Engineering Models Research Articles

A new sine-inspired probability model: Theoretical features with statistical modeling of the music engineering and reliability scenarios

It is a proven authenticity that probability-schooled methodologies have a cardinal role in making decisions under the vagueness circumstances. Thus, to reach meaningful and fruitful decisions under vagueness circumstances, numerous probability-focused methodologies have been introduced, promoted, and implemented. This paper also contributes to adding another useful probability model to the existing literature for data modeling in engineering-oriented areas. The new model is called the weighted sine exponentiated exponential (WSEE) distribution, which is formed by implementing the exponentiated exponential model together with a sine-adapted method. Certain properties of the WSEE distribution are given. The parameter estimation of the WSEE distribution along with the simulation study is also provided. Last, in order, we apply the WSEE distribution for analyzing three data sets taken from the musical engineering and reliability settings. The optimality of the WSEE distribution is proved by comparing its results with other prominent modifications of the exponentiated exponential distribution. Using 4 statistical criteria as judgment tools, it was shown numerically and pictorially that the WSEE distribution is an optimal and suitable model for the musical engineering reliability data sets.

Building a Sustainable Future: A Three-Stage Risk Management Model for High-Permeability Power Grid Engineering

Building a Sustainable Future: A Three-Stage Risk Management Model for High-Permeability Power Grid Engineering

Exact exploration of time fractional-based magnetized flow of a generalized second grade fluid through an oscillating rectangular duct

To model several engineering and physical models, the approach of the fractional derivative is highly anticipated. As compared to the ordinary derivatives, the fractional derivatives with more flexibility can estimate the data due to the involvement of the fractional-order derivatives. Due to these advantages of the fractional approach, this study communicates with the determination of the fractional-based exact outcomes of an oscillatory rectangular duct problem of a generalized second-grade fluid. The approach of the fractional operator is involved in the relationship of the constitutive equations. For cosine oscillation of the rectangular duct, exact results of the magnetized unsteady flow problem are evaluated through the technique of Laplace transform with double finite Fourier sine transform. This study concludes that the velocity field exhibits escalating behavior relative to the improved fractional parameter. Moreover, the magnetic parameter with increasing values declines the flow field while the accelerating values of the fluid parameter enhance the velocity field.

Synchronised WindScanner field measurements of the induction zone between two closely spaced wind turbines

Abstract. Field measurements of the flow interaction between the near wake of an upstream wind turbine and the induction zone of a downstream turbine are scarce. Measuring and characterising these flow features in wind farms under various operational states can be used to evaluate numerical flow models and design of control systems. In this paper, we present induction zone measurements of a utility-scale 3.5 MW turbine with a rotor diameter of 126 m in a two-turbine wind farm operating under waked and unwaked conditions. The measurements were acquired by two synchronised continuous-wave WindScanner lidars that could resolve longitudinal and lateral velocities by dual-Doppler reconstruction. An error analysis was performed to quantify the uncertainty in measuring complex flow situations with two WindScanners. This is done by performing a large-eddy simulation while using the same measurement layout, modelling the WindScanner sensing characteristics and simulating similar inflow conditions observed in the field. The flow evolution in the induction zone of the downstream turbine was characterised by performing horizontal-plane dual-Doppler scans at hub height. The measurements were conducted for undisturbed, fully waked and partially waked flows. Evaluation of the engineering models of the undisturbed induction zone showed good agreement along the rotor axis. In the full-wake case, the measurements indicated a deceleration of the upstream turbine wake due to the downstream turbine induction zone as a result of the very short turbine spacing. During a wake steering experiment, the interaction between the laterally deflected wake of the upstream turbine and the induction zone of the downstream turbine could be measured for the first time in the field. Additionally, the analyses highlight the affiliated challenges while conducting field measurements with synchronised lidars.

