Engineering Simulation in Automotive

The largest automotive manufacturers use engineering simulation software as a mandatory tool in their design processes. The designs of vehicles and their components are verified through engineering simulation and are optimized based on the results. This way, the number of physical prototypes required in the manufacturing workflow can be significantly reduced. The performance, efficiency, and cost reduction are among the benefits of using this powerful technology.

Whether talking about F1 cars, airplanes, family automobiles, buses, or trucks, their aerodynamics are always tested, as it can influence many different crucial aspects related to the vehicle. Computational fluid dynamics (CFD) play an important role in truck aerodynamics, allowing engineers to investigate the airflow and better understand how air moves around the vehicle. Creating an aerodynamic vehicle is essential to ensure enhanced performance, increased speed, noise reduction, and fuel consumption.

CFD for Truck Design

Each component of the truck design process offers opportunities to reduce aerodynamic drag. Any supplementary component added or modified has an impact on the performance of other components.

There are different ways to optimize a truck design. Some experts suggest that the greatest aerodynamic drag reduction comes from using devices in three main areas: gap, underbody, and rear. Improving truck aerodynamics in these three points can generate the greatest fuel savings for a large majority of fleets [1].

A better cabin design or simple accessories like trailer fairings can improve a truck’s performance by:

• Improving truck stability and rollover
• Reducing splash and spray
• Better weight distribution on the truck chassis
• Reducing fuel consumption
• Improving long-distance behavior
• Reducing driver fatigue

Why is Optimization in Truck Aerodynamics So Important?

17 North American truck fleets, operating more than 62,000 tractors and 217,000 trailers, saw a 3% increase in fuel economy in 2015. This represents a cumulative saving of $501 million on fuel when compared to the 2015 national average fuel spend of 1.7 million over-the-road class 8 trucks.

According to the Annual Fleet Fuel Study released by the North American Council for Freight Efficiency (NACFE), they eliminated operational deficiencies by purchasing a variety of fuel efficiency technologies. As a result, fleet-wide fuel consumption in miles per gallon increased from 6.87 mpg to 7.06 mpg in 2015, the largest margin of improvement since 2012 [2].

In truck design, the testing process can go through several of these methods:

• Engineering simulation, especially computational fluid dynamics and finite element analysis
• Wind tunnel testing – for tractor/trailer vehicles in a wind tunnel, where environmental conditions can be controlled
• Track testing – a large variety of test protocols are used on a test track
• On-road testing – involve physical testing with special devices installed on a truck, for long-distance driving in different roads conditions
• Fleet Composite Evaluation – based on different fleet recording for total miles driven, freight carried, and fuel purchased

Engineering simulation is usually used in combination with physical testing, but the main benefit is that it allows the engineers to test their design early, and make several iterations until an optimized CAD design is created. Once they have a final design, moving towards physical prototyping, with a significant amount of cost-saving, is the next step.

Still, until recently engineering simulation was not an easy approach to testing either. On-premises traditional simulation software starts from $40k, lacking flexibility or collaboration options. They also require advanced technical expertise to be efficiently used. Four years ago, however, the world’s first 100% cloud-based simulation platform—SimScale—was launched, breaking these barriers.

Completely web-based, SimScale doesn’t need investment in hardware and works from any PC or laptop. And with hundreds of learning resources available, becoming an expert in engineering simulation is much easier.

SimScale’s CEO David Heiny tests the capabilities of the platform to solve a real-life engineering problem. Fill in the form and watch this free webinar to learn more!

Watch the Webinar

Ready-to-Use Truck Design Simulation Templates

The SimScale Public Projects library is a place where you can find thousands of simulations performed by SimScale engineers or community users. These projects can be copied and modified to help you set up your own CFD, FEA, or thermal analysis in only a few minutes. Here is a selection of three truck aerodynamics simulations:

Simple Truck Aerodynamics Analysis

This project is a CFD simulation of airflow around a truck moving at 10m/s & 20m/s.

The k-omega SST model was used for this analysis, and the values of “k” and “omega” were calculated using the standard approach based on the velocity and turbulent intensity.

The simulation results include a different screenshot with the stream tracer around the truck for different speeds.

Aerodynamics Simulation of a Semi-Trailer Truck

This project simulates the external aerodynamics of a semi-trailer truck. A steady state turbulent flow analysis with the k-omega SST model was used for the study of turbulence distribution.

To reduce computational efforts, we could also take symmetry conditions along the directional flow. The inlet velocity is 28 m/s with a pressure boundary condition at the outlet.

The simulation investigates the mean flow field around the vehicle. The results processed with ParaView provide insights into how the flow behaves around the vehicle body. The figures also show a high-pressure distribution on the front face and a large flow recirculation region behind the trailer.

Aerodynamics Simulation of a Moving Truck

The project represents a mix between a CFD and scalar moving simulation for a driving truck with the exhaust gases coming out its exhaust pipe.

Due to the asymmetry created, the user considered two scenarios for the geometry adoption: (1) no trailer (2) asymmetric model for the exhaust pipe positioning.

Hence the simulation conditions adopted are 15 m/s speed for the truck moving and 3 m/s for the exhaust gases coming out the pipe.

If you’d like to learn more about the SimScale cloud-based simulation platform and its features, download this overview.

The post Truck Design: 3 CFD Simulations for Improving Truck Aerodynamics appeared first on SimScale.

Open Source Vs. Proprietary A Historical Debate in Developers' Communities

In the 80s, people with a more commercial outlook began to foresee the potential in using individual licensing agreements for software applications, without granting open access to the software code. For such proprietary software, original authors have the exclusive property rights to the source code. This makes them the only ones legally allowed to copy or modify it—so the person, team, or organization who created the code maintains full control over it.

The free developers’ community continued to promote their open-source philosophy as a set of values known as „the open-source way” [1]. Any open-source initiative, project or product is meant to be created in the spirit of collaborative participation, free exchange, transparency, rapid prototyping, meritocracy, and community support. From the early days of the software industry, heated debates were generated between the two communities, with commercial reasons on one side, and ethical disputes on code copyrights on the other.

Open Source Vs. Proprietary What is the Difference Between Proprietary and Open-Source CAE Software?

From a licensing perspective, the main differences are related to the cost and the conditions of using the software. Being primarily commercial, the majority of engineering software applications are proprietary. They are built with hidden source code and offered in a perpetual licensing system. Users must agree to licensing conditions—esentially consenting not to do anything with the software that the authors have not explicitly permitted.

