Engineering Models Research Articles

Incremental model transformations with triple graph grammars for multi-version models and multi-version pattern matching

AbstractLike conventional software projects, projects in model-driven software engineering require adequate management of multiple versions of development artifacts, importantly allowing living with temporary inconsistencies. In previous work, we have introduced multi-version models for model-driven software engineering, which allow checking well-formedness and finding merge conflicts for multiple versions of the same model at once. However, situations where different models are linked via automatic model transformations also have to be handled for multi-version models. In this paper, we propose a technique for jointly handling the transformation of multiple versions of a source model into corresponding versions of a target model. This enables the use of a more compact representation that may afford improved execution time of both the transformation and further analysis. Our approach is based on the well-known formalism of triple graph grammars and the aforementioned encoding of model version histories called multi-version models. In addition to batch transformation of an entire history, the technique covers incremental synchronization of changes in the framework of multi-version models. Our solution is complemented by a dedicated pattern matching technique for multi-version models. We show the correctness of our approach with respect to the standard semantics of triple graph grammars and conduct an empirical evaluation to investigate the performance of our technique regarding execution time and memory consumption. Our results indicate that the proposed solution affords lower memory consumption and may improve execution time for batch transformation of large version histories, but can also come with computational overhead in unfavorable cases.

Advancing Parameter Estimation in Differential Equations: A Hybrid Approach Integrating Levenberg–Marquardt and Luus–Jaakola Algorithms

This study presents an integrated approach that combines the Levenberg–Marquardt (LM) and Luus–Jaakola (LJ) algorithms to enhance parameter estimation for various applications. The LM algorithm, known for its precision in solving non-linear least squares problems, is effectively paired with the LJ algorithm, a robust stochastic optimization method, to improve accuracy and computational efficiency. This hybrid LM-LJ methodology is demonstrated through several case studies, including the optimization of MESH equations in distillation processes, modeling controlled diffusion in biopolymer films, and analyzing heat and mass transfer during the drying of cylindrical quince slices. By overcoming the convergence issues typical of gradient-based methods and performing global searches without initial parameter bounds, this approach effectively handles complex models and closely aligns simulation results with experimental data. These capabilities highlight the versatility of this approach in engineering and environmental modeling, significantly enhancing parameter estimation in systems governed by differential equations.

On a Stable Multiplicative Calculus-Based Hybrid Parallel Scheme for Nonlinear Equations

Fractional-order nonlinear equation-solving methods are crucial in engineering, where complex system modeling requires great precision and accuracy. Engineers may design more reliable mechanisms, enhance performance, and develop more accurate predictions regarding outcomes across a range of applications where these problems are effectively addressed. This research introduces a novel hybrid multiplicative calculus-based parallel method for solving complex nonlinear models in engineering. To speed up the method’s rate of convergence, we utilize a second-order multiplicative root-finding approach as a corrector in the parallel framework. Using rigorous theoretical analysis, we illustrate how the hybrid parallel technique based on multiplicative calculus achieves a remarkable convergence order of 12, indicating its effectiveness and efficiency in solving complex nonlinear equations. The intrinsic stability and consistency of the approach—when applied to nonlinear situations—are clearly indicated by the symmetry seen in the dynamical planes for various parameter values. The method’s symmetrical behavior indicates that it produces accurate findings under a range of scenarios. Using a dynamical system procedure, the ideal parameter values are systematically analyzed in order to further improve the method’s performance. Implementing the aforementioned parameter values using the parallel approach yields very reliable and consistent outcomes. The method’s effectiveness, reliability, and consistency are evaluated through the analysis of numerous nonlinear engineering problems. The analysis provides a detailed comparison with current techniques, emphasizing the benefits and potential improvements of the novel approach.

Penerapan Model Incremental Delivery pada Pengembangan Aplikasi Sistem Informasi Hotel Cipta Jakarta

Along with the development of the times, we see many hotels experiencing changes. A change that is rarely reviewed as a news topic is the software overhaul that occurs in hotels from offline to online systems. Lay people only see changes in hotels from a visible perspective, without having to know whether the hotel's system and system applications have changed. One of the factors in system development at Hotel Cipta is the need for hotel management to prioritize the development of system applications from offline to online systems, even web-based. It is not easy to overhaul an existing system application compared to creating a new system application. There are several software engineering methods and models that are commonly used to analyze system needs and design systems in the software development process. One of them is the incremental delivery model. This makes it interesting to try to apply the incremental delivery model to the development of the Hotel Cipta information system application by taking place at the Hotel Cipta on KH. Wachid Hasyim Street, Central Jakarta. In this study, the object of research is the Front Office department.

