Double-row spherical roller bearings are frequently employed as supporting components in mining machinery. In practice, these bearings are lubricated manually, and the bearing chamber is filled with grease, which can lead to issues such as grease wastage and increased bearing temperatures. Additionally, factors such as load and speed also influence the bearing temperature, collectively contributing to inadequate lubrication and potential bearing failure. Consequently, it is essential to investigate the temperature of the bearing under operating conditions. Utilizing tribology theory and the principles of bearing heat generation, a numerical model of fluid–structure interaction heat transmission within the bearing was developed, and finite element analysis was conducted through the ANSYS-Fluent module. The model was verified, and the temperature field of the bearing under varying operating conditions was studied. The findings of this research are as follows: (1) The numerical model demonstrates high accuracy, with a relative error of less than 5% when comparing the experimental temperature values of the jaw crusher bearing to the simulated values. (2) Under diverse operating conditions, the inner ring of the bearing has the highest temperature of all parts of the bearing, while the bearing cavity’s flow field has the lowest temperature. (3) The average temperature amplitude across different areas of the bearing system will rise as a result of increases in radial load or the bearing rotational speed. (4) When the grease filling volume increases from minimal to maximal, the average temperature in each bearing area initially decreases before subsequently rising, with the optimal grease filling amount identified as 60%. In operational scenarios, if the bearing temperature exceeds 70 °C, it is imperative to shut down the machine immediately to avert bearing failure. This study on bearing lubrication has practical guiding significance.

To effectively confront the acute challenge of global warming, at the present stage, the Chinese government has designated carbon reduction as the core objective to accomplish the coordinated control of greenhouse gas and pollutant emissions. As China is a major manufacturing country, with the continuous improvement of air emission standards, it is particularly necessary to carry out the design of more efficient volatile organic pollutant emission devices. This study takes a treatment system with a waste gas ventilation volume of 6 × 10⁴ m³·h⁻¹ as an example, adopts the end treatment approach of adsorption and catalytic combustion coupling, and designs a purification device composed of multistage oil-mist recovery, electrostatic adsorption, dry filtration, activated-carbon adsorption and desorption, catalytic combustion, etc. It also employs the fuzzy proportional-integral-derivative fine temperature control algorithm, and the temperature overshoot was decreased by 85%. The average emission concentration of volatile organic compounds at the equipment outlet is 6.56 mg·m⁻³, and the average removal rate is 93.99%, far surpassing the national emission standards. The device operates efficiently and stably, confirming that the end-coupled treatment system based on the adaptive fuzzy proportional-integral-derivative temperature control strategy can effectively handle volatile organic compounds with oil mist and holds significant promotion and research value.

Accurate prediction and inversion of time-dependent gap deformation between a passenger aircraft door and its frame remain difficult under slowly varying pressurization because the response is governed by coupled geometry, lock restraint, and nonlinear contact. This study develops a digital-twin-driven inversion framework for door–frame gap deformation. A physics-based parametric twin is established using feature associations and a unified assembly datum, and the outer skin, stiffeners, load-bearing frame, inner panel, locks, and door frame are represented in a nonlinear quasi-static finite-element model. Geometric nonlinearity and a separable door–frame contact are retained so that load-dependent opening, local slip, and constraint effects can be captured. A full-scale test platform with binocular vision provides three-directional relative-displacement measurements. The numerical model is updated through equivalent-stiffness correction, local nodal adjustment, and global scaling, with physical restrictions imposed on the adjustable region and correction amplitudes to avoid unconstrained point-wise fitting; corrected simulation and experimental data are then fused for surrogate training. An improved Gaussian process regression model uses the normalized pressure level, spatial coordinates of the registered key-point pairs, and displacement-direction encoding as inputs and the corresponding fused directional gap displacement as the output; a squared-exponential kernel and input-dependent noise model represent nonlinear response and uncertainty. Under an identical within-profile training–validation partition, the reported GPR implementation yields lower aggregate error than the corresponding RBF-NN and random-forest benchmark runs, with a root-mean-square error of 0.21, a mean relative error of 2.8%, and a coefficient of determination of 0.985. These metrics quantify interpolation within the calibrated slowly varying quasi-static loading domain rather than validated extrapolation to an independent unseen load history. The framework provides a mechanism-consistent route for virtual–physical updating and surrogate-based inversion of aircraft door–frame gap states.