Abstract Geopolymers are X-ray amorphous materials with appealing properties such as high flexural strength and high compressive strength. Yet, the influence of the heterogeneity and porosity on the constitutive behavior is not fully understood. We formulate a multiscale physics-based mechanistic model to describe the strength behavior of geopolymer composites. Using an energy-based approach, we derive novel solutions to describe the effective yield criterion of fiber-reinforced and particle-reinforced metakaolin geopolymers. We calibrate our theoretical model using nanoindentation tests and validate our theoretical framework via flexural strength tests on metakaolin-based geopolymer composites. Geopolymer composites are found to exhibit a pressure-dependent granular behavior. We subdivide the porosity into nanoporosity and microporosity. Our results indicate that the nanoporosity is solely influenced by the chemistry and is not influenced by the processing and the presence of reinforcement. Due tothe presence of nanoporosity, the strength-total porosity relationships are not unique. However, an approximate one-to-one correspondence exists between the strength and the microporosity. The nanogranular structure and the chemical composition at the nanometer scale have a profound influence on the effective mechanical response.Our conceptual framework is an important step in the mechanistic modeling of the behavior of geopolymer composites.

Abstract High-resolution spatiotemporal data is crucial for characterizing, modeling, and monitoring the space–time dynamics of complex systems in manufacturing. However, the acquisition of such data is generally expensive and time-consuming. Spatiotemporal interpolation aims to predict the values at unmeasured locations using measured data, and emerges as a promising solution to cost-effectively characterizing spatiotemporal processes. Since the interpolation performance is largely influenced by the available measurement data, an intelligent measurement strategy is an important prerequisite to the success of interpolation methods. In this paper, a hierarchical measurement strategy is developed to achieve a balance between interpolation precision and measurement cost in spatiotemporal interpolation. A hierarchical decision-making problem is formulated to determine the observation times and measurement locations at each observation. To expedite the solution search process, hierarchical genetic algorithm is adopted and implemented using high-performance computing. Moreover, a new form of the covariance function is developed using a Bessel additive periodic variogram to more accurately model the periodic spatial variations in spatiotemporal processes. Case studies using real-world data collected from ultrasonic metal welding are reported to demonstrate the effectiveness of the proposed method.

As a type of architectured material, knitted textiles exhibit global mechanical behavior which is affected by their microstructure defined at the scale at which yarns are arranged topologically given the type of textile manufactured. To relate local geometrical, interfacial, material, kinematic and kinetic properties to global mechanical behavior, a first-order, two-scale homogenization scheme was developed and applied in this investigation. In this approach, the equivalent stress at the far field and the consistent material stiffness are explicitly derived from the microstructure. In addition, the macrofield is linked to the microstructural properties by a user subroutine which can compute stresses and stiffness in a looped finite element (FE) code. This multiscale homogenization scheme is computationally efficient and capable of predicting the mechanical behavior at the macroscopic level while accounting directly for the deformation-induced evolution of the underlying microstructure.

Direct numerical simulations (DNS) of knitted textile mechanical behavior are for the first time conducted on high performance computing (HPC) using both the explicit and implicit finite element analysis (FEA) to directly assess effective ways to model the behavior of such complex material systems. Yarn-level models including interyarn interactions are used as a benchmark computational problem to enable direct comparison in terms of computational efficiency between explicit and implicit methods. The need for such comparison stems from both a significant increase in the degrees-of-freedom (DOFs) with increasing size of the computational models considered as well as from memory and numerical stability issues due to the highly complex three-dimensional (3D) mechanical behavior of such 3D architectured materials. Mesh and size dependency, as well as parallelization in an HPC environment are investigated. The results demonstrate a satisfying accuracy combined with higher computational efficiency and much less memory requirements for the explicit method, which could be leveraged in modeling and design of such novel materials.

Abstract Larger supercomputers allow the simulation of more complex phenomena with increased accuracy. Eventually this requires finer and thus also larger geometric discretizations. In this context, and extrapolating to the Exascale paradigm, meshing operations such as generation, deformation, adaptation/regeneration or partition/load balance, become a critical issue within the simulation workflow. In this paper we focus on mesh partitioning. In particular, we present a fast and scalable geometric partitioner based on Space Filling Curves (SFC), as an alternative to the standard graph partitioning approach. We have avoided any computing or memory bottleneck in the algorithm, while we have imposed that the solution achieved is independent (discounting rounding off errors) of the number of parallel processes used to compute it. The performance of the SFC-based partitioner presented has been demonstrated using up to 4096 CPU-cores in the Blue Waters supercomputer.

With the rapid development of sensing, communication, and computing technologies and infrastructure, today’s manufacturing industry is marching towards a big data era and a new generation of digitalization and intelligence. The availability of big data provides us with a golden opportunity to promote smart manufacturing. Nevertheless, the deployment and popularization of big data analytics in manufacturing is still at its nascent stage. One critical challenge results from the lack of high-performance computing (HPC) capability, which is crucial for responsive and intelligent decision-making in the modern manufacturing industry. To address this challenge, this paper proposes a framework and some general guidelines for implementing big data analytics in an HPC environment. The details of the whole workflow, from the prototype to the final application, are high-lighted. A case study for intelligent 3D sensing with real-world manufacturing data is presented to demonstrate the effectiveness of the proposed framework.