Corrosion: Causes and prevention. An engineering problem. By F. N. Speller. 2nd ed. Pp. xiii + 694. London: McGraw-Hill Publishing Co., Ltd., 1935. 42s

1990 ◽  
Vol 54 (44) ◽  
pp. 961-961
Author(s):  
Arthur Marsden
Keyword(s):  
Engineering Problem

Influence of Cognitive, Affective, and Conative Elements in Promoting Engineering Problem Solving Skills

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Norasyikin Omar ◽  
◽  
Mimi Mohaffyza Mohamad ◽  
Marina Ibrahim Mukhtar ◽  
Aini Nazura Paimin ◽  
...  
Keyword(s):  
Problem Solving ◽  
Engineering Problem ◽  
Problem Solving Skills ◽  
Engineering Problem Solving

scholarly journals Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1022
Author(s):  
Hoang T. Nguyen ◽  
Kate T. Q. Nguyen ◽  
Tu C. Le ◽  
Guomin Zhang
Keyword(s):  
Artificial Intelligence ◽  
Flame Retardancy ◽  
Material Science ◽  
Construction Materials ◽  
Engineering Problem ◽  
Future Research ◽  
Fire Performance ◽  
Structural Components ◽  
Comprehensive Overview ◽  
Fire Engineering

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

scholarly journals Approximation of continuous random variables for the evaluation of the reliability parameter of complex stress–strength models

2021 ◽  
Author(s):  
Alessandro Barbiero ◽  
Asmerilda Hitaj
Keyword(s):  
Order Approximation ◽  
Linear Optimization ◽  
Closed Form Solution ◽  
Random Variables ◽  
Discrete Distribution ◽  
Computation Time ◽  
Form Solution ◽  
Engineering Problem ◽  
Approximation Technique ◽  
Reliability Parameter

AbstractIn many management science or economic applications, it is common to represent the key uncertain inputs as continuous random variables. However, when analytic techniques fail to provide a closed-form solution to a problem or when one needs to reduce the computational load, it is often necessary to resort to some problem-specific approximation technique or approximate each given continuous probability distribution by a discrete distribution. Many discretization methods have been proposed so far; in this work, we revise the most popular techniques, highlighting their strengths and weaknesses, and empirically investigate their performance through a comparative study applied to a well-known engineering problem, formulated as a stress–strength model, with the aim of weighting up their feasibility and accuracy in recovering the value of the reliability parameter, also with reference to the number of discrete points. The results overall reward a recently introduced method as the best performer, which derives the discrete approximation as the numerical solution of a constrained non-linear optimization, preserving the first two moments of the original distribution. This method provides more accurate results than an ad-hoc first-order approximation technique. However, it is the most computationally demanding as well and the computation time can get even larger than that required by Monte Carlo approximation if the number of discrete points exceeds a certain threshold.

scholarly journals Strengthening the PSO algorithm with a new technique inspired by the golf game and solving the complex engineering problem

2021 ◽  
Author(s):  
Serkan Dereli ◽  
Raşit Köker
Keyword(s):  
Pso Algorithm ◽  
Engineering Problem ◽  
Golf Ball ◽  
New Technique ◽  
Successful Implementation ◽  
Robot Arm ◽  
Bee Colony ◽  
Scientific World ◽  
Heuristic Technique ◽  
A New Technique

AbstractThis study has been inspired by golf ball movements during the game to improve particle swarm optimization. Because, all movements from the first to the last move of the golf ball are the moves made by the player to win the game. Winning this game is also a result of successful implementation of the desired moves. Therefore, the movements of the golf ball are also an optimization, and this has a meaning in the scientific world. In this sense, the movements of the particles in the PSO algorithm have been associated with the movements of the golf ball in the game. Thus, the velocities of the particles have converted to parabolically descending structure as they approach the target. Based on this feature, this meta-heuristic technique is called RDV (random descending velocity) IW PSO. In this way, the result obtained is improved thousands of times with very small movements. For the application of the proposed new technique, the inverse kinematics calculation of the 7-joint robot arm has been performed and the obtained results have been compared with the traditional PSO, some IW techniques, artificial bee colony, firefly algorithm and quantum PSO.

Model-Eliciting Activities: A Research-Based Method for Inquiry Learning and Professional Development in the Engineering Classroom

2008 ◽  
Author(s):  
Tamara J. Moore
Keyword(s):  
Complex Systems ◽  
Student Development ◽  
Engineering Problem ◽  
First Year ◽  
Foundation Engineering ◽  
Underrepresented Populations ◽  
Model Eliciting Activities ◽  
Engineering Problems ◽  
The Given ◽  
Mathematics And Science

