In this guide, we’ll look into what magnetic lifting is, how magnetic lifting devices work, and how SimScale improves their design through its electromagnetics simulation tool.

What is Magnetic Lifting?

Magnetic lifting is a method used in mechanical and industrial settings to move heavy metal objects without direct contact. The lifting process is both safe and efficient, particularly when moving large metal items. This technique relies on magnets to create a strong magnetic field that securely attaches to ferrous (iron-containing) materials like steel (not stainless steel, due to its specific metal structure, which makes it non-magnetic).

Smaller magnetic lifting devices can lift between 200 and 400 pounds (~ 100 to 200 kg), suitable for lighter tasks. Larger models are capable of handling hefty loads ranging from 6,000 to 13,000 pounds (~ 2700 to 6000 kg), ideal for more demanding industrial operations. These devices are particularly useful for transporting steel plates, forgings, die castings, and other similar items commonly found in workshops, warehouses, and processing plants.

Types of Magnetic Lifting Devices

Magnetic lifting devices are essential tools in various industrial settings, each type designed for specific applications and capacities. Here are the main types:

Electromagnetic Lifting Magnet

Electromagnetic lifting magnets use an electrical current to create a magnetic field, enabling them to attract and lift ferromagnetic materials. They consist of a coil wound around a ferromagnetic core. When electricity flows through the coil, it generates a magnetic field, allowing the magnet to hold a load securely. The lifting capacity of these magnets can be adjusted by varying the electric current.

They differ from permanent magnets as they require a continuous power source to maintain their magnetic field. Electromagnetic lifting magnets are widely used in industries like scrap yards, manufacturing, and recycling.

Permanent Lifting Magnet

Permanent lifting magnets are built with permanent magnet materials like neodymium or ferrite. These magnets produce a constant magnetic field without needing an external power source. They’re typically used for lifting smaller objects and have a fixed lifting capacity.

These magnets include a block with a main body and a rotor, each containing two magnets. When these magnets are aligned, they generate a magnetic flux that reaches the metal objects to be lifted. One key advantage is their functionality, even during power failures. They’re often found in material handling, sorting, and assembly line applications.

Electropermanent Lifting Magnet

Electropermanent lifting magnets use a mix of permanent magnets and electromagnets to create a magnetic field. Once established, this magnetic field can be maintained without a continuous power supply but can also be turned on or off using an electrical control system. This feature makes them useful when power failure is a concern, such as in steel mills, shipyards, and heavy equipment manufacturing.

When the two sets have the same magnetic direction, the magnet can attract ferromagnetic workpieces. If their magnetic directions are opposite, they cancel each other out, and no magnetic force is generated for clamping. These magnets consist of two magnetic power sources: one set of high intrinsic coercive force (Hci) magnets and another set of low Hci magnets wrapped in electrical wire coils. Changing the direction of the current pulse in the coils can alter the direction of the magnets’ orientation.

Applications of Magnetic Lifting

Here are some examples of magnetic lifting applications in different sectors.

Optimizing Magnetic Lifting Performance through Electromagnetic Simulation

Electromagnetic simulation plays a crucial role in enhancing the performance and efficiency of magnetic lifting devices. Here are several ways in which simulation can optimize magnetic lifting performance.

Detailed Magnetic Field Analysis

Electromagnetic simulations can provide a detailed map of the magnetic field’s strength across the lifting surface. For example, they help in ensuring uniform field strength when lifting irregularly shaped objects like curved metal plates or cylindrical steel rolls.

Through magnetic lifting analysis, engineers can detect areas where magnetic flux leakage occurs, which could lead to reduced lifting efficiency or unintended attraction to nearby metal objects.

Load Capacity Optimization

By simulating various load types, including asymmetric and unevenly distributed loads, designers can optimize the magnetic lifter for a wide range of scenarios, such as adapting the lifter design to handle elongated steel beams safely.

