Parametric Mission Cost Model - Overview
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parametric mission model overview↗
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Parametric Cost Modeling of Space Missions Using the Develop New Projects (DMP) Implementation Process
This paper presents an overview of a parametric cost model that has been built at JPL to estimate costs of future, deep space, robotic science missions. Due to the recent dramatic changes in JPL business practices brought about by an internal reengineering effort known as develop new products (DNP), high-level historic cost data is no longer considered analogous to future missions. Therefore, the historic data is of little value in forecasting costs for projects developed using the DNP process. This has lead to the development of an approach for obtaining expert opinion and also for combining actual data with expert opinion to provide a cost database for future missions. In addition, the DNP cost model has a maximum of objective cost drivers which reduces the likelihood of model input error. Version 2 is now under development which expands the model capabilities, links it more tightly with key design technical parameters, and is grounded in more rigorous statistical techniques. The challenges faced in building this model will be discussed, as well as it's background, development approach, status, validation, and future plans.
Rosenberg, Leigh↗
Parametric Cost Modeling of Space Missions Using the Develop New Projects (DNP) Implementation Process
This paper presents an overview of a parametric cost model that has been built at JPL to estimate costs of future, deep space, robotic science missions.
Systems engineering cost model parametric cost mod↗
Cost Modeling for low-cost planetary missions
This presentation will provide an overview of the JPL parametric cost models used to estimate flight science spacecrafts and instruments. This material will emphasize the cost model approaches to estimate low-cost flight hardware, sensors, and instrumentation, and to perform cost-risk assessments. This presentation will also discuss JPL approaches to perform cost modeling and the methodologies and analyses used to capture low-cost vs. key cost drivers.
cost model↗
Parametric Systems Analysis of a Mid-Lift/Drag Entry System for Human Mars Mission
Mars entry systems are complex with many competing design objectives. This paper presents a data-driven and physics-based approach to systems analysis and tradespace exploration for a human Mars entry system using a Mid-Lift/Drag aerodynamic decelerator. The baseline entry system includes elements for Mars aerocapture, entry, descent, and landing segments of the mission. The approach uses a set of parametric models that enable a system-level analysis, vehicle sizing, sensitivity analysis, and tradespace exploration. The paper provides a brief introduction of the Mid-Lift/Drag concept, an overview of parametric models, discussions on alternative structural concepts, results from system-level analysis, and summary remarks.
Jamshid A Samareh↗
Power System Overview for the Small RPS Centaur Flyby and the Mars Polar Hard Lander NASA COMPASS Studies
The NASA Glenn Research Center (GRC) Radioisotope Power System Program Office (RPSPO) sponsored two studies lead by their mission analysis team. The studies were performed by NASA GRCs Collaborative Modeling for Parametric Assessment of Space Systems (COMPASS) team. Typically a complete toplevel design reference mission (DRM) is performed assessing conceptual spacecraft design, launch mass, trajectory, science strategy and sub-system design such as, power, propulsion, structure and thermal.
Space Missions↗
Overview of NASA's Integrated Design and Engineering Analysis (IDEA)Environment
Historically, the design of subsonic and supersonic aircraft has been divided into separate technical disciplines (such as propulsion, aerodynamics and structures) each of which performs their design and analysis in relative isolation from others. This is possible in most cases either because the amount of interdisciplinary coupling is minimal or because the interactions can be treated as linear. The design of hypersonic airbreathing vehicles, like NASA s X-43, is quite the opposite. Such systems are dominated by strong non-linear interactions between disciplines. The design of these systems demands that a multi-disciplinary approach be taken. Furthermore, increased analytical fidelity at the conceptual design phase is highly desirable as many of the non-linearities are not captured by lower fidelity tools. Only when these systems are designed from a true multi-disciplinary perspective can the real performance benefits be achieved and complete vehicle systems be fielded. Toward this end, the Vehicle Analysis Branch at NASA Langley Research Center has been developing the Integrated Design & Engineering Analysis (IDEA) Environment. IDEA is a collaborative environment for parametrically modeling conceptual and preliminary launch vehicle configurations using the Adaptive Modeling Language (AML) as the underlying framework. The environment integrates geometry, configuration, propulsion, aerodynamics, aerothermodynamics, trajectory, closure and structural analysis into a generative, parametric, unified computational model where data is shared seamlessly between the different disciplines. Plans are also in place to incorporate life cycle analysis tools into the environment which will estimate vehicle operability, reliability and cost. IDEA is currently being funded by NASA s Hypersonics Project, a part of the Fundamental Aeronautics Program within the Aeronautics Research Mission Directorate. The environment is currently focused around a two-stage-to-orbit configuration with a turbine based combined cycle (TBCC) first stage and reusable rocket second stage. This paper provides an overview of the development of the IDEA environment, a description of the current status and detail of future plans.
