arXiv subjects

Adrián Lozano-Durán

Publications and source records attributed to Adrián Lozano-Durán.

At least 19 recordsLinked to original sources

arXiv2026

Global modal/non-modal analysis of high-enthalpy Martian entry vehicles

We analyze laminar-to-turbulent transition over a blunt capsule representative of Martian entry at Mach 26 using global modal and transient-growth analyses about a thermochemical base flow characteristic of high-altitude entry conditions. Across the Reynolds-number range considered ($Re_\infty = 10^4$--$10^6$), the axisymmetric global eigenspectrum remains stable, ruling out temporally unstable axisymmetric global modes as a viable route to transition. The flow nevertheless supports strong transient growth concentrated in the shear--entropy layer generated by bow-shock curvature. Energy-budget analysis reveals a two-stage process: Reynolds-stress production in the mean shear first amplifies kinetic disturbances, which then generate entropy fluctuations as they traverse the strong base-flow entropy gradient. The optimal gain scales linearly with the Reynolds number based on the capsule nose radius, whereas boundary-layer-localized disturbances become competitive at the highest Reynolds numbers examined. These results identify shear--entropy-layer transient growth as a plausible linear pathway for seeding transition in high-enthalpy blunt-entry vehicles.

physics.flu-dyn↗

arXiv2026

HYMOR: An open-source package for global modal, non-modal, and receptivity analysis in high-enthalpy hypersonic vehicles

We present HYMOR (HYpersonic MOdal/non-modal, and Receptivity), an open-source computational framework for the linear stability analysis of high-enthalpy hypersonic flows. The toolkit includes MATLAB and Julia implementations and is released under the MIT license. HYMOR provides global modal, non-modal, and freestream receptivity analyses capable of capturing interactions among spatially separated physical mechanisms that are inaccessible to traditional local methods. A shock-fitting formulation is employed to treat the bow shock as a sharp discontinuity, ensuring that the interaction of infinitesimal disturbances with the shock reproduces the exact response predicted by linear interaction analysis. The code also solves the nonlinear equations for base-flow computation and automatically linearizes the resulting discrete operators for the stability analyses. Several thermochemical models are available for treatment of real-gas effects in high-enthalpy regimes. The numerical implementation is verified against a collection of benchmark cases that demonstrate the accuracy and capabilities of the toolkit across its modal, non-modal, and receptivity analysis modes.

physics.flu-dyn↗

arXiv2026

Bow-shock instability in entry, descent, and landing vehicles under high-enthalpy conditions

Laminar--turbulent transition remains a major uncertainty in the aerothermal design of entry, descent, and landing (EDL) vehicles. We show that, under high-enthalpy Mars-entry conditions, the detached bow shock and shock-generated shear--entropy layer can become unstable under freestream disturbances, leading to nonlinear breakdown and enhanced wall heating. The analysis spans freestream Mach numbers ($M_\infty$) up to 30 for both Earth and Mars at high altitude, with Mars being more susceptible. The receptivity analysis shows that disturbance amplification occurs through a three-step mechanism: (i) transmission and amplification of acoustic and entropic freestream components across the bow shock; (ii) further convective amplification within the post-shock shear--entropy layer; and (iii) bow-shock corrugation driven by the downstream pressure field, which reinforces the instability. The dominant response is localized in the shock layer, with no classical boundary-layer mode required. The total optimal energy gain scales as $\overline{G}_T^{\rm opt}\sim γ_2^*M_\infty^2 \exp[(ρ_2/ρ_1)/C-B/\sqrt{Re_\infty}]$, where $γ_2^*$ is an effective specific-heat ratio, $ρ_1$ and $ρ_2$ the pre- and post-shock densities, $Re_\infty$ the freestream Reynolds number, and $B$, $C$ geometry-dependent constants. For a representative EDL vehicle during Mars entry, amplification factors reach order $10^6$. Flight measurements from the Mars Science Laboratory (MSL) and Mars 2020/Perseverance capsules are consistent with these results, as are wall-modeled large-eddy simulations of MSL under representative Mars-entry conditions. These results suggest that bow-shock instabilities may constitute a transition mechanism for blunt hypersonic entry vehicles, either alone or combined with others.

physics.flu-dyn↗

arXiv2026

Numerically Consistent Non-Boussinesq Subgrid-scale Stress Model with Enhanced Convergence

