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The most common and lethal weapons against military vehicles are the improvised explosive devices (IEDs). In an explosion, critical cabin’s penetrations and high accelerations can cause serious injuries and death of military personnel. This investigation uses single and multi-fidelity Bayesian optimization (BO) to design sandwich composite armors for blast mitigation. BO is an efficient methodology to solve optimization problems that involve black-box functions. The black-box function of this work is the finite element (FE) simulation of the armor subjected to blast. The main two components of BO are the surrogate model of the black-box function and the acquisition function that guides the optimization. In this investigation, the surrogate models are Gaussian Process (GP) regression models and the acquisition function is the multi-objective expected improvement (MEI) function. Information from low and high fidelity FE models is used to train the GP surrogates.

In-cylinder ion sensing is a subject of interest due to its application in spark-ignited (SI) engines for feedback control and diagnostics including: combustion knock detection, rate and phasing of combustion, and mis-fire On Board Diagnostics (OBD). Further advancement and application is likely to continue as the result of the availability of ignition coils with integrated ion sensing circuitry making ion sensing more versatile and cost effective. In SI engines, combustion knock is controlled through closed loop feedback from sensor metrics to maintain knock near the borderline, below engine damage and NVH thresholds. Combustion knock is one of the critical applications for ion sensing in SI engines and improvement in knock detection offers the potential for increased thermal efficiency. This work analyzes and characterizes the ionization signal in reference to the cylinder pressure signal under knocking and non-knocking conditions.

Sensory jury testing was utilized to characterize vibration levels perceived by the operator, with respect to levels measured using instrumentation, in order to develop a tool for the evaluation of vibration at the operator interfaces. Details of the jury testing and jury data processing method are highlighted as well as the refinement of vibration characterization for a specific application. The vibration at user interface locations of both snowmobiles and ATVs was measured along with subjective feedback from a panel of jurists. Statistical analysis was performed on the jury data to provide both a qualitative and quantitative number to represent the opinion of the jury. Correlations were developed between the measured levels of vibration and the opinions of the jury. Finally, a set of correlation functions suitable for design predictions was developed.

Researchers desire to evaluate system reliability uniquely and efficiently. Despite its strong technical demand, little progress has been made on system reliability analysis in the last two decades. Up to now, bound methods for system reliability prediction have been dominant. For system reliability bounds, the second order bound method gives fairly accurate prediction for system reliability assuming that the probabilities of second-order joint events are accurately obtained. Two primary challenges in system reliability analysis are evaluation of the probabilities of second-order joint events and no unique system reliability for design optimization. Firstly, the greatest technical demand is found in an accurate and efficient method to numerically evaluate the probability of a second-order joint event.

This paper presents an innovative approach for quality engineering using the Eigenvector Dimension Reduction (EDR) Method. Currently industry relies heavily upon the use of the Taguchi method and Signal to Noise (S/N) ratios as quality indices. However, some disadvantages of the Taguchi method exist such as, its reliance upon samples occurring at specified levels, results to be valid at only the current design point, and its expensiveness to maintain a certain level of confidence. Recently, it has been shown that the EDR method can accurately provide an analysis of variance, similar to that of the Taguchi method, but is not hindered by the aforementioned drawbacks of the Taguchi method. This is evident because the EDR method is based upon fundamental statistics, where the statistical information for each design parameter is used to estimate the uncertainty propagation through engineering systems.

The need for greater capacity in automotive transportation (in the midst of constrained resources) and the convergence of key technologies from multiple domains may eventually produce the emergence of a “swarm” concept of operations. The swarm, a collection of vehicles traveling at high speeds and in close proximity, will require management techniques to ensure safe, efficient, and reliable vehicle interactions. We propose a shared-autonomy approach in which the strengths of both human drivers and machines are employed in concert for this management. A fuzzy logic-based control implementation is combined with a genetic algorithm to select the shared-autonomy architecture and sensor capabilities that optimize swarm operations.

New tire model is under development in European Commission research project called VERT (Vehicle Road Tire Interaction, BRPR-CT97-0461). The objective of the project is to create a physical model for tire/surface contact simulation. One of the subtasks has been to develop a method for snow surface characterization. The aim is simulate winter tire on snow surface with FEM software. This kind of simulation has been earlier done with snow model parameters from laboratory experiments. A snow shear box device has been developed in Helsinki University of Technology to measure mechanical properties of snow in field conditions. Both shear and compression properties can be measured with the device. With the device, a large number of snow measurements have been done at the same time with VERT winter tire testing in Nokian Tyres'' test track in Ivalo Finland. Measurement data have been postprocessed afterwards and parameters for material models have been evaluated.

