Fenglei Li

To further refine rehabilitation protocols for transverse patellar fractures, this study conducted numerical simulations in Abaqus to investigate the interaction between a nickel–titanium shape-memory-alloy patellar concentrator and fractured patellar bone under variations in patellar loading, quadriceps muscle tension, knee-flexion angle, and fracture-site width. Lower knee-flexion angles primarily generated bending moments in the patellar concentrator, whereas greater flexion angles primarily generated tensile forces. Across the simulated knee-flexion range of 0–150°, the predicted quadriceps muscle force ranged from 240 to 670 N. Based on the simulated mechanical response, under the prescribed modeling assumptions, the 60–90° knee-flexion range showed a comparatively favorable balance between predicted fracture closure and implant loading. Comparison with previously published computational studies showed that the loading and angular conditions examined in this study were within the ranges reported in the literature, while extending the analysis to less frequently studied flexion conditions. However, because direct experimental validation, mesh-convergence testing, and comprehensive sensitivity analysis were not included, these values should be interpreted as model-based estimates requiring further experimental and clinical validation.

The construction cost of offshore wind farms (OWFs) is heavily influenced by vessel scheduling and meteorological uncertainties. To address these challenges, this paper proposes a constraint-driven hierarchical optimization framework for the coordinated scheduling of installation vessels (IVs) and transport vessels (TVs). First, a Mixed-Integer Linear Programming (MILP) model is established to describe the operational constraints, which is then decomposed into two interrelated sub-problems: vessel path planning and scheduling optimization. For path planning, the problem is modeled as a Multiple Traveling Salesman Problem (MTSP) to ensure balanced fleet workloads. This stage is solved via a tailored three-stage heuristic combining balanced sweep clustering and penalized local search. For scheduling optimization, a hybrid Earliest Deadline First (EDF)-Simulated Annealing (SA) strategy is employed, where EDF generates a strictly feasible baseline to warm-start the SA optimization. Furthermore, a stochastic optimization approach integrates historical meteorological data to ensure schedule robustness against weather uncertainty. The validity of the framework is supported by two real-world OWF cases, which demonstrate total cost reductions of 15.44% and 13.20%, respectively, under stochastic weather conditions. These results demonstrate its effectiveness in solving high-constraint offshore engineering problems.

DH36 high-strength steel is widely used in shipbuilding and other fields due to its excellent strength, low-temperature toughness, wear resistance, and corrosion resistance. However, the harsh deep-sea environment seriously reduces the service life of welds. In this study we subjected DH36 welded joints to laser shock peening at three different energy levels (5 J, 7 J, 9 J) to investigate its effects on microhardness, microstructure, high-cycle fatigue, and residual stress of the DH36 welded joints. Results indicate that LSP can significantly enhance the surface microhardness of welded joints. Notably, the 7 J energy treatment increased the weld zone microhardness from 195 HV_0.2 to 231 HV_0.2 (18.5% improvement) and the heat-affected zone microhardness from 194 HV_0.2 to 234 HV_0.2 (20.6% improvement). Residual tensile stress on the specimen surface was offset and replaced by residual compressive stress after LSP. Concurrently, the high-cycle fatigue limit of the specimens was significantly improved, with the most pronounced improvement observed in specimens subjected to 5 J energy—increasing from 258 MPa to 295 MPa, representing an increase of 14.34%.

In squeeze casting, the injection parameters including fit clearance, speed, temperature, and their uncertainties significantly impact the forming quality. Robust optimization can improve the design reliability and reduce the influence of uncertainty while using a suitable metamodel is beneficial for prediction accuracy and efficiency. This paper proposes a robust optimization method based on a hybrid metamodel with Simplified Pareto and Updated Training Set (SPUTS) to improve the prediction accuracy along the Pareto front and in the whole design space. After the first round of robust optimization based on a general metamodel, the training set is updated by simplifying the Pareto solution set. A finite element simulation is performed to construct a high-precision metamodel that combines the kriging and radial basis function (RBF) models to run a new robust optimization. The proposed method was validated by application to the robust optimization of an injection mechanism with a large inner diameter. The results indicated that the SPUTS hybrid metamodel greatly reduced the prediction errors in the test set. The optimized design showed better reliability and robustness and had a greater clearance ratio than the initial design.

This study investigated the effect of laser shock peening (LSP) treatment on the fatigue performance of Q355D steel butt-welded joints. The results demonstrate that LSP sig-nificantly enhances joint fatigue resistance through gradient hardening in surface lay-ers, introduction of high-magnitude residual compressive stress fields, and micro-structural refinement. Specifically, microhardness increased across all joint zones with gradient attenuation of strengthening effects within an approximately 700 μm depth. LSP effectively suppressed residual tensile stress concentration in regions beyond 4 mm on both sides of the weld. Fatigue tests confirmed that LSP substantially extended joint fatigue life: by 113–165% in the high-stress region (250–270 MPa) and 46–63% in the medium-low-stress region (230–240 MPa). Fractographic analysis further revealed reduced fatigue striation spacing and lower microcrack density in LSP-treated speci-mens, reflecting the synergistic effect of residual compressive stress fields and micro-structural refinement in retarding crack propagation. This work substantiates LSP as an effective method for enhancing fatigue resistance in Q355D steel welded joints.

For anomaly identification of predicted data in machinery condition monitoring, traditional threshold methods have problems during residual testing. It is difficult to make decisions when the residuals are close to the threshold and fluctuate. This paper proposes a Bayesian dynamic thresholding method that combines Bayesian inference with neural network signal prediction. The method makes full use of historical prior data to build an anomaly identification and warning model applicable under single variable or multidimensional variables. A long short-term memory signal prediction model is established, and then a Bayesian hypothesis testing-based anomaly identification strategy is presented to quantify the probability of anomaly occurrence and issue early warnings for anomalies beyond a certain probability. The model was applied to open data sets of a pumping station and actual operating data of a nuclear power turbine. The results indicate that the model successfully predicts the failure probability and failure time. The effectiveness of the proposed method is verified.

A Bayesian framework-based approach is proposed for the quantitative validation and calibration of the kriging metamodel established by simulation and experimental training samples of the injection mechanism in squeeze casting. The temperature data uncertainty and non-normal distribution are considered in the approach. The normality of the sample data is tested by the Anderson–Darling method. The test results show that the original difference data require transformation for Bayesian testing due to the non-normal distribution. The Box–Cox method is employed for the non-normal transformation. The hypothesis test results of the calibrated kriging model are more reliable after data transformation. The reliability of the kriging metamodel is quantitatively assessed by the calculated Bayes factor and confidence. The Bayesian factor and the confidence level results indicate that the kriging model demonstrates improved accuracy and is acceptable after data transformation. The influence of the threshold ε on both the non-normally and normally distributed data in the model is quantitatively evaluated. The threshold ε has a greater influence and higher sensitivity when applied to the normal data results, based on the rapid increase within a small range of the Bayes factors and confidence levels.