Robinson, Jeffrey S.↗
Parametric Mass Modeling for Mars Entry, Descent and Landing System Analysis Study
This paper provides an overview of the parametric mass models used for the Entry, Descent, and Landing Systems Analysis study conducted by NASA in FY2009-2010. The study examined eight unique exploration class architectures that included elements such as a rigid mid-L/D aeroshell, a lifting hypersonic inflatable decelerator, a drag supersonic inflatable decelerator, a lifting supersonic inflatable decelerator implemented with a skirt, and subsonic/supersonic retro-propulsion. Parametric models used in this study relate the component mass to vehicle dimensions and mission key environmental parameters such as maximum deceleration and total heat load. The use of a parametric mass model allows the simultaneous optimization of trajectory and mass sizing parameters.
Samareh, Jamshid A.↗
An Overview of NASA's Integrated Design and Engineering Analysis (IDEA) Environment
Historically, the design of subsonic and supersonic aircraft has been divided into separate technical disciplines (such as propulsion, aerodynamics and structures), each of which performs design and analysis in relative isolation from others. This is possible, in most cases, either because the amount of interdisciplinary coupling is minimal, or because the interactions can be treated as linear. The design of hypersonic airbreathing vehicles, like NASA's X-43, is quite the opposite. Such systems are dominated by strong non-linear interactions between disciplines. The design of these systems demands that a multi-disciplinary approach be taken. Furthermore, increased analytical fidelity at the conceptual design phase is highly desirable, as many of the non-linearities are not captured by lower fidelity tools. Only when these systems are designed from a true multi-disciplinary perspective, can the real performance benefits be achieved and complete vehicle systems be fielded. Toward this end, the Vehicle Analysis Branch at NASA Langley Research Center has been developing the Integrated Design and Engineering Analysis (IDEA) Environment. IDEA is a collaborative environment for parametrically modeling conceptual and preliminary designs for launch vehicle and high speed atmospheric flight configurations using the Adaptive Modeling Language (AML) as the underlying framework. The environment integrates geometry, packaging, propulsion, trajectory, aerodynamics, aerothermodynamics, engine and airframe subsystem design, thermal and structural analysis, and vehicle closure into a generative, parametric, unified computational model where data is shared seamlessly between the different disciplines. Plans are also in place to incorporate life cycle analysis tools into the environment which will estimate vehicle operability, reliability and cost. IDEA is currently being funded by NASA?s Hypersonics Project, a part of the Fundamental Aeronautics Program within the Aeronautics Research Mission Directorate. The environment is currently focused around a two-stage-to-orbit configuration with a turbine-based combined cycle (TBCC) first stage and a reusable rocket second stage. IDEA will be rolled out in generations, with each successive generation providing a significant increase in capability, either through increased analytic fidelity, expansion of vehicle classes considered, or by the inclusion of advanced modeling techniques. This paper provides the motivation behind the current effort, an overview of the development of the IDEA environment (including the contents and capabilities to be included in Generation 1 and Generation 2), and a description of the current status and detail of future plans.
Robinson, Jeffrey S.↗
Differential absorption lidar (DIAL) for water vapor and aerosol profiling from airborne and space-based platforms
Many current challenges in weather and climate prediction involve cloud and aerosol processes, which are intimately linked to processes that initiate or take place within the planetary boundary layer (PBL). The current space-based observing system provides a wealth of information on the spatial distribution of key atmospheric variables such as profiles of water vapor (WV) and temperature, but lacks the sensitivity, accuracy, and vertical resolution in the PBL to overcome the observational gaps that inhibit the parametrization and advancement of current climate and weather models. To overcome some of these observational gaps and improve understanding and monitoring of these key processes, mature remote sensing techniques like Differential Absorption Lidar (DIAL) can be employed from various platforms to capture processes on a wide range of spatial scales. This presentation will provide an overview of NASA’s water vapor DIAL capabilities, present the strengths, weaknesses, and synergies of the DIAL technique relative to traditional methods, and provide representative data examples from the HALO airborne water vapor DIAL which was designed as a technology testbed for future space-based DIAL missions as well as a workhorse instrument in support of suborbital process studies. HALO is an airborne multifunction lidar system, simultaneously profiling WV with the DIAL technique and aerosol and cloud optical properties and PBL heights via the high spectral resolution lidar (HSRL) technique. HALO has a compact design that allows it to fly on most research aircraft, enabling nadir profiling from the lower stratosphere down through the PBL, which is critical for studying processes such as convection and cloud formation that can link the PBL and free troposphere. We will end with an outlook on the benefits and challenges of transitioning the mature DIAL technique to space to compliment other active and passive sounders and enable new PBL observations for the future Earth Observing System. The Atmospheric Boundary layer Lidar pathfindEr (ABLE), a program aimed at advancing technologies to enable the first demonstration of DIAL from space with cross-cutting observables of PBL heights, aerosol and cloud profiling, and methane columns from space will also be presented.
