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Ključne besede: visoko šolstvo, univerze, Slovenija, zgodovinski pregledi
Objavljeno v DiRROS: 28.03.2025; Ogledov: 123; Prenosov: 36
Celotno besedilo (11,89 MB)

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: 103; Prenosov: 62
Celotno besedilo (2,59 MB)

Objavljeno v DiRROS: 28.03.2025; Ogledov: 166; Prenosov: 83
Celotno besedilo (1,40 MB)
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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: 132; Prenosov: 56
Celotno besedilo (425,51 KB)
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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: 117; Prenosov: 83
Celotno besedilo (2,66 MB)
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