Modular Model Research Articles

Defining hybrid hierarchical models in πHYFLOW

Defining hybrid hierarchical models in <i>π</i>H<scp>Y</scp>F<scp>LOW</scp>

Quark and lepton modular models from the binary dihedral flavor symmetry

Inspired by the structure of top-down derived models endowed with modular flavor symmetries, we investigate the yet phenomenologically unexplored binary dihedral group 2D3. After building the vector-valued modular forms in the representations of 2D3 with small modular weights, we systematically classify all (Dirac and Majorana) mass textures of fermions with fractional modular weights and all possible 2 + 1-family structures. This allows us to explore the parameter space of fermion models based on 2D3, aiming at a description of both quarks and leptons with a minimal number of parameters and best compatibility with observed data. We consider the separate possibilities of neutrino masses generated by either a type-I seesaw mechanism or the Weinberg operator. We identify a model that, besides fitting all known flavor observables, delivers predictions for six not-yet measured parameters and favors normal-ordered neutrino masses generated by the Weinberg operator. It would be interesting to figure out whether it is possible to embed our model within a top-down scheme, such as \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb{T}}^{2}/{\\mathbb{Z}}_{4}$$\\end{document} heterotic orbifold compactifications.

Universal predictions of Siegel modular invariant theories near the fixed points

We analyze a general class of locally supersymmetric, CP and modular invariant models of lepton masses depending on two complex moduli taking values in the vicinity of a fixed point, where the theory enjoys a residual symmetry under a finite group. Like in models that depend on a single modulus, we find that all physical quantities exhibit a universal scaling with the distance from the fixed point. There is no dependence on the level of the construction, the weights of matter multiplets and their representations, with the only restriction that electroweak lepton doublets transform as irreducible triplets of the finite modular group. Also the form of the kinetic terms, which here are assumed to be neither minimal nor flavor blind, is irrelevant to the outcome. The result is remarkably simple and the whole class of examined theories gives rise to five independent patterns of neutrino mass matrices. Only in one of them, the predicted scaling agrees with the observed neutrino mass ratios and lepton mixing angles, exactly as in single modulus theories living close to τ = i.

A modular ontology modeling approach to developing digital product passports to promote circular economy in the built environment

The significant impact of the built environment on resource consumption and waste production has led to calls for a shift towards a circular economy model that maximizes the efficient use of resources. This study explores the use of digital product passports (DPPs) to improve how we manage products throughout their lifecycle. However, dealing with the complexity and large volume of data in DPPs can be challenging in terms of effective information management and utilization. We address this issue by adopting a modular ontological approach to systematically capture product lifecycle information from its origin to its end-of-life phase. To ensure interoperability and reusability of the ontology, we annotate key concepts and relationships using International Organization for Standardization (ISO) standards that promote circular economy. Our research led to the development of several ontology modules derived from literature reviews and interviews conducted with industry and academia experts who specialize in sustainability. These modules were then integrated to create a digital product passport ontology. The study demonstrates the feasibility of using a modular ontology approach to manage the complex information inherent in DPPs paving the way for more sustainable management practices in the built environment sector.

Parametric analysis of the Charge-Hold-Vent method for cryogenic propellant tank chilldown

In the absence of external heat exchangers, the on-orbit transfer of cryogenic propellants requires the receiver tank to first be quenched to a sufficiently low energy state to allow for a continuous no-vent fill to avoid unnecessary venting of liquid. One proposed method for tank chilldown that minimizes the potential for venting liquid is the charge hold vent (CHV) method. CHV follows a cyclic process that gradually removes thermal energy from the receiver tank by injecting liquid with the vent valve closed and allowing the fluid and wall to reach near-thermal equilibrium before venting the superheated vapor. However, the CHV method must be optimized to minimize complexity, mass, and time. This paper presents a modular CHV analytical model used to quantify the number of cycles and propellant mass consumed based on first principles. The model is used to examine the effect of eight parameters: receiver tank material, volume, mass, maximum expected operating pressure, and initial pressure, liquid injection pressure and temperature, and the target temperature. The model is validated against the only two available CHV datasets. Based on results, the tank mass-to-volume ratio is the most important factor in determining the number of CHV cycles and thus degree of difficulty in tank chilldown. The model can easily be used for early-stage design, sizing, and analysis of cryogenic propellant transfer systems.

