Analysis Of Engineering Systems Research Articles

Lean-Stratified Combustion System with Miller Cycle for Downsized Boosted Application - Part I

<div class="section abstract"><div class="htmlview paragraph">Automotive manufacturers relentlessly explore engine technology combinations to achieve reduced fuel consumption under continued regulatory, societal and economic pressures. For example, technologies enabling advanced combustion modes, increased expansion to effective compression ratio, and reduced parasitics continue to be developed and integrated within conventional and hybrid propulsion strategies across the industry. A high-efficiency gasoline engine capable for use in conventional or hybrid electric vehicle platforms is highly desirable. This paper is the first to two papers describing the development of a combustion system combining lean-stratified combustion with Miller cycle for downsized boosted applications. The work was completed under a multi-year US DOE project. The goal was to define a light-duty engine package capable of achieving a 35% fuel economy improvement at US Tier 3 emission standards over a naturally aspirated stoichiometric baseline vehicle. A multi-mode combustion system was developed enabling highly efficient lean-stratified operation at light-load and stoichiometric Miller-cycle operation at mid- to high-loads. Some of the unique challenges in synergistically combining the two concepts are highlighted in this paper. A central direct-injection four-valve layout was designed with high-tumble ports and a bowl-in-piston capable of stable operation under highly dilute (lean plus EGR) mixtures when properly matched to fuel spray characteristics, a multiple-injection strategy, and a high-energy ignition system. Other challenges specific to meeting exhaust emissions requirements with a high-efficiency engine are also explored in this study. In this Part 1 of the paper, the single-cylinder combustion system design, analysis, and testing is described. Multi-cylinder engine system design, analysis and development is described in Part 2 of the paper [<span class="xref">1</span>].</div></div>

STACKED: The building design, systems engineering and performance analysis of plant factories for urban food production

Expanding cities across the world rely increasingly on the global food network, but should they? Population growth, urbanisation and climate change place pressure on this network, bringing its resilience into question. For decades urban agriculture has been discussed in popular media and academia as a potential solution to improve food security, quality and sustainability. The new idol in this discussion is the plant factory: A fully closed system for crop production. Arrays of LEDs provide light and hydroponics provide water and nutrients to vertically stacked layers of crops, hence the term vertical farming. The plant factory features more extensive climate control than high-tech greenhouses. The question remains whether this level of climate control is necessary, effective and/or efficient. The scope of this research is therefore to investigate the potential and limitations of plant factories for urban food production. The STACKED method was developed to address the performance of plant factories across multiple scales, from leaf to facility to city. The role of plant processes in the total energy balance was outlined first. Performance was assessed by analysing the resource requirements, including energy, electricity, water, CO2 and land area use, for the production of fresh vegetables. The impact of facade and cooling system design was analysed in detail. Lastly, the effects of local food production on the urban energy balance were assessed for various scenarios. The results of this dissertation can serve as a foundation for future studies on the application of plant factories in both theoretical and real world applications.Expanding cities across the world rely increasingly on the global food network, but should they? Population growth, urbanisation and climate change place pressure on this network, bringing its resilience into question. For decades urban agriculture has been discussed in popular media and academia as a potential solution to improve food security, quality and sustainability. The new idol in this discussion is the plant factory: A fully closed system for crop production. Arrays of LEDs provide light and hydroponics provide water and nutrients to vertically stacked layers of crops, hence the term vertical farming. The plant factory features more extensive climate control than high-tech greenhouses. The question remains whether this level of climate control is necessary, effective and/or efficient. The scope of this research is therefore to investigate the potential and limitations of plant factories for urban food production. The STACKED method was developed to address the performance of plant factories across multiple scales, from leaf to facility to city. The role of plant processes in the total energy balance was outlined first. Performance was assessed by analysing the resource requirements, including energy, electricity, water, CO2 and land area use, for the production of fresh vegetables. The impact of facade and cooling system design was analysed in detail. Lastly, the effects of local food production on the urban energy balance were assessed for various scenarios. The results of this dissertation can serve as a foundation for future studies on the application of plant factories in both theoretical and real world applications.

