Modeling Of Processes Research Articles

Measurement of Oscillatory and Dissipative Resonator Characteristics of Solid-State Wave Gyroscopes: Algorithms for Operational Correction of Systematic Signal Drift

The article discusses the possibilities of increasing the output signal accuracy of integrating solid-state wave gyroscopes manufactured with a reduction in the requirements for the residual characteristics of the multi-frequency and multi-factor ratios of their resonators, as well as for the centering of the control ring electrode. These capabilities involve the use of a more complex output function that includes an additional part due to the effect of these errors on the systematic drift function. However, its formation requires introduction of an interval shutdown of the parametric excitation system so as to increase the purity of identification of the listed factors. To reveal this topic, the following is sequentially described: mathematical formulation of the problem; general analysis of the systematic drift of the integrating gyroscope signal caused by mechanical errors in the design of its resonator; autonomous algorithmic reduction of the influence of the dominant residual differentials in the mode of interval disconnection of excitation; autonomous algorithmic reduction of the influence of the dominant residual frequency difference in the mode of interval disconnection of excitation; simultaneous algorithmic compensation of the influence of residual Q and Frequency in the interval disconnection mode of excitation; estimation of the signal drift component of the integrating gyroscope, due to errors in the control loops, as well as the possibility of its reduction. The analysis of these problems is carried out on the basis of a model of wave processes in the gyroscope resonator, obtained only on the basis of the laws of classical mechanics. There were no additional errors due to the imperfection of the electrical circuits for processing measurement and control signals. Various computational schemes discussed in the article can also be useful for performing operational adjustments of the gyroscope measurement system, which may be required as a result of the design aging factor, as well as after its intensive use in a wide range of external disturbances.

Dynamic processes in current sheets and experimental laboratory astrophysics

The results of experimental research of the dynamics of current sheets, which are formed in laboratory experiments at the IOF RAS, are presented as a brief review. It is shown that the most significant features of phenomena like solar flares can be reproduced in laboratory conditions. These features include the relatively slow accumulation of the magnetic energy in the course of the current sheet formation, the rapid release of the energy during the disruption of the current sheet, acceleration of plasma flows, ultrafast plasma heating, and effective particle’s acceleration. A qualitative similarity has been established between the basic characteristics of current sheets in the tail region of the Earth’s magnetosphere and in laboratory conditions. A comparison of a number of fundamental dimensionless parameters indicates the possibility of quantitative laboratory modeling of processes occurring in the magnetosphere. It is concluded that experimental research of the dynamics of current sheets and magnetic reconnection processes represent one of the promising areas of the laboratory astrophysics.

Flame Front Model with the Clusters Condensation

The processes model in a flame during the n-alkanes air mixture combustion initiation is proposed, taking into account the supramolecular structures formation possibility in the peroxide clusters form. This approach is justified by the n-alkanes melting temperatures correlation with their autoignition temperatures and anti-knock indexes. The condensation possibility is provided for such high molecular structures. Boiling temperatures values at flame front pressures characteristic were evaluated. To predict the peroxide clusters melting temperatures, a formula developed earlier for the hydrocarbons condensed state was used, which takes into account the length and molecular weight of modeled clusters. Expected peroxide clusters melting temperatures were predicted for conditions of dimeric and tetrameric structures. A linear dependence was used to recalculation the obtained values in boiling temperatures. It is shown that the calculated clusters phase transitions characteristic temperatures can be realized in the flame front preparatory zone. Based on the condensation theory, the flame front thickness and the minimum non-extinguishing sphere radius during ignition were estimated: the obtained data closely coincide with these parameters known values.

Development of optimized ensemble machine learning-based prediction models for wire electrical discharge machining processes

This paper proposes development of optimized heterogeneous ensemble models for prediction of responses based on given sets of input parameters for wire electrical discharge machining (WEDM) processes, which have found immense applications in many of the present-day manufacturing industries because of their ability to generate complicated 2D and 3D profiles on hard-to-machine engineering materials. These ensembles are developed combining predictions of the three base models, i.e. random forest, support vector machine and ridge regression. These three base models are first framed utilizing the training datasets, providing predictions for all the responses under consideration. Based on these predictions, two optimization problems are formulated for each of the responses, while minimizing root mean squared error and mean absolute error, for subsequent development of two optimized ensembles whose predictions are the weighted sum of the predictions of the base models. The prediction performance of all the five models is ascertained through nine statistical metrics, after which a cumulative quality loss-based multi-response signal-to-noise (MRSN) ratio for each model is computed, for each of the responses, where a higher MRSN ratio indicates greater accuracy in prediction. This study is conducted using two experimental datasets of WEDM process. Overall, the optimized ensemble models having higher MRSN ratios than the base models are indicated to deliver better prediction accuracy.

