Complex Engineering Systems Research Articles

АСПЕКТЫ ПРИМЕНЕНИЯ ЭКСЕРГИИ ДЛЯ АНАЛИЗА ЭНЕРГЕТИЧЕСКОГО КОМПЛЕКСА ПРЕДПРИЯТИЙ ПЕРЕРАБОТКИ УГЛЕВОДОРОДНОГО СЫРЬЯ

The paper describes methodological approaches of applying the thermodynamic analysis to assess the efficiency of complex chemical and engineering systems, as well as to identify the scientifically-based areas for improving the energy sector. Modern chemical and engineering systems including the hydrocarbon processing plants are characterized by the high specific energy intensity and complex engineering topology. The sophistication of such facilities can be objectively assessed based on the exergy analysis. The exergy analysis makes it possible to carry out a quantitative and qualitative assessment of any type of energy at the enterprise. Moreover, it provides an opportunity to adequately evaluate the potential of the energy utilization including secondary energy resources, combustible hydrocarbon gases, industrial wastes, and effluents. The paper presents methodological approaches to determine the exergy types and calculate the exergy balance, considering the specifics of hydrocarbon processing plants. The exergy balance of the study subject represented in the form of a Grassmann diagram has been calculated using the above provisions. Besides, the exergy of all types of fuel consumed has also been calculated, as well as the industrial processes consuming the largest amount of the fuel exergy for functioning have been determined. Finally, according to the data presented, the main directions for improving the efficiency of the hydrocarbon processing plants have been proposed.

RBMDO Using Gaussian Mixture Model-Based Second-Order Mean-Value Saddlepoint Approximation

Actual engineering systems will be inevitably affected by uncertain factors. Thus, the Reliability-Based Multidisciplinary Design Optimization (RBMDO) has become a hotspot for recent research and application in complex engineering system design. The Second-Order/First-Order Mean-Value Saddlepoint Approximate (SOMVSA/FOMVSA) are two popular reliability analysis strategies that are widely used in RBMDO. However, the SOMVSA method can only be used efficiently when the distribution of input variables is Gaussian distribution, which significantly limits its application. In this study, the Gaussian Mixture Model-based Second-Order Mean-Value Saddlepoint Approximation (GMM-SOMVSA) is introduced to tackle above problem. It is integrated with the Collaborative Optimization (CO) method to solve RBMDO problems. Furthermore, the formula and procedure of RBMDO using GMM-SOMVSA-Based CO(GMM-SOMVSA-CO) are proposed. Finally, an engineering example is given to show the application of the GMM-SOMVSA-CO method.

Comprehensive Evaluation and Decision for Goaf Based on Fuzzy Theory in Underground Metal Mine

In underground metal mines, goaf brings huge safety risks. It is an important part of mine to evaluate goaf stability and determine the best goaf handling measures. However, the evaluation and decision are often separated; they need to be unified. Meanwhile, stability evaluation and decision for goaf are a complex system engineering of rock masses in underground mining, and subjectivity exists in the evaluation and decision process. Under these conditions, it is necessary to minimize subjectivity, and the results of stability evaluation also need to be considered comprehensively in determining handling measures. In this paper, the fuzzy theory was adopted based on the fact that the goaf stability evaluation and handling measure decision were fuzziness. Firstly, the goaf stability model was established by a two‐layer fuzzy comprehensive evaluation. It took into account 12‐factor indexes of goaf with engineering empirical approaches and divided them into 3 categories according to their engineering categories. The model improved the applicability of the goaf stability evaluation results, and the results were the basis for goaf handling measures as well. Secondly, a decision model of the goaf handling measures was established by multiobjective fuzzy optimization. It consisted of five goaf handling measures and five evaluation indexes. The model provided a comprehensive decision and optimal scheme for goaf handling. Two models were also applied to the Paishanlou gold mine and achieved a good handling result. The practical application showed that the two models were feasible.

Non-intrusive semi-analytical uncertainty quantification using Bayesian quadrature with application to CFD simulations

