Systems Engineering Research Articles

Melting and solidification in periodically modulated thermal convection

Melting and solidification in periodically time-modulated thermal convection are relevant for numerous natural and engineering systems, for example, glacial melting under periodic sun radiation and latent thermal energy storage under periodically pulsating heating. It is highly relevant for the estimation of melt rate and melt efficiency management. However, even the dynamics of a solid–liquid interface shape subjected to a simple sinusoidal heating has not yet been investigated in detail. In this paper, we offer a better understanding of the modulation frequency dependence of the melting and solidification front. We numerically investigate periodic melting and solidification in turbulent convective flow with the solid above and the melted liquid below, and sinusoidal heating at the bottom plate with the mean temperature equal to the melting temperature. We investigate how the periodic heating can prevent the full solidification, and the resulting flow structures and the quasi-equilibrium interface height. We further study the dependence on the heating modulation frequency. As the frequency decreases, we found two distinct regimes, which are ‘partially solid’ and ‘fully solid’. In the fully solid regime, the liquid freezes completely, and the effect of the modulation is limited. In the partially solid regime, the solid partially melts, and a steady or unsteady solid–liquid interface forms depending on the frequency. The interface height can be derived based on the energy balance through the interface. In the partially solid regime, the interface height oscillates periodically, following the frequency of modulation. Here, we propose a perturbation approach that can predict the dependency of the oscillation amplitude on the modulation frequency.

Memoization in Model Checking for Safety Properties with Multi-Swarm Particle Swarm Optimization

In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checking, the task of finding specification errors is reduced to deterministic search techniques such as Depth-First Search. Recent research has shown that swarm intelligence offers a powerful search capability compared to traditional techniques. In particular, multi-swarm Particle Swarm Optimization is known to be efficient and can mitigate the state-space explosion problem, i.e., the exponential increase in the search space with a linear increase in the problem size. However, the state-space explosion problem is still significant when verifying very large systems. Further performance improvement is needed. To achieve this, we propose a novel memoization or cache mechanism for storing tentative solutions for reuse in the later stages of the search procedure. For each stage, a candidate solution computed by a swarm is summarized efficiently and heuristically to consolidate similar solutions into a single representative solution. We store the summary and its associated solutions in key-value maps. Instead of computing known solutions repeatedly, we retrieve the solution if the stored key matches the summary. We incorporated the proposed mechanism into a model-checking technique with multi-swarm Particle Swarm Optimization and evaluated the search performance. We show in this paper that the proposed mechanism improved time and space consumption while maintaining solution quality.

Componentizing autonomous underwater vehicles by physical-running algorithms

Autonomous underwater vehicles (AUV) constitute a specific type of cyber-physical system that utilize electronic, mechanical, and software components. A component-based approach can address the development complexities of these systems through composable and reusable components and their integration, simplifying the development process and contributing to a more systematic, disciplined, and measurable engineering approach. In this article, we propose an architecture to design and describe the optimal performance of components for an AUV engineering process. The architecture involves a computing approach that carries out the automatic control of a testbed using genetic algorithms, where components undergo a ‘physical-running’ evaluation. The procedure, defined from a method engineering perspective, complements the proposed architecture by demonstrating its application. We conducted an experiment to determine the optimal operating modes of an AUV thruster with a flexible propeller using the proposed method. The results indicate that it is feasible to design and assess physical components directly using genetic algorithms in real-world settings, dispensing with the corresponding computational model and associated engineering stages for obtaining an optimized and tested operational scope. Furthermore, we have developed a cost-based model to illustrate that designing an AUV from a physical-running perspective encompasses extensive feasibility zones, where it proves to be more cost-effective than an approach based on simulation.