For open-source software, the copyright belongs to the author, or a third party (for instance, software like OpenFOAM for CAE). Here, the vendor only plays the role of the distributor. A variety of open-source CAE software licenses is available for users, providing considerably more freedom and flexibility, and these are offered by the author within the license agreement. The Open Source Initiative [2] better explains these freedoms and the criteria open-source software must comply with:

• The license shall not restrict any party from selling or giving away the software
• The program must include source code and must allow distribution of source code, as well as a compiled form
• The license must explicitly permit the distribution of software built from modified source code (it may require derived works to carry a different name or version number)
• The license must be technology-neutral
• The license must not restrict other software
• The license must not be specific to a product
• No discrimination against fields of endeavor
• No discrimination against persons or groups
• The license must allow modifications and derived works
• The rights apply to all to whom the program is redistributed without an additional license by those parties

Open Source Benefits of Open-Source Software

The freedom to run the software for any purpose, on any number of machines, can significantly reduce costs, which is usually what drives a company’s decision to adopt open-source software. A lower Total Cost of Ownership (TCO) is considered to be one of the most important advantages of open-source solutions. Accurately estimating the TCO is not always simple, since the costs that need to be taken into account include (but are not limited to) various expenditures associated with administration, licensing, hardware and software updates, training and development, maintenance, technical support, and more. However, a TCO analysis often serves as the main planning and decision-making tool—and opting for proprietary software tends to increase costs across all dimensions.

Main Barriers to Using Proprietary Software

• It is developed for a single purpose, applications are separately packaged;
• Licenses and maintenance are very expensive;
• Low level of customization and adaptability;
• You are dependent on the developer for all updates, support, and fixes;
• Security issues—loopholes are slower to be discovered and patched;
• Vendor support is conditional to maintenance subscription.

Most Important Open-Source Engineering Software Benefits

• It’s essentially free;
• It’s continually evolving and less prone to bugs;
• You are not locked into using a particular vendor’s system;
• You can adapt the software to fit your own business requirements;
• You get access to the support offered by technical community;
• Increased degree of modularity and scalability.

Features of Open Engineering Platforms

The differences between proprietary and open-source solutions, which we have discussed above, apply to engineering/CAE platforms as well. In addition to a lower TCO and other financial benefits, an engineering platform user should be aware of the many qualitative advantages that CAE platforms, built “the open-source way,” provide:

• Open exchange — free exchange of ideas creates an environment where people can learn from each other and contribute to the creation of new ideas.
• Collaboration — open participation enables co-creation and faster problem-solving. With an open CAE platform, engineers and designers with different specializations can share information within their community and collaborate with suppliers and manufacturers to come up with better products.
• Rapid prototyping — can generate rapid failures, but ultimately results in better solutions.
• Meritocracy — in open-source communities, the best ideas win and everyone has access to the same information.
• Community — people with the same principles bring together diverse ideas and share their work.

Why Isn’t Everyone using Open-Source Software?

Despite the many benefits mentioned above, there are a number of reasons why open-source software adoption has not been as widespread as proprietary alternatives. Since creating a commercial software that would generate revenue is not a requirement, the needs of the end user tend to be neglected in favor of developers’ preferences. As a result, there are several disadvantages of using purely open-source software:

• The software interface is much less “user-friendly,” and often difficult to use unless you have extensive coding experience (as is the case with OpenFOAM–learn more: Why OpenFOAM Users Should Try SimScale).
• When you run into problems, you only have the community of your fellow users to rely on for support, which they provide on a voluntary basis.
• One benefit of an open system is that many people are identifying and fixing the bugs; however, that also means that it will be vulnerable to users with malicious intentions.

So how can one take advantage of the benefits of open-source without compromising on convenience and security?

SimScale – A CAE Platform Based on Open-Source Solvers

One solution would be to try to take the best of both worlds. The SimScale simulation platform is based on cutting-edge open-source solver technology. It has a user-friendly interface, dedicated support, maintenance and data security measures. In the spirit of “the open-source way,” it provides a free Community account with full access to all its features. Additionally, its Professional Plan offers many perks of traditional proprietary software with much more flexible and affordable pricing plans.

By adopting the most popular and reliable open-source tools of the industry, SimScale is able to rapidly implement new technology and ensure full compatibility with third-party software tools. Behind the intuitive user interface, the following open-source solvers enable the powerful simulation functionalities of the platform:

OpenFOAM — is a free, open-source CFD software that was primarily developed by OpenCFD Ltd, and is distributed by OpenCFD Ltd and the OpenFOAM Foundation [3]. It has a large user base across most areas of engineering and science, from both commercial and academic organizations.

OpenFOAM has an extensive range of features that are capable of solving anything from complex fluid flows involving chemical reactions, turbulence, and heat transfer, to solid mechanics and electromagnetics. Many industry leaders—such as Airbus, BMW, Ford Motor, General Electric, Siemens, and Volkswagen—are using this toolbox for various applications.

Code_­­Aster — developed by EDF, Code_Aster offers a full range of multi-physics analysis and modeling methods that go well beyond the standard functions of a thermomechanical calculation code. These range from seismic analysis to porous media via acoustics, fatigue, stochastic dynamics, etc. Its modeling algorithms and solvers are constantly evolving and improving (1,200,000 lines of code, 200 operators)[4]. Code_Aster is used for various applications by industry-leading companies, including Airbus, Rolex, and Valeo.

Other open-source solvers integrated on the SimScale platform:

CALCULIX — a package developed by a team from MTU Aero Engines as a tool to build, calculate, and post-process finite element models.

YADE — an extensible open-source framework for discrete numerical models, which enables virtual simulation of bulk material behavior within industrial applications.

MESHING ALGORITHMS — as the results of a simulation also depend on the mesh quality, the SimScale platform combines a large number of powerful meshing algorithms which allow users to quickly create accurate and robust computing grid.

Learn more about the cutting-edge open-source solver technology that the SimScale platform is based on, and browse our validation library with a large number of evaluated simulations of standard scientific cases.

Discover all the simulation features provided by SimScale. Download the document below.

Download SimScale Features Overview

The post Open-Source or Proprietary Software — What Is Best for Users? appeared first on SimScale.

Pump Design Main Pump Types

Simply classified, pumps fall into 2 major categories: centrifugal pumps (or rotodynamic pumps) and positive displacement pumps [1].

Centrifugal pumps produce a head and a flow by increasing the liquid’s velocity through the machine, with the help of a rotating vane impeller. These pumps include radial, axial, and mixed flow units. Centrifugal pumps can further be classified as:

• end suction pumps
• in-line pumps
• double suction pumps
• vertical multi-stage pumps
• horizontal multi-stage pumps
• submersible pumps
• self-priming pumps
• axial-flow pumps
• regenerative pumps

Positive displacement pumps operate by alternating between filling a cavity and displacing a given volume of liquid. The positive displacement pump delivers a constant volume of liquid for each cycle against varying discharge pressure or head. The positive displacement pumps can be classified as:

• Reciprocating pumps — piston, plunger, and diaphragm
• Power pumps
• Steam pumps
• Rotary pumps — gear, lobe, screw, vane, regenerative (peripheral), and progressive cavity

Pump Design

Pump design needs continuous improvement in all product development stages, from concept to design, laboratory testing, engineering validation, and finally manufacturing.