High-Low Level Support Vector Regression Prediction Approach (HL-SVR) for Data Modeling with Input Parameters of Unequal Sample Sizes

Support vector regression (SVR) has been widely used to reduce the high computational cost of computer simulation. SVR assumes the input parameters have equal sample sizes, but unequal sample sizes are often encountered in engineering practices. To solve this issue, a new prediction approach based on SVR, namely as high-low level SVR approach (HL-SVR) is proposed for data modeling of input parameters of unequal sample sizes in this paper. The proposed approach consists of low-level SVR models for the input parameters of larger sample sizes and high-level SVR model for the input parameters of smaller sample sizes. For each training point of the input parameters of smaller sample sizes, one low-level SVR model is built based on its corresponding input parameters of larger sample sizes and their responses of interest. The high-level SVR model is built based on the obtained responses from the low-level SVR models and the input parameters of smaller sample sizes. A number of numerical examples are used to validate the performance of HL-SVR. The experimental results indicate that HL-SVR can produce more accurate prediction results than SVR. The proposed approach is applied to the stress analysis of dental implant, in which the structural parameters have massive samples but the material of implant can only be selected from Ti and its alloys. The obtained prediction results of the HL-SVR approach are much better than SVR. The proposed approach can be used for the design, optimization, and analysis of engineering systems with input parameters of unequal sample sizes.

Foreword by Guest Editor LTC James H. Schreiner, PhD, PMP, CPEM

FOREWORD
 This special issue of the Industrial and Systems Engineering Review highlights top papers from the 2020 annual General Donald R. Keith memorial capstone conference held at the United States Military Academy in West Point, NY. The conference was certainly a first of its kind virtual conference including asynchronous delivery of paper presentations followed by synchronous question and answer sessions with evaluation panels. Following a careful review of 63 total submissions, eleven were selected for publication in this journal. Unique to this year’s special edition is the mixed selection of seven project team capstone papers, and four honors research papers. Each paper incorporated features of systems or industrial engineering and presented detailed and reflective analysis on the topic. Although there are many elements which cut across the works, three general bodies of knowledge emerged in the papers including: systems engineering and decision analysis, systems design, modeling and simulation, and system dynamics.
 Systems Engineering and Decision Analysis topics included three unique contributions. Recognized as ‘best paper’ at the 2020 virtual conference, the work of Robinson et al. designed a multi-year predictive cost engineering model enabled through an MS O365 Power BI decision support interface to support U.S. Army Corps of Engineer (USACE) inland waterway national investment strategies. Schloo and Mittal’s work presents research in testing and evaluation of the Engagement Skills Trainer (EST) 2000 towards improving real-world soldier performance. Gerlica et al. employs a robust and scalable K-means clustering methodology to improve decision making in defensive shift schemes for Air Force Baseball outfield personnel.
 Systems Design works included three unique contributions. Binney et al. worked to design evaluation criteria for military occupational specialties associated with open-source intelligence (OSINT) analysts for the Army’s OSINT Office. Hales et al. interdisciplinary work aided in the design of search and identification systems to be incorporated on autonomous robotics to enable survivability improvements for the Army’s chemical, biological, radiological, nuclear, and explosives (CBRNE) units. Burke and Connell evaluated and designed a performance measurement-based assessment methodology for U.S. Pacific Command’s Key Leader Engagement process.
 System modeling and simulation included three unique contributions: Arderi et al. simulated and assessed how the Hyper-Enabled Operator (HEO) project improves situational awareness for U.S. Special Forces using the Infantry Warrior Simulation (IWARS). Blanks et al. employed a VBA module and Xpress software for a scheduling optimization model for enhancement of final exam scheduling at the U.S. Air Force Academy. Kelley and Mittal utilized a Batch Run Analysis and Simulation Studio (BRASS) program to batch multiple iterations of IWARS scenarios to study the integration of autonomous systems alongside military units.
 Finally, two unique contributions utilizing system dynamics (SD) modeling is presented: Dixon and Krueger developed a Vensim SD model to examine how policy recommendations across Central America could restrict gang activities while positively promoting women’s involvement in society. Cromer et al. utilized systems design approaches and a K-means clustering machine learning techniques to develop SD models in support of the U.S. Africa Command and Defense Threat Reduction Agency to examine the interdependence of threats across the Horn of Africa.
 Thank you and congratulations to the 2020 undergraduate scholars and all authors who provided meaningful contributions through steadfast intellectual efforts in their fields of study! Well done!
 
