Benchmark Examples

The effort to find the best solution can be reduced by defining an appropriate guiding Junction. A guiding function (third line of the algorithm) attempts to select those nodes from W, which are assessed to be best or cheapest. If a perfect guiding function was given (similar to a scheduler automaton which knows the optimal solution), it would always make the optimal decisions and step-by-step select the nodes in Table 10.1 from W. 

In this section, the TA-based modeling and solution approach is applied to a popular benchmark scheduling problem from Kallrath [1], It is a multistage and multiproduct chemical batch plant. 

230 I 70 Scheduling Based on Reachability Analysis of Timed Automata 

A manual batch sizing procedure was used to determine constant batch sizes for all operations. Batch sizes for tasks running on the resources i i, R3. R, R, i 9 are fixed to 10. The batch size of T2, which was experimentally identified as a bottleneck, was chosen as 20. With respect to R(, and R7, the batch sizes for the 

The fact that the unstable products S, S9, S10, S12 cannot be stored (NIS) is modeled as follows separate storages with infinite capacities are defined for these 

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For the benchmark example mentioned in [10], a 2-hot/2-cold streams problem along with heating steam and cooling water, the maximal allowable variation of various (6) possible minimum-utility HEN structures is examined by... 

The second contribution spans an even larger range of length and times scales. Two benchmark examples illustrate the design approach polymer electrolyte fuel cells and hard disk drive (HDD) systems. In the current HDDs, the read/write head flies about 6.5 nm above the surface via the air bearing design. Multi-scale modeling tools include quantum mechanical (i.e., density functional theory (DFT)), atomistic (i.e., Monte Carlo (MC) and molecular dynamics (MD)), mesoscopic (i.e., dissipative particle dynamics (DPD) and lattice Boltzmann method (LBM)), and macroscopic (i.e., LBM, computational fluid mechanics, and system optimization) levels. 

The need for computer simulations introduces some constraints in the description of solvent-solvent interactions. A simulation performed with due care requires millions of moves in the Monte Carlo method or an equivalent number of time steps of elementary trajectories in Molecular Dynamics, and each move or step requires a new calculation of the solvent-solvent interactions. Considerations of computer time are necessary, because methodological efforts on the calculation of solvation energies are motivated by the need to have reliable information on this property for a very large number of molecules of different sizes, and the application of methods cannot be limited to a few benchmark examples. There are essentially two different strategies. 

The first strategy maintains the QM description of the solvent molecules but reduces their number and adopts a different description for other molecules (often adopting a continuum distribution) to take account of bulk effects in the calculation. These QM simulation methods, of which the first and most frequently used is the Car-Parrinello method [2], are in use since several years, and have largely passed the stage of benchmark examples. This strategy is the most satisfactory under the formal aspects we have at present, and will surely be employed more and more with increasing computer power, but will certainly not completely replace, in the foreseeable future, other strategies. 

The electronic spectra of the five-membered ring compounds have been intensively studied by the experimental and theoretical works. These molecules are fundamental units in many important biological systems. Furthermore, their excitation spectra are benchmark examples for theoretical studies of molecular excited states [51,55-58]. For furan and thiophene, various types of excitation spectra were measured the vacuum ultraviolet (VUV) spectrum, electron energy-loss (EEL) spectrum and magnetic circular dichroism (MCD) spectrum. The SAC-Cl method offered consistent interpretations of these electronic spectra [51-53]. 

To define normal performance, it is desirable for a company to identify benchmark examples so that various time study analysts can develop consistency in performance rating. The benchmark examples should be supplemented by a clear description of the characteristics of an employee carrying out a normal performance. 

I. Shavitt, F. B. Brown, and P. G. Bnrton, Confign-ration Selection and Extrapolation in Multireference Configuration Interaction Calcnlations The (H2)2 van der Waals Complex as a Benchmark Example, Int. J. Quantum Chem. 31,507-520 (1987). 

FIGURE 18.2. Benchmarking example of monitoring steam losses. 

Such information can be combined to generate performance indicators to monitor the efficiency or productivity of different program units and can be estimated by combining output and input information or by comparing process indicators over time or t ainst a benchmark. Examples of such indicators are the number of beneficiaries served per program staff member, the level of administrative costs for every US 1,000 transferred, and the number of applications processed in less than a week. 

The results of tq>plying Hebe to some benchmark examples are shown in Figure 8. The designs include the DAIO receiver DAIOjtv) and phase decoder DAI04>Ki, encoder ECCjcnc) and decoder (ECCjdec) portions of an... 

Hercules and Hebe have been applied to the design examples in the benchmark suite proposed by the High-level Synthesis Workshop [Wor89]. Most of these benchmark examples have been rewritten in HardwareC and synthesized to logic-level implementations. 

Table 11.16 Synthesis results for the benchmark examples, assuming 5 cycle 16-bit and 32-bit multiply (4 cycle multiply + 1 cycle latch).

Krueger, R., 2011. Development and Application of Benchmark Examples for Mode II Static Delamination Propagation and Fatigue Growth Predictions. Report Prepared for NASA, NASA/CR-2011-217305, Hampton, Virginia. 

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