A framework for data-driven structural analysis in general elasticity based on nonlinear optimization: The dynamic case

In this article, we present an extension of the formulation recently developed by the authors to the structural dynamics setting. Inspired by a structure-preserving family of variational integrators, our new formulation relies on a discrete balance equation that establishes the dynamic equilibrium. From this point of departure, we first derive an “exact” discrete-continuous nonlinear optimization problem that works directly with data sets. We then develop this formulation further into an “approximate” nonlinear optimization problem that relies on a general constitutive model. This underlying model can be identified from a data set in an offline phase. To showcase the advantages of our framework, we specialize our methodology to the case of a geometrically exact beam formulation that makes use of all elements of our approach. We investigate three numerical examples of increasing difficulty that demonstrate the excellent computational behavior of the proposed framework and motivate future research in this direction.     

Numerical approach of cyclic behaviour of 316LN stainless steel based on a polycrystal modelling including strain gradients

A non-local polycrystal approach, taking into account strain gradients, is proposed to simulate the 316LN stainless steel fatigue life curve in the hardening stage. Material parameters identification is performed on tensile curves corresponding to several 316LN polycrystals presenting different grain sizes. Applied to an actual 3D aggregate of 316LN stainless steel of 1200 grains, this model leads to an accurate prediction of cyclic curves. Geometrical Necessary Dislocation densities related to the computed strain gradient are added to the micro-plasticity laws. Compared to standard models, this model predicts a decrease of the local stresses as well as a grain size effect.     

A novel approach for supplier selection based on the Kano model and fuzzy MCDM

This paper proposes a three-phase approach for supplier selection based on the Kano model and fuzzy Multi Criteria Decision-Making. Since the supplier selection problem involves different criteria, quality attributes have been assumed to denote the importance weight of the criteria for supplier selection. Furthermore, to consider the inherent vagueness of human thought, a fuzzy logic has been utilised. Initially, the importance weight of the criteria has been calculated using a fuzzy Kano questionnaire and fuzzy analytic hierarchy process. In the second phase, the Fuzzy TOPSIS technique has been used to screen out in capable suppliers. Finally, in the third phase, the filtered suppliers which are qualified, once again will be evaluated by the same approach for the final ranking. The proposed approach has also been examined in a case study.     

基于高斯过程回归的光纤陀螺温度漂移补偿

基于实测数据分析了全温度全速率下的陀螺漂移特性,提出了一种基于高斯过程回归的补偿新方法.该方法通过超参数训练直接建立了温度、陀螺输出和载体角速率之间的映射关系,弥补了传统方法对零偏和标度因数分别进行建模导致引入两次补偿误差的不足,在提高补偿精度的同时,简化了补偿步骤.仿真结果表明,相较于最小二乘支持向量回归方法,由高斯过程回归方法训练得到的模型能够更加准确地描述温度漂移特性,具有较高的预测补偿精度和良好的泛化能力,预测均方根误差小于0.003(°)/s,有效抑制了温度对光纤陀螺精度的影响.     

A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system

This paper presents a similarity-based approach for prognostics of the Remaining Useful Life (RUL) of a system, i.e. the lifetime remaining between the present and the instance when the system can no longer perform its function. Data from failure dynamic scenarios of the system are used to create a library of reference trajectory patterns to failure. Given a failure scenario developing in the system, the remaining time before failure is predicted by comparing by fuzzy similarity analysis its evolution data to the reference trajectory patterns and aggregating their times to failure in a weighted sum which accounts for their similarity to the developing pattern. The prediction on the failure time is dynamically updated as time goes by and measurements of signals representative of the system state are collected. The approach allows for the on-line estimation of the RUL. For illustration, a case study is considered regarding the estimation of RUL in failure scenarios of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS).     

An efficient approach for statistical moments estimation of structural response based on a novel adaptive hybrid dimension-reduction method

Statistical moments estimation is one of the main topics for the analysis of a stochastic system, but the balance among the accuracy, efficiency, and versatility for different methods of statistical moments estimation still remains a challenge. In this paper, a novel point estimate method (PEM) based on a new adaptive hybrid dimension-reduction method (AH-DRM) is proposed. Firstly, the adaptive cut-high-dimensional model representation (cut-HDMR) is briefly reviewed, and a novel AH-DRM is developed, where the high-order component functions of the adaptive cut-HDMR are further approximated by multiplicative forms of the low-order component functions. Secondly, a new point estimation method (PEM) based on the AH-DRM is proposed for statistical moments estimation. Finally, several examples are investigated to demonstrate the performance of the proposed PEM. The results show the proposed PEM has fairly high accuracy and good versatility for statistical moments estimation.     