This paper deals with the dynamic characterization of an automotive shock absorber, a continuation of an earlier work [1]. The objective of this on-going research is to develop a testing and analysis methodology for obtaining dynamic properties of automotive shock absorbers for use in CAE-NVH low-to-mid frequency chassis models. First, the effects of temperature and nominal length on the stiffness and damping of the shock absorber are studied and their importance in the development of a standard test method discussed. The effects of different types of input excitation on the dynamic properties of the shock absorber are then examined. Stepped sine sweep excitation is currently used in industry to obtain shock absorber parameters along with their frequency and amplitude dependence. Sine-on-sine testing, which involves excitation using two different sine waves has been done in this study to understand the effects of the presence of multiple sine waves on the estimated dynamic properties.

A variable displacement engine with the capability to vary stroke length and compression ratio independent of one another has been designed, prototyped, and successfully operated. Reasons for investigation of such an engine are the potential for improvement in fuel economy and/or performance. Literature has shown that engines with variable compression ratio can significantly decrease specific fuel consumption. Engines with variability in stroke length can maintain peak efficiency running conditions by adjusting power output through displacement change verses through the efficiency detriment of throttling. The project began with the synthesis of a planar 2-dimensional rigid body mechanism. Various synthesis techniques were employed and design took place with a collection of computer software. MATLAB code performed much of the synthesis, kinematic, and dynamic analysis.

The development of diesel engines is constantly leading to greater increases in the power density. The heat load into the combustion chamber walls increases with the increased power density. Estimating correct local heat fluxes inside the combustion chamber is one of the most challenging tasks in engine simulation. In this study, the heat load of the piston was estimated with the help of the modern simulation tools CFD and FEM. The objective of the work was to evaluate the thermal stress of a research engine designed for an exceptionally high maximum and mean pressure. The local heat transfer coefficient and gas temperature were simulated with a CFD code with the standard and modified wall functions and used as boundary values for the FEM analysis. As a reference case, a model of a production engine with measured piston surface temperatures was used to validate the combined CFD and FEM analysis.

This paper presents the Statistical Energy Approach (SEA) method for estimating the gross response in complex interconnected structural systems. The method is intended to compensate for the difficulties present in evaluating parameters and excitation needed when attempting to use traditional methods of linear vibration analysis. The amount of information needed to apply the method is modest and the formulas are easy to use. Some limitation on application is demonstrated by a detailed example.

A common scenario in engineering design is the availability of several black-box functions that describe an event with different levels of accuracy and evaluation cost. Solely employing the highest fidelity, often the most expensive, black-box function leads to lengthy and costly design cycles. Multi-fidelity modeling improves the efficiency of the design cycle by combining information from a small set of observations of the high-fidelity function and large sets of observations of the low-fidelity, fast-to-evaluate functions. In the context of Bayesian optimization, the most popular multi-fidelity model is the auto-regressive (AR) model, also known as the co-kriging surrogate. The main building block of the AR model is a weighted sum of two Gaussian processes (GPs). Therefore, the AR model is well suited to exploit information generated by sources that present strong linear correlations.

The steering assembly is a part of an automotive suspension system that provides control and stability. It provides control of direction, stability, and control over placement of the car. Optimization of the vehicle in weight results in enhanced performance and low fuel consumption, more so for an all-terrain race car. Optimization in this paper loosely refers to weight reduction and achieving the optimum stiffness to weight ratio of each component. This research encompasses various aspects linked to conceptualizing, designing, analysing, optimizing, and finally manufacturing the steering sub-system. Analytical calculations for mechanical design were performed using data from various experiments and jigs. CAD was developed using SolidWorks, and various analyses were performed using Altair HyperWorks. Finite Element Analysis (FEA) was primarily used to build stress plots and locate weak spots aiding optimization.

This paper describes research to analyze widespread fatigue damage in lap joints. The particular objective is to determine when large numbers of small cracks could degrade the joint strength to an unacceptable level. A deterministic model is described to compute fatigue crack growth and residual strength of riveted panels that contain multiple cracks. Fatigue crack growth tests conducted to evaluate the predictive model are summarized, and indicate good agreement between experimental and numerical results. Monte Carlo simulations are then performed to determine the influence of statistical variability on various analysis parameters.