We extend the data-assimilation approach of Ling and Lozano-Durán (AIAA 2025-1280) to develop machine-learning-based subgrid-scale stress (SGS) models for large-eddy simulation (LES) that are consistent with the numerical scheme of the flow solver. The method accounts for configurations with two inhomogeneous directions and is applied to turbulent boundary layers (TBL) under adverse pressure gradients (APG). To overcome the limitations of linear eddy-viscosity closures in complex flows, we adopt a non-Boussinesq SGS formulation along with a dissipation-matching training loss. A second improvement is the integration of a multi-task learning strategy that explicitly promotes monotonic convergence with grid refinement, a property that is often absent in conventional SGS models. A posteriori tests show that the proposed model improves predictions of the mean velocity and wall-shear stress relative to the Dynamic Smagorinsky model (DSM), while also achieving monotonic convergence with grid refinement.

physics.flu-dyn↗

arXiv2025

Dimensionless learning based on information

Dimensional analysis is one of the most fundamental tools for understanding physical systems. However, the construction of dimensionless variables, as guided by the Buckingham-$π$ theorem, is not uniquely determined. Here, we introduce IT-$π$, a model-free method that combines dimensionless learning with the principles of information theory. Grounded in the irreducible error theorem, IT-$π$ identifies dimensionless variables with the highest predictive power by measuring their shared information content. The approach is able to rank variables by predictability, identify distinct physical regimes, uncover self-similar variables, determine the characteristic scales of the problem, and extract its dimensionless parameters. IT-$π$ also provides a bound of the minimum predictive error achievable across all possible models, from simple linear regression to advanced deep learning techniques, naturally enabling a definition of model efficiency. We benchmark IT-$π$ across different cases and demonstrate that it offers superior performance and capabilities compared to existing tools. The method is also applied to conduct dimensionless learning for supersonic turbulence, aerodynamic drag on both smooth and irregular surfaces, magnetohydrodynamic power generation, and laser-metal interaction.

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arXiv2025

Cause-and-effect approach to turbulence forecasting

Traditional approaches to turbulence forecasting often rely on correlation-based criteria for input selection. These methods may select variables that correlate with the target without truly driving its dynamics, which limits interpretability, generalization, and efficiency. In this work, we introduce a causality-based approach for input selection in turbulence forecasting based on the Synergistic-Unique-Redundant Decomposition (SURD) of causality. This method decomposes the information from candidate inputs into unique, redundant, and synergistic causal contributions and links them to the fundamental limits of predictive accuracy achievable by any model. In practice, we implement the approach using neural mutual-information estimators and demonstrate its application to wall-shear-stress forecasting from direct numerical simulation data of turbulent channel flow. Our findings show that input variables with strong unique or synergistic causal contributions enable compact forecasting models with high predictive power, whereas redundant variables can be excluded without degrading accuracy. We first validate these capabilities in two benchmark cases involving collider effects, and then apply the methodology to three turbulent flow configurations with different interaction types. In each case, we demonstrate how SURD causalities guide optimal input selection by constructing forecasting models based on various input combinations. We also compare the results with standard space-time correlation analysis and show that SURD provides a more reliable basis for input selection, as it captures nonlinear dependencies, distinguishes redundant, unique, and synergistic interactions, and remains invariant under invertible transformations of the variables. Overall, we believe this enables more interpretable and compact models by reducing input dimensionality without sacrificing performance.

physics.flu-dyn↗

arXiv2025

Observational causality by states and interaction type for scientific discovery

Causality plays a central role in understanding interactions between variables in complex systems. These systems often exhibit state-dependent causal relationships, where both the strength and direction of causality vary with the value of the interacting variables. In this work, we introduce a state-aware causal inference method that quantifies causality in terms of information gain about future states. The effectiveness of the proposed approach stems from two key features: its ability to characterize causal influence as a function of system state, and its capacity to distinguish between redundant and synergistic interactions. The method is validated across a range of benchmark cases in which the direction and strength of causality evolve in a prescribed manner with the state of the system. We further demonstrate the applicability of our approach in two real scenarios: the interaction between motions across scales in a turbulent boundary layer, and the Walker circulation phenomenon in tropical Pacific climate dynamics. Our results show that, without accounting for state-dependent causality as well as redundant and synergistic effects, traditional approaches to causal inference may lead to incomplete or misleading conclusions.

