Studying effects of joint characteristics on the performance of a cantilever beam shaped piezoelectric energy harvester through joint identification

In energy harvesting systems, specifications of the generated electrical energy depend on the structure’s dynamics. This dependence can be used to identify the system’s joint characteristics. To this end, an innovative frequency-response-function (FRF) based identification method is presented. The investigated system is a cantilever beam shaped structure with an embedded bimorph piezoelectric bender, connected to a base via bolted joint as a depiction for wing of a UAV connected to fuselage. The implemented FRF is ratio of the piezoelectric output voltage to the base input displacement. The joint identification procedure consists of analytical modeling of the system with joint, experimental testing of the system and a real-coded Genetic Algorithm (GA) method. The joint is modeled as a combination of longitudinal and torsional springs, whose stiffnesses are obtained using the GA method. The obtained results indicate that the analytical model has good correlation with the experimental data. Then, effects of the joint characteristics on the energy harvester’s performance are investigated by comparison of the system with two different joint assumptions, namely, rigid and realistic joint. Finally, the effects of various joint characteristics on the energy harvester’s performance are presented and approaches to achieve the maximum performance of the system are suggested.

Contaminant Source Identification in Aquifers: A Critical View

Forty years and 157 papers later, research on contaminant source identification has grown exponentially in number but seems to be stalled concerning advancement towards the problem solution and its field application. This paper presents a historical evolution of the subject, highlighting its major advances. It also shows how the subject has grown in sophistication regarding the solution of the core problem (the source identification), forgetting that, from a practical point of view, such identification is worthless unless it is accompanied by a joint identification of the other uncertain parameters that characterize flow and transport in aquifers.

Joint DNA-based disaster victim identification

We address computational and statistical aspects of DNA-based identification of victims in the aftermath of disasters. Current methods and software for such identification typically consider each victim individually, leading to suboptimal power of identification and potential inconsistencies in the statistical summary of the evidence. We resolve these problems by performing joint identification of all victims, using the complete genetic data set. Individual identification probabilities, conditional on all available information, are derived from the joint solution in the form of posterior pairing probabilities. A closed formula is obtained for the a priori number of possible joint solutions to a given DVI problem. This number increases quickly with the number of victims and missing persons, posing computational challenges for brute force approaches. We address this complexity with a preparatory sequential step aiming to reduce the search space. The examples show that realistic cases are handled efficiently. User-friendly implementations of all methods are provided in the R package dvir, freely available on all platforms.

Joint DNA-based Disaster Victim Identification

We address computational and statistical aspects of DNA-based identification of victims in the aftermath of disasters. Current methods and software for such identification typically consider each victim individually, leading to suboptimal power of identification and potential inconsistencies in the statistical summary of the evidence. We resolve these problems by performing joint identification of all victims, using the complete genetic data set. Individual identification probabilities, conditional on all available information, are derived from the joint solution in the form of posterior pairing probabilities. A closed formula is obtained for the a priori number of possible joint solutions to a given DVI problem. This number increases quickly with the number of victims and missing persons, posing computational challenges for brute force approaches. We address this complexity with a preparatory sequential step aiming to reduce the search space. The examples show that realistic cases are handled efficiently. User-friendly implementations of all methods are provided in the R package dvir, freely available on all platforms.

Efficient joint identification and fluted segment modelling of shrink-fit tool assemblies by updating extended tool models

In milling, the dynamic behavior of the tool center point is crucial for estimating surface quality of the workpiece as well as the process stability behavior. Experimental-analytical receptance coupling can be used for predicting the tool tip dynamics but requires accurate analytical modelling of the holder-tool assembly. This includes the reliable identification of the holder-tool joint properties as well as the correct modelling of the fluted segment of end mills. However, the modelling effort associated with accurately representing the dynamic behavior of the fluted segment is significant. In addition, the joint identification requires a reference tool tip frequency response function of the tool assembly clamped in the machine spindle. This is inefficient and can also lead to incorrect estimation of joint properties. This paper provides an efficient method for joint identification and fluted section modelling using an offline, free–free excitation approach. The objective of this paper is to enable a direct comparison of the dynamic behavior of the freely constrained analytical tool assembly model with that of the real freely constrained tool assembly. The comparison of displacement to force frequency response at certain points on the tool assembly allows for the identification of tool model parameters such as the joint properties and effective diameter of the fluted segment. The comparability is realized by extending the analytical holder-tool beam model to include the receptance model of the standard spindle-holder interface. In this study, as an example, a thermal shrink-fit holder-tool beam model is extended to include an HSK-A63 interface. Subsequently, frequency response functions at two points on the real freely constrained tool assembly are measured in order to identify the joint stiffness and effective diameter of the fluted segment using the corresponding proposed formulations. The updated holder-tool model is then coupled with a 4-axis milling machine and validated. Despite the reduced modelling effort, a good prediction accuracy could be achieved for different holder-tool combinations.