Dynamics of novel soliton and periodic solutions to the coupled fractional nonlinear model

This study secures the soliton solutions of the (2+1)-dimensional Davey–Stewartson equation (DSE) incorporating the properties of the truncated M-fractional derivative. The DSE and its coupling with other systems have extensive applications in many fields, including physics, applied mathematics, engineering, hydrodynamics, plasma physics, and nonlinear optics. Various solutions, such as dark, singular, bright-dark, bright, complex, and combined solitons, are derived. In addition, exponential, periodic, and hyperbolic solutions are also generated. The newly designed integration method, known as the modified Sardar subequation method (MSSEM), has been applied in this study for extracting the solutions. The approach is efficient in explaining fractional nonlinear partial differential equations (FNLPDEs) by confirming pre-existing solutions and producing new ones. Furthermore, we plot the density, 2D, and 3D graphs with the associated parameter values to visualize the solutions. The outcomes of this work indicate the effectiveness of the method utilized to improve nonlinear dynamical behavior. We anticipate that our work will be helpful for a large number of engineering models and other related problems.

O-173 Gastruloids: a pluripotent stem cell model of gastrulation and body plan engineering in development and disease

Abstract Gastrulation is the process whereby the mass of cells that results from fertilization is shaped into the body plan of an organism. Understanding this process in humans is important as the origin of several syndromes and birth defects, map to this period. However, addressing this period experimentally is difficult due to technical, and more importantly, obvious ethical constrains. Over the last few years, alternatives to study gastrulation have emerged from the realization of the self-organizing abilities of Pluripotent Stem Cells and led to a number of cellular models of the early stages of mammalian development. I will discuss these models, with a special focus on gastruloids, highlighting their strengths as well as their limitations.

Integrated multi-view modeling for reliable machine learning-intensive software engineering

Development of machine learning (ML) systems differs from traditional approaches. The probabilistic nature of ML leads to a more experimentative development approach, which often results in a disparity between the quality of ML models with other aspects such as business, safety, and the overall system architecture. Herein the Multi-view Modeling Framework for ML Systems (M3S) is proposed as a solution to this problem. M3S provides an analysis framework that integrates different views. It is supported by an integrated metamodel to ensure the connection and consistency between different models. To facilitate the experimentative nature of ML training, M3S provides an integrated platform between the modeling environment and the ML training pipeline. M3S is validated through a case study and a controlled experiment. M3S shows promise, but future research needs to confirm its generality.

AI Systems Modeling Enhancer (AI‐SME): Initial Investigations into a ChatGPT‐enabled MBSE Modeling Assistant

AbstractAs artificial intelligence (AI) becomes integral across industries, there is a growing opportunity to transform the generation of models for systems engineering. This research investigates the integration of OpenAI's GPT‐4 Turbo into CATIA Magic for Model‐Based Systems Engineering (MBSE), resulting in the creation of AI Systems Modeling Enhancer (AI‐SME). This study explores the comparison between models generated by AI, specifically OpenAI's GPT, and those crafted by human systems engineers. While recognizing challenges in AI‐generated models, this research underscores the potential of AI assistants to enhance the speed and accuracy of SysML model creation. Results demonstrate AI‐SME's successes in generating requirements, block definition diagrams, and internal block diagrams. Despite identified limitations such as redundancy and lack of cohesiveness in AI‐generated models, the study concludes that AI‐SME represents a notable advancement in AI‐assisted systems engineering.

The Early Determination Method of Reservoir Drive of Oil Deposits Based on Jamalbayli Indexes

