After manufacture, every military vehicle experiences a unique history of dynamic loads, depending on loads carried, missions completed, etc. Damage accumulates in vehicle structures and components accordingly, leading eventually to failures that can be difficult to anticipate, and to unpredictable consequences for mission objectives. The advent of simulation-based fatigue life prediction tools opens a path to Digital Twin based solutions for tracking damage, and for gaining control over vehicle reliability. An incremental damage updating feature has now been implemented in the Endurica CL fatigue solver with the aim of supporting such applications. The incremental updating feature is demonstrated via the example of a simple transmission mount component. The damage state of the mount is computed as it progresses towards failure under a series of typical loading histories.
We present an approach in which we use simulation to capture the two-way coupling between the dynamics of a military vehicle and that of a fluid that sloshes in a tank attached to the vehicle. The simulation is carried out in Chrono and builds on support provided by two modules: Chrono::FSI (Fluid-Solid Interaction) and Chrono::Vehicle. The dynamics of the fluid phase is governed by the mass and momentum (Navier-Stokes) equations, which are discretized in space via a Lagrangian approach called Smoothed Particle Hydrodynamics. The vehicle dynamics is the solution of a set of differential algebraic equations of motion. All equations are discretized in time via a half-implicit symplectic Euler method. This solution approach is general -- it allows for fully three dimensional (3D) motion and nonlinear transients. We demonstrate the solution in conjunction with the simulation of a vehicle model that performs a constant radius turn and double lane change maneuver.
Objective: The objective of this study was to optimize the occupant restraint systems for a light tactical vehicle in frontal crashes through a combination of sled testing and computational modeling. Methods: Two iterations of computational modeling and sled testing were performed to find the optimal restraint designs (including seatbelt and airbag) for protecting occupants represented by three size of ATDs, namely Hybrid-III 5th female, 50th male, and 95th male ATDs, and two military gear configurations, namely improved outer tactical vest (IOTV) and SAW Gunner configuration using a tactical assault panel (TAP). A parametric study using computational simulations were first conducted to find the best combinations of seatbelt and airbag designs for different sizes of ATDs and military gear configurations involving both driver and front seat passenger. Following the parametric study, 12 sled tests were conducted with the simulation-recommended restraint designs.
This paper investigates the fuel saving potential of a series hybrid military truck using a combined battery pack design and powertrain supervisory control optimization strategy. The design optimization refers to the sizing of the Lithium-ion battery pack in the hybridized configuration. On the other hand, the powertrain supervisory control optimization finds the most efficient way to split power demands between the battery pack and the engine. Most of the previous literatures implement them separately. Combining the sizing and optimal control problem in a single optimization routine might produce better fuel economy in a more efficient manner. This study proposes a novel unified framework to couple Genetic Algorithm (GA) with Pontryagin’s Minimum Principle (PMP) to determine the battery pack sizing and the power split control sequence simultaneously.
Abstract Improving injury prediction accuracy and fidelity for mounted Warfighters has become an area of focus for the U.S. military in response to improvised explosive device (IED) use in both Iraq and Afghanistan. Although the Hybrid III anthropomorphic test device (ATD) has historically been used for crew injury analysis, it is only capable of predicting a few select skeletal injuries. The Computational Anthropomorphic Virtual Experiment Man (CAVEMAN) human body model is being developed to expand the injury analysis capability to both skeletal and soft tissues. The CAVEMAN model is built upon the Zygote 50th percentile male human CAD model and uses a finite element modeling approach developed for high performance computing (HPC). The lower extremity subset of the CAVEMAN human body model presented herein includes: 28 bones, 26 muscles, 40 ligaments, fascia, cartilage and skin.
Abstract Under body blast (UBB) loading to military transport vehicles is known to cause foot-ankle fractures to occupants due to energy transfer from the vehicle floor to the feet of the soldier. The soldier posture, the proximity of the event with respect to the soldier, the personal protective equipment (PPE) and age/sex of the soldier are some variables that can influence injury severity and injury patterns. Recently conducted experiments to simulate the loading environment to the human foot/ankle in UBB events (~5ms rise time) with variables such as posture, age and PPE were used for the current study. The objective of this study was to determine statistically if these variables affected the primary injury predictors, and develop injury risk curves. Fifty below-knee post mortem human surrogate (PMHS) legs were used for statistical analysis. Injuries to specimens involved isolated and multiple fractures of varying severity.
This SAE Standard provides ordering information for any SAE 20R5 hose type (such as "EC, HT, LT" or combination thereof.) This is a wire-reinforced hose for coolant circulating systems of automotive type engines. This hose consists of a convoluted section with plain ends. The hose shall contain a wire helix or helices in the convoluted section. It is a supplement for Government use but may be used by others.