Modeling Of Processes Research Articles

Mathematical understanding (hermeneutic aspects of educational activities)

The article discusses the issues of mathematical understanding as an educational practice of identifying the algorithmic meanings of mathematical (mathematized) educational texts. The hermeneutic orientation towards following the “guiding thread of language” (G.-G. Gadamer), as it is shown, cannot be narrowed to the attitude towards natural languages inherent in humanitarian educational practices. The “common place” of mathematical understanding in educational practices is: some of created languages (of scientific communication) being consolidated around the mathematical installation are not reducible to its sign universality or educational generalizations. But it often turns out to be outside the field of view of practicing teachers, in a didactically abstract position. The article substantiates the possibility of rehabilitating the hermeneutic attitude in mathematical education in connection with its development and strengthening in understanding the prerequisites for the algorithmic solution of mathematical problems (the need for additional information, non-standard solution techniques, transfer of educational goal setting to the area of interdisciplinary relationships or life experience, etc.). The author shows and comments on the effectiveness of the hermeneutic approach in mathematics education using examples of solving problems in algebra, geometry and in constructing mathematical models of various physical processes.

CFD Analysis of Gas-Dynamic and Heat Transfer Processes in a Propulsion System Using Polymer Fuel

In the presented work, a computer model of thermodynamic and gas-dynamic processes in a rocket engine of a new type, which uses solid polymers as fuel is considered. The design feature is the presence of a central body - a gasification chamber, where solid polymer fuel is decomposed into volatiles. To conduct the research, a preliminary thermodynamic analysis of the combustion of gasification products was carried out. Based on the results of the thermodynamic analysis using the mathematical model of the turbulent flow of viscous compressible gas, the processes of gas dynamics and heat transfer in the chamber and nozzle of the rocket engine were simulated. Analyses are made for the geometry of a real experimental sample of a polymer-fueled rocket engine developed at Oles Honchar Dnipro National University (Ukraine), which has passed the first successful tests. As a result of CFD simulation, dynamic and thermal fields in the combustion chamber and engine nozzle were obtained, and ways of further improvement of the design were determined.

Applying Design Thinking to Develop AI-Based Multi-Actor Decision-Support Systems: A Case Study on Human Capital Investments

Artificial intelligence, particularly machine learning, has revolutionized organizational decision-making processes by assuming many decision responsibilities traditionally allocated to humans. In this scenario, decision-support systems based on AI have gained considerable relevance, although the attitudes of managers toward intelligent agents are still unbalanced towards human intervention in decision-making. An additional level of complexity arises when the development of these systems occurs within the context of investments in human capital, such as startup funding or organizational development. In this field, decision-making becomes even more critical, since it implies the will, goals, and motivations of every human actor involved: the investors and those seeking investments. termed multi-actor decision-making, this process involves multiple individuals or groups of individuals who, starting from non-coincident objectives, must reach a mutual agreement and converge toward a common goal for the success of the investment. Considering these challenges, this study aims to apply the design thinking technique as a human-centered methodology to support the design of an AI-based multi-actor decision-support system, conceived by Mylia (The Adecco Group), in the field of organizational development. Additionally, the integration of strategic organizational counseling will be introduced to facilitate the modeling of internal DM processes within the provider organization, enabling the seamless flow of internal behaviors from the decision-support system’s conceptualization to its integration in the external market.

Integrated modeling of urban mobility, flood inundation, and sewer hydrodynamics processes to support resilience assessment of urban drainage systems.

With the increasing frequency of extreme weather events and a deepening understanding of disasters, resilience has received widespread attention in urban drainage systems. The studies on the resilience assessment of urban drainage systems are mostly indirect assessments that did not simulate human behavior affected by rainfall or semi-quantitative assessments that did not build simulation models, but few research characterizes the processes between people and infrastructure to assess resilience directly. Our study developed a dynamic model that integrates urban mobility, flood inundation, and sewer hydrodynamics processes. The model can simulate the impact of rainfall on people's mobility behavior and the full process including runoff generation, runoff entering pipes, node overflow, flood migration, urban mobility, and residential water usage. Then, we assessed the resilience of the urban drainage system under rainfall events from the perspectives of property loss and urban mobility. The study found that the average percentage increase in commuting time under different return periods of rainfall ranged from 6.4 to 203.9%. Calculating the annual expectation of property loss and traffic obstruction, the study found that the annual expectation loss in urban mobility is 9.1% of the annual expectation of property loss if the rainfall is near the morning commuting peak.