Modal criteria for optimizing the acoustic behaviour of a coupled fluid–structure system based on a systemic approach

This paper proposes modal criteria to represent various noise sources within a complex structure, such as an automobile. By optimizing a complex system using criteria linked to modal mass and stiffness matrices, different modes of noise propagation can be investigated separately. Several criteria are thus suggested, each related to a vibrational propagation path. Since the system is studied using modal analysis, criteria can be found based on modes associated with the structure's hollow parts, plates, and cavities. These different criteria are analysed based on the assumption of a complex vibroacoustic system. It is shown that by analysing such criteria, one can determine which part of the structure needs to be optimized. The optimization of such a system could constitute a research topic in its own right, and is beyond the scope of the present paper. Copyright © 2007 John Wiley & Sons, Ltd.     

Developing a novel approach to assess the efficiency of resource utilisation in organisations: a case study for an automotive supplier

There are different business processes in each organisation. Each business process utilises some resources to perform its related activities, produces specified outputs (products/services) and reaches organisational goals. In this paper, an approach is presented to assess the ability of business processes to utilise resources. To apply the presented framework, a manufacturing firm in the automotive industry was selected. Initially, four main business process groups were chosen for the assessment. Then, 15 processes of the four determined process groups and resources utilised by each process were identified. All of the recognised 19 resources were classified into six major categories, including physical, relational, organisational, informational, human and legal resources. Afterward, a hierarchical top-down analysis was performed to determine the ability of process groups and processes to utilise resource categories and resources. The results of the analysis show which resources have been strongly/weakly utilised by which business processes. In other words, by applying the suggested framework, it is possible to accurately identify the strengths and weaknesses of the resource utilisation. Therefore, the company can focus on weaknesses, prioritise them and develop improvement actions to increase the ability to utilise resources in the specified areas.     

Development of a genetic algorithm‐based lookup table approach for efficient numerical integration in the method of finite spheres with application to the solution of thin beam and plate problems

It is observed that for the solution of thin beam and plate problems using the meshfree method of finite spheres, Gaussian and adaptive quadrature schemes are computationally inefficient. In this paper, we develop a novel technique in which the integration points and weights are generated using genetic algorithms and stored in a lookup table using normalized coordinates as part of an offline computational step. During online computations, this lookup table is used much like a table of Gaussian integration points and weights in the finite element computations. This technique offers significant reduction of computational time without sacrificing accuracy. Example problems are solved which demonstrate the effectiveness of the procedure. Copyright © 2006 John Wiley & Sons, Ltd.     

A novel test configuration design method for inverse identification of in‐plane moduli of a composite plate under the PFEUM framework

We propose a novel sensitivity based approach that predicts and explains the accuracy of material parameter identification for a composite plate using the Projected Finite Element Update Method. A typical experiment using the Projected Finite Element Update Method technique involves a plate specimen held at 3 or 4 supports and bent under the application of a point load. Two‐Dimensional Digital Image Correlation is used to measure the pseudo displacements resulting from the projection of out‐of‐plane deflection of the plate onto the image plane. A cost function relating the projected numerical and experimental displacement fields is then minimised to obtain the material parameters. It is shown that the contribution of a specific material parameter in the observed displacement field influences the accuracy of its identification. The contributions from material parameters are first quantified in terms of sensitivity criterion that may be tailored by changing the elements of test configuration such as location of supports, the load application point, and the specimen geometry. Several test configurations are designed by maximising the sensitivities corresponding to individual material parameters. The relevance of proposed sensitivity criterion in these configurations is then validated through material identification in simulated experiments with added Gaussian noise. Finally, a thin CFRP plate is tested under these configurations to demonstrate the practical use of this approach. The proposed approach helps in robust estimation of the in‐plane elastic moduli from a bent composite plate with a simple Two‐Dimensional Digital Image Correlation setup without requiring measurement of the actual plate deflection or curvatures.     

A facile sonochemical approach based on graphene carbon nitride doped silver molybdate immobilized nafion for selective and sensitive electrochemical detection of chromium (VI) in real sample

The hexavalent Chromium (Chromium (VI) (or) Cr⁶⁺) is considered as the most toxic element that directly affects the environment and the human cycle adversely. Therefore, in the present study we proposed a sensitive and selective electrochemical sensor for the detection of Cr⁶⁺ in various water samples using graphene carbon nitride decorated with silver molybdate immobilized with nafion (g-C₃N₄/AgM/Nf) modified glassy carbon electrode (GCE). The nanocomposite was synthesized by sonochemical method and characterized by various analytical and spectroscopic techniques. The g-C₃N₄/AgM/Nf/GCE was exceptional and showed massive electrocatalytic change via the appearance of peaks in amperometric i-t technique. Cr⁶⁺ has a reduction peak appearing at 0.1–0.7 μM, with a low limit of detection of 0.0016 μM, and sensitivity of 65.8 µAµM⁻¹cm⁻². The g-C₃N₄/AgM/Nf/GCE sensor for Cr⁶⁺ exhibited a good selectivity, stability, sensitivity, and reproducibility, which certified that the g-C₃N₄/AgM/Nf/GCE is a promising electrode. The electrochemical sensing method also accessed for the recognition of Cr⁶⁺ in different water samples and showed good feasibility with superior recovery.     

