Indiana University-Purdue University Indianapolis (IUPUI) / While computed tomographic angiography (CTA) has emerged as a powerful noninvasive option that allows for direct visualization of arterial stenosis(AS), it cant assess the hemodynamic abnormality caused by an AS. Alternatively, trans-stenotic pressure gradient (TSPG) and fractional flow reserve (FFR) are well-validated hemodynamic indices to assess the ischemic severity of an AS. However, they have significant restriction in practice due to invasiveness and high cost. To fill the gap, a new computational modality, called InVascular has been developed for non-invasive quantification TSPG and/or FFR based on patient's CTA, aiming to quantify the hemodynamic abnormality of the stenosis and help to assess the therapeutic/surgical benefits of treatment for the patient. Such a new capability gives rise to a potential of computation aided diagnostics and therapeutics in a patient-specific environment for ASs, which is expected to contribute to precision planning for cardiovascular disease treatment. InVascular integrates a computational modeling of diseases arteries based on CTA and Doppler ultrasonography data, with cutting-edge Graphic Processing Unit (GPU) parallel-computing technology. Revolutionary fast computing speed enables noninvasive quantification of TSPG and/or FFR for an AS within a clinic permissible time frame. In this work, we focus on the implementation of inlet and outlet boundary condition (BC) based on physiological image date and and 3-element Windkessel model as well as lumped parameter network in volumetric lattice Boltzmann method. The application study in real human coronary and renal arterial system demonstrates the reliability of the in vivo pressure quantification through the comparisons of pressure waves between noninvasive computational and invasive measurement. In addition, parametrization of worsening renal arterial stenosis (RAS) and coronary arterial stenosis (CAS) characterized by volumetric lumen reduction (S) enables establishing the correlation between TSPG/FFR and S, from which the ischemic severity of the AS (mild, moderate, or severe) can be identified. In this study, we quantify TSPG and/or FFR for five patient cases with visualized stenosis in coronary and renal arteries and compare the non-invasive computational results with invasive measurement through catheterization. The ischemic severity of each AS is predicted. The results of this study demonstrate the reliability and clinical applicability of InVascular.

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The present research work has been carried out to develop a unified knowledge-based hybrid methodology combining regression-based, lumped parameter and finite element analyses that can be implemented in the initial phase of vehicle design resulting in a superior side crash performance. As a first step, a regression-based model (RBM) is developed between the injury parameter Thoracic Trauma Index (TTI) of the rear SID and characteristic side impact dynamic response variables such as rear door velocity (final) and intrusion supplementing an existing RBM for front TTI prediction. In order to derive the rear TTI RBM, existing public domain vehicle crash test data provided by NHTSA has been used. A computer-based tool with a Graphical User Interface (GUI) has been developed for obtaining possible solution sets of response variables satisfying the regression relations for both front and rear TTI. As a next step in the formulation of the present hybrid methodology for vehicle side impact safety design, a new Lumped Parameter Model (LPM) representing NHTSA side impact is developed. The LPM developed consists of body sub-systems like B-pillar, front door, rear door and rocker (i.e. sill) on the struck side of the vehicle, MDB, and “rest of the vehicle” as lumped masses along with representative nonlinear springs between them. It has been envisaged that for the initial conceptual design to progress, the targets of dynamic response variables obtained from RBM should yield a set of spring characteristics broadly defining the required vehicle side structure. However, this is an inverse problem of dynamics which would require an inordinate amount of time to be solved iteratively. Hence a knowledge-based approach is adopted here to link the two sets of variables i.e., the dynamic response parameters (such as average door and B-pillar velocities, door intrusion, etc.) and the stiffness and strength characteristics of the springs present in LPM. In effect, this mapping is accomplished with the help of an artificial neural network (ANN) algorithm (referred to as ANN_RBM_LPM in the current work). To generate the required knowledge database for ANN_RBM_LPM, one thousand cases of LPM chosen with the help of the Latin Hypercube technique are run with varying spring characteristics. The goal of finding the desired design solutions describing vehicle geometry in an efficient manner is accomplished with the help of a second ANN algorithm which links sets of dynamic spring characteristics with sets of sectional properties of doors, B-pillar and rocker (referred as ANN_LPM_FEM in the current work). The implementation of this approach requires creation of a knowledge database containing paired sets of spring characteristics and sectional details just mentioned. The effectiveness of the hybrid methodology comprising both ANN_RBM_LPM and ANN_LPM_FEM is finally illustrated by improving the side impact performance of a Honda Accord finite element model. Thus, the unique knowledge-based hybrid approach developed here can be deployed in real world vehicle safety design for both new and existing vehicles leading to enormous saving of time and costly design iterations.

Suspension systems and wheels play a critical role in vehicle dynamics performance of a car in areas such as ride comfort and handling. Lumped parameter models (LPMs) are commonly used for assessing the performance of vehicle suspension systems. However, there is a lack of clarity with regard to the relative capabilities of different LPM configurations. A comprehensive comparative study of three most commonly used LPMs of increasing complexity has been carried out in the current work. The study reported here has yielded insights into the capabilities of the considered LPMs in predicting response time histories which may be used for assessing ride comfort. A shortcoming of available suspension system models appears to be in representation of harsh situations such as jounce movement which cause full compression of springs leading to ‘jerks’ manifested as high values of rate of change of acceleration of sprung mass riding on a wheel. In the current research work, a modified nonlinear quarter-car model is proposed to account for the contact force that results in jerk-type response. The numerical solution algorithm is validated through the simulation of an impact test on a car McPherson strut in a Drop Weight Impact Testing Tower developed in CAR Laboratory, CPDM. This is followed by a detailed comparison of HCM and QCM to examine their suitability for such analysis. For decades, wheel bearings in vehicles have been designed using simplified analytical approaches based on Hertz contact theory and test data. In the present work, a hybrid approach has been developed for assessing the load bearing capacity of a wheel ball bearing set. According to this approach, the amplitude of dynamic wheel load can be obtained from a lumped parameter analysis of a suspension system, which can then be used for detailed static finite element analysis of a wheel bearing system. The finite element modelling approach has been validated by successfully predicting the load bearing capacity of an SKF ball bearing set for an acceptable fatigue life. For the first time, using a powerful commercial explicit finite element analysis tool, a detailed dynamic analysis has been carried of a deep groove ball bearing with a rotating inner race. The analysis has led to a consistent representation of complex motions consisting of rotations and revolutions of rolling elements, and generated insights into the stresses developed in the various components such as balls and races. In conclusion, a simple yet effective fuzzy logic-based yaw control algorithm has been presented in the current research. According to this algorithm, two inputs i.e. a yaw rate error and a driver steering angle are used for generating an output in the form of an additive steering angle which potentially can aid a driver in avoiding straying from an intended path.