Browse Topic: Vehicle performance
The modern battlefield is increasingly transparent, generating large volumes of open-source data on the use, damage, and loss of military vehicles. This paper presents a structured methodology to exploit such data for deriving operational requirements for future vehicles. It uses a mixed-method framework combining qualitative reporting with quantitatively verified loss data. Daily battlefield reports are analyzed with large language models to extract operational context, employment patterns, and tactical conditions. These insights are cross-referenced with loss data to assess how operational factors affect vehicle survivability, with the findings being used to prioritize requirements that improve vehicle performance. The approach is demonstrated through a case study of Leopard tanks in the Russia-Ukraine war, using Institute for the Study of War reports and Oryxspioenkop loss data. Results show how open-source intelligence can systematically inform survivability, mobility, and combat effectiveness in modern vehicle design.
The effects of hover operations near a partial boundary structure were assessed for a free-flying quadrotor platform under both wind-off and wind-on conditions. The partial boundary structure was selected to replicate a building facade or urban vertiport environment, providing a realistic operational context for these free-flight tests. Test points were chosen to investigate operations near the partial boundary wall and edge, and across a range of partial ground effect conditions to capture the progressive onset of ground effect characteristics. Regions of degraded vehicle performance, quantified primarily by rotor thrust coefficient (CT ) and power requirements, emerged near the partial boundary edge. These performance trends were attributed to localized changes in rotor inflow profile, characterized by near-field rotor pressure measurements. Partial ground effect was found to not resemble full ground effect until much of the vehicle had traversed over the partial boundary, with the vehicle airframe and fuselage serving as the primary factor driving the onset and development of ground effect characteristics. Under wind-on conditions, the interaction between the wind freestream and the partial boundary structure significantly shaped vehicle performance and handling qualities. The most adverse handling qualities coincided with regions of peak flow vorticity, highly unsteady flow, and large velocity gradients at the shear layer above the partial boundary.
The performance of chassis suspension mechanisms critically affects vehicle handling, ride comfort, and safety. Implementing real-time health monitoring for chassis systems contributes to preventing severe consequences such as increased body roll or loss of handling stability caused by shock absorber softening or spring stiffness degradation under deteriorating operating conditions, while circumventing the substantial costs associated with professional facility-based chassis inspections. With the rapid development of sensing and data analytics technologies, data-driven approaches are increasingly used in health monitoring. This study aims to achieve online monitoring of chassis suspension performance degradation using a deep neural network (DNN). First, a half-car model incorporating both vertical and pitch motions was established to simulate bumpy road conditions, with the aim of constructing a dataset that includes key vehicle suspension parameters and vehicle states related to their degradation characteristics. Subsequently, a DNN model comprising three hidden layers is developed to assess suspension performance degradation. To optimize model performance, the effects of different numbers of neurons and hidden layers on model accuracy are explored. Experimental results show that the maximum absolute percentage errors of the DNN model in predicting suspension stiffness and damping coefficients are less than 0.13% and 0.17%, respectively, with average absolute percentage errors below 0.046% and 0.06%. The coefficients of determination (R2) exceed 0.999. The proposed method accurately predicts the trend of key suspension parameters, providing robust data support for health management and maintenance decision-making. This is expected to reduce safety risks and maintenance costs while enhancing overall vehicle performance and reliability.
Aerodynamic simulations are crucial in vehicle design and performance evaluation. Traditionally, these simulations utilize Computational Fluid Dynamics (CFD) techniques to compute flow quantities such as velocity, pressure, and wall-shear stresses. Accurate prediction of these quantities is vital for estimating drag and lift forces, which directly impact fuel efficiency, stability, and acoustics. This study focuses on developing an AI surrogate for aerodynamic design of production mideo-size SUVs using NVIDIA’s PhysicsNeMo framework. Firstly, high-fidelity 3D CFD data are generated using first-principles solvers on 102 different geometry variants at a uniform inlet velocity of 38.89 m/s and a fixed set of boundary conditions. The DoMINO (Decomposable Multiscale Iterative Neural Operator) AI model, part of the PhysicsNeMo framework, is then used to train on this dataset, accurately predicting surface pressure and flow fields around vehicles for rapid estimation of critical aerodynamic metrics such as drag and lift. DoMINO is a neural operator that learns local geometry representations from point cloud data and predicts PDE solutions on discrete points using dynamically constructed computational stencils in local regions. By leveraging both short- and long-range geometric features, the DoMINO model predicts solution fields on the vehicle’s surface and in the surrounding flow domain—capabilities essential for informed design and engineering decisions in industrial applications. In this study, the DoMINO model is evaluated on a realistic production mid-size SUV vehicle designed by General Motors. Comprehensive hyperparameter tuning is conducted to optimize model performance, along with an analysis of input grid sensitivity. The findings highlight DoMINO's effectiveness as a robust and accurate tool for aerodynamic analysis in the automotive sector, enabling accelerated design cycles and enhanced vehicle performance.
