Ammonia (NH3)/mathanol (CH3OH) co-firing is a promising clean combustion strategy, while hydrogen (H2) production through NH3 cracking and CH3OH reforming can further enhance fuel reactivity. Therefore, studying NH3/H2/CH3OH ternary flames can provide a basis for the individual and combined utilization of these three fuels. In this study, spherical expanding NH3/H2/CH3OH flames were investigated at 473 K and 0.5 MPa over different equivalence ratios (phi), H2 fractions, and CH3OH fractions. Flame-shape deformation, crack length, and cell number were quantified by image processing, and linear stability theory was used to evaluate critical conditions and perturbation growth rates. The results showed that CH3OH addition weakens thermal-diffusion instability (TDI) on the lean side governed by molecular diffusion, while the effect becomes stronger on the rich side. H2 addition shows the opposite trend. With H2 addition only, the critical flame radius increases monotonically with phi. With further CH3OH addition, critical flame radius decreases first and then increases with phi. The turning point shifts toward a higher phi than that of NH3/air flame. The combined addition of H2 and CH3OH does not simply produce additive effects, but changes the competition between TDI and hydrodynamic instability (HI). Specifically, H2 mainly enhances preferential-diffusion effects under lean-burn conditions, whereas high CH3OH addition strengthens the relative contribution of HI by reducing flame thickness and increasing the thermal expansion ratio. Adding H2 and 30% CH3OH intensify late-stage crack development and cellularization under lean-burn and stoichiometric conditions. At 60% CH3OH, stronger HI accelerates cell splitting and causes exponential growth of crack length. NH3/air flame is more prone to large-scale asymmetric deformation of flame profiles. With CH3OH and H2 addition, higher flame speed suppresses large-scale asymmetric deformation of flame profile. Meanwhile, it amplifies electrode-induced disturbances and makes mediumto-large-scale wrinkling more pronounced. Abbreviations: BI, Buoyancy instability; CVCC, Constant-volume combustion chamber; db4, Daubechies 4 wavelet; HI, Hydrodynamic instability; LC, Linear curvature model; LS, Linear stretch model; NQ, Nonlinear quasi-steady model; TDI, Thermal-diffusion instability
Microchannel flow boiling has emerged as a promising technology for thermal management of electronic devices due to its high heat transfer coefficient, low pumping power, and compact cooling architecture. This review presents a comprehensive overview of recent advances in microchannel flow boiling heat transfer and performance optimization strategies. The fundamental mechanisms governing microchannel flow boiling are first summarized with emphasis on bubble dynamics, microscale confinement effects, interfacial phase change, and thin film evaporation. Then the effects of channel geometry, surface modification, and working fluid modification on heat transfer performance are further analyzed by elucidating how these strategies regulate the underlying mechanisms of microchannel flow boiling. Furthermore, emerging optimization strategies are discussed in terms of hybrid active passive regulation, coordinated regulation of multiple boiling mechanisms, and artificial intelligence (AI) assisted prediction and optimization. Representative approaches include topology-optimized microchannel structures, adaptive shape memory alloy (SMA)-based regulation, jet microchannel coupled cooling, and AI-assisted prediction and optimization methods. The practical limitations of existing strategies are also discussed in terms of manufacturability, durability, operational stability, cost, packaging compatibility, and system level integration. Finally, the current challenges and future research directions in microchannel flow boiling are outlined. This review provides guidance for the design and optimization of next generation thermal management systems for high power electronic and energy systems.
High-mix low-volume discrete manufacturing faces a critical practical challenge, as its tightly coupled flexible job shop processing and multi-level assembly decisions are often scheduled separately in practice. Existing assembly-aware scheduling studies provide limited support for early sub-assembly and flexible assembly task selection, making them insufficient for such integrated production environments. This paper studies the flexible job shop scheduling problem with multi-level assembly operations and considers makespan minimization as the primary objective. A sequence-based mixed integer linear programming model is first developed to formulate the integrated problem. To solve larger instances efficiently, an improved memetic algorithm is then proposed, in which problem-specific encoding and decoding mechanisms explicitly support feasible assembly selection under multi-level constraints, while priority rule-based initialization and critical-path neighborhood search enhance global exploration and local exploitation. Computational experiments on four groups of benchmark instances show that the proposed model can solve small instances efficiently, and the proposed algorithm consistently outperforms the comparison methods on larger instances. Ablation studies further demonstrate the contribution of each customized operator and their combined effect. A real-world case study also illustrates the potential of the proposed approach for large-scale engineering applications.
This work proposes a hybrid computational framework that integrates the Virtual Element Method (VEM) with neural network techniques to efficiently solve three-dimensional hyperelastic problems. The framework combines graph neural networks (GNNs), VEM, and physics-informed neural networks (PINNs), and incorporates a transfer learning strategy to improve the model generalization ability. Owing to its ability to operate on arbitrary polyhedral meshes, VEM provides a flexible and accurate discretization for hyperelastic problems involving complex geometries. Building on this property, the proposed framework employs VEM to construct high-fidelity physics constraints, utilizes GNNs to encode the topology of polyhedral meshes, and adopts PINNs to solve the governing equations under these constraints. Furthermore, the introduced transfer learning strategy enables rapid adaptation of the trained model to new material parameters and loading conditions. Numerical experiments demonstrate that the proposed framework achieves accuracy comparable to that of traditional high-fidelity numerical simulations while improving computational efficiency by one to two orders of magnitude. In addition, the method exhibits strong robustness and generalization capability under variations in parameters and loading scenarios. These results indicate that the proposed approach provides a promising and efficient solution for the rapid analysis of multi-scenario and multi-parameter three-dimensional hyperelastic problems.