All‐nonlinear static‐dynamic neural networks versus Bayesian machine learning for data‐driven modelling of chemical processes

AbstractIn recent decades, the utilization of machine learning (ML) and artificial intelligence (AI) approaches have been explored for process modelling applications. However, different types of ML models may have contrasting advantages and disadvantages, which become critical during the optimal selection of a specific data‐driven model for a particular application as well as estimation of parameters during model training. This paper compares and contrasts two different types of data‐driven modelling approaches, namely the series/parallel all‐nonlinear static‐dynamic neural network models and models from a Bayesian ML approach. Both types of AI modelling approaches considered in this work have shown to significantly outperform several state‐of‐the‐art steady‐state and dynamic data‐driven modelling techniques for various performance measures, specifically, model sparsity, predictive capabilities, and computational expense. The performances of the proposed model structures and algorithms have been evaluated for two nonlinear dynamic chemical engineering systems—a plug‐flow reactor for vapour phase cracking of acetone for production of acetic anhydride and a pilot‐plant for post‐combustion CO2 capture using monoethanolamine as the solvent. For the validation data from the CO2 capture pilot plant, root mean squared error (RMSE) for flue gas outlet temperature, flowrate and CO2 concentration is 0.05%, 1.07%, and 5.0%, respectively, for the all‐nonlinear static‐dynamic neural networks and 0.1%, 1.75%, and 14.14%, respectively, for the Bayesian ML models. For the plug flow reactor data, the Bayesian ML models yield superior RMSE compared to the all‐nonlinear static‐dynamic neural networks when the measurement data are corrupted with Gaussian, auto‐correlated, or cross‐correlated noise.

Evaluation of influence of thermoplastic slurry flow conditions on heat transfer coefficient during beryllium ceramic formation

The article presents the results of calculation of the influence of thermoplastic slurry flow conditions on the total heat transfer coefficient when forming ceramic products. Methods of detailed description of casting processes and thermal calculations have been developed to ensure reliable correspondence of calculated data and experimental results implemented on the basis of calculational experiment. The modeling of physical processes occurring during the formation of products allows you to discover new opportunities for improving the quality of castings by allowing you to more closely track the change in the temperature-phase fields of the process and clearly present the solidification kinetics depending on the casting modes. The speed of heat removal from the casting during the solidification period determines the rate of movement of the slurry, along with the temperature field on which the width of the transition region depends. These factors have a direct influence on the formation of the structure of beryllium ceramics. The study of the heat exchange process in the formation of ceramic products depending on temperature, heat at phase transition is the main task, since they largely determine the technological and operational characteristics of beryllium ceramics. The design data allows to determine the optimal conditions of the ceramic casting process and to obtain a solidified product with a uniform structure at the outlet.

Review of the Modelling of Corrosion Processes and Lifetime Prediction for HLW/SF Containers—Part 2: Performance Assessment Models

Review of the Modelling of Corrosion Processes and Lifetime Prediction for HLW/SF Containers—Part 2: Performance Assessment Models

Introducing bimodal sea-states in a hybrid model for nearshore wave processes

HySwash has been recently developed as a fast and effective hybrid method to predict nearshore wave processes under unimodal wave conditions. However, global wave climates, and especially those in the tropical regions where coral reefs are hosted, are usually exposed to multiple incoming wave systems, resulting in several energy peaks corresponding to coexisting swells and wind seas. Moreover, although the full distribution of wave runup can have a significant impact on the assessment of vulnerable low-lying tropical regions, predictive models of flooding usually synthesize wave runups to an extreme percentile value, overlooking its full distribution. To enhance the capabilities of HySwash, in the present work, the inclusion of bimodality, as well as the prediction of the complete wave runup distribution is presented. This involves adapting the sampling, selection, and interpolation algorithms together with the hydrodynamic modeling that constitutes the original HySwash methodology. The positive mathematical validation reinforces the applicability of HySwash for a variety of coastal applications. Furthermore, a comparison is conducted between extreme wave runups induced by bimodal sea states and unimodal sea states, providing insights into the impact of multimodality on wave runup extremes.

