Influences of heating temperatures on physical properties, spray characteristics of bio-oils and fuel supply system of a conventional diesel engine

Alternative fuels need to satisfy the strict requirements of the use for diesel engines aiming at enhancing the performance and reducing pollutant emissions. The use of straight bio-oils for diesel engines entails improving their disadvantages such as high density, high surface tension and kinematic viscosity (tri-physical parameters). There have been some as-used methods for reduction of the above-mentioned negative effects related to straight bio-oil disadvantage, however, the adequately-heating method may be considered as a simple one helping the physical parameters of straight bio-oils to reach stable and highly-confident values which are close to those of traditional diesel fuel. As a consequence, the spray and atomization, combustion, performance, and emissions of diesel engines fueled with preheated bio-oils are improved. In this work, a study of the dependence of the density, surface tension and kinematic viscosity of coconut oil (a type of bio-oils) on temperatures (from 40-110oC) within a wide variety are conducted. In the first stage, the influence study of temperature on tri-physical parameters is carried out on the basis of experimental correlation and as-described mathematical equation. In the second stage, the influence study of tri-physical parameters on spray and atomization parameters including penetration length (Lb) and Sauter mean diameter (SMD), and the influence of tri-physical parameters on fuel supply system are investigated. The optimal range of temperature for the as-used bio-oils is found after analyzing and evaluating the obtained results regarding the physical properties and spray characteristics, as well as compared with those of diesel fuel. The confident level over 95% from the regression correlation equation between the above-mentioned tri-physical parameters and temperature is presented. Additionally, the measured spray parameters, the calculated values of frictional head loss and fuel flow rate are thoroughly reported. 

Dynamic system identification based on selective sensitivity

System identification is often associated with the evaluation of damage for existing structures. Usually, dynamic test data are utilized to estimate the parameter values for a given structural model. This requires the solution of an inverse problem. Unfortunately, inverse problems in general are ill-conditioned, particularly with a large number of parameter to be determined. This means that the accuracy of the estimated parameter values is not sufficiently high in order to enable a damage identification. The goal of this study was to develop an experimental procedure which allows to identify the system parameters in substructures with high reliability. For this purpose, the method of selective sensitivity was employed to define special dynamic excitations, namely selectively sensitive excitation. Two different approaches have been introduced, which are the quasi-static approach and the iteratively experimental procedure. The former approach is appropriate for statically determinate structures and excitation frequencies below the structure's fundamental frequency. The latter method, which uses a-priori information about the parameters to be identified to set up an iterative experiment, can be applied to statically indeterminate structures. The viability of the proposed iterative procedure in detection of small changes of structure's stiffness was demonstrated by a simple laboratory experiment. The applicability of the strategy, however, depends largely on experimental capacity. It was also experienced that such a test is associate with expensive cost of equipments and time-consuming work.Systemidentifikation wird oft als Werkzeug im Zusammenhang mit der Beurteilung von Schädigungen an Strukturen eingesetzt. Oftmals erfolgt eine Abschätzung der Parameter eines vorgegebenen Strukturmodells mittels der in dynamischen Versuchen gemessenen Daten. Dies erfordert die Lösung eines inversen Problems. Insbesondere bei einer großen Anzahl von zu bestimmenden Parametern sind inverse Probleme in der Regel schlecht konditioniert. Dies bedeutet, dass die Präzision der identifizierten Parameterwerte oft nicht ausrei-chend hoch ist, um die ursprünglich gestellte Frage nach einer Schädigungsidentifikation beantworten zu können. Das Ziel dieser Arbeit war, eine experimentelles Verfahren zu entwickeln, das es erlaubt, die Systemparameter in Substrukturen mit hoher Verlässlichkeit zu identifizieren. Zu diesem Zweck wurde die Methode der selektiven Sensitivität eingesetzt, um spezielle dynamische Anregungen, nämlich selektiv sensitive Anregung zu bestimmen. Zwei verschiedene Ansätze wurden eingeführt, der quasistatische Ansatz und das iterativ experimentelle Verfahren. Der erste Ansatz ist für den Versuch einer statisch bestimmten Struktur mit Anregungsfrequenz unter der Grundfrequenz der Struktur geeignet. Der zweite Ansatz verwendet a-priori Information über die zu identifizierenden Parameter, um einen iterativen Versuch aufzubauen, und kann auch auf statisch unbestimmte Strukturen angewendet werden. Die Realisierbarkeit des vorgesclagten iterativen Verfahrens zur Detektion von kleinen lokalen Steifikeitsänderungen wurde durch einen einfachen Versuch im Labor demonstriert. Die Anwendbarkeit des Vorgehen hängt jedoch größtenteils von experimenteller Kapazität ab. Es wurde auch festgestellt, dass ein solcher Versuch mit einem erheblichen versuchstechnischen Mehraufwand und zeitraubender Arbeit verbunden ist

Support Vector Machine for Regression of Ultimate Strength of Trusses: A Comparative Study

Thanks to the rapid development of computer science, direct analyses have been increasingly used in the design of structures in lieu of member-based design methods using the effective length factor. In a direct analysis, the ultimate strength of a whole structure can be sufficiently estimated, so that the need for member capacity checks is eliminated. However, in complicated structural design problems where many structural analyses are required, the use of direct analyses requires an excessive computation cost. In such cases, Machine Learning (ML) algorithms are used to build metamodels that can predict the structural responses without performing costly structural analysis. In this paper, the support vector machine (SVM) algorithm is employed for the first time to develop a metamodel for predicting the ultimate strength of trusses using direct analysis. Several kernel functions for the SVM model, including linear, sigmoid, polynomial, radial basis function (RBF), are considered. A planar 39-bar nonlinear inelastic steel truss is taken to study the performance of the kernel functions. The results confirm the applicability of the SVM-based metamodel for predicting the ultimate strength of trusses. In particular, the RBF appears to be the best kernel among others. This investigation also provides a deeper understanding of the effect of the parameters on the efficiency of the kernel functions

ADAPTIVE EXCITATION FOR SELECTIVE SENSITIVITY-BASED STRUCTURAL IDENTIFICATION

Major problems of applying selective sensitivity to system identification are requirement of precise knowledge about the system parameters and realization of the required system of forces. This work presents a procedure which is able to deriving selectively sensitive excitation by iterative experiments. The first step is to determine the selectively sensitive displacement and selectively sensitive force patterns. These values are obtained by introducing the prior information of system parameters into an optimization which minimizes the sensitivities of the structure response with respect to the unselected parameters while keeping the sensitivities with respect to the selected parameters as a constant. In a second step the force pattern is used to derive dynamic loads on the tested structure and measurements are carried out. An automatic control ensures the required excitation forces. In a third step, measured outputs are employed to update the prior information. The strategy is to minimize the difference between a predicted displacement response, formulated as function of the unknown parameters and the measured displacements, and the selectively sensitive displacement calculated in the first step. With the updated values of the parameters a re-analysis of selective sensitivity is performed and the experiment is repeated until the displacement response of the model and the actual structure are conformed. As an illustration a simply supported beam made of steel, vibrated by harmonic excitation is investigated, thereby demonstrating that the adaptive excitation can be obtained efficiently

Fuzzy analysis of laterally-loaded pile in layered soil

A fuzzy finite-element method for analysis of laterally loaded pile in multi-layer soil with uncertain properties is presented. The finite-element formulation is established using a beam-on-two-parameter foundation model. Uncertainty propagation of the soil parameters to the pile response is evaluated by a perturbation technique. This approach requires a few number of classical finite-element equations to be solved and provides reasonable results. A comparison with vertex method is made in a numerical example