Brian J. Carroll↗
Modal identification experiment
The Modal Identification Experiment (MIE) is a proposed on-orbit experiment being developed by NASA's Office of Aeronautics and Space Technology wherein a series of vibration measurements would be made on various configurations of Space Station Freedom (SSF) during its on-orbit assembly phase. The experiment is to be conducted in conjunction with station reboost operations and consists of measuring the dynamic responses of the spacecraft produced by station-based attitude control system and reboost thrusters, recording and transmitting the data, and processing the data on the ground to identify the natural frequencies, damping factors, and shapes of significant vibratory modes. The experiment would likely be a part of the Space Station on-orbit verification. Basic research objectives of MIE are to evaluate and improve methods for analytically modeling large space structures, to develop techniques for performing in-space modal testing, and to validate candidate techniques for in-space modal identification. From an engineering point of view, MIE will provide the first opportunity to obtain vibration data for the fully-assembled structure because SSF is too large and too flexible to be tested as a single unit on the ground. Such full-system data is essential for validating the analytical model of SSF which would be used in any engineering efforts associated with structural or control system changes that might be made to the station as missions evolve over time. Extensive analytical simulations of on-orbit tests, as well exploratory laboratory simulations using small-scale models, have been conducted in-house and under contract to develop a measurement plan and evaluate its potential performance. In particular, performance trade and parametric studies conducted as part of these simulations were used to resolve issues related to the number and location of the measurements, the type of excitation, data acquisition and data processing, effects of noise and nonlinearities, selection of target vibration modes, and the appropriate type of data analysis scheme. The purpose of this talk is to provide an executive-summary-type overview of the modal identification experiment which has emerged from the conceptual design studies conducted to-date. Emphasis throughout is on those aspects of the experiment which should be of interest to those attending the subject utilization conference. The presentation begins with some preparatory remarks to provide background and motivation for the experiment, describe the experiment in general terms, and cite the specific technical objectives. This is followed by a summary of the major results of the conceptual design studies conducted to define the baseline experiment. The baseline experiment which has resulted from the studies is then described.
Kvaternik, Raymond G.↗
X-38 Experimental Aerothermodynamics
The X-38 program seeks to demonstrate an autonomously returned orbital test flight vehicle to support the development of an operational Crew Return Vehicle for the International Space Station. The test flight, anticipated in 2002, is intended to demonstrate the entire mission profile of returning Space Station crew members safely back to earth in the event of medical or mechanical emergency. Integral to the formulation of the X-38 flight data book and the design of the thermal protection system, the aerothermodynamic environment is being defined through a synergistic combination of ground based testing and computational fluid dynamics. This report provides an overview of the hypersonic aerothermodynamic wind tunnel program conducted at the NASA Langley Research Center in support of the X-38 development. Global and discrete surface heat transfer force and moment, surface streamline patterns, and shock shapes were measured on scaled models of the proposed X-38 configuration in different test gases at Mach 6, 10 and 20. The test parametrics include angle of attack from 0 to 50 degs, unit Reynolds numbers from 0.3 x 10 (exp 6) to 16 x 10 (exp 6)/ ft, rudder deflections of 0, 2, and 5 deg. and body flap deflections from 0 to 30 deg. Results from hypersonic aerodynamic screening studies that were conducted as the configuration evolved to the present shape at, presented. Heavy gas simulation tests have indicated that the primary real gas effects on X-38 aerodynamics at trim conditions are expected to favorably influence flap effectiveness. Comparisons of the experimental heating and force and moment data to prediction and the current aerodynamic data book are highlighted. The effects of discrete roughness elements on boundary layer transition were investigated at Mach 6 and the development of a transition correlation for the X-38 vehicle is described. Extrapolation of ground based heating measurements to flight radiation equilibrium wall temperatures at Mach 6 and 10 were made and generally compared to within 50 deg F of flight prediction.
