Search Results (132)

Gear whine noise is governed not only by intentional microgeometry modifications but also by unavoidable pitch (indexing) deviation. This study presents a workflow that couples a tooth-resolved surface scan with a calibrated pitch-deviation table, both imported into a multibody dynamics (MBD) model built in MSC Adams View. Three operating scenarios were evaluated—ideal geometry, measured microgeometry without pitch error, and measured microgeometry with pitch error—at a nominal speed of 1000 r min⁻¹. Time domain analysis shows that integrating the pitch table increases the mean transmission error (TE) by almost an order of magnitude and introduces a distinct 16.66 Hz shaft order tone. When the measured tooth topologies are added, peak-to-peak TE nearly doubles, revealing a non-linear interaction between spacing deviation and local flank shape. Frequency domain results reproduce the expected mesh-frequency side bands, validating the mapping of the pitch table into the solver. The combined method therefore provides a more faithful digital twin for predicting tonal noise and demonstrates why indexing tolerances must be considered alongside profile relief during gear design optimization. Full article

The study aims to increase productivity in the cold room door manufacturing industry by addressing non-value-adding operations, identifying bottlenecks, and reducing processing time through digital twin (DT)-based simulation. The goal is to eliminate the need for supply chain outsourcing and increase overall efficiency. The research involves developing a DT of the existing production process for five distinct categories of cold room doors: flush door, single door, double door, face-mounted door, and sliding door. Simulation was used to uncover problems at multiple stations, encompassing curing, welding, and packing. Lean principles were used to identify the causes of inefficiency, and the process was improved using TRIZ principles. These changes produced a 42.90% improvement in productivity, a 20% dependence reduction on outsourcing and an increase of 10.5% added inventory to the shortage demand level. The approach presented is provided for a particular manufacturer of cold room doors, but the methods and techniques used are generally applicable to other manufacturing companies to support systematic innovation. Combining DT simulation, lean techniques and TRIZ principles, this study presents a strong approach to addressing the productivity challenges in manufacturing. The incorporation of these methods has brought considerable operational efficiency and has minimised dependency on external outsourcing. Full article

In lunar exploration missions, rovers must navigate multiple waypoints within strict time constraints while avoiding dynamic obstacles, demanding real-time, collision-free path planning. This paper proposes a digital twin-enhanced hierarchical planning method, A*-D3QN-Opt (A-Star-Dueling Double Deep Q-Network-Optimized). The framework combines the A* algorithm for global optimal paths in static environments with an improved D3QN (Dueling Double Deep Q-Network) for dynamic obstacle avoidance. A multi-dimensional reward function balances path efficiency, safety, energy, and time, while priority experience replay accelerates training convergence. A high-fidelity digital twin simulation environment integrates a YOLOv5-based multimodal perception system for real-time obstacle detection and distance estimation. Experimental validation across low-, medium-, and high-complexity scenarios demonstrates superior performance: the method achieves shorter paths, zero collisions in dynamic settings, and 30% faster convergence than baseline D3QN. Results confirm its ability to harmonize optimality, safety, and real-time adaptability under dynamic constraints, offering critical support for autonomous navigation in lunar missions like Chang’e and future deep space exploration, thereby reducing operational risks and enhancing mission efficiency. Full article

Double-column self-centering segmentally assembled bridges (SC-SABs) present greater design complexity compared to single-column systems, primarily due to vertical stiffness discontinuities at segmental spandrel abutments, which critically affect the refinement of their seismic design methods. To address these challenges, this study conducts a systematic investigation into the mechanical behavior and seismic performance of double-column SC-SAB. First, leveraging fundamental mechanical principles and stress-strain relationships, the coupling mechanism between the two columns is analytically established. An analytical expression for the elastic stiffness of a double-column SC-SAB, when simplified to an equivalent single-column system, is derived. This establishes the equivalent stiffness conditions for reducing a double-column system to a single-column model, and the overall equivalent stiffness of the double-column system is formulated. To validate the theoretical framework, a finite element model of the double-column SC-SAB is developed using OpenSees (1.0.0.1 version). An equivalent single-column model is constructed based on the derived stiffness equivalence conditions. By comparing the peak displacement and bearing capacity between the double-column and equivalent single-column models, the accuracy and feasibility of the simplification approach are confirmed. The numerical results further validate the derived overall equivalent stiffness, providing a robust theoretical foundation for simplified engineering applications. Additionally, pushover analysis and hysteretic response analysis are performed to systematically evaluate the influence of key design parameters on the seismic performance of double-column SC-SAB. The results demonstrate that the prestressed twin-column system exhibits excellent self-centering capability, effectively controlling residual displacements, aligning with seismic resilience goals. This research advances the seismic design methodology for SC-SAB by resolving critical challenges in stiffness equivalence and joint behavior quantification. The findings of this study can be utilized to derive equivalent damping ratios and equivalent periods. Based on the displacement response spectrum, the pier-top displacement and maximum force can be determined, thereby enabling a displacement-based seismic design approach. This research holds significant theoretical and practical value for advancing seismic design methodologies for self-centering segmental bridge piers and enhancing the seismic safety of bridge structures. Full article