Photocatalytic Degradation of Algal Organic Matter Using TiO2/UV and Persulfate/UV

Eutrophication in water reservoirs releases algal organic matter (AOM), a key precursor to the formation of disinfection by-products (DBPs) during the disinfection process. Typical drinking water treatment is not efficient for AOM removal, and advanced treatments are necessary for the removal of residual AOM before chlorination. UV-based technology with PS and TiO2 is widely used as a pre-oxidation step in water treatment; however, no publications have focused on them for AOM degradation. In this context, this work investigated the effect of oxidant concentration (0.1 to 0.5 g∙L−1) and pH (6 to 10) on AOM degradation with TiO2/UV and persulfate (PS)/UV using response surface methodology. In general, PS/UV was more effective in removing protein, while TiO2/UV was more effective in carbohydrate degradation. TiO2/UV removals varied from 27 to 57% for protein and from 48 to 86% for carbohydrates. The optimal condition (57% for protein and 86% for carbohydrates) was obtained using 0.5 g∙L−1 TiO2 at pH 10. PS/UV removals varied from 33 to 81% for protein and from 24 to 53% for carbohydrates. The optimal condition (81% for protein and 53% for carbohydrates) was obtained using 0.5 g∙L−1 PS concentration at pH 8. Degradation kinetics showed a good fit to the pseudo-first-order model (R2 > 95%) for both processes. The DBP formation reductions observed with TiO2/UV—trihalomethane (THM) (85 to 86%) and chloral hydrate (CH) (94 to 96%)—were similar to the efficiencies observed for PS/UV—THM (87 to 89%) and CH (83 to 88%). These results show the efficiency of UV-based technology for AOM degradation and the control of DBP formation.

Blockchain-Based Quality Assurance System for Academic Programs

Nowadays, technology is increasingly being adopted in different kinds of businesses to process, store, and share sensitive information in digital environments that include enormous numbers of users. However, this has also increased the likelihood of cyberattacks and misuse of information, potentially causing severe damage. One promising technology, which can provide the required security services with an improved level of efficiency, is blockchain. This research explores the use of Ethereum blockchain and smart contracts to create a secure and efficient quality assurance system (QAS) for academic programs. By utilizing blockchain and smart contracts, the proposed approach improves the integrity and reliability of sensitive information processed by the QAS, promotes transparency and governance, and reduces the time and effort required for quality operations. The current approach uses an additional access control layer to further enhance user privacy. Smart contracts automate various quality transactions and saves time and resources, and hence increases the efficiency of the QAS. The interplanetary file system (IPFS) is used to address the challenge of size limitations in blockchain. Additionally, this research investigates the use of various cryptographic schemes to provide robust security services at the application layer. The experimental results showed that the use of a hybrid cryptosystem relying on an Elliptic curve digital signature and AES encryption (AES_ECCDSA) outperforms other counterparts’ cryptosystems using an RSA digital signature and AES encryption (AES_RSADSA) and Elliptic Curve Integrated Encryption Scheme (ECIES) in terms of speed. The performance results showed that AES_ECCDSA consumes 188 ms to perform the required cryptographic operations for a standard-quality document with a size of 8088 KB, compared to the 231 ms and 739 ms consumed by the AES_RSADSA and ECIES schemes, respectively. This study presents a prototype implementation of the blockchain-based QAS, which outlines the processing model and system requirements for key QAS processes. It has been found that the cost and time required for blockchain operations vary depending on the size of the input data—a larger data size requires more time and costs more to process. The results of the current study showed that the time delay for blockchain transactions ranges from 15 to 120 s, while the cost ranges from USD 50 to USD 400. This research provides evidence that blockchain and smart contract technologies have the potential to create a secure, efficient, and trustworthy QAS environment for academic programs.

Analytical Model for the Molecular Ionization Energy in an External Electric Field.