SolarDesign: An online photovoltaic device simulation and design platform

Abstract SolarDesign (https://solardesign.cn/) is an online photovoltaic device simulation and design platform that provides engineering modeling analysis for crystalline silicon solar cells, as well as emerging high-efficiency solar cells such as organic, perovskite, and tandem cells. The platform offers user-updatable libraries of basic photovoltaic materials and devices, device-level multi-physics simulations involving optical-electrical-thermal interactions, and circuit-level compact model simulations based on detailed balance theory. Employing internationally advanced numerical methods, the platform accurately, rapidly, and efficiently solves optical absorption, electrical transport, and compact circuit models. It achieves multi-level photovoltaic simulation technology from “materials to devices to circuits” with fully independent intellectual property rights. Compared to commercial software, the platform achieves high accuracy and improves speed by more than an order of magnitude. Additionally, it can simulate unique electrical transport processes in emerging solar cells, such as quantum tunneling, exciton dissociation, and ion migration.

Unifying Model Execution and Deductive Verification with Interaction Trees in Isabelle/HOL

Model execution allows us to prototype and analyse software engineering models by stepping through their possible behaviours, using techniques like animation and simulation. On the other hand, deductive verification allows us to construct formal proofs demonstrating satisfaction of certain critical properties in support of high-assurance software engineering. To ensure coherent results between execution and proof, we need unifying semantics and automation. In this paper, we mechanise Interaction Trees (ITrees) in Isabelle/HOL to produce an execution and verification framework. ITrees are coinductive structures that allow us to encode infinite labelled transition systems, yet they are inherently executable. We use ITrees to create verification tools for stateful imperative programs, concurrent programs with message passing in the form of the CSP and Circus languages, and abstract system models in the style of the Z and B methods. We demonstrate how ITrees can account for diverse semantic presentations, such as structural operational semantics, a relational program model, and CSP's failures-divergences trace model. Finally, we demonstrate how ITrees can be executed using the Isabelle code generator to support the animation of models.

Design and Technical-Economic-Environmental Evaluation of a Biorefinery Using Non-Marketable Edible Mushroom Waste

Edible mushrooms are gaining popularity as functional foods, not only for their distinctive taste and subtle flavor but also for their potential health benefits. They are rich in essential nutrients, including carbohydrates, proteins, fiber, minerals, and vitamins. However, it is estimated that up to 30% of total mushroom production generates non-marketable waste, which is currently used as animal feed, fertilizer, or compost. From a circular economy perspective, the objective of this work is to propose a conceptual design for a biorefinery to produce high-value-added products from non-marketable edible mushroom waste and to conduct a comprehensive assessment using modeling and simulation tools for process engineering. The general approach first involves identifying high-potential products, then determining the best processing routes, followed by developing a process flowsheet diagram for a case study (A. bisporus), and ultimately conducting a comprehensive assessment of the biorefinery to evaluate its sustainability. As a result, the proposed biorefinery may produce chitin, citric acid, and fertilizer, demonstrating the technical feasibility of waste valorization in terms of product yields, with positive profitability in terms of the rate of return on investment and payback period, and low environmental impact in terms of water and energy consumption as well as CO2 emissions.

Detailed analysis on primary and secondary break up characteristics of liquid fuel jet in crossflow using homogeneous mixture model and large eddy simulation

The detailed breakup characteristics of the liquid jet fuel, Jet-A, in crossflow to various ranges of momentum flux ratios and Weber numbers have been investigated using a high-fidelity compressible multi-phase numerical technique. Multi-phase large eddy simulation with adaptive mesh refinement and Eulerian to Lagrangian transformation are applied to the homogeneous mixture model. The liquid surface instabilities and their frequencies inside the injector orifice are observed and analyzed. Interactions between liquid jet and crossflow result in phenomena such as horse-shoe vortex formation, boundary separation, liquid trailing, and counter-rotating vortex pair. Liquid column breakup characteristics and wave structures are analyzed from both temporal and spatial viewpoints. The Sauter mean diameter distribution and cumulative distributions of droplets resulting from secondary breakup are presented, along with the droplet size distribution derived from the momentum flux ratio and Weber number. Several engineering models, including the instability frequencies, the breakup length, and the penetration depth, are proposed in terms of the momentum flux ratio and Weber number, providing valuable insights for injector and combustor design.