To improve the safety, reliability, and performance of complex engineering systems, it is crucial to understand and quantify uncertainties. This paper presents a framework to non-intrusively and semi-analytically quantify the parametric uncertainty within CFD simulations using Bayesian quadrature (BQ). An in-house uncertainty quantification (UQ) code based upon this mathematical framework is developed. The code is then validated by applying it to quantify the uncertainty due to a varying parameter in a simple analytical test function. The mean and variance obtained using BQ are compared with those obtained from the analytical solution and stochastic simulation using the Latin hypercube sampling (LHS) method. The validation test case shows that BQ outperforms the LHS approach in terms of computational efficiency and accuracy. The UQ code is then utilised to characterise the uncertainty (due to the unknown inlet flow profile) of CFD predicted operating parameters of an industrial scale butterfly valve, as well as the uncertainties (due to the unknown high-wavenumber damping factor Cs) of a SAS-SST simulated bluff-body flow. It is found that the entry flow profile presents non-ignorable effects on the valve operating parameters. Meanwhile, the variance of the valve operating parameters changes with the valve opening. For the bluff-body flow, large variances of predicted flow properties exist in the region where the separate shear layer dominates because of varyingCs. Moreover, the effect of Cs is more significant on the turbulence quantities, as it acts on the generation of turbulent eddies directly.

Certifiable Risk-Based Engineering Design Optimization

Reliable, risk-averse design of complex engineering systems with optimized performance requires dealing with uncertainties. A conventional approach is to add safety margins to a design that was obtained from deterministic optimization. Safer engineering designs require appropriate cost and constraint function definitions that capture the risk associated with unwanted system behavior in the presence of uncertainties. The paper proposes two notions of certifiability. The first is based on accounting for the magnitude of failure to ensure data-informed conservativeness. The second is the ability to provide optimization convergence guarantees by preserving convexity. Satisfying these notions leads to certifiable risk-based design optimization (CRIBDO). In the context of CRIBDO, risk measures based on superquantile (aka conditional value-at-risk) and buffered probability of failure are analyzed. CRIBDO is contrasted with reliability-based design optimization (RBDO), in which uncertainties are accounted for via the probability of failure through a structural and a thermal design problem. A reformulation of the short column structural design problem leading to a convex CRIBDO problem is presented. The CRIBDO formulations capture more information about the problem to assign the appropriate conservativeness, exhibit superior optimization convergence by preserving properties of underlying functions, and alleviate the adverse effects of choosing hard failure thresholds required in RBDO.

Formalization of bond graph using higher-order-logic theorem proving

Bond graph is a unified graphical approach for describing the dynamics of complex engineering and physical systems and is widely adopted in a variety of domains, such as, electrical, mechanical, medical, thermal and fluid mechanics. Traditionally, these dynamics are analyzed using paper-and-pencil proof methods and computer-based techniques. However, both of these techniques suffer from their inherent limitations, such as human-error proneness, approximations of results and enormous computational requirements. Thus, these techniques cannot be trusted for performing the bond graph based dynamical analysis of systems from the safety-critical domains like robotics and medicine. Formal methods, in particular, higher-order-logic theorem proving, can overcome the shortcomings of these traditional methods and provide an accurate analysis of these systems. It has been widely used for analyzing the dynamics of engineering and physical systems. In this paper, we propose to use higher-order-logic theorem proving for performing the bond graph based analysis of the physical systems. In particular, we provide formalization of bond graph, which mainly includes functions that allow conversion of a bond graph to its corresponding mathematical model (state–space model) and the verification of its various properties, such as, stability. To illustrate the practical effectiveness of our proposed approach, we present the formal stability analysis of a prosthetic mechatronic hand using HOL Light theorem prover. Moreover, to help non-experts in HOL, we encode our formally verified stability theorems in MATLAB to perform the stability analysis of an anthropomorphic prosthetic mechatronic hand.

Transfer prior knowledge from surrogate modelling: A meta-learning approach

Surrogate modelling has emerged as a useful technique to study complex physical and engineering systems in various disciplines, especially for engineering analysis. Previous studies mostly focused on developing new surrogate models and/or applying existing surrogate models to practical problems. Despite the computational efficiency, the surrogate for a new task is often built from scratch and the knowledge gained from previous surrogate modelling for similar tasks is neglected. As the need for quickly modifying simulation models to reflect design changes has significantly increased, one potential solution is to utilize prior knowledge from surrogate modelling. In this study, a novel meta-learning-based surrogate modelling framework is presented. The framework includes two phases: a meta-training and a few-shot learning phase. A meta-model that represents a family of tasks and the adaptation of this model to a new task with few data points are the results of the first and second phase, respectively. The study specifies the scope of the framework by classifying similar tasks. Applications of the framework to global sensitivity analysis, optimization, and reliability analysis are also addressed. Four numerical experiments are performed to demonstrate the feasibility and applicability of the framework.