A model for the molecular ionization energy in an applied electric field is presented on the basis of a perturbation expansion in the electric field. The leading term arises from the Frenkel approach, which is the same for all molecules normally used in the Poole-Frenkel model for conductivity in an electric field. For a set of test molecules, the quality of the results is comparable to that of previous results using constrained density functional theory. We conclude that the Frenkel term is dominant and sufficient at relatively low fields and that the dipole and polarizability terms, the leading terms dependent on the properties of the individual molecule, make a significant contribution only at high fields and for relatively large molecules. Because the presented model is analytical, quantum chemical calculations are avoided for a variety of electric field strengths and molecular orientations, and the model can therefore be applied directly in coarse-grained models for electronic processes in dielectric condensed phases.

Giant Sediment-Wave Field and Supercritical Flows in a Distally Steepened Ramp, Fort Payne Formation (Lower Mississippian), Kentucky-Tennessee, U.S.A.

Sediment waves are common in confined to unconfined settings on modern marine slopes and basin floors worldwide. Their morphology and stratal patterns show that many of them migrate upslope and upflow, a characteristic thought to record Froude-supercritical flow conditions associated with sediment gravity flows and density cascades. Few sediment waves of this type have been observed in the ancient rock record. This study reports the discovery of a giant (over 20,000 km2) sediment-wave field in Lower Mississippian carbonates and shales of the Fort Payne Formation in Tennessee and Kentucky. Sediment waves are present in the clinothem slopes and basin floor of a distally steepened ramp as seismic-scale bedforms ranging from 100 to 700 m long and 15-50 m high. Dominant facies include crinoidal shales, packstones, and rudstones. Many of the beds are sharp-based and graded, indicating sediment-gravity-flow deposition. Upslope-inclined rudstones (backsets) and wavy beds with upflow and downflow laminae are common and indicate supercritical flow conditions. These observations and interpretations are in stark contrast to previous interpretations of crinoidal bioherms and Waulsortian-type mounds. Basin physiography, a cool-water heterozoan carbonate factory and coastal upwelling were key factors in establishing a rapidly prograding ramp characterized by high rates of sediment production and basinward shedding. Sediment gravity flows and density cascades transported crinoidal grains downslope and entrained crinoids that inhabited the slope. Flows frequently reached Fr-supercritical flow conditions that led to the formation of sediment waves similar to those in modern marine slopes. This sediment-wave field is one of the first to be documented in the ancient rock record, and its extent is enormous. The near absence of other reported ancient examples must be due to misinterpretation (mounds, slumps) and the problem of observational scale and resolution. Sediment waves are often larger than outcrops. Lack of recognition of sediment waves in subsurface seismic sections is probably due to misinterpretation and the insufficient resolution. Perhaps this study demonstrates the need for revisiting and updating existing facies models of carbonate-sediment transport and depositional processes across slopes and basin floors.

Exploring time-series transformers for spatio-temporal prediction of microstructural evolution of polycrystalline grain

This study introduces a novel microstructure prediction model (polycrystalline grain transformers: PGTs) for metal solidification processes, leveraging the capabilities of transformer networks. The model’s primary purpose is to predict the dynamic evolution of polycrystalline microstructures within additive manufacturing conditions. To facilitate the training of the PGTs model, we generated a comprehensive dataset through a two-dimensional multi-phase field numerical simulation model. The inherent strength of transformer networks lie in its multi-head attention mechanism, which enables the PGTs model to autonomously identify crucial feature information within the input sequence, particularly related to structural evolution. Our research outcomes underscore the robustness of the PGTs model, as it closely aligns with predictions obtained from conventional phase-field calculations. Importantly, this model exhibits a remarkable improvement in computational efficiency, surpassing traditional phase-field methods by several orders of magnitude. This breakthrough opens up new avenues for research, offering an efficient and cost-effective strategy for delving deep into the intricate relationship between solidification processes and material mechanical properties. Notably, we mark this study as the pioneering application of transformer networks in numerical simulations of the metal solidification process, showcasing its immense potential across various domains, including material design.

