Proposed Modeling Framework Research Articles

Examining avascular tumour growth dynamics: A variable-order non-local modelling perspective

This study investigates the growth of an avascular tumour described through the interchange of mass among its constituents and the production of inelastic distortions induced by growth itself. A key contribution of this research examines the role of non-local diffusion arising from the complex and heterogeneous tumour micro-environment. In our context, the non-local diffusion is enhanced by a variable-order fractional operator that incorporates crucial information about regions of limited nutrient availability within the tissue. Our research also focuses on the identification of an evolution law for the growth-induced inelastic distortions recast through the identification of non-conventional forces dual to suitable kinematic descriptors associated with the growth tensor. The establishment of such evolution law stems from examining the dissipation inequality and subsequently determining a posteriori connections between the inelastic distortions and the source/sink terms in the mass balance laws. To gain insights into the dynamics of tumour growth and its response to the proposed modelling framework, we first study how the variables governing the tissue evolution are affected by the introduction of the new growth law. Second, we investigate how regions of limited diffusion within the tumour, encoded into a fractional operator of variable-order, influence its growth.

A continuum scale chemo-mechanical model for multi-trap hydrogen transport in deformed polycrystalline metals

This study presents a coupled multi-trap hydrogen diffusion and crystal plasticity model within a thermodynamically consistent framework. The developed modeling scheme incorporates the hydrogen-enhanced localized plasticity (HELP) and hydrogen-enhanced strain-induced plasticity (HESIV) based on thermodynamic considerations. The theoretical formulations are implemented in the finite element method, which considers the concentration- and chemical potential-based balance equations. The validity of the coupled model is established through preliminary single element simulations under isothermal conditions, showcasing the predictability of evolution of hydrogen concentration and occupancies in the deformed microstructure of polycrystalline metal. Then, the developed computational approach is applied to two materials exhibiting hydrogen-induced hardening and softening behaviors in accordance with the HELP mechanism and vacancy-hydrogen complex (VaH) formation under isothermal condition. The model successfully reproduces the change of mechanical behavior induced by hydrogen in both materials. The effects of hydrogen diffusivity and the binding energy of defects on hydrogen transport are also investigated. Furthermore, the sensitivity of hydrogen diffusion to different trapping mechanisms, local equilibrium trapping and kinetics trapping, is examined. The proposed modeling framework can be applied to new material design strategies for developing metals resistant to hydrogen embrittlement.

Numerical evaluation of cutting strategies for thin-walled parts

Static form errors due to in-process deflections is a major concern in flank milling of thin-walled parts. To increase both productivity and part geometric accuracy, there is a need to predict and control these form errors. In this work, a modelling framework for prediction of the cutting force-induced form errors, or thickness errors, during flank milling of a thin-walled workpiece is proposed. The modelled workpiece geometry is continuously updated to account for material removal and the reduced stiffness matrix is calculated for nodes in the engagement zone. The proposed modelling framework is able to predict the resulting thickness errors for a thin-walled plate which is cut on both sides. Several cutting strategies and cut patterns using constant z-level finishing are studied. The modelling framework is used to investigate the effect of different cut patterns, machining allowance, cutting tools and cutting parameters on the resulting thickness errors. The framework is experimentally validated for various cutting sequences and cutting parameters. The predicted thickness errors closely correspond to the experimental results. It is shown from numerical evaluations that the selection of an appropriate cut pattern is crucial in order to reduce the thickness error. Furthermore, it is shown that an increased machining allowance gives a decreased thickness error for thin-walled plates.

Prediction method of carding process production quality based on digital twin technology

In recent years, digital twin technology has developed rapidly, which has great advantages in improving production quality and saving production costs. Therefore, it is of great significance to apply digital twin technology to the spinning field to realize the prediction of spinning production quality. This study takes the carding process in yarn manufacturing as an example. First, a prediction model framework for carding process production quality is proposed based on the digital twin modeling mechanism. Each key component’s modeling principles and methods are analyzed, and the modeling process and coupling between models are clarified. Next, the carding process production quality prediction model is established based on the proposed modeling framework and actual working conditions. Then, a set of comparative experiments on a generative adversarial network and a set of comparative experiments between different prediction models were designed. The results showed that the production quality prediction model of the carding process established according to the modeling method proposed in this paper meets the requirements of spinning production quality prediction, and the average error rate of the model was 5.7%. Finally, the modeling method and experimental results are discussed, and future research directions are considered. The paper has great significance for further research on quality prediction of yarn manufacturing, providing a reference for applying digital twin technology and promoting intelligent manufacturing in the spinning field.

