White paper

Breaking down silos with Business Process Management

Business Process Management maximizes the scope of SPDM software solutions by ensuring full traceability and interconnectivity in the engineering design processes.


Solve complex MDAO studies in a fraction of the time using a validated advanced panel method

ESTECO and Research in Flight showcase the optimization of a propeller geometry to meet stakeholders' goals in a complex, changing environment of multiple competing requirements and key performance metrics.

A detail of Morpheus Hotel building design
Success Story

Balancing multiple disciplines in AEC

ESTECO Technology helped Bouygues Construction automate the simulation process to identify appropriate designs quicker.

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White paper
From flying taxis to autonomous planes: How MDO is designing the aircraft of the future
From the recent pandemic to rising awareness of climate change, innovation is essential for the survival of today’s aerospace industry. While emergent aircraft can deliver the required level of transformative change, many challenges exist to unlock these next-generation designs. Multidisciplinary Design Optimization (MDO) is the perfect methodology to design new concepts and address complex engineering challenges, helping engineers iterate and discover innovation solutions. When working in the conceptual and preliminary design phases, engineers have the freedom to explore the entire design space using MDO. In this white paper, you'll read about: issues associated with designing the next generation of aircraft, how to choose the right MDO framework.
White paper
How to Accelerate and Democratize Your Design Processes
Within the world of simulation, automation is a leading digital technology that’s proving its worth, accelerating and democratizing every step in the design process. Usability, though, is a key issue for many working with automated solutions. Regardless of their level of expertise, individuals are faced with complex algorithms and must identify the right optimization strategies to get the best result, while taking into account the available resources. In this white paper, you'll read how the ESTECO Autonomous Optimization technology can help: automate every sort of simulation process, free up time and human resources, democratize design space exploration and expedite decision-making processes.
White paper
The road towards safer vehicles goes through Design Optimization
Passive safety systems protect the occupants and other road users in the event of an impact. But designing such systems is a complex undertaking where many variables must be considered. To design passive safety systems, engineers need to meet contrasting objectives and consider multiple conditions, including different impact modes, crash test dummy sizes and types, and a broad range of vehicle variants. Simulation process automation and design optimization are essential to expedite the development process, while balancing different variables and requirements. In this white paper, you'll read about: the multidisciplinary nature of passive safety, the issues faced when designing such systems, how automation and optimization can address these challenges.
White paper
Democratize product development with Simulation Process and Data Management (SPDM)
Simulation process and data management (SPDM) software has become an essential design and analysis capability, especially for large organizations that might be running dozens of simulations at any time, and then needing to track, share and recall that data at the appropriate time in the design lifecycle. Specifically, the SPDM technology is critical to: Automate the execution of complex simulation and enable more engineers to perform routine analysis. Master simulation processes and data with version control, dependency management and traceability. Democratize the access to simulation data among users throughout the company. In this white paper, you'll read how the right SPDM tool helps digitalize product development processes and accelerate time-to-market by scaling up the use of simulation models across global organizations and teams, in the aerospace, automotive, manufacturing and other industries.
Optimization of a high power electric traction machine
This webinar demonstrates the use of the electro-mechanical FEA solution JMAG for modeling and analysis of the electric machine and modeFRONTIER for simulation process automation and optimization. Adarsh Viji Elango from ESTECO, and Sainan Xue and Dheeraj Bobba from Powersys Inc. present the design process of a highly constrained multi-dimensional rotor geometry for an electric traction machine using principles of multi-objective optimization. Agenda: Introduction to Synchronous Reluctance Machines (SynRM) Baseline SynRM design and model setup in JMAG Input design space definition and performance criterion Integration of JMAG model in modeFRONTIER DOE and Optimization Set-Up in modeFRONTIER Optimization result analysis and design selection
Success Story
Pipistrel: flying straight from simulation to production
The ESTECO optimization technology as a way to skip any prototype phase for a hybrid-electric aircraft propeller Pipistrel, an aviation & aerospace company based in Slovenia, relied on ESTECO Technologies to design the propeller for a highly efficient, hybrid-electric aircraft. The work was part of the EU-funded project MAHEPA (Modular Approach to Hybrid Electric Propulsion Architecture), that had the aim of advancing two variants of a low emission, serial hybrid-electric propulsion architecture to TRL (Technology Readiness Level) 6. The modeFRONTIER process automation and optimization software allowed automation in the simulation process and identification of innovative and optimized designs in a limited time. Challenge Engineers at Pipistrel had the challenge to design a propeller, driven by hybrid-electric propulsion system taking into account the different conditions the aircraft meets during the four flight phases: takeoff, climb, cruise and descent. Considering speed, power and thrust requirements changing during the flight, the objective was to maximize takeoff thrust and recuperation power during descent and minimize power during climb and cruise phase. The optimization involved three stages: the preliminary propeller optimization, the airfoil optimization, and the final propeller optimization. ## Solution For this multi-phase optimization project, Rok Lapuh and David Eržen, aero-dynamics engineers at Pipistrel, used modeFRONTIER coupled with CHARM (Comprehensive Hierarchical Aeromechanics Rotorcraft Model) and XFOIL, an interactive program for the design and analysis of subsonic isolated airfoils. Benefiting from the ESTECO process automation technology, Pipistrel could automate the simulation workflows, simultaneously evaluate thousands of designs and identify innovative optimized results. This process was conducted in a fully autonomous way leaving Pipistrel’s engineers the task to select the most appropriate design. With the first propeller optimization, Pipistrel optimized the chord and twist distribution to get the