To address the imbalance between the shearing–mixing quality and energy efficiency of deepwater drilling mud mixers and breakthrough the limitations of existing independent single-objective analytical perspectives, the Eulerian solid–liquid two-phase numerical simulation was adopted in this study. Combined with a modified shear rate algorithm and a triple energy coupling analysis of shear rate, Lamb vortex energy and Enstrophy, the energy conversion and particle dispersion mechanisms inside the mixer under variable flow rates and solid concentrations were systematically investigated, and the performance differences between the first-generation and optimized mixers were clarified. Structural optimizations including an additional modular stator with a designed shear gap of 2 mm, improved blade profiles and shear angles to 14.2°, and miniaturized radial dimensions of the impeller and volute were implemented to achieve compact structural upgrading. The results demonstrate that high-energy regions are concentrated in the rotor–stator gap. After optimization, the peak shear rate increases from 12,010 s⁻¹ to 17,092 s⁻¹, representing a 42.3% enhancement. The peak Lamb vortex energy and the mean Enstrophy rise by 8.6% and 18.9%, respectively. Shear rate correlates weakly positively with Lamb vortex energy and strongly negatively with Enstrophy, revealing vortex sensitivity to flow velocity and tight coupling of viscous dissipation to particle concentration. The outlet coefficient of variation C_v decreases by 59.6%. Higher flow rates strengthen the coupling of shear and vortex energy, and higher solid concentrations weaken stator shear performance. The optimized mixer achieves synergistic improvements in shear efficiency and mixing quality, with over 50% enhancement in mud dispersion stability and more than 15%.

Heat pump technology can efficiently recover waste heat from oil and gas gathering, processing, and transportation. However, the energy transfer mechanism of high-speed rotating internal flow in the scroll compressor remains unclear under unbalanced load conditions, leading to low equipment energy efficiency and high operation and maintenance costs. This study adopted dynamic grid technology, finite element analysis and one-way thermal–fluid–solid coupling method to quantitatively study the flow field characteristics and mechanical response of four characteristic phases. The results showed that the internal pressure and temperature fields of the compressor presented a non-uniform distribution. The deformation of the scroll wraps was mainly concentrated in the compression chamber, and the maximum stress was concentrated at the wraps’ root. Further analysis revealed that temperature loading played a dominant role in the structural responses. At a spindle rotation angle of 0°, under temperature loading alone, the maximum deformation and maximum stress were 28.605 μm and 521.81 MPa, respectively, while the corresponding values under pressure loading alone were small. In addition, the deformation and stress under coupled loading were not a linear superposition of the individual loading effects, with a deformation deviation of 0.938 μm and a stress deviation of 7.18 MPa at a spindle rotation angle of 0°. In this study, a numerical model of the scroll compressor was established and experimentally verified, which provided a theoretical basis for optimizing scroll profile design, suppressing wrap tip wear and improving the energy efficiency of heat pump systems.

As critical equipment in the petroleum industry, coiled tubing is prone to safety hazards, including stress concentrations and fatigue failure, under complex operating conditions. An online enhanced metal magnetic memory detection method was employed to reduce noise in surface magnetic field signals from tubing subjected to 35 MPa of internal pressure across different fatigue cycles. Conventional signal processing methods have difficulty effectively extracting characteristic magnetic field signals in high-noise environments; therefore, a comprehensive comparison of the noise reduction effectiveness of five common signal processing techniques in stress-distorted regions was conducted, an in-depth analysis of the limitations of different methods was performed, and a hybrid noise reduction framework combining wavelet threshold denoising (WTD) and sequential variational modal decomposition (SVMD) was established. Concurrently, the whale optimization algorithm (WOA), which possesses global search capabilities and demonstrates good adaptability to multi-parameter coupling issues in hybrid denoising frameworks, was innovatively proposed for key parameter optimization. Using fuzzy entropy (FE) as an evaluation metric, the experimental results demonstrated that magnetic field signals in all directions achieved at least a 1.03% reduction in FE and a minimum increase of 33.1% in integrated side lobe ratio (ISLR). This provided effective technical support for reliably detecting stress-distortion zones on coiled-tubing surfaces and established the engineering necessity of implementing preventive maintenance.