NOx formation model for utility boilers using robust two-step steady-state detection and multimodal residual convolutional auto-encoder

BackgroundTo achieve carbon neutrality, thermal power plants will take on greater peak-shaving responsibilities, resulting in utility boilers running at more frequently-changing states. This variation inevitably contributes to dirtier measurement data, and the data will follow new distributions that represent asynchronous operational characteristics. MethodTo predict the NOx formation concentration in this new context, a novel modelling framework is proposed. First, a two-step steady-state detection algorithm, including a basic and a precise identification procedure, is proposed to select steady-state samples from operational data. This algorithm also combines with a bayesian optimization–variational mode decomposition (BO–VMD) approach to enhance its robustness by eliminating high-frequency noise, and with an empirical cumulative distribution based outlier detection (ECOD) to remove outliers. Therefore, a high-quality steady-state dataset is acquired. Based on these data, a multimodal residual convolutional auto-encoder is developed to extract the latent variables that vary with the operation modes. These variables are then used to construct the multimodal NOx prediction model. Significant findingsThe findings on a 660 MW utility boiler reveal that, the proposed modelling framework has a lower misdiagnosis rate in steady-state identification compared to the one-step detection technique, and achieves higher fitting and generalization accuracy than the global modelling method.

Epidemic Spreading in Metapopulation Networks Coupled With Awareness Propagation.

Understanding the feedback loop that links the spatiotemporal spread of infectious diseases and human behavior is an open problem. To study this problem, we develop a multiplex framework that couples epidemic spreading across subpopulations in a metapopulation network (i.e., physical layer) with the spreading of awareness about the epidemic in a communication network (i.e., virtual layer). We explicitly study the interactions between the mobility patterns across subpopulations and the awareness propagation among individuals. We analyze the coupled dynamics using microscopic Markov chains (MMCs) equations and validate the theoretical results via Monte Carlo (MC) simulations. We find that with the spreading of awareness, reducing human mobility becomes more effective in mitigating the large-scale epidemic. We also investigate the influence of varying topological features of the physical and virtual layers and the correlation between the connectivity and local population size per subpopulation. Overall the proposed modeling framework and findings contribute to the growing literature investigating the interplay between the spatiotemporal spread of epidemics and human behavior.

LES of a pressurized sooting aero-engine model burner using a computationally efficient discrete sectional method coupled to tabulated chemistry

This work is focused on the modeling and analysis of soot formation and oxidation in the pressurized ethylene-based model burner investigated at DLR. This burner features a dual swirler configuration for the primary air supply and includes secondary dilution jets inside the combustion chamber, showing reacting flow characteristics representative of the RQL combustor technology. Large-eddy simulations (LES) of the DLR burner are conduced here to assess a coupling approach between flamelet generated manifold (FGM) chemistry and discrete sectional method (DSM) based soot model with clustering method. First, a validation of the numerical results is conducted for the gas velocity and temperature fields, and good agreement is obtained for both mean and fluctuating quantities. The Soot Volume Fraction (SVF) computed from LES shows a satisfactory agreement with the experimental data in both SVF distribution and magnitude. The analysis also includes a numerical investigation of the soot production and the Particle Size Distributions (PSD). Finally, the configuration without secondary air is evaluated and an accurate prediction of the SVF field is also obtained. In this case, the absence of dilution air strongly influences the central region of the combustion chamber, and soot distribution and PSD are mainly affected by transport and dilution, not oxidation. It is finally concluded the proposed modeling framework is capable of predicting the soot field and particle size distributions inside the combustor for both operating conditions.

