Zircon U-Pb geochronology and emplacement history of intrusive rocks in the Ardestan section, central Iran

The Urumieh-Dokhtar Magmatic Arc (UDMA) is part of the Alpine–Himalayan orogenic belt and interpreted to be a subduction-related Andean-type magmatic arc. Along this belt, Eocene volcanics and some gabbroic to granitic bodies crop out. The main rock types of the studied intrusion are granite, granodiorite, and diorite. They have geochemical features typical of magnesian, calc-alkaline, metaluminous to slightly peraluminous granites and I-type intrusive rock that have a strong enrichment in Large-Ion Lithophile (LIL) elements (e.g. Rb, Ba, Sr), and a depletion in High Field Strength (HFS) elements (e.g. Nb, Ti, P), typical of subduction-related magmas. Zircon U-Pb dating was applied to determine the emplacement ages of the different intrusions in the Ardestan area. Among them the Kuh-e Dom diorite is 53.9±0.4Ma old; the Kuh-e Dom granodiorite is 51.10±0.4Ma old; the Mehrabad granodiorite is 36.8±0.5Ma old, the Nasrand granodiorite is 36.5±0.5Ma old, the Zafarghand granodiorite is 24.6±1.0Ma old, and the Feshark granodiorite is 20.5±0.8Ma old. These results delineate more accurately the magmatic evolution related to the Neotethyan subduction from the Lower Eocene to Lower Miocene, and the subsequent Zagros orogeny that resulted from the Arabia-Eurasia collision. The emplacement of these intrusive rocks inside the UDMA, which has a close relationship with the collisional orogeny, is transitional from a subduction-related setting to post-collisional setting in the Ardestan area

A Quantized State Approach to On-line Simulation for Spacecraft Autonomy

Future space applications will require an increased level of operational autonomy. This calls for declarative methods for spacecraft state estimation and control, so that the space-craft engineer can focus on modeling the spacecraft rather than implementing all details of the on-line system. Celebrated model based methods such as Kalman filtering techniques and Model Predictive Control (MPC) rely on an on-line model of the system under control that can be simulated in faster than real-time. This becomes a severe challenge when the paradigm of modeling employed is that of hybrid systems where discrete and continuous dynamics co-exists. This paper describes the design and implementation of an efficient engine for simulation of hybrid systems, specifically tailored for on-line applications. The simulation engine, contrary to traditional simulations systems, does not rely on discretization of time, but instead it works on a discretized state-space where the update of states is determined by a projection of points in time where the trajectory enters a new region. With this approach each state in the model is integrated separately, meaning that sparsity is exploited well. In addition hybrid transitions are located conservatively, i.e. without the need to ever “roll back ” the simulation in time. Nomenclature Hybrid systems: Q location index set X continuous state-space U continuous input-space Y continuous output-space E input/output event labels F forcing functions on the continuous state-space G continuous output map T transition map DEVS Specification: X inputs Y outputs S internal states δint(S) state mapping function, for internal events δext: (e,S, x) state mapping function for external events λ: S output mapping ta: S the time advance e duration since the last internal or external event x set of input

ABSTRACT HYBRID DISCRETE EVENT SIMULATION WITH MODEL PREDICTIVE CONTROL FOR

Simulation modeling combined with decision control can offer important benefits for analysis, design, and operation of semiconductor supply-chain network systems. Detailed simulation of physical processes provides information for its controller to account for (expected) stochasticity present in the manufacturing processes. In turn, the controller can provide (near) optimal decisions for the operation of the processes and thus handle uncertainty in customer demands. In this paper, we describe an environment that synthesizes Discrete-EVent System specification (DEVS) with Model Predictive Control (MPC) paradigms using a Knowledge Interchange Broker (KIB). This environment uses the KIB to compose discrete event simulation and model predictive control models. This approach to composability affords flexibility for studying semiconducto