On Average Optimality for Non-Stationary Markov Decision Processes in Borel Spaces

This paper studies Borel models of nonstationary Markov decision processes (MDPs) with average criteria. We establish a suitable fixed point theorem, which is used to show the existence of solutions to the average optimality equation (AOE), without using contractions. The existence of optimal policies follows from the obtained solutions to the AOE. Furthermore, we show that versions of the rolling horizon algorithm can be used to produce an optimal policy or an ϵ-optimal policy. Finally, we compare the optimality conditions imposed in this paper with the existing ones in the literature, and demonstrate that they can be satisfied while the previous ones are not. Funding: This research was supported by National Key Research and Development Program of China [2022YFA1004600], National Natural Science Foundation of China [No. 12301170], Guangdong Basic and Applied Research Foundation [2023A1515012644], and the Fundamental Research Funds for the Central Universities, Sun Yat-Sen University [No. 23qnpy39].

Mapping secondary data gaps for social simulation modelling: A case study of the journeys of Syrian asylum seekers to Europe

Simulation models of social processes may require data that are not readily available, have low accuracy, are incomplete or biased. The paper presents a formal process for collating, assessing, selecting, and using secondary data as part of creating, validating, and documenting an agent-based simulation model of a complex social process, in this case, asylum seekers’ journeys to Europe. The process starts by creating an inventory of data sources, and the associated metadata, followed by assessing different aspects of data quality according to pre-defined criteria. As a result, based on the typology of available data, we are able to produce a thematic map of the area under study, and assess the uncertainty of key data sources, at least qualitatively. We illustrate the process by looking at the data on Syrian migration to Europe in 2011–21. In parallel, successive stages of the development of a simulation model allow for identifying key types of information which are needed as input into empirically grounded modelling analysis. Juxtaposing the available evidence and model requirements allows for identifying knowledge gaps that need filling, preferably by collecting additional primary data, or, failing that, by carrying out a sensitivity analysis for the assumptions made. By doing so, we offer a way of formalising the data collection process in the context of model-building endeavours, while allowing the modelling to be predominantly question-driven rather than purely data-driven. The paper concludes with recommendations with respect to data and evidence, both for modellers, as well as model users in practice-oriented applications.

Model-based assembly process planning for flexible aircraft cabin architectures

AbstractThe configuration and equipment of an aircraft cabin has a significant impact on the passenger’s flight experience. To meet the needs of passengers and improve the flight experience, airlines repeatedly demand customised adaptations to the cabin design. The implementation of these changes requires a high degree of flexibility in production and can lead to difficulties in setting up a fixed final assembly line. In addition, unforeseen events and changes in the OEMs’ supply chain require a rapid response in aircraft production to be able to deliver on time. Digitalisation in product development and production planning makes it possible to overcome these challenges and makes an important contribution to flexibility, time and cost efficiency. This paper presents a digital approach to modelling and flexible planning of assembly processes of the cabin. To this end, aircraft design data is automatically linked to the assembly system to react quickly to design changes in the assembly planning process. The integration is realised in a system architecture model depicted with the systems modelling language (SysML). The architecture model follows the formal process description defined in VDI3682. A planning algorithm then uses the production architecture parameters to optimise the assembly processes, e.g. in terms of time. The approach presented is demonstrated using the example of scheduling the pre-assembly processes of the "crown module". The latter includes all structural and functional components above the window panels, such as overhead bins, electrics and air conditioning. The results show how changes in aircraft and cabin design or in the production plant and resources can be flexibly evaluated. This allows conceptual changes to be evaluated and traded before the cabin design is frozen and transferred to real production. These advantages contribute to the time and cost optimisation of aircraft production. This work thus makes a valuable contribution to the further development of digital solutions in aircraft production.