It’s easy to simulate how different ferrous materials respond to the magnetic field, considering factors like:

• Material thickness
• Alloy composition
• Surface condition

Enhancing Operational Safety

Engineers can focus more on operational safety by simulating mechanical stresses and strains on the lifting device under different load conditions, such as analyzing the stress distribution on the lifting arm when lifting near the device’s maximum capacity. Magnetic lifter designers can assess the durability of the insulation and electrical wiring, particularly under extreme conditions like high temperature or humidity.

The multiphysics post-processing results, including electromagnetics and thermal analysis, can predict heat generation in the coils and other components during operation. For devices meant for continuous use, simulation helps design systems that can operate for extended periods without overheating.

Energy Efficiency and Sustainability

Engineers can test how quickly the magnetic field can be altered in response to changing conditions, which is crucial in automated systems where rapid adaptation to different loads is required. By adjusting parameters such as the number of coil turns, wire diameter, and coil dimensions, designers can achieve the desired magnetic field strength with lower energy input.
For example, a simulation might reveal that reducing the wire diameter in the coil while increasing the number of turns achieves the same lifting strength with less electrical power required.

Magnetic Lifting Simulation with SimScale Electromagnetics

SimScale’s electromagnetic simulation capabilities offer a comprehensive solution for engineers and designers working on magnetic lifting devices.

Browser-Based Electromagnetic Simulation

SimScale allows you to simulate the electromagnetic (EM) performance of electromechanical devices without the need for expensive hardware or complex software installations. You can run multiple simulations in parallel directly in your web browser. This approach significantly accelerates the design process, enabling faster innovation and real-time collaboration.

Magnetostatics Tool for Detailed Analysis

The Magnetostatics simulation tool is a core feature of SimScale for magnetic lifting applications. It enables engineers to perform various low-frequency electromagnetics simulations, such as analyzing:

• Magnetic flux density
• Magnetic field strength
• Linear magnetic permeability
• Non-linear magnetic permeability

Simulate Your Magnetic Lifting Machines Using SimScale

Magnetic lifting devices offer a safe, efficient, and contactless method of transport in various industries. SimScale’s Electromagnetic Simulation simplifies the complex task of designing and testing magnetic lifters. Sign up now to start using SimScale, or request a demo to see it in action. You can also learn through our step-by-step tutorial focused on magnetic lifting simulations.

Get started right away with SimScale’s easy-to-use, web-based platform by clicking below—no need for special software or hardware.

The post Magnetic Lifting – Mechanism, Types, and Simulation appeared first on SimScale.

For the result fields of harmonic simulations, this is true in variables that are linearly proportional to the input loads, such as:

• Displacement components
• Strain components
• Stress components

On the other hand, derived quantities expressed as nonlinear functions of the result fields naturally do not exhibit a harmonic behavior. This applies, for example, to:

• Total displacement
• Principal strains and stresses
• Von Mises stress
• Tresca stress

Moreover, the harmonic quantities are expressed in terms of their peak value (module or magnitude are terms used interchangeably for it) and a phase delay angle. This expression is encoded in a single complex number, often called a “phasor.”

In a practical sense, a harmonic quantity at any point will oscillate between a maximum and a minimum value given directly by the module of the response. Therefore, this value can be directly used in engineering assessments, e.g., to know the maximum deflection, maximum strain, peak bending stress, etc.

In contrast to this direct method, an issue arises when the analyst is required to perform their assessment using one of the nonlinear derived quantities, for example, maximum von Mises stress. In this case, the simple harmonic peak value evaluation is no longer available, and a more involved process is needed in order to provide an equivalent simple measure, a.k.a. the “Maximum over phase.”

Max Over Phase Von Mises Stress in SimScale

A feature to compute the max over phase von Mises stress in FEA harmonic simulation results was recently released in SimScale. This feature leverages the analytic formulation of the von Mises stress as a function of the principal stresses to find its maximum value across one harmonic phase cycle. The method first finds the phase sweep angle at which the peak value occurs and then computes the value. Only the max over phase value is delivered in the result fields.