 LTC James H. Schreiner, PhD, PMP, CPEM
 Program Director, Systems and Decision Sciences (SDS)
 Department of Systems Engineering
 United States Military Academy
 Mahan Hall, Bldg 752, Room 423
 West Point, NY 10996, USA
 james.schreiner@westpoint.edu

Integration of simulation and Markov Chains to support Bayesian Networks for probabilistic failure analysis of complex systems

Development of failure analysis techniques for complex engineering systems is evolving rapidly. Complexity in these systems refers to the complex interrelations among system components, variables, factors, and parameters as well as the large number of components to include in the study. It is not an easy task to include all interrelationships of a complex system into one representation. New dynamic and uncertain factors affecting engineering systems, like climate change, new technologies, and new uses, make it clear that the water reservoir systems operations and performance are under probabilistic inputs from many different factors. This means that failure of such systems should be assessed using multidisciplinary probabilistic uncertainty measures. Bayesian Networks (BNs) provide a flexible way of representing such complex systems and their interrelating components probabilistically and in a single unified representation. Compared to other techniques such as fault tree and event tree analyses methods, BN is useful in representing complex networks that have multiple events and different types of variables in one representation, with the ability to predict the effects, or diagnose the causes leading to a certain effect. In this paper, two proposed methodologies are developed to support BNs in dealing with the failure analysis of complex engineering systems, i.e. Simulation Supported Bayesian Networks (SSBNs), and Markov Chain Simulation Supported Bayesian Networks (MCSSBNs). For complex networks, whose failures are affected by a large number of uncertain interconnected variables, these proposed methods are used for efficiently predicting failure probabilities. Compared to exhaustive simulation, the new tools have the distinction of decomposing the complex system into many sub-systems, which makes it easier for understanding the network and faster for simulating the entire network while taking multiple operation scenarios into consideration. The efficiency of these techniques is demonstrated through their application to a pilot system of two dam reservoirs, where the results of SSBNs and MCSSBNs are compared with those of the simulation of entire system operations.

Analysis, synthesis and modelling of air individual smart rescue system

The morphological approach is a successful analytical tool for development schematic, conceptual and preliminary design of new engineering systems in Aerospace. This investigation used a morphological approach to engineering solutions development as well as to tools for creating promising individual smart rescue system on conceptual design stage. The problem was solved in two stages. At a higher level of the hierarchy, the principle of action in morphological box was chosen. In the second phase, specific engineering solutions were explored. Analysis of engineering systems, solutions and solving technique and systems and their potential opportunities with technique provide correct guidelines to supply the reasonable decisions. Flight simulations, depending on different initial conditions, were performed using the LANE program.

Process System Engineering (PSE) Analysis on Process and Optimization of the Isomerization Process

Oil refineries are facing an ever-strict restriction in terms of fuel specification in view of the several policies by the environmental impact agency (EIA). Strict rules have been enforced on the gasoline product specification. Isomerization is one of the key processes for increasing the octane number of gasoline. Hence a case study has been performed using the concept of hydrogen once-through technology to analyze the process constraints for optimal operation of the process using Aspen HYSYS. In addition to this, a rigorous process model of isomerate stabilizer was also used to compare the results of the stabilizer model and optimize those various variables affecting the octane number of gasoline. The model was validated by observing the effects of constraints on the efficiency of the process by comparing it with the operational isomerization unit to further verify the authenticity of the case study. Furthermore, a calculator has been generated for the reactor temperature with respect to high benzene contents in the feedstock (benzene in feed Vs reactors ΔT).