Crucial in pump design is ensuring it fits the efficiency parameters. For a heating circulating pump, the total efficiency can be calculated by multiplying the motor efficiency and the hydraulic efficiency.

Depending on the pump type and size, efficiency can vary. For glandless pumps, the total efficiency can be from 5% to 54%, compared to pumps with glands for which the same factor is between 30% and 80% [3].

Learn More about CFD in

Pump Design Why is Simulation Important for Pump Design?

Any design and optimization process should be based on a complex set of solid mechanics, fluid dynamics, and thermal simulations. The main advantage of engineering simulation is that it allows the user to virtually test the CAD model early in the design process, and consequently iterate until finding the best possible version. As simulations can be done with a computer, the number of physical prototypes required is massively reduced.

What SimScale brings is the possibility, for the first time in history, to create these simulations in the cloud, by only using a web browser and without the need to invest in a powerful computer or licenses.

Used by pump designers worldwide, SimScale has the same web-based environment for all the simulation features, enabling one to validate experimental results, run parametric studies, and optimize designs.

Here are a few examples of pump simulation projects:

Centrifugal Pump with MRF

This is a simulation project for a centrifugal water pump using the steady state, the Multiple Reference Frame method (MRF) and the k-omega SST turbulence model.

The MRF model is one of the two approaches for multiple zones and is a steady state approximation in which individual cell zones can be assigned different rotational and/or translational speeds.

Performing MRF simulations is computationally much less demanding than transient modelling. MRF provides good approximations with less computational effort and considerably less computation time. The center of rotation, rotation axis, and angular velocity define the MRF rotation.

In this case, the geometry includes a standard backward type pump impeller and volute casing. The flow intake is done axially from the inlet pipe, exiting from the shown outlet. In addition, a separate region specifies the MRF zone in the vicinity of the impeller. For a given pump, it is vital to know the flow parameters in order to attain the accurate pressure rise. Here arbitrary values were used to demonstrate an operational example. The simulation investigates the mean velocity and pressure field in the pump.

The results, therefore, show the velocity vectors and pressure rise at the outlet for a cut section of the pump. The simulation provides insight as to how much pressure rise the pump creates for a given mass flow and rotational speeds.

Centrifugal Pump with AMI

This project simulates a transient analysis of a centrifugal pump with a rotating mesh using the Arbitrary Mesh Interface (AMI) approach and k-omega SST turbulence model.

In the AMI approach, a mesh interface is created between the moving and stationary parts of the mesh. AMI simulations are full “Transient” and therefore are computationally much more intensive than MRF. Also, they take all transient effects into account and are usually sensitive to the time step length. In addition, AMI could be specified as oscillating or full rotating motion.

In this case, the geometry has a backward type pump impeller with a volute casing around it. The flow enters the domain from the inlet and exits from the outlet shown. An AMI zone created around the impeller defines the rotating mesh region.

The simulation analyzes the instantaneous flow in the pump. The computed results show the instantaneous velocity field and instantaneous pressure profile for a section plane. The simulation provides insight into the time-dependent changes in the flow.

SimScale’s CEO David Heiny tests the capabilities of the platform to solve a real-life engineering problem. Fill in the form and watch this free webinar to learn more!

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Just upload a design and improve it!

SimScale’s Public Project library is an open collaborative platform offering different models of pumps and suggested simulations. Basic pump design simulations are available for free and any engineer or designer can copy and modify them for free:

Here are a few links to some interesting pump projects in the SimScale Public Project library.

Also, I invite you to read this case study and learn how CFD technology would have helped optimize the centrifugal pump design in less time and with less effort. As simulation technology has become available to all engineers, this is definitely a step to be integrated into the pump design process.

Download this case study for free to learn how the SimScale CFD platform was used to investigate a ducting system and optimize its performance.

Download Case Study

The post Pump Design Optimization with Computational Fluid Dynamics appeared first on SimScale.

Pipe Design How to Begin a Piping Design?

Here are a few essential key factors to consider in the piping design process:

• Establish the optimum pipe diameter to guarantee optimum fluid flows for the pipeline system, depending on the specific fluid transportation needs
• Establish maximum and working flow pressure and temperatures
• Analyzing optimal pipe material composition depending on the chemical fluid properties
• Design pipeline network architecture for optimal fluid velocity
• Ensure safety factors and quality criteria for the whole pipeline design and pipeline network construction

The most common engineering problems that occur in piping design involve the fluid flow behavior and the analysis of stress in pipes networks. CFD simulation, as a fluid simulator, offers advanced methods to test them.

To easily understand the most important advantages offered by simulation software, let’s review some piping design simulation examples available in the SimScale Public Projects Library:

Thermal Thermal-Structural Analysis of a Cracked Pipe

Due to their extensive use, it is important to consider durability and damage tolerance for the whole life cycle of pipes. A crack in a pipe can result in an important loss of fluid, heat or steam with unpredictable consequences and significant damages costs. According to Pipeline & Hazardous Materials Safety Administration, in the last 20 years, pipeline incidents have caused over $6.3 billion in property damages. Statistics show 250 pipeline incidents per year, with an average of 2.5 million barrels spilled. [1]

In this thermal-structural analysis of a cracked pipe, the same simulation scenario considers a non-cracked and a cracked pipe. The CAD model was created with Onshape and then imported into the SimScale platform. For this analysis, a steady state thermal-structural analysis was selected, since the temperature and pressure were considered to be static.

If you’d like to learn more about how to use Onshape and SimScale together for a completely cloud-based design process, watch this free webinar recording.

CFD Scalar Transport in T-Junction Pipe

Experiments on piping design flow show that triggering turbulence depends sensitively on initial conditions. In real life, fluid flow turbulence generates many of the incidents, especially in the pipeline junction areas. This project simulates scalar transport mixing in turbulent T-junction pipe flow.

This project validates the mixing of a passive scalar quantity in a single phase flow at a Reynolds number of 24900 via the Reynolds-Averaged Navier–Stokes (RANS) approach and k-omega SST turbulence model. The geometry for this case is a T-junction pipe with two inlets.

The project simulates the mixing process at the junction and the scalar distribution downstream in the mixing pipe. The results give the contours of the scalar quantity and the flow field in the vicinity and downstream of the T-junction.

FEA Bending Analysis of an Aluminium Pipe

Structural analysis is frequently used in piping design and simulation process due to the elastoplastic behavior of aluminum or other piping materials. In this project, the bending of an aluminum pipe is done via a rolling process.

A nonlinear static structural analysis was selected. The penalty contact with higher stiffness was used for the contact between the pipe, roller, and molder. The results show the von Mises stress and total nonlinear strain formed in the bent aluminum pipe.

SIM Pipeline Network

The long-distance piping networks and junction’s architecture generate many problems for the piping maintenance in oil and gas and industrial water transportation.

