The aim of this study is to improve cabin temperature homogeneity by comparing serial and parallel coolant line configurations in Anadolu Isuzu’s new midibus model. A one-dimensional hydraulic model was developed in KULI using supplier-provided pressure drop versus flow rate curves for the preheater, defroster, convectors, and pumps. Pipe lengths and inner diameters were obtained from the CATIA vehicle layout. The model was assessed against physical flow rate measurements for both configurations. The maximum deviations were 11.6% for the serial layout and 6.2% for the parallel layout. KULI was used only for coolant flow rate prediction; all temperature results were obtained from physical vehicle tests. At the 30th min, the measured left–right convector outlet temperature difference decreased from 6.6°C in the serial layout to 1.0°C in the parallel layout. For the investigated vehicle and operating condition, the parallel configuration provided a more balanced flow distribution and improved outlet temperature homogeneity.
Reliability testing and validation are essential parts of any automobile development project. These tests mainly depend on two things: how much of the product’s target life is covered and whether enough samples are tested to capture variations. Today, most projects face tight timelines and cost constraints, which makes it important to design an optimized reliability test plan. Accelerated testing is widely used to reduce testing time, but finding the correct acceleration factor for a complex system like an automotive engine is difficult. Existing studies describe methods such as simple stress tests, step-stress tests, and degradation tests to shorten test duration. However, these methods need more samples at different stress levels to establish a clear relationship between stress and life and then calculate the acceleration factor. While such models work well at the component level, they become much more complicated at the system level because different components can have different acceleration behaviors. In this paper, a method is proposed to derive the acceleration factor by correlating engine test bed data with field failure and usage data. Test engineers often use standard accelerated test cycles for engines used in similar applications. As a result, organizations usually have historical test data and field data from earlier, similar products. The proposed approach models accelerated test data and field failure data separately and then links them to determine the acceleration factor. This derived factor can later be used in future engine development projects to convert test cycles into equivalent field mileage coverage. Weibull analysis and regression techniques are used to support the case studies presented in this work.
The precise detection and localization of 'low-slow-small' (LSS) unmanned aerial vehicles (UAVs) pose significant challenges in terminal defense scenarios. These non-cooperative targets have extremely sparse representations in radar point clouds because of their minimal radar cross-sections and appear as textureless, blurred patches in visual images. Furthermore, traditional multi-modal fusion paradigms rely heavily on rigid geometric projection matrices, which frequently fail under the mechanical vibrations, thermal deformations, and extrinsic perturbations inherent in rapid deployment environments, which often results in severe feature misalignment and contamination. To overcome these critical bottlenecks, we present an end-to-end cross-modal detection framework built on Bidirectional Collaborative Energy Fields (Co-Field). By redefining the 3D object detection task as an energy field reconstruction process within a latent multimodal feature space, we entirely remove the need for absolutely precise extrinsic parameters. Our framework uses a novel bidirectional mutual-guided interaction mechanism. Specifically, a vision-guided radar branch employs deformable cross-attention to adaptively aggregate highly discrete radar geometric features around visual semantic centers, achieving a feature-level soft alignment that implicitly compensates for calibration drift. Concurrently, a radar-corrected vision branch utilizes radar-derived geometric occupancy probabilities as depth anchors to gate and calibrate visual features, effectively suppressing the inherent divergence of monocular depth estimation. Ultimately, the framework regresses the target's precise 3D states and fine-grained categories directly from the extrema of the reconstructed collaborative energy field, circumventing complex heuristic post-processing. Experiments on the curated MMAUD dataset show that the proposed Co-Field method achieves better robustness. It remains highly effective even under severe calibration errors up to 10 degrees and achieves a 74.2% recall rate at extreme sparse distances ranging from 70 to 100 meters, and consistently outperforms state-of-the-art rigid and soft fusion baselines.
Real-time monitoring of metal wear debris in lubricating oil is crucial for accurately assessing aerospace equipment health and preventing catastrophic mechanical failures. Although three-coil differential inductive sensors are widely used due to their high sensitivity and robust structure, their detection performance inevitably degrades under the influence of multi-physics coupling effects inherent in complex industrial environments. Existing analytical models were primarily limited to single electromagnetic field simulations, failing to capture the intricate electromagnetic-thermal-mechanical interactions, which reduced analytical accuracy and hindered the optimization of sensor designs for extreme conditions. To overcome these challenges, this study established a comprehensive parametric simulation model to conduct a rigorous electromagnetic-thermal-mechanical coupled analysis of the differential sensor. Specifically, the research analyzed the underlying mechanisms by which wide temperature variations from -55 °C to 200 °C and specific debris kinematics exert varying degrees of influence on the induced voltage. Quantitative results indicated that both the isolated thermal and mechanical fields altered the voltage by a maximum of 0.649%, demonstrating that the sensor maintains a relatively stable response across extreme thermal gradients. Furthermore, the analysis revealed that increased debris velocity decreased the induced voltage amplitude due to the intensified motion-induced eddy current effects. Notably, in complex multiple debris scenarios, the strong dominant signals generated by ferromagnetic wear debris severely masked the weaker responses from non-ferromagnetic ones, indicating a substantial missed detection risk under mixed contamination conditions. Ultimately, this research successfully filled the critical simulation gap for evaluating these sensors under multi-physics coupling, providing robust theoretical guidance for their reliable deployment in complex industrial applications.
This paper investigates cooperative task allocation for multi-satellite clusters under onboard functional payload constraints in many-to-few low Earth orbit non-cooperative servicing scenarios involving heterogeneous space targets. Although the servicing satellite cluster has a numerical advantage, the effectiveness of a single satellite is limited by orbital maneuver cost and the stringent requirements of onboard functional payload systems on rendezvous accuracy and interaction timing. As a result, multiple satellites are generally required to cooperate to establish local numerical superiority for effective task execution. Meanwhile, differences in object strategic importance and resistance capability may lead to excessive resource concentration on high-value objects, thereby degrading overall operational efficiency. To address these issues, an existing multi-satellite cooperative task planning framework is adopted to characterize the matching relationship between resource supply and task demand by jointly considering maneuver cost, target value heterogeneity, and coalition-size requirements. On this basis, a distributed cooperative mechanism is introduced to enable the satellite cluster to adaptively form coordinated task coalitions matched to object value. Simulation results show that, in representative many-to-few LEO servicing scenarios, the resulting allocation scheme can establish local cooperative resource concentration while satisfying full target coverage and no-idle-satellite constraints. It also suppresses excessive competition for high-value objects and improves the balance of resource utilization, thereby demonstrating the effectiveness of the proposed approach for cooperative task allocation in multi-satellite LEO servicing scenarios.