physics.data-an↗

arXiv2024

Decomposing causality into its synergistic, unique, and redundant components

Causality lies at the heart of scientific inquiry, serving as the fundamental basis for understanding interactions among variables in physical systems. Despite its central role, current methods for causal inference face significant challenges due to nonlinear dependencies, stochastic interactions, self-causation, collider effects, and influences from exogenous factors, among others. While existing methods can effectively address some of these challenges, no single approach has successfully integrated all these aspects. Here, we address these challenges with SURD: Synergistic-Unique-Redundant Decomposition of causality. SURD quantifies causality as the increments of redundant, unique, and synergistic information gained about future events from past observations. The formulation is non-intrusive and applicable to both computational and experimental investigations, even when samples are scarce. We benchmark SURD in scenarios that pose significant challenges for causal inference and demonstrate that it offers a more reliable quantification of causality compared to previous methods.

physics.data-an↗

arXiv2024

Are the dynamics of wall turbulence in minimal channels and larger domain channels equivalent? A graph-theoretic approach

This work proposes two algorithmic approaches to extract critical dynamical mechanisms in wall-bounded turbulence with minimum human bias. In both approaches, multiple types of coherent structures are spatiotemporally tracked, resulting in a complex multilayer network. Network motif analysis, i.e., extracting dominant non-random elemental patterns within these networks, is used to identify the most dominant dynamical mechanisms. Both approaches, combined with network motif analysis, are used to answer whether the main dynamical mechanisms of a minimal flow unit (MFU) and a larger unconstrained channel flow, labeled a full channel (FC), at $Re_τ\approx 180$, are equivalent. The first approach tracks traditional coherent structures defined as low- and high-speed streaks, ejections, and sweeps. It is found that the roll-streak pairing, consistent with the current understanding of self-sustaining processes, is the most significant and simplest dynamical mechanism in both flows. However, the MFU has a timescale for this mechanism that is approximately $2.83$ times slower than that of the FC. In the second approach, we use semi-Lagrangian wavepackets and define coherent structures from their energetic streak, roll, and small-scale phase space. This method also shows similar motifs for both the MFU and FC. It indicates that, on average, the most dominant phase-space motifs are similar between the two flows, with the significant events taking place approximately $2.21$ times slower in the MFU than in the FC. This value is more consistent with the implied timescale ratio of only the slow speed streaks taking part in the roll-streak pairing extracted using the first multi-type spatiotemporal approach, which is approximately $2.17$ slower in the MFU than the FC.

physics.flu-dyn↗

arXiv2023

Information-theoretic causality and applications to turbulence: energy cascade and inner/outer layer interactions

We introduce an information-theoretic method for quantifying causality in chaotic systems. The approach, referred to as IT-causality, quantifies causality by measuring the information gained about future events conditioned on the knowledge of past events. The causal interactions are classified into redundant, unique, and synergistic contributions depending on their nature. The formulation is non-intrusive, invariance under invertible transformations of the variables, and provides the missing causality due to unobserved variables. The method only requires pairs of past-future events of the quantities of interest, making it convenient for both computational simulations and experimental investigations. IT-causality is validated in four scenarios representing basic causal interactions among variables: mediator, confounder, redundant collider, and synergistic collider. The approach is leveraged to address two questions relevant to turbulence research: i) the scale locality of the energy cascade in isotropic turbulence, and ii) the interactions between inner and outer layer flow motions in wall-bounded turbulence. In the former case, we demonstrate that causality in the energy cascade flows sequentially from larger to smaller scales without requiring intermediate scales. Conversely, the flow of information from small to large scales is shown to be redundant. In the second problem, we observe a unidirectional causality flow, with causality predominantly originating from the outer layer and propagating towards the inner layer, but not vice versa. The decomposition of IT-causality into intensities also reveals that the causality is primarily associated with high-velocity streaks.

physics.flu-dyn↗

arXiv2023

Near-Field Wall-Modeled Large-Eddy Simulation of the NASA X-59 Low-Boom Flight Demonstrator

Wall-modeled large-eddy simulation (WMLES) is utilized to analyze the experimental aircraft X-59 Quiet SuperSonic Technology (QueSST) developed by Lockheed Martin at Skunk Works for NASA's Low-Boom Flight Demonstrator project. The simulations utilize the charLES solver and aim to assess the ability of WMLES to predict near-field noise levels under cruise conditions, considering various subgrid-scale (SGS) models and grid resolutions. The results are compared with previous numerical studies based on the Reynolds-averaged Navier-Stokes (RANS) equations. Our findings demonstrate that WMLES produces near-field pressure predictions that are similar to those of RANS simulations at a comparable computational cost. Some mild discrepancies are observed between the WMLES and RANS predictions downstream the aircraft. These differences persist for finest grid refinement considered, suggesting that they might be attributed to underresolved interactions of shock waves and expansions waves at the trailing edge.