Summary Historically, the concept of “reservoir drive” aimed to simplify the mathematical modeling in reservoir engineering. Within this framework, the energy of the reservoir, particularly its aquifer, was idealized, leading to classifications such as “partial waterdrive,” “full waterdrive,” “gas cap drive,” and so forth. However, in reality, all existing energy sources interact simultaneously within reservoirs. Accordingly, this study aims to develop a new concept for a more realistic description of reservoir drive mechanisms and evaluation of reservoir energy performance. Numerous computer simulations have revealed a strong correlation between the ratio of relative changes in pore volume to relative changes in reservoir pressure and the reservoir’s energy nature and activity level. Moreover, the noted ratio did not depend on production technology, pressure/volume/temperature properties of hydrocarbon systems, rheological properties of reservoir rocks, or other factors. Based on this correlation, specific parameters termed as Jamalbayli Indexes (JI) have been identified to quantitatively describe reservoir energetic performance. JI consist of two parameters. One of them describes the relative change in pore volume per unit of relative change in reservoir pressure, and the second is the relative change in pore volume per unit of relative change in formation porosity. Here, “relative change” means a change in a parameter relative to its original value. These parameters are dimensionless and can have values around or equal to unity. A new conceptual framework for describing reservoir drive mechanisms based on JI has been formulated. According to this framework, reservoir drive mechanism is determined by comparing the computed JI values with unity rather than relying on subjective assessments of the trend of some functional dependencies. For the first time, it has become possible to express the reservoir drive performance quantitatively and determine the level of energy activity of the reservoirs with the help of JI. Additionally, a technique has been developed to evaluate the numerical values of JI for specific oil (including volatile oil) deposits based on the production data at any stage of production. The proposed methodology was tested using data from the eighth horizon of the Russkiy Khutor field in Russia. The test results not only confirmed the reliability of the obtained model but also demonstrated the adequacy of the proposed concept as a whole. Summarizing the results of other works by the authors, the adequacy of the proposed concept for both oil, gas, and gas condensate deposits has been confirmed. The research findings are expected to contribute to updating the traditional principles used for the mathematical problem statements in fluid flow in porous formations. Additional Key Terms and Phrases Reservoir Drive Mechanism; Early Determination Methods; Reservoir Performance Analysis; Production Data Interpretation; Enhanced Oil Recovery; Reservoir Management; Drive Mechanism Identification; Reservoir Fluid Dynamics; Production Rate Analysis; Reservoir Engineering Techniques; Field Development Planning; Reservoir Simulation; Pressure/Volume/Temperature (PVT) Analysis; Production Monitoring and Evaluation drive mechanisms; production data analysis; volatile oil; Jamalbayli indexes; drive performance indexes

Automating computational design with generative AI

AbstractAI image generators based on diffusion models have recently garnered attention for their capability to create images from simple text prompts. However, for practical use in civil engineering they need to be able to create specific construction plans for given constraints. This paper investigates the potential of current AI generators in addressing such challenges, specifically for the creation of simple floor plans. We explain how the underlying diffusion‐models work and propose novel refinement approaches to improve semantic encoding and generation quality. In several experiments we show that we can improve validity of generated floor plans from 6% to 90%. Based on these results we derive future research challenges considering building information modeling. With this we provide: (i) evaluation of current generative AIs; (ii) propose improved refinement approaches; (iii) evaluate them on various examples; (iv) derive future directions for diffusion models in civil engineering.

Using VR to Validate and Visualize MBSE‐Designed Interfaces

AbstractSeveral unique challenges arise in the new field of integration between Model‐Based Systems Engineering (MBSE) models and game‐based digital twin visualizations. First, no known standardized interfaces between the MBSE model and game engine visualization are defined, which can lead to custom or stove‐piped solutions. Additionally, visualizing digital twin models in true to life scale is insufficient with typical desktop computers. The Bi‐Directional Interface Requirements Operational System Test (BIFROST) prototype, funded by the US Army Program Executive Office (PEO) Aviation, seeks to address these challenges.Progress of the BIFROST prototype is covered in the paper. The prototype aims to determine the feasibility and challenges of validating interfaces through visualizing changes to an MBSE model in a 3D game engine. Research was performed to visualize part of an uncrewed aircraft system ground control station in 3D using the Unity game engine. A Mission Control Architecture MBSE model, developed by PEO Aviation, is used to drive the digital twin of the ground control station through a set of virtual reality (VR) controls. Users can visualize, analyze, and test the human‐machine interaction of the 3D models in VR prior to real‐world system changes. This paper presents the recommended interfaces between the MBSE model and 3D engine, lessons learned, and future research areas.

Learning the Randleman Criteria in Refractive Surgery: Utilizing ChatGPT-3.5 Versus Internet Search Engine.

Introduction Large language models such as OpenAI's (San Francisco, CA)ChatGPT-3.5 hold immense potential to augment self-directed learning in medicine, but concerns have risen regarding its accuracy in specialized fields. This study compares ChatGPT-3.5 with an internet search engine in their ability to define the Randleman criteria and its five parameters within a self-directed learning environment. Methods Twenty-three medical students gathered information on the Randleman criteria. Each student was allocated 10 minutes to interact with ChatGPT-3.5, followed by 10 minutes to search the internet independently. Each ChatGPT-3.5 conversation, student summary, and internet reference were subsequently analyzed for accuracy, efficiency, and reliability. Results ChatGPT-3.5 provided the correct definition for 26.1% of students (6/23, 95% CI: 12.3% to 46.8%), while an independent internet search resulted in sources containing the correct definition for 100% of students (23/23, 95% CI: 87.5% to 100%, p = 0.0001). ChatGPT-3.5 incorrectly identified the Randleman criteria as a corneal ectasia staging system for 17.4% of students (4/23), fabricated a "Randleman syndrome" for 4.3% of students (1/23), and gave no definition for 52.2% of students (12/23). When a definition was given (47.8%, 11/23), a median of two of the five correct parameters was provided along with a median of two additional falsified parameters. Conclusion Internet search engine outperformed ChatGPT-3.5 in providing accurate and reliable information on the Randleman criteria. ChatGPT-3.5 gave false information, required excessive prompting, and propagated misunderstandings. Learners should exercise discernment when using ChatGPT-3.5. Future initiatives should evaluate the implementation of prompt engineering and updated large-language models.

