Browse Topic: Power and Propulsion
Multiphase compressible flow problems are widespread in aviation, aerospace, transportation, military, and industrial fields, for instance, in underwater explosion bubble dynamics, fuel injection for hypersonic vehicles, liquid sloshing in propellant tanks, and supercavitating underwater vehicles. This paper proposes an improved THINC (Tangent of Hyperbola for Interface Capturing) method for multiphase flow simulations, based on a selective reconstruction strategy for the dominant material. The core of the strategy is to apply the THINC reconstruction exclusively to the material with the largest volume fraction within a multiphase mixed cell, which numerically governs the local interface evolution. The volume fractions of non-dominant materials are then obtained through a proportional distribution that inherently ensures the summation (Σαk = 1) and boundedness (0 ≤ αk> ≤ 1) constraints are met without explicit corrections. This approach reduces the number of THINC reconstructions for each time step in a multiphase mixed cell from Nm (the number of materials) to one, significantly simplifying the algorithm and lowering computational cost. It thereby avoids the error accumulation and complex renormalization procedures associated with conventional schemes that reconstruct all materials. While strictly maintaining volume fraction conservation, the proposed method preserves interface sharpness through the underlying THINC framework. The method is implemented in a diffuse-interface, multiphase Eulerian framework and validated with a series of challenging benchmarks, including shock-helium bubble interaction, triple-point problem, gas impact, and the more complex modified gas impact. Numerical results show that, compared with conventional multiphase THINC approaches that reconstruct every material, the proposed scheme can reduce CPU time by about 40.0% without compromising the accuracy of key physical quantities.
Steady advancement is observed in global research on eco-friendly and sustainable transportation. Rapid technological evolution of hybrid electric vehicles (HEVs) is documented. Lower overall noise output and more compact structures are achieved in HEV engines relative to conventional internal combustion engines. The perceptibility of harmonic impulsive sounds is significantly enhanced by these design characteristics. A close correlation is observed between these acoustic phenomena and negative human auditory perceptions. These events are treated as a core focus for HEV noise, vibration, and harshness optimization. Accurate quantification of harmonic impulsive sounds is not achieved by conventional objective indicators. A favorable balance between reliability and accuracy is not established by existing subjective prediction models. Practical engineering applications of these methods are severely restricted. A novel objective quantification method for harmonic impulsive sounds is proposed in this study. The method is established based on time–frequency masking theory and tonal strength. Bench tests in a semi-anechoic chamber and subjective evaluation experiments with standardized rating scales are performed for data collection. Collected sound signals are decomposed through an integrated approach of wavelet transform and variational mode decomposition. Targeted feature extraction is completed for harmonic impulsive sounds. A quantitative index incorporating human auditory temporal and frequency masking effects is developed. The proposed index exhibits a significantly stronger correlation with subjective evaluation results than traditional objective metrics, confirming its superior ability to reflect actual perceived sound quality. An interval prediction model for sound quality evaluation is established based on support vector machines and kernel density estimation. Traditional objective metrics and the proposed index are introduced as key input parameters. Effective and reliable prediction of HEV engine noise subjective satisfaction is achieved by the model.
With the continuous improvement of performance requirements for aviation equipment, the importance and complexity of hydraulic systems as the core carrier of flight control are becoming increasingly prominent. The cleanliness of aircraft hydraulic pipelines directly affects the reliability and flight safety of hydraulic systems, and it is necessary to use specialized cleaning and testing equipment during design and manufacturing to achieve efficient cleaning. The design of traditional cleaning equipment relies on experience-driven development, with mechanical, hydraulic, and electrical systems developed independently. There are problems such as unclear requirement definitions, low efficiency of interdisciplinary collaboration, and lagging validation, making it difficult to achieve the goal of forward design. Therefore, this study introduces Model-based Systems Engineering (MBSE) method in the development process of pipeline cleaning test equipment, proposes a modeling process based on RFLP (Requirements-Function-Logical-Physical), and uses SysML system modeling language to construct a top down design model system for aircraft hydraulic pipeline cleaning equipment. Through requirement analysis modeling, functional behavior definition, and system architecture design, the significant advantages of MBSE method in the development of complex aviation test equipment have been verified, effectively improving the bold design capability and top down design efficiency. MBSE method can not only improve the design efficiency of equipment, but also promote the intelligent and efficient operation of equipment, which has important significance for the development of intelligent manufacturing and electromechanical integration technology.
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