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Frictional loss in bend pipes: numerical simulation and data driven modeling

SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2023年第3期48卷 1-16页

作者： Vasa, Adarsh Chaudhury, Kaustav Natl Inst Technol Rourkela Dept Mech Engn Rourkela 769008 India

Here, we present a quasi-universal correlation for the friction factor in 90-degree and 180-degree bend pipes using the ensemble of metamodels (EOM). Using computational simulations, we obtain frictional losses at curvature ratios from 0.01 to 0.2 and Reynolds numbers up to 0.1 million. With the help of this data, we develop the EOM based correlation. The extensive window of parameters considered in the present study encompasses a vast design and operating conditions of 90-degree and 180-degree bend pipes. The large parametric space considered here essentially represents a variety of hydrodynamic scenarios. We use a sufficient yet nominal set of parameters representative of the variation and perform numerical simulations over the sampled set. Using the simulation data, we then train and validate the EOM to develop the required correlation. The k-fold cross-validation technique is employed to the EOM to optimize the model's predictive capabilities over the changing flow regimes. We believe that this modeling strategy, along with the database, may act as a base framework towards the further development of data augmentation techniques in pipes of various configurations, in general. Using this EOM based correlation, we further generate a large database for the friction factor in the two smooth bend pipe configurations at various curvature ratio and different Reynolds number. This approach can be viewed as an alternative to the Moody chart, however, for pipe bends.

关键词： data driven modeling ensemble of metamodels bend pipes friction factor

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data driven modeling of pseudopalisade pattern formation

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JOURNAL OF MATHEMATICAL BIOLOGY 2023年第1期87卷 4-4页

作者： Athni Hiremath, Sandesh Surulescu, Christina TU Kaiserslautern Mech & Proc Engn Gottlieb Daimler Str 42 D-67663 Kaiserslautern Rhineland Palat Germany TU Kaiserslautern Felix Klein Zentrum Math Paul Ehrlich Str 31 D-67663 Kaiserslautern Rhineland Palat Germany

Pseudopalisading is an interesting phenomenon where cancer cells arrange themselves to form a dense garland-like pattern. Unlike the palisade structure, a similar type of pattern first observed in schwannomas by pathologist J.J. Verocay (Wippold et al. in AJNR Am J Neuroradiol 27(10):2037-2041, 2006), pseudopalisades are less organized and associated with a necrotic region at their core. These structures are mainly found in glioblastoma (GBM), a grade IV brain tumor, and provide a way to assess the aggressiveness of the tumor. Identification of the exact bio-mechanism responsible for the formation of pseudopalisades is a difficult task, mainly because pseudopalisades seem to be a consequence of complex nonlinear dynamics within the tumor. In this paper we propose a data-driven methodology to gain insight into the formation of different types of pseudopalisade structures. To this end, we start from a state of the art macroscopic model for the dynamics of GBM, that is coupled with the dynamics of extracellular pH, and formulate a terminal value optimal control problem. Thus, given a specific, observed pseudopalisade pattern, we determine the evolution of parameters (bio-mechanisms) that are responsible for its emergence. Random histological images exhibiting pseudopalisade-like structures are chosen to serve as target pattern. Having identified the optimal model parameters that generate the specified target pattern, we then formulate two different types of pattern counteracting ansatzes in order to determine possible ways to impair or obstruct the process of pseudopalisade formation. This provides the basis for designing active or live control of malignant GBM. Furthermore, we also provide a simple, yet insightful, mechanism to synthesize new pseudopalisade patterns by linearly combining the optimal model parameters responsible for generating different known target patterns. This particularly provides a hint that complex pseudopalisade patterns could be synthesized by

关键词： Optimal control Pseudopalisades data driven modeling Pattern formation

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data driven modeling of the Reactive Oxygen Species Stimulated by Photon Energy in Light Therapies

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IEEE ACCESS 2020年 8卷 18196-18206页

作者： Dong, Jianfei Wang, Tianfeng Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Suzhou 215123 Peoples R China Delft Univ Technol Dept Microelect NL-2628 Delft Netherlands