This simulation project captures the nuances of turbulent pipe flow. With it, it is possible to visualize the flow phenomena at pipelines bends, junctions, and nozzles. The engineer created the geometry with Salome 7.4.0 and meshed on SimScale using the snappyHexMesh algorithm.

Non-Newtonian Flow of Concrete

In many practical applications, the piping system transports a mix of fluids with different physical properties and different behavior during the flow. One of these applications frequently used in the construction industry consists of concrete mixes. High slump or “flowing” concrete mixes are economical ready-mix products that allow a maximum flow without sacrificing strength by adding water at the job site.

This project simulates the flow of a non-Newtonian fluid (concrete) through a pipeline. The mixture mass proportions are 45% Gravel, 29% Sand, 9% Fine Sand, 17% Cement. The geometry constructed in Salome uses a CAD model of a ball valve.

The simulation shows the capability of the SimScale platform to model the behavior of non-Newtonian fluids; in this case, concrete.

Comparison of Pipe Junction Designs

In pipeline design projects, engineers need to understand the pipe’s infrastructure behavior in different circumstances. Also, it is efficient to design two or more versions for the same pipe junctions.

Here is a good example of two different piping designs, simulated considering their downstream behavior. This project shows also how CFD can help in choosing the best design version. The main difference between the two models is the way the small pipe connects to the main pipe at the junction. Version one is a simple connection of both pipes. In version two, the small pipe continues into the larger pipe and opens up in the middle of the larger pipe.

For this project, the engineer chose an incompressible, steady-state simulation setup applying a k-omega SST turbulence model. The same simulation setup is used for both versions. If this model is used in the mixed flow of two fluids with very similar physical characteristics, the pipeline engineer will finally choose the version one, for a better efficiency. The simulation of both junction models also provides an immediate feedback on the performance. In this way, the design process progresses significantly faster.

Pipe Hollow Drawing

This project demonstrates a simple simulation of a hollow manufacturing process. The simulation uses the nonlinear static stress analysis method.

Performed on an 8-core machine, the whole simulation took less than 2 hours.

The project also allows the analysis of the stress field, that results from the manufacturing process.

Aerodynamics of a Pipe with Vent Holes

Aerodynamics analyses are common across many industries. Even in the healthcare equipment industry, product engineers can simulate the flow aerodynamics inside different pieces of medical equipment.

This project shows firstly how to analyze the internal airflow through a medical device. For it, a fixed volume flux was applied at the inlet and a zero-gradient boundary condition at the outlet. A k-omega SST model is also used to account for turbulence effects. Finally, the steady-state simulation needed around 320 iterations to reach satisfying convergence criteria which took around 30 minutes on a 4-core machine. These kinds of simulation results can be also used to optimize the design in terms of the velocity peaks and the pressure drop of the device.

Creep Analysis of a Superheater Boiler Pipe

Superheaters increase the thermal efficiency of boiler equipment. They consist of several pipes which take the saturated steam and convert it into super-heated steam, in order to use it in steam engines, steam reforming, and other processes.

Working in a high steam pressure, superheater materials should also be resistant to industrial higher pressures and temperatures for extended periods of time.

In this project, the superheater pipe is tested for the high steam pressure and then the stresses relaxed for maximum 100,000 hours. Due to symmetry, simulation considers only part of the pipe.

Cyclic boundary conditions according to symmetry can be applied in order to represent the simulation for the whole pipe. The steam pressure of 10 MPa was also applied to the inner surface of a pipe.

Pipe Elbow Joint

In piping design, engineers can combine straight tubular pipes in different configurations and for different tubing sections. The elbow joint is probably one of most common piping fit. Also, this is usually installed between two lengths of pipe to allow a change of direction. Here the vertical displacement is similar to bending in beams. The results show the von Mises stress, the vertical displacement, as well as weakest point of the elbow, which is the joint between the fixed flange and the elbow body at the bolt hole.

The post 10 Piping Design Simulations: Fluid Flow and Stress Analyses appeared first on SimScale.

A few decades ago, aerospace became the first industry to embrace engineering simulation. From nanotechnologies and micro-composite materials to futuristic wings profiles, many research projects in aircraft design focus on finding better solutions and bringing them to market faster, while simultaneously reducing costs.

Using engineering simulation software as part of their development process, aerospace companies and engineers can evaluate different designs earlier in the development process. This streamlines the design process by reducing the number of required physical prototypes.

SimScale provides the opportunity to simulate and test designs completely in the web browser, giving access to all analysis capabilities and collaboration options. As a cloud-based CAE platform, SimScale makes it possible to perform powerful CFD simulations or FEA from any device.

Here are a few CFD software simulations related to the aerospace industry from the SimScale Public Projects Library, which engineers can copy and use as templates for their own CFD analyses of different aircraft systems and components.

Aircraft Design Aircraft Design Aerodynamics Analysis

This aircraft simulation shows the airflow distribution around an aircraft design at low subsonic compressible flow regime. The project was used in the Aerospace Workshop featuring EUROAVIA organized by SimScale, which you can watch immediately by filling out this form.

The flow of air around the commercial aircraft model was simulated via the Reynolds-averaged Navier-Stokes (RANS) method. The flow conditions were Mach number M = 0.35, Angle of Attack = 2 degrees, Pressure P = 100000 pa and temperature T = 0 degrees Celsius. For turbulence modeling, the k-omega SST model was used with the wall function approach. A ramping of the velocity boundary condition was applied up to the free stream value for better convergence.

If you want to try your own simulation project, see our step-by-step tutorial on how to set up and perform this analysis.

Aircraft Simulation Aircraft Landing Gear CFD Analysis

CFD simulation of a landing gear carried out with SimScale

This landing gear simulation shows how SimScale can be used for an airflow analysis around an aircraft landing gear. Landing gears are among the most critical components of an aircraft. During take-off and landing operations, the wheels can cause problems that may affect the safety and security of the plane and passengers.

In this airflow analysis, the large eddy simulation (LES) method was used. The geometry is a simplified version of a common front landing gear configuration for any commercial aircraft. The free stream flow velocity was 35m/s under standard conditions. A ramping of the velocity boundary condition was applied to gradually increase the velocity from a low value to the free stream value for faster convergence. The simulation investigates the instantaneous velocity profiles and the wake vortices. The CFD simulation results give an insight into the flow field in the wake regions.

Aircraft Design Optimization Optimization of a Wing with CFD Simulations

Aircraft wing simulation with SimScale

The shape and positioning of the wings are what determines the efficiency of an airplane in flight.

This project simulates two designs of aircraft wings and their aerodynamic effects. This project was also a homework exercise for the SimScale Aerospace Workshop – Session 2. This exercise involves simulating the aircraft wing with applied bending and torsional load due to wind pressure.

