Method For Determination Of Parameters Research Articles

The Influence of Material Properties and Wall Thickness on Predicted Wall Stress in Ascending Aortic Aneurysms: A Finite Element Study.

Finite element analysis (FEA) has been used to predict wall stress in ascending thoracic aortic aneurysm (ATAA) in order to evaluate risk of dissection or rupture. Patient-specific FEA requires detailed information on ATAA geometry, loading conditions, material properties, and wall thickness. Unfortunately, measuring aortic wall thickness and mechanical properties non-invasively poses a significant challenge, necessitating the use of non-patient-specific data in most FE simulations. This study aimed to assess the impact of employing non-patient-specific material properties and wall thickness on ATAA wall stress predictions. FE simulations were performed on 13 ATAA geometries reconstructed from computed tomography angiography (CTA) images. Patient-specific material properties and wall thicknesses were made available from a previous study where uniaxial tensile testing was performed on tissue samples obtained from the same patients. The ATAA wall models were discretised with hexahedral elements and prestressed. For each ATAA model, FE simulations were conducted using patient-specific material properties and wall thicknesses, and group-mean values derived from all tissue samples included in the same experimental study. Literature-based material property and wall thickness were also obtained from the literature and applied to 4 representative cases. Additional FE simulations were performed on these 4 cases by employing group-mean and literature-based wall thicknesses. FE simulations using the group-mean material property produced peak wall stresses comparable to those obtained using patient-specific material properties, with a mean deviation of 7.8%. Peak wall stresses differed by 20.8% and 18.7% in patients with exceptionally stiff or compliant walls, respectively. Comparison to results using literature-based material properties revealed larger discrepancies, ranging from 5.4% to 28.0% (mean 20.1%). Bland-Altman analysis showed significant discrepancies in areas of high wall stress, where wall stress obtained using patient-specific and literature-based properties differed by up to 674kPa, compared to 227kPa between patient-specific and group-mean properties. Regarding wall thickness, using the literature-based value resulted in even larger discrepancies in predicted peak stress, ranging from 24.2% to 30.0% (mean 27.3%). Again, using the group-mean wall thickness offered better predictions with a difference less than 5% in three out of four cases. While peak wall stresses were most affected by the choice of mechanical properties or wall thickness, the overall distribution of wall stress hardly changed. Our study demonstrated the importance of incorporating patient-specific material properties and wall thickness in FEA for risk prediction of aortic dissection or rupture. Our future efforts will focus on developing inverse methods for non-invasive determination of patient-specific wall material parameters and wall thickness.

Fan noise radial mode detection based on [formula omitted] regularization method

Radial mode detection has been widely employed in fan noise research. Many radial mode detection algorithms by conducting the convex L1 norm optimization have been proposed by taking advantage of the fact that the fan noise sound field is usually dominated by a limited set of radial modes. Nevertheless, L1 norm optimization has been shown to be suboptimal and cannot enforce further sparsity. A fan noise radial mode detection method based on L1/2 regularization algorithm is proposed in this paper to improve its robustness and dynamic range. This method employs the L1/2 regularization to directly solve the in-duct sound propagation model. Synthesized simulations indicates that the L1/2 regularization method can detect the correct radial mode order and amplitude under low signal-to-noise ratio. Moreover, the new method is insensitive to regularization parameter. An experimental system of radial rake is implemented to validate the new method. It is shown that the L-curve method, which do not require any prior information of the acoustic mode distribution or contaminating measurement noise, is an appropriate choice of the regularization parameter determination method for the L1/2 regularizer. It is demonstrated that the new L1/2 regularization method can enhance the mode sparsity, especially under low signal-to-noise ratio. Moreover, the resolution and dynamic range can be improved by the L1/2 regularization method, which makes it an effective and robust fan noise radial mode detection technique in practical applications.

Dynamic Determination Method of Line Drop Compensator Parameters for Voltage Regulators Based on Mixture of Experts Using Real‐Time Information

In this paper, a method for determining line drop compensator (LDC) parameters for step voltage regulators and on‐load tap changers is proposed to avoid voltage violations in distribution systems with voltage fluctuations due to photovoltaic (PV) generation and electric vehicle (EV) charging. Distribution system operators need to effectively solve voltage problems caused by the widespread installation of distributed energy resources with the existing voltage regulators to construct an efficient and rational infrastructure. Our focus was on improving the method for determining the LDC parameters for the existing voltage regulators based on LDC control. A mechanism to dynamically change the LDC parameters depending on the situations in the distribution system was developed by using a mixture of experts, one of the machine learning techniques, and real‐time voltage and power flow information from information technology switches. The power flow calculations were performed using a distribution system model constructed based on the topology, loads, and generation characteristics of an actual distribution system in the Hokuriku region. The effectiveness of the proposed method was evaluated in terms of its improved effect on PV and EV hosting capacity, as well as the impact on the frequency of tap operations of voltage regulators. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The Influence of Citric Acid on the Corrosion Process of Different Metallic Materials