Light therapies can be used to treat fungal infections. A general mechanism is attributed to the generation of cytotoxic reactive oxygen species (ROS) due to light stimulation. The effectiveness of these therapies has been widely studied in the literature via conducting biological experiments, where fungi are exposed to light with various wavelengths and power. However, despite the large amount of work reporting the experimental results, few efforts have been given to build a mathematical model that describes the amount of generated ROS as a function of the photon energy and power of the stimulating light. The lack of such a model still hinders the optimization of the light doses. In this work, we propose a novel modeling method based on experimental data, so as to establish a mathematical relationship between the ROS concentration and the stimulating photon energy and light fluence (energy density). The anti-fungal experiments were performed on Candida albicans (C: albicans) using four LED light sources with different wavelengths ranging from 385nm to 450nm. Both the viability of the fungi and the ROS concentration therein were measured during the experiments. High fitting accuracy has been achieved by the model, which therefore demonstrates the effectiveness of the proposed modeling techniques.

关键词： Biomedical engineering data driven modeling light-emitting diodes light therapy parameter estimation

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data-driven modeling of Li-Ion Battery Based on the Manufacturer Specifications and Laboratory Measurements

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 3485-3493页

作者： Di Fonso, Roberta Cecati, Carlo Teodorescu, Remus Stroe, Daniel-Ioan Bharadwaj, Pallavi Aalborg Univ DK-9220 Aalborg Denmark Univ LAquila I-67100 Laquila Italy Indian Inst Technol Gandhinagar 382355 India

Accurate modeling of Lithium-ion battery is essential in the development and testing of state estimation and lifetime prediction algorithms. The desired features of the model include flexibility, fast development, accuracy and reliability. There are many different ways to model a battery, depending on the level of abstraction desired, the data available and the target application environment. This paper shows how to extract equivalent circuit model parameters from manufacturer datasheets and laboratory measurement to build robust battery simulation models. A step-by-step methodology for data preparation is presented for both datasheet and measurement-based methods. The benefits and the disadvantages of both approaches are also discussed. A simple equivalent circuit model is firstly derived from manufacturer specification and its robustness is enhanced by collecting more extensive experimental data in the laboratory. Furthermore, an advanced model to better capture the battery dynamics is developed. The aging effects are added to this battery model, to reflect the internal parameters variation according to the health condition of the battery. To measure the accuracy of the developed models, the relative error is computed. An initial relative error of 2.8% of the model build with manufacturer specifications is reduced to 1.0% using laboratory measurements and finally to less than 0.4% by incorporating aging effects.

关键词： Batteries Integrated circuit modeling data models Load modeling Computational modeling State of charge Aging Voltage Battery charge measurement Accuracy Lithium-ion battery equivalent circuit model datasheet specifications data driven modeling state of health

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data-driven surrogate modeling of dielectric reflectarray antenna using 3D printing technology for X band applications

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2024年第5期66卷 e34153-e34153页

作者： Bereket, Mehmet Belen, Mehmet A. Iskenderun Tech Univ Dept Elect & Elect Engn Hatay Turkiye

Reflectarray (RA) antennas are gaining recognition as a high-performance alternative for antenna design. They combine the functionalities of standard parabolic reflectors and phased array antennas without a feed network. However, the design of RA models requires a model that exhibits exceptional performance while meeting contradictory requirements such as size and operation band. An effective and computationally efficient approach is to employ a rapid and precise unit element model for the design of reflective antennas. Artificial intelligence (AI) regression techniques have been developed to predict electromagnetic behavior efficiently and accurately in microwave device design. A cross-shaped 3D printable RA was examined as a case study, and a design-ready surrogate model was developed with a mean absolute error of approximately 5 degrees using only 800 data samples. The method can be considered a feasible substitute for current surrogate-assisted RA design techniques due to its computational efficacy and dependability.