Modular flavor symmetric models

We review the modular flavor symmetric models of quarks and leptons focusing on our works. We present some flavor models of quarks and leptons by using finite modular groups and discuss the phenomenological implications. The modular flavor symmetry gives interesting phenomena at the fixed point of modulus. As a representative, we show the successful texture structure at the fixed point [Formula: see text]. We also study CP violation, which occurs through the modulus stabilization. Finally, we study SMEFT with modular flavor symmetry by including higher dimensional operators.

Development of modular dynamic code for steam power conversion system in sodium-cooled fast reactor

Sodium-cooled fast reactor (SFR) is one of the most promising Generation IV reactor types. To improve operational safety and economy, SFR requires accurate prediction of the steam power conversion system. Therefore, a one-dimensional thermal-hydraulic analysis code of the steam power conversion system of SFR is developed in this study where each equipment is packaged as a separate module with modular modeling method. JFNK is adopted to solve ODE in this code, which expand the universality of the code. The model is validated by comparing with design results from the Daya Bay nuclear power plant and the BN-600 steam generator. The maximum relative errors for pressure and temperature are 5.44% and 2.40%, respectively. After validation of the code, the steady-state analysis of the SFR steam power conversion circuit is carried out where the flow and heat transfer process of key equipment can be accurately simulated considering a complete steam power conversion cycle. This work provides reference value for the thermal-hydraulic characteristics of steam power conversion system in SFR.

Bridging the gap to practicality: A green methanesulfonic acid system enhanced with synergistic additives for efficient and high-quality lead electro-refining

Green methanesulfonic acid (MSA) system offers a promising alternative to the traditional fluosilicic acid (H2SiF6) method for lead electro-refining. However, the MSA system has yet to produce efficient electro-refining process, high-quality cathodic lead and stable anodic dissolved layer concurrently, which is crucial for its practical application. Herein, we propose an MSA-based system — enhanced with Lugalvan NES (NES) and calcium lignosulfonate (CL) — for efficient and high-quality lead electro-refining. In both proof-of-concept and separate high-flux flow operations, we achieve a low energy consumption of 73.06 kWhe/t Pb and a high Faradaic efficiency of 99.11 %. Dense deposits with ultra-low grain sizes under 20 nm and high purities of over 99.99 %, and uniform dissolved layers with moderately adhesive anode mud are obtained. Theoretical simulations reveal the HOMO and LUMO energy levels of the additives and their adsorption energy on the Pb interface. Electrochemical analysis indicates a cathodic three-dimensional nucleation process and an anodic mechanism of uniform dissolution. A modular micro-kinetics model is developed to elucidate energy performance. These advances represent a significant step forward for lead electro-refining, achieving efficient operation and high-quality output while reducing energy use and surpassing the efficacy of the traditional method.

Exploring transitions of sewer wastewater infrastructure towards decentralisation using the modular model TURN-Sewers

We present a new modular model called TURN-Sewers for exploring different adaptations of centralised wastewater infrastructure towards more decentralised wastewater systems under different urban development scenarios. The modular model is flexible and computationally efficient in exploring transitions at the city scale, allowing for the comparison of different policies and management strategies for sanitary wastewater infrastructure. TURN-Sewers includes independent modules that simulate the generation, dimensioning, deterioration, management, and calculation of performance indicators for different wastewater systems. This model can use readily available spatial information to support infrastructure planners and other stakeholders in exploring different transition pathways from centralised to decentralised wastewater infrastructure. An illustrative example demonstrates how TURN-Sewers can generate multiple future alternatives, define different infrastructure management strategies regarding system expansion, rehabilitation and transition, and assess the economic, hydraulic and structural impacts.