Abstract The paper presents the results from a study of the corrosion behaviour of different metallic materials, such as copper, aluminium and OL 52 steel in the presences of various concentrations of citric acid added in neutral solution (Na2SO4 0.25 M). Citric acid (2-hydroxy-1, 2, 3-propanetricarboxylic acid) is among the most commonly utilized compounds in the food and beverage sectors. This non-toxic chemical can be derived from natural, renewable sources such as citrus fruits, pineapples, kiwis, raspberries, strawberries, and both black and red currants. Corrosion studies carried out previously demonstrated a mixed effect of citric acid, it can be a good inhibitor for aluminum alloys in a strongly acidic or strongly basic media as well as in neutral NaCl media, for low carbon-steel and copper in an acidic environment, but also, a good, accelerated corrosion agent in an alkaline solution for metals such as iron, copper, zinc and tin. Potentiodynamic polarization and chronoamperometry techniques were used in the experimental part of this paper to evaluate the inhibition efficiency of the citric acid in test solutions. Also, Tafel method for the kinetic parameters determination for studied corrosion process.

Balance Sparse Decomposition Method with Nonconvex Regularization for Gearbox Fault Diagnosis

As an important part of rotating machinery, gearboxes often fail due to their complex working conditions and harsh working environment. Therefore, it is very necessary to effectively extract the fault features of the gearboxes. Gearbox fault signals usually contain multiple characteristic components and are accompanied by strong noise interference. Traditional sparse modeling methods are based on synthesis models, and there are few studies on analysis and balance models. In this paper, a balance nonconvex regularized sparse decomposition method is proposed, which based on a balance model and an arctangent nonconvex penalty function. The sparse dictionary is constructed by using Tunable Q-Factor Wavelet Transform (TQWT) that satisfies the tight frame condition, which can achieve efficient and fast solution. It is optimized and solved by alternating direction method of multipliers (ADMM) algorithm, and the non-convex regularized sparse decomposition algorithm of synthetic and analytical models are given. Through simulation experiments, the determination methods of regularization parameters and balance parameters are given, and compared with the L1 norm regularization sparse decomposition method under the three models. Simulation analysis and engineering experimental signal analysis verify the effectiveness and superiority of the proposed method.

A novel molecular structure parameter determination method for biomass thermochemical conversion mechanism investigation

The low-grade energy properties and high O content of biomass limit its efficient energy utilization. Torrefaction is known to be one of the most promising thermochemical conversion methods for upgrading biomass. 250 °C Gas-pressurized (GP) torrefaction realizes deep decomposition of hemicellulose in biomass to as high as 99.7% by deoxygenation and aromatization. However, the above thermochemical conversion mechanism at molecular-level is currently scant. Here, a novel molecular structural parameter method and pure hemicellulose torrefaction were employed to investigate the thermochemical conversion mechanism. Results demonstrate that O content of 250 °C GP torrefied hemicellulose was as low as 28.8 wt%, which was considerably lower than 44.6 wt% of traditional torrefied hemicellulose. There are five deoxygenation mechanisms of hemicellulose during the GP torrefaction: deacetylation, decarbonylation, dehydroxylation, ring opening and cyclization reactions to produce CO2/CH3COOH, CO, H2O, ketones/aldehydes and furans. The deoxygenation and aromatization reactions cause the chemical structure of hemicellulose evolution significantly, these mechanisms are: active hemicellulose undergo aromatization reaction through cyclization and deoxidation to generate preliminary 2-ring furans polymer, followed by stepwise polymerization reactions to form 4-ring and 6-ring furans polymers. These polymerization reactions further greatly enhance deoxygenation reaction. A thermochemical conversion mechanism model is developed based on the above investigation. This work provides a novel method for determining the molecular structure and reaction mechanism of thermochemical converted biomass.