Artificial Neural Network-Based Fault Detection on Nigerian 330kv Power Transmission Line

In Nigeria, there will inevitably be ongoing difficulties with 330kv power transmission lines. This drop in insulation strength between the phase conductors and the earthed screen encircling the conductors may be the cause of the difficulties that have emerged on the electric transmission circuit. Many scholars have approached these problems in different ways. Fast Fourier Transform (FFT), Wavelet Transform, Fourier Transform, S-Transform, and Fourier Series are a few of the techniques utilized to address these transmission line problems. It has not been widely and thoroughly adopted to solve problems in Power System Engineering, according to numerous study studies on artificial neural networks (ANNs). Consequently, the 330kV Power Transmission Line problems have been addressed in this research by using artificial neural networks (ANNs). the training efficacy of any ANN challenges diagnosis system. The MATLAB/Simulink R2018a was utilized to conduct simulations on an ANN challenges detector. The ANN detector was trained with pre-fault and fault signals as inputs, allowing for the identification of different types of line difficulties. Results indicated that at Mean Square Error (MSE) of 1.6856e-5, the best training performance for the challenges was attained. At Regression, it approaches zero (9.8958e-1). Because it met the requirements, the simulation's outcome is acceptable. Keywords: Power System, Transmission Line, Matlab/Simulink, Three Phase, Artificial neural networks, fault detection.

Diagnosis of marine internal combustion engines by means of rapidly variable temperature and composition of exhaust gas as an alternative or support for currently used diagnostic methods

The article points out relevance of parametric diagnostics of ship engines and analyzes the state of research in this field. A method is proposed for diagnosing engine systems on the basis of rapidly variable exhaust temperature while measuring its composition. A method for determining diagnoser tools from the signal within one engine cycle and mathematical and statistical treatment of test results is presented. The products of numerical moddeling in the Diesel-RK software and the products of laboratory research on a Farymann Diesel test engine were analyzed. Affect of the most popular defects on the analyzed parameters was defined. Criteria for matching a diagnoser tool in accordance with the type of damage in a ship engine was presented. A methodology was proposed for adapting the presented method to metering on a ship engine in operation.

Analytical modeling and computational optimization for a 1-DOF compliant mechanism

The first natural frequency directly affects the operational conditions of compliant mechanisms in precision engineering systems. To address this challenge, a computational method based on the surface response method is proposed to optimize the frequency of a new one degree of freedom (DOF) compliant mechanism. Initially, a 1-DOF 3D model of a compliant mechanism is built. The dynamic equation and the frequency response are formulated via equivalent stiffness and the Lagrange method. Subsequently, a series of numerical simulations are conducted to find the fundamental frequency of the mechanism. The initial dimensions of the flexible joints are determined, and the initial frequency is analyzed by using finite element analysis. Next, the flexible joints in the designed mechanism are optimized by a variant of the genetic algorithm. The optimized dimensions of the mechanism are found with the thickness of the circular joint of 1.10 mm, the thickness of the leaf joint of 1.19 mm, and the length of the leaf joint of 54.50 mm. The optimized result showed that there is a significant improvement in the frequency of the mechanism, increasing from an initial design of 53.218 Hz to an optimal design of 75.927 Hz with an improvement of 42.6%. This study provides important reference materials for future research on compliant mechanisms.

Trends and Developments in Engineering Education: A Bibliometric Analysis of Students' Competencies Research (2015-2024)

In this study, CiteSpace and VOSviewer bibliometrics software were used to analyze 257 papers on the topic of student competencies in engineering education included in the Core Collection of Web of Science databases from 2015 to 2024, revealing the research hotspots and development trends in this field. The study found that innovative research in engineering education mainly focuses on the areas of interdisciplinary integration, technology application and sustainable development, especially the application of project- based learning (PBL), augmented reality (AR) technology, and modeling tools have shown significant results in enhancing students' practical ability and innovative thinking. the extensive application of models such as BIM and LCSD further enhances students' professional skills in system analysis and engineering design. Meanwhile, the innovative curriculum design and the cultivation of innovative self-efficacy played an important role in the development of students' creativity. This study not only summarizes the hotspots of existing research, but also proposes future research directions in technology integration, global competency development and social responsibility education, providing a strong theoretical basis for the future development of engineering education.

