A Neuro-Evolution Heuristic Using Active-Set Techniques to Solve a Novel Nonlinear Singular Prediction Differential Model

In this study, a novel design of a second kind of nonlinear Lane–Emden prediction differential singular model (NLE-PDSM) is presented. The numerical solutions of this model were investigated via a neuro-evolution computing intelligent solver using artificial neural networks (ANNs) optimized by global and local search genetic algorithms (GAs) and the active-set method (ASM), i.e., ANN-GAASM. The novel NLE-PDSM was derived from the standard LE and the PDSM along with the details of singular points, prediction terms and shape factors. The modeling strength of ANN was implemented to create a merit function based on the second kind of NLE-PDSM using the mean squared error, and optimization was performed through the GAASM. The corroboration, validation and excellence of the ANN-GAASM for three distinct problems were established through relative studies from exact solutions on the basis of stability, convergence and robustness. Furthermore, explanations through statistical investigations confirmed the worth of the proposed scheme.

Strain and interface energy of ellipsoidal inclusions subjected to volumetric eigenstrains: shape factors

Thermodynamic modeling of the development of non-spherical inclusions as precipitates in alloys is an important topic in computational materials science. The precipitates may have markedly different properties compared to the matrix. Both the elastic contrast and the misfit eigenstrain may yield a remarkable generation of elastic strain energy which immediately influences the kinetics of the developing precipitates. The relevant thermodynamic framework has been mostly based on spherical precipitates. However, the shapes of actual particles are often not spherical. The energetics of such precipitates can be met by adapting the spherical energy terms with shape factors. The well-established Eshelby framework is used to evaluate the elastic strain energy of inclusions with ellipsoidal shapes (described by the axes a, b, and c) that are subjected to a volumetric transformation strain. The outcome of the study is two shape factors, one for the elastic strain energy and the other for the interface energy. Both quantities are provided in the form of easy-to-use diagrams. Furthermore, threshold elastic contrasts yielding strain energy shape factors with the value 1.0 for any ellipsoidal shape are studied.

Numerical Simulation of Non-Spherical Submicron Particle Acceleration and Focusing in a Converging–Diverging Micronozzle

Submicron particles transported by a Laval-type micronozzle are widely used in micro- and nano-electromechanical systems for the aerodynamic scheme of particle acceleration and focusing. In this paper, the Euler–Lagrangian method is utilized to numerically study non-spherical submicron particle diffusion in a converging–diverging micronozzle flow field. The influence of particle density and shape factor on the focusing process is discussed. The numerical simulation shows how submicron particle transporting with varying shape factors and particle density results in different particle velocities, trajectories and focusing in a micronozzle flow field. The particle with a larger shape factor or larger density exhibits a stronger aerodynamic focusing effect in a supersonic flow field through the nozzle. In the intersection process, as the particle size increases, the position of the particle trajectory intersection moves towards the throat at first and then it moves towards the nozzle outlet. Moreover, the influence of the thermophoretic force of the submicron particle on the aerodynamic focusing can be ignored. The results will be beneficial in technological applications, such as micro-thrusters, microfabrication and micro cold spray.

Discriminative Power of Geometric Parameters of Different Cultivars of Sour Cherry Pits Determined Using Machine Learning

The aim of this study was to develop models based on linear dimensions or shape factors, and the sets of combined linear dimensions and shape factors for discrimination of sour cherry pits of different cultivars (‘Debreceni botermo’, ‘Łutówka’, ‘Nefris’, ‘Kelleris’). The geometric parameters were calculated using image processing. The pits of different sour cherry cultivars statistically significantly differed in terms of selected dimensions and shape factors. The discriminative models built based on linear dimensions produced average accuracies of up to 95% for distinguishing the pit cultivars in the case of ‘Nefris’ vs. ‘Kelleris’ and 72% for all four cultivars. The average accuracies for the discriminative models built based on shape factors were up to 95% for the ‘Nefris’ and ‘Kelleris’ pits and 73% for four cultivars. The models combining the linear dimensions and shape factors produced accuracies reaching 96% for the ‘Nefris’ vs. ‘Kelleris’ pits and 75% for all cultivars. The geometric parameters with high discriminative power may be used for distinguishing different cultivars of sour cherry pits. It can be of great importance for practical applications. It may allow avoiding the adulteration and mixing of different cultivars.

Parameterizing the Effects of Tumor Shape in Magnetic Nanoparticle Thermotherapy Through a Computational Approach

In this work effects of tumor shape on magnetic nanoparticle hyperthermia (MNPH) are investigated and evaluated using four categories (spherical, oblate, prolate, and egg-shape) of tumor models having different morphologies. These tumors have equal volume; however, due to the differences in their shapes, they have different surface areas. The shape of tumors is quantified in terms of shape factor (ζ). Simulations for MNPH are done on the physical model constituting tumor tissue enclosed within the healthy tissue. Magnetic hyperthermia is applied (frequency 150 kHz, and magnetic field amplitude 20.5 kA/m) to all tumor models, for 1 hour, after injection of magnetic nanoparticles (MNPs) at the respective tumor centroids. The distribution of MNPs after injection is considered Gaussian. The governing model (Pennes' bioheat model) of heat transfer in biological media is solved with the finite volume-immersed boundary (FV-IB) method to simulate MNPH. Therapeutic effects are calculated using the Arrhenius tissue damage model, cumulative equivalent minutes at 43°C (CEM 43), and heterogeneity in temperature profiles of the tumors. Results show that the therapeutic effects due to MNPH depend significantly on the shape of a tumor. Tumors with higher shape factors receive less therapeutic effects in comparison to the tumors having lower shape factors. An empirical thermal damage model is also developed to assess the MNPH efficacy in real complex-shaped tumors.

