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The present work describes the constraint analysis methodology to perform initial sizing for generic wing-in-ground-effect (WIG) vehicles which can be used for their initial conceptual design and the design space exploration. Constraint formulations based on classical equations of motion were adapted for different types of WIG craft to obtain a compact set of equations to visually represent the feasible design space. Relations for classical WIG, power-augmented WIG craft (PARWIG), and dynamic air cushioning WIG craft (DACWIG) were developed to account for various vehicle configurations and model their specific features. Apart from theoretical and available semi-empirical formulations for WIG craft, additional constants and correction factors were recommended using verified time-dependent simulations of several reference WIG craft to enhance the accuracy of the method and perform verification studies. Constraint diagrams and corresponding design spaces were compared to existing data of WIG craft and demonstrated strong capabilities of the method to create the design space with reasonable wing loadings and thrust (power)-to-weight ratios. Finally, directions to apply, improve, and further verify the formulations are suggested. • The constraint analysis methodology was developed for wing-in-ground effect vehicle conceptual design. • The constraint analysis methodology was developed for WIG, PARWIG, and DACWIG craft sizing. • The methodology was applied to seven reference cases and showed acceptable accuracy for all vehicles. [ABSTRACT FROM AUTHOR]

• The paper presents a simple, reliable and fast dynamics model of off-road track vehicle. • The numerical model was formulated by a new method – DBD (Discrete Body Dynamics). • The proposed method was demonstrated using an M113 track vehicle. • The time computation of the new code was faster compared with that obtained from commercial Multi-Body Dynamic programs. • The method is simple enough to be used in prediction of trafficability for off-road applications. The paper presents a simple, fast, and reliable dynamic model for an off-road track vehicle operating on terrain with obstacles. The method has been proven previously for wheeled-vehicle formulation. The model is based on a discrete body dynamics (DBD) method, which leads to simplistic linear decoupled motion equations. In this method, joints and bodies with relatively small mass are replaced with stiff springs and dampers, eliminating the system's constraints and reducing the number of system bodies; this is important for accelerating the simulation runtime of the track vehicle model. The track in this approach is based on modeling each link as a point-mass. Two consecutive links are connected by stiff springs and dampers. This approach reduces the calculation time and increases system stability. The track–soil interaction was modeled using Bekker's and Janosi's formulation (Bekker, 1956; Hanamoto and Janosi, 1961). Specific soil properties were obtained for each link–soil interaction from soil classification and GIS. The link–ground contact was determined from topographic surface and adjustment of the force and direction acting on the track. The results of the simulation using the DBD method were compared with Siemens' VL commercial multibody dynamics program and with experiments reported in the literature. Results using the proposed method were found to be similar to the commercial program based on published experiments. The solution runtimes obtained for unpaved soil were two orders faster with the DBD method compared with the Siemens' VL program. The model was written as an independent software infrastructure, enabling easy integration with any other software component, such as a control system. The algorithm is in a suitable form for parallel processing calculation to speed up the runtime simulation close to real-time. [ABSTRACT FROM AUTHOR]

• The paper presents a reliable dynamics model of off-road vehicle operation in RT. • The numerical model was formulated by a new method – DBD (Discrete Body Dynamics). • The proposed method was demonstrated using an ATV vehicle. • DBD is faster than Multi-Body Dynamics programs. This paper presents a simple, reliable dynamics model of off-road vehicle operation in real-time (RT) on terrain with obstacles. The numerical model was formulated by a new method – DBD (Discrete Body Dynamics). The new method is based on a discrete-element method, where the equations of motion are linear and simple to solve. In this new method, the suspension systems are composed of soft and stiff springs and dampers (instead of suspension arms and joints constrains), to present the kinematics and dynamics of real suspension. Reduction of the number of bodies and avoidance of constraints significantly improves model efficiency and simplicity. The tires–soil interaction was modeled using Brixius prediction. Specific soil properties were obtained from the classification system for each tire–soil interaction, size, and geometric area. The tire–ground contact was determined by topographic surface and adjustment of the forces and direction acting on the tires. The proposed method allows quick and simple definition of a vehicle. The model is written as an independent software infrastructure, enabling easy integration with any other software component. Simulation results were compared with Siemens' VL commercial multibody dynamics program. The performance of the proposed method was very similar to the commercial program (R2 > 0.9), with the significant advantage of much higher RT performance. [ABSTRACT FROM AUTHOR]