A Review of Particle Packing Models and Their Applications to Characterize Properties of Sand-Silt Mixtures

This paper reviews particle packing models and explores their application in geotechnical engineering, specifically for sand-silt mixtures. The review covers key models, including limiting case, linear, and non-linear packing models, focusing on their mathematical structures, physical principles, assumptions, and limitations through the concept of excess free volume. The application of particle packing models in geotechnical engineering is explored in characterizing the properties of sand-silt mixtures, offering insights into maximum, minimum, and critical void ratios and inter-granular void ratio, and the prediction of mechanical properties.

Leveraging Transformer Model for Data-Driven Requirement Engineering: A Case Study of NLP4RE E-Commerce Business Apps in Malaysia and Indonesia

Leveraging Transformer Model for Data-Driven Requirement Engineering: A Case Study of NLP4RE E-Commerce Business Apps in Malaysia and Indonesia

Acoustically tunable intra-droplet assembly of organoids towards high-throughput tumor model construction

Three-dimensional cell organoids and spheroids are increasingly recognized as physiologically relevant models for ex vivo research and therapeutic screening. However, there remains a need for a cost-effective and convenient platform to fabricate these models with well-defined architectures for downstream applications. Here, this work proposes micro models constructing in the droplet platform, by acoustic forces, that can obtain 3D cellular organoids with tunable structures at high yield. By modulating signals applied to acoustic devices, a periodic potential array with adjustable structures is formed, enabling uniform trapping and patterning of droplets. Simultaneously, the localized potential is also formed inside droplets, resulting in the active assembly of cells. Then, hydrogel droplets containing the constructed models are crosslinked, providing ECM to mimic the 3D scaffolds. In experiment, MCF-7 cells are assembled into spherical, linear and cross structures. This method can generate thousands of tumor spheroids in one minute. The long-term culturing results indicate an enhanced efficiency in forming mature tumor models compared with control groups. This biomanufacturing technology has the advantages of being high-throughput, low-cost, and tunable, making it a robust tool for droplet-based applications such as tissue engineering, drug screening, and disease modeling etc.

Examining the Role of Surface Temperature on Boundary Layer Stability and Transition to Turbulence

This study investigates the influence of varying surface temperature on the critical Reynolds number to understand the onset of turbulence in the boundary layer of a flat plate. Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent have been utilized for the purpose of this study. The study systematically varies surface temperature to measure its impact on flow stability. The research demonstrates that an increase in surface temperature results in a lower critical Reynolds number (leading to an earlier transition from laminar to turbulent flow) by modeling air as the working fluid and applying the k-ω SST turbulence model. The results align with theoretical predictions which highlight the destabilizing effect of temperature-induced viscosity changes. This finding has practical implications for aerospace engineering, HVAC systems, and environmental modeling where controlling turbulence can optimize performance and energy efficiency. The study acknowledges limitations due to the use of a 2D model and suggests future work using 3D simulations and other flow conditions to expand the applicability of the findings.

AI-Augmented Chemical Engineering Models for Predictive Battery Maintenance in IoT Applications

This research presents a cutting-edge approach that combines chemical engineering principles with artificial intelligence (AI) to enhance battery maintenance in Internet of Things (IoT) applications. The study develops predictive maintenance models capable of accurately forecasting battery degradation, thereby optimizing usage patterns and extending battery life. By integrating electrochemical data with AI-driven predictive analytics, the project enables real-time monitoring of battery health, achieving over 90% accuracy in anticipating maintenance needs, which reduces unplanned downtime and lowers operational costs. Employing machine learning frameworks, data analysis tools, and chemical engineering simulation software, the focus is on lithium-ion and solid-state batteries, commonly used in IoT networks across sectors such as smart cities, agriculture, and healthcare. Key results include a 20−30% increase in battery lifespan and a 15% improvement in energy efficiency, significantly reducing electronic waste and environmental footprint. This interdisciplinary approach provides a foundation for sustainable battery technology in IoT technology, paving the way for future advancements in AI-augmented predictive maintenance and chemical engineering models for next-generation applications. The integration of AI with chemical engineering principles allows for the development of models that adapt to the unique electrochemical behavior of different battery types, such as lithium-ion and solid-state batteries. By analyzing lifecycle data and device usage patterns, predictive models can account for variables that influence battery performance over time, including temperature fluctuations and charging cycles. This project utilizes neural networks and reinforcement learning algorithms to train models capable of learning from historical data, enhancing their predictive capabilities. A significant aspect of this research involves the preprocessing of battery lifecycle data to ensure high data quality, which is critical for accurate model training and validation.