As the development of oilfields in China enters its middle-to-late stage, the old oilfields still occupy a dominant position in the production structure. The seepage process of reservoirs in the high Water Content Period (WCP) presents significant nonlinear and non-homogeneous evolution characteristics, and the traditional seepage-modeling methods are facing the double challenges of accuracy and adaptability when dealing with complex dynamic scenarios. In recent years, Deep Learning technology has gradually become an important tool for reservoir seepage field prediction by virtue of its powerful feature extraction and nonlinear modeling capabilities. This paper systematically reviews the development history of seepage field prediction methods and focuses on the typical models and application paths of Deep Learning in this field, including FeedForward Neural networks, Convolutional Neural Networks, temporal networks, Graphical Neural Networks, and Physical Information Neural Networks (PINNs). Key processes based on Deep Learning, such as feature engineering, network structure design, and physical constraint integration mechanisms, are further explored. Based on the summary of the existing results, this paper proposes future development directions including real-time prediction and closed-loop optimization, multi-source data fusion, physical consistency modeling and interpretability enhancement, model migration, and online updating capability. The research aims to provide theoretical support and technical reference for the intelligent development of old oilfields, the construction of digital twin reservoirs, and the prediction of seepage behavior in complex reservoirs. Full article

Marine voyage optimization determines the optimal route and speed to ensure timely arrival. The problem becomes particularly complex when incorporating a dynamic environment, such as future expected weather conditions along the route and unexpected disruptions. This study explores two model-free Deep Reinforcement Learning (DRL) algorithms: (i) a Double Deep Q Network (DDQN) and (ii) a Deep Deterministic Policy Gradient (DDPG). These algorithms are computationally costly, so we split optimization into an offline phase (costly pre-training for a route) and an online phase where the algorithms are fine-tuned as updated weather data become available. Fine tuning is quick enough for en-route adjustments and for updating the offline planning for different dates where the weather might be very different. The models are compared to classical and heuristic methods: the DDPG achieved a 4% lower fuel consumption than the DDQN and was only outperformed by Tabu Search by 1%. Both DRL models demonstrate high adaptability to dynamic weather updates, achieving up to 12% improvement in fuel consumption compared to the distance-based baseline model. Additionally, they are non-graph-based and self-learning, making them more straightforward to extend and integrate into future digital twin-driven autonomous solutions, compared to traditional approaches. Full article

In high-capacity and short-reach applications, double-sideband self-coherent detection (DSB-SCD) has garnered significant attention due to its ability to recover optical fields of DSB signals without requiring a local oscillator. However, DSB-SCD is fundamentally constrained by the non-ideal receiver transfer function, necessitating a guard band between the carrier and signal. While the conventional twin-single-sideband (twin-SSB) modulation scheme addresses this requirement, it incurs substantial implementation complexity. In this paper, we propose a spectrum inversion-based double-sideband (SI-DSB) modulation scheme, where spectral inversion shifts the DSB signal to the high-frequency region, creating a guard band around the zero frequency. After photodetector detection, baseband signal recovery is achieved through subsequent spectral inversion. Compared with the twin-SSB modulation scheme, this approach significantly reduces DSP complexity. The simulation exploration two modulation formats of pulse–amplitude modulation and quadrature-amplitude modulation, demonstrating a comparable system performance between SI-DSB and twin-SSB modulation schemes. We also illustrate the parameter optimization process for the SI-DSB modulation scheme, including carrier-to-signal power ratio and guard band. Furthermore, validation with three FADD receivers further demonstrates the superior performance of the proposed SI-DSB modulation in DSB-SCD systems. Full article