Optimized reusable modular 3D-printed models of choledochal cyst to simulate laparoscopic and robotic bilioenteric anastomosis

Laparoscopic and robotic surgery is a challenge to the surgeon's hand–eye coordination ability, which requires constant practice. Traditional mentor training is gradually shifting to simulation training based on various models. Laparoscopic and robotic bilioenteric anastomosis is an important and difficult operation in hepatobiliary surgery. We constructed and optimized the reusable modular 3D-printed models of choledochal cyst. The aim of this study was to verify the ability of this optimized model to distinguish between surgeons with different levels of proficiency and the benefits of repeated practice. A total of 12 surgeons with different levels participated in the study. Operation completion time and OSATS score were recorded. The model was validated by Likert scale. Surgeons were shown the steps and contents before performing laparoscopic or robotic bilioenteric anastomosis using the model. Surgeons with different levels of experience showed different levels when performing laparoscopic bilioenteric anastomosis on this model. Repeated training can significantly shorten the time of laparoscopic bilioenteric anastomosis and improve the operation scores of surgeons with different levels of experience. At the same time, preliminary results have shown that the performance of surgeons on the domestic robotic platform was basically consistent with their laparoscopic skills. This model may distinguish surgeons with different levels of experience and may improve surgical skills through repeated practice. It is worth noting that in order to draw more reliable conclusions, more subjects should be collected and more experiments should be done in the future.

Modular approximate discrete modeling for blisk system with triple closed-loop equivalence method

At present, the lumped-parametric model is often used in the preliminary design of bladed disks. However, the lack of systematic modeling methods leads to high errors. In order to develop the systematic modeling method and improve the equivalence accuracy, this paper considers the comprehensive equivalence from the aspects of natural characteristics, transient forced responses, and steady-state forced responses under aerodynamic excitations. This paper includes two novelties. First, based on the modular equation of blisk system, an approximate discrete model is derived for computational efficiency. Second, a triple closed-loop equivalence method with modal weight optimization is developed innovatively for optimal equivalence parameters. The method can significantly improve the accuracy of element parameters in the lumped-parametric model and achieve the comprehensive equivalence to actual blisk. Compared with the existing equivalence method, the average errors of natural frequencies in the whole mode interval and in the high-density mode interval are reduced by 41.75 % and 36.76 %, respectively. The maximum and average displacement errors of the transient response are reduced by 51.76 % and 58.98 %, respectively. Furthermore, vibration characteristics of the mass and stiffness mistuned blisks are analyzed under traveling wake excitation during acceleration and deceleration processes by approximate discrete model and determined parameters.

Modular system pond cultivation model for the survival of milkfish (Chanos chanos): case study of Pangkajene Kepulauan Regency, Indonesia

Modular system pond cultivation model for the survival of milkfish (Chanos chanos): case study of Pangkajene Kepulauan Regency, Indonesia

Outline of an evolutionary morphology generator towards the modular design of a biohybrid catheter.

Biohybrid machines (BHMs) are an amalgam of actuators composed of living cells with synthetic materials. They are engineered in order to improve autonomy, adaptability and energy efficiency beyond what conventional robots can offer. However, designing these machines is no trivial task for humans, provided the field's short history and, thus, the limited experience and expertise on designing and controlling similar entities, such as soft robots. To unveil the advantages of BHMs, we propose to overcome the hindrances of their design process by developing a modular modeling and simulation framework for the digital design of BHMs that incorporates Artificial Intelligence powered algorithms. Here, we present the initial workings of the first module in an exemplar framework, namely, an evolutionary morphology generator. As proof-of-principle for this project, we use the scenario of developing a biohybrid catheter as a medical device capable of arriving to hard-to-reach regions of the human body to release drugs. We study the automatically generated morphology of actuators that will enable the functionality of that catheter. The primary results presented here enforced the update of the methodology used, in order to better depict the problem under study, while also provided insights for the future versions of the software module.