A Random-Field-Environment-Based Multidimensional Time-Dependent Resilience Modeling of Complex Systems

Over the past few decades, many research efforts have been dedicated to qualitatively and quantitatively evaluate resilience in different domains. As compared with research areas in social science and ecology, the concept of resilience in the engineering domain is relatively new. In the engineering domain, studies on resilience mostly focus on civil infrastructure. It is important to extend the concept of resilience to a broad range of engineering applications. The field environments of complex engineering systems vary with different applications. Even with the same component/system applied in different field environments, the ability, time, and resources required by failure detection, diagnosis, and restoration can be different. Hence, it is critical to introduce a new dimension, random field environment (RFE), into the development of the mathematical model for quantifying resilience. This article first introduces a new definition of resilience and then proposes a general RFE-based multidimensional time-dependent resilience model connecting reliability, vulnerability, and recoverability. Besides, we present a specific RFE-based multidimensional time-dependent resilience model by considering the specified functions of the impact of the RFE on system performance and recovery. Furthermore, we extend the proposed resilience model by incorporating multiple failure paths of complex systems. Finally, we apply the proposed resilience model to vehicular edge computing networks to evaluate the vehicular network resilience with the disruptive events on the communication links.

WAYS TO IMPROVE THE ENGINEERING AND TECHNICAL SYSTEM IN THE AGRO-INDUSTRIAL COMPLEX

The engineering and technical system in the agro-industrial complex is currently an insufficiently studied area of activity and the need to analyze the state of this system is obvious. (Research purpose). The research purpose is in considering the state of the engineering and technical system of the agro-industrial complex and determining ways of its improving. (Materials and methods). The implementation of technological processes for maintaining and restoring the parameters of the technical condition of agricultural machinery with proper quality is possible only with the availability of intelligent technological equipment at specialized enterprises of the agro- industrial complex engineering system, which will provide high-resource repair and maintenance. The article proposes a complete register of operating service enterprises at all levels of ownership, identifying of their potential and developing a strategy for the modernization of this system. (Results and discussion). The article presents for consideration the measures to improve the engineering and technical component in the agro-industrial complex of Russia. At present the system is characterized by a weakening of the quality of technical support of agricultural machines throughout their entire life cycle. One of the important reasons associated with these studies is that ensuring the operational reliability of agricultural machinery for seasonal field work is carried out mainly by agricultural producers, most of whom do not have the ability to carry out high-level operations for technical service of machines. (Conclusions). The most promising enterprises in the engineering sphere of the agro-industrial complex for the needs of agricultural producers are those equipped with modern technological equipment, intelligent technologies and highly qualified specialists.

Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review

With the increase of the scale and complexity of mechanical equipment, traditional intelligent fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the demand of coupling faults. In the past decades, the vigorous development of deep learning (DL) brings new opportunities for IFD, especially the representation learning based on Autoencoder (AE) theory has been widely applied. To provide a more comprehensive reference, the theoretical foundations of multi-type AEs and the training method of stacked autoencoder (SAE) are briefly introduced. Then the application advances of AE are reviewed from optimization and combination aspects, which are aiming at improving the representation learning ability. To provide ways for the application of AE-based methods, two typical study cases for ideal and complex engineering systems are illustrated respectively. Finally, the challenges and prospects of AE-based representation learning are reported from four aspects, which give a guidance for the future research direction.

Risk analysis virtual ENvironment for dynamic event tree-based analyses

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also known as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used DPRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). Since 2012, the Idaho National Laboratory (INL) is developing its own tool to perform Dynamic PRA: RAVEN (Risk Analysis and Virtual ENvironment). RAVEN has been designed in a high modular and pluggable way to enable easy integration of different programming languages (i.e., Python, C++) and coupling with other application including, among the others, several thermal–hydraulic and severe accident codes (e.g., RELAP5-3D, MELCOR, MAAP5, TRACE, etc.). RAVEN is aimed to provide a framework/container of capabilities for engineers and scientists to analyze the response of systems, physics and multi-physics, employing advanced numerical techniques and algorithms. Moreover, RAVEN models stochastic events, such as components failures, and performs uncertainty quantification (UQ). Such stochastic modeling is employed by using sampling strategies among which both MC and DET algorithms, which are going to be employed in this paper. In addition, RAVEN processes the large amount of data generated by sampling the physical models using data-mining based algorithms and risk assessment techniques. This paper provides an overview of the DET methodologies that have been deployed within the RAVEN framework, showing the potential of such techniques for the analysis of complex systems. A brief background of classical methodologies and their limitation is also reported and represent the motivation for the deployment of such dynamic technique. In addition, results from a pressurized water reactor loss of coolant accident scenario, using RELAP5-3D as physical model, are reported.