In this study, this feature is tested qualitatively and quantitatively for the accuracy of the results by essentially comparing them to a brute force phase sweep history expansion. All the details of the approach are provided below.

Test Case

The test model captures a round shaft in a cantilever bending configuration. The bending load rotates around the center axis of the shaft while staying orthogonal to it.

The parameters of the model are:

• Geometry
  • Length = 0.3 m
  • Diameter = 0.05 m
• Material
  • Young’s modulus = 2.05e11 Pa
  • Poisson ratio = 0.28
  • Density = 7850 kg/m3
• Load
  • Peak value = 1000 N
  • Direction = orthogonal to the shaft axis
  • Vibration = 500 rotations per second

FEA Model

The rotating load is modeled as two orthogonal components spaced at a 90° phase angle:

$$ \vec F = \{F_x, F_y, F_z\} $$

$$ F_x = 0 $$

$$ F_y = 1000 @ 0° $$

$$ F_z = 1000 @ 90° $$

The FEA mesh is implemented with a sweep refinement, giving the following statistics:

• Number of nodes = 49,767
• Number of elements = 11,400
  • Tetrahedrals = 0
  • Hexahedrons = 10,600
  • Prisms= 800
  • Pyramids = 0
• Element type = 2nd order

Methodology

For the numerical verification of the method, we start with the computed stress components in complex form. The stress components results are assumed to be correct and are not tested in this exercise, but only the derived quantities of principal stresses and von Mises stress.

The core idea is to perform a ‘phase expansion’ of the stress components’ histories and then compute the derived quantities, such as the von Mises stress, for each point in the history. This ‘brute force’ approach is completely different from the methodology implemented in the platform and should provide quality reference results for comparison.

Two variations of this methodology were implemented in this exercise:

1. Direct computation of the VMS history from the stress components history
2. Computation of the VMS history passing through the principal stresses

Following is a detailed explanation of the equations and methods implemented.

Phase Sweep Expansion

The ‘phase (sweep) expansion’ for one variable is performed by using the definitions of the harmonic functions and the relations to the complex form, using Euler’s formula. For instance, for a harmonic variable x, we have:

$$ x(t) = A cos(\omega t +\phi) $$

$$ x(t) = Re\{Ae^{j(\omega t +\phi)}\} $$

Note: using the cosine definition of the harmonic variable is important because the phase angle directly relates to the ‘phase of max’ or the sweep angle at which the peak value of the function happens. In the case of a sine function, the phase angle relates to the zero-crossing, which is not as useful.

The complex variable form for the variable contains the information of the amplitude and phase angle (this type of variable is also known as a “phasor”):

$$ X = Ae^{j\phi} $$

This is the quantity that is found in the harmonic FEA results for the linearly proportional fields (components of displacement, strain, and stress). It is presented as a set of two variables, and can take two different shapes:

• Magnitude and phase:

$$ Magnitude = A $$

$$ Phase = \phi $$

• Real and imaginary components:

$$ Real \ Part = Re\{X\} = A cos(\phi) $$

$$ Imaginary \ Part = Im \{X\} = A sin(\phi)$$

In the latter case, the complex variable is expressed as:

$$ X = Re\{X\} + jIm\{X\} $$

Thus, to find the time history of the variable starting from the complex form we can simply perform the operation:

$$ x(t) = Re\{Xe^{j\omega t}\} $$

This expression is furtherly simplified to abstract away the frequency of oscillation, by expressing the independent variable in terms of the ‘phase sweep angle’:

$$ \theta = \omega t \in [0, 2π] $$

Then the time history of the variable is presented as a function of this angle instead of time.

Derived Quantities

After having the phase sweep expansion of the linear variables, the computation of the history of a derived quantity is done simply by applying the corresponding formula at each angle.