Offshore wind turbines are prone to structural damage over time due to environmental factors, which increases operational costs and the risk of accidents. Early detection of structural damage through monitoring systems can help reduce maintenance costs. However, under complex external conditions and varying structural parameters, existing methods struggle to accurately and quickly detect damage. Understanding the factors that influence structural health is critical for effective long-term monitoring, as these factors directly affect the accuracy and timeliness of damage identification. This study comprehensively analyzed 5 MW offshore wind turbine measurement data, including constructing a digital twin model, establishing a surrogate model, and performing a sensitivity analysis. For monopile-based turbines, sensors in x and y directions were installed at four heights on the pile foundation and tower. Via Bayesian optimization, the finite element model’s structural parameters were updated to align its modal parameters with sensor data analysis results. The update efficiencies of different objective functions and the impacts of neural network hyperparameters on the surrogate model were examined. The sensitivity of the turbine’s structural parameters to modal parameters was studied. The results showed that the modal flexibility matrix is more effective in iteration. A 128-neuron, double-hidden-layer neural network balanced computational efficiency and accuracy well in the surrogate model for modal analysis. Flange damage and soil degradation near the pile mainly impacted the turbine’s health. Full article

This paper presents the application of reinforcement learning algorithms in the tuning of PID controllers for the control of some classes of continuous nonlinear systems. Tuning the parameters of the PID controllers is performed with the help of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which presents a series of advantages compared to other similar methods from machine learning dedicated to continuous state and action spaces. The TD3 algorithm is an off-policy actor–critic-based method and is used as it does not require a system model. Double Q-learning, delayed policy updates and target policy smoothing make TD3 robust against overestimation, increase its stability, and improve its exploration. These enhancements make TD3 one of the state-of-the-art algorithms for continuous control tasks. The presented technique is applied for the control of a biotechnological system that has strongly nonlinear dynamics. The proposed tuning method is compared to the classical tuning methods of PID controllers. The performance of the tuning method based on the TD3 algorithm is demonstrated through a simulation, illustrating the effectiveness of the proposed methodology. Full article

Permanent magnet synchronous motors (PMSMs) are widely used in industrial automation and electric vehicles due to their high efficiency and excellent dynamic performance. However, controlling PMSMs presents challenges such as parameter variations and system nonlinearities. This paper proposes a twin delayed deep deterministic policy gradient (TD3)-based energy-saving optimization control method for PMSM drive systems. The TD3 algorithm uses double networks, target policy smoothing regularization, and delayed actor network updates to improve training stability and accuracy. Simulation experiments under two operating conditions show that the TD3 algorithm outperforms traditional proportional–integral (PI) controllers and linear active disturbance rejection control (LADRC) controllers in terms of reference trajectory tracking, q-axis current regulation, and speed tracking error minimization. The results demonstrate the TD3 algorithm’s effectiveness in enhancing motor efficiency and system robustness, offering a novel approach to PMSM drive system control through deep reinforcement learning. Full article

A numerical simulation of the circular cylinder as an obstacle in a double forward-facing (DFFS) step was performed. The size and position of the upstream cylinder (c₁) and downstream cylinder (c₂) were varied to explore their role in heat transfer in both laminar and turbulent conditions. Comparative results of the upper and lower half of the downstream cylinder were plotted as results to understand the heat transfer and flow characteristics around the downstream cylinder due to the effect of the upstream cylinder’s dimension and position. For Re = 800, when the c₁ is placed near the bottom of the wall, it results in a pair of rear-side symmetrical vortices, and the c₂ cylinder vortices become larger when the c₁ is shifted towards the top wall. Additional flow separation happens adjacent to the steps when c₁ is greater than c₂. These vortices strongly influence the convection heat transfer from the step. However, when Reynolds number (Re) is increased from 800 to 80,000, these vortices’ size is decreased. When c₁ moves from 0.375H to 0.75H, the average Nusselt number is increased significantly. Moreover, a hike in Re results in a higher average Nusselt number irrespective of the position of obstacles. The upstream cylinder significantly enhances the Nusselt number when it is placed near the top wall rather than the bottom wall. Full article