The task requires the user to set up six different configurations with three different models and compare the results. Its purpose is to demonstrate how the deformation and stresses change with each structural optimization of a wing. The figure shows the possible load configurations with the initial model.

Aircraft Wingtip Design Wingtip Vortices Simulation

Velocity field for a wing with winglet

A span-wise difference in lift generation creates wingtip vortices. These vortices cause a destabilization and loss of performance in the form of a reduction in lift. This project demonstrated that winglets could be the best solution and an effective measure to reduce the strength of wingtip vortices.

The simulation investigated the velocity fields in two parallel models, for a wing with and without a wingtip attachment. The simulation clearly demonstrated losses of performance in the model without a wingtip attachment. This was due to the vortices that reduce the lift effect on the wing.

In the second model, wings with winglets generate larger lift than those without, when all other parameters are the same.

Aircraft Design Aircraft Cabin Ventilation

CFD analysis of the ventilation inside an aircraft cabin with SimScale

The aircraft ventilation system is essential for passengers’ comfort. Here is a project that simulates the airflow inside an aircraft cabin.

This CFD analysis shows two cabin configurations and their effect on the airflow pattern. It initially involves a ventilation simulation in an aircraft cabin. To find the best solution, six alternative configurations of inlet and outlet are investigated.

This project was also used in the SimScale Aerospace Workshop. Watch the recordings for free.

Set up your own simulation via web in minutes by creating a free account on the SimScale platform. No installation, special hardware or credit card is required.

Community Plan Professional Trial

The post 5 Ready-to-Use CFD Simulations for Aircraft Design appeared first on SimScale.

It is widely known that OpenFOAM is not the most user-friendly software, however. Acknowledging its power and aiming to make OpenFOAM’s functionality easier to use, SimScale integrated it into its cloud-based simulation platform. With an easy-friendly interface that integrates various solvers including OpenFOAM, Code_Aster, and CalculiX, SimScale developed further, becoming an open ecosystem where simulation functionality, content, as well as people, are brought together in one place. SimScale is not open source, but through the Community account, engineers and designers can join free of charge and collaborate effectively with their peers.

Challenges in Classical Simulation Methods

Current simulation challenges are related to the integration and automation of simulation tools in a very complex CAE environment, including automatic geometry retrieval, surface and volume meshing, and sensitivity and optimization studies. In terms of the solver settings and user expertise, computational fluid dynamics is considerably behind finite element analysis, making the problems of software development and usability more pressing.

Range and quality of physical models, solver settings, and solution algorithms, as well as the lack of robust automatic solution control, brings considerable complexity to the user. The current state of solver development aims to produce monolithic general purpose tools, trying to tackle all physical problems for all users. These are few consequences which can arise [1]:

• Simulation software becomes exceedingly complex due to the interaction between numerous physical models, solution strategies, and solver settings. This leads to development bottlenecks and difficulties in testing and validation;
• User requirements may involve experimental material properties, additional equations in the system or coupling with multiple external packages into simulation networks;
• Monolithic software necessarily implies that for any set of physics only a small subset of functionality is being used. The impact of unused or incompatible model combinations remains, typically in unnecessary memory usage;
• A drawback of monolithic tools is the tendency to use identical discretization and numerics even when they are clearly sub-optimal, simply because they “fit into the framework”.

What is OpenFOAM?

OpenFOAM is free, offering to users the freedom to run, copy, distribute, study, change, and improve the software. OpenFOAM was developed primarily by OpenCFD Ltd in 2004 and distributed by OpenCFD Ltd and the OpenFOAM Foundation. It has a large user base across most areas of engineering and science, from both commercial and academic organizations. It has an extensive range of features to solve anything from complex fluid flows involving chemical reactions, turbulence, and heat transfer, to acoustics, solid mechanics, and electromagnetics.

OpenFOAM is first and foremost a C++ library, used primarily to create executables, known as applications. The applications fall into two categories: solvers—each of them designed to solve a specific problem in continuum mechanics—and utilities—designed to perform tasks that involve data manipulation. New solvers and utilities can be created by its users with some pre-requisite knowledge of the underlying method, physics, and programming techniques involved [2].

OpenFOAM is a collection of approximately 250 applications built upon a group of over 100 software libraries (modules). Each application performs a specific task, for example, the snappyHexMesh application that can generate meshes for complex geometries, such as for a vehicle. The simpleFoam application could then be used to simulate steady-state, turbulent, incompressible flow around the vehicle.

The main resource for OpenFOAM’s community of developers is the OpenFOAM User Guide, which

• Examines the setup of input data files for a CFD analysis. The input data includes time information (start time, end time, and time step) and controls for reading and writing data (time, format, and compression)
• Describes the setting of numerical schemes that affect accuracy and stability of a simulation. Matrix solver controls and algorithm controls are also explained that affect computational time and stability
• Includes a chapter on meshing, beginning with the mesh structure of OpenFOAM and the handling of boundaries and boundary conditions
• Includes applications that convert meshes from well-known formats into the OpenFOAM format and detailed coverage is given to the principal conversion applications (e.g., fluentMeshToFoam)
• OpenFOAM is shipped with a version of ParaView that enables the visualization of elements used commonly in CFD such as geometry surfaces, cutting planes, vector plots, and streamlines. Animations can be generated conveniently from ParaView.

In this case study, the SimScale cloud-based CAE platform was used to investigate a ducting system and optimize its performance. Download it for free to learn how.

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CFD Analysis Covered by OpenFOAM

OpenFOAM is gaining considerable popularity in academic research and among industrial users, both as a research platform and a black-box CFD and structural analysis solver. The main ingredients of its design are:

• Expressive and versatile syntax, allowing easy implementation of complex physical model
• Extensive capabilities, including wealth of physical modeling, accurate and robust discretization, and complex geometry handling, to the level present in commercial CFD
• Open architecture and open source development, where complete source code is available to all users for customization and extension at no cost

OpenFOAM does not have a generic solver applicable to all cases. Instead, its users must choose a specific solver for a class of problems to solve. The solvers with the OpenFOAM distribution are in the SOLVERS directory, reached quickly by typing app at the command line. This directory is further subdivided into several directories by category of continuum mechanics, for example, incompressible flow, heat transfer, multiphase, Lagrangian, and combustion. Each solver is given a name that is descriptive.

The current list of solvers distributed with OpenFOAM is covering a wide spectrum of CFD analysis:

• ‘Basic’ CFD codes
• Incompressible flow
• Compressible flow
• Multiphase flow
• Direct numerical simulation (DNS)
• Combustion
• Particle-tracking flows
• Molecular dynamics methods
• Direct simulation Monte Carlo methods
• Electromagnetics

Why is SimScale Important for OpenFOAM Users?

SimScale is based on cutting-edge open source solver technology, including OpenFOAM, which is currently used by leading companies in a large number of industries such as automotive (BMW, Ford, Volkswagen), aerospace, (Airbus), process technology (Siemens), and power generation (General Electric).