Let’s take, for instance, the maximum total displacement at a given point. The procedure to compute its phase expansion would be as follows:

1. Obtain the complex results for the displacement components at the desired location \(X\), \(Y\), \(Z\)
2. Perform the phase sweep expansion for the displacement components and obtain the curves \(x(\theta)\), \(y(\theta)\), \(z(\theta)\) with \(\theta \in [0, 2] \)
3. For each angle in the phase sweep history, apply the corresponding formula \(\omega(\theta) = \sqrt{x(\theta)^2 + y(\theta)^2 + z(\theta)^2} \)
4. Finally, examine the phase sweep history of the total displacement to extract the maximum value and, optionally, the phase angle at which it occurs (“phase of max”).

Notice that the total displacement is not a linear function of the displacement components, and therefore it is not a harmonic function by itself and can not be expressed in the complex form. Notice how its frequency of oscillation is double the frequency of oscillation of the displacement components.

A similar approach is followed to obtain other derived quantities, such as:

• Principal stresses: at each point in the phase sweep history of the stress components, the stress tensor is built, and the eigenvalue problem is solved for it, obtaining the phase expansion of the principal stresses.
• Von Mises stress: can be computed starting from the phase expansion of the stress components or from the phase expansion of the principal stresses. For both cases, simple formulas are available which can be applied point-by-point in the history:

$$ \sigma_{eq} = \sqrt{\frac{1}{2}[(\sigma_{xx} – \sigma_{yy})^2 + (\sigma_{yy} – \sigma_{zz})^2 + (\sigma_{zz} – \sigma_{xx})^2 + 6(\sigma_{xy}^2 + \sigma_{xz}^2 + \sigma_{yz}^2)]} $$

$$ \sigma_{eq} = \sqrt{\frac{1}{2}[(\sigma_1 – \sigma_2)^2 + (\sigma_2 – \sigma_3)^2 + (\sigma_3 – \sigma_1)^2]} $$

Results

SimScale

The result distribution and max over phase value for the von Mises stress are shown in the following figure.

Maximum value of Von Mises stress, max over phase = 54.64 MPa

Competitor Software

The same model was implemented in a competitor FEA tool, which also offers the ‘Max Over Phase’ calculation feature. The implemented model has the following statistics:

• Number of nodes = 37,094
• Number of elements = 8 550
  • Tetrahedrals = 0
  • Hexahedrons (Hex20) = 8,450
  • Prisms (Wedge15) = 100
  • Pyramids = 0
• Element type = 2nd order

The max over phase distribution shows the same behavior as SimScale, with the following numeral results:

Maximum Von Mises stress, max over phase = 54.599 MPa

Deviation with respect to the SimScale results = 0.08 %

Numerical Verification

By applying the verification methodology at the point reported for the maximum value of the von Mises stress (max over phase), we obtain the following results.

First, we see the phase sweep expansion of the stress components, appreciating the harmonic functions, peak values, phase angles, etc.

From here, we can compute the history of the von Mises stress by applying the formula at each point of the phase sweep:

We can appreciate the doubling of the frequency due to the squaring operations in the formula. Examination of the curve yields a maximum value of 55.4335 MPa.

At this same mesh node, the SimScale results show a max over phase value of 54.6369 MPa. The computed deviation at this point is 1.44%.

If we apply the same procedure for all of the mesh nodes in the results, the Normalized Mean Absolute Difference error measurement gives a weighted deviation of 3.9% (here the larger values are emphasized). This error measure is necessary to weigh down the close-to-zero results, for which the relative deviations are always very high.

The NMAD is computed as:

$$ NMAD = 100 \frac{\lt \vert e – p \vert \gt}{max(\lt \vert e \vert \gt, \lt \vert p \vert \gt)} $$

Where:

• The result values from the simulation e
• There reference computed values p
• The average operator (over all the components)
• The absolute value operator |x|

In order to investigate the cause of the discrepancy, we can note that the SimScale methodology is to compute the von Mises stress starting from the principal stresses. Moreover, the principal stresses are computed from a stress tensor formed by the complex stress components.

