Contrary to various claims, IGA is not a new paradigm in the numerical approximation of partial differential equations; it is simply an alternative implementation of the p-version of the finite element method. IGA is one possible implementation of the p-version of the finite element method. Its distinguishing features are: (a) it retains the CAD geometry, (b) the basis functions are the same as those used for CAD representation, typically B-splines or NURBS, and (c) it provides for the enforcement of the inter-element continuity of the derivatives of the basis functions.
It is generally recognized that explainable artificial intelligence (XAI) will play an important role in numerical simulation where it will impose the requirements of reliability, traceability, and auditability. These requirements will necessitate clear thinking about the nature of mathematical models, the trustworthiness of their predictions, and ways to improve their reliability.
Meshless methods, also known as mesh-free methods, are computational techniques used for the approximation of the solutions of partial differential equations in the engineering and applied sciences. The advertised advantage of the method is that users do not have to worry about meshing. However, eliminating the meshing problem has introduced other, more complex issues. Oftentimes, advocates of meshless methods fail to mention their numerous disadvantages.
The World-Wide Failure Exercise (WWFE) was an international research project with the goal of assessing the predictive performance of competing failure models for composite materials. Part I (WWFE-I) focused on failure in fiber-reinforced polymer composites under two-dimensional (2D) stresses and ran from 1996 until 2004. Part II was concerned with failure criteria under both 2D and 3D stresses, and ran between 2007 and 2013. Part III, also launched in 2007, was concerned with damage development in multi-directional composite laminates.
Engineering students first learn statics, then strength of materials, and progress to the theories of plates and shells, continuum mechanics, and so on. As the course material advances from simple to complex, students often think that each theory (model) stands on its own, overlooking the fact that simpler models are special cases of the more complex ones. This view guided the development of the finite element (FE) method in the 1960s and 70s, and ultimately led to legacy FE codes adopting an “element-centric” approach.
Model development projects are essentially scientific research projects. As such, they are subject to the operation of the Kuhn Cycle, named after Thomas Kuhn, who identified five stages in scientific research projects: Normal Science, Model Drift, Model Crisis, Model Revolution, and Paradigm Change. The Kuhn cycle is a valuable concept for understanding how mathematical models evolve. It highlights the importance of paradigms in shaping model development and the role of paradigm shifts in the process.
From the beginning of FEM acceptance, a significant communication gap existed between the engineering and mathematical communities. Engineers did not understand why mathematicians would worry so much about the number of square-integrable derivatives, and mathematicians did not understand how it is possible that engineers can find useful solutions even when the rules of variational calculus are violated (variational crimes). This gap widened over the years: On one hand, the art of finite element modeling became an integral part of engineering practice. On the other hand, the science of finite element analysis became an established branch of applied mathematics.
At present, a very substantial unrealized potential exists in numerical simulation. Simulation technology has matured to the point where management can realistically expect the reliability of predictions based on numerical simulations to match the reliability of observations in physical experimentation. This will require management to upgrade simulation practices through exercising simulation governance.
Digital transformation is a multifaceted concept with plenty of room for interpretation. Its common theme emphasizes the proactive adoption of digital technologies to reshape business practices with the goal of gaining a competitive edge. The scope, timeline, and resource allocation of digital transformation projects depend on the specific goals and objectives. Here, we address digital transformation in the engineering sciences, focusing on numerical simulation.
The idea of a digital twin originated at NASA in the 1960s as a “living model” of the Apollo program. When Apollo 13 experienced an oxygen tank explosion, NASA utilized multiple simulators and extended a physical model of the spacecraft to include digital simulations, creating a digital twin. This twin was used to analyze the events leading up to the accident and investigate ideas for a solution. The term “digital twin” was coined by NASA engineer John Vickers much later. While the term is commonly associated with modeling physical objects, it is also employed to represent organizational processes. Here, we consider digital twins of physical entities only.
“Of the three FEA products we use, StressCheck gives me the most confidence when it comes to performing post-processing quality checks on multi-body contact analysis results. StressCheck has the capability to extract any data of interest and convergence information for that data at any location in the model domain; the other two FEA products do not. And when it comes to interference fit multi-body contact solutions, I haven’t found any software to outperform StressCheck.”
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