Data-driven method for electric vehicle charging demand analysis: Case study in Virginia

Electric vehicle (EV) adoption in the U.S. will be accelerated by the historic $7.5 billion public investments in EV charging infrastructure. Careful analysis of EV charging demands plays a vital role in understanding the energy requirements, power grid impact, and smart charging management opportunities of EVs. To this end, this paper develops a data-driven trip-chaining-based modeling framework including five steps: Trip data acquisition and preprocessing, EV adoption modeling, travel itinerary synthesis, EV charging demand simulation and EV load profile generation. The developed analysis framework was demonstrated using real-world data for one region in Virginia, U.S. The results show that the proposed modeling framework can work effectively. For the study region in 2040, the predicted number of plug-in EVs is 470,114, resulting in a weekly charging demand of 38,078,127 kWh (55 % home, 9 % work, and 36 % public) in September and 45,920,358 kWh (61 % home, 9 % work, and 30 % public) in February.

How to Comprehensively Evaluate Firm Performance from Operational, Financial, and Sustainability Perspectives? A Two-Stage Data Envelopment Analysis Approach

ABSTRACT This study proposes a two-stage modeling framework to evaluate the efficiency of firm performance based on sustainability (environmental, social, and governance (ESG)), operational and financial measures. Diverging from previous studies, this study conceptualizes firm performance as two serially linked stages, with operational performance as the first stage and market performance as the second stage. Acknowledging that ESG activities may reflect firm value and that these activities depend on a firm’s operational performance, ESG scores are modeled simultaneously as an output of the operational performance stage and as an input for the market stage. Under this performance appraisal framework, two-stage data envelopment analysis models capturing overall and stage-level efficiency are constructed. The application of the proposed modeling framework is demonstrated using a sample of large Saudi listed firms as a case study of an emerging market. The main conclusion is that ESG legislation, regulations, and disclosure guidelines should be well defined in emerging markets to increase the overall efficiency of listed firms’ performance.

Future sea level rise exacerbates compound floods induced by rainstorm and storm tide during super typhoon events: A case study from Zhuhai, China

Compound floods are becoming a growing threat in coastal cities against a background of global sea level rise (SLR), and may cause increasing impacts on societal safety and economy. How to quantify the impact of SLR and compound effects among various flood causes on compound flood have become important challenges. We propose a modeling framework which integrates atmospheric, storm tide and urban flood (IASTUF) models to characterize the various physical processes related to compound flood. Future SLR projections under various shared socioeconomic and respective concentration pathway emission scenarios are considered. Hengqin Island (Zhuhai City, China) frequently experiences typhoon conditions combined with rainstorm and storm surge events. Its population has increased more than sixfold during the past decade, stimulating urgent demands for assessments of the potential risks associated with future compound floods in the context of potential SLR. A compound flood event in northern Hengqin Island, caused by the super typhoon Mangkhut in 2018, is selected as a case study to verify the proposed modeling framework. Results show that the IASTUF modeling framework can capture well the combined processes of typhoon, rainstorm, storm tide and inland flooding and demonstrates good performance in quantifying compound flood magnitudes. Compared to the current scenario, the node flooding volume (from the drainage system) and the maximum inundation area (with inundation depths >1 m) in 2050 are projected to increase by 20–26 % and 41–85 %, respectively, and these increases rise to 46–84 % and 23–71 times by 2100. The inundation volumes and water depths due to compound events are larger than the sum of those caused by the corresponding single-cause events, indicating that concurrent rainstorm and storm surge induce positive compound effects on flood magnitude. These findings can provide guidance for the management and mitigation of future compound flood hazards driven by super typhoon events.

Realization of adaptive mesh refinement for phase-field model of thermal fracture within the FEniCS framework

This paper presents an adaptive phase field model for simulating fracture due to coupled interactions (such as thermal quenching and hot cracking in additive manufacturing). The proposed model is implemented in an open-source finite element framework, FEniCS. The model considers spatial variations of the fracture toughness and differential coefficients of thermal shrinkage. Several paradigmatic case studies are addressed to demonstrate the potential of the proposed modeling framework. Specifically, we (a) benchmark our crack predictions for mechanical and thermal boundary condition interactions with the results from alternative numerical methods, (b) accurately reproduce experimentally observed complex crack trajectories due to thermal quenching and hot cracking in additive manufacturing, and (c) demonstrate the ease of extending of the proposed framework to thermal cracking problems in three dimensions. The current implementation provides the basic for an efficient framework for fracture problems due to multi-physical interactions for practical engineers with less programming expertise.