That is, a complex-valued stress tensor, for which the eigenvalue problem is solved. The result of this computation is a set of three complex eigenvalues that are, in turn, interpreted as the phasor representation of harmonic principal stresses. A phase expansion can be performed for them, for instance.

It is important to emphasize how this methodology assumes that the principal stresses are indeed harmonic functions. Upon further analysis, this does not seem to be a correct assumption, primarily because the eigenvalue computation is not a linear operation on the stress components. Furthermore, a numerical verification is also performed.

In order to test this hypothesis numerically, a comparison was made between the two approaches for computing the phase expansion of the principal stresses:

1. “Method A” – This is the method implemented in SimScale, where the principal stresses are assumed to be harmonic functions, and their complex representations are obtained by solving the eigenvalue problem for the complex stress tensor. The phase expansion is then performed on the complex principal stresses.
2. “Method B” – This method makes no assumptions about the principal stresses, and works by first obtaining the phase history of the stress components, then forming a real stress tensor at each point in the history. The eigenvalue problem is solved at each point to yield the history of the principal stresses.

The results of the two methods are shown below:

At first glance, it is apparent that the two results are similar. But a closer inspection shows how the actual eigenvalues are not harmonic functions (method B, the dashed lines in the plot), which is expected. The curves are close at the peaks but diverge at the zero crossings.

The real history of the principal stresses seems to be kind of an ‘envelope’ of the assumed harmonic, simplified principal stresses.

Nonetheless, the assumption of the principal stresses as harmonic functions seems to properly capture relevant information, such as the peak values and the phase of max.

Also, it can be seen from the figure that a small deviation is found between each harmonic principal stress and the reference curves (method B). The deviations at the peaks are:

• Deviation in P1 = 0.7%
• Deviation in P2 = 0.5%
• Deviation in P3 = 2.2%

Finally, the von Mises stress computed from method A and B principal stresses are compared, in order to measure the deviations between them:

The curves display the same trend, with the phase of max coinciding on both. But the computation of the von Mises stress seems to aggregate the deviations from the computation of the principal stress (aka the assumption of harmonic shape) to showcase a larger deviation.

As related to the results from the analysis of the principal stress methods, the deviations are larger at the valleys and smaller at the peaks.

The values at the peak are:

• VMS max over phase, method A = 54.6369 MPa
• VMS max over phase, method B = 55.4335 MPa
• Deviation = 1.44%

The value for the von Mises stress using method A coincides with the reported results from the SimScale simulation, which confirms that the assumptions and methodologies are correctly captured in this analysis.

The results show that the methodology implemented in SimScale suffers from the limitations of assuming a harmonic shape for the principal stresses and the corresponding impact on the precision of the results. On the other hand, this method is efficient and fast, which offers a profitable tradeoff between computation time and accuracy.

This lack of precision is minimized when the focus of the analysis is to obtain the max over phase, or the peak value of the history because, at this point, the minimum error is expected to happen if the results of this analysis can be generalized. On the same trend, if the interest would shift to deliver the phase sweep history or the minimum values, then the imprecisions of the method would be of greater impact.

Conclusions

A test methodology is presented for the validation of the results given by SimScale’s implementation of the “Von Mises Stress – Max Over Phase” feature.

The methodology works by implementing two alternative ways of computing the max over phase values starting from the harmonic stress components and comparing the final numerical results.

The methodology was implemented on the test case of a round cantilever beam under the action of a rotating bending force. The case was solved using SimScale FEA Harmonic simulation to obtain the stress results.

The comparison of results shows that the SimScale maximum von Mises stress (max over phase) has a deviation of 1.4% with respect to the reference methodology.

The comparison of results with the same model implemented in the competitor software shows a difference of 0.08% in the maximum value, which seems to indicate that a similar methodology is implemented in that product. The qualitative distribution of the results is also comparable.