Advanced Bayesian-Inspired Multilayer Effective Parameter Determination Method for Automated ANN Model Generation of Microwave Components

Advanced Bayesian-Inspired Multilayer Effective Parameter Determination Method for Automated ANN Model Generation of Microwave Components

Solutions of crack constraint parameter in 3D models −− II: Through T-stress

To more accurately analyze the crack front fields, especially under the low constraint conditions which are more common in practical engineering structures, two-parameter approaches (e.g. J-A(A2), J-Q, etc.) are proposed. Currently, no theoretical solutions of the constraint parameters (e.g. A(A2), Q, etc.) in two-parameter approaches are available. Meanwhile, numerical methods for constraint parameter determination are not practical because of significant resource and time consumption, especially for 3D solid models or engineering structures. Therefore, theory and formulae for the estimation of constraint parameter A in 3D solid models are developed in the current work based on T-stress, so as to make it possible to apply two-parameter approaches in practical engineering structures under normal working condition. Two actual structures are used to validate the engineering application of the developed estimate method and formulae.

Micro-scale crystallization thermodynamics study of typical energetic compounds integrating optofluidics and machine learning

With the aim of investigating the changing law of crystallization driving force of typical energetic compounds under micro-scale crystallization conditions, a thermodynamic parameter determination method based on optofluidics was proposed. Aimed at nitro, nitramine and nitrate explosives in energetic compounds, hexanitrostilbene (HNS), cyclotetramethylene tetranitramine (HMX) and pentaerythritol tetranitrate (PETN) were selected as representatives, the solubility of the three kinds of energetic compounds in their respective commonly used solvents (HNS: in DMF, DMSO, NMP; HMX: in DMF, DMSO, CYC; PETN: in DMF, DMSO, EAc) at different temperatures were determined. Furthermore, microfluidics and machine learning were combined, the solubility data of the explosives were processed using BP neural network. Moreover, the metastable zone widths of HNS, HMX and PETN in each solvent were determined using on-line Raman technique. Additionally, crystalline thermodynamic parameters such as solid–liquid interfacial tension, crystal surface entropy factor, enthalpy of dissolution and etc. were calculated for each system.

Multi-criteria group emergency decision-making method considering knowledge granularity

Emergencies are typically characterized by abruptness, time urgency, and complexity, which give rise to challenges such as incomplete information, compromised information effectiveness, and reduced efficiency. To address these issues, this study proposes a novel multi-criteria group emergency decision-making (MCGEDM) method considering knowledge granularity. Within the framework of a hierarchical criterion system (HCS), decision makers' (DMs') judgment information is extracted using belief distributions (BDs) on knowledge chunks, based on the conceptualization of knowledge granularity. This enables DMs to make judgments as effectively as possible, thereby improving efficiency in terms of time and enhancing the effectiveness of information. The generalized combination (GC) rule is applied for individual information fusion within basic nests, demonstrating internal revision and complementation of information. Automatic parameter determination methods are proposed to enhance the effectiveness of information and the efficiency of MCGEDM. Finally, the proposed method is demonstrated through a simulative case of an oil spill emergency, and the subsequent sensitivity analysis and comparisons verify its feasibility and effectiveness.

UAV-based LiDAR Bathymetry at an Alpine Mountain Lake

Abstract. LiDAR bathymetry provides an efficient and comprehensive way to capture the topography of water bodies in shallow water areas. However, the penetration depth of this measurement method into the water column is limited by the medium water and water turbidity, resulting in a limited detectability of the bottom topography in deeper waters. An increase of the analyzable water depth is possible by the use of extended evaluation methods, in detail full-waveform stacking methods. So far, however, this has only been investigated for water depths of up to 3.50 m due to water turbidity. In this article, the potential of these extended data processing methods is investigated on an alpine mountain lake with low water turbidity and thus high analyzable water depth. Compared to the standard data processing, the penetration depth could be significantly increased by 58%. In addition, methods for depth-resolved water turbidity parameter determination on the basis of LiDAR bathymetry data were successfully tested.

Initial structure parameters determination and aberration optimization method for ultra-wide angle optical system with large aperture

Initial structure parameters determination and aberration optimization method for ultra-wide angle optical system with large aperture

A Comprehensive Study on Image Segmentation Using Pulse-Coupled Neural Networks

Pulse-Coupled Neural Network (PCNN) operates as a matrix of neurons, each uniquely corresponding to a pixel in the image being processed. Unlike traditional neural networks, PCNN does not require any training, making it highly suitable for image segmentation tasks. However, the complexity of PCNN arises from its numerous parameters, making parameter selection a challenging endeavor. In this work, we introduce a simplified PCNN architecture that includes an automatic parameter determination method. This approach is specifically designed for binary image segmentation and has been tested on various images, yielding results with distinct and desirable features. The proposed network iterates only four times, enhancing its efficiency. During pre-processing, RGB images are converted to HSV color space, and the V component undergoes further processing. This component is first filtered using an averaging filter, followed by a sharpening filter. The parameters for the PCNN are then generated automatically, eliminating the need for manual selection and making the network highly suitable for real-time image processing. The performance of the proposed network has been verified through tests on a variety of images. Key Words: Neural Networks, Pulse Coupled Neural Networks, Image Processing