Augmented Reality Applications in Mechanical System Design and Prototyping

With the integration of computer-generated images and data into the real world, augmented reality (AR) is a game-changing technology that improves users' perceptions and interactions with their surroundings. This technology, which can overlay digital data on real-world environments, has great potential for use in several fields, such as mechanical system design and prototyping. The use of AR in this industry is not just a theoretical idea; rather, it is a rapidly developing reality with enormous potential for creativity, effectiveness, and accuracy. Augmented Reality (AR) is a technology that combines the physical and digital worlds to provide a composite vision that improves the user's sense of reality. Augmented Reality (AR) superimposes digital data in real-time over the actual world, in contrast to Virtual Reality (VR), which submerges users in a fully digital environment. Usually, gadgets like tablets, smartphones, AR glasses, and head-mounted displays (HMDs) help with this integration. The term "mechanical system design" describes the entire process of imagining, creating, and perfecting mechanical parts and systems; it includes everything from rough concept drawings to intricate engineering models and simulations. Before moving on with full-scale manufacturing, prototyping entails building early models or samples of a product to test and verify design concepts.

Experimental studies of confined detonations of plasticized high explosives in inert and reactive atmospheres

When explosives detonate in a confined space, repeated boundary reflections result in complex shock interactions and the formation of a uniform quasi-static pressure (QSP). For fuel-rich explosives, mixing of partially oxidized detonation products with an oxygen-rich atmosphere results in a further energy release through rapid secondary combustion or ‘afterburn’. While empirical formulae and thermochemical modelling approaches have been developed to predict QSP, a lack of high-fidelity experimental data means questions remain around the deterministic quality of confined explosions, and the magnitude and mechanisms of afterburn reactions. This article presents experimental data for RDX- and PETN-based plastic explosives, demonstrating the high repeatability of the QSP generated in a sealed chamber using pressure transducers and high-speed infrared thermometry. Detonations in air, nitrogen and argon atmospheres are used to identify the contribution of afterburn to total QSP, to estimate the duration of afterburn reactions and to speculate on the flame temperature associated with this mechanism. Computational fluid dynamic modelling of the experiments was also able to accurately predict these effects. Understanding and quantifying explosions in complex environments are critical for the design of effective protective structures: the mechanisms described here provide a significant step towards the development of fast-running engineering models for internal blast events.

Influencing Factors Analysis in Railway Engineering Technological Innovation under Complex and Difficult Areas: A System Dynamics Approach

The geological complexity, environmental sensitivity, and ecological fragility inherent in complex and difficult areas (CDAs) present new opportunities and challenges for technological innovation in railway engineering development in China. At the current stage in China, the process of technological innovation in railway engineering within CDAs still faces a series of pressing issues that need addressing. The paper identifies and determines 22 influencing factors for technological innovation in railway engineering within CDAs across five dimensions. Subsequently, a technological innovation model for railway engineering in such areas is constructed based on system dynamics (SD), which is followed by simulation and sensitivity analysis to identify the key influencing factors. The results indicate that key influencing factors for technological innovation in railway engineering within CDAs include technological innovation capability, the adaptability of technology to the environment, R&D funding investment, technological product requirements, technological innovation incentive mechanisms, and the level of technological development. The importance ranking of each dimension is as follows: technological factors > technical factors > management factors > resource factors > environmental factors. The paper provides new insights for promoting technological innovation and management development in complex and challenging railway engineering projects. It offers a fresh perspective to enhance the technological innovation efficiency of railway projects in complex and challenging areas.