Assessing Reusable Packaging: The Importance of Methodological Choices in Carbon Footprint Calculation

The reliability and clarity of environmental assessments represent an important prerequisite for measures towards the sustainable transformation of our economic system. Studies examining the environmental performance of reusables are often used to derive arguments for and against their use. Accordingly, it is important to have clarity about the influence of methodological decisions on the results of such studies. This paper analyses possible approaches to the allocation of environmental impacts of transport processes to reusable shipping packaging in the context of parcel deliveries. A model was developed to conduct comparative analyses of carbon emissions (carbon footprint) from the use of single-use vs. reusable shipping packaging and was subsequently applied to two reusable shipping packaging systems currently available on the market. The results showed that using different allocation models led to significant variations in the results for the carbon footprint of the analysed packaging (single-use and reusable), while at the same time, the calculated environmental break-even point in the comparison between the single-use and reusable options remained rather stable. The results highlight the importance of a clear and standardized methodological framework for the communication of footprint information for reusable shipping packaging. Moreover, for determining the environmental break-even point, the results suggest that aspects like the comparison scenario (i.e., the selection of the single-use packaging) are more important than the methodological choice of the allocation model for transportation processes.

Treed Gaussian processes for animal movement modeling.

Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and management. One challenge to modeling telemetry data is that animal movement often varies greatly in pattern over time, and current continuous-time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation. We demonstrate a novel application of treed Gaussian process (TGP) modeling, a Bayesian machine learning approach that automatically captures the nonstationarity and abrupt transitions present in animal movement. The machine learning formulation of TGPs enables modeling to be nearly automated, while their Bayesian formulation allows for the derivation of movement descriptors with associated uncertainty measures. We demonstrate the use of an existing R package to implement TGPs using the familiar Markov chain Monte Carlo algorithm. We then use estimated movement trajectories to derive movement descriptors that can be compared across individuals and populations. We applied the TGP model to a case study of lesser prairie-chickens (Tympanuchus pallidicinctus) to demonstrate the benefits of TGP modeling and compared distance traveled and residence times across lesser prairie-chicken individuals and populations. For broad usability, we outline all steps necessary for practitioners to specify relevant movement descriptors (e.g., turn angles, speed, contact points) and apply TGP modeling and trajectory comparison to their own telemetry datasets. Combining the predictive power of machine learning and the statistical inference of Bayesian methods to model movement trajectories allows for the estimation of statistically comparable movement descriptors from telemetry studies. Our use of an accessible R package allows practitioners to model trajectories and estimate movement descriptors, facilitating the use of telemetry data to answer applied management questions.

Graphical Specification of Sensitive Business Processes

This paper aims at developing a new BPMN extension, called “BPMN4SBP”, supporting the multi-dimensional modeling of Sensitive Business Processes (SBPs) (i.e., the knowledge, functional, organizational, behavioral, informational and intentional dimensions). It aims to explicitly integrate all the relevant issues and aspects relevant at the coupling of the business process modeling (BPM) domain and the knowledge management (KM) domain for improving the identification and management of crucial knowledge which are mobilized and created by these processes. This paper provides the analysis of requirements and relevant concepts for modeling SBP. Based on a core domain ontology, need for extension is identified and the valid BPMN4SBP extension is designed (according to the BPMN extension mechanism) by the construction of a conceptional domain model and the corresponding BPMN extension model. This extended BPMN4SBP meta-model is derived by applying model transformation rules and by adapting, also, the UML profile mechanism to BPMN. The applicability of the proposed BPMN extension is demonstrated by modeling an SBP in a real case study in the healthcare domain.

Optimization of cooling rate of Q-P treated 42SiCr steel using AI digital twinning

In the continuously advancing field of mechanical engineering, digitalization is bringing a major transformation, specifically with the concept of digital twins. Digital twins are dynamic digital models of real-world systems and processes, crucial for Industry 4.0 and the emerging Industry 5.0, which are changing how humans and machines work together in manufacturing. This paper explores the combination of physics-based and data-driven modeling using advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques. This approach provides a comprehensive understanding of mechanical systems, improving materials design and manufacturing processes. The focus is on the advanced 42SiCr alloy, where AI-driven digital twinning is used to optimize cooling rates during Quenching and Partitioning (Q-P) treatments. This leads to significant improvements in the mechanical properties of 42SiCr steel. Given its complex properties influenced by various factors, this alloy is perfect for digital twinning. The Q-P heat treatment process not only restores the material's deformability but also gives it advanced high-strength steel (AHSS) properties. The findings show how AI and ML can effectively guide the development of high-strength steels and enhance their treatment processes. Additionally, integrating digital twins with new technologies like the Metaverse offers exciting possibilities for simulated production, remote monitoring, and collaborative design. By establishing a clear workflow from physical to digital twins and presenting empirical results, this paper connects theoretical modeling with practical applications, paving the way for smarter manufacturing solutions in mechanical engineering. Furthermore, this paper analyzes how digital twins can be integrated into advanced technologies like the Metaverse, opening up new possibilities for simulated production, remote monitoring, design collaboration, training simulations, analytics, and complete supply chain visibility. This integration is a crucial step toward realizing the full potential of digitalization in mechanical engineering.

