An Efficient Kriging Modeling Method Based on Multidimensional Scaling for High-Dimensional Problems

Kriging-based modeling has been widely used in computationally intensive simulations. However, the Kriging modeling of high-dimensional problems not only takes more time, but also leads to the failure of model construction. To this end, a Kriging modeling method based on multidimensional scaling (KMDS) is presented to avoid the “dimensional disaster”. Under the condition of keeping the distance between the sample points before and after the dimensionality reduction unchanged, the KMDS method, which mainly calculates each element in the inner product matrix due to the mapping relationship between the distance matrix and the inner product matrix, completes the conversion of design data from high dimensional to low dimensional. For three benchmark functions with different dimensions and the aviation field problem of aircraft longitudinal flight control, the proposed method is compared with other dimensionality reduction methods. The KMDS method has better modeling efficiency while meeting certain accuracy requirements.

A monolithic approach toward coupled electrodynamic–thermomechanical problems with regard to weak formulations

Weak formulations of boundary value problems are the basis of various numerical discretization schemes. They are classically derived applying the method of weighted residuals or a variational principle. For electrodynamical and caloric problems, variational approaches are not straightforwardly obtained from physical principles like in mechanics. Weak formulations of Maxwell’s equations and of energy or charge balances thus are frequently derived from the method of weighted residuals or tailored variational approaches. Related formulations of multiphysical problems, combining mechanical balance equations and the axioms of electrodynamics with those of heat conduction, however, raise the additional issue of lacking consistency of physical units, since fluxes of charge and heat intrinsically involve time rates and temperature is only included in the heat balance. In this paper, an energy-based approach toward combined electrodynamic–thermomechanical problems is presented within a classical framework, merging Hamilton’s and Jourdain’s variational principles, originally established in analytical mechanics, to obtain an appropriate basis for a multiphysical formulation. Complementing the Lagrange function by additional potentials of heat flux and electric current and appropriately defining generalized virtual powers of external fields including dissipative processes, a consistent formulation is obtained for the four-field problem and compared to a weighted residuals approach.

Stochastic Method to Predict Effects of Roll Grinding Deviations on Sheet Flatness in Cold Rolling

Industrial measurements of the diameter profiles of work-rolls used in cold sheet rolling are applied with a stochastic roll-stack model to better understand how residual error from the roll grinding process affects the rolled sheet flatness quality. Roll diameter measurements taken via a non-contact, optical device on new, warm, and worn work-rolls show that the diameter deviations vary along the roll lengths, across roll samples, and at different operational states, suggesting a multi-dimensional random field problem. Studies are conducted for a 4-high rolling mill with 301 stainless steel sheet to investigate the reliability in achieving target flatness considering the work-roll diameter random field. Also investigated is the sensitivity of the flatness reliability to roll diameter deviations at different locations along the roll lengths, and for the three operational states (newly machined, warm, and worn following several passes). The results lead to several key findings. Foremost, it is shown that an assumption of statistical independence among the residual grinding errors at different roll axis locations is improper. Further, it is demonstrated that, for the measured grinding error correlation patterns, the roll diameter deviations external to the roll/sheet contact region play an important role in contributing to flatness defects within the sheet, and that these influences vary according to the roll operational state (new, warm, worn). The presented stochastic model and applied measurement data thus provide for a new understanding into how roll grinding performance influences dimensional quality in the sheet rolling process.

Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control

Increasing the growth of big data, particularly in healthcare-Internet of Things (IoT) and biomedical classes, tends to help patients by identifying the disease early through methods for the analysis of medical data. Hence, nanotechnology-based IOT biosensors play a significant role in the medical field. Problem. However, the consistency continues to decrease where missing data occurs in such medical data from nanotechnology-based IOT biosensors. Furthermore, each region has its own special features, which further lowers the accuracy of prediction. The proposed model initially reconstructs lost or partial data in order to address the challenge of handling the medical data structures with incomplete data. Methods. An adaptive architecture is proposed to enhance the computing capabilities to predict the disease automatically. The medical databases are managed by unpredictable environments. This optimized paradigm for diagnosis produces the fuzzy, genetically categorized decision tree algorithm. This work uses a normalized classifier namely fuzzy-based decision tree (FDT) algorithm for classifying the data collected via nanotechnology-based IOT biosensors, and this helps in the identification of nondeterministic instances from unstructured datasets relating to the medical diagnosis. The FDT algorithm is further enhanced by using genetic algorithms for effective classification of instances. Finally, the proposed system uses two larger datasets to verify the predictive precision. In order to describe a fuzzy decision tree algorithm based upon the fitness function value, a modified decision classification rule is used. The structure and unstructured databases are configured for processing. Results and Conclusions. This evaluation of test patterns helps to track the efficiency of FDT with optimized rules during the training and testing stages. The proposed method is validated against nanotechnology-based IOT biosensors data in terms of accuracy, sensitivity, specificity, and F -measure. The results of the simulation show that the proposed method achieves a higher rate of accuracy than the other methods. Other metrics relating to the model with and without feature selection show an improved sensitivity, specificity, and F -measure rate than the existing methods.

