Natural plant fibers (NPFs) have emerged as a sustainable alternative in the manufacture of composites due to their renewability and low environmental impact. This has led to a significant increase in the use of natural plant fiber-reinforced polymers (NPFRPs) in a variety of industries. The diversity of NPF types brings a wide range of properties and functionalities to NPFRPs, which in turn highlights the urgent need to improve the properties of fiber materials in order to enhance their performance and suitability. This paper provides insight into the processing mechanisms behind NPF fiber treatments, exploring how these treatments affect the mechanical, thermal and environmental properties of NPFRPs. It also offers a critical assessment of the advantages and disadvantages of physical, chemical, biological and nanotechnological treatments. The findings of our analysis provide a basis for the development of future treatments that aim to enhance the material properties of NPFRPs, thereby increasing their competitiveness with conventional synthetic fiber-reinforced polymers. Finally, a novel thermoplastic resin composite system, Elium–NPFRP, is proposed that embodies the principles of green development. The system has been designed with the objective of capitalizing on the environmental benefits of NPFs while simultaneously addressing the challenges associated with the integration of NPFs into polymer matrices. The Elium–NPFRP composite system not only exemplifies the potential of NPFs for sustainable materials science, but is also a practical solution that can be implemented in a diverse range of applications, spanning automotive components to construction materials. This has the potential to reduce carbon footprints and promote a circular economy.

Experimental and numerical investigations are presented for a theory-guided machine learning (ML) model that combines the Hashin failure theory (HFT) and the classical lamination theory (CLT) to optimize and accelerate the design of composite laminates. A finite element simulation with the incorporation of the HFT and CLT were used to generate the training dataset. Instead of directly mapping the relationship between the ply angles of the laminate and its strength and stiffness, a multi-layer interconnected neural network (NN) system was built following the logical sequence of composite theories. With the forward prediction by the NN system and the inverse optimization by genetic algorithm (GA), a benchmark case of designing a composite tube subjected to the combined loads of bending and torsion was studied. The ML models successfully provided the optimal layup sequences and the required fiber modulus according to the preset design targets. Additionally, it shows that the machine learning models, with the guidance of composite theories, realize a faster optimization process and requires less training data than models with direct simple NNs. Such results imply the importance of domain knowledge in helping improve the ML applications in engineering problems.

Numerous studies have demonstrated that under low-velocity, low-energy impact conditions, although the surface damage to fiber-reinforced composite laminates may be minimal, significant internal damage can occur. Consequently, a progressive damage finite element model was specifically developed for thermoplastic carbon fiber-reinforced composite laminates subjected to low-speed impact loads, with the objective of analyzing the damage behavior of laminates under impacts of varying energy levels. The model utilizes a three-dimensional Hashin criterion for predicting intralayer damage initiation, with cohesive elements based on bilinear traction–separation law for predicting interlaminar delamination initiation, and incorporates a damage constitutive model based on equivalent displacement to characterize fiber damage evolution, along with the B-K criterion for interlaminar damage evolution. The impact response of laminates at energy levels of 5 J, 10 J, 15 J, 20 J, and 25 J was analyzed through numerical simulation, drop-hammer experiments, and XCT non-destructive testing. The results indicated that the simulation outcomes closely correspond with the experimental findings, with both the predicted peak error and absorbed energy error maintained within a 5% margin, and the trends of the mechanical response curves aligning closely with the experimental data. The damage patterns predicted by the numerical simulations were consistent with the results obtained from XCT scans. The study additionally revealed that the impact damage of the laminates primarily stems from interlaminar delamination and intralayer tensile failure. Initial damage typically presents as internal delamination; hence, enhancing interlaminar bonding performance can significantly augment the overall load-bearing capacity of the laminate.

Using two different methods, a previously developed elliptic blending model (the original $\mathrm{STT} k\text{-}\omega \text{-}\phi \text{-}\alpha$ model) is modified for sensitization to streamline curvature. One method involves modifying the dissipation term in the turbulent dissipation equation, while the other constructs a new formulation for the turbulent kinetic energy production term based on an explicit algebraic stress model. The capabilities of the proposed models are evaluated by applying them to three flows with curved surfaces; namely, the two-dimensional (2D) infinite serpentine passage flow, the 2D U-turn duct flow, and the 2D periodic hill flow. The $\mathrm{STT} k\text{-}\omega$ model with rotation and curvature correction (the $\mathrm{STT} k\text{-}\omega \text{-}CC$ model) is also used for comparison. The computed results are compared with the relevant direct numerical simulation, experimental, and large eddy simulation data from the literature. It is found that the two proposed models significantly improve upon the original $\mathrm{STT} k\text{-}\omega \text{-}\phi \text{-}\alpha$ model. Compared with the $\mathrm{STT} k\text{-}\omega \text{-}CC$ model, the two proposed models produce better results in the 2D infinite serpentine passage flow and the 2D periodic hill flow. The proposed models are similarly competitive with the $\mathrm{STT} k\text{-}\omega \text{-}CC$ model in the 2D U-turn duct flow.