Rapid analysis method for process parameter determination of transient thermal tensioning during welding buckling distortion mitigation

The effect of transient thermal tensioning (TTT) on mitigating buckling distortion is significantly influenced by the process parameters. TTT and conventional welding (CW) experiments were both performed on butt welded joints using induction heating as an additional heat source. The thermal elastic plastic (TEP) FE simulation was then carried out with consideration of material nonlinearity and geometrical nonlinearity to investigate the temperature, buckling distortion fields of the butt welded joints. A good agreement between computational results and the measurement was represented, and the buckling distortion of the butt welded joints with TTT was much smaller than that with CW. Based on the validated FE simulation, the sensitivity of process parameters (amount, sizes, tensioning temperature Ttmax, locations of induction heating) for TTT was examined in detail. The results show that amount and sizes exerted an evident impact on buckling distortion mitigation, which were determined by equipment. Tensioning temperature Ttmax and the offset from weld centerline d1 are more important parameters on effectively mitigating welding buckling distortion of thin plate structures than the offset from welding arc d2. Through optimizing Ttmax and d1, buckling distortion of examined butt welded joint can be reduced by more than 80 %. Moreover, a method for determining the process parameters of TTT towards engineering applications was proposed. Firstly, the amount and sizes of additional heat sources are maximized per the available equipment. Secondly, Ttmax is recommended as yield temperature to implement TTT and d1 is optimized through an experiment or TEP FE simulation. Eventually, the offset from welding arc d2 is further optimized when the manufacturing precisions are not reached.

Regional features of camel milk composition and properties in the Republic of Kazakhstan

Camel milk, renowned for its distinctive nutritional qualities, has captured the interest of scientific researchers due to its potential health benefits. This study aims to compare the biochemical composition of camel milk sourced from two distinct regions of Kazakhstan: Jetisu and Mangystou. Analytical methods were employed to achieve this objective, including gas chromatography for fatty acid analysis, chemical methods for physicochemical parameter determination, and assessment of amino acid, fatty acid, mineral, and vitamin content. In samples from the Jetisu region, protein content ranged from 3.61% to 3.70%, fat from 3.85% to 4.64%, and lactose from 4.80% to 4.85%. In comparison, samples from the Mangystou region exhibited protein content ranging from 3.65% to 3.81%, fat from 4.72% to 5.75%, and lactose from 4.21% to 4.28%. Regarding amino acid composition, Mangystou region samples contained more essential amino acids per 100 g of protein than Jetisu region samples: 41.29 g versus 38.20 g, respectively. Additionally, the Jetisu region sample contained 64.291% saturated fatty acids, while the Mangystou region sample had 62.135%, indicating differences in fatty acid composition based on geographical origin. In terms of mineral composition, camel milk from the Mangystou region exhibited higher calcium and zinc content compared to Jetisu region samples, with calcium and zinc content measured at 124.50 mg/100 g and 490.15 μg/100 g, respectively, for Mangystou samples, and 112.50 mg/100 g and 321.24 μg/100 g, respectively, for Jetisu samples. Overall, the study underscores regional variances in camel milk's biochemical composition, which can impact its nutritional and biological value. These findings provide dairy product producers in Kazakhstan with enhanced opportunities to create healthy, high-quality dairy products.

Biophysical phenotyping of single-cell based on impedance and application for individualized precision medicine

Single-cell biophysical characterization based on impedance measurement is an advantageous approach due to its label-free, high-efficiency, cost-effective and real-time capability. Biophysical phenotyping can yield timely and rich information on physiological and pathological state of cells for disease diagnosis, drug screening, precision medicine, etc. However, precise measurement on single-cell impedance is challenging, particularly hard to figure out the detailed biophysical parameters of single cell due to coupling and complexity of impedance model. Here, we propose an analytic determination method to decode single-cell electrophysiological parameters (including cell-substrate interface capacitance, cell membrane capacitance, cell membrane conductivity, and cytoplasm conductivity) from the impedances measured at optimized frequencies by using analytic solution rather than spectrum fitting. With this simple and fast analytic solution method, the physiological parameters of single cell in natural adhesion state can be accurately determined in real time. We validate this cell parameter determination method in monitoring the change of cell adhesion under hydraulic effects and exploring electrophysiological differences among MCF-7, HeLa, Huh7, and MDA-MB-231 cell lines. Particularly, we apply the approach to optimize tumor treating fields (TTFields) therapy, realizing individualized precision medicine. Our work provides an accurate and efficient approach for characterizing single-cell biophysical properties with real-time, in-situ, label-free, and less invasive advantages.