Numerical study of the stress strain state during tests of polymer composite materials samples

To date, a large number of experimental methods for determining the mechanical properties of composite materials is known. However, the results of these methods. i.e. their comparison features, are relative and give good repeatability in strict compliance with the sample production and experiment technology. In this case, the stress-strain state in the working area of sample structures should be numerically evaluated to determine the generally accepted characteristics. This paper constructs and develops the simplest models of the mechanical behavior of polymer composite material samples. The stress-strain state is numerically simulated in the working areas of samples with various geometric parameters and external loading features. Modeling is made using the ANSYS engineering analysis system. According to the results of calculations for each of the samples, the fields of stresses, deformations, and displacements under compression and tension are obtained for medium models: effective isotropic medium, effective anisotropic medium, and layered composite with effective anisotropic medium texture layers.

AC-DC 복합 전력계통 해석을 위한 대규모 전력계통 실시간 모의 기술 개발

In the case of the KEPCO power system, the installation of power electronic devices such as FACTS/HVDC is increasing to maximize the power transmission capability. The installation of these power electronic devices increase the utilization of the conventional transmission facilities. However, it can also cause power system instability due to the control instability and interaction between conventional facilities and other power electronic devices. By these reasons, the importance of the AC-DC integrated power system analysis technology is increasing. This technology provide the capability to analysis interaction among conventional ac facilities and power electronic based devices. In this paper, AC-DC integrated power system analysis technology based on the real-time simulation. As a case study, the result of the dynamic performance tests of the facilities that operating on the KEPCO power system are provided.

A novel looped space tether transportation system with multiple climbers for high efficiency

This paper proposes a novel concept of looped tether transportation system with multiple climbers for highly efficient transportation of payloads. It is formed by connecting two parallel tether transportation systems or partial space elevators with multiple climbers per tether at upper and lower ends. A high-fidelity and accurate model for the system is built up to capture the high order flexural modes of tethers based on the nodal position finite element method in the arbitrary Lagrangian-Eulerian description. Numerical analysis is conducted to understand the dynamic characteristics of the looped tether transportation system compared to the tether transportation system with a single tether. The results show that the high-order flexural modes of tethers must be considered in the system's engineering analysis. The analysis also shows the interaction between two tethers caused by climbers on different tethers moving in different directions could be beneficial. It will reduce the overall libration of the system while keeping the magnitude of tether tension comparable to the tether transportation system with a single tether. In addition, the analysis of high order flexural modes of tether indicates that there exists a risk of tether collision in the payload transportation, which indicates again the necessity to include the high order flexural modes of tether in the analysis. Finally, the study shows the risk of tether collision could be avoided by optimizing the number of climbers per tether, the climber's moving profiles, and the distance between climbers using the optimal control methodology.

IoT-Based Intelligent Construction System for Prefabricated Buildings: Study of Operating Mechanism and Implementation in China

At present, the low degree of informatization in the construction industry chain is one of the prominent problems impeding the application efficiency of prefabrication. To break through the bottleneck of information integration and interaction in the construction process of prefabricated buildings, this paper proposes a conceptual framework of an Intelligent Construction System for Prefabricated Buildings based on the Internet of Things (ICSPB-IoT) according to the needs of government supervision departments, contractors and owners. Then this paper describes the formation of the “information network” from “information flow”, based on which the operating mechanism of the ICSPB-IoT is discussed using the method of system engineering analysis. Finally, the proposed ICSPB-IoT is graded into different implementation levels according to the degree of intelligence of the Internet of Things information, and the implementation path is proposed for construction enterprises with different levels of digitalization. The results show that the ICSPB-IoT provides cross-phase, cross-organizational information interaction for government supervision departments, contractors and owners to improve the efficiency of communication and collaboration between them. This paper also provides decision-making support for the design, layout and implementation of the Internet of Things for prefabrication construction enterprises.

Design of a compact microwave-absorbing load based on sintered material FeSiAl

The dummy load in the terminal of traveling wave tubes is an important component of compact high-power electron linear accelerators. To develop high-power loads and miniaturize the accelerators, a microwave-absorbing material FeSiAl coated by a special sintering process is employed to enhance the efficiency of attenuation. Based on the method of multistate and multithickness, the complex permittivity and permeability of the material FeSiAl was obtained. And the design of a compact S-band dummy load was done by means of an engineering design and analysis system. With a very short length of less than 250mm, this dummy load is able to dissipate 10kW residual power to ensure the security of the accelerator. It can provide an important reference for the design of the high-power compact dummy load.