The computation of the principal stresses was also tested, which revealed that the probable cause of the imprecision in the SimScale methodology lies in the (arguably mistaken) assumption that the principal stresses are harmonic functions.

It is also found that, although imprecise, the SimScale methodology is fast and resource-efficient. It properly captures information such as the “phase of max” and a close “max over phase”, with an error of 1.4% on the peak value of the von Mises stress and of 2.2% on the peak value of the third principal stress.

The post Max Over Phase in Harmonic Response Analysis appeared first on SimScale.

But that’s not all. In this article, we will explore how simulation can not only help study mechanical phenomena but also enable better-informed decision-making early in the design process. In other words, we will see how engineers can benefit from one particular aspect of simulation that provides them with more accessibility, collaboration opportunities, and efficiency in both time and money.

What is Solid Mechanics?

Solid mechanics is a branch of physical science that focuses on studying the movement and deformation of solid materials under external loads such as forces, displacements, and accelerations. These loads can cause different effects on the materials, such as inertial forces, changes in temperature, chemical reactions, and electromagnetic forces. This field plays a critical role in various engineering disciplines, including aerospace, automotive, civil, mechanical, and materials engineering.

Solid mechanics focuses on understanding the mechanical properties of solid materials and their response to different types of loading. These materials include metals, alloys, composites, polymers, and others. By studying how materials behave under different conditions and in different environments, engineers can gain insights into designing and optimizing structures, components, and systems to ensure their safety, reliability, and performance.

In solid mechanics, there are two fundamental elements:

• The object’s internal resistance that acts to balance the external forces, represented by stress
• The object’s deformation and change in shape as a response to external forces, represented by strain

The relationship between stress and strain is described by Young’s Modulus, which states that strain occurring in a body is proportional to the applied stress as long as the deformation is relatively small – i.e., within the elastic limit of the solid body. This can be visualized in the stress-strain curve shown below.

What is Solid Mechanics Used for?

The importance of solid mechanics lies in its practical applications and contributions to engineering and the industry. The key reasons why solid mechanics is not only practical but crucial for engineers can be categorized as follows:

• Design analysis
• Failure analysis and prevention
• Material selection and optimization
• Structural safety and load-bearing capacity
• Performance optimization and efficiency

Design Analysis

Solid mechanics provides the foundation for designing and analyzing structures and components. By applying principles of solid mechanics, engineers can assess the structural integrity and performance of systems and ensure they meet design requirements and safety standards.

It enables them to predict and understand factors such as stresses, strains, and deformations, which are vital in designing structures that can withstand expected loading conditions and environmental factors.

Failure Analysis and Prevention

Solid mechanics helps engineers investigate and analyze failures in structures or components. By understanding the causes of failure, such as excessive stress, material fatigue, or deformation, engineers can improve design practices, materials selection, and manufacturing processes to prevent failures and enhance the reliability and durability of products.

Material Selection and Optimization

Solid mechanics plays a significant role in material selection and optimization. Engineers need to evaluate the mechanical properties of different materials and assess their suitability for specific applications.

By considering factors such as strength, stiffness, toughness, and fatigue resistance, solid mechanics helps engineers choose the most appropriate materials to meet performance requirements while considering factors such as weight, cost, and manufacturability.

Structural Safety and Load-bearing Capacity

Solid mechanics allows engineers to assess the safety and load-bearing capacity of structures and objects. Through analysis and simulations, engineers can determine the structural stability, response to external forces, and ability to withstand static and dynamic loads.

This knowledge is essential in ensuring the integrity of critical structures, such as bridges, buildings, and aircraft, where failure could have severe consequences.

Performance Optimization and Efficiency

Solid mechanics helps engineers optimize designs to improve performance and efficiency. By analyzing stress distributions, material usage, and structural behavior, engineers can identify areas for improvement, reduce unnecessary material and weight, and optimize designs for enhanced strength, rigidity, or energy efficiency. This optimization process leads to cost savings, improved product performance, and reduced environmental impact.

