Editorial for the Special Issue on Editorial Board Members’ Collection Series: Modeling and Simulation of Composite Materials

Advanced composite materials, consisting of stiff reinforcements embedded in a compliant matrix, exhibit superior mechanical and functional performance compared with monolithic materials [1,2]. These advantages have led to their widespread adoption in aerospace, automotive, construction, and energy applications [3,4]. However, the same microstructural degrees of freedom that enable property optimization also give rise to a high-dimensional and highly nonlinear design space. Key parameters—including fiber volume fraction, orientation, and multiscale defects—strongly couple across length scales to govern stiffness, thermal response, durability, and failure behavior [5,6]. A systematic experimental characterization of these interactions is costly, time-consuming, and often destructive [7]. Consequently, predictive modeling and high-fidelity simulation have become central to composite mechanics. Recent advances in multiscale constitutive modeling, finite element analysis, and data-driven methods enable the efficient prediction of elastic properties, damage evolution, and residual stresses within hours rather than months [8].

This Special Issue compiles recent research advancements concerning the effective properties and numerical modeling of diverse composite materials. The findings presented herein hold substantial importance for enhancing out understanding of current composite materials, facilitating the optimization of existing formulations and guiding the development of novel multi-component and/or multiphase materials.

Patro et al. proposed a bilinear traction separation law for fiber-reinforced polymer adhesive joints under variable-amplitude loading, which is driven by the fatigue crack growth rate [9]. Their model incorporates a load interaction parameter within a Paris-type cohesive framework. This approach accurately captured damage accumulation trends and significantly improved life prediction accuracy.

Cakiroglu et al. developed highly accurate machine learning models to predict the tensile strength of eco-friendly concrete containing natural fibers and recycled aggregates [10]. This study successfully applies SHAP (Shapley Additive Explanations) analysis to interpret model predictions, identifying key factors such as specimen age and fiber type. Its primary innovation is an accessible online tool that delivers instant strength predictions, addressing a gap in design codes for sustainable composites.

Zitouni et al. reveal that wall survivability under firebrand assault hinges on insulation phase-change and coating emissivity rather than bulk mass [11]. Their transient finite element model considers primary design parameters for thermal reactions of various wall specimens during firebrand accumulation. The study redirects future codification from prescriptive thickness toward material-specific thermophysical thresholds, offering a rational path to performance-based envelope design.

Tsivouraki et al. present a self-contained numerical twin that couples cohesive-zone fatigue delamination with random vibration response to decode damage diagnosability through virtual testing [12]. Their framework exposes frequency down-shift as a deterministic signature of inter-ply fracture, converting stochastic fatigue scatter into a predictable spectral gradient and enabling the virtual optimization of vibration-based monitoring strategies for thermoplastic composites.

Tomasi et al. welded filament-wound carbon-fiber architecture to an elliptical cross-section, iteratively tuning ply orientation and curvature until the protector met buckling, modal, and regulatory demands at minimum mass [13]. The study shows that composite failure criteria and process constraints can steer the earliest load path definition for an industrial vehicle part. Thus, they achieved the goals of both weight and fuel consumption reduction.

Tarawneh et al. benchmarked five shear design models against a curated set of ultra-high-performance concrete (UHPC) girders, revealing that only the iterative tension localization approach preserves conservatism across reinforcement ratios while alternatives drift toward unsafe predictions as transverse steel increases, thereby establishing a rational basis for updating safety factors in forthcoming UHPC bridge specifications [14].

Jia et al. embedded a ceramic–metal gradient beam within a three-parameter viscoelastic foundation and solved its damped eigenproblem via an n-th-order generalized beam theory coupled to a modified differential quadrature algorithm, demonstrating that foundation damping decouples frequency from decay and enabling frequency-tuned designs without altering amplitude attenuation [15].

Rayhan et al. developed a homogenization scheme with a calibrated deep feed-forward network that mapped constituent stiffness and volume fraction directly to the full elasticity tensor of unidirectional composites, proving that data-driven regression can deliver virtual test results indistinguishable from representative volume element computations and offering a design tool orders of magnitude faster than finite element upscaling [16].

Mofidi et al. recasted the pull-out of post-installed fiber-reinforced polymer anchors as a fracture mechanics eigenvalue problem, deriving a closed-form solution that coupled bilinear bond slip to equilibrium along an evolving debond front [17]. The resulting equation forecasted capacity within a few percentage points of the test data, turning a formerly empirical connection into a design-ready mechanics expression that could be embedded directly in future structural codes.