Systematic Study of the Effects of High Shear Granulation Parameters on Process Yield, Granule Size, and Shape by Dynamic Image Analysis

The aim of the work was to analyze the influence of process parameters of high shear granulation on the process yield and on the morphology of granules on the basis of dynamic image analysis. The amount of added granulation liquid had a significant effect on all monitored granulometric parameters and caused significant changes in the yield of the process. In regard of the shape, the most spherical granules with the smoothest surface were formed at a liquid to solid ratio of ≈1. The smallest granules were formed at an impeller speed of 700 rpm, but the granules formed at 500 rpm showed both the most desirable shape and the highest process yield. Variation in the shape factors relied not only on the process parameters, but also on the area equivalent diameter of the individual granules in the batch. A linear relationship was found between the amount of granulation liquid and the compressibility of the granules. Using response surface methodology, models for predicting the size of granules and process yield related to the amount of added liquid and the impeller speed were generated, on the basis of which the size of granules and yield can be determined with great accuracy.

Analyzing the Impacts of Meshing and Grid Alignment in Dual-Porosity Dual-Permeability Upscaling

Summary The discrete fracture network (DFN) model is widely used to simulate and represent the complex fractures occurring over multiple length scales. However, computational constraints often necessitate that these DFN models be upscaled into a dual-porositydual-permeability (DPDK) model and discretized over a corner-point grid system, which is still commonly implemented in many commercial simulation packages. Many analytical upscaling techniques are applicable, provided that the fracture density is high, but this condition generally does not hold in most unconventional reservoir settings. A particular undesirable outcome is that connectivity between neighboring fracture cells could be erroneously removed if the fracture plane connecting the two cells is not aligned along the meshing direction. In this work, we propose a novel scheme to detect such misalignments and to adjust the DPDK fracture parameters locally, such that the proper fracture connectivity can be restored. A search subroutine is implemented to identify any diagonally adjacent cells of which the connectivity has been erroneously removed during the upscaling step. A correction scheme is implemented to facilitate a local adjustment to the shape factors in the vicinity of these two cells while ensuring the local fracture intensity remains unaffected. The results are assessed in terms of the stimulated reservoir volume calculations, and the sensitivity to fracture intensity is analyzed. The method is tested on a set of tight oil models constructed based on the Bakken Formation. Simulation results of the corrected, upscaled models are closer to those of DFN simulations. There is a noticeable improvement in the production after restoring the connectivity between those previously disconnected cells. The difference is most significant in cases with medium DFN density, where more fracture cells become disconnected after upscaling (this is also when most analytical upscaling techniques are no longer valid); in some 2D cases, up to a 22% difference in cumulative production is recorded. Ignoring the impacts of mesh discretization could result in an unintended reduction in the simulated fracture connectivity and a considerable underestimation of the cumulative production.

Upscaling of Realistic Discrete Fracture Simulations Using Machine Learning

Upscaling of discrete fracture networks to continuum models such as the dual porosity/dual permeability (DPDP) model is an industry-standard approach in modelling of fractured reservoirs. While flow-based upscaling provides more accurate results than analytical methods, the application of flow-based upscaling is limited due to its high computational cost. In this work, we parametrize the fine-scale fracture geometries and assess the accuracy of several convolutional neural networks (CNNs) to learn the mapping between this parametrization and the DPDP model closures such as the upscaled fracture permeabilities and the matrix-fracture shape factors. We exploit certain similarities between this task and the problem of image classification and adopt several best practices from the state-of-the-art CNNs used for image classification. By running a sensitivity study, we identify several key features in the CNN structure which are crucial for achieving high accuracy of predictions for the DPDP model closures, and put forward the corresponding CNN architectures. Obtaining a suitable training dataset is challenging because i) it requires a dedicated effort to map the fracture geometries; ii) creating a conforming mesh for fine-scale simulations in presence of intersecting fractures typically leads to bad quality mesh elements; iii) fine-scale simulations are time-consuming. We alleviate some of these difficulties by pre-training a suitable CNN on a synthetic random linear fractures’ dataset and demonstrate that the upscaled parameters can be accurately predicted for a realistic fracture configuration from an outcrop data. The accuracy of the DPDP results with the predicted model closures is assessed by a comparison with the corresponding fine-scale discrete fracture-matrix (DFM) simulation of a two-phase flow in a realistic fracture geometry. The DPDP results match well the DFM reference solution, while being significantly faster than the latter.

Design of 3D printable prosthetic foot to implement nonlinear stiffness behavior of human toe joint based on finite element analysis

Toe joint is known as one of the critical factors in designing a prosthetic foot due to its nonlinear stiffness characteristic. This stiffness characteristic provides a general feeling of springiness in the toe-off and it also affects the ankle kinetics. In this study, the toe part of the prosthetic foot was designed to improve walking performance. The toe joint was implemented as a single part suitable for 3D printing. The various shape factors such as curved shape, bending space, auxetic structure, and bending zone were applied to mimic human foot characteristics. The finite element analysis (FEA) was conducted to simulate terminal stance (from heel-off to toe-off) using the designed prosthetic foot. To find the structure with characteristics similar to the human foot, the optimization was performed based on the toe joint geometries. As a result, the optimized foot showed good agreement with human foot behavior in the toe torque-angle curve. Finally, the simulation conditions were validated by comparing with human walking data and it was confirmed that the designed prosthetic foot structure can implement the human foot function.