Novel Response Relation Method for Sensor Data Analysis of Complex Engineering Systems

Technology of Internet of Things (IoT) offers extensive applications for industrial productivity and safety improvement. Advanced miniature sensors are available for monitoring multiple process parameters in a complex industrial or an engineering system. An industrial plant's overall operational status is captured using a network of sensors and stored on a cloud storage platform, where it is evaluated using the machine learning algorithms to produce valuable insights. Finding the correlation among these sensor variables is essential before feeding the same to machine learning algorithms. The present study proposes a novel approach to choose a few critical sensors out of numerous sensors based on the Response Relationship methodology. The Response Relationship method enables the system to be fully autonomous and helps find the interrelation among variables. The Response Relationship among variables is quantified and used for calculating the Remaining Useful Life of a complex engineering system. The proposed methodology is also applied to binary and multi-class classification to demonstrate the efficiency of the Response Relationship method. The results obtained are compared with standard methods of prediction and classification in terms of suitable metrics.

Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids

Constitutive models are widely used for modeling complex systems in science and engineering, where first-principle-based, well-resolved simulations are often prohibitively expensive. For example, in fluid dynamics, constitutive models are required to describe nonlocal, unresolved physics such as turbulence and laminar–turbulent transition. However, traditional constitutive models based on partial differential equations (PDEs) often lack robustness and are too rigid to accommodate diverse calibration datasets. We propose a frame-independent, nonlocal constitutive model based on a vector-cloud neural network that can be learned with data. The model predicts the closure variable at a point based on the flow information in its neighborhood. Such nonlocal information is represented by a group of points, each having a feature vector attached to it, and thus the input is referred to as vector cloud. The cloud is mapped to the closure variable through a frame-independent neural network, invariant both to coordinate translation and rotation and to the ordering of points in the cloud. As such, the network can deal with any number of arbitrarily arranged grid points and thus is suitable for unstructured meshes in fluid simulations. The merits of the proposed network are demonstrated for scalar transport PDEs on a family of parameterized periodic hill geometries. The vector-cloud neural network is a promising tool not only as nonlocal constitutive models and but also as general surrogate models for PDEs on irregular domains.

Landing gear disassembly sequence planning using multi-level constraint matrix ant colony algorithm


 
 
 The life cycle of most complex engineering systems is greatly a function of maintenance. Generally, most maintenance operation usually requires the removal of failed part. Disassembly sequence planning is an optimization program that seeks to identify the optimal sequence for the removal of the failed part. Most studies in this area usually, use single constraint matrix while implementing varied complex algorithm to identify the optimal sequence that saves time associated with carrying out maintenance operation. The used of single constraint matrix typically has the drawback of computer higher storage requirement as well as time consumption. To address this problem, this study proposes Multi-Level Constraint Matrix Ant Colony Algorithm (MLCMACA). MLCMACA efficiency was validated using complex aircraft landing gear systems in comparison with genetic algorithms. The result shows MLCMACA superior performance from the perspective of reduced search time and faster tracking of optimal disassembly sequence. Hence is recommended for handling of disassembly sequence planning problems.
 
 

Time and space-variant system reliability analysis through adaptive Kriging and weighted sampling

The performance of complex engineering systems often changes over time and space, potentially leading to multiple failure modes. Such time and space variance presents a challenge to reliability analysis and design methods. To address this issue, this paper first analyzes three categories of approaches to four types of time-variant reliability problems, and then develops an adaptive uniform design-based Kriging (AUDK) framework with an improved weighted sampling method (IWSM), termed the AUDK-IWSM. In particular, a Kriging response surface of the extreme function for time and space-variant limit states is constructed, and then an existing weighted sampling method is improved for high precision to prepare the sampling points for reliability analysis on the Kriging surface. To construct the AUDK model and update it with the most probable point, an adaptive iterative algorithm is developed based on the IWSM. Thus, through integrating the reliability index of the generated Kriging response surface, the resultant AUDK-IWSM algorithm improves reliability calculations and enhances computational efficiency of the reliability analysis. The AUDK-IWSM is illustrated, and its effectiveness is verified through four case studies for engineering practice, in comparison with a number of state-of-the-art techniques reported in the literature.

