arXiv subjects

Zahra Sharif Khodaei

Publications and source records attributed to Zahra Sharif Khodaei.

9 recordsLinked to original sources

arXiv2026

Exceptional-point conjugate symmetry and migration in fluid-loaded elastic waveguides: restructuring the physical dispersion spectrum

The dispersion spectrum of a fluid-loaded elastic waveguide is a non-Hermitian system with eigenmodes on a two-sheeted Riemann manifold. Although exceptional points (EPs) organize avoided crossings, the topological rules by which fluid loading restructures the observable spectrum (both physically admissible modes and their connectivity) remain unknown, and conventional solvers fail to recover complete branches. Here we establish these rules. We prove that, for real elastic moduli and real fluid parameters, EPs obey a conjugate-pair symmetry, and that a pair on the physical sheet enforces an avoided crossing of real wavenumbers between the two modes it controls, yielding a topological criterion for mode identification. We then identify two independent reconstruction mechanisms. First, the physical observation space expands continuously: a mode observable above its vacuum cut-off can extend downward to the sound line, admitting solutions in a frequency band empty in vacuum; vacuum-anchored seeds above cut-offs continued downward capture such branches systematically. Second, EP migration rewires mode connectivity: as fluid density increases, conjugate pairs leaving the physical sheet (signaled by a sign reversal of the imaginary part of either the in-plane or the vertical wavenumber) leave the two previously coupled physical branch segments unconnected by any EP within the observation space; they become independent curves that may intersect freely on the real frequency axis, with the narrowest veerings losing their topological protection first. Mirror-symmetry breaking births additional EPs generically in the trapped regime but only conditionally in the leaky regime. Numerical computations on symmetric and asymmetric composite laminates under single- and double-sided water loading validate the framework, recovering multiple leaky branches missed by standard solvers.

physics.comp-ph↗

arXiv2026

Inter-turbine spacing and flow unsteadiness effects on wake-induced blade dynamics

Wind turbines operating downstream of others in a farm are routinely exposed to waked inflow, with reduced mean velocity and elevated turbulence driving power deficits and additional structural fatigue. The direct effect of wakes on blade-level structural loading remains under-explored experimentally, owing partly to the sparse spatial coverage of conventional point-based strain sensors. Here, we present a wind-tunnel study of wake-induced blade dynamics using two $1\,\mathrm{m}$-diameter turbine models, in which one blade of a downstream turbine ($WT_2$) is instrumented with distributed Rayleigh-backscattering fibre-optic strain sensors, providing spatially continuous strain measurement across the blade span. By changing the relative position of the upstream turbine ($WT_1$) to the downstream, waked turbine $WT_2$ across the streamwise and spanwise extent, we map power output, spanwise strain, and accumulated representative fatigue relevant loading across the wake profile. Full wake impingement suppresses blade loading through the associated velocity deficit, while partial wake overlap generates the strongest load intermittency and highest relative fatigue relevant loading, despite an intermediate power recovery. A combined performance-to-loading metric shows this partial-wake regime offers the least favourable trade-off between energy yield and structural loading. These results show that minimising partial-wake exposure, not only mean velocity deficits, should be a design consideration for wind-farm layout and turbine spacing.

physics.flu-dyn↗

arXiv2026

Steering topology distributions for unified generative design of architected metamaterials

Architected metamaterials derive their functions from structure, creating vast opportunities to program physical responses through topology design. However, existing design methods are often tailored to individual design problems, making limited use of topology knowledge for effective and broadly applicable design as objectives, constraints, and physical functions change. Here we introduce Generative Topology Optimization (GenTO), a unified framework that turns a learned topology prior into a reusable design engine. GenTO trains a diffusion model on a large full-order topology dataset and then iteratively steers the resulting topology distribution toward task-specific high-performing regions using user-defined physical objectives and constraints. This shifts the object of optimization from a single structure to a task-adapted topology distribution. Across topology design problems spanning thermal extremization, multi-objective morphology control, property-targeted auxetic design, and vibration transmission design, GenTO reuses pretrained topology priors for heterogeneous tasks, preserves structural diversity, and reaches high-performing solutions supported by numerical benchmarks and experimental validation. These results establish reusable topology knowledge as a unified principle for effective and scalable architected metamaterial design.

cs.AI↗

arXiv2026

Homotopy continuation of viscoelastic waveguide dispersion curves: from intra-manifold tracking to inter-manifold transport