maximum thrust and minimum power for a given set of airfoils. The results were then used as requirements for the airfoil optimization. The design team used modeFRONTIER to design the airfoil under specific geometry constraints (thickness, cur- vature or leading-edge radius), while increasing the lift and reducing the drag. They started a Design of Experiments phase and then used the HYBRID genetic algorithm to successfully run the airfoil optimization and get the Pareto front with the optimal designs. At last, they used the optimum airfoil for the final propeller optimization. With the ESTECO optimization algorithms, engineers at Pipistrel could evaluate almost five thousand designs in a limited time and increase the thrust by 30% during takeoff. ## Benefits Before using modeFRONTIER, Pipistrel went through a manual process to simulate multiple designs and choose the preferred one. With the introduction of ESTECO Technology, Pipistrel engineers not only were able to automate this process, but could evaluate options not considered otherwise. “modeFRONTIER optimization technology gave me the opportunity to think outside of the box - said Rok Lapuh, Aerodynamics Engineer at Pipistrel - We could find a design that is completely different from what we’re used to, but that may work even better”. They also dramatically reduced the go-to-market time as they moved from simulation directly to the production. “We trust the results we get with modeFRONTIER so much that we don’t expect we’ll require a prototype - said David Eržen, Aerodynamics Engineer at Pipistrel - We go straight into production”.
White paper
Driving Change for Autonomous Vehicles
Automation is the next frontier for automotive companies, with a range of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) systems under development. For these ADAS/AD systems to reach the market, design teams need a robust validation strategy. The challenge lies in finding the right balance between minimizing the number of system failures while maximizing the comfort of travel in complex conditions. But existing physical test strategies are unfeasible, taking too long to complete and millions of hours of drive time. In this white paper, you'll read: how simulation can help validate next-generation vehicles using a scenario-based verification plan. how ESTECO technology was key to develop a white-box verification system, which takes advantage of statistical tools to post-process road testing and derive scenarios that are a combination of events identified during road testing.
White paper
Unlock the potential of generative design for Architecture and Construction
The architecture and construction industries are under pressure to improve building performance, reliability and comfort while reducing operational costs and project timescales. As traditional design processes fail to address these challenges, the simulation-based generative design concept is a viable solution. This white paper highlights how it allows engineers to: evaluate the impact of their design decisions, run multiple requirement-led simulations, optimize project-wide build parameters to expedite the best design solution. Find out how our software technologies have helped the world’s architects and structural engineers build a solid ROI and supported the profitability and growth of the construction industry.
Success Story
Optimizing a perfect race engine. ESTECO Academy Design Competition winner
modeFRONTIER enabled Michael Bambula of the University of Florida to run the workflow, integrate third-party software, automate the design exploration process and perform post-process analysis. The winner, Michael Bambula of the University of Florida, presented a top-notch design project, in which he achieved significant performance improvements (64.2 hp @16500 rpm) while developing a complete model for a Moto3 bike and realistic simulations that also considered the specifics of the race track. Organized in partnership with Aprilia Racing and Gamma Technologies, the competition was open to teams of undergraduate and graduate engineering students. The challenge was to improve the design of a 4 stroke single cylinder engine through multidisciplinary optimization (using modeFRONTIER) and 1-D simulation of the engine system with GT-SUITE. The competition award included an internship opportunity at the APRILIA Racing team, which counts several World Championship Awards. The goal of the project was to maximize engine power. Due to the constrained engine architecture, an optimization of the Intake/Exhaust system was performed. Gamma Technologies supplied a set of simulation tools (GT-Suite) to develop the 1-D model of the high-performance engine. Various aspects of the base engine architecture were constrained such as Bore, Stroke, Con Rod Length, Engine Speed, Max Valve Diameters, Max Valve Lift, Max Throttle Diameter, Max Compression Ratio, Non-variable Cam Timing, and Naturally Aspirated. Considering these constraints, the optimization of the cylinder filling (Wave Dynamics) was seen as the logical design direction. modeFRONTIER workflow was used to automate the design exploration process and integrate Excel and GT-Suite for computing lifts value intake and exhaust valve lift profiles and simulating the engine power output. During the development of the 1-D Engine Model there were inherently many unknowns, therefore Michael made assumptions supported by rigorous research. The design variables related to the intake/ exhaust system were automatically found by modeFRONTIER to optimize the output results: sum of engine power across engine speeds speeds from lowest to highest respectively (11500 rpm to 17500 rpm). “modeFRONTIER ran 1000 different designs that varied the input parameters. The Hybrid Algorithm did an amazing job at finding the optimum solutions based on the objective of maximizing the engine power” said Michael Bambula, University of Florida Racing Team. “The analysis went beyond just determining the most powerful engine”, continued Bambula, “in fact the final objective, aimed at determining whether a certain design is sufficient for motorsports, was to compare it to lap times. This is why it was decided that the final group of optimum results from modeFRONTIER would be simulated in OptimumLap software considering, among other assumptions, a Moto3 motorcycle model traversing the Phillip Island Grand Prix Circuit in Australia”.
Advanced design automation with modeFRONTIER & nTop platform
This webinar demonstrates the added value of integrating nTop platform with modeFRONTIER Multidisciplinary Design Optimization (MDO) software through nTopCL to act as an unbreakable generative geometry node. This opens up new and unique possibilities for advanced engineering design exploration. Gaurish Sharma of ESTECO and Evan Pilz of nTopology show how to set up, run, and evaluate the results of an automated Topology Optimization DoE to identify a truly optimal solution. Agenda: Automate DoE in modeFRONTIER and pass design parameters to nTop Platform Generate part geometry using topology optimization and automated post-processing Evaluate the results in ANSYS and select the best design using statistical methods