Synergistic enhancement for energy-saving, emission reduction and profit improvement in iron and steel manufacturing system: Strategies for parameter regulation and technologies integration

The steel industry, characterized by high energy consumption and carbon emission, poses a significant threat to global energy and environmental security. In response to the energy conservation and dual-carbon targets, the steel industry must urgently find sustainable pathways for transformation and development. Steel production structure optimization and the application of cutting-edge technologies have been proven to be highly effective. However, the complexity and variability of actual production conditions highlight the absence of a methodology capable of adapting to real production scenarios, elucidating influencing principles across multiple factors, analyzing synergistic mechanisms of multiple indicators, and proposing collaborative control strategies. Furthermore, the potential for technology applications that are closely integrated with the company’s resource endowment remains uncertain, leading to an unclear overall strategy for energy saving and carbon reduction. This study develops a multi-scale optimization and evaluation model for ISMP, incorporating interconnected and matching processes, as well as nested multi-indicators, in response to the context. Subsequently, the disturbance mechanisms of various factors are accurately identified and a multi-indicator synergistic control strategy and framework are proposed. Following optimization, the exergy loss, energy consumption, carbon emission intensity, cost, and pollutant emissions of ISMP per tonne of steel are reduced by 1418.70 MJ, 42.93 kgce, 154.79 kg, 171.21 CNY, and 0.0168 kg, respectively. Additionally, measures such as reducing moisture content in coking coal can effectively regulate multiple indicators. Based on the proposed sensitivity quantification method, the temperature of hot blast is identified as the most effective controllable factor. Technologies like hydrogen-rich fuel injection, integrated waste heat recovery, and solar photovoltaic & energy storage have shown favorable prospects, according to analysis conducted from various angles regarding economic viability and potential for energy-saving and carbon reduction. However, the application of aforementioned technologies may be limited by financial consideration, necessitating cautious and judicious investment choices.

A Universal Framework for Skill-Based Cyber-Physical Production Systems

In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and a shortage of skilled labor. This paper proposes a universal framework for skill-based cyber–physical production systems (CPPS) that formalizes production knowledge into machine-processable formats. Key contributions include a novel conceptual model for skill-based production processes and an automated method to derive production plans from high-level CPPS skills for production planning and execution. This framework aims to enhance smart manufacturing by enabling more efficient, transparent, and automated production planning, thereby addressing the critical gap in current manufacturing practices. The framework’s benefits include making production processes explainable, optimizing multi-criteria systems, and eliminating human biases in process selection. A case study illustrates the framework’s application, demonstrating its current capabilities and potential for modern manufacturing.

Modeling enzyme competition in eicosanoid metabolism in macrophage cells using a cybernetic framework

Cellular metabolism is a complex process involving the consumption and production of metabolites, as well as the regulation of enzyme synthesis and activity. Modeling of metabolic processes is important to understand the underlying mechanisms, with a wide range of applications in metabolic engineering and health sciences. Cybernetic modeling is a powerful technique that accounts for unknown intricate regulatory mechanisms in complex cellular processes. It models regulation as goal-oriented, where the levels and activities of enzymes are modulated by the cybernetic control variables to achieve the cybernetic objective. This study used cybernetic model to study the enzyme competition between arachidonic acid (AA) and eicosapentaenoic acid (EPA) metabolism in murine macrophages. AA and EPA compete for the shared enzyme cyclooxygenase. Upon external stimuli, AA produces proinflammatory 2-series prostaglandins and EPA metabolizes to antiinflammatory 3-series prostaglandins, where proinflammatory and antiinflammatory responses are necessary for homeostasis. The cybernetic model adequately captured the experimental data for control and EPA-supplemented conditions. The model is validated by performing an F-test, conducting leave-one-out-metabolite cross-validation, and predicting an unseen experimental condition. The cybernetic variables provide insights into the competition between AA and EPA for the cyclooxygenase enzyme. Predictions from our model suggest that the system undergoes a switch from a predominantly proinflammatory state in the control to an antiinflammatory state with EPA-supplementation. The model can also be used to analytically determine the AA and EPA concentrations required for the switch to occur. The quantitative outcomes enhance understanding of proinflammatory and antiinflammatory metabolism in RAW 264.7 macrophages.

