Organic Rankine Cycles (ORCs) generate power from low temperature heat. To make the best use of the diverse low temperature heat sources, the cycle is tailored to each application. The objective is to maximize process performance by optimizing both process parameters and the working fluid. Today, process optimization and working fluid selection are typically addressed separately in a two-step approach: working fluids are selected using heuristic knowledge; subsequently, the process is optimized. Such an approach can lead to suboptimal solutions, since the optimal fluid might be excluded by the heuristics. We therefore present a framework for the holistic design of ORCs enabling the simultaneous optimization of the process and the working fluid based on process performance. The simultaneous optimization is achieved by exploiting the rich molecular picture underlying the PC-SAFT equation of state in a continuous-molecular targeting approach (CoMT-CAMD). To allow for the prediction of caloric properties, a quantitative structure–property relationship (QSPR) for the ideal gas heat capacity is proposed that relies on pure component parameters of PC-SAFT. The framework is used for the optimization of a geothermal ORC in a case study. A sound holistic design of process and working fluid is achieved.

SUMMARY Inverse methods are useful tools not only for deriving estimates of unknown parameters of the subsurface, but also for appraisal of the thus obtained models. While not being neither the most general nor the most efficient methods, Bayesian inversion based on the calculation of the Jacobian of a given forward model can be used to evaluate many quantities useful in this process. The calculation of the Jacobian, however, is computationally expensive and, if done by divided differences, prone to truncation error. Here, automatic differentiation can be used to produce derivative code by source transformation of an existing forward model. We describe this process for a coupled fluid flow and heat transport finite difference code, which is used in a Bayesian inverse scheme to estimate thermal and hydraulic properties and boundary conditions form measured hydraulic potentials and temperatures. The resulting derivative code was validated by comparison to simple analytical solutions and divided differences. Synthetic examples from different flow regimes demonstrate the use of the inverse scheme, and its behaviour in different configurations.

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SUMMARYIn implicit upwind methods for the solution of linearized Euler equations, one of the key issues is to balance large time steps, leading to a fast convergence behavior, and small time steps, needed to sufficiently resolve relevant flow features. A time step is determined by choosing a Courant-FriedrichsLevy (CFL) number in every iteration. A novel CFL evolution strategy is introduced and compared with two existing strategies. Numerical experiments using the adaptive multiscale finite volume solver QUADFLOW demonstrate that all three CFL evolution strategies have their advantages and disadvantages. A fourth strategy aiming at reducing the residual as much as possible in every time step is also examined. Using automatic differentiation, a sensitivity analysis investigating the influence of the CFL number on the residual is carried out confirming that, today, CFL control is still a difficult and open problem.

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The rigorous optimization of the geometry of a glass cell with computational fluid dynamics (CFD) is performed. The cell will be used for non-invasive nuclear magnetic resonance (NMR) measurements on a single droplet levitated in a counter current of liquid in a conical tube. The objective function of the optimization describes the stability of the droplet position required for long-period NMR measurements.The direct problem and even more the optimization problem require an efficient method to handle the high numerical complexity implied. Here, the flow equations are solved two-dimensionally and in steady state with the finite-element code SEPRAN for a spherical droplet with ideally mobile interface. The optimization is performed by embedding the CFD solver SEPRAN in the optimization environment EFCOSS. The underlying derivatives are computed using the automatic differentiation software ADIFOR. An overall concept for the optimization process is developed, requiring a robust scheme for the discretization of the geometries as well as a model for horizontal stability in the axially symmetric case. The numerical results show that the previously employed measuring cell described by Schröter is less suitable to maintain a stable droplet position than the new cell.

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