The key benefits offered by SimScale to OpenFOAM users or other open source solvers are deeply related to the open source principles:

• Quick integration – The SimScale platform can rapidly implement new technology and ensure the greatest compatibility between SimScale and third-party software tools, with high accuracy.
• Easy scripts integration – Full functionality of OpenFOAM CAE in a friendly interface; the facilities of integration and ease of use SimScale reduces the headaches of simulation technology users who are not conversant with scripts.
• Open exchange environment – SimScale creates an environment where people can learn and use the open information to creating new ideas.
• Open participation – Using SimScale, engineers with different specializations can share information about the products with designers within the product development teams, between divisions or with suppliers.
• Open collaboration – SimScale promotes free collaboration which generates creation and open support for problem-solving.
• Open prototyping – Can generate rapid failures, but can lead to better solutions found faster. For CAE and specifically OpenFOAM users, this is one of the most important advantages of creating high-quality products and fast release on the market.
• Open access to meritocracy – In open source communities the best ideas win and everyone has access to the same information. With the SimScale Public Projects, the benefit is the same, as every user with a Community account creates public simulations that other members can copy, change or improve according to their needs, everything while having the option to share or comment on projects.
• Open community – People with the same principles bring together diverse ideas and share their work. In engineering simulation, the CAE community contribution is essential to innovation.

These were eight reasons why OpenFOAM users should give SimScale a try. With more than 100,000 engineers and designers relying on SimScale for their simulation projects, the platform is so much more than an interface for different solvers. It is a powerful online simulation software, with plenty of features to choose from and a CAE community wherein everyone can collaborate and share their work with their peers. Download this overview to learn about all its features.

The post Why OpenFOAM Users Should Try SimScale appeared first on SimScale.

Building Thermal Comfort Why is Thermal Comfort in Buildings Important?

It’s simple: feeling comfortable in an interior space directly impacts people’s mood. In office buildings, working in optimal conditions enables us to think and work better, and thermal comfort contributes not only to well-being but even to productivity. Eliminating potential health hazards is also a very important aspect of maintaining ideal thermal comfort.

It is relatively simple to design adequate HVAC systems from the inception stages, particularly when it comes to individual rooms or single office spaces. Things become somewhat more complicated when considering the design of complete buildings, where each room and floor have quite different thermal comfort parameters: different number of people, different sizes, placing of windows may differ, diversity of the electronic equipment or the vicinity of special areas such as server rooms, central heating systems, staircases, and other service premises may alter the thermal requirements.

All of these factors can be taken into account in the early stages of the design stage with the help of engineering simulation. Download this free white paper to learn how to ensure comfort in buildings with the aid of cloud-based CFD simulation.

Thermal Comfort What is Thermal Comfort?

According to the international standard EN ISO 7730, thermal comfort is: “that condition of mind which expresses satisfaction with the thermal environment”. In simple words, is the comfortable condition where a person is not feeling too hot or too cold. [1]

Human thermal comfort cannot be expressed in degrees and can’t be defined by an average range of temperatures. It is a very personal experience and a function of many criteria, which differs from person to person in the same environmental space. The Health and Safety Executive estimates that reasonable comfort can be established when a minimum of 80% indoor occupants are feeling comfortable with the thermal environment. [3]

Download this free case study to learn how the SimScale cloud-based CFD platform was used to investigate a ducting system and optimize its performance.

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Thermal Factors What Influences Thermal Comfort in Buildings?

Thermal comfort is a cumulative effect resulting from a series of environmental and personal factors. Environmental factors include [1]:

• Air temperature — The air contact temperature measured by the dry bulb temperature (DBT)
• Air velocity (AV) — The air contact velocity measured in m/s
• Radiant temperature (RT) — The temperature of a person’s surroundings; generally expressed as mean radiant temperature (MRT) which is a weighted average of the temperature of the surfaces surrounding a person and any strong mono-directional radiation, such as the solar radiation
• Relative humidity (RH) — The ratio between the current amount of vapor in the air and the maximum amount of water vapor that the air can hold at that air temperature, expressed as a percentage

Personal factors are also important and are independent of the environment:

• Clothing — Clothes insulate a person from exchanging heat with the surrounding air and surfaces.
• Metabolic heat — The heat produced by physical activity. Usually, a person who stays still feels cooler than those who are moving.

There are other contributing factors that could be considered such as the availability of drinks and food, acclimatization device, or health status of the individual.

Thermal Simulation How to Control Thermal Comfort with Simulation Software

Whether you are a civil engineer, a mechanical engineer or an HVAC designer, engineering simulation software can be used to simulate optimal thermal conditions. Inlet and outlet vanes sizes and positions can be optimized to minimize energy costs. Moreover, the SimScale cloud-based CAE platform can be used for preliminary virtual testing of ventilation systems, fans or entire building designs to easily visualize airflow and predict performance.

The optimization of HVAC systems is based on CFD and thermal simulations. Airflow distribution and dynamics can be simulated in any building space, starting from basic aspects such as the infusion of fresh air and removal of stale air, the heating produced by electronic devices, walls insulation, office cubicles or windows/doors exposure to external factors.

You can find more details about the simulation of optimal thermal comfort, the factors that influence it and other practical aspects related to HVAC system and building design in this: “How to Improve Thermal Comfort in an Office Environment”.

Moreover, in the SimScale Public Projects Library, you can find many free templates that you can copy and use to set up your own simulation to predict thermal comfort in buildings design or optimize HVAC designs for different office spaces, commercial buildings or residential ones.

The post Thermal Comfort in Buildings: How to Better Control and Predict appeared first on SimScale.

Computer simulation technology has developed in close relation with both the computer industry and engineering processes. Associated with the process manufacturing industries, simulation has been customarily used as a tool to increase the production capacity.

In the early stages, computer simulation was not very accessible; a lengthy process, the ambiguity of resulting models, and a large number of required specialists resulted in prohibitively expensive associated costs. Until the wide-scale adoption of computing algorithms in 1970’s, industrial engineers learn about computer simulation models in schools but rarely apply them.

Development in computing, modern programming language, visualization tools, and graphics have had a huge impact on the evolution of simulation technology.