A novel method for determination of the filling level in the feed frame of a rotary tablet press

Aim: The aim of this study was to investigate whether the filling level within the feed frame of a rotary tablet press can be quantified by laser triangulation combined with the angle recognition of one paddle wheel via rotary encoder.

Significance: Rotary tablet press feed frames are supposed to assure a uniform die filling and, thus, to guarantee the weight and content uniformity of the resulting tablets. Therefore, a constant bulk availability and flow within the feed frame is crucial and has to be ensured by the feed frame design and the operating conditions. So far, there is no instrument available to monitor the bulk filling level or the bulk distribution within feed frames.

Methods: Calcium phosphate dihydrate was used as model powder. The powder surface level was determined via laser triangulation and the angle position of the paddle wheel was monitored via incremental rotary encoder. The data of both parameters was acquired synchronously and evaluated by in-house written software.

Results: Different powder masses led to significantly different filling level signals. The experiments showed a high reproducibility of the determined filling levels. Furthermore, an influence of the rotational speed on the powder distribution was observed.

Conclusions: The developed instrument may be used for quantification of the volumetric filling level within rotary tablet press feed frames. It may either be used to better understand the powder behavior within feed frames or for improvement of the die filling process by implementing the device into a feedback loop.     

A global scheme for controlling the mean and standard deviation of a process based on the optimal design of gauges

Control charts for variables are a reference tool for the statistical monitoring of a quantitative variable. However, there are processes in which the exact measurement of the variable is highly complex or costly. In these cases, one option is to base the monitoring on the number of units that are classified by a gauge as being above, below, or between a pair of reference limits. With this approach, the process control achieves the economy and agility of control by attributes. In the literature, one can find different alternatives to such schemes. In this paper, a more general formulation is introduced that subsumes many of the previously proposed schemes into a new control scheme by means of an additional parameter. Through a specially designed optimization procedure, it is possible to obtain the parameters that maximize the scheme's performance against a specified process shift.     

A novel approach for predicting risk of vector-borne disease establishment in marginal temperate environments under climate change: West Nile virus in the UK

Vector-borne diseases (VBDs), such as dengue, Zika, West Nile virus (WNV) and tick-borne encephalitis, account for substantial human morbidity worldwide and have expanded their range into temperate regions in recent decades. Climate change has been proposed as a likely driver of past and future expansion, however, the complex ecology of host and vector populations and their interactions with each other, environmental variables and land-use changes makes understanding the likely impacts of climate change on VBDs challenging. We present an environmentally driven, stage-structured, host–vector mathematical modelling framework to address this challenge. We apply our framework to predict the risk of WNV outbreaks in current and future UK climates. WNV is a mosquito-borne arbovirus which has expanded its range in mainland Europe in recent years. We predict that, while risks will remain low in the coming two to three decades, the risk of WNV outbreaks in the UK will increase with projected temperature rises and outbreaks appear plausible in the latter half of this century. This risk will increase substantially if increased temperatures lead to increases in the length of the mosquito biting season or if European strains show higher replication at lower temperatures than North American strains.     

A novel method for evaluating the secondary voltages of transformers in primary substations based on sensitivity coefficients

This paper provides a novel method to evaluate the secondary voltage of transformers in primary substations. The method is focused on the secondary voltage deviation which is a function of real power, reactive power, tap position, and primary voltage of the transformer in transmission systems. It helps dispatchers calculate and predict voltage deviation before introducing voltage regulation and reactive power compensation. The proposed approach is first to derive the formula of secondary voltage sensitivity coefficients from power flow equations. Real operational data are then substituted into the formula to get sensitivity coefficients. Finally, the corresponding secondary voltage is calculated with the sensitivity coefficients obtained. Implementing historical data of three substations in the above algorithm shows that the errors between calculated and actual voltages are less than 0.15%. The proposed method can not only accurately estimate the secondary voltage of the primary substation, but also be used to calculate the compensating reactive power as well as determine the tap position of the transformer, thereby improving the voltage and reactive power control strategies of power transmission systems.     

A global view on ARAMIS, a risk assessment methodology for industries in the framework of the SEVESO II directive

The ARAMIS methodology was developed in an European project co-funded in the fifth Framework Programme of the European Commission with the objective to answer the specific requirements of the SEVESO II directive. It offers an alternative to purely deterministic and probabilistic approaches to risk assessment of process plants. It also answers the needs of the various stakeholders interested by the results of the risk assessment for land use or emergency planning, enforcement or, more generally, public decision-making. The methodology is divided into the following major steps: identification of major accident hazards (MIMAH), identification of the safety barriers and assessment of their performances, evaluation of safety management efficiency to barrier reliability, identification of reference accident scenarios (MIRAS), assessment and mapping of the risk severity of reference scenarios and of the vulnerability of the plant surroundings. The methodology was tested during five case studies, which provided useful information about the applicability of the method and, by identifying the most sensitive parts of it opened way to new research activity for an improved industrial safety.