Building credibility for human systems integration in model‐based systems engineering

AbstractRobust and trusted digital human representations are necessary to successfully account for human considerations in model‐based systems engineering (MBSE). Multiple domains and modeling frameworks leverage verification, validation, and accreditation (VV&A) processes to characterize when and under what conditions a model is valid to establish credibility. A literature review was completed on mathematical, physics‐based, software development, discrete event simulation, agent‐based, system dynamics, and MBSE models with the goal of proposing a process for performing VV&A on digital engineering (DE) and MBSE models for sociotechnical systems. However, this research also revealed the need for a broader framework to characterize the risk associated with using these models for making high‐consequence decisions. While accomplishing the literature review, another approach to building credibility was identified that is used heavily in the financial industry, namely model risk management (MRM). This process is extended by leveraging MRM approaches from within the financial community to propose a framework for sociotechnical model users to characterize the risk of using MBSE models to make programmatic decisions. The primary contribution of this work is to document a meta‐analysis of model VV&A while proposing an alternative approach to characterizing and communicating credibility that was discovered during this analysis. This approach could be a viable option for ensuring the credibility of human systems integration in MBSE models.

Latent Pitfalls in Microstructure-Based Modeling for Thermally Aged 9Cr-1Mo-V Steel (Grade 91)

Abstract A case study was conducted on a mechanistic model development that predicted tensile strength deterioration with thermal aging of 9Cr-1Mo-V steel in supporting the 60-year design life expected for advanced nuclear reactors. For property prediction beyond practical testing times, mechanistic modeling is highly desired, as it taps into the physics of structure–property relationships and therefore can generate reliable results for extrapolation. Meanwhile, as mechanistic models are often complicated, reflecting the intricacy of microstructure and strengthening mechanisms, pitfalls that are difficult to detect often exist. This paper discusses latent pitfalls that are common in mechanistic modeling or specific in this 9Cr-1Mo-V case development through using the American Society of Mechanical Engineers verification and validation in computational solid mechanics (ASME V&V 10) standard for evaluating credibility of modeling in materials engineering. Suggestions are also made for enhancing reliability of microstructure-based modeling.

Engineering modelling requirements for integration of assistive technology as a pathway for inclusive education in the disability unit

The phenomenon of 'massification in higher education ', which refers to the significant increase in the number of students enrolling in universities, has made it difficult for inclusive education to be attainable owing to unknown reasons that are encumbering the students living with disability from having a sense of belonging. Hence, this study aims to evaluate the relationship between students living with disability and social justice through inclusivity in higher education with the aid of Assistive Technology (AT) to conceptualise a framework. The researchers employed a mixed methods approach to collect and analyse data statistically and descriptively. The requirements for integrating the model to ease the lack of consideration of students living with disability in the digital age were evaluated and critically reviewed regarding its impact on students’ sense of belonging in the digital age. The major findings from this study elucidate the factors perceived as the central requirements for mitigating the lack of inclusivity and social justice in higher education. The procurement of ATs was topical as most respondents (83%) categorically opined the need to improve this process by making timely purchases of the proper ATs from reliable service providers. The successful implementation of this framework could significantly enhance the educational experience of students with disabilities at the University of Venda, making it a more inclusive and supportive learning environment. The findings on the appropriateness of ATs for students overwhelmingly indicated that many of the available devices and technologies meant to assist learners were not fit for the tasks they were intended for. The university should establish a procurement committee and a high-level action plan outlining the requirements for delivering assistive products & services.