Main History Milestones

1943 – ENIAC (Electronic Numerical Integrator and Computer) was the first digital machinery construction and started in secret at the Moore School of Electrical Engineering, University of Pennsylvania. Its main goal was ballistic trajectory calculation [1];

1945 – Jon Von Neumann developed the “merge sort” algorithm, which was integrated with one of the first computer simulation programs running on the DVAC (Electronic Discrete Variable Automatic Computer) [2];

1952 – John McLeod, a pioneer in modern simulation, founded the first Simulation Council, known today as Society for Computer Simulation (SCS) [3];

1961 – IBM presented the “Gordon Simulator” to Norden (systems design company), which comprised a tool used to design a system to distribute weather information to general aviation [4];

1964 – CACI Products Company released SIMSCRIPT, a powerful free-form simulation language designed to simplify writing programs for simulation models, used especially in inventory simulations [3];

1967 – Norwegian Computer Center developed the language Simula67 [3];

1967 – Continuous System Simulation Language (CSSL) was developed by the Society for Computer Simulation [3];

1979 – Alan Pritsker developed the first version of SLAM, a FORTRAN based computer simulation language;

1998 – Micro Saint v2.0 for Windows 95 provided automatic data collection, optimization, and a new Windows interface, without any programming language requirement;

2008 – NASA released the Standard for Development of Models and Simulations;

2012 – Barna Szabo and Ricardo Actis introduced simulation governance as a technical requirement for mechanical design [5];

2013 – SimScale officially released the world’s first and only 100% web-based engineering simulation platform [6];

What is Computer Simulation Technology Offering Today?

Today’s modern versions of simulation technology regularly provide a set of features:

• A uniquely structured environment that facilitates models with rapid geometry setup function
• Automatically details generation, windows interfaces, and pop-up menus
• Easy and quick to use, with lower risks of errors
• Built-in material handling patterns and templates
• Product design verified and tested faster, offering 3D views alternatives
• 3D graphics automatically created as the user enters data
• Simulations results can be instantly viewed in 3D animation [3]

Real-time simulation technology is used today in various industrial applications in the fields of manufacturing, energy and power systems, industrial equipment, valves, pumps, automotive, and airplane engines. The key challenges in the industrial simulation are digital model integration, reducing time to market, computational processing power, energy efficiency, and the associated cost reduction.

The first step in the simplification of the simulation process was the separation from traditionally designed applications, achieved by the universal recognition of major project files with standard extensions. This offered total independence between product design and the simulation process.

The migration of computer simulation software into the Cloud has made a massive impact on product cost reduction, quality improvement, and market-ready effectiveness. All major CAE providers have started offering alternative services to the traditional on-premises simulation software, but not covering all benefits Cloud has to offer.

What is Next in Engineering Simulation?

Some industry analysis trends emerged at the last edition of the NAFEMS World Congress—(International Association for the Engineering Modelling, Analysis, and Simulation Community established in 1983)[6].

• Design-centric workflow – already adopted in digital industry models
• Ease of use and/or usability – applications should be friendly, for large numbers of users
• Analysis and simulation of CAD – as part of modern digital processes
• The impact of the Web, Cloud, and mobile devices – opening access and communication facilities
• Capturing and reuse of knowledge – by embedding digital data science models
• Systems approach to combining heterogeneous models – multiphysics simulations
• Speed and model fidelity – improved by Cloud infinite computational power
• Unattractive technical issues – limited by opening access to knowledge
• Changes to licensing models – due to essential differences offered by Cloud subscription Software as a Service (SaaS) models
• Nano simulations – finite element analysis and simulation at the nanoscale opens up a vast applicability in the field of biological engineering

These trends are linked to a major initiative to expand the use and benefit of engineering simulation to larger user categories, “The goal is to gain better advantage and growth of CAE software given the business drivers that push the need for more innovation and creative competitiveness”, says Joe Walsh, CEO of IntrinSIM [6].

Experts appreciate that the simulation democratization process has currently three obstacles: software costs, hardware expenses, and expertise training. The Cloud models offer a solution for first two of these stumbling blocks, opening the doors to vendors which offer freemium versions of simulation analysis on a pay-by-use model. “We need to reduce the level of expertise required to do simulation,” says Walsh in an interview for Engineering.com [6]. “This is referred to as design-centric workflow rather than simulation-centric. This way simulation is used to derive and drive design decisions as opposed to just using it to do an analysis.”

“The Next” is Here, with Reliable Alternatives

With its web-based engineering simulation platform, SimScale offers real answers to all three major obstacles in the computer-aided engineering (CAE) democratization process. SimScale has created a completely new approach to how CAE technology can be used, by making it accessible, cost-efficient, and easy-to-learn and use.

Accessibility – any user has access to necessary powerful computer simulation technology running in a simple web browser, without any supplementary hardware, software or maintenance resources;

Cost-efficiency – using SimScale, users pay only for what they use, achieving the best cost/ performance in the engineering process;

Breaking knowledge barriers – any SimScale operation is easy to learn and use. “With SimScale, we have this unique situation that for the first time in the history of simulation software, the functionality itself, the people, the content, and the know-how are all brought together in one place, on one platform which can ultimately help everybody to learn simulation faster and apply it more effectively,” said David Heiny, Managing Director and Co-Founder SimScale in an interview for DEVELOP3D LIVE magazine.

SimScale’s CEO David Heiny tests the capabilities of the platform to solve a real-life engineering problem. Fill in the form and watch this free webinar to learn more!

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The post Role of Computer Simulation Technology in Modern Engineering appeared first on SimScale.

When investigating prospective air conditioning systems that supply multiple areas, minimal thermal conditions need to be taken into account. These include refreshing the air quality, saving energy resources, and boosting performance. Modern air conditioning systems are integrated with other utility services, forming an ecosystem of smart resources that can help users live and work more comfortably and efficiently.

Air Conditioning Systems Designing Air Conditioning Systems with Simulation

Improving the efficiency of AC systems is a vital part of keeping ecosystems functional. A considerable factor in this efficiency and optimization processes is engineering simulation techniques. CFD, FEA, and thermal analyses help air conditioning designers and engineers improve the equipment functionality in home environments, offices, cars or industrial applications.

SimScale is an ideal tool for air conditioning designers and engineers in their performance optimization efforts. SimScale’s 3D simulation platform enables rapid product improvement that addresses many aspects such as endurance, reliability, performance, noise reduction, thermal comfort, and energy efficiency for air conditioning systems.

Offices in Smart Buildings

Modern research is looking towards the intelligent integration of the Internet of Things (IoT) sensors in smart building systems. Smart air conditioning systems involve the interconnection of mobile phones, smart sensors, and wearable devices placed on the human body. The feedback signals with the occupants’ information are provided by smartphones and personal bracelets that in turn adjust air conditioners accordingly. Experimental results show that the indoor temperature can be controlled accurately within a range of error less than ±0.1 °. [1]

Herewith are some SimScale simulation examples showing how easy and efficient simulation analyses are for optimizing air conditioning systems in office spaces. One example is that of an airflow analysis inside an office space.

The analysis was set up using the natural convective heat transfer analysis type. A quite simple boundary condition setup was chosen (fixed temperature at the walls and inlet, fixed inlet velocity condition), but this could easily be applied to other boundary conditions, such as warm or cold windows and adiabatic walls. The resultant images show a streamlined visualization of the velocity field and a temperature contour plot that indicates where it is warmer and colder within the office space.

