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  • Multidisciplinary Design Optimization of Vehicle Weight Reduction

    In this study from ESTECO North America, a six-step MDO process is illustrated by solving a 2001 Taurus weight reduction problem with two formulations AAO and CO. The chosen optimization platform is modeFRONTIER plus SOMO. Conclusions show the advantages to using AAO methodology in MDO.

  • Development and Applications of Enterprise Multi-disciplinary Design Optimization (EMDO) Systems

    This new EMDO system is a web-based and mobile-enabled technology, which provides a collaborative and distributed execution platform to manage the complexity of growing demands of large-scale vehicle design projects. It has potential to make significant contributions for cross-attribute program developments in term of weight saving, fuel economy increase, meeting or exceeding multiple attribute requirements, as well as reducing product development time and engineering cost. A large-scale vehicle design case study will be used to demonstrate the capabilities and usefulness of the new EMDO system.

  • Ford Adopts ESTECO Technology to Develop an Enterprise Multidisciplinary Design Optimization System

    We are proud to announce that Ford Motor Company has adopted SOMO, the enterprise collaboration and distributed execution framework developed by ESTECO, as a key tool to enable an Enterprise Multidisciplinary Design Optimization (EMDO) System. 

  • A New Automated Underhood Thermal Management CFD DoE Workflow with ModeFRONTIER

    Ford engineers are challenged to find a cost efficient cooling system concept for the underhood thermal management that suits different powertrains and environments, respecting standards (NACA duct) and without compromising the vehicle’s aerodynamic performance. From a thermodynamics perspective, the problem to address regards the high temperatures deriving from the friction inside the PTU (Power Take-off Unit) and surrounding parts which can cause the oil to overheat.

  • Vehicle Aerodynamic Shape Optimization

    Recent advances in morphing, simulation, and optimization technologies have enabled analytically driven aerodynamic shape optimization to become a reality. This paper will discuss the integration of these technologies into a single process which enables the aerodynamicist to optimize vehicle shape as well as gain a much deeper understanding of the design space around a given exterior theme.

  • Auto-Correlation of Occupant Restraint System Model Using Bayesian Model Validation Metric

    The presentation focuses on the auto-correlation of occupant restraint system model using Bayesian Model Validation Metric

  • Shape Optimization of A Floor Duct For Weight and Pressure Drop Using modeFRONTIER and Sculptor

    The following presentation focuses on the shape optimization of a floor duct for weight and pressure drop using modeFRONTIER and Sculptor

  • Optimization Strategies to Explore Multiple Optimal Solutions and Its Application to Restraint System Design

    This paper focuses on algorithm selection for single objective formulation in order to fin multiple equally good solutions. The four algorithms considered in this paper include Multi-Objective Genetic Algorithms II  (MOGA-II), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Simulated Annealing (MOSA) and Mixed Integer Programming Sequential Quadratic Programming (MIPSQP). modeFRONTIER was used to evaluate the capability of achieving multiple optimal solutions of the selected optimization algorithms. 

  • History and Applications of modeFRONTIER at Ford Motor Company

    In Ford modeFRONTIER is used in different disciplines: safety analysis, NVH (noise, vibration and harshness), aerodynamics and fuel cells. An example is presented for each of those disciplines, as well as the main advantages of the optimization platform.

  • A method for selecting surrogate models in crashworthiness optimization

    In past decades the simulation results of vehicle impacts have reached high confidence levels. However, impact simulation remains computationally expensive, even though it is commonly used. Surrogate model or response surface based design optimization has been widely adopted as a common process in automotive industry and high fidelity models are often required.


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