Sultana et al. interleaved finite element simulations with response surface statistics to reveal that yarn spacing and fabric thickness dominate the stiffness landscape of jute woven composites, whereas shear angle governs only twill architectures, thereby offering a rational route to tailor natural-fiber reinforcements without exhaustive prototyping [18].

Escalante-Tovar et al. developed machine learning models to predict the flexural strength of fiber-reinforced UHPC [19]. They applied SHAP analysis to interpret predictions and identify key influencing factors, as well as build a hybrid neural network that integrated top-performing models, achieving high accuracy validated through independent experiments.

Queirós et al. stacked band-pass filtered shearographic phase maps to suppress speckle noise and magnify nanometer-level strain gradients, revealing barely visible impact, disbond, and hole damage with crisper contours than thermography [20]. By converting subtle stiffness loss into high-contrast anomaly images, the method enables engineers to size hidden flaws and lighten future composite structures.

Fikry et al. interleaved a polyamide mesh at ply-drop resin pockets, transforming the discontinuity into a crack-deflecting interface that markedly raises interlaminar toughness under shear [21]. Localized inserts redistribute strain and delay delamination, while full embedding sustains load without sacrificing tensile strength, offering a lightweight, manufacturing-friendly route to damage-tolerant composites.

Rahman et al. traced the full-range response of pultruded glass fiber joints with a progressive damage model, revealing how fracture energies steer failure from brittle shear-out to ductile bearing [22]. Their parametric map offers designers a rapid route to optimize end distance and bolt layouts for damage-tolerant composite connections without exhaustive testing.

Chrysochoidis et al. embedded nonlinear contact and frictional slip inside a layer-wise beam model, allowing the delamination faces to open, close, and slide [23]. Guided-wave simulations matched to piezoelectric measurements showed that the resulting time-of-flight stretch and amplitude drop were sensitive to crack length even at minor damage levels. By converting interface kinematics into measurable transient signatures, the study offers a physics-based route to size barely visible delamination in real time.

Yoshida et al. translated ply-curving termination from external to internal drop-offs, revealing that cover plies redistribute through-thickness tension and suppress the curved-fiber cracks which previously limited fatigue benefit [24]. X-ray tomography shows delamination is confined within the curved zone even at high load, extending life tenfold. By converting a stress concentrator into a gradual load transfer region, the study enables lighter and more durable composite tapers for primary aircraft structures.

Felipe-Sesé et al. introduced a novel methodology combining digital image correlation (DIC) with frequency analysis to accurately measure low Poisson’s ratios in composites [25]. The key contribution is the successful application of lock-in filtering to DIC data, which provides stable strain distributions and significantly reduces measurement uncertainty. By incorporating frequency-based signal processing into tensile testing, this approach enables a precise characterization of subtle periodic mechanical behaviors at low strain ranges.

Li et al. developed an optimization strategy for CMC (ceramic matrix composite) combustion liners in aero-engines, focusing on maximizing natural frequencies to suppress vibration [26]. Using regression analysis, they identified wall thickness, stiffener thickness, and lamination scheme as key factors. The study demonstrated that a 45° lamination scheme significantly enhanced performance. This study provides a systematic method for optimizing CMC structures, offering practical guidance for vibration-resistant design and maximizing service life in high-temperature applications.

Ribeiro et al. presented a comprehensive finite element scheme to assess the fire behavior of a novel mineral wool composite slab [27]. This unified analytical approach concurrently predicts post-fire load-bearing capacity and associated carbon emissions. Their work harmonizes safety with environmental stewardship and highlights the potential for synergistic optimization in structural fire protection.

Li et al. developed cross-property relations for continuous fiber-reinforced ceramic matrix composites using micromechanics models and finite element analysis [28]. The study reveals that composites with a fixed fiber–interphase volume ratio exhibit nearly linear cross-property relations, while interphase property variations cause these relations to shift downward. This methodology establishes a multiphysics framework that enables the inference of mechanical properties from thermal data or vice versa to address measurement challenges in extreme thermo-mechanical environments.

Despite growing interest in the modeling and analysis of composite materials and the introduction of novel approaches through emerging technologies such as machine learning, significant opportunities remain for further advancement in the modeling and performance evaluation of composite materials. The studies compiled in this Special Issue are expected to facilitate progress in composite material technology, thereby expanding the scope of potential applications.