Iskanje po repozitoriju

A+ | A- | | SLO | ENG

Povzetek: A benchmark activity on two-fluid simulations of high-pressure boiling upward flows in a pipe is performed by 12 participants using different MCFD (Multiphase Computational Fluid Dynamics) codes and closure relationships. More than 30 conditions from DEBORA experiment conducted by CEA are considered. Each case is characterised by the flow rate, inlet temperature, wall heat flux and outlet pressure. High-pressure Freon (R12) at 14 bar and 26 bar is boiled in a 19.2 mm pipe heated over 3.5 m. Flow rates range from 2000 kg m−2 s −1 to 5000 kg m−2 s −1 and exit quality � ranges from single-phase conditions to � = 0.1 which leads to a peak void fraction of � = 70%. In these high pressure conditions, bubbles remain small and there is no departure from the bubbly flow regime (François et al., 2011; Hösler, 1968). However, different kind of bubbly flows are observed: wall-peak, intermediate peak or core-peak, depending on the case considered. Measurements along the pipe radius near the end of the heated section are compared to code predictions. They include void fraction, bubble mean diameter, vapour velocity and liquid temperature. The benchmark covered two phases. In the first phase of the benchmark activities, experimental data were given to the participants, allowing to compare the simulation results and to develop, to select or to adjust the models in the CMFD codes. The second phase included blind cases where the participants could not compare to the measurements. In between the two phases, possible additional model adjustments or calibrations were performed. Overall, the benchmark involved very different closures and a wide range of models’ complexity was covered. Yet, it is extremely difficult to have a robust closure for all conditions considered, even knowing experimental measurements. The wall-to-core peak transition is not captured consistently by the models. The degree of subcooling and the void fraction level are also difficult to assess
Objavljeno v DiRROS: 28.03.2025; Ogledov: 134; Prenosov: 75
Celotno besedilo (2,59 MB)

Objavljeno v DiRROS: 28.03.2025; Ogledov: 198; Prenosov: 104
Celotno besedilo (1,40 MB)
Gradivo ima več datotek! Več...

Povzetek: Equation discovery, also known as symbolic regression, is the field of machine learning that studies algorithms for discovering quantitative laws, expressed as closed-form equations or formulas, in collections of observed data. The latter is expected to come from measurements of physical systems and, therefore, noisy, moving the focus of equation discovery algorithms towards discovering approximate equations. These loosely match the noisy observed data, rendering them inappropriate for applications in mathematics. In this article, we introduce Diofantos, an algorithm for discovering equations in the ring of integers that exactly match the training data. Diofantos is based on a reformulation of the equation discovery task into the task of solving linear Diophantine equations. We empirically evaluate the performance of Diofantos on reconstructing known equations for more than 27,000 sequences from the online encyclopedia of integer sequences, OEIS. Diofantos successfully reconstructs more than 90% of these equations and clearly outperforms SINDy, a state-of-the-art method for discovering approximate equations, that achieves a reconstruction rate of less than 70%.
Ključne besede: symbolic regression, equation discovery, online encyclopedia of integer sequences
Objavljeno v DiRROS: 27.03.2025; Ogledov: 160; Prenosov: 71
Celotno besedilo (425,51 KB)
Gradivo ima več datotek! Več...

Povzetek: Ordinary differential equations (ODEs) are a widely used formalism for the mathematical modeling of dynamical systems, a task omnipresent in scientific domains. The paper introduces a novel method for inferring ODEs from data, which extends ProGED, a method for equation discovery that allows users to formalize domain-specific knowledge as probabilistic context-free grammars and use it for constraining the space of candidate equations. The extended method can discover ODEs from partial observations of dynamical systems, where only a subset of state variables can be observed. To evaluate the performance of the newly proposed method, we perform a systematic empirical comparison with alternative state-of-the-art methods for equation discovery and system identification from complete and partial observations. The comparison uses Dynobench, a set of ten dynamical systems that extends the standard Strogatz benchmark. We compare the ability of the considered methods to reconstruct the known ODEs from synthetic data simulated at different temporal resolutions. We also consider data with different levels of noise, i.e., signal-to-noise ratios. The improved ProGED compares favourably to state-of-the-art methods for inferring ODEs from data regarding reconstruction abilities and robustness to data coarseness, noise, and completeness.
Ključne besede: ordinary differential equations, equation discovery, mathematical modeling, system identification, symbolic regression, partial observability
Objavljeno v DiRROS: 27.03.2025; Ogledov: 146; Prenosov: 98
Celotno besedilo (2,66 MB)
Gradivo ima več datotek! Več...

Povzetek: Pipe bends disrupt the flow, resulting in an asymmetric velocity field across the pipe diameter (D). We examined the recovery length required for the flow to return to a symmetric velocity profile downstream of a sharp elbow. The wall-resolved Large Eddy Simulation (LES) approach was applied to reproduce turbulent fluid flow at Reynolds numbers (�� ) of 5600 and 10,000. An additional case in the transitional laminar-turbulent-laminar regime was analyzed at ��=1400 . This analysis explored the behavior of the Dean vortices downstream of the elbow and revealed that, in turbulent cases, these vortices reverse their vorticity direction in the region between 8 D and 10 D. However, they eventually decay in structure as far as 25 D from the elbow. Flow asymmetry was analyzed in a 100 D long pipe section downstream of the elbow using four different criteria: wall shear stress (WSS), streamwise velocity, its fluctuations, and vorticity fields. This study found that in turbulent flows, the distance required for flow recovery is a few tens of D and decreases with increasing ��. However, in the transitional case, the flow separation within the elbow induces instabilities that gradually diminish downstream, and flow asymmetry persists even longer than the 100 D length of our outlet pipe section. WSS proved sensitive for detecting asymmetry near walls, whereas flow profiles better revealed bulk asymmetry. It was also shown that asymmetry indicators derived from velocity fluctuations and vorticity were less sensitive than those obtained from streamwise velocity.
Ključne besede: cevno koleno, flow asymmetry, pipe elbows, secondary flow, flow separation, turbulent flow, recovery length, Large Eddy Simulation
Objavljeno v DiRROS: 27.03.2025; Ogledov: 185; Prenosov: 63
Celotno besedilo (6,74 MB)
Gradivo ima več datotek! Več...