Using Simulation in Solid Mechanics

Understanding how solid materials behave under different conditions is crucial for a wide range of engineering and design applications. By simulating the behavior of solid materials, engineers and designers can optimize their designs and reduce the need for costly physical prototyping.

Using simulation software, engineers and designers can create virtual models of their designs and analyze their performance under various conditions. They can simulate stresses, strains, and deformations in solid materials.

The example below is a structural analysis of a wheel loader arm. This simulation project enabled the design engineer to study the relative movement between the components and assess the stress performance simultaneously. This assessment was done by calculating the Von Mises stress distribution within the arm. Such an approach almost eliminates the need for physical prototyping in the early stages of the design process.

Finite Element Modeling in Solid Mechanics

Knowing that most engineering cases of solid mechanics are nonlinear by nature, analyzing them with analytical solutions may not be feasible. That’s where numerical modeling comes into play.

To simulate solid mechanics cases and assess the material behavior, engineers use finite element modeling (FEM), a numerical method upon which a simulation technique called Finite Element Analysis (FEA) is based.

FEA involves dividing a complex solid model into a finite number of smaller, interconnected elements to approximate the behavior of the structure. By applying appropriate boundary conditions and material properties, FEA can simulate the response of the structure to different loads, allowing engineers to assess stress, strain, displacement, and deformation patterns.

To further understand the details of FEA, check out our dedicated guide to Finite Element Analysis (FEA).

The FEA software in SimScale, for instance, helps engineers and designers virtually test and predict the behavior of solid bodies. This enables them to solve complex structural engineering problems under static or dynamic loading conditions.

Yet, with all this, you might still be wondering what exactly the single aspect of simulation benefiting engineers today is. Well, it goes beyond the mathematical side of simulation and capitalizes on the integration of another technology: the cloud.

Simulating Faster with SimScale

SimScale combines the capabilities of simulation with the benefits of cloud computing to enable engineers to analyze accurately, collaborate better, and innovate faster.

Using SimScale’s cloud simulation, you can access your simulation projects anytime, anywhere. All you need is a web browser. You simply sign up to SimScale, import your 3D design, and start simulating.

Furthermore, not only are your projects accessible to you, but you can also very easily share them with your colleagues and teams to collaborate on them, improve your designs quickly, and shorten your workflow significantly.

For example, the global engineering and manufacturing company Bühler uses SimScale to enable the collaboration between 15% of its mechanical and process engineers spread across 25 departments in ten business units on four continents.

The post Solid Mechanics Simulation and Analysis with SimScale appeared first on SimScale.

In medicine, disposable pumps are used for accurately dispensing medication, such as antibiotics, chemotherapy or pain management therapies. Allowing doctors and nurses to choose the volume and flow rates, they are very important to suit patient’s individual needs of medication for general infusion use. But the medical sector is not the only one using them, as disposable pumps are present in the pharmaceutical, food, beverage, consumer, and industrial sectors as well.

Talking about disposable pump technology cannot be done without mentioning the industry leader—Quantex. The company supplies single-use pumps and also provides design and development services for custom applications. Quantex’s engineers and designers are on hand to provide design expertise throughout the development process and assistance into volume production.

Quantex’s products actually evolved from the biomedical market, where disposable pumps were required for hygienic reasons. Its pumps replaced the old peristaltic pump technology, improving accuracy and ease of drug administration. With significant experience in the medical field and seeing the potential of disposable pumps, Quantex expanded its product base to other industries, such as food processing.

Challenge: Increasing Membrane Thickness while Keeping the Same Mold

Making a new, bigger model posed multiple challenges. Newly designed parts of the pumps included very thin deflective polypropylene membranes.

Producing the membranes at the desired thickness and with very small tolerances created significant difficulties for the manufacturer. Expensive iterative modifications to the molds would have to be applied and the whole production process would have become much longer.