Tire normal forces play a crucial role in vehicle dynamic control systems (VDCs), and an accurate estimation of them could substantially improve vehicle handling and safety. Total normal forces on tires are the combination of the static and dynamic values. These values change by varying the vehicle static parameters (vehicle mass and CG position), the road grade, and vehicle dynamic states. All of the mentioned forces and parameters are used in the design of vehicle dynamics controllers. Therefore, to have an efficient estimation solution to access all different target controllers, the proposed estimation algorithm is divided into two parts. The first part is an ANN-based vehicle mass and CG position estimation algorithm and the second one is a deep-learning-based algorithm and an integrated hardware–software method to estimate dynamic normal forces. The first part includes two neural network (NN) blocks related to vehicle roll and pitch dynamics. The outputs of the NN blocks are load distributions for the axes associated with each dynamics. Based on the measurement unit data, the vehicle's maneuvers are classified into one of many categories used for downstream tasks. The rules of the fuzzy classification logic determine which of the mentioned blocks to be activated. At the same time, an experimentally validated 9-DOF vehicle model instantaneously observes the estimated values. In the second part, long short-term memory (LSTM), gated recurrent unit (GRU), and an integrated hardware–software method were developed to assess the tire's dynamic normal forces during maneuvers. The performed simulations and the results of field tests show that the proposed algorithm can accurately estimate the considered parameters in the presence of noise and disturbances. It is shown that the proposed algorithm is more accurate and faster than the extended Kalman filter and recursive least square estimation methods to estimate static values. • Development of a new estimation algorithm in two parts considering all different target controllers. • The first part is an ANN-based vehicle mass and CG position estimation algorithm to achieve tire static normal forces. • The second part is a deep learning-based algorithm and an integrated hardware-software method to estimate tire dynamic normal forces. • A new validated 9-DOF model is developed which integrates mathematical motion equations with a GPS/IMU hardware. • The proposed algorithm can estimate the tire normal forces with less than 8% error. [ABSTRACT FROM AUTHOR]

• Vehicle modal parameters are identified using the free responses from the field tests of a vehicle passing over humps. • The road roughness can be estimated accurately using the measured vehicle responses from the field tests. • The proposed method can be easily and effectively performed with a few accelerometers only. Accurate information about vehicle parameters and road roughness is of great significance in vehicle dynamic analysis, road driving quality, etc. In this study, a method for estimating vehicle parameters and road roughness was developed using the measured vehicle responses from field tests which is efficient, economical, and accurate. First, the full-vehicle model was introduced. Then, vehicle modal parameters were identified using the consequent free responses of a vehicle passing over bumps. Second, the expression of the vehicle frequency response function (FRF) with respect to the wheel contact point was derived from the vehicle equation of motion, and a road roughness estimation method based on the vehicle FRF was developed. Third, field tests in which the vehicle passes over bumps were performed for vehicle model identification. Finally, field tests for road roughness estimation were carried out using a calibrated vehicle to verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

• A semi-recursive vehicle model is validated in modeling and accuracy. • Constant and adaptive time-step algorithms for real-time simulation are presented. • An accurate real-time simulation of a vehicle is completed in an affordable laptop. • A benchmark problem for the multibody dynamics community is put forward. Semi-recursive formulations and their various versions have made it possible to describe complex nonlinear systems such as vehicles precisely while still solving the relevant equations of motion in real time. An optimal combination of an efficient multibody formulation and a fast numerical time integration scheme are needed to accurately simulate complex systems in real time. This paper introduces a double-step semi-recursive multibody formulation and analyzes its performance with high-order numerical time-integration algorithms for real-time simulation. The Runge-Kutta, Gill, Runge-Kutta-Fehlberg, Adams-Bashforth-Moulton, and adaptive time step Runge-Kutta numerical methods are explained and compared. Results are verified against a commercial multibody software solution. A 15-degree-of-freedom sedan vehicle model serves as a benchmark to verify the theoretical results. The results highlight the differences between the numerical algorithms and suggest appropriate approaches for a nonlinear vehicle dynamics model, particularly for cases where simulation times are long. [ABSTRACT FROM AUTHOR]