Психологический потенциал профессиональной надежности инженеров на этапе обучения

The article deals with the problem of professional reliability and efficiency. The prerequisites for the development of grounds for the study of this psychological category on the example of engineering specialties are indicated. Special attention is paid to laying of structure of professionally important qualities and professional competences (knowledge, abilities, skills) at a stage of vocational education in a higher educational institution. A number of labor fields are given, for whose specialists this characteristic is especially significant. An attempt is made to clarify the criteria for the professional reliability of an organization employee in the work of domestic scientists. It has been established that psychophysiological, individual-psychological, personal and moral parameters are most often mentioned. 
 The methods most often used by numerous authors in revealing the mechanisms of reliability of activity within the framework of labor psychology are listed. It is noted that the initial achievements were carried out on professional activities related to the operation of complex technical means and systems (engineering). Lists the traditional types of engineering activities and modern trends in the dynamics of engineering professions. Based on theoretical analysis and the professional method, universal psychological reliability indicators are derived for engineers of different professional orientations. On the basis of the center for psychological support of the Tver State Technical University, psychodiagnostic tools are selected and the corresponding student contingent (students in technical areas) are examined. The results of the study are described and conclusions are formed. A general opinion on the article is drawn up.

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

A hybrid-learning decomposition algorithm for competing risk identification within fleets of complex engineering systems

There is an increasing interest in the reliability of complex engineering systems, especially in the systems’ through-life risk analysis. A complex system, like the civil aircraft engine studied in this paper, contains multiple potential failure modes throughout its life that are contributed by various sub-system and component failures going through different deterioration processes. In order to fulfill the requirements of efficient swap and replacement maintenance strategies in the aviation industry, it is important to quantify the individual component risks within a complex system to enable an accurate prediction of spare parts demands. We propose a novel data-driven hybrid-learning algorithm with three building blocks: pre-defined reliability model based on the Weibull distribution, automated unsupervised clustering, and the quality check & output. The algorithm enables the identification of the riskiest sub-systems and the associated reliability models are quantitatively calculated. As all component risks follow the Weibull distribution, the parameters can be obtained. A case study carried out on a fleet of civil aircraft engines shows that the algorithm enables a better understanding of sub-system level risks from system level performance records, improving the efficient execution of the maintenance strategy.

Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes

Uncertainty propagation in complex engineering systems often poses significant computational challenges related to modeling and quantifying probability distributions of model outputs, as those emerge as the result of various sources of uncertainty that are inherent in the system under investigation. Gaussian Processes regression (GPs) is a robust meta-modeling technique that allows for fast model prediction and exploration of response surfaces. Multi-fidelity variations of GPs further leverage information from cheap and low fidelity model simulations in order to improve their predictive performance on the high fidelity model. In order to cope with the high volume of data required to train GPs in high dimensional design spaces, a common practice is to introduce latent design variables that are typically projections of the original input space to a lower dimensional subspace, and therefore substitute the problem of learning the initial high dimensional mapping, with that of training a GP on a low dimensional space. In this paper, we present a Bayesian approach to identify optimal transformations that map the input points to low dimensional latent variables. The “projection” mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters, conditioned on the available training data. The proposed Bayesian inference scheme relies on a two-step iterative algorithm that samples from the marginal posteriors of the GP parameters and the projection matrix respectively, both using Markov Chain Monte Carlo (MCMC) sampling. In order to take into account the orthogonality constraints imposed on the orthonormal projection matrix, a Geodesic Monte Carlo sampling algorithm is employed, that is suitable for exploiting probability measures on manifolds. We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together. We validate our framework on three synthetic problems with a known lower-dimensional subspace. The benefits of our proposed framework, are illustrated on the computationally challenging aerodynamic optimization of a last-stage blade for an industrial gas turbine, where we study the effect of an 85-dimensional shape parameterization of a three-dimensional airfoil on two output quantities of interest, specifically on the aerodynamic efficiency and the degree of reaction.

An integrated methodological approach for optimising complex systems subjected to predictive maintenance

The present paper addresses the relevant topic of maintenance management, widely recognised as a fundamental issue involving complex engineering systems and leading companies towards the optimisation of their assets while pursuing cost efficiency. With this regard, our research aims to provide companies with a hybrid methodological approach based on Multi-Criteria Decision-Making (MCDM) capable to deal with the main failures potentially involving complex systems subjected to predictive maintenance. Such an approach is going to be integrated within the framework of traditional Failure Mode Effects and Criticality Analysis (FMECA), whose strengths and weaknesses are considered. In particular, the ELimination Et Choix Traduisant la REalité (ELECTRE) TRI is suggested to sort failure modes into risk priority classes while the Decision Making Trial and Evaluation Laboratory (DEMATEL) is proposed to highlight the most influencing failures within each risk class. The approach is applied to a real service system, whose critical components are monitored by sensors and subjected to predictive maintenance. Final results clearly demonstrate as highlighting the elements impacting the occurrence of other failures within specific risk classes is a significant driver towards the implementation of effective maintenance, maximising the whole level of performance of the analysed system over its lifecycle.