Economic Load Dispatch Problem Analysis Based on Modified Moth Flame Optimizer (MMFO) Considering Emission and Wind Power

In electrical power system engineering, the economic load dispatch (ELD) problem is a critical issue for fuel cost minimization. This ELD problem is often characterized by non-convexity and subject to multiple constraints. These constraints include valve-point loading effects (VPLEs), generator limits, emissions, and wind power integration. In this study, both emission constraints and wind power are incorporated into the ELD problem formulation, with the influence of wind power quantified using the incomplete gamma function (IGF). This study proposes a novel metaheuristic algorithm, the modified moth flame optimization (MMFO), which improves the traditional moth flame optimization (MFO) algorithm through an innovative flame selection process and adaptive adjustment of the spiral length. MMFO is a population-based technique that leverages the intelligent behavior of flames to effectively search for the global optimum, making it particularly suited for solving the ELD problem. To demonstrate the efficacy of MMFO in addressing the ELD problem, the algorithm is applied to four well-known test systems. Results show that MMFO outperforms other methods in terms of solution quality, speed, minimum fuel cost, and convergence rate. Furthermore, statistical analysis validates the reliability, robustness, and consistency of the proposed optimizer, as evidenced by the consistently low fitness values across iterations.

The machine system as a factor of scientific and technological progress in agro-industrial complex

The article proposes a principle for forming a machine system using the object-process approach to modeling technical systems as a methodology of systems engineering. It has been shown that the central concept of a machine system is scientific and technological progress (STP): a machine system should simultaneously promote progress in the agro-industrial complex (AIC) and reflect it. The article presents a definition of a machine system, its functions, main goal, architecture, stakeholders, and external environment. A machine system is positioned as a mixed system that combines conceptual, substantive, and physical components. The conceptual part is an information product – the machine system itself, presented in the form of a computer program, website, standards, databases, on paper, etc. The physical component is a group of experts in developing a machine system. It has been shown that in a market economy, a machine system should be built not on the basis of specific brands and models of technical means, but on the basis of their types. A definition of the type was proposed based on the division of technical means characteristics into functional and non-functional. To substantiate the machine types, an approximate list of types of non-functional characteristics of agricultural machinery was formed. Diagrams of the architecture of the machine system and the development of types of agricultural machines were constructed. The architecture of the machine system is a combination of the structure of the machine system and the activities of experts in the development of the machine system, which ensures the performance of the machine system of its functions, the main ones being the systematization of agricultural machines and technologies and the formation of ideas among interested parties about the state and directions of scientific and technological progress in the agro-industrial complex.

An efficient surrogate model for system reliability with multiple failure modes

AbstractThe failure modes of complex systems are often highly dependent. Aiming at the reliability analysis of engineering systems with multiple failure modes, an efficient surrogate model for system reliability with multiple failure modes is proposed, namely, the Copula Adaptive Kriging surrogate model (CAKSM). First, the initial Kriging model is established based on the initial samples. In order to select more effective added samples to update the Kriging model, a new learning function, namely, Copula Unites with Expected Improvement Function (CUEIF), is established. The function can make a reasonable sample selection by considering the failure correlation. Then, based on the optimal Copula function, the reliability data of each failure mode are coupled and analyzed. In order to adapt to various computing scales, regularization is introduced to improve the prediction speed of the surrogate model, and the probability density function of each performance function is obtained by introducing nonparametric kernel density estimation. Finally, the effectiveness of the CAKSM method and the learning function CUEIF is verified through four examples.

Requirements Engineering at a Turning Point: Key Research Trends Shaping the Future of Systems and Software Integration

Requirements Engineering (RE) is experiencing a significant transformation that requires urgent attention and creativity from industry experts to address the challenges of future systems and software development. At the core of this evolution is Requirements Engineering (RE), an essential process that defines, records, and maintains the fundamental aspects of software and systems design. With the rapid increase in complexity and interconnectedness across different industries, the issues linked to requirements engineering are becoming more pressing, significantly affecting costs, timelines, and overall performance. This article outlines current research trends in requirements engineering, focusing on emerging challenges and future pathways. We investigate advancements in RE techniques, methodologies, and tools, assessing their effects on building complex systems. Furthermore, we emphasize the growing significance of stakeholder engagement and agile practices in shaping requirements, highlighting their contributions to improving collaboration and adaptability. By evaluating the present state of practice and pinpointing critical areas of research concern, this article offers a guide for future requirements engineering studies, enabling researchers and practitioners to address the changing needs of software and systems engineering.