Kinematics-guided data-driven energy surrogate model for robotic additive manufacturing

With the increase in the usage of industrial robots for manufacturing applications, there will be a corresponding rise in energy consumption. Especially, robotic additive manufacturing (AM) has demonstrated significant potential for working autonomously in hazardous or extraterrestrial settings, as well as for producing large-scale structures. Recent advancements have made it possible for teams of robots to collaborate for printing structures even larger than their sizes together. Utilizing robots efficiently for AM is essential for achieving energy sustainability goals. The literature on energy models for robots and AM processes lacks a comprehensive energy model for robotic AM. To address this gap, a multi-point trajectory-based energy model is proposed for robotic AM. The model is created to help users manufacture parts in an energy-efficient way. These simulations require a significant amount of computation time, which limits their usage to offline purposes only. Hence, we propose two energy surrogate models for real-time predictions: a pure data-driven model and a kinematics-guided data-driven model. The kinematics-guided approach is an enhanced version of pure data-driven model which learns from inverse kinematics solutions along the trajectory to understand the robot’s dynamics and kinematics. There were two test cases conducted. The first test case consisted of paths that were feasible, printable, and within the robot’s reach, while the second test case consisted of a mixture of paths that were both feasible and infeasible. In comparison to the pure data-driven approach, the kinematics-guided approach stood out in both testing cases. Two real-world case studies have been used to demonstrate that the proposed approach can be used in lieu of simulations for real-time applications. Especially in the second case, where the part was placed very close to the robot making a portion of the part infeasible to print, the proposed approach correctly identified that it was not possible to print the geometry due to the robot’s kinematics. The proposed kinematics-guided approach is 195 and 700 times faster than the simulation results, respectively, in two case studies, and thus supporting the use of the proposed approach for real-time applications.

Coupled heat and fluid transport in pulled extrusion of optical fiber preforms

In the fabrication of optical fibers, the viscosity of the glass varies dramatically with temperature so that heat transfer plays an important role in the deformation of the fiber geometry. To date, models of fiber drawing processes have either assumed isothermal conditions or that advection is the dominant heat-transfer mechanism, with conduction therefore neglected. While advective heat transfer and neglect of conduction are valid in the fast process of drawing a preform to a fiber, heat conduction is also important in the slow process of extruding a fiber preform. In this paper, we derive and solve, in the context of pulled extrusion, a coupled heat and fluid flow model that incorporates heat conduction. The dramatic variation of viscosity with temperature means that this problem is extremely challenging to solve via standard numerical techniques and we therefore develop a novel finite-difference numerical solution method that proves to be highly robust. We use this method to show that conduction significantly affects the size of internal holes at the exit of the device and hence has important consequences for the process of manufacturing optical fibers.

Towards a Systemic Concept of the Brain Ishemia Stroke: Monte Carlo Driven in silico Model.

The work proposes a new mathematical model of dynamic processes of a typical spatially heterogeneous biological system, and sets and solves a mathematical problem of modeling the dynamics of the system of neurovascular units of the brain in conditions of ischemic stroke. There is a description of only a small number of mathematical models of stroke in the literature. This model is being studied and a numerical and software implementation of the corresponding mathematical problem is proposed. This work is the first attempt ever aiming to employ a Monte Carlo computational approach for In Silico simulation of the most critical parameters in molecular and cellular pathogenesis of the brain ischemic stroke. In this work, a new mathematical model of the development of ischemic stroke is proposed in the form of a discrete model based on neurovascular units (NVU) as elements. As a result of testing the program with the assignment of empirically selected coefficients, data were obtained on the evolution of the states of the lattice of the cellular automaton of the model for the spread of stroke in a region of the brain tissue. A resulting new theoretical model of the particular pathologically altered biosystem might be taken as a promising tool for further studies in neurology; general pathology and cell biology. For the first time, a mathematical model has been constructed that allows us to represent the spatial dynamics of the development of the affected area in ischemic stroke of the brain, taking into account neurovascular units as single morphofunctional structures.

