The scientific performances of the Giant Magellan Telescope (GMT) Observatory are divided into Observatory Performance Mode (OPM) formally defined in the GMT Observatory Requirement Document (ORD) as a direct flow down from the GMT Science Requirement document (SRD). There are 3 main OPM categories: Natural Seeing, GLAO, and High Angular Resolution (e.g., NGAO, LTAO) that branched out into several sub-categories. For each OPM, system engineering has defined image quality metric standards: Key Performance Parameters (KPP) that acts as bounds to the Observatory overall performance within which design parameters are traded. During the course of the project, system engineering must assess the compliance of the current design solutions with respect to the KPPs. The GMT project has build an exhaustive integrated modeling computing framework allowing for bottom-up end-to-end modeling of the entire GMT Observatory. This integrated modeling framework brings together finite element, control, optical, thermal and fluid dynamics models. This paper introduces the integrated modeling framework and describes the whole process that is setting up bottom-up end-to-end simulations of GMT OPMs. For example, analytical error budgets and the project risk registers are used to identify and to down select the most relevant parameters and features of the telescope design that must be included into the GMT integrated model while keeping the size of the simulation manageable from a computing load standpoint. The paper also reports on how the model validation unfold with model audits at both system and subsystem levels using software management best practices. Finally, simulation results for several OPMs are presented and discussed in terms of their statistical meaning with respect to the foreseen on-sky estimation of the KPPs during the Assembly, Integration and Validation phase of the project.
The GMT strategy for advancing subsystem design using aerothermal modeling is presented. The focus is not on the models themselves but on the procedure used to answer specific questions posed to the GMT System Engineering Integrated Modeling team by the various subsystem groups. Work in progress from an aerothermal point of view will be presented in several major subsystems. The scheduling challenges and resource management, both computational and human, to ensure timely responses are also addressed.
System engineering at GMTO is using a comprehensive integrated model that integrates seamlessly, in a unified framework, finite element, optics, and control models. A computational fluid dynamics (CFD) model of the observatory is also used to estimate dome seeing, wind jitter, structural thermal deformations, and observatorywide design optimization. The GMT integrated modeling group realizes various studies for different subsystems of the project that provides the basis for the subsystem level design trades. It also assists system engineering by performing top-down and bottom-up requirements verification, error budget derivation, and operational strategies optimization. Integrated modeling will also support system engineering during the assembly, integration, verification, and commissioning phase of the project. For example, system engineering relies on the integrated model to estimate the key performance parameters (KPP) of the project. The KPP are performance metrics that will be used to validate the completion of the observatory and to confirm its readiness with respect to the start of science observation. In the paper, we give a system-level overview of the integrated model, including a description of each sub-model and of the framework that binds them together. The paper also describes how system engineering is using the integrated model for the derivation of the error budgets and of the top-down requirements flowing down from the science requirements to the lower level of subsystem engineering requirements; and how as the design of the subsystems progress, integrated modeling is then used to validate, bottom-up, the same requirements from subsystem engineering requirements back up to the science requirements with respect to the observatory performance metrics.
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