关键词： 3D printer artificial intelligence data driven modeling deep learning optimization surrogate modeling

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data-driven modeling of band-pass filter for sub-5G applications

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2023年第8期65卷 2210-2216页

作者： Belen, Aysu Belen, Mehmet A. Iskenderun Tech Univ Iskenderun Vocat Sch Higher Educ Hybrid & Elect Vehicle Technol Hatay Turkiye Iskenderun Tech Univ Dept Elect Elect Engn Hatay Turkiye

Radiofrequency noise is one of the challenging problems in the design of high-performance wireless communication systems, for which microstrip band-pass filters are one of the most commonly used design solutions for this challenge. However, for having high-performance designs. usage of a 3D Electromagnetic simulation tool is a must in which the computation efficiency of the whole design optimization process might not be acceptable or even feasible. An efficient solution is utilization of AI-based algorithms to create data-driven surrogate models of the handled problem. In this paper, for achieving design optimization of an edge-coupled band-pass filter AI-based algorithms have been used to create a data-driven surrogate model. To achieve this, by using a 3D full-wave simulator a data set for the aimed bandpass filter is generated. Then a series of state-of-the-art regression algorithms, Support Vector Regression Machine, MultiLayer Perceptron, Ensemble Learning, Gaussian Process Regression, and Convolutional Neural Network have been used to create a data-driven surrogate model for the aimed filter design. In the third step, the obtained data-driven surrogate model is used to assist an optimization process directed by the Bayesian optimization technique to optimally determine geometrical design parameters of the desired band-pass filter for sub-5G applications at frequency of 3.4 GHz. The obtained results of the surrogate model are compared with experimental results and found to be in high agreement level. Furthermore, the performance of the optimally designed filter is compared with the counterpart designs in literature. Thus, based on the obtained results, it can be said that the proposed surrogate-assisted optimization process is not only an efficient method in terms of computational costs but also is an efficient method to obtain high-performance microwave filter designs.

关键词： 5G artificial intelligence data driven modeling microwave filter surrogate modeling

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data-driven modeling of optimal intensity measure of soil-nailed wall structures

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STRUCTURAL ENGINEERING AND MECHANICS 2023年第1期86卷 85-92页

作者： Bayat, Massoumeh Bayat, Mahdi Bayut, Mahmoud Islamic Azad Univ Dept Comp Engn Mashhad Branch Mashhad Iran Islamic Azad Univ Dept Civil Engn Roudehen Branch Roudehen Iran Texas State Univ Ingram Sch Engn Smart Sustainable & Resilient Infrastruct SSRI Lab San Marcos TX 78666 USA

This article examines the seismic vulnerability of soil nail wall structures. Detailed information regarding finite element modeling has been provided. The fragility function evaluates the relationship between ground motion intensities and the probability of surpassing a specific level of damage. The use of incremental dynamic analysis (IDA) has been applied to the soil nail wall against low to severe ground motions. In the nonlinear dynamic analysis of the soil nail wall, a set of twenty seismic ground motions with varying PGA ranges are used. The numerical results demonstrate that the soil-nailed wall reaction is extremely sensitive to earthquake ground vibrations under different intensity measures (IM). In addition, the analytical fragility curve is provided for various intensity values.

关键词： data driven modeling finite element modeling nonlinear analysis nonlinear time history analysis optimal intensity measure

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data driven surrogate modeling of horn antennas for optimal determination of radiation pattern and size using deep learning

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2024年第1期66卷 e33702-e33702页

作者： Piltan, Onur Can Kizilay, Ahmet Belen, Mehmet A. A. Mahouti, Peyman Yildiz Tech Univ Dept Elect & Commun Engn Istanbul Turkiye Iskenderun Tech Univ Dept Elect & Elect Engn Hatay Turkiye