Thermal profile modeling and microstructural evolution in laser processing of Inconel 625 plates by comparison of analytical and numerical methods

Microstructural evolution of materials under specified process conditions and parameters can be predicted by thermal modeling of additive manufacturing laser processes. The objective of this study was to develop, analyze and compare two methods for prediction: an analytical method and a numerical method for laser processing of Inconel 625 material. These methods were compared with experimental results for thermal profiling, and the effect of thermal profiles on microstructure of the experimental samples was explored. Maximum temperature and cooling rate of the numerical method were shown in good agreement, while the analytical method proved more challenging when compared to the experimental results for three laser parameters. Cooling curves were correlated with microstructure in terms of grain size, morphology, and orientation, with findings trending with parameter adjustments. This research supports the numerical modeling approach as a method for examining optimal laser processing conditions for Inconel 625 that is ideally suited for complex fluid flow analyses.

Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression

The rapid expansion of wind power worldwide underscores the critical significance of engineering-focused analytical wake models in both the design and operation of wind farms. These theoretically derived analytical wake models have limited predictive capabilities, particularly in the near-wake region close to the turbine rotor, due to assumptions that do not hold. Knowledge discovery methods can bridge these gaps by extracting insights, adjusting for theoretical assumptions, and developing accurate models for physical processes. In this study, we introduce a genetic symbolic regression (SR) algorithm to discover an interpretable mathematical expression for the mean velocity deficit throughout the wake, a previously unavailable insight. By incorporating a double Gaussian distribution into the SR algorithm as domain knowledge and designing a hierarchical equation structure, the search space is reduced, thus efficiently finding a concise, physically informed, and robust wake model. The proposed mathematical expression (equation) can predict the wake velocity deficit at any location in the full-wake region with high precision and stability. The model's effectiveness and practicality are validated through experimental data and high-fidelity numerical simulations.

Computational modeling of multi-track multi-layer laser directed energy deposition process

This study presents the development of a 3D fully coupled thermomechanical finite element model to evaluate the melt pool geometry, defects, and micro-hardness of multi-track multi-layer deposition in the powder feed laser-directed energy deposition (LDED) technique. Fabrication strategies can significantly affect the melt pool geometry and material behavior of the manufactured components. Understanding the effect of process parameters in a multi-track multi-layer LDED is important. Optimal process parameters are crucial for achieving high-quality deposition in terms of deposition geometry and integrity. The model can aid in mitigating issues such as inadequate inter-track or track-substrate fusion, porosity, cracks, and other defects. A multi-track multi-layer finite element model (FEM) can realistically predict melt pool evolution, mechanical properties, and defects in laser-LDED. This work focuses on the development of a fully coupled thermomechanical model for multi-track multi-layer deposition processes using ABAQUS®. The finite element framework employs Johnson-Cook’s flow stress model for constitutive behavior and the Koistein-Marburger equation for predicting hardness. Two tracks and two layers of SS316L powder are deposited on an SS304 substrate in finite element simulations and experiments. The model has been validated for the melt-pool depth in the substrate, deposition geometry, heat-affected zone, and hardness. It has been observed that there is a ∼10 % error in the prediction of melt pool geometry characterization and a ∼12 % error in hardness as compared to experimental results. The melt-pool width, depth, substrate dilution, and hardness have been characterized as a function of process parameters via simulations and experiments. The model is capable of predicting inter-track and interface defects. In addition, the effect of interlayer cooling on the melt-pool geometry and hardness has been studied.

Challenges and Perspectives of the Startup Ecosystem for the Development of Innovative Economy in Georgia

The processes of introduction of technology and innovation ecosystem in the economic development of the country have attracted special attention in the global world in the conditions of pandemic and post-pandemic. Accordingly, the research focuses on the formation and development models of modern start-up ecosystems, processes and determinants that ensure their transformation into innovations and the transfer of resources for economic development in the regions. The aim of the paper is to determine the main directions that will contribute to the development of startup ecosystems, taking into account global trends and state-specific practices; The research is based on the methods of grouping, comparison, analogy, analysis, generalization and systemic-structural approaches. As a result of the research, startup ecosystems were defined as an institutional mechanism that is open to the expansion of intersectoral networks and changes the competitive market environment in favor of the architecture of a stable innovative economy. The examples of Estonia, Spain, and Britain show the potential of developing a national startup ecosystem model that prioritizes long-term development, state technical self-sufficiency, and the ability to expand to new markets. Keywords: innovative economy, ecosystems, technoparks, startups, innovations, R&D, economic development

