4 results
Filters
Settings
Now showing1 - 4 of 4
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Novel hybrid ANN-interpolation techniques for predicting mean residence time in wet twin screw granulation application: a critical reviewIn twin-screw granulation (TSG), the mean residence time (MRT) of materials significantly influences granule properties, such as size distribution and density, impacting the quality of the final product. Accurately estimating MRT is crucial because deviations can lead to overwetting, compaction issues, or insufficient granulation. This study presents a hybrid approach that combines machine learning and data interpolation techniques to model MRT as a function of process parameters, including feed flow rate, screw speed, screw configuration, and liquid-to-solid ratio. Our goal is to develop a predictive tool capable of handling coarse datasets for precise MRT estimation. By optimising the MRT, process control, efficiency, and batch-to-batch consistency can be improved, ensuring adherence to product specifications and facilitating cost-effective scale-up. This study explores the integration of various univariate and multivariate spline interpolation techniques with the nonlinear autoregressive with exogenous inputs (NARX) and multilayer perceptron (MLP) machine learning methods to enhance the accuracy of MRT. While numerous studies have utilised large datasets, this study examines a coarse dataset, applying various interpolation techniques to enhance data resolution and consequently improve the performance of the NARX machine learning model. This study examined training and testing datasets of different sizes, demonstrating the versatility and applicability of the coupled methodology. Our findings demonstrate the advantages of multivariate cubic spline interpolation with the NARX approach over MLP and Kriging with univariate interpolation methods. This paper presents a comprehensive review of existing interpolation techniques and their impact on modeling performance, addressing a critical gap in the current literature. The results show that the multivariate cubic spline interpolation with the NARX approach achieved a 72% reduction in the root mean square error (RMSE) and an 85% increase in the adjusted compared to the existing Kriging interpolation technique (Ismail et al. in Powder Technol 343:568–577, 2019). In terms of computational efficiency, the NARX approach with univariate and multivariate spline interpolations are 16 times more efficient than the Kriging interpolation technique.Publicationtype: Journal Article15 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Effects of B.1.1.7 and B.1.351 on COVID-19 dynamics: a campus reopening study(Springer, 2021-08-23); ; ; ; The timing and sequence of safe campus reopening has remained the most controversial topic in higher education since the outbreak of the COVID-19 pandemic. By the end of March 2020, almost all colleges and universities in the United States had transitioned to an all online education and many institutions have not yet fully reopened to date. For a residential campus like Stanford University, the major challenge of reopening is to estimate the number of incoming infectious students at the first day of class. Here we learn the number of incoming infectious students using Bayesian inference and perform a series of retrospective and projective simulations to quantify the risk of campus reopening. We create a physics-based probabilistic model to infer the local reproduction dynamics for each state and adopt a network SEIR model to simulate the return of all undergraduates, broken down by their year of enrollment and state of origin. From these returning student populations, we predict the outbreak dynamics throughout the spring, summer, fall, and winter quarters using the inferred reproduction dynamics of Santa Clara County. We compare three different scenarios: the true outbreak dynamics under the wild-type SARS-CoV-2, and the hypothetical outbreak dynamics under the new COVID-19 variants B.1.1.7 and B.1.351 with 56% and 50% increased transmissibility. Our study reveals that even small changes in transmissibility can have an enormous impact on the overall case numbers. With no additional countermeasures, during the most affected quarter, the fall of 2020, there would have been 203 cases under baseline reproduction, compared to 4727 and 4256 cases for the B.1.1.7 and B.1.351 variants. Our results suggest that population mixing presents an increased risk for local outbreaks, especially with new and more infectious variants emerging across the globe. Tight outbreak control through mandatory quarantine and test-trace-isolate strategies will be critical in successfully managing these local outbreak dynamics.Publicationtype: Journal ArticleScopus© Citations 4 165 212 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A selection of benchmark problems in solid mechanics and applied mathematicsIn this contribution we provide benchmark problems in the field of computational solid mechanics. In detail, we address classical fields as elasticity, incompressibility, material interfaces, thin structures and plasticity at finite deformations. For this we describe explicit setups of the benchmarks and introduce the numerical schemes. For the computations the various participating groups use different (mixed) Galerkin finite element and isogeometric analysis formulations. Some programming codes are available open-source. The output is measured in terms of carefully designed quantities of interest that allow for a comparison of other models, discretizations, and implementations. Furthermore, computational robustness is shown in terms of mesh refinement studies. This paper presents benchmarks, which were developed within the Priority Programme of the German Research Foundation ‘SPP 1748 Reliable Simulation Techniques in Solid Mechanics—Development of Non-Standard Discretisation Methods, Mechanical and Mathematical Analysis’.Publicationtype: Journal ArticleScopus© Citations 32 448 209 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , System-Based Approaches for Structural Optimization of Flexible Mechanisms(2018-07-01); ; ; This paper reviews the state-of-the-art methods to perform structural optimization of flexible mechanisms. These methods are based on a system-based approach, i.e. the formulation of the design problem incorporates the time response of the mechanism that is obtained from a dynamic simulation of the flexible multibody system. The system-based approach aims at considering as precisely as possible the effects of nonlinear dynamic loading under various operating conditions. Also, the optimization process enhances most existing studies which are limited to (quasi-) static or frequency domain loading conditions. This paper briefly introduces flexible multibody system dynamics and structural optimization techniques. Afterwards, the two main methods, named the weakly and the fully coupled methods, that couple both disciplines are presented in details and the influence of the multibody system formalism is analyzed. The advantages and drawbacks of both methods are discussed and future possible research areas are mentioned.Publicationtype: Journal Article159