Modular Finite Element Modeling of Heavy Plate Rolling Processes Using Customized Model Reduction Approaches

Heavy plates are indispensable semi-finished products. Quality is strongly linked with production, so the rolling process must be performed within well-defined narrow tolerances. To meet this challenge, adequate modeling has become a necessity. In contrast to continuous strip rolling, where the workpiece can be modeled as a semi-infinite strip and 2D modeling can be argued quite well, this strategy is insufficient for the comprehensive modeling of heavy plate rolling. The geometry of the heavy plate favors an inhomogeneous distribution of relevant state variables, such as temperature. In addition, if the process involves longitudinal and spreading passes, the required plate rotation spoils the assumption of a symmetric arrangement that might have been acceptable before rotation. Consequently, the derivation of suitably reduced models is not trivial, and modeling tailored to the specific objective of investigation is of utmost importance. Models intended to resolve the evolution of inhomogeneities in the field variables are demanding and computationally expensive. An effective modular modeling strategy was developed for such models to be used offline. Mutually complementing and interchangeable modules may constitute an efficient modeling strategy valid for the specific subject of interest. The presented approach reduces the enormous cost of complete 3D simulation as much as the model purpose allows for.

Analysis of a stand alone battery-converter device from a simulative systems engineering perspective

A design analysis method for the concept of a modular battery pack based on physical modeling is presented, particularly for a prototype still under development. The originality of this work is threefold: A system engineering framework that aims to provide a process for design-based analysis. The construction of a modular battery system model and a focused examination on the technological implications of its design. This is achieved by a reliable assessment of the primary system components, the battery and dc-to-dc converter, proving consistency with recent insights from experiments and literature. The process is not limited to a specific design but can be applied to any battery system and serves as a starting point for further design optimization.

A Fuzzy Neural Network-Based Intelligent Warning Method for Financial Risk of Enterprises

The fast warning for financial risk of enterprises has always been a realistic demand for their managers. Currently, this mainly relies on expert experience to make comprehensive analysis from massive business data. Benefitting from the strong computational performance of deep learning, this paper proposes a fuzzy neural network (FNN)-based intelligent warning method for financial risk of enterprises. An improved FNN structure with time-varying coefficients and time-varying time lags is established to extract features of enterprises from complex financial context. The algorithm of fuzzy C-means and fuzzy clustering based on sample data are studied. In this paper, the fuzzy C-means algorithm is used to cluster the samples, the input sample set is preprocessed, a new set of learning samples is formed, and then the neural network is trained. The enterprise financial risk sample and its modular FNN model are established, and the evaluation of the enterprise financial risk sample is simulated. Then, a decision part is added following the FNN part to output the warning results. After that, we have also conducted a case study as simulation experiments to evaluate the proposed technical framework. The obtained results show that it can perform well in the fast warning of financial risk for enterprises.

The Impact of Modular Professional Nursing Care Model (Modification of The Primary-Team Model) on Patient Satisfaction in Health Services: A Literature Review

Optimal health services are considerably increasing patient satisfaction. Optimization of health services could be implemented using the Modular Professional Nursing Care Method (MAKP Modular). MAKP Modular provides a clear division of tasks and responsibilities for nurses in accordance with the distribution of nursing staff. This literature review aimed to identify the effect of MAKP Modular in increasing patient satisfaction through the optimization of health services. The research design is a literature review. The journal article was conducted on the Google Scholar journal database website and 10 journal articles were selected according to inclusion criteria. The results of the review show that the application of MAKP Modular affects the quality of implementation of nursing care standards. The performance of nurses manifested in the implementation of MAKP Modular also influences patient satisfaction. However, there are still some deficiencies in the application of modular MAKP in the health service setting. These deficiencies are influenced by factors of knowledge, attitudes, and motivation of nurses. The application of MAKP Modular to the healthcare system is conducted by combining the team method and the primary method. The combination of these two methods allows for a more effective and efficient process of providing nursing care. Evaluation of the implementation of MAKP Modular in clinical and hospital settings needs to be engaged on an ongoing basis to determine the effectiveness of implementation.