Conventional mode tracking operates in the dark: it traces dispersion branches on the non-Hermitian eigenvalue manifold using only local continuity, unaware of the global Riemann-sheet topology. When exceptional points (EPs) lie close to the real frequency axis, the eigenvector similarity that local trackers rely on degrades, and mode tracking becomes unreliable, failing silently. This paper replaces blind intra-manifold tracking with inter-manifold transport. A material attenuation parameter s in [0,1] continuously maps the target lossy problem to an auxiliary lossless one whose Hermitian eigenvalue problem yields a well-posed anchor manifold on which each dispersion branch possesses a globally unique and continuous identity. These identities are defined once on the elastic anchor and then transported to the viscoelastic target via predictor-corrector homotopy continuation; as long as the path avoids all EPs, branch identity is preserved throughout the transport. For any mode pair whose EPs have not crossed the real frequency axis (Type I), the transported identities are inherited automatically. In contrast, when an EP crosses the real axis and becomes Type II, the topology differs from the elastic anchor and a label swap is required. The framework is validated on symmetric and unsymmetric laminates, with most cases at loss factors of 0.003 to 0.02; for all Type I pairs in these cases the identities are inherited without alteration. For a challenging unsymmetric laminate at 0.05, several EP pairs have become Type II, yet the homotopy transport still produces numerically accurate solutions. Two diagnostic signatures--an extremely sharp imaginary-part crossing and a marked discrepancy between spectral group velocity and energy flux velocity--identify where the underlying EP topology demands a label swap.

math.NA↗

arXiv2026

Discrete Solution Operator Learning for Geometry-Dependent PDEs

Neural operator learning accelerates PDE solution by approximating operators as mappings between continuous function spaces. Yet in many engineering settings, varying geometry induces discrete structural changes, including topological changes, abrupt changes in boundary conditions or boundary types, and changes in the computational domain, which break the smooth-variation premise. Here we introduce Discrete Solution Operator Learning (DiSOL), a complementary paradigm that learns discrete solution procedures rather than continuous function-space operators. DiSOL factorizes the solver into learnable stages that mirror classical discretizations: local contribution encoding, multiscale assembly, and implicit solution reconstruction on an embedded grid, thereby preserving procedure-level consistency while adapting to geometry-dependent discrete structures. Across geometry-dependent Poisson, advection-diffusion, linear elasticity, as well as spatiotemporal heat conduction problems, DiSOL produces stable and accurate predictions under both in-distribution and strongly out-of-distribution geometries, including discontinuous boundaries and topological changes. These results highlight the need for procedural operator representations in geometry-dominated problems and position discrete solution operator learning as a distinct, complementary direction in scientific machine learning.

cs.LG↗

arXiv2026

Wind-turbine wake effects on the rate of accumulation of fatigue damage in overhead conductors

Guidance relating to the safe distance within which overhead conductors (OHCs) should not be built with respect to a wind turbine, and vice versa, varies from country to country. In the United Kingdom the recommendation is that OHCs are not installed within three rotor diameters (3D) of wind turbines due to concerns over wake-induced fatigue. To assess the physical basis for this recommendation, wind-tunnel experiments were conducted using a scaled wind turbine placed upstream of an instrumented conductor, in conditions representative of forested terrain. The conductor was equipped with distributed fibre-optic strain sensing based on Rayleigh backscattering, providing spatially resolved measurements at 2.6 mm resolution. Three conductor heights and four turbine-conductor spacings were tested, together with a no-turbine baseline, while maintaining a constant incident wind speed of 7 m/s. The results identify a critical region near the clamp where mean strain is maximised. Neither mean nor fluctuating strain at this location increased substantially due to the presence of the turbine wake. Strain fluctuations were dominated by aeolian vibration, with the wake increasing vibration amplitude whilst reducing its characteristic frequency. Rainflow counting fatigue analysis shows that damage accumulation depends strongly on conductor height. While fatigue damage rates increase when the conductor is fully immersed in the wake, reduced damage rates are observed for lower heights and closer spacings. These results suggest that, under forested atmospheric conditions, conductor-turbine separations smaller than the current 3D guidance may be feasible provided the conductor is not fully immersed in the turbine wake.

physics.flu-dyn↗

arXiv2025

The effect of tip-speed ratio and free-stream turbulence on the coupled wind turbine blade/wake dynamics