International Distribution and Development of Rock Mass Classification: A Review

AbstractSince the 1950s, numerous rock mass classification systems (RMCS) have emerged for applications such as tunneling, mining, and slope engineering. The continued proliferation of these systems reflects the complexity of the issue and the absence of a universally accepted approach within the rock engineering community. This review offers an up-to-date examination of the global utilization of RMCS across applications such as tunnels, caverns, mining slope stability, and more. A “family tree of rock mass classification systems” is presented that traces their evolution over the decades. The family tree illustrates a prolific period of system development between 1970 and 2000, followed by a decline in progress and increasing specialization of systems. Based on surveys within the rock engineering community carried out between 2022 and 2024, the dominant systems for underground engineering and slope-related tasks were found to be GSI, RMR, and the Q-system, which have demonstrated their practical efficacy over 5 decades. Based on the surveys, two “world maps of rock mass classification” have been produced that visualize the international RMCS distribution. However, the survey shows that only some new or derived systems developed in the last 25 years have gained global acceptance. The paper closes with an outlook to possible future developments in rock mass classification and characterization.

Metabolic Engineering of Corynebacterium glutamicum for the Production of the Four-Carbon Platform Chemicals γ-Hydroxybutyrate and γ-Butyrolactone.

γ-Hydroxybutyrate (GHB) is an important C4 platform chemical, serving as a crucial precursor for the synthesis of various bulk chemicals, including γ-butyrolactone (GBL) and 1,4-butanediol (1,4-BDO). In this study, we report the systematic metabolic engineering of Corynebacterium glutamicum for the biological production of GHB from glucose via the introduction of a glutamate-derived pathway. We showed that C. glutamicum is a promising host for producing GHB due to its higher tolerance to GHB as compared to other chassis. By screening key enzymes capable of converting glutamate into GHB and blocking byproduct synthesis pathways, an engineered C. glutamicum strain was developed that achieved a GHB production titer of 30.6 g/L. Comparative transcriptome analysis was subsequently employed to identify previously uncharacterized aldehyde dehydrogenases responsible for succinate accumulation, and knockout of the corresponding genes led to an increased GHB titer of 33.7 g/L. Ultimately, the integration of a phosphoketolase-mediated nonoxidative glycolysis (NOG) pathway further enhanced GHB production, resulting in an accumulation of 38.3 g/L of GHB with a yield of 0.615 mol/mol glucose during batch fermentation. The GHB in the fermentation broth can be efficiently converted into GBL by acid treatment with a yield of 0.970 mol/mol.

Electric Vehicle Thermal System Concept Development for Multiple Variants Using Digital Prototype and AI

The automotive industry is experiencing a surge in system complexity driven by the ever-growing number of interacting components, subsystems, and control systems. This complexity is further amplified by the expanding range of component options available to original equipment manufacturers (OEMs). OEMs work in parallel on more than one vehicle model, with multiple vehicle variants for each vehicle model. With the increasing number of vehicle variants needed to cater to diverse regional needs, development complexity escalates. To address this challenge, modern techniques like Model-Based Systems Engineering (MBSE), digitalization, and Artificial Intelligence (AI) are becoming essential tools. These advancements can streamline concept development, optimize thermal and HVAC system design across variants, and accelerate the time-to-market for next-generation EVs. The development of battery electric vehicles (BEVs) needs a strong focus on thermal management systems (TMSs) and heating, ventilation, and air conditioning (HVAC) systems. These systems play a critical role in maintaining optimal battery temperature, maximizing range and efficiency, and ensuring passenger comfort. This article proposes a digital prototype (DP) and AI-based methodology to specify BEV thermal system and HVAC system components in the concept phase. This methodology uses system and variant thinking in combination with digital prototype (DP) and AI to verify BEV thermal system architecture component specifications for future variants without extensive simulation. A BEV cabin cooling requirement of 22 °C to be achieved within 1800s at a high ambient temperature (45 °C) is required, and its verification is used to prove this methodology.