An Algorithm for Calculating the Parameter Selection Area of a Doubly-Fed Induction Generator Based on the Guardian Map Method

Large-scale wind farms incorporating doubly-fed induction generators (DFIGs) are considered a promising direction for modern energy supply systems due to their role in reducing dependence on fossil energy sources. However, the dynamic interactions between DFIGs and AC grids sometimes lead to sub-synchronous oscillation (SSO) that threatens the safe and stable operation of wind power systems. Therefore, it is essential to develop a mathematical model and design an algorithm to quantitatively design the control parameters. Such algorithms are helpful in preventing or mitigating system stability problems coming from wind power connected to the grid and reducing damage to power equipment. The traditional state-space model is mainly established to determine the stable operating point and analyze the influence of parameters on the system operating mode. However, this method does not provide the selection area for the system parameters. To address this shortcoming, this paper introduces a modular state-space model for DFIGs containing series compensation lines and proposes an algorithm for calculating the parameter selection area based on the Guardian map method. First, a detailed modular state-space model based on the virtual synchronous generator (VSG) control is established. The modular model helps to reflect the relationship between state variables and focuses on describing the operating state of DFIGs in wind farms. Second, this paper focuses on the influence of VSG control parameters and compensation capacitance on SSO. It aims to clarify the role of the series compensation level and control parameters on SSO based on VSG control. Then, an algorithm for the parameter selection area based on the Guardian map is proposed and the area of the VSG-controlled DFIG is obtained. Finally, the accuracy and validity of the algorithm are verified by time domain simulation in MATLAB/Simulink and HIL experiment.

WR21 marine gas turbine thermodynamic simulator for ship propulsion studies

An original modular mathematical model, based on physical laws, for steady state and dynamics performance simulation of the WR21 gas turbine is presented in the paper. The model, developed in Matlab-Simulink language, is organized in modular form. Each gas turbine component (i.e. compressor, turbine, combustor, heat exchanger) is modelled in a specific simulator block, that simulates the performance of its pertinent component by means of steady state performance maps and/or correlations, time dependent momentum, energy and mass equations, nonlinear algebraic equations. The great application flexibility of the component simulator modules, allows to modelling any gas turbine layout (i.e. axial or radial flow compressor and turbine, rotating shafts number, heat exchangers). The WR21gas turbine, developed mainly for marine propulsion application, is a three shafts gas turbine with thermal regeneration. It is characterized by a high thermodynamic efficiency, which remains high up to 30% of the design power. The WR21 simulator results are validated by comparison with manufacturer or literature data. The WR21 gas turbine main performances are compared to those of the LM 2500 gas turbine, obtained by a previously author paper. The LM 2500 is the gas turbine currently most used in naval propulsion plants. In the article, a comparison between the steady state working data of the two gas turbines is reported in tabular and graphical form and commented.

Modular invariant holomorphic observables

In modular invariant models of flavor, observables must be modular invariant. The observables discussed so far in the literature are functions of the modulus τ and its conjugate, τ¯. We point out that certain combinations of observables depend only on τ, i.e. are meromorphic, and in some cases even holomorphic functions of τ. These functions, which we dub “invariants” in this Letter, are highly constrained, renormalization group invariant, and allow us to derive many of the models' features without the need for extensive parameter scans. We illustrate the robustness of these invariants in two existing models in the literature based on modular symmetries, Γ3 and Γ5. We find that, in some cases, the invariants give rise to robust relations among physical observables that are independent of τ. Furthermore, there are instances where additional symmetries exist among the invariants. These symmetries are relevant phenomenologically and may provide a dynamical way to realize symmetries of mass matrices.