Wind turbines operating within wind farms experience complex aerodynamic loading arising from the interplay between wake-induced velocity deficits, enhanced turbulence, and varying operational conditions. Understanding the relationship between the blade's structural response to the different operating regimes and flow structures generated in the turbine's wake is critical for predicting fatigue damage and optimizing turbine performance. In this work, we implement a novel technique, allowing us to simultaneously measure spatially distributed blade strain and wake dynamics for a model wind turbine under controlled free-stream turbulence (FST) and tip-speed ratio ($λ$) conditions. A $1$ $\mathrm{m}$ diameter three-bladed rotor was instrumented with distributed Rayleigh backscattering fibre-optic sensors, while synchronised hot-wire anemometry captured wake evolution up to $4$ rotor diameters downstream. Experiments were conducted covering a wide $\{\mathrm{FST}, λ\}$ parameter space -- $21$ cases in total. Results reveal that aerodynamic-induced strain fluctuations peak at $λ\approx 3.5$, close to the design tip -speed ratio ($λ_d = 4$), with the blade's tip experiencing a contribution from the aerodynamically-driven strain fluctuations of up to $75\%$ of the total fluctuating strain at design conditions. Spectral analysis shows frequency-selective coupling between wake flow structures and the blade response, dominated by flow structures dynamically related to the rotor's rotating frequency (\textit{eg.} tip vortex structure). The novel experimental methodology and results establish a data-driven foundation for future aeroelastic models' validation, and fatigue-informed control strategies.

physics.flu-dyn↗

arXiv2025

Finite-PINN: A Physics-Informed Neural Network with Finite Geometric Encoding for Solid Mechanics

PINN models have demonstrated capabilities in addressing fluid PDE problems, and their potential in solid mechanics is beginning to emerge. This study identifies two key challenges when using PINN to solve general solid mechanics problems. These challenges become evident when comparing the limitations of PINN with the well-established numerical methods commonly used in solid mechanics, such as the finite element method (FEM). Specifically: a) PINN models generate solutions over an infinite domain, which conflicts with the finite boundaries typical of most solid structures; and b) the solution space utilised by PINN is Euclidean, which is inadequate for addressing the complex geometries often present in solid structures. This work presents a PINN architecture for general solid mechanics problems, referred to as the Finite-PINN model. The model is designed to effectively tackle two key challenges, while retaining as much of the original PINN framework as possible. To this end, the Finite-PINN incorporates finite geometric encoding into the neural network inputs, thereby transforming the solution space from a conventional Euclidean space into a hybrid Euclidean-topological space. The model is comprehensively trained using both strong-form and weak-form loss formulations, enabling its application to a wide range of forward and inverse problems in solid mechanics. For forward problems, the Finite-PINN model efficiently approximates solutions to solid mechanics problems when the geometric information of a given structure has been preprocessed. For inverse problems, it effectively reconstructs full-field solutions from very sparse observations by embedding both physical laws and geometric information within its architecture.

cs.CE↗

arXiv2024

The influence of free-stream turbulence on the fluctuating loads experienced by a cylinder exposed to a turbulent cross-flow

The impact of several $``\text{flavours}"$ of free-stream turbulence (FST) on the structural response of a cantilevered cylinder, subjected to a turbulent cross-flow is investigated. At high enough Reynolds numbers, the cylinder generates a spectrally rich turbulent wake which significantly contributing to the experienced loads. The presence of FST introduces additional complexity through two primary mechanisms: $\textbf{directly}$, by imposing a fluctuating velocity field on the cylinder's surface, and $\textbf{indirectly}$, by altering the vortex shedding dynamics, modifying the experienced loads. We employ concurrent temporally resolved Particle Image Velocimetry (PIV) and distributed strain measurements using Rayleigh backscattering fibre optic sensors (RBS) to instrument the surrounding velocity field and the structural strain respectively. By using various turbulence-generating grids, and manipulating their distance to the cylinder, we assess a broad FST parameter space allowing us to individually explore the influence of transverse integral length scale ($\mathcal{L}_{13}/D$), and turbulence intensity ($TI$) of the FST on the developing load dynamics. The presence of FST enhances the magnitude of the loads acting on the cylinder. This results from a decreased vortex formation length, increased coherence of regular vortex shedding, and energy associated with this flow structure in the near-wake. The cylinder's structural response is mainly driven by the vortex shedding dynamics, and their modification induced by the presence of FST, ie. the indirect effect outweighs the direct effect. From the explored FST parameter space, $TI$ was seen to be the main driver of enhanced loading conditions, presenting a positive correlation with the fluctuating loads magnitude at the root.

physics.flu-dyn↗