Summary
Simulation approaches for fluid‐structure‐contact interaction, especially if requested to be consistent even down to the real contact scenarios, belong to the most challenging and still ...unsolved problems in computational mechanics. The main challenges are 2‐fold—one is to have a correct physical model for this scenario, and the other is to have a numerical method that is capable of working and being consistent down to a zero gap. Moreover, when analyzing such challenging setups of fluid‐structure interaction, which include contact of submersed solid components, it gets obvious that the influence of surface roughness effects is essential for a physical consistent modeling of such configurations. To capture this system behavior, we present a continuum mechanical model that is able to include the effects of the surface microstructure in a fluid‐structure‐contact interaction framework. An averaged representation for the mixture of fluid and solid on the rough surfaces, which is of major interest for the macroscopic response of such a system, is introduced therein. The inherent coupling of the macroscopic fluid flow and the flow inside the rough surfaces, the stress exchange of all contacting solid bodies involved, and the interaction between fluid and solid are included in the construction of the model. Although the physical model is not restricted to finite element–based methods, a numerical approach with its core based on the cut finite element method, enabling topological changes of the fluid domain to solve the presented model numerically, is introduced. Such a cut finite element method–based approach is able to deal with the numerical challenges mentioned above. Different test cases give a perspective toward the potential capabilities of the presented physical model and numerical approach.
The aim of this work is to develop a novel computational approach to facilitate the modeling of angiogenesis during tumor growth. The preexisting vasculature is modeled as a 1D inclusion and embedded ...into the 3D tissue through a suitable coupling method, which allows for nonmatching meshes in 1D and 3D domain. The neovasculature, which is formed during angiogenesis, is represented in a homogenized way as a phase in our multiphase porous medium system. This splitting of models is motivated by the highly complex morphology, physiology, and flow patterns in the neovasculature, which are challenging and computationally expensive to resolve with a discrete, 1D angiogenesis and blood flow model. Moreover, it is questionable if a discrete representation generates any useful additional insight. By contrast, our model may be classified as a hybrid vascular multiphase tumor growth model in the sense that a discrete, 1D representation of the preexisting vasculature is coupled with a continuum model describing angiogenesis. It is based on an originally avascular model which has been derived via the thermodynamically constrained averaging theory. The new model enables us to study mass transport from the preexisting vasculature into the neovasculature and tumor tissue. We show by means of several illustrative examples that it is indeed capable of reproducing important aspects of vascular tumor growth phenomenologically.
We develop a versatile computational framework for modeling and simulation of vascular tumor growth. The preexisting vasculature is consistently embedded into the surrounding 3D tissue as a 1D inclusion. The neovasculature is represented as a distinct phase including blood flow and species transport by a homogenized, porous medium approach. Suitable coupling strategies are implemented to study the formation of the neovasculature and transport from the preexisting vasculature to tumor neovasculature and tissue. Realistic tumor growth patterns can be reproduced.
This article presents a novel alternative subgraphs assembly line balancing problem with resource selection and parallel stations. The model considers both the choice between different process ...alternatives and the selection of production resources for the various tasks. It also allows parallelization of stations in the balanced line, thereby improving the flexibility of the algorithm. The objective of optimization is to minimize the total cost of the planned line. A genetic algorithm is used for solving the model. To evaluate its functionality, the approach was applied to 486 well-known reference problems. In addition, the reference problems were expanded to create a new set of reference problems that serve as a benchmark for the algorithm's new capabilities. The results show that the presented algorithm is feasible for solving the formulated assembly line balancing problem.
This article presents a novel approach for the automated 3D-layout planning of multi-station assembly lines. The planning method is based on a comprehensive model of the used production resources, ...including their geometry, kinematic properties, and general characteristics. Different resource types can be included in the planning system. A genetic algorithm generates and optimizes possible layouts for a line. The optimization aims to minimize the line's area and the costs for assembling the line while simultaneously optimizing the resources' positioning to perform their tasks. The line's cycle time is considered as a boundary condition. For the evaluation of different layout alternatives, a multi-body simulation is performed. A parameter study is used to set the algorithm's parameters. Afterward, the algorithm is applied to three increasingly complex examples to validate and evaluate its functionality. The approach is promising for industrial applications as it allows the integration of various resource types and individualization of the optimization function.