Quantex engineer Jonathan Ford decided to investigate the deflection of the membrane undergoing pressure load depending on variable plastic thickness. The CAD tool used at Quantex already had structural analysis capabilities, but it did not include nonlinear FEA analysis. The extra calculation package was very expensive and this is when Jonathan discovered how cost-effective SimScale is.

Solution: Testing the Disposable Pumps Designs With FEA

The first step in analyzing the new pump design was to extract the geometry of the membrane from the model. A reference, square membrane was tested at this step. Obtaining a good 3D mesh of a geometry that has high aspect ratios is not easy. The user needs to make sure that the mesh is sufficiently fine to capture the actual behavior of the system while keeping the number of nodes as small as possible, to avoid long calculation times. Taking advantage of the geometrical symmetry is always good—in the case of the studied membrane, two symmetry planes could be applied. Still, the first test mesh was first order and had only one element across the thickness of the membrane.

On the other hand, a precise study of the system during the transition phase was not required and the mesh was used regardless. The square-shaped geometry was already studied experimentally. The comparison of final maximum deflection of the simulation and the physical test has shown good agreement. This meant that the coarse mesh could be used to analyze the new design.

Jonathan’s experience with the pumps that used square-shaped membranes pointed out the difficulties with precision manufacturing of the edges. With an increased size of the pump, the problem would be even bigger. A new shape was proposed, where the edges would be rounded, and the whole membrane would look more like an oval. The main variable parameter now was the actual thickness of the membrane. Jonathan wanted to know what would be the deflection difference when he increased the thickness of the part by 50%.

Having the square membrane case done earlier, creating proper meshes for both new variants of the design was easy. The selected calculation type was nonlinear advanced structural analysis. Thanks to the customization features of SimScale, Jonathan was able to utilize the polypropylene as the material used in the actual product. Jonathan mentioned that the SimScale support team was definitely helpful when choosing the proper symmetry boundary conditions to ensure the best stability of the computation.

To deform the membrane, a pressure load was applied to its top surface. At first, the pressure was distributed uniformly across the surface. The selection of such condition on the platform was quick and straightforward. At the same time, it was a bit far from reality, since reference cases have shown that the pressure force is proportional to the deflection of the membrane, decreasing with deflection. Fortunately, SimScale provided the possibility of non-uniform force distribution. Jonathan came up with a smart functional definition of the pressure load, which better mimicked reality.

The final element of the simulation setup was the choice of numerical schemes. Jonathan favored fast calculation, so relative convergence criteria were selected and the default tolerances were loosened up. The geometry was expected to undergo big deformations as the force was ramped up. With strict convergence tolerances, it would create significant trouble with getting a solution in this transition state. The tolerance of the calculation at maximum deflection was much tighter than defined by the convergence criteria which added confidence to the result.

Optimization Results with the Simulation Approach

The simulation runs took around 25 min each. The data was downloaded and analyzed using ParaView and the results were very encouraging.

The decrease of displacement of the membrane when increasing its thickness by 50% was definitely in an acceptable range. Knowing that the manufacturing defects would generally never exceed the tested maximum thickness, the production tolerance limits were increased by 50%. This gave a larger processing window and increased confidence level that the pump was production capable. Furthermore, several expected expensive iterative modifications to the molds and the production process were avoided.

Finally, the calculated deformed shape of the membrane was overlaid on the pump’s geometry. It appeared that additional improvements to the pumping area were now possible, which would result in an increase of devices’ performance.

Overall, the CAE approach to the design optimization allowed Quantex to improve its product, safely investigate a new design, and save money and time. With the new pump being manufactured, further studies are planned for optimization and customization of the product.

Quantex’s original success story can be found on the SimScale website.

If you want to give cloud-based simulation a try, start with a free community account.

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 Webinar

The post Increasing Manufacturing Tolerance for Disposable Pumps by 50% with FEA appeared first on SimScale.