There is a rising demand for advanced new materials in food packaging to meet the growing economic and sustainability requirements. Natural fibers have excellent mechanical and thermal isolation properties and lower production costs than synthetic fibers, making them suitable candidates for the development of sustainable food packaging. Other characteristics of natural fibers, i.e., low cost, easy availability, and low impact on the environment, making these materials a promising option for food packaging. The primary focus of this study is to utilize multivariable analysis, an analytic hierarchy process and a multi-criteria decision analysis to select appropriate natural fiber reinforcement for food packaging from commonly used plant fibers (coir, date palm, hemp, sisal, jute, flax, corn stalk, banana, bamboo, cotton). The multivariable selection system is used to compare crucial food packaging materials’ requirements and production parameters gathered different properties including density, Young modulus, elongation to break, moisture content, thermal conductivity, cost, availability, etc, to help identify appropriate natural fibers. From the results, selecting the most appropriate natural fiber depends on the design requirements, cost, and availability; all these parameters are also affected by the geological conditions and customer needs. Single parameter comparison is used to compare the investigated natural fiber; then analytic hierarchy process is used to rank the highest priority parameters for the natural fiber selection process. The results showed that density (22%), cost (13.6%), moisture content (11.7%), thermal conductivity (10.7%), elongation at break (7.7%), and Young modulus (7.4%) are rated in the order of priority. Hemp, banana and sisal fibers are ranked as the best choices for the food packaging by the multi-criteria decision analysis, i.e. TOPSIS. Examples are presented considering the multivariable analysis using radar charts to identify the best natural fibers source for countries such as Kingdom of Saudi Arabia, Pakistan, Bangladesh, and India. Date palm is a preferred natural fiber source for food packaging reinforcement in Saudi Arabia and Pakistan, whereas bamboo and jute are better choices for India and Bangladesh.

In the present work, Cerium (IV)-Zirconium (IV) oxide nanoparticles (CeO₄ZrNPs) was successfully dispersed into Chitosan/15Gelatin nanocomposites with different quantities. The obtained chitosan-based nanocomposites represented remarkable improvements in structural, morphological, mechanical, and thermal properties. Roughness increased from 74 nm to 6.4 nm, Young’s Modulus enhanced from 1.36 GPa to 2.99 GPa. The influence of dispersed CeO₄ZrNPs contents on the phase transition temperature (T _g) and the non-isothermal degradation processes of chitosan-based nanocomposites were examined using Differential Scanning Galorimetry (DSC) with different heating rates. Kinetic parameters of the thermal degradation for chitosan-based nanocomposites were evaluated using Kissinger-Akahira-Sunose (KAS) and Kissenger (KIS) procedures. Chitosan-based nanocomposites showed an increase in the thermal degradation temperature with higher activation energies, indicating improved thermal stability. Thermal analysis demonstrated that chitosan-based nanocomposites became more ordered by increasing CeO₄ZrNPs as inferred from the negative entropy increase. Moreover, the degradation of chitosan-based nanocomposites has been described as a non-spontaneous process. The resulting information is particularly important in applications in which there is a need to obtain chitosan nanocomposites with improved mechanical and thermal properties such as food packing industry.

Aluminum alloys are the most essential part of all shaped castings manufactured, mainly in the automotive, food industry, and structural applications. There is little consensus as to the precise relationship between grain size after grain refinement and corrosion resistance; conflicting conclusions have been published showing that reduced grain size can decrease or increase corrosion resistance. The effect of Al–5Ti–1B grain refiner (GR alloy) with different percentages on the mechanical properties and corrosion behavior of Aluminum-magnesium-silicon alloy (Al–Mg–Si) was studied. The average grain size is determined according to the E112ASTM standard. The compressive test specimens were made as per ASTM: E8/E8M-16 standard to get their compressive properties. The bulk hardness using Vickers hardness testing machine at a load of 50 g. Electrochemical corrosion tests were carried out in 3.5 % NaCl solution using Autolab Potentiostat/Galvanostat (PGSTAT 30).The grain size of the Al–Mg–Si alloy was reduced from 82 to 46 µm by the addition of GR alloy. The morphology of α-Al dendrites changes from coarse dendritic structure to fine equiaxed grains due to the addition of GR alloy and segregation of Ti, which controls the growth of primary α-Al. In addition, the mechanical properties of the Al–Mg–Si alloy were improved by GR alloy addition. GR alloy addition to Al–Mg–Si alloy produced fine-grained structure and better hardness and compressive strength. The addition of GR alloy did not reveal any marked improvements in the corrosion properties of Al–Mg–Si alloy.

Mass and cost tradeoffs by deploying three optimized spars, made of all-glass, hybrid and all-carbon composites, applied to a publicly available large-scale composite blade of 100 m in length for a 13.2 MW wind turbine, are explored. The blade mass and cost minimizations are calculated for two design load cases, generating the worst aerodynamic loads for parked and rotating rotor blades, while meeting the stiffness, strength, stability and resonance design requirements, as recommended by the wind turbine standards. The optimization cases are formulated as a single-objective, multi-constraint optimization problem, while taking into account the manufacturability of hybrid spars in particular, and it is solved using a genetic algorithm method. The blade mass lowers in the range of 8.1–13.3%, 18.5–20.7% and 25.7–26.4% for the optimized all-glass, hybrid and all-carbon spars, respectively, while the cost decreases for the optimized all-glass spars only. The cost increases in a range of 1.2–13.6% and 24.5–31.5% when the optimized hybrid and all-carbon spars are used. Further, the hybrid spar optimization using the blade mass and cost objective functions, as well as the effects of spar optimization on the blade’s structural performance in terms of tip deflection, strength, buckling resistance and first natural frequency, are discussed.

The limitations of the established and existing creep failure models have inspired the development of a new creep prediction model. Models like Norton–Bailey and Omega are unable to model the tertiary creep curve for engineering materials. Kachanov–Rabotnov, Theta Projection, and Sine hyperbolic models rely on specific material properties for accurate damage predictions. In order to overcome these weaknesses, a new creep model combining the Norton–Bailey and Kachanov–Rabotnov models has been further devised for the creep life prediction of metallic materials. The model combination helps in covering the limitations of one model over another and to benefit from each other’s strengths. A technique of user subroutine scripting was adapted to implement the new creep model in finite element (FE) software of ABAQUS, manufactured by Dassault Systemes, version 2020. The new model was tested on an FE dog bone stainless steel 304 specimen; the analysis showed excellent agreement with the experimental creep deformation data at 600 °C to 700 °C. The creep strain rate curves obtained by the method of user subroutine scripting were found to be 90.69% accurate to the 1000 h experimental creep strain rate curve. Similarly, while comparing with the 336 h experimental creep test, the new model accuracy was found to be 92.66% for the creep strain rate curve. The new model’s precision was 91.56% when compared with the Omega and Norton–Bailey models for creep strain rate for the same conditions. The quantitative accuracy of the new creep model is better as compared to the existing creep models and can be an improved source of alternatives to existing creep models for the deformation predictions.