Perspectives for Mitigation of CO2 Emission due to Development of Electromobility in Several Countries

The creep trend method is used for the analysis of the development of electric car production in three regions: The United States, the European Union and Japan. Based on vehicle registration and population growth data for each year the creep trend method using historical data for the years 2007–2017 is applied for forecasting development up to 2030. Moreover, the original method for calculating the primary energy factor (PEF) was applied to the analysis of power engineering systems in the regions investigated. The assessment of the effects of electromobility development on air quality has been performed, reduction values for pollutant and greenhouse gas emissions have been determined, which was the main objective of this manuscript. Mitigation of air pollutant emissions, i.e., carbon dioxide (CO2), carbon monoxide (CO) and nitrogen oxides (NOx) was estimated and compared to the eventual expected increase of emissions from power plants due to an increase of the demand for electricity. It can be concluded that electricity powered cars along with appropriate choices of energetic resources as well as electricity distribution management will play the important role to achieve the sustainable energy economy. Based on the emission reduction projections resulting from the projected increase in the number of electric cars, (corrected) emissions will be avoided in 2030 in the amount of over 14,908,000 thousand tonnes CO2 in European Union, 3,786,000 thousand tonnes CO2 in United States and 111,683 thousand tonnes CO2 in Japan.

Sensitivity-based adaptive sequential sampling for metamodel uncertainty reduction in multilevel systems

Decomposition-based technique is often used in the analysis and design of complex engineering systems for reducing the computational complexity by studying the subsystems decomposed from multilevel systems. Metamodels, as a replacement of original simulation models, can further alleviate the computational burden. However, discrepancy between the simulation models and metamodels, which is defined as metamodel uncertainty, may be introduced in the analysis process of multilevel systems owing to the lack of data. The metamodel uncertainties of sub-models will be further amplified because of the hierarchical uncertainty propagation and interaction between uncertainties, which will have a great impact on the system results. An adaptive sequential sampling strategy based on sensitivity is proposed in this paper so as to improve the prediction accuracy of system response. In this strategy, polynomial-chaos expansion is used to realize the forward propagation of metamodel uncertainty quantified by the Kriging model. The forward propagation is combined with optimization based on maximum variance criterion for searching the input locations that results in the largest variance of system response. Then, the indices of subsystems are obtained to make decisions about which subsystem needs extra samples by combining Karhunen-Loeve expansion and sensitivity analysis. The effectiveness of the proposed sequential sampling strategy method is verified by two mathematical examples and a multiscale composite material.

Multi-level, multi-variate, non-stationary, random field modeling and fragility analysis of engineering systems

Engineering systems can often be represented considering models at multiple levels. Different properties within each level are typically inhomogeneous in space and cross-correlated, while properties of different levels are physically dependent on each other. In addition, system properties usually vary in time due to effects of external environmental conditions. Current analysis techniques cannot model the spatially inhomogeneous, intra-level correlated, inter-level dependent, and temporally varying system properties. This paper proposes a random field-based formulation for the modeling of engineering systems that overcome the limitation. To consider the inhomogeneous spatial variability and intra-level correlation, we propose a multi-variate non-stationary random field formulation. To consider the inter-level dependency, we propose a multi-level formulation that considers lower-level properties as regressors in the random field of higher-level properties. To consider the temporal variability, we use a state-dependent model that enables the updating of the multi-level random fields over time. As a special case, the paper shows that the proposed formulation can be used in system fragility analysis, considering capacities and demands, and fragilities as levels. The paper implements the proposed formulation in the modeling of a deteriorating transportation system considering six different levels. The first three levels are defined by state variables at different scales, while the last three levels are defined by bridge capacities and demands, bridge fragilities, and network fragilities. The results are significantly different from those obtained by using non-random field models.