Structures with multiple materials have now become one of the perceived necessities for automotive industry to address vehicle design requirements such as light-weight, safety, and cost. The objective of this study is to develop a design methodology for multi-material structures accountable for vehicle crash durability. The heuristic topology synthesis approach of Hybrid Cellular Automaton (HCA) framework is implemented to generate multi-material structures with the constraint on the volume fraction of the final design. The HCA framework is integrated with ordered-SIMP (solid isotropic material with penalization) interpolation, artificial material library, as well as statistical analysis of material distribution data to ensure a smooth transition between multiple practical materials during the topology synthesis.

The study of spray-wall interaction is of great importance to understand the dynamics during fuel-surface impingement process in modern internal combustion engines. The identification of droplet post-impingement pattern (contact, transition, non-contact) and droplet characteristics can quantitatively provide an estimation of energy transfer for spray-wall interaction, thus further influencing air-fuel mixing and emissions under combusting conditions. Theoretical criteria of single droplet post-impingement pattern on a dry wall have been experimentally and numerically studied by many researchers to quantify the hydrodynamic droplet behaviors. However, apart from model fidelity, another issue is the scalability. A theoretical criterion developed from one case might not be well suited to another scenario. In this paper, a data-driven approach for single droplet-dry wall post-impingement pattern utilizing arithmetical machine learning classification methods is proposed and demonstrated.

Based on RADAR and LiDAR measurements of deer with RADAR and LiDAR in the Spring and Fall of 2014 [1], we report the best fit statistical models. The statistical models are each based on time-constrained measurement windows, termed test-points. Details of the collection method were presented at the SAE World Congress in 2015. Evaluation of the fitness of various statistical models to the measured data show that the LiDAR intensity of reflections from deer are best estimated by the extreme value distribution, while the RCS is best estimated by the log-normal distribution. The value of the normalized intensity of the LiDAR ranges from 0.3 to 1.0, with an expected value near 0.7. The radar cross-section (RCS) varies from -40 to +10 dBsm, with an expected value near -14 dBsm.

The formation of ice on aircraft is a highly dynamic process during which ice will expand and contract upon freezing and undergoing changes in temperature. Finite element analysis (FEA) simulations were performed investigating the stress/strain response of an idealized ice sample bonded to an acrylic substrate subjected to a uniform temperature change. The FEA predictions were used to guide the placement of strain gages on custom-built acrylic and aluminum specimens. Tee rosettes were placed in two configurations adjacent to thermocouple sensors. The specimens were then placed in icing conditions such that ice was grown on top of the specimen. It was hypothesized that the ice would expand on freezing and contract as the temperature of the interface returned to the equilibrium conditions.

Sandwich structures with honeycomb core are widely used in the lightweight design and impact energy absorption applications in automotive, sporting, and aerospace industries. Recently, the auxetic honeycombs with negative Poisson's ratio attract substantial attention for different engineering products. In this study, we implement Additive Manufacturing technology, experimental testing, and Finite Element Analysis (FEA) to design and investigate the mechanical behavior of a novel unit cell for sandwich structure core. The new core model contains the conventional and auxetic honeycomb cells beside each other to create a Hybrid Honeycomb (HHC) for the sandwich structure. The different designs of unit cells with the same volume fraction of 15% are 3D-printed using Fused Deposition Modeling technique, and the comparative study on the mechanical behavior of conventional honeycomb, auxetic honeycomb, and HHC structures is conducted.

In regions at war, the increasing use of improvised explosive devices (IEDs) is the main threat against military vehicles. Large cabin”s penetrations and high gross accelerations are primary threats against the occupants” survivability. The occupants” survivability under an IED event largely depends on the design of the vehicle armor. Under a blast load, a vehicle armor should maintain its structural integrity while providing low cabin penetrations and low gross accelerations. This investigation employs Bayesian global optimization (BGO) and non-uniform rational B-splines (NURBS) to design sandwich composite armors that simultaneously mitigate the cabin”s penetrations and the reaction force at the armor”s supports. The armors are made of four layers: steel, carbon fiber reinforced polymer (CFRP), aluminum honeycomb, and CFRP.