Horn antenna designs are favored in many applications where ultra-wide-band operation range alongside of a high-performance radiation pattern characteristics are requested. Scattering-parameter characteristics of antennas is an important design metric, where inefficiency in the input would drastically lower the realized gain. However, satisfying the requirement for scattering parameters are not enough for having an antenna with high-performance results, where the radiation characteristic of the design can be changed independently than the scattering parameters behavior. A design might have a high-efficiency performance, but the radiation characteristics might not be acceptable. Furthermore, there are other design considerations such as size and volume of the design alongside of these conflicting characteristics, which directly affect the manufacturing cost and limits the possible applications. In this work, by using data-driven surrogate modeling, it is aimed to achieve a computationally efficient design optimization process for horn antennas with high radiation performance alongside of being small in or within the limits of the desired application limits. Here, the geometrical design variables, operation frequency, and radiation direction of the design will be taken as the input, while the realized gain of the design is taken as the output of the surrogate model. Series of powerful and commonly used artificial intelligence algorithms, including Deep Learning had been used to create a data-driven surrogate model representation for the handled problem, and 80% computational cost reduction had been obtained via proposed approach. As for the verification of the studied optimization problem, an optimally designed antenna is prototyped via the use of three-dimensional printer and the experimental results ware compared with the results of surrogate model.

关键词： artificial intelligence 3D printer data driven modeling deep learning optimization surrogate modeling

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data driven Fuzzy modeling Using Level Sets

Data Driven Fuzzy Modeling Using Level Sets

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Leite, Daniel Gomide, Fernando Yager, Ronald Adolfo Ibanez Univ Fac Engn & Sci Santiago Chile Univ Estadual Campinas Sch Elect & Comp Engn Campinas SP Brazil Iona Coll Inst Machine Intelligence New Rochelle NY 10801 USA

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

The paper looks at the structure of fuzzy rule based models from the point of view of a function relating membership grades of inputs with rule outputs. This view in turn is generalized by an approach that produces the output functions of the fuzzy rules using input and output data. In this view, a formulation to compute the output of the model consists of estimating the parameters of the output functions. Essentially, the paper suggests an alternative method for fuzzy modeling based on output functions constructed from level sets and input and output data. The data driven method provides an easy and efficient way to develop and process fuzzy models. Examples of function estimation problems show that the data driven level set method is very effective when compared with alternative modeling techniques.

关键词： fuzzy modeling data driven modeling

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PHYSICS-BASED AND data-driven modeling OF HYBRID ROBOT MOVEMENT ON SOFT TERRAIN

PHYSICS-BASED AND DATA-DRIVEN MODELING OF HYBRID ROBOT MOVEM...

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作者： Wang, Guanjin University of Maryland

学位级别：博士

Navigating an unmapped environment is one of the ten biggest challenges facing the robotics community. Wheeled robots can move fast on flat surfaces but suffer from loss of traction and immobility on soft ground. However, legged machines have superior mobility over wheeled locomotion when they are in motion over flowable ground or a terrain with obstacles but can only move at relatively low speeds on flat surfaces. A question to answer is as follows: If legged and wheeled locomotion are combined, can the resulting hybrid leg-wheel locomotion enable fast movement in any terrain condition? To investigate the rich physics during dynamic interactions between a robot and a granular terrain, a physics-based computational framework based on the smoothed particle hydrodynamics (SPH) method has been developed and validated by using experimental results for single robot appendage interaction with the granular system. This framework has been extended and coupled with a multi-body simulator to model different robot configurations. Encouraging agreement is found amongst the numerical, theoretical, and experimental results, for a wide range of robot leg configurations, such as curvature and shape. Real-time navigation in a challenging terrain requires fast prediction of the dynamic response of the robot, which is useful for terrain identification and robot gait adaption. Therefore, a data-driven modeling framework has also been developed for the fast estimation of the slippage and sinkage of robots. The data-driven model leverages the high-quality data generated from the offline physics-based simulation for the training of a deep neural network constructed from long short-term memory (LSTM) cells. The results are expected to form a good basis for online robot navigation and exploration in unknown and complex terrains.

关键词： Computational physics Engineering Fluid mechanics data driven modeling Dynamics Granular Material Robot Navigation Smoothed Particle Hydrodynamics Terramechanics

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