An extended Birnbaum importance-based two-stage heuristic for component assignment problems under uncertainty

The component assignment problem (CAP) is to find the optimal assignment of n components to n positions in a system that maximizes the system reliability. Due to the insufficiency and the inaccuracy of related data and system complexities, there are different types of uncertainties in real-world engineering system analysis. This paper proposes an extended Birnbaum importance (BI)-based two-stage (EBITS) heuristic using evidential network and interval-valued BI measures for solving CAPs under parametric and model uncertainties. The parametric uncertainty is the uncertainty about component reliabilities and the model uncertainty is the uncertainty about the system structure. The proposed method is applied to find the optimal assignment of valves in a ship’s fuel service system.

A SEQUENTIAL AUGMENTED METAMODELING METHOD FOR ENGINEERING RELIABILITY ANALYSIS

Metamodeling methods provide useful tools to replace expensive numerical simulations in engineering reliability analysis and design optimization. The radial basis functions (RBFs) and augmented RBFs can be used to create accurate metamodels; therefore they can be integrated with a reliability analysis method such as the Monte Carlo simulations (MCS). However the model accuracy of RBFs depends on the sample size, and the accuracy generally increases as the sample size increases. Since the optimal sample size used to create RBF metamodels is not known before the creation of the models, a sequential RBF metamodeling method was studied. In each iteration of reliability analysis, augmented RBFs were used to generate metamodels of a limit state or performance function, and the failure probability was calculated using MCS. Additional samples were generated in subsequent analysis iterations in order to improve the metamodel accuracy. Numerical examples from literature were solved, and the failure probabilities based on the RBF metamodels were found to have a good accuracy. In addition, only small numbers of iterations were required for the reliability analysis to converge. The proposed method based on sequential RBF metamodels is useful for probabilistic analysis of practical engineering systems.

Understanding Interdependencies between Mechanical Velocity and Electrical Voltage in Electromagnetic Micromixers.

Micromixers are critical components in the lab-on-a-chip or micro total analysis systems technology found in micro-electro-mechanical systems. In general, the mixing performance of the micromixers is determined by characterising the mixing time of a system, for example the time or number of circulations and vibrations guided by tracers (i.e., fluorescent dyes). Our previous study showed that the mixing performance could be detected solely from the electrical measurement. In this paper, we employ electromagnetic micromixers to investigate the correlation between electrical and mechanical behaviours in the mixer system. This work contemplates the “anti-reciprocity” concept by providing a theoretical insight into the measurement of the mixer system; the work explains the data interdependence between the electrical point impedance (voltage per unit current) and the mechanical velocity. This study puts the electromagnetic micromixer theory on a firm theoretical and empirical basis.

CAD Model Segmentation Via Deep Learning

Computer aided design (CAD) models are widely employed in the current computer aided engineering or finite element analysis (FEA) systems that necessitate an optimal meshing as a function of their geometry. To this effect, the sub-mapping method is advantageous, as it segments the CAD model into different sub-parts, with the aim mesh them independently. Many of the existing 3D shape segmentation methods in literature are not suited to CAD models. Therefore, we propose a novel approach for the segmentation of CAD models by harnessing deep learning technologies. First, we refined the model and extracted local geometric features from its shape. Subsequently, we devised a convolutional neural network (CNN)-inspired neural network trained with a custom dataset. Experimental results demonstrate the robustness of our approach and its potential to adapt to augmented datasets in future.

A real time methodology of cluster-system theory-based reliability estimation using k-means clustering

With rapidly increase in the design and application of complex system engineering, life and reliability analysis methods for these engineering have received much attention. A novel real time reliability analysis methodology is proposed based on the cluster-system theory and k-means clustering. Firstly, the system consisting of three or more identical or similar sub-systems facing the same load or task is defined as cluster-system. By analyzing the key performance parameters, sub-systems with similar performance are divided into a family-system, so that each sub-system can be used as reference sample for other sub-systems in the identical family-system. The cubic spline interpolation method under first-order boundary conditions is used to fit the average variation of key performance parameters of family-systems. The residual life of family-system can be obtained by determining the threshold of key performance parameters of sub-system through experiments or experience. Then reliability of the whole system is estimated by the contribution of each sub-system in cluster-system to solve the problem that there is no fault data and reference samples in the reliability analysis of complex system. Application in the Five-hundred-meter Aperture Spherical Telescope (FAST) demonstrates the effectiveness of the proposed method, compared to traditional reliability analysis methods based on experimental statistics.