On the Mathematical Background of Sliding Mode-Based Friction Compensation of a Micro-Telemanipulation System

Modeling of various phenomena in engineering work is always a kind of simplification of real processes, aiming at a model where a certain level of mathematical theory and computational procedures is sufficient. If the complexity of the required theory corresponds to the general mathematical competence of engineers, then technical problems can be treated separately in engineering (or physical) models without regard to the mathematical background. However, in some advanced engineering fields, the harmonized development of engineering and mathematical models and toolboxes is necessary to find efficient solutions. For example, modeling variable structure systems in ideal sliding mode requires a mathematical toolbox that goes far beyond general engineering competence through the theory of discontinuous right-hand-side differential equations. Although sliding mode control is popular in practice and the concept of sliding mode allows a significant reduction of model complexity, its exact mathematical description is rarely encountered. The problem of friction compensation of a micro-telemanipulator using sliding mode control demonstrates a harmonized application of the mathematical and engineering approaches. Based on Filippov’s theory, the ideal sliding mode can be discussed. Although an ideal system cannot be implemented in reality, the real systems can be kept close enough to it; therefore, the discussion of the solution of the ideal model is important for practical applications. Although several elements of the topic are available in the literature, in this paper a unique complex approach is given for users of sliding mode control with experimental considerations, different engineering models, and codes. The paper concludes that sliding mode control is a case where engineering and mathematical modeling are inseparable and requires the competence of both fields.

Predictive Representations: Building Blocks of Intelligence.

Adaptive behavior often requires predicting future events. The theory of reinforcement learning prescribes what kinds of predictive representations are useful and how to compute them. This review integrates these theoretical ideas with work on cognition and neuroscience. We pay special attention to the successor representation and its generalizations, which have been widely applied as both engineering tools and models of brain function. This convergence suggests that particular kinds of predictive representations may function as versatile building blocks of intelligence.

Physicochemical Modulations in MXenes for Carbon Dioxide Mitigation and Hydrogen Generation: Tandem Dialogue between Theoretical Anticipations and Experimental Evidences

The dawn of MXenes has fascinated researchers under their intriguing physicochemical attributes that govern their energy and environmental applications. Modifications in the physicochemical properties of MXenes pave the way for efficient energy-driven operations such as carbon capture and hydrogen generation. The physicochemical modulations such as interface engineering through van der Waals coupling with homo/hetero-junctions render the tunability of optoelectronic variables driving the photochemical and electrochemical processes. Herein, we have reviewed the recent achievements in physicochemical properties of MXenes by highlighting the role of intercalants/terminal groups, atomic defects, surface chemistry and few/mono-layer formation. Recent findings of MXenes-based materials are systematically surveyed in a tandem manner with the future outlook for constructing next-generation multi-functional catalytic systems. Theoretical modelling of MXenes surface engineering proffers the mechanistic comprehension of surface phenomena such as termination, interface formation, doping and functionalization, thereby enabling the researchers to exploit them for targeted applications. Therefore, theoretical anticipations and experimental evidences of electrochemical/photochemical carbon dioxide reduction and hydrogen evolution reactions are synergistically discussed.

A multiphase flow model of water droplets dielectrophoretic-induced air dehumidification phenomena

Air humidity in indoor spaces plays a critical role in human comfort and health. Dehumidification systems are used for building humidity controls, but they can take significant energy consumption, especially in geographic locations with high outdoor humidity and warm climates. Consequently, there is a growing demand for innovative dehumidification processes that consume minimal energy. Dielectrophoretic air dehumidification represents one such promising approach. However, it has not garnered significant attention due to the absence of engineering models and simulation tools capable of evaluating its performance and limitations at large-scale airflows. A new numerical multiphase CFD model, which is also experimentally validated, is developed in a customized Reacting Foam solver based on OpenFOAM® version 9. The newly developed model seeks to decrease substantial energy consumption and lower costs by leveraging the dielectrophoretic phenomenon to regulate moisture levels in the air. The solver integrates a hybrid Eulerian-Lagrangian framework to track the droplet's trajectory and growth rate while solving the continuum equations for the moist air. An electrospray produces electrically charged droplets, which grow during their in-flight trajectories as water vapor condenses onto their surfaces. The role of electrostatic forces in promoting vapor condensation within a high-gradient electrical field is investigated, and the dielectrophoretic vapor nucleation process on charged water droplets is discussed. The CFD model was validated against results from the literature and from proof-of-concept experiments conducted by the authors, which showed a 2 % air dehumidification with a single electrospray and airflow rate of 5 cubic feet per minute. The simulation results indicated that augmenting the number of electrically charged spray droplets increased the dehumidification of the air to 25 %. The initial mean droplet diameter, the orientation of the injector and relative humidity significantly influence the assessment of dehumidification. Scaling up this approach to larger airflow volumes is identified as a potential future research direction.