Characterization of a filamentary discharge ignited in a gliding arc plasmatron operated in nitrogen flow

A gliding arc plasmatron (GAP) is a promising warm plasma source for the use in gas conversion applications but lacks an understanding of the plasma dynamics. In this paper, the gliding arc plasma conditions of a GAP operated with nitrogen flow (10 slm) are characterized using optical emission spectroscopy (OES) and numerical simulation. A simultaneously two-wavelength OES method and Abel inversion of the measured images with a spatial resolution of 19.6 μm are applied. The collisional radiative model used in this study includes Coulomb collisions of electrons. An iterative method of plasma parameter determination is applied. The determined values of the electric field up to 49 Td and electron density up to 2.5∙1015 cm−3 fit well to the plasma parameters received with different diagnostics methods in comparable plasma sources. Additionally, the electric current, which is calculated using the determined reduced electric field and electron density, is compared with the measured one.

Modeling and free vibration analysis of bolted composite flanged cylindrical-cylindrical shells under partial bolt loosening conditions

The bolted composite coupled cylindrical shell is a common substructure in modern engineering, which may encounter the issue of local loosening conditions during service. Meanwhile, the influence of flanges on the structural vibration characteristics is often overlooked in dynamic numerical analysis. In this paper, a semi-analytical dynamic model for the bolted composite flanged cylindrical-cylindrical shell structure under arbitrary connection conditions is established, and the free vibration characteristics of locally loose structures are emphatically studied. In the model, the annular plate modeling method is developed to achieve the simulation of the flange part. To fully reflect the connection characteristics of the bolted joint, a spring surface model and the corresponding parameter determination method based on fractal contact theory are developed. The equations of motion of the structure are obtained utilizing the Lagrange equations. The accuracy of the model and its predictive ability for the vibration characteristics of the bolted composite flanged cylindrical-cylindrical shell under local loosening conditions are fully demonstrated through numerous comparisons with experimental results from other literature and modal test results obtained under various working conditions. Furthermore, the influence of the number and degree of loosening bolts on the free vibration characteristics of the structure and the coupling phenomenon between different modes are discussed. The research in this paper is expected to provide references for the design of bolted composite flanged cylindrical shell structures and the monitoring of bolt connection defects.

A parameter determination method of powder burn model based on Levenberg-Marquardt optimization

ABSTRACT The powder burn model is a mathematical model used to characterize the combustion behavior of energetic materials. In this paper, a parameter determination method for the powder burn model is proposed based on the Levenberg-Marquardt (L-M) optimization algorithm, establishing connections between internal ballistics parameters and powder burn model parameters. Taking a 4/1 single-base propellant as an example, the detailed fitting process is presented, and error analysis is conducted. The fittings of the gas state equation and reaction rate equation of powder burn model converge at the 15th and 22nd iterations, respectively. Both fittings displayed a high level of accuracy, with an R-squared value of 0.999. The comparison with the results of the closed bomb experiments demonstrates excellent agreement between pressure-time curves calculated by the powder burn model and the internal ballistics model, confirming their suitability for describing the combustion behavior of propellants. This method provides a foundation for numerically simulating the internal ballistics combustion behavior of energetic materials.

Investigation on damage creep constitutive model of rock under the coupled effect of freeze–thaw cycles and loading

AbstractThe investigation on damage creep properties of rock under freeze–thaw conditions are essential for assessing the long‐term stability of rock mass engineering in cold regions. This research analyzed the damage characteristics of rock under the coupled effect of freeze–thaw cycles and loading; the damage variable under the coupled effect of freeze–thaw cycles and loading was proposed. A damage creep constitutive model was developed, and the determination method for the model parameters was proposed. The rationality of the model was calibrated using test data, and the calculation results of the proposed model were compared with classical Nishihara model. Additionally, the research analyzed the variation of model parameters with the number of freeze–thaw cycles and discussed the damage creep mechanisms of rock under the coupled effect of freeze–thaw cycles and loading.