Horvath, Thomas J.↗
Advanced Statistical Methods in Spacecraft Flight Software Cost Estimation: Bayesian Regression and Nonlinear Principal Components Analysis to Support System Engineering in the Early Project Lifecycle
This paper provides an overview of the new features and model updates in the upcoming release of the NASA Analogy Software Cost Tool (ASCoT). ASCoT, hosted within the Online NASA Space Estimation Tools (ONSET) on the One NASA Cost Engineering (ONCE) Database, is a web-based tool that provides a suite of estimation tools to support early lifecycle NASA flight software cost analysis. In addition to the traditional parametric flight software costing method COCOMO II, ASCoT contains a Bayesian linear regression to predict total flight software development cost as a function of total spacecraft cost, as well as four analogic methods: k-Nearest Neighbors (kNN) and Clustering models to predict Effort (in work-months) and total source lines of code (SLOC). These methods are designed to work primarily with system-level inputs such as mission type (orbiter, lander, etc.), mission destination (Earth, Inner Planetary, etc.), and the number of instruments and deployables. Nonlinear principal components analysis (NLPCA) is performed to find the principal features of the data composed of both categorical and numerical variables and is necessary prior to defining our analogic methods. Sensitivity analyses and in- and out-of-sample model performance results are presented for the Bayesian CER and the analogic models.
Johnson, James K.↗
Development of NASA/DOE NTP System Performance Models
A critical enabling technology in the evolutionary development of Nuclear Thermal Propulsion (NTP) is the ability to predict the system performance under a variety of operating conditions. The ability to predict the system performance is critical for mission analysis and for control subsystem testing, as well as for the modeling of various failure modes. Performance must be accurately predicted during steady-state and transient operation, such as start-up, shut-down and after-cooling. The development and application of verified and validated system models has the potential to reduce testing, cost and time required for the technology to again reach flight-ready status. An integrated NASA/DOE team was formed in late 1991 to develop and implement a strategy for modeling NTP systems. It is the intent of the interagency team to develop several levels of computer programs, which vary in detail, to simulate NTP systems based on either prismatic, particle or advanced fuel forms. This paper presents an overview of the models under development by the interagency team. In addition, the status of the development and validation efforts for the Level 1 steady-state parametric model is discussed.
Walton, James T.↗
Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies
Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihood–based classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrence–based texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms.
Pasolli, Edoardo↗
Validation of High Speed Earth Atmospheric Entry Radiative Heating from 9.5 to 15.5 km/s
This paper presents an overview of the analysis and measurements of equilibrium radiation obtained in the NASA Ames Research Center's Electric Arc Shock Tube (EAST) facility as a part of recent testing aimed at reaching shock velocities up to 15.5 km/s. The goal of these experiments was to measure the level of radiation encountered during high speed Earth entry conditions, such as would be relevant for an asteroid, inter-planetary or lunar return mission. These experiments provide the first spectrally and spatially resolved data for high speed Earth entry and cover conditions ranging from 9.5 to 15.5 km/s at 13.3 and 26.6 Pa (0.1 and 0.2 Torr). The present analysis endeavors to provide a validation of shock tube radiation measurements and simulations at high speed conditions. A comprehensive comparison between the spectrally resolved absolute equilibrium radiance measured in EAST and the predictive tools, NEQAIR and HARA, is presented. In order to provide a more accurate representation of the agreement between the experimental and simulation results, the integrated value of radiance has been compared across four spectral regions (VUV, UV/Vis, Vis/NIR and IR) as a function of velocity. Results have generally shown excellent agreement between the two codes and EAST data for the Vis through IR spectral regions, however, discrepancies have been identified in the VUV and parts of the UV spectral regions. As a result of the analysis presented in this paper, an updated parametric uncertainty for high speed radiation in air has been evaluated to be [9.0%, -6.3%]. Furthermore, due to the nature of the radiating environment at these high shock speeds, initial calculations aimed at modeling phenomena that become more significant with increasing shock speed have been performed. These phenomena include analyzing the radiating species emitting ahead of the shock and the increased significance of radiative cooling mechanisms.
Equilibrium↗