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The investigation of specific interactions among biological molecules must take into consideration the stereochemistry of the structures. Thus, models of the molecules are essential for describing the spatial organization of potentially interacting groups, and estimations of conformation are required for a description of spatial organization. Both the function of visualizing molecules, and that of estimating conformation through calculations of energy, are part of the molecular modeling system described in the present paper. The potential uses of the system in investigating some aspects of the origin of life rest on the assumption that translation of conformation from genetic elements to catalytic elements would have been required for the development of the first replicating systems subject to the process of biological evolution.
Model Based Systems Engineering (MBSE) has recently been gaining significant support as a means to improve the traditional document-based systems engineering (DBSE) approach to engineering complex systems. In the spacecraft design domain, there are many perceived and propose benefits of an MBSE approach, but little analysis has been presented to determine the tangible benefits of such an approach (e.g. time and cost saved, increased product quality). This thesis presents direct examples of how developing a small satellite system model can improve traceability of the mission concept to its requirements. A comparison of the processes and approaches for MBSE and DBSE is made using the NASA Ames Research Center SporeSat CubeSat mission as a case study. A model of the SporeSat mission is built using the Systems Modeling Language standard and No Magics MagicDraw modeling tool. The model incorporates mission concept and requirement information from the missions original DBSE design efforts. Active dependency relationships are modeled to analyze the completeness and consistency of the requirements to the mission concept. Overall experience and methodology are presented for both the MBSE and original DBSE design efforts of SporeSat.
Model Based Systems Engineering (MBSE) has recently been gaining significant support as a means to improve the "traditional" document-based systems engineering (DBSE) approach to engineering complex systems. In the spacecraft design domain, there are many perceived and propose benefits of an MBSE approach, but little analysis has been presented to determine the tangible benefits of such an approach (e.g. time and cost saved, increased product quality). This paper presents direct examples of how developing a small satellite system model can improve traceability of the mission concept to its requirements. A comparison of the processes and approaches for MBSE and DBSE is made using the NASA Ames Research Center SporeSat CubeSat mission as a case study. A model of the SporeSat mission is built using the Systems Modeling Language standard and No Magic's MagicDraw modeling tool. The model incorporates mission concept and requirement information from the mission's original DBSE design efforts. Active dependency relationships are modeled to demonstrate the completeness and consistency of the requirements to the mission concept. Anecdotal information and process-duration metrics are presented for both the MBSE and original DBSE design efforts of SporeSat.
System models are often touted as engineering tools that promote better understanding of systems, but these models are typically created during system design. The Ground Data System (GDS) team for the Dawn spacecraft took on a case study to see if benefits could be achieved by starting a model of a system already in operations. This paper focuses on the four steps the team undertook in modeling the Dawn GDS: defining a model structure, populating model elements, verifying that the model represented reality, and using the model to answer system-level questions and simplify day-to-day tasks. Throughout this paper the team outlines our thought processes and the system insights the model provided.
The spectre of cyber attacks on aerospace systems can no longer be ignored given that many of the components and vulnerabilities that have been successfully exploited by the adversary on other infrastructures are the same as those deployed and used within the aerospace environment. An important consideration with respect to the mission/safety critical infrastructure supporting space operations is that an appropriate defensive response to an attack invariably involves the need for high precision and accuracy, because an incorrect response can trigger unacceptable losses involving lives and/or significant financial damage. A highly precise defensive response, considering the typical complexity of aerospace environments, requires a detailed and well-founded understanding of the underlying system where the goal of the defensive response is to preserve critical mission objectives in the presence of adversarial activity. In this paper, a structured approach for modeling aerospace systems is described. The approach includes physical elements, network topology, software applications, system functions, and usage scenarios. We leverage Model-Based Systems Engineering methodology by utilizing the Object Management Group's Systems Modeling Language to represent the system being analyzed and also utilize model transformations to change relevant aspects of the model into specialized analyses. A novel visualization approach is utilized to visualize the entire model as a three-dimensional graph, allowing easier interaction with subject matter experts. The model provides a unifying structure for analyzing the impact of a particular attack or a particular type of attack. Two different example analysis types are demonstrated in this paper: a graph-based propagation analysis based on edge labels, and a graph-based propagation analysis based on node labels.
The EVA systems cost model presented is based on proposed EVA equipment for the space shuttle program. General information on EVA crewman requirements in a weightless environment and an EVA capabilities overview are provided.
Understanding the human water footprint and its impact on the hydrological cycle is essential to inform water management under climate change. Despite efforts in estimating irrigation water withdrawals in earth system models, uncertainties and discrepancies exist within and across modeling systems conditioned by model structure, irrigation parameterization, and the choice of input datasets. Achieving model reliability could be much more challenging for data-sparse regions, given limited access to ground truth for parameterization and validation. Here, we demonstrate the potential of utilizing remotely sensed vegetation and soil moisture observations in constraining irrigation estimation in the Noah-MP land surface model. Results indicate that the two constraints together can effectively reduce model sensitivity to the choice of irrigation parameterization by 7%–43%. It also improves the characterization of the spatial patterns of irrigation and its impact on evapotranspiration and surface soil moisture by correcting for vegetation conditions and irrigation timing. This study highlights the importance of utilizing remotely sensed soil moisture and vegetation measurements in detecting irrigation signals and correcting for vegetation growth. Integrating the two remote sensing datasets into the model provides an effective and less feature engineered approach to constraining the uncertainty of irrigation modeling. Such strategies can be potentially transferred to other modeling systems and applied to regions across the globe.