A Unified Treatment of Kepler Occurrence to Trace Planet Evolution. II. The Radius Cliff Formed by Atmospheric Escape

The Kepler mission enabled us to look at the intrinsic population of exoplanets within our galaxy. In period-radius space, the distribution of the intrinsic population of planets contains structure that can trace planet formation and evolution history. The most distinctive feature in period-radius space is the radius cliff, a steep drop-off in occurrence between 2.5 and 4R ⊕ across all period ranges, separating the sub-Neptune population from the rarer Neptunes orbiting within 1 au. Following our earlier work to measure the occurrence rate of the Kepler population, we characterize the shape of the radius cliff as a function of orbital period (10–300 days) as well as insolation flux (9500S ⊕–10S ⊕). The shape of the cliff flattens at longer orbital periods, tracking the rising population of Neptune-sized planets. In insolation, however, the radius cliff is both less dramatic and the slope is more uniform. The difference in this feature between period space and insolation space can be linked to the effect of EUV/X-ray versus bolometric flux in the planet’s evolution. Models of atmospheric mass loss processes that predict the location and shape of the radius valley also predict the radius cliff. We compare our measured occurrence rate distribution to population synthesis models of photoevaporation and core-powered mass loss in order to constrain formation and evolution pathways. We find that the models do not statistically agree with our occurrence distributions of the radius cliff in period space or insolation space. Atmospheric mass loss that shapes the radius valley cannot fully explain the shape of the radius cliff.

THE ENVIRONMENTAL DIMENSION IN NEW PRODUCTS DESIGN – EDUCATIONAL TRAINING IN INDUSTRIAL DESIGN

Industrial design (product) was one of the most impacted professions by the changes imposed by the insertion of the environmental issue in the project. Initially contemplated in the so-called eco-design, industrial design activities underwent adjustments and improvements. These result in an expanded vision, contemplating the modern vision of sustainability, in its economic, social, and environmental dimensions. The teaching and academic applications of design in the research and extension spheres were consequently altered, demanding complementary visions throughout the process, including the PPCs of the courses and the qualification needs of the faculty. The article presents models of project processes and reflects on the need to insert sustainability issues in project teaching.

Частная полумарковская модель как инструмент снижения сложности задачи оценивания устойчивости функционирования элементов информационной инфраструктуры, подверженной воздействию угроз

Decision-making on information infrastructure (II) security for its sustainable functioning in the face of threats requires a tool to assess the sustainability of its individual elements. The application of the semi-Markov model to assess the stability of the functioning of elements of II exposed to threats in a direct setting is associated with the increasing complexity of the description of the object of delineation (parametric space) in step progression from the number of the considered impacts, which reduces its practical significance. However, no studies have been found in the scientific literature to reduce the complexity of the semi-Markov model. The article presents an approach to reducing the complexity of modeling by adopting correct assumptions when forming the initial data. Given the conditions under which it is possible to take a series. It was a cost to limit the applicability of the model by significantly reducing the complexity of the modelling. The problem statement and the modified transition graph are given. The novelty of the problem statement is to take into account the limitations on the available resources for the restoration of functionality of the element. To explain the physical essence of the modeling process, a thought experiment with a model is introduced. To solve the problem, the following methods were used: a) expert methods for extraction of initial data; b) mathematical models of private semi-Markov processes; c) methods of transformation of Laplace; r) methods of planning of experiment. Illustrative examples and graphs accompany the task sequence demonstration. As a result of the experiment, the regularities of the studied process, the existence of which was proved formally. As a result of the experiment, the regularities of the studied process were revealed, the existence of which was officially proved. The results of the study broaden knowledge about the application of methods of Markov processes to assess the stability of the functioning of AI elements in relation to the conditions of the impact of threats.