The Gravity Recovery and Climate Experiment Follow-On (GRFO) mission precisely measures the inter-satellite range between the centers of mass of its twin satellites to map the earth’s gravity field. The baseline ranging measurement is achieved using the K-band ranging (KBR) system, which is sensitive to satellite attitude variations caused by the offset between the satellite center of mass and the KBR antenna phase center. Accurate decoupling of the KBR range from attitude variations requires precise determination of the KBR’s antenna offset vectors (AOVs). To address this, GRFO conducted eight KBR calibration maneuvers on 17 and 28 September 2020. However, these maneuvers exaggerated the impact of microwave multipath noise, complicating AOV estimation. Existing studies have not fully mitigated this noise. This study introduces a new frequency-domain method to estimate AOVs by leveraging double-difference signals and analyzing their spectral characteristics, along with those of the KBR range during calibration maneuvers, to suppress multipath noise. Our recalibrated AOVs achieve good alignment between the KBR and laser ranging interferometer (LRI) ranging signals. We validate our recalibrated AOVs by comparing the residuals between the LRI and KBR ranging signals corrected using both recalibrated AOVs and documented AOVs. The results show that, for the majority (58.4%) of the analyzed period (from January 2020 to June 2023), the residuals corrected by the recalibrated AOVs are closer to the LRI ranging signal. These findings demonstrate the effectiveness of the proposed method in addressing multipath noise and improving the accuracy of KBR range measurements. This work provides a framework for future gravity missions requiring precise calibration of multipath effects in inter-satellite ranging systems. Full article

With the advent of digitization, digital twin technology is gradually becoming one of the core technologies of the Industry 4.0 era, highlighting the increasing importance of digital twin project management. Despite its potential, DT projects face significant risks during implementation, stemming from technical, managerial, and operational complexities. To address these challenges, this study proposes an improved failure mode and effect analysis (FMEA) framework by integrating double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This framework converts qualitative assessments into quantitative metrics and calculates weights using a hybrid approach, enabling more precise risk prioritisation. Application of the model to an automotive manufacturing company’s DT project identified key risks, particularly in the iteration and upgrade phase, emphasising the importance of cross-departmental collaboration and robust digital infrastructure. The proposed model provides a systematic framework for enterprises to assess and mitigate risks, ensuring the successful deployment of DT projects. Full article

To tackle the energy-saving optimization issue of plug-in hybrid electric trucks traversing multiple traffic light intersections continuously, this paper presents a double-layer energy management strategy that utilizes the dynamic programming–twin delayed deep deterministic policy gradient (DP-TD3) algorithm to synergistically optimize the speed planning and energy management of plug-in hybrid electric trucks, thereby enhancing the vehicle’s passability through traffic light intersections and fuel economy. In the upper layer, the dynamic programming (DP) algorithm is employed to create a speed-planning model. This model effectively converts the nonlinear constraints related to the position, phase, and timing information of each traffic signal on the road into time-varying constraints, thereby improving computational efficiency. In the lower layer, an energy management model is constructed using the twin delayed deep deterministic policy gradient (TD3) algorithm to achieve optimal allocation of demanded power through the interaction of the TD3 agent with the truck environment. The model’s validity is confirmed through testing on a hardware-in-the-loop test machine, followed by simulation experiments. The results demonstrate that the DP-TD3 method proposed in this paper effectively enhances fuel economy, achieving an average fuel saving of 14.61% compared to the dynamic programming–charge depletion/charge sustenance (DP-CD/CS) method. Full article

In recent years, twin stacker crane units have been increasingly integrated into large automated storage and retrieval systems (AS/RSs) in shipyards to enhance operational efficiency. These common rail units often encounter conflicts, and the additional time costs incurred during collision avoidance significantly diminish AS/RS efficiency. Therefore, addressing the twin stacker cranes’ scheduling problem (TSSP) with a collision-free constraint is essential. This paper presents a novel approach to identifying and avoiding collisions by approximating the stacker crane’s trip trajectory as a triangular envelope. Utilizing the collision identification equation derived from this method, we express the collision-free constraint within the TSSP and formulate a mixed-integer programming model. Recognizing the multimodal characteristics of the TSSP objective function, we introduce the dung beetle optimizer (DBO), which excels in multimodal test functions, as the foundational framework for a heuristic optimizer aimed at large-scale TSSPs that are challenging for exact algorithms. To adapt the optimizer for bi-level programming problems like TSSPs, we propose a double-layer code mechanism and innovatively design a binary DBO for the binary layer. Additionally, we incorporate several components, including a hybrid initialization strategy, a Cauchy–Gaussian mixture distribution neighborhood search strategy, and a velocity revision strategy based on continuous space discretization, into the improved dung beetle optimizer (IDBO) to further enhance its performance. To validate the efficacy of the IDBO, we established a numerical experimental environment and generated a series of instances based on actual environmental parameters and operational conditions from an advanced AS/RS in southeastern China. Extensive comparative experiments on various scales and distributions demonstrate that the components of the IDBO significantly improve algorithm performance, yielding stable advantages over classical algorithms in solving TSSPs, with improvements exceeding 10%. Full article