Attracting students to engineering is a challenge. In addition, ABET requires that engineering graduates be able to work on multi-disciplinary teams and apply mathematics and science when solving engineering problems. One manner of integrating teamwork and engineering contexts in a first-year foundation engineering course is through the use of Model-Eliciting Activities (MEAs) — realistic, client-driven problems based on the models and modeling theoretical framework. A Model-Eliciting Activity (MEA) is a real-world client-driven problem. The solution of an MEA requires the use of one or more mathematical or engineering concepts that are unspecified by the problem — students must make new sense of their existing knowledge and understandings to formulate a generalizable mathematical model that can be used by the client to solve the given and similar problems. An MEA creates an environment in which skills beyond mathematical abilities are valued because the focus is not on the use of prescribed equations and algorithms but on the use of a broader spectrum of skills required for effective engineering problem-solving. Carefully constructed MEAs can begin to prepare students to communicate and work effectively in teams; to adopt and adapt conceptual tools; to construct, describe, and explain complex systems; and to cope with complex systems. MEAs provide a learning environment that is tailored to a more diverse population than typical engineering course experiences as they allow students with different backgrounds and values to emerge as talented, and that adapting these types of activities to engineering courses has the potential to go beyond “filling the gaps” to “opening doors” to women and underrepresented populations in engineering. Further, MEAs provide evidence of student development in regards to ABET standards. Through NSF-funded grants, multiple MEAs have been developed and implemented with a MSE-flavored nanotechnology theme. This paper will focus on the content, implementation, and student results of one of these MEAs.

scholarly journals Fluid Genetic Algorithm (FGA)

2017 ◽  
Vol 4 (2) ◽  
pp. 158-167 ◽  
Author(s):  
Ruholla Jafari-Marandi ◽  
Brian K. Smith
Keyword(s):  
Genetic Algorithm ◽  
Success Rate ◽  
Optimization Problems ◽  
Engineering Problem ◽  
Review Of The Literature ◽  
Learning Rates ◽  
New Concepts ◽  
Convergence Control ◽  
Multi Level ◽  
Paper Method

Abstract Genetic Algorithm (GA) has been one of the most popular methods for many challenging optimization problems when exact approaches are too computationally expensive. A review of the literature shows extensive research attempting to adapt and develop the standard GA. Nevertheless, the essence of GA which consists of concepts such as chromosomes, individuals, crossover, mutation, and others rarely has been the focus of recent researchers. In this paper method, Fluid Genetic Algorithm (FGA), some of these concepts are changed, removed, and furthermore, new concepts are introduced. The performance of GA and FGA are compared through seven benchmark functions. FGA not only shows a better success rate and better convergence control, but it can be applied to a wider range of problems including multi-objective and multi-level problems. Also, the application of FGA for a real engineering problem, Quadric Assignment Problem (AQP), is shown and experienced. Highlights This work presents a novel Genetic Algorithm alteration. Chromosome concept and structure in FGA is more similar to the real genetic world. FGA comprises global and individual learning rates. We show FGA enjoys higher success rate, and better convergence control.

scholarly journals Implementation of dimensional management methodology and optimisation algorithms for pre-drilled hinge line interfaces of critical aero assemblies – A case study

2020 ◽  
pp. 095440542097810
Author(s):  
Konstantinos C Bacharoudis ◽  
David Bainbridge ◽  
Alison Turner ◽  
Atanas A Popov ◽  
Svetan M Ratchev
Keyword(s):  
Degrees Of Freedom ◽  
Engineering Problem ◽  
Hole Diameter ◽  
Design Stage ◽  
Assembly Process ◽  
Mechanical Assembly ◽  
Hinge Line ◽  
Management Procedure ◽  
Dimensional Management ◽  
Optimisation Algorithms

A dimensional management procedure is developed and implemented in this work to deal with the identification of the optimum hole diameter that needs to be pre-drilled in order to successfully join two subassemblies in a common hinge line interface when most of the degrees of freedom of each subassembly have already been constrained. Therefore, an appropriate measure is suggested that considers the assembly process and permits the application of optimisation algorithms for the identification of the optimum hole diameter. The complexity of the mechanical subassemblies requires advanced 3D tolerance analysis techniques to be implemented and the matrix method was adopted. The methodology was demonstrated for an industrial, aerospace engineering problem, that is, the assembly of the joined wing configuration of the RACER compound rotorcraft of AIRBUS Helicopter and the necessary tooling needed to build the assembly. The results indicated that hinge line interfaces can be pre-opened at a sufficiently large size and thus, accelerate the assembly process whilst the suggested methodology can be used as a decision-making tool at the design stage of this type of mechanical assembly.

The Clifford Paterson Lecture, 1993, Accurate measurement in in vivo magnetic Resonance: an engineering problem?

1994 ◽  
Vol 349 (1691) ◽  
pp. 357-388
Keyword(s):  
Magnetic Resonance ◽  
Signal To Noise Ratio ◽  
Engineering Problem ◽  
Resonance Imaging ◽  
Significant Extent ◽  
Performance System ◽  
Imaging And Spectroscopy ◽  
Intractable Problem ◽  
Conceptual Difficulties

Measurement in in vivo magnetic resonance — both in imaging and spectroscopy — has proved to be a much more intractable problem than extrapolation from conventional high resolution studies might have suggested. Although this paper concentrates mainly on some of the complications of magnetic resonance imaging, the same conceptual difficulties (compounded by much reduced signal levels) affect in vivo spectroscopy. Tissue is an extremely complex system and many of the difficulties studying it arise from the interactions that are unintentionally engendered when it is observed. Patient motion is a potent source of artifact to the technical challenge of making better measurements, and different forms of motion are likely to be the ultimate limitation on the sensitivity and discrimination of the technique as a whole. In this context it is observed that the traditional criterion of performance — system signal-to-noise ratio — should be replaced by a signal-to-artifact estimate, and that this may affect the design and implementation of detector systems to a significant extent.

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