Joint operation planning of drivers and trucks for semi-autonomous truck platooning

Semi-autonomous truck platooning, a futuristic and promising traveling mode of trucks on highways, has received extensive attention as autonomous driving gains more maturity. Under the semi-autonomous platooning mode, rather than requiring a human driver for each truck in the platoon, only one human driver is needed for the leading truck, and all following trucks can be controlled by autonomous driving and wireless communication technology. Therefore, in addition to saving energy, such a technology can help reduce labor costs by using fewer drivers to fulfill the same demand. Compared to traditional truck platooning, the schedules of drivers under semi-autonomous truck platooning may not be exactly the same as trucks, which are usually neglected in recent studies. Therefore, in this paper, we make the first attempt to develop a mathematical modeling framework to optimise the operation plan of drivers and trucks interdependently. To be mentioned, the truck fleet is equipped with the semi-autonomous platooning function and time windows. Specifically, the operation plan will dictate the number of drivers to be dispatched, and traveling routes and time schedules of drivers and trucks interdependently with the objective to minimise the total operation cost, including drivers' fixed dispatch cost, on-road labor cost, and energy consumption cost while fulfilling all delivery demands. To tackle the large-scale problem in a timely manner, a tailored Lagrangian Relaxation approach is proposed to solve the model. Numerical experiments are conducted to demonstrate the performance of the proposed modeling framework and validate the feasibility and efficiency of the proposed solution algorithm.

Computationally efficient neural hybrid automaton framework for learning complex dynamics

This paper proposes a computationally efficient and effective data-driven modeling framework for dynamical systems. The proposed modeling framework employs a collection of shallow neural networks known as Extreme Learning Machines (ELMs) to model local system behaviors along with data-driven inferred transitions among local models to establish a neural hybrid automaton model. First, the sampled system inputs are mapped to the corresponding feature spaces to obtain data-driven partitions, which subsequently define the transitions and invariants of the neural hybrid automaton model through a novel data-driven mode clustering process. Then, a collection of ELMs are trained to approximate the local dynamics. The learning processes integrate a segmented data merging procedure for location identification and a local dynamics modeling process. The proposed neural hybrid automaton models can capture behaviors of complex dynamical systems with high modeling precision but significantly lower computational complexities in computationally expensive tasks such as training and verification, which are traditionally considered to be computationally expensive tasks for neural network models. A computationally efficient set-valued reachability analysis method which is commonly used in safety verification is then developed based on interval analysis and a novel Split and Combine process. Finally, applications to modeling the limit cycle and human handwritten motions are presented to show the effectiveness and efficiency of our approach.

A unifying switching regime regression framework with applications in health economics

Motivated by three health economics-related case studies, we propose a unifying and flexible regression modeling framework that involves regime switching. The proposal can handle the peculiar distributional shapes of the considered outcomes via a vast range of marginal distributions, allows for a wide variety of copula dependence structures and permits to specify all model parameters (including the dependence parameters) as flexible functions of covariate effects. The algorithm is based on a computationally efficient and stable penalized maximum likelihood estimation approach. The proposed modeling framework is employed in three applications in health economics, that use data from the Medical Expenditure Panel Survey, where novel patterns are uncovered. The framework has been incorporated in the R package GJRM, hence allowing users to fit the desired model(s) and produce easy-to-interpret numerical and visual summaries.

A fractional derivative aerodynamic model at high angles of attack

A fractional derivative aerodynamic model is put forward based on Grünwald-Letnikov fractional derivative in this paper. In order to express the hysteresis property of aerodynamic coefficients, a fractional derivative state-space equation is developed from Goman’s differential equation. A fast and high-accuracy calculation method of Grünwald-Letnikov fractional derivative is proposed. Then, the model is demonstrated by several experiments data from literatures. The simulation results show that the presented fractional derivative aerodynamic model can preferably express the nonlinear unsteady aerodynamic characteristics of airfoils and aircrafts. The proposed modelling framework is meaningful to the designation of flight and control system.

