Green hydrogen production is emerging as a crucial component in global decarbonization efforts. This review focuses on the role of computational approaches and artificial intelligence (AI) in optimizing green hydrogen technologies. Key approaches to improving electrolyzer efficiency and scalability include computational fluid dynamics (CFD), thermodynamic modeling, and machine learning (ML). As an instance, CFD has achieved over 95% accuracy in estimating flow distribution and polarization curves, but AI-driven optimization can lower operational expenses by up to 24%. Proton exchange membrane electrolyzers achieve efficiencies of 65–82% at 70–90 °C, but solid oxide electrolyzers reach up to 90% efficiency at temperatures ranging from 650 to 1000 °C. According to studies, combining renewable energy with hydrogen production reduces emissions and improves grid reliability, with curtailment rates of less than 1% for biomass-driven systems. This integration of computational approaches and renewable energy ensures a long-term transition to green hydrogen while also addressing energy security and environmental concerns.

Aluminum (Al)–aluminum–carbon nanotube (Al–CNT) dual-matrix (DM) composites are a novel class of nanocomposite materials that combine the ductility of the Al matrix with the hardness and wear resistance of the Al–CNT composite and thus can offer a unique combination of properties that make them suitable for a wide range of wear applications such as cutting tools, bearings, brake pads, wear-resistant coatings, etc. However, the specific properties of the DM Al–(Al–CNT) composite will depend on several factors related to the material’s composition and the friction conditions. This study investigates the wear behavior of DM Al–(Al–CNT) composites consisting of a primary matrix of soft aluminum in which milled hard particles of (Al–CNT) are dispersed as affected by five parameters: (1) wt.% CNT in the reinforcement particles, (2) mixing ratio between the reinforcement particles and the soft Al matrix, (3) sliding speed, (4) applied load, and (5) distance. The experimental design used a Taguchi fractional factorial orthogonal array (OA) L₂₇ to reduce the number of experiments, and analysis of variance (ANOVA) was used to determine the significance and to model the effect of each control parameter. Results showed that the wear rate could be reduced by up to 80% by tailoring the composition and controlling the friction conditions. It was found that the mixing ratio significantly impacts the wear behavior of DM Al–(Al–CNT) composites. A mixing ratio of 50% and a CNT content of 3 wt.% at the lowest applied load gave the lowest wear rate and coefficient of friction. Scanning electron microscopy investigations showed fragmentations in the reinforced matrix at higher loads and mixing ratios, which negatively impacted the wear behavior. Our findings confirm that DM Al–(Al–CNT) composites are promising for wear applications. However, the wear behavior depends on the composition and microstructural design of the composite, which needs to be carefully studied and understood.

This study examines the effects of sliding speed, applied load, and rotational speed (RPM) on the coefficient of friction (COF) and abrasive wear of semi-metallic automotive brake pads using a pin-on-disc test rig. Results indicate an inverse correlation between COF and RPM, with COF decreasing 78% (0.51–0.112) as speed increased from 200 to 1000 RPM, primarily due to thermal effects and tribo-layer formation. Conversely, sliding velocity positively influenced COF, increasing 21.7% (0.637–0.775) from 0.4 to 0.8 m/s, enhancing the stability of the friction layer for consistent braking. Abrasive wear escalated with higher loads and speeds, increasing 234% (0.384–1.284 g/N) at 30 N and 179% (0.404–1.13 g·s/m) with velocity, indicating a shift to severe wear mechanisms. Findings highlight the role of SiC and MgO reinforced composites in improving wear resistance and maintaining stable COF in high-performance braking systems.

In this work, three composite formulations BP1, BP2, and BP3 are developed and tested in order to examine the wear characteristics of automotive brake pads. Each composite uses powder metallurgy to combine performance optimized materials, such as graphite, and powdered materials as suitable reinforcements. Under various pressure conditions, the brake pad samples' wear rate, hardness, braking force, noise, vibration, and coefficient of friction were all examined. Particularly at high pressures, BP1 showed greater braking force and frictional effectiveness; however, this came at the expense of increased wear and noise. With moderate noise, vibration, and wear resistance, BP2 provided a well-balanced characteristic. While having less friction and braking force, BP3 demonstrated the lowest wear rate and the least amount of noise, which makes it a good choice for applications that value longevity and quieter operation. According to the results, choosing the best brake pad composition relies on the particular performance needs, with BP1 being best suited for high-force applications and BP3 with a longer lifetime and less noise.