Green Buildings with Intelligent Industrial Cooling

The concept of green industrial buildings is based on specific materials and healthy ventilation systems able to satisfy energy savings, environmental regulations, building standards, and industry regulations. Ventilation design thinking is at the forefront of a paradigm shift. In the past, the thermal properties of air within a zone determined the heating, ventilation, and air conditioning specifications. Going forward, however, occupant-specific and highly responsive systems will become the norm. Natural ventilation, displacement ventilation, and micro-zoning with subfloor plenums, along with the use of point-of-source heat control and point-of-use sensors, will evolve to create a `smart,’ responsive, and dynamic ventilation system [2].

One of the most frequent industrial applications with a high “green” impact are cooling systems for server rooms. In this SimScale project—Server Room Cooling—the air temperature and velocity inside a server room are analyzed using a thermo-fluid analysis type.

The simulation was set up using the natural convective heat transfer analysis. Two different simulations were set up: one assuming a laminar flow field as a rough estimation and the second one using a k-epsilon RANS turbulence model.

Also, different boundary conditions were used: in one simulation, the room walls have been assumed to be adiabatic and in the other—a fixed temperature was assigned.

The simulation results show the resulting velocity and temperature field inside the server room, allowing the evaluation of the necessary power of the cooling system under different operation conditions.

Moreover, different layouts of the server room including the ventilation and air conditioning system can be evaluated from very early in the design phase with less physical testing.

This simulation example demonstrates how SimScale can be used to answer “what if” scenarios very fast and efficiently. The simulation took only 20 minutes, running on a 16 core machine.

Download this case study for free to learn how the SimScale CFD platform was used to investigate a ducting system and optimize its performance.

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Smart Vehicles with Less Consumption

Industry practices and automotive researchers claim that air conditioning systems in vehicles are considered a high supplementary consumer source.

AC loads account for more than 5% of the fuel used annually for light-duty vehicles in the United States [3]. At the same time, air conditioning loads can significantly impact electric vehicle (EV), plug-in hybrid electric vehicle (PHEV), and hybrid electric vehicle (HEV) performance up to a 50%, as proven by research conducted by Mitsubishi. [4].

Consequently, increased cooling demands from the battery thermal management system may impact the vehicle’s air conditioning system.

Cabin climate conditioning is one of the main problems for long distance trucks during driver rest periods.

In the US, trucks that travel more than 500 miles per day use 838 million gallons of fuel annually for rest periods of idling [5].

In this SimScale project—car cabin airflow analysis—the simulation analysis is based on a steady-state convective heat transfer with the k-omega SST model for turbulence modeling.

The cabin interior has 4 inlet air conditioning ducts (2 in the center and 2 at the sides) and one outlet. The simulation investigates the flow field and temperature distribution inside the cabin. The results show that flow and temperatures vary at different sections inside the cabin. The whole process was run on 8 computer cores and took around 12.5 hours.

All projects described in this article and many others can be copied and used as templates for free. This way, you can modify them and start your own simulation in the easiest way possible. Just visit the SimScale Public Projects library and use any project you need.

The post How to Design Better Air Conditioning Systems With CFD Simulation appeared first on SimScale.

Engineering simulation is being increasingly adopted in the design process as a strategy to address these challenges and to ensure the energy efficiency and optimal performance of HVAC equipment.

HVAC Design Challenges in HVAC System Design Processes

A classic HVAC system design process is based on four elementary steps:

Design > Physical Prototyping > Testing > Design Change

There are several critical issues that arise in relation to the quality, time, and costs related to the prototyping cycle.

Finding an acceptable solution in a reasonable time—and with reasonable costs—can only be achieved by working with very experienced HVAC system design teams. Otherwise, the testing process can involve hundreds of prototype changes and iterations, leading to unpredictable scheduling and costs that are impossible to predict. The unpredictable costs associated with the supply and the need for testing laboratory activities also need to be considered from the outset. The process of prototyping and testing can take weeks, and sometimes HVAC designers or architects don’t have this time available in the design schedule.

HVAC Simulation Saving Time and Money with Simulation

By implementing simulation activities, the HVAC system design process is radically simplified:

Design > Simulation (CAE) > Design Changes

For HVAC systems, designers and engineers will typically perform fluid dynamics (CFD) and thermal simulations to improve the air conditioning circulation and comfort factors for different indoor or outdoor scenarios. HVAC product designers can drastically enhance the characteristics of equipment by analyzing simulation charts for factors like temperature, pressure, and velocity distribution. The study of aerodynamic or thermodynamic forces is very useful in identifying the optimal equipment position for a specific location.

Noise reduction is another important factor in creating a pleasant living and working environment. The most common sources of noise pollution in the work environment are related to fans, variable air volume systems, grilles and diffusers, chillers, compressors, pumps, standby generators, boilers, and cooling towers. The alternative solutions that can help to reduce noise pollution are silent ventilation equipment, silencers and insulation materials for HVAC protection, or implementing a noise optimization analysis.

HVAC Systems 5 Tips for HVAC System Design

1. Engineer – Designer Tandem Collaboration

In a typical product development process, engineers and product designers work together, but they often have different goals for their projects. Engineers are more interested in the product’s reliability, quality, dimensional details, materials supply, or manufacturing processes. In contrast, designers are more oriented towards features, customer experience, and competitive alternatives. It is important that all team members work in tandem to achieve the main goals of the overall project. All modern simulation solutions facilitate seamless communication and collaboration to ensure these goals are met.

2. Looking for a Perfect Fit

Acting as essential creation poles in the HVAC product development chain, the role of an HVAC designer is to ensure a perfect fit between product functionality and design. Any new product should primarily be simple to use and attractive by its simplicity.

3. Improving Existing Products

Customers need more practical, functional, and higher quality products than ever before. Before starting working on the design concept, the product designer should study existing HVAC products and investigate competitor’s key features and functionalities.

4. Testing

HVAC system design teams should benchmark new products, evaluating how new features and functionalities can be implemented into the existing product design.

5. Trusting Simulation

Simulation technology is a valuable part of the design process. When considering industry purposes, HVAC system design specialists should make use of the virtual prototyping offered by Computer-Aided Engineering. There are many traditional simulation software packages available on the market with a wide range of available features and capabilities.

Many of these technologies are, however, inaccessible to most professionals and companies due to the large upfront investment that is required, lack of technical expertise, and accessibility restrictions.

Since the launch of SimScale’s cloud-based CAE platform, however, the world of engineering simulation has begun to change. Engineers and designers now have access to powerful simulation capabilities within an easy-to-use, cloud-based platform that can be accessed with a standard laptop or desktop computer.

Download this case study for free to learn how the SimScale CFD platform was used to investigate a ducting system and optimize its performance.

Download Case Study

The post HVAC System Design: Why Engineering Simulation is a Must appeared first on SimScale.