Acoustic Simulation of the Electric Vehicle Motor

People pay more and more attention to NVH (N-noise, V-vibration, and H-harshness) characteristics of electric vehicles and motors. The vibration and noise of the electric vehicle motor is a complex multi physical field problem, related to electromagnetic, mechanical, structural and sound fields. In this paper, firstly, the Maxwell electromagnetic field analysis software is used to build the electromagnetic simulation model of PMSM(Permanent magnet synchronous motor) motor, and then the electromagnetic excitation is calculated. Then, the magnetic solid coupling analysis is carried out by using the Maxwell module and harmonic response module of ANSYS Workbench, and the harmonic response of the motor structure under the action of electromagnetic excitation is simulated. After the simulation of magnetic solid coupling is completed, the BEM(Boundary element method) simulation of the permanent magnet synchronous motor is carried out with the acoustic simulation software LMS Virtual Lab. The acoustic simulation of the permanent magnet synchronous motor shows that the sound pressure level at three measurement points have obvious peak value at 3700Hz, 4500Hz, 5400Hz and 6400Hz respectively. The acoustic simulation results lay the foundation for the vibration and noise reduction of the motor.

A Block Arnoldi Algorithm Based Reduced-Order Model Applied to Large-Scale Algebraic Equations of a 3-D Field Problem

In simulations of three-dimensional transient physics filled through a numerical approach, the order of the equation set of high-fidelity models is extremely high. To eliminate the large dimension of equations, a model order reduction (MOR) technique is introduced. In the existing MOR methods, the block Arnoldi algorithm-based MOR method is numerically stable, achieving a passively reduced order model. Nevertheless, this method performs poorly when it is applied to very wide-frequency transients. To eliminate this deficiency, multipoint MOR methods are emerging. However, it is hard to directly apply an existing multipoint MOR method to a 3-D transient field equation set. The implementation issues in a reduction process (such as the selection of expansion points, the number of moments matched at a point and the error bound) have not been explored in detail. In this respect, an adaptive multipoint model reduction model based on the Arnoldi algorithm is proposed to obtain the reduced-order models of a 3-D temperature field. The originality of this study is the proposal of a novel adaptive algorithm for selecting expansion points, matching moments automatically, using a posterior-error estimator based on temperature response coupled with a network topological method (NTM). The computational efficiency and accuracy of the proposed method are evaluated by the numerical results from solving the temperature field of a prototype insulated-gate bipolar transistor (IGBT).

Screening Criteria for Dump-flood Projects Implementation in Indonesia Fields

Dump-flooding is a process of water flowing from higher-pressure-aquifer/ -water-bearing-reservoir to lower and depleted-pressure-oil reservoir through casing of the same well. This method is introduced to resolve groundwater scarcity of remote field problem. Furthermore, dump flood is a more cost-friendly method compare with conventional water injection, as it does not require water surface facility, more injection wells, and injection pipelines. Although dump-flooding has been successfully applied in many oilfields in the world, yet there is no standardized screening criteria that have been published. Therefore, this paper intends to generate a dump flood-organized-screening table to acquire potential oil reservoir candidates in Indonesia through dumpflooding projects based on the screening results. The screening table was constructed by gathering reservoir, aquifer, and fluid property data from positively-applied-dump flood project-oilfield. Two sensitivity methods – radar plot and CORRELL – were used to define critical and non-critical parameters which affected the oil recovery factor value. After being analyzed, the sensitivity results from CORRELL method were selected considering it is used frequently to measure the strength of the relationship between two variables. There are seven critical parameters (oil viscosity, reservoir permeability and porosity, depth, reservoir temperature, and aquifer porosity and permeability) that influence the decision to perform dump-flooding in one field. There are 134 out of 264 oilfields in Indonesia were tested with confident of 90% that were screened afterward. In addition, there are two factors to determine “go/no-go” decision, those are: range of tolerance and uncertainty. The status of the project can be declared as “go” if there is no out-of-tolerance-range-parameter and the uncertainty accumulation parameters is below 30%. After screening, 75 out of 134 fields were passed, where the majority of them were sandstone reservoirs with a dominant light oil compositions and been previously water flooded