physics.flu-dyn↗

arXiv2023

Machine learning building-block-flow wall model for large-eddy simulation

A wall model for large-eddy simulation (LES) is proposed by devising the flow as a combination of building blocks. The core assumption of the model is that a finite set of simple canonical flows contains the essential physics to predict the wall-shear stress in more complex scenarios. The model is constructed to predict zero/favourable/adverse mean pressure gradient wall turbulence, separation, statistically unsteady turbulence with mean flow three-dimensionality, and laminar flow. The approach is implemented using two types of artificial neural networks: a classifier, which identifies the contribution of each building block in the flow, and a predictor, which estimates the wall-shear stress via combination of the building-block flows. The training data are directly obtained from wall-modelled LES (WMLES) optimised to reproduce the correct mean quantities. This approach guarantees the consistency of the training data with the numerical discretisation and the gridding strategy of the flow solver. The output of the model is accompanied by a confidence score in the prediction that aids the detection of regions where the model underperforms. The model is validated in canonical flows (e.g. laminar/turbulent boundary layers, turbulent channels, turbulent Poiseuille-Couette flow, turbulent pipe) and two realistic aircraft configurations: the NASA Common Research Model High-lift and NASA Juncture Flow experiment. It is shown that the building-block-flow wall model outperforms (or matches) the predictions by an equilibrium wall model. It is also concluded that further improvements in WMLES should incorporate advances in subgrid-scale modelling to minimise error propagation to the wall model.

physics.flu-dyn↗

arXiv2023

POD-based study of turbulent plane Poiseuille flow: comparing structure and dynamics between quasi-linear simulations and DNS

Turbulence in the restricted nonlinear (RNL) dynamics is analyzed and compared with DNS of Poiseuille turbulence at $R=1650$. The structures are obtained by POD analysis of the two components of the flow partition used in RNL dynamics: the streamwise-mean flow and fluctuations. POD analysis of the streamwise-mean flow indicates that the dominant POD modes, in both DNS and RNL, are roll-streaks harmonic in the spanwise. However, we conclude that these POD modes do not occur in isolation but rather are Fourier components of a coherent roll-streak structure. POD analysis of the fluctuations in DNS and RNL reveals similar complex structures consisting in part of oblique waves collocated with the streak. The origin of these structures is identified by their correspondence to POD modes predicted using a stochastic turbulence model (STM). These predicted POD modes are dominated by the optimally growing structures on the streak, which the STM predicts correctly to be of sinuous oblique wave structure. This close correspondence between the roll-streak structure and the associated fluctuations in DNS, RNL and the STM implies that the self-sustaining mechanism operating in DNS is essentially the same as that in RNL, which has been previously associated with optimal perturbation growth on the streak.

physics.flu-dyn↗

arXiv2022

Wall-modeled large-eddy simulation based on building-block flows

A unified subgrid-scale (SGS) and wall model for large-eddy simulation (LES) is proposed by devising the flow as a collection of building blocks that enables the prediction of the eddy viscosity. The core assumption of the model is that simple canonical flows contain the essential physics to provide accurate predictions of the SGS tensor in more complex flows. The model is constructed to predict zero-pressure-gradient wall-bounded turbulence, adverse pressure gradient effects, separation and laminar flow. The approach is implemented using a Bayesian classifier, which identifies the contribution of each building block in the flow, and a neural-network-based predictor, which estimates the eddy viscosity based on the building-block units. The training data are directly obtained from wall-modeled LES with an exact SGS/wall model for the mean quantities to guarantee consistency with the numerical discretization. The model is validated in canonical flows and the NASA High-Lift Common Research Model and shown to improve the predictions with respect to current modeling approaches.

physics.flu-dyn↗

arXiv2022

Causality analysis of large-scale structures in the flow around a wall-mounted square cylinder