Property Valuation in Latvia and Brazil: A Multifaceted Approach Integrating Algorithm, Geographic Information System, Fuzzy Logic, and Civil Engineering Insights

This study aimed to predict residential apartment prices in Latvia and Brazil using algorithms from machine learning, fuzzy logic, and civil engineering principles, with a focus on overcoming multicollinearity challenges. To explore the market dynamics, we conducted four initial experiments in the central regions of Riga and Jelgava (Latvia), as well as São Paulo and Niterói (Brazil). Data were collected from real estate advertisements, supplemented by civil engineering inspections, and analyzed following international valuation standards. The research integrated human decision-making behavior with machine learning and the Apriori algorithm. Our methodology followed five key stages: data collection, data preparation for association rule mining, the generation of association rules, fuzzy logic analysis, and the interpretation of model accuracy. The proposed method achieved a mean absolute percentage error (MAPE) that ranged from 5% to 7%, indicating strong alignment with market trends. These findings offer valuable insights for decision making in urban development, particularly in optimizing renovation priorities and promoting sustainable growth.

X-RMTV: An Integrated Approach for Requirement Modeling, Traceability Management, and Verification in MBSE

Formal requirements modeling and traceability management are essential for effectively implementing Model-Based Systems Engineering (MBSE). However, few studies have explored the integration of requirement modeling, traceability management, and verification within MBSE-based systems engineering methodologies. Moreover, the predominant modeling language for MBSE, SysML, lacks sufficient capabilities for requirement description and traceability management and for depicting physical attributes and executable capabilities, making it challenging to verify functional and non-functional requirements collaboratively. This paper proposes an integrated approach for requirement modeling, traceability management, and verification, building on the previously proposed integrated modeling and the simulation language called X language. Our contributions primarily include defining the ReqXL specification for MBSE-oriented requirement modeling based on X language, proposing an algorithm for automatically generating requirement traces, and an integrated framework for requirements modeling, traceability management, and verification was developed by combining the X language with ReqXL. These functionalities were customized on the self-developed integrated modeling and simulation platform, XLab, which is specifically tailored for the X language. Furthermore, we showcase the efficacy and promise of our approach through a case study involving the design of an aircraft electrical system.

Explainability of convolutional neural networks for damage diagnosis using transmissibility functions

Structural health monitoring (SHM) is crucial for ensuring the safety and efficiency of engineering systems. Vibration-based signals have been widely employed in numerous studies for damage detection, localization and quantification, thanks to their high sensitivity to structural damages and versatile applicability across various structures. Among these, transmissibility functions (TFs) have emerged as particularly promising because they can be derived from output-only measurements, making them practical for real-world applications. Vibration-based features, including those captured by TFs, provide a wealth of damage-related information, but extracting these features can be challenging with traditional methods and often requires a priori knowledge. To address this challenge, deep learning has gained significant attention for its capacity to handle intricate patterns and extract hidden features. However, deep learning algorithms are often perceived as black-box models due to their complex and heterogeneous layers, which hinder transparency and raise concerns, especially in critical domains. Although numerous studies have highlighted the diagnostic potential of deep learning in SHM with vibration-based data, there has been limited exploration of the rationale behind using deep neural networks (DNNs) for processing TFs. This paper addresses this gap by introducing explainable artificial intelligence (XAI) methods in two distinct TF-based damage diagnostic case studies: a numerical aluminium structural beam and a large-scale experimental steel frame. In the former, damage is simulated as local stiffness reductions, while in the latter, damage is introduced by loosening joint bolts. In both cases, a convolutional neural network (CNN) is used to process raw TF data for damage detection, localization, and quantification in the beam, and for detection and localization in the frame. Subsequently, an XAI method based on the layer-wise relevance propagation (LRP) algorithm is employed to identify the most relevant input features. A pattern analysis is presented to explain recurrent patterns, and a comparison with other XAI methods is proposed for further validation. The results demonstrate that the CNN provides accurate predictions for both case studies, with its focus on damage-sensitive features aligning closely with findings in the existing literature.