Model-Based Systems Engineering (MBSE) promotes increased consistency between a system's design and its design documentation through the use of an object-oriented system model. The creation of this system model facilitates data presentation by providing a mechanism from which information can be extracted by automated manipulation of model content. Existing MBSE tools enable model creation, but are often too complex for the unfamiliar model viewer to easily use. These tools do not yet provide many opportunities for easing into the development and use of a system model when system design documentation already exists. This study creates a Systems Modeling Language (SysML) Document Traceability Framework (SDTF) for integrating design documentation with a system model, and develops an Interactive Visualization Engine for SysML Tools (InVEST), that exports consistent, clear, and concise views of SysML model data. These exported views are each meaningful to a variety of project stakeholders with differing subjects of concern and depth of technical involvement. InVEST allows a model user to generate multiple views and reports from a MBSE model, including wiki pages and interactive visualizations of data. System data can also be filtered to present only the information relevant to the particular stakeholder, resulting in a view that is both consistent with the larger system model and other model views. Viewing the relationships between system artifacts and documentation, and filtering through data to see specialized views improves the value of the system as a whole, as data becomes information
The design and development of the adaptive modeling system is described. This system models how a user accesses a relational database management system in order to improve its performance by discovering use access patterns. In the current system, these patterns are used to improve the user interface and may be used to speed data retrieval, support query optimization and support a more flexible data representation. The system models both syntactic and semantic information about the user's access and employs both procedural and rule-based logic to manipulate the model.
With the appearance of general off-the-shelf software packages for the simulation of mechanical systems, modelling and simulation of mechanisms has become an easier task. The authors have recently used one such package, DADS, to model the dynamics of rigid and flexible-link robotic manipulators. In this paper, we present this overview of our learning experiences with DADS, in the hope that it will shorten the learning process for others interested in this software.
This paper presents insights that conform to numerous system modeling languages/representation standards. The insights are drawn from best practices of Information Visualization as applied to aerospace-based applications.
The overall technology model is outlined and the objectives and descriptions of the primary and secondary propulsion drives presented. The primary propulsion driver missions are the geostationary platform; the coherent optical system of modular imaging collectors (COSMIC); the 100 meter thinned aperature telescope; and the orbiting deep space relay station (ODSRS). The secondary propulsion driver missions are space platform alpha, the space station, and the automated planetary station.
A model support system used to support a model in a wind tunnel test section is described. The model comprises a metric, or measured, half-span supported by a nonmetric, or nonmeasured half-span which is connected to a sting support. Moments and forces acting on the metric half-span are measured without interference from the support system during a wind tunnel test.
We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.
Model-Based Systems Engineering has been enabled by the development of the SysML language and software tools to create systems models. Systems models described in SysML incorporate frames (Diagrams) that represent behaviors (activities, sequences, state machines, use cases), requirements, and structure (definitions, internal structure, parametric formulation, and packaging). The SysML models are, in turn, used by applications to do analysis and studies of the designs and operational capabilities. These uses of the model are based on simulations, and do not include hardware. This paper presents a software environment and processes that enables more comprehensive systems models for MBSE, and use of these rich models for real-time operations. The paper describes a software platform that enables creation of comprehensive models, beyond what is now possible with SysML and related software tools, called the NASA Platform for Autonomous Systems (NPAS). The platform encapsulates a paradigm and infrastructure for creating systems models with complexity levels comparable to the ones handled by SysML software tools, but with additional fidelity that includes detailed design diagrams encompassing sensors, components, and design topologies. Furthermore, NPAS enables incorporation of data, information, and knowledge (DIaK) to implement autonomy and Integrated System Health Management (ISHM) and the inherent integration of content encompassing SysML structure and behavior diagrams throughout the NPAS modelAnd lastly, the NPAS models are used in real-time operations, taking advantage of the fidelity and complexity encompassed in the models in order to implement “thinking” ISHM and/or autonomous operations. . Incorporation of SysML model content into an NPAS model is briefly discussed.
The DYSCO program has been under development since 1979. It has been funded by Army and Air Force laboratories and by the Kaman Aerospace Corporation. It is presently available at a number of government and nongovernment installations. It has been used to analyze a very broad range of dynamics problems. A principle feature of the software design of DYSCO is the separation of the executive from the technology. The executive, which controls all the operations, is intelligent in the sense that it knows that its function is to assemble differential equations and to prepare them for solution. The technology library contains FORTRAN routines which perform standard functions, such as, computing the equation coefficients of an element (or component) given the local state at any time. The technology library also contains algorithms and procedures for solving the coupled system equations. The system was designed to allow easy additional of technology to the library. Any linear or nonlinear structural entity, control system, or set of ordinary differential equations may be simply coded and added to the library, as well as algorithms for time or frequency domain solution. The program is described with emphasis on its usefulness in easily modeling unusual concepts and configurations, performing analysis of damage, evaluating new algorithms, and simulating dynamic tests.