The aim of this work is to analyse the formation mechanisms of large-scale coherent structures in the flow around a wall-mounted square cylinder, due to their impact on pollutant transport within cities. To this end, we assess causal relations between the modes of a reduced-order model obtained by applying proper-orthogonal decomposition to high-fidelity-simulation data of the flow case under study. The causal relations are identified using conditional transfer entropy, which is an information-theoretical quantity that estimates the amount of information contained in the past of one variable about another. This allows for an understanding of the origins and evolution of different phenomena in the flow, with the aim of identifying the modes responsible for the formation of the main vortical structures. Our approach unveils that vortex-breaker modes are the most causal modes, in particular, over higher-order modes, and no significant causal relationships were found for vortex-generator modes. We validate this technique by determining the causal relations present in the nine-equation model of near-wall turbulence developed by Moehlis et al. (New J. Phys, vol. 6, 2004, p. 56), which are in good agreement with literature results for turbulent channel flows.

physics.flu-dyn↗

arXiv2022

A Database for Reduced-Complexity Modeling of Fluid Flows

We present a publicly accessible database designed to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. Availability of high-quality flow data is essential for all of these aspects of model development for both data-driven and physics-based methods. The database contains time-resolved data for six distinct datasets: a large eddy simulation of a turbulent jet, direct numerical simulations of a zero-pressure-gradient turbulent boundary layer, particle-image-velocimetry measurements for the same boundary layer at several Reynolds numbers, direct numerical simulations of laminar stationary and pitching flat-plate airfoils, particle-image-velocimetry and force measurements of an airfoil encountering a gust, and a large eddy simulation of the separated, turbulent flow over an airfoil. These six cases span several key flow categories: laminar and turbulent, statistically stationary and transient, tonal and broadband spectral content, canonical and application-oriented, wall-bounded and free-shear flow, and simulation and experimental measurements. For each dataset, we describe the flow setup and computational/experimental methods, catalog the data available in the database, and provide examples of how these data can be used for reduced-complexity modeling. All data can be downloaded using a browser interface or Globus. Our vision is that the common testbed provided by this database will aid the fluid mechanics community in clarifying the distinct capabilities of new and existing methods.

physics.flu-dyn↗

arXiv2022

Information-theoretic formulation of dynamical systems: causality, modeling, and control

The problems of causality, modeling, and control for chaotic, high-dimensional dynamical systems are formulated in the language of information theory. The central quantity of interest is the Shannon entropy, which measures the amount of information in the states of the system. Within this framework, causality is quantified by the information flux among the variables of interest in the dynamical system. Reduced-order modeling is posed as a problem related to the conservation of information in which models aim at preserving the maximum amount of relevant information from the original system. Similarly, control theory is cast in information-theoretic terms by envisioning the tandem sensor-actuator as a device reducing the unknown information of the state to be controlled. The new formulation is used to address three problems about the causality, modeling, and control of turbulence, which stands as a primary example of a chaotic, high-dimensional dynamical system. The applications include the causality of the energy transfer in the turbulent cascade, subgrid-scale modeling for large-eddy simulation, and flow control for drag reduction in wall-bounded turbulence.

math.DS↗

arXiv2021

Performance of wall-modeled LES with boundary-layer-conforming grids for external aerodynamics

We investigate the error scaling and computational cost of wall-modeled large-eddy simulation (WMLES) for external aerodynamic applications. The NASA Juncture Flow is used as representative of an aircraft with trailing-edge smooth-body separation. Two gridding strategies are examined: i) constant-size grid, in which the near-wall grid size has a constant value and ii) boundary-layer-conforming grid (BL-conforming grid), in which the grid size varies to accommodate the growth of the boundary-layer thickness. Our results are accompanied by a theoretical analysis of the cost and expected error scaling for the mean pressure coefficient ($C_p$) and mean velocity profiles. The prediction of $C_p$ is within less than $5\%$ error for all the grids studied, even when the boundary layers are marginally resolved. The high accuracy in the prediction of $C_p$ is attributed to the outer-layer nature of the mean pressure in attached flows. The errors in the predicted mean velocity profiles exhibit a large variability depending on the location considered, namely, fuselage, wing-body juncture, or separated trailing-edge. WMLES performs as expected in regions where the flow resembles a zero-pressure-gradient turbulent boundary layer such as the fuselage ($<5\%$ error). However, there is a decline in accuracy of WMLES predictions of mean velocities in the vicinity of wing-body junctions and, more acutely, in separated zones. The impact of the propagation of errors from the underresolved wing leading-edge is also investigated. It is shown that BL-conforming grids enable a higher accuracy in wing-body junctions and separated regions due to the more effective distribution of grid points, which in turn diminishes the streamwise propagation of errors.

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