controller passes analyst specified parameters to the models and data among the models. An integrator workbook calls a history based parametric analysis cost model to determine the costs. Also, the integrator estimates the flight rates, launched masses, and architecture benefits over the years of the campaign. An accumulator workbook presents the analytical results in a series of bar graphs. In no way does ATLAS compete with a CEE; instead, ATLAS complements a CEE by ensuring that the time of the experts is well spent Using ATLAS, an architecture analyst can perform technology sensitivity analysis, study many scenarios, and see the impact of design decisions. When the analyst is satisfied with the system configurations, technology portfolios, and deployment strategies, he or she can present the concepts to a team, which will conduct a detailed, discipline-oriented analysis within a CEE. An analog to this approach is the music industry where a songwriter creates the lyrics and music before entering a recording studio.
The author has identified the following significant results. Digital techniques have been developed and used to apply precision-grade radiometric and geometric corrections to ERTS MSS and RBV scenes. Geometric accuracies sufficient for mapping at 1:250,000 scale have been demonstrated. Radiometric quality has been superior to ERTS NDPF precision products. A configuration analysis has shown that feasible, cost-effective all-digital systems for correcting ERTS data are easily obtainable. This report contains a summary of all results obtained during this study and includes: (1) radiometric and geometric correction techniques, (2) reseau detection, (3) GCP location, (4) resampling, (5) alternative configuration evaluations, and (6) error analysis.
For mission planners and evaluators alike, value in cost models comes from a mean or median prediction, an understanding of the uncertainty on that prediction, and an understanding of model performance. Here we apply advanced statistical and machine learning methods to spacecraft flight software cost, effort, and SLOC estimation, and present the results in the latest version of the Analogy Software Cost Tool (ASCoT). We present in- and out-of-sample performance metrics for our models, each of which incorporate some amount of epistemic uncertainty. ASCoT, hosted on the One NASA Cost Engineering (ONCE) database via the Online NASA Space Estimation Tool (ONSET), was first showcased in 2016 as a number of analogy-based models and methods (kNN and Clustering) to support early project formulation. This ASCoT update improves upon the previous analogic methods by incorporating uncertainty in the data transformations. In particular, we use a Nonlinear Principal Components Analysis (NLPCA) to deal with ordinal data.
The need for a predictive budget/funging model is obvious. The current models used by the Resource Analysis Office (RAO) are used to predict the total costs of satellite projects. An effort to extend the modeling capabilities from total budget analysis to total budget and budget outlays over time analysis was conducted. A statistical based and data driven methodology was used to derive and develop the model. Th budget data for the last 18 GSFC-sponsored satellite projects were analyzed and used to build a funding model which would describe the historical spending patterns. This raw data consisted of dollars spent in that specific year and their 1989 dollar equivalent. This data was converted to the standard format used by the RAO group and placed in a database. A simple statistical analysis was performed to calculate the gross statistics associated with project length and project cost ant the conditional statistics on project length and project cost. The modeling approach used is derived form the theory of embedded statistics which states that properly analyzed data will produce the underlying generating function. The process of funding large scale projects over extended periods of time is described by Life Cycle Cost Models (LCCM). The data was analyzed to find a model in the generic form of a LCCM. The model developed is based on a Weibull function whose parameters are found by both nonlinear optimization and nonlinear regression. In order to use this model it is necessary to transform the problem from a dollar/time space to a percentage of total budget/time space. This transformation is equivalent to moving to a probability space. By using the basic rules of probability, the validity of both the optimization and the regression steps are insured. This statistically significant model is then integrated and inverted. The resulting output represents a project schedule which relates the amount of money spent to the percentage of project completion.
The three main cost areas of unmanned satellite development are discussed. The areas are identified as: (1) the spacecraft platform (SCP), (2) the payload or experiments, and (3) the postlaunch ground equipment and operations. The SCP normally accounts for over half of the total project cost and accurate estimates of SCP costs are required early in project planning as a basis for determining total project budget requirements. The development of single formula SCP cost estimating relationships (CER) from readily available data by statistical linear regression analysis is described. The advantages of single formula CER are presented.
During the past two years, available NASA imagery has been applied to a broad spectrum of problems of concern to Michigan-based agencies. These demonstrations include the testing of remote sensing for the purposes of (1) highway corridor planning and impact assessments, (2) game management-area information bases, (3) multi-agency river basin planning, (4) timber resource management information systems, (5) agricultural land reservation policies, and (6) shoreline flooding damage assessment. In addition, cost accounting procedures have been developed for evaluating the relative costs of utilizing remote sensing in land cover and land use analysis data collection procedures.
Following from early experience with polyimides on the SST program and Shuttle aft flap studies the Boeing Company is now working on collaborative programs with its principal nacelle suppliers to pursue the development of Gr/PMR-15 nacelle components. Two programs are currently in effect. The first program is directed specifically towards the flight test and service evaluation at the earliest possible date of a 747 nacelle core cowl structure. The second program seeks to firmly establish the producibility and cost of a 757 thrust reverser C duct in a production environment. The near term objectives of these programs include: (1) the comparison of estimated cost and weight of Gr/PMR-15 versus metal structure, (2) the engine test of representative composite structure, (3) the preliminary design and analysis of the C duct structure, and (4) the preparation of cost data and time schedules for the development and producibility program. In addition to powerplant structure, the propulsion ducting system has shown to be a strong candidate for Gr/PMR-15 application. Currently, the Boeing 747 Organization is evaluating the use of PMR-15 matrix composites to replace nearly 800 lbs of titanium ducting per airplane.
The performance of the quasi-procedural analysis system in optimization tasks is investigated. In particular, the quasi-procedural method is applied to the complete mission optimization of a medium-sized transport aircraft using a vortex-lattice aerodynamic model and finite element structural analysis of the wing and the tail. Some modifications and improvements to the system, required in order to complete this task, are described. The performance of the system is compared with that of the standard procedural system, and it is shown that the quasi-procedural approach reduces the analysis required to reach the optimum. The overall cost is shown to be strongly dependent on the analysis architecture and data structure.