We present a dynamic vascular tumor model combining a multiphase porous medium framework for avascular tumor growth in a consistent Arbitrary Lagrangian Eulerian formulation and a novel approach to ...incorporate angiogenesis. The multiphase model is based on Thermodynamically Constrained Averaging Theory and comprises the extracellular matrix as a porous solid phase and three fluid phases: (living and necrotic) tumor cells, host cells and the interstitial fluid. Angiogenesis is modeled by treating the neovasculature as a proper additional phase with volume fraction or blood vessel density. This allows us to define consistent inter-phase exchange terms between the neovasculature and the interstitial fluid. As a consequence, transcapillary leakage and lymphatic drainage can be modeled. By including these important processes we are able to reproduce the increased interstitial pressure in tumors which is a crucial factor in drug delivery and, thus, therapeutic outcome. Different coupling schemes to solve the resulting five-phase problem are realized and compared with respect to robustness and computational efficiency. We find that a fully monolithic approach is superior to both the standard partitioned and a hybrid monolithic-partitioned scheme for a wide range of parameters. The flexible implementation of the novel model makes further extensions (e.g., inclusion of additional phases and species) straightforward.
•We present a new method to predict changes in food insecurity risks due to income shocks such as the COVID-19 global pandemic at a granular level.•Our method only uses existing household survey data ...and external information about income shock at the aggregate level.•We apply the method to predict food insecurity changes during the pandemic at the district level in Vietnam.•Although the national average change of food insecurity is predicted to be small, changes can be quite large in certain districts.
As COVID-19 threatens the food security of vulnerable populations across the globe, there is an increasing need to identify places that are affected most in order to target aid. We propose a two-step approach to predict changes in food insecurity risk caused by income shocks at a granular level using existing household-level data and external information on aggregate income shocks. We apply this approach to assess changes in food insecurity risk during the pandemic in Vietnam. Using national household survey data between 2010 and 2018, we first estimate that a 10% decrease in income leads to a 3.5% increase in food insecurity. We then use the 2019 national Labor Force Survey to predict changes in the share of food-insecure households caused by the income shocks during the pandemic for 702 districts. We find that the small, predicted change in food insecurity risk at the national level masks substantial variation at the district level, and changes in food insecurity risk are larger among young children. Food relief policies, therefore, should prioritize a small number of districts predicted to be severely affected.
In this paper, we propose a finite element–based immersed method to treat the mechanical coupling between a deformable porous medium model (PM) and an immersed solid model (ISM). The PM is formulated ...as a homogenized, volume‐coupled two‐field model, comprising a nearly incompressible solid phase that interacts with an incompressible Darcy‐Brinkman flow. The fluid phase is formulated with respect to the Lagrangian finite element mesh, following the solid phase deformation. The ISM is discretized with an independent Lagrangian mesh and may behave arbitrarily complex (it may, eg, be compressible, grow, and perform active deformations). We model two distinct types of interactions, namely, (1) the immersed fluid‐structure interaction (FSI) between the ISM and the fluid phase in the PM and (2) the immersed structure‐structure interaction (SSI) between the ISM and the solid phase in the PM. Within each time step, we solve both FSI and SSI, employing strongly coupled partitioned schemes. This novel finite element method establishes a main building block of an evolving computational framework for modeling and simulating complex biomechanical problems, with focus on key phenomena during cell migration. Cell movement is strongly influenced by mechanical interactions between the cell body and the surrounding tissue, ie, the extracellular matrix (ECM). In this context, the PM represents the ECM, ie, a fibrous scaffold of structural proteins interacting with interstitial flow, and the ISM represents the cell body. The FSI models the influence of fluid drag, and the SSI models the force transmission between cell and ECM at adhesions sites.
We propose a comprehensive, versatile computational framework for modeling and simulation of an individual cell interacting with its surrounding tissue. The cell is modeled as a visco‐hyperelastic solid interacting with a biphasic poroelastic medium model representing the extracellular matrix coupled to interstitial flow. A novel immersed finite element approach and a combination of monolithic and strongly coupled partitioned solution schemes is utilized to discretize and solve the arising three field multiphysics problem in a robust manner.
This study aimed to evaluate the efficacy and side effects of first-line afatinib treatment in a real-world setting in Vietnam.
This retrospective study was conducted across nine hospitals in ...Vietnam. Advanced epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC) patients who received afatinib as first-line therapy between April 2018 and June 2022 were included, and patient medical records were reviewed. Key outcomes were overall response rate (ORR), time-to-treatment failure (TTF), and tolerability.