In recent years, premium vehicles have increasingly incorporated suspension systems capable of adjusting ride height. The primary function of these systems is to enable the vehicle to traverse uneven terrain by elevating the chassis, thereby preventing contact between the underbody and the road surface. Notably, air spring-based mechanisms enhance ride comfort by modulating the wheel rate. The system proposed in this study achieves ride height adjustment through vertical displacement of the spring’s lower seat. By constructing a detailed mechanical topology model using a dynamic simulation tool, this research aims to evaluate the feasibility of improving driving performance not only through height regulation but also by actively controlling the vehicle’s posture during motion.
The Nissan Sentra has provided straightforward behavior and performance for sedan shoppers in the U.S. for over forty years. For 2026, Nissan took the solid 2025 model and made enough mechanical tweaks and visual changes to call it an all-new vehicle. This might sound like a bit of a stretch, but given how the advancements add up to an improved drive experience in a better-looking vehicle, we'll let it slide. Available in four grades - S, SV, SR and SL that range from $22,400 to $27,990, before destination fees and packages - the 2026 Sentra puts on airs like it's a simple vehicle, hiding some of its advanced technology to keep the interior clean and clear, from the driver's screen to the steering wheel buttons. Wireless device charging and wireless Apple CarPlay/Android Auto minimize wire clutter. The standard 12.3-in NissanConnect infotainment touchscreen hides its options in a selection of tiles, and it has a single round volume button that makes it easy to turn down quickly.
This study investigates the performance and vibration characteristics of representative lift rotors for a notional lift+cruise electric vertical takeoff and landing (eVTOL) configuration. As new eVTOL concepts continue to be developed and others progress towards FAA certification, it is crucial to understand the performance and vibratory considerations associated with different lift rotor design choices, including the number of blades and the method of thrust control (e.g., variable blade pitch/fixed RPM vs. fixed blade pitch/variable RPM). The NASA Revolutionary Vertical Lift Technology (RVLT) Lift+Cruise configuration was chosen as the baseline vehicle for this analysis (Ref 1). The investigation includes the evaluation of multiple lift rotors with 2-, 3-, and 4-bladed configurations as well as variable- vs. fixed-pitch designs. Both isolated rotor and full vehicle simulations were assessed to demonstrate some of the design variables applicable to the full vehicle performance and vibratory content. Industry-standard rotorcraft comprehensive analysis software, the Rotorcraft Comprehensive Analysis System (RCAS) (Ref 3), was used to evaluate and compare each configuration for performance and vibratory loads at key points in the rotors and in the fuselage
In automotive engineering, understanding driving behavior is crucial for decision on specifications of future system designs. This study introduces an innovative approach to modeling driving behavior using Graph Attention Networks (GATs). By leveraging spatial relationships encoded in H3 indices, a graph-based model constructed, which captures dependencies between various vehicle operational parameters and their operational regions using H3 indices. The model utilizes CAN signal features such as speed, fuel efficiency, engine temperature, and categorical identifiers of vehicle type and sub-type. Additionally, regional indices are incorporated to enrich the contextual information. The GAT model processes these heterogeneous features, learning to identify patterns indicative of driving behavior. This approach offers several significant advantages. Firstly, it enhances the accuracy of driving behavior modeling by effectively capturing the complex spatial and operational dependencies inherent in vehicle data. The use of GATs allows for the dynamic weighting of different features, ensuring that the most relevant information is prioritized in the analysis. Secondly, the integration of regional indices provides a deeper contextual understanding, enabling the model to discern region-specific driving patterns that might otherwise be overlooked. Furthermore, this method facilitates the identification of abnormal behavioral trends, offering valuable insights for design engineers. By understanding region-based driving behavior, engineers can modify vehicle systems to better meet the needs of specific areas, leading to improved performance and user satisfaction. The combination of graph-based methods with attention mechanisms represents a significant advancement in vehicle performance monitoring, paving the way for a more comprehensive understanding of driving behavior across different regions.
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