Performance-based transit subsidy allocation scheme to fulfill multiple policy criteria

ABSTRACT Performance-based subsidy allocation schemes are typically intended to satisfy a single policy goal (e.g. social welfare maximization) and often do not include objective approaches to quantifying bus service efficiency. This paper proposes a framework for network data envelopment analysis and introduces an innovative transit subsidy allocation scheme with a fixed budget that fulfills multiple policy criteria. The proposed basic scheme minimizes the maximum deviation from efficient allocation in proportion to the operating scales across all routes in a two-stage production system. Multiple policy criteria are then added to the basic scheme as additional goal components. The proposed scheme is evaluated by using an empirical dataset of 344 subsidized intercity bus routes collected from 20 Taiwanese bus operators. The results demonstrate that the subsidies allocated on the basis of the proposed scheme can satisfy the policy criteria incorporated into the scheme. The proposed modeling framework is flexible, and therefore, local governments can easily incorporate policy criteria into the scheme according to their own interests and needs.

Forecasting Day-Ahead Brent Crude Oil Prices Using Hybrid Combinations of Time Series Models

Crude oil price forecasting is an important research area in the international bulk commodity market. However, as risk factors diversify, price movements exhibit more complex nonlinear behavior. Hence, this study provides a comprehensive analysis of forecasting Brent crude oil prices by comparing various hybrid combinations of linear and nonlinear time series models. To this end, first, the logarithmic transformation is used to stabilize the variance of the crude oil prices time series; second, the original time series of log crude oil prices is decomposed into two new subseries, such as a long-run trend series and a stochastic series, using the Hodrick–Prescott filter; and third, two linear and two nonlinear time series models are considered to forecast the decomposed subseries. Finally, the forecast results for each subseries are combined to obtain the final day-ahead forecast result. The proposed modeling framework is applied to daily Brent spot prices from 1 January 2013 to 27 December 2022. Six different accuracy metrics, pictorial analysis, and a statistical test are performed to verify the proposed methodology’s performance. The experimental results (accuracy measures, pictorial analysis, and statistical test) show the efficiency and accuracy of the proposed hybrid forecasting methodology. Additionally, our forecasting results are comparatively better than the benchmark models. Finally, we believe that the proposed forecasting method can be used for other complex financial time data to obtain highly efficient and accurate forecasts.

A framework for modelling spatio-temporal trends in crop production using generalised additive models

Satellite imagery provides opportunities for inference of trends in crop production across space and time. However, the large size of these datasets has made application of statistical modelling approaches computationally difficult. Recent advances in computational techniques and infrastructure have allowed generalised additive models to be fitted to very large datasets. We propose a framework for inferring trends in crop production across space and time using generalised additive models which accounts for inter-annual trends (main effect of year), spatial distribution (main effect of space), crop ontogeny (main effect of month), inter-annual changes in seasonality (interaction between year and month), and inter-annual changes in spatial distribution (interaction between year and space). Application of the proposed model to farm scale, multi-field sites in the Ord River Irrigation Area, Western Australia, demonstrates that this approach is able to decompose variation into the aforementioned effects. Furthermore, comparison of grain production observations and estimates for the Western Australian Wheatbelt as ground-truth data showed agreement with inferences drawn from the proposed model, with prediction terms for the main effect of year positively correlated with estimated tonnes produced from 2013 to 2021 (p = 0.03). Finally, application to Madagascar, which has been experiencing a food crisis, revealed a decreasing trend in cropland Normalised Difference Vegetation Index (NDVI) from 2014 to 2021 of 2.5%, raising concerns about ongoing food security. The proposed modelling framework is adaptable to numerous agricultural research problems.

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control.

Modeling and simulation of the two-tank system within a hybrid framework

<p><span lang="EN-US">Most real-world dynamical systems are often involving continuous behaviors and discrete events, in this case, they are called hybrid dynamical systems (HDSs). To properly model this kind of systems, it is necessary to consider both the continuous and the discrete aspects of its dynamics. In this paper, a modeling framework based on the hybrid automata (HA) approach is proposed. This hybrid modeling framework allows combining the multi-state models of the system, described by nonlinear differential equations, with the system’s discrete dynamics described by finite state machines. To attest to the efficiency of the proposed modeling framework, its application to a two-tank hybrid system (TTHS) is presented. The TTHS studied is a typical benchmark for HDSs with four operating modes. The MATLAB Simulink and Stateflow tools are used to implement and simulate the hybrid model of the TTHS. Different simulations results demonstrate the efficiency of the proposed modeling framework, which allows us to appropriately have a complete model of an HDS.</span></p>