A total of 343 patients on first-line afatinib were eligible for the study. EGFR exon 19 deletion (Del19) alone was detected in 46.9% of patients, L858R mutation alone in 26.3%, and other uncommon EGFR mutations, including compound mutations, in 26.8%. Patients with brain metastases at baseline were 25.4%. Patients who received 40 mg, 30 mg, and 20 mg as starting doses of afatinib were 58.6%, 39.9%, and 1.5%, respectively. The ORR was 78.1% in the overall population, 82.6% in the Del19 mutation subgroup, 73.3% in the L858R mutation subgroup, and 75.0% in the uncommon mutation subgroup (p > 0.05). The univariate and multivariate analyses indicate that the ORR increased when the starting dose was 40 mg compared to starting doses below 40 mg (83.9% vs. 74.3%, p = 0.034). The median TTF (mTTF) was 16.7 months (CI 95%: 14.8-18.5) in all patients, with a median follow-up time of 26.2 months. The mTTF was longer in patients in the common EGFR mutation subgroup (Del19/L858R) than in those in the uncommon mutation subgroup (17.5 vs. 13.8 months, p = 0.045) and in those without versus with brain metastases at baseline (17.5 vs. 15.1 months, p = 0.049). There were no significant differences in the mTTF between subgroups based on the starting dose of 40 mg and < 40 mg (16.7 vs. 16.9 months, p > 0.05). The most common treatment-related adverse events (any grade/grade ≥ 3) were diarrhea (55.4%/3.5%), rash (51.9%/3.2%), paronychia (35.3%/5.0%), and stomatitis (22.2%/1.2%).
Afatinib demonstrated clinical effectiveness and good tolerability in Vietnamese EGFR-mutant NSCLC patients. In our real-world setting, administering a starting dose below 40 mg might result in a reduction in ORR; however, it might not have a significant impact on TTF.
A long-term field study on corrosion of STK400 steel was established at Phu My port. The large steel pile, which was equivalent to real structures, was placed in all environmental zones to evaluate ...the overall corrosive effects of the brackish water. After 5 years, the corrosion rate over the entire length of the steel pile was measured and the rust at the boundary altitude between the zones was characterized. The corrosion rate profile shows the polarization of the entire pile length into two large anodic areas at the highest water level and submerged zones, where had high corrosion rate and pitting corrosion form. X-ray diffraction and metallographic of rust layers showed that Fe
3
O
4
phase increased with water depth, allowing to strong diffusion of Fe
2+
ion from steel substrate into the environment. The change in organism populations along water altitude in the tidal and submerged zones made the rust layers more complicated. Such distribution of polarized electrodes and composition of rust were due to the renewal of the corrosive agent by tidal cycles and organism population in water. As a result, the steel substrate in the tidal and mud zones was protected as cathodic areas.
Graphical Abstract
Titanium alloys are notoriously difficult to machine. They are used in the manufacture of various types of lightweight components. It is therefore important to improve their machinability and thus ...achieve sustainability in machining such alloys, by selecting appropriate influential factors: cutting parameters, tool material, geometric form, coolant types, and hybrid machining methods, to deliver efficient output. Nowadays, meta-heuristic algorithms effectively solve multi-objective optimization in machining problems instead of single-objective one. Along with that, the mathematical predictive models used for single-objective optimization are gradually being replaced by machine learning algorithms, which are highly robust and efficient in terms of prediction performance. Therefore, this work addresses the prediction and optimization of average surface roughness (Ra) and tool wear (VB) in Ti
6
Al
4
V alloy turning, using a WC tool coated by chemical vapor deposition (CVD) and physical vapor deposition (PVD), with dry machining. We apply a two-pronged approach combining machine learning (ML) and Non-Dominated Sorting Genetic Algorithm (NSGA-II), to model and optimize Ra and VB. The four ML models - Linear Regression (LIN), Support Vector Machine Regression (SVR), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) - are used to predict Ra and VB. The input variables of the turning process - feed rate, depth of cut, cutting speed, cutting time, and tool materials - are the major factors affecting surface quality and tool wear. By the error metrics such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R
2
), ANN is found to offer the best predictive performance. An ML and NSGA-II-based approach is then employed for multi-objective optimization of cutting parameters in Ti
6
Al
4
V turning. Fifty Pareto solutions are identified in the range of Ra and VB between (1.332 and 1.441 µm) and (0.100 and 0.125 mm), respectively. In this work, the Pareto solutions are selected based on their ranked performances. This aligns with the decision criterion employed to select the most robust cutting parameters. The definitive optimal Ra and VB are obtained by formulating a robust decisive multi-criterion function which integrates performance, preferred decision criterion, and trustworthiness. Finally, this produces the optimal solution for Ra and VB − 1.439 µm and 0.100 mm, respectively. Experimental validation confirms that the final optimum solution is within the acceptable range.
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