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Journal of Physics: Conference Series - latest papers

Latest articles for Journal of Physics: Conference Series

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  • A Causal Decomposition of the Energy Containing Eddies in Compressible Minimal Flow Units
    The causal mechanisms between velocity and thermodynamic quantities in the near-wall region of a minimal flow unit are studied for a subsonic and a supersonic case. The flow dynamics are here represented by the time evolution of POD mode coefficients associated to near-wall streaks. These coefficients describe both the velocity field and the thermodynamic variables associated to them. The velocity mode coefficients are further split into their dilatational and solenoidal components to directly measure the effect of compressibility. Causality is quantified via the Synergistic-Unique-Redundant Decomposition (SURD) developed in Martínez-Sánchez et al. (2024). We note similarities in the prediction of the future state of the velocity signals for the subsonic and supersonic case and differences in the thermodynamic variables. We further highlight the role of compressibility in the supersonic case by contrasting the causal relationships of the dilatational velocity and density with those in the subsonic case. These distinctions are absent when considering the full velocity and density signals. Finally, we highlight the role of the unique, redundant, and synergistic causalities through a simple quadratic model for the prediction of the density signal.

  • Preface
    Sixth Madrid Summer School on Turbulence Editor: Javier Jiméenez The sixth biennial Summer Workshop on Turbulence at the School of Aeronautics of the Universidad Politécnica de Madrid took place from June 2 to July 4, 2025. It was funded by the Caust grant of the European Research Council, whose goal is to identify causal relations in turbulence and in other fields of physics. As in the five preceding Workshops [1–5], the present one aimed to provide a meeting place for theoreticians, experimentalists and simulators in which to develop and test new ideas on turbulence physics and structure, including new techniques such as machine learning and massive Monte Carlo simulations. Around seventy, mostly young, participants from thirty one international groups met for five weeks of collaborative work, primarily using the computational data archived in the receiving institution and, in many cases, also contributing their own. Although the format included some invited seminars and periodic plenary meetings, most of the work took place in small groups whose composition often changed during the workshop. These proceedings reflect the results of the work of these groups, which, in many cases, has later continued in the form of new collaborations. List of Workshop organiser is available in this PDF.

  • Peer Review Statement
    All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Open Review • Conference submission management system: Own • Number of submissions received: 31 • Number of submissions sent for review: 30 • Number of submissions accepted: 29 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 93.5 • Average number of reviews per paper: 1.2 • Total number of reviewers involved: 22 • Contact person for queries: Name: Javier Jimenez Email: javier.jimenezs@upm.es Affiliation: Universidad Politecnica de Madrid - Universidad Politecnica de Madrid Escuela Tecnica Superior de Ingenieria Aeronautica y del Espacio

  • Inter-scale energy transfer in turbulent channels
    We investigate the energy cascade in wall-bounded turbulence by analysing the inter-scale transfer between wall-parallel lengthscales in periodic channels. This transfer originates from the nonlinear interactions in the advective term of the Navier-Stokes equations, which satisfy the classical triadic compatibility relations. Each triadic interaction is examined individually, and the corresponding nonlinear energy transfers are mapped to assess their relative importance in sustaining turbulence. Motivated by the anisotropy of the flow, we interpret each of the nine contributions to the advection term arising from the different combinations of the streamwise, spanwise and wall-normal velocities as carrying distinct physical information, and therefore analyse them separately. Time-averaged maps of the energy transfer across all lengthscales and wall-normal positions for a channel flow at Reτ ≈ 180 are used to explore the mechanisms underlying the cascade process. As a proof of concept, reduced-order simulations are performed by retaining only the interactions identified as responsible for significant energy transfer based on our framework. Turbulent dynamics are successfully reproduced when 30% or more of the total interactions are included, while noticeable deviations emerge in the near-wall region when this proportion is further reduced.

  • Reduced-order modeling of a viscoelastic turbulent jet with hybrid machine learning models
    Adding flexible polymers to a Newtonian solvent confers complex properties to the resulting solution. The additional complexity substantially increases the computational cost of numerical simulations, which often makes them prohibitively expensive. Here, we propose hybrid reduced-order models to accelerate simulations of viscoelastic turbulent jets. The model combines modal decompositions with deep networks: we use proper orthogonal decomposition to obtain a compact representation of the data, and a neural network is trained to predict the mode coefficients in the low-dimensional space. Results show that the hybrid model effectively captures the long-term behavior of the viscoelastic jet, that we demonstrate by computing relevant statistics of the jet. While small models are capable of predicting large-scale dynamics more than one-step at a time, thus facilitating greater accelerations, larger models are mandatory for forecasting smaller-scale dynamics, with skip connections the most effective strategy for deeper and generalizable models. The proposed methodology underpins the potential of hybrid approaches for compact and robust reduced-order models of viscoelastic turbulent jets.