Sliding mode control design using a generalized reduced-order fractional model for chemical processes

This article presents a comprehensive study that evaluates the potential advantages of adopting a unified, systematic, and organized general fractional model within the Sliding Mode Control (SMC) design framework. The exploration extends to the model's applicability in a variety of disciplines. Although prior research has utilized SMC and fractional-order systems independently, no previous work has established a mathematical framework that integrates a generic fractional-order SMC model. The study addresses this gap and highlights potential implications for control design and applications in a variety of fields, particularly for chemical processes.

Partial molar volumes of 1–1 electrolytes at high T and P: correlations and predictions

Knowledge of the partial molar volumes of aqueous ions allows accurate calculation of the pressure dependence of equilibrium constants, solubility of minerals, etc., thus being useful for thermodynamic modeling of hydrothermal processes. This study analyzed methods to correlate and predict the values of the partial molar volumes at infinite dilution, V2o, for 1–1 electrolytes and singly charged ions at elevated T and P. Since the precise experimental values of the dielectric constant of water are measured only up to 873 K, we were interested only in non-electrostatic ways to correlate V2o data. First of all, we compiled the V2o values at T > 373 K for the following 1–1 electrolytes: HCl, LiCl, LiI, LiNO3, LiOH, NaF, NaCl, NaBr, NaI, NaNO3, NaOH, NaHCO3, NaClO4, NaH2PO4, NaTr (Tr stands for triflate), KF, KCl, KBr, KI, KNO3, KOH, CsBr, and NH4Cl. Relations, following from the “density” model and from the Fluctuation Solution Theory (FST) were employed to analyze data. It was concluded that at the current state of knowledge of V2o the FST-relations for electrolytes are recommended mainly to reject strongly deviating experimental outliers. However, the “density” model provides a simple and fairly accurate way to describe the compiled set of data with only two parameters for each ion, n and Vhc, values of which were evaluated for the following singly-charged ions: H+, Li+, Na+, K+, Cs+, NH4+, F-, Cl-, Br-, I-, OH–, NO3–, H2PO4-, HCO3–, ClO4-, Tr- (Tr = triflate). Following Mesmer et al. (1988), we consider the fitting parameters Vhc and n to be related to the intrinsic volume of the ion and to the number of water molecules transferred from the bulk water to a hydration shell around the ion, respectively.Although the values of n and Vhc were fitted from data at temperatures from 373 to 673 K and water densities, do, from 0.5 to 1.1 g cm−3, apparently, they predict realistic V2o values at temperatures up to 1600 K and do ranges from 0.2 to 1.6 g cm−3.

Harnessing artificial neural networks and linear regression models for modeling thermal modification processes: Characterization by FTIR and prediction of the mechanical properties of eucalyptus wood

In the present study, an artificial neural network model and multiple linear regression method were constructed to establish a relationship between the parameters of the thermal modification process and the mechanical properties of Eucalyptus camaldulensis wood samples. Three influencing parameters on the mechanical properties during heat treatment were considered input variables, namely water absorption, volumetric mass, and mass loss; the other parameters were kept constant (treatment temperature: 200, 220, 240, and 260 °C, and the duration: 5, 60, and 90 minutes). There were five neurons in the hidden layer that were used, as well as an output layer for the mechanical property. According to the results obtained, the mean square error (MSE) for the training, validation, and testing datasets were determined to be 0.21, 0.25, and 0.22 in the prediction modulus of rupture (MOR) and 0.019, 0.017, and 0.023 in the prediction modulus of elasticity (MOE). Higher coefficients of determination (R2) ranging from 0.93 to 0.98 were obtained for all datasets with the proposed ANN models, while the multiple linear regression models found the MSE to be 1.03 and 1.40 for MOR and MOE, respectively, as well as R2 to be 0.97 and 0.80, respectively. These results show that ANN models can be successfully used to predict the change in mechanical properties of wood during heat treatment.