Simulation cycle

A combination of the two techniques was shown to be a useful method for the determination of solution structures of weakly coupled dicopper(II) complexes (Fig. 9.4)[119]. The MM-EPR approach involves a conformational analysis of the dimeric structure, the simulation of the EPR spectrum with the geometric parameters resulting from the calculated structures and spin hamiltonian parameters derived from similar complexes, and the refinement of the structure by successive molecular mechanics calculation and EPR simulation cycles. This method was successfully tested with two dinuclear complexes with known X-ray structures and applied to the determination of a copper(II) dimer with unknown structure (Fig. 9.5 and Table 9.9)[119]. 

S //Asa mediator between CFD calculations and macro-scale process simulations, the reactor geometry is represented by a relatively small number of cells which are assumed to be ideally mixed. The basic equations for mass, impulse and energy balance are calculated for these cells. Mass transport between the cells is considered in a network-of-cells model by coupling equations which account for convection and dispersion. The software is capable of optimizing a process in iterative simulation cycles in a short time on a standard PC, but it also requires experimentally-based data to calibrate the software modules to a specific micro reactor. 

When the first assignment statement executes, TM does not get updated immediately but is scheduled to be assigned at the end of the current simulation cycle. Since all statement executions occur sequentially and in zero time, so when the third statement is executed, the old value of TM is used to compute the value of TO (TM has not been assigned its new value yet). Consequently, the output TZ does not reflect the and-or-invert behavior of the logic. The problem is that TM, TN, and TO all get updated at the end of the current simulation cycle and these updated values are not used again to reevaluate the logic. 

The effect of the magnitude of dx on the efficiency of transition path sampling can be systematically analyzed by calculating correlation functions of various quantities as a function of the number of simulations cycles. Ideally, such correlation functions decay quickly, indicating that path space is sampled with high efficiency. In [11], we carried out such an efficiency analysis for transition path sampling of isomerizations of a model dimer immersed in a soft sphere liquid. In that study, we calculated correlation functions... 

Does the semantics model the simulation cycle or the hardware corresponding to the VHDL description ... 

A major tradeoff is whether the semantics models the entire simulation cycle or the hardware corresponding to the VHDL description. Clearly, it is simpler to model a large VHDL subset if the semantics includes a formalization of the simulator. However, there is extra complexity involved in modeling the simulation engine. A related tradeoff is the nature of the translation from VHDL. Most existing approaches are automatable. A manual translation may suffer from bugs that are introduced during translation. 

VanTassel, J.P. (March 1992) A Formalization of the VHDL Simulation Cycle. Cambridge Tech Report 249. 

The VHDL-AMS LRM draft expresses the semantics of the language through a natural language description of the basic simulation cycle. This is satisfactory for a working document that must be accessible to a large forum. But, to... 

The VHDL standard views the simulation of a completed VHDL model in terms of a monolithic controlling kernel which executes a simulation cycle. It repeatedly advances time to that of the next scheduled event, then checks if the event unblocks any processes. If so, it executes those processes (in zero time), allowing them to schedule future events, until they all become blocked again, waiting on some event. A new cycle then starts. 

The following is an analysis of VHDL and VHDL-AMS simulation cycles in terms of CSP. We show first that a VHDL model has the semantics of the synchronous parallel composition of the semantics for its component processes. In other words, the VHDL kernel can be eliminated by incorporating a local partial copy into each process as demontrated below. We then show that the VHDL-AMS model can be treated in a similar way. Each process model gets a copy of a fragment of the kernel plus a copy of the VHDL-AMS analog solver. 

As it happens, VHDL is - intentionally - deterministic, but this is not directly built into even the operational semantics given in the VHDL standard, although it is stated as an aim. Determinism derives from the detailed semantics of the simulation cycle and, in fact, strict determinism was lost with the 93 revision of the standard, which allowed writing to shared global variables at start-up, but we are not considering that aspect of that revision here. 

System is made of 8 elements with series-parallel structure Fig. 1. Simulation experiment was made for 10000 simulations. During simulation cycle, maximum event was chosen from parallel structure. This event was used in series structure and minimal event was chosen for system failure. Elements are described with time to failure distributions according to computer language MATLAB (HOLUB, R. VINTR, Z.). 

The design flow is not a waterfall diagram, as it has a number of iterative processes in which the designer can return to or redo any step until the proper functionality is achieved. Within each step, with the exception of the fabrication process, there is a complete generate—simulate cycle (not shown explicitly), in which the design components are developed and then simulated to ensure correct functionality before moving on to the next step. 

The association of Predicat-Transition Petri Net with differential algebraic equations (DAE) systems are particularly well fitted to describe dynamic and comportmental aspects. Places with DAE system are called hybrid places and are drawn with two concentric circles. The kernel of the simulator is split up into 3 units a discrete solver, a continuous solver and a simulation manager which manages the interactions between the solvers. Figure 2 shows the simulation cycle. 

More in general, our approach would be integrated in experimenl/simulation cycles for optimization of biomimetic cell-like systems of different complexity, especially when chemical exchanges from and to vesicles are key parameters to regulate and control. 

Fig. 8.10 The ctmelation of the sensitivity vector of enzyme Cln2 with the sensitivity vectras of all other variables of the cell cycle model. The investigated parameters were the following kasbf, kisbf, esbfnS, BCKO, CLN3MAX, Dn3 and Jn3. It is clear that cos 0 is close to 1 during most of the time period of the simulated cycle and for most variables, indicating that these sensitivity vectors are locally similar (Lovrics et al. 2008)...

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