Mathematical modeling and numerical simulation in sciences and engineering

Mathematical modeling and numerical simulation in sciences and engineering

РАЗРАБОТКА ИНФОРМАЦИОННОЙ СИСТЕМЫ УПРАВЛЕНИЯ И ОПТИМИЗАЦИИ ТЕПЛОЭНЕРГЕТИЧЕСКИМИ ПРОЦЕССАМИ

This article describes the development of an information system based on the 1C platform using arti-ficial intelligence to control thermal power processes. The economic efficiency is analyzed and engi-neering modeling is carried out

3D Multispheroid Assembly Strategies towards Tissue Engineering and Disease Modeling.

Cell spheroids (esp. organoids) as 3D culture platforms are popular models for representing cell-cell and cell-extracellular matrix (ECM) interactions, bridging the gap between 2D cell cultures and natural tissues. 3D cell models with spatially organized multiple cell types are preferred for gaining comprehensive insights into tissue pathophysiology and constructing in vitro tissues and disease models because of the complexities of natural tissues. In recent years, an assembly strategy using cell spheroids (or organoids) as living building blocks has been developed to construct complex 3D tissue models with spatial organization. Here, a comprehensive overview of recent advances in multispheroid assembly studies is provided. The different mechanisms of the multispheroid assembly techniques, i.e., automated directed assembly, noncontact remote assembly, and programmed self-assembly, are introduced. The processing steps, advantages, and technical limitations of the existing methodologies are summarized. Applications of the multispheroid assembly strategies in disease modeling, drug screening, tissue engineering, and organogenesis are reviewed. Finally, this review concludes by emphasizing persistent issues and future perspectives, encouraging researchers to adopt multispheroid assembly techniques for generating advanced 3D cell models that better resemble real tissues.

Application of Ant Colony Programming Approach for Solving Systems of Stochastic Differential Equations

Stochastic differential equations (SDE) have wide applications in natural phenomena, engineering, finance, and biological models. Obtaining analytic solutions for an SDE is often complex, and the complexity increases for an SDE system. The paper introduces ant colony programming (ACP) as a novel approach for solving SDE system. Ant colony programming was developed in two directions, the first is to add the Wiener process as a variable to the terminals and functions, and the second is to construct the appropriate fitness function . ACP constructs mathematical expressions and evaluates them using the fitness function . The ACP proposed effectiveness has been demonstrated by applying to 2,3 and 4-dimensional SDE systems. The most important finding of this work is that ACP generates symbolic stochastic processes that represent solutions for SDE system. Methods for solving SDE systems are important tools for study phenomena that involve noise or randomness.

Lifetime assessment of semi-submersible wind turbines by Gaidai risk evaluation method

As the global agenda turns more towards the so-called challenge of climate change and lowering carbon emissions, research into green, renewable energy sources becoming nowadays more and more popular. Offshore wind power, produced by FOWTs (i.e., Floating Offshore Wind Turbines), is one such substitute. It is a significant industrial part of the contemporary offshore wind energy industry and produces clean, renewable electricity. Accurate operational lifetime assessment for FOWTs is an important technical safety issue, as environmental in situ loads can lead to fatigue damage as well as extreme structural dynamics, which can cause structural damage. In this study, in situ environmental hydro and aerodynamic environmental loads, that act on FOWT, given actual local sea conditions have been numerically assessed, using the FAST coupled nonlinear aero-hydro-servo-elastic software package. FAST combines aerodynamics and hydrodynamics models for FOWTs, control and electrical system dynamics models, along with structural dynamics models, enabling coupled nonlinear MC simulation in the real time. The FAST software tool enables analysis of a range of FOWT configurations, including 2- or 3-bladed horizontal-axis rotor, pitch and stall regulation, rigid and teetering hub, upwind and downwind rotors. FAST relies on advanced engineering models—derived from the fundamental laws, however with appropriate assumptions and simplifications, supplemented where applicable with experimental data. Recently developed Gaidai reliability lifetime assessment method, being well suitable for risks evaluation of a variety of sustainable energy systems, experiencing nonlinear, potentially extreme in situ environmental loads, throughout their designed service life. The main advantage of the advocated Gaidai risks evaluation methodology being its ability to tackle simultaneously a large number of dynamic systems' degrees of freedom, corresponding to the system's critical components.