Modelling pressure dynamics in a gas pipeline with an in-line sampling

Gas pipelines, whether main, field, or urban, often operate in non-stationary modes. Changes in the operating modes of pumping stations, equipment start-up and shutdown, an in-line sampling or pumping and various factors are causes of instability of pressure, velocity, gas flow rate. Main pipelines are complex engineering systems with the pipeline itself being the main element. Fail-safety is the major factor in the operation of this system. Ensuring operational safety requires studying the movement regimes of the transported medium, particularly study of the dynamics of pressure duringstart-up or shutdown and at specific extraction points. This article aims to build a mathematical model and study the pressure dynamics in a gas pipeline with a sampling point. The theory of non-stationary motion liquid in round pipes has been strongly developed in the works of I. A. Charnyj. These works consider a large complex of engineering tasks taking into account viscous properties of the transported medium and pipe resistance in the hydraulic approximation. In the article on the basis of I. A. Charnyj's researches on the motion of real liquid in circular pipes the equation in partial derivatives of hyperbolic type is compiled. The equation describes the unsteady pressure of a horizontal section of gas pipeline with an extraction point. Using the Dirac delta function allows the formulation of the problem in the form of a single equation. Pressures are set at the ends of a given section, and the initial velocity is related to the Dirac delta function. By applying the finite Fourier sine transform, the partial differential equation is transformed into an ordinary differential equation and solved. Solution of equation is vision of a solution to the initial task. Inverse transform formulas based on Fourier theory allowed us to proceed to the solution of this task. Explicit dependences for the dynamics of unsteady pressure are obtained. The qualitative analysis of the formulas indicates the wave motion of a medium during the initial phase of operation, transitioning into a stationary mode after a brief period. The duration of the transition period depends on factors such as the hydraulic resistance coefficient and the velocity of the transported medium. Coefficient of hydraulic resistance and the velocity of the transported medium are the main factors. An example is considered for a horizontal section under isothermal flow conditions. With assumed numerical parameters, the transition to a stationary state occurs approximately 17 minutes after the process begins, as illustrated in the graphs provided in the article. Engineers can use mathematical models of the liquid and gas motion through pipes in the design of pipelines, as well as in solving tasks that arise during their operation. These tasks include monitoring the condition of the pipeline system, optimizing the operation, accumulation capacity estimates and others.

Genotype from Phenotype: Using CRISPR Screens to Dissect Lymphoma Biology.

Genome-wide screens are a powerful technique to dissect the complex network of genes regulating diverse cellular phenotypes. The recent adaptation of the CRISPR-Cas9 system for genome engineering has revolutionized functional genomic screening. Here, we present protocols used to introduce Cas9 into human lymphoma cell lines, produce high-titer lentivirus of a genome-wide sgRNA library, transduce and culture cells during the screen, select cells with a specified phenotype, isolate genomic DNA, and prepare a custom library for next-generation sequencing. These protocols were tailored for loss-of-function CRISPR screens in human B-cell lymphoma cell lines but are highly amenable for other experimental purposes.

Performance analysis of ER fluid-lubricated two-lobe hole-entry hybrid journal bearings with bionic and spherical texture surfaces optimized by genetic algorithm

This study is done into the multifaceted world of ER (Electro-rheological) fluid-lubricated optimized textured hybrid hole-entry journal bearings to improve dynamic performance of the bearing and stability. These innovative techniques harness the dynamic properties of ER fluids in conjunction with optimized textured spherical and bionic textured surfaces to achieve enhanced performance and reliability. This study aims to explore the influence of ER fluid on the tribological behaviour and controllability of hybrid hole-entry journal bearings. Further it proposes the optimized design of bionic and spherical textured surfaces, capitalizing on their ability to influence fluid flow, load-carrying capacity, and friction reduction. The results demonstrate promising improvements in bearing performance metrics, including dynamic stability and fluid film damping, load-carrying capacity, friction reduction, and controllability, facilitated by the integration of ER fluid and the optimization of textured spherical and bionic textured surfaces. This study offers valuable insights into the potential applications of ER fluids and biomimicry-inspired textured surfaces in bearing system engineering. The inclusion of bionic textured hole-entry hybrid bearing lubricated with ER fluid improves the stability by up to 112.57 % and decrease the friction coefficient up to 23.37 %. The findings are expected to inform the design and development of high-performance hole-entry hybrid journal bearings with diverse industrial applications.