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For internal flows with small values of the Reynolds number, there is often at a considerable distance from the pipe inlet cross-section a change of the flow form from laminar to turbulent. To describe this phenomenon of laminar-turbulent transition in the pipe, also parallel-plate channel flow, a modified algebraic intermittency model was used. The original model for bypass transition developed by S. Kubacki and E. Dick was designed for simulating bypass transition in turbomachinery.

A modification of mentioned model was proposed. Modified model is suitable for simulating internal flows in pipes and parallel-plate channels. Implementation of the modified model was made using the OpenFOAM framework. Values of several constants of the original model were modified.

For selected Reynolds numbers and turbulence intensities (Tu), localization of laminar breakdown and fully turbulent flow was presented. Results obtained in this work were compared with corresponding experimental results available in the literature. It is particularly worth noting that asymptotic values of wall shear stress in flow channels and asymptotic values of axis velocity obtained during simulations are similar to related experimental and theoretical results.

The modified model allows precision numerical simulation in the area of transitional flow between laminar, intermittent and turbulent flows in pipes and parallel-plate channels. Proposed modified algebraic intermittency model presented in this work is described by a set of two additional partial differential equations corresponding with k-omega turbulence model presented by Wilcox (Wilcox, 2006).

In order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based on the positive and inverse grey relational degrees.

Based on 82 soil sample data collected in Daiyue District, Tai'an City, Shandong Province, firstly, the spectral data of soil samples are transformed by the first order differential and logarithmic reciprocal first order differential and so on, the correlation coefficients between the transformed spectral data and soil organic matter content are calculated, and the estimation factors are selected according to the principle of maximum correlation. Secondly, the positive and inverse grey relational degree model is used to identify the samples to be identified, and the initial estimated values of the organic matter content are obtained. Finally, based on the difference information between the samples to be identified and their corresponding known patterns, a modified model for the initial estimation of soil organic matter content is established, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the methods of logarithmic reciprocal first order differential and the first-order differential of the square root for transforming the original spectral data are more effective, which could significantly improve the correlation between soil organic matter content and spectral data. The modified model for hyperspectral estimation of soil organic matter has high estimation accuracy, the average relative error (MRE) of 11 test samples is 4.091%, and the determination coefficient (R²) is 0.936. The estimation precision is higher than that of linear regression model, BP neural network and support vector machine model. The application examples show that the modified model for hyperspectral estimation of soil organic matter content based on positive and inverse grey relational degree proposed in this article is feasible and effective.

The model in this paper has clear mathematical and physics meaning, simple calculation and easy programming. The model not only fully excavates and utilizes the internal information of known pattern samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation of soil organic matter. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

The paper succeeds in realizing both a modified model for hyperspectral estimation of soil organic matter based on the positive and inverse grey relational degrees and effectively dealing with the randomness and grey uncertainty in spectral estimation.

Piezoelectric actuators (PEAs) exhibit hysteresis nonlinearity in open-loop operation, which may lead to unwanted inaccuracy and limit system performance. Classical Preisach model is widely used for representing hysteresis but it requires a large number of first-order reversal curves to ensure the model accuracy. All the curves may not be obtained due to the limitations of experimental conditions, and the detachment between the major and minor loops is not taken into account. The purpose of this paper is to propose a modified Preisach model that requires relatively few measurements and that describes the detachment, and then to implement the inverse of the modified model for compensation in PEAs.

The classical Preisach model is modified by adding a derivative term in parallel. The derivative gain is adjusted to an appropriate value so that the measured and predicted hysteresis loops are in good agreement. Subsequently, the new inverse model is similarly implemented by adding another derivative term in parallel with the inverse classical Preisach model, and is then inserted in open-loop operation to compensate the hysteresis. Tracking control experiments are conducted to validate the compensation.

The hysteresis in PEAs can be accurately and conveniently described by using the modified Preisach model. The experimental results prove that the hysteresis effect can be nearly completely compensated.

The proposed modified Preisach model is an effective and convenient mean to characterize accurately the hysteresis. The compensation method by inserting the inverse modified Preisach model in open-loop operation is feasible in practice.

Proposing an improved model for evaluating criticality of non-value added (waste) in operation is necessary for realizing sustainable manufacturing practices. The purpose of this paper is concerning on improvement of the decision support model for evaluating risk criticality lean waste occurrence by considering the weight of modified FMEA indices and the influence of waste-worsening factors causing the escalation of waste risk magnitude.

Integration of entropy and Taguchi loss function into decision support model of modified FMEA is presented to rectify the limitation of previous risk reprioritization models in modified FMEA studies. The weight of the probability components and loss components are quantified using entropy. A case study from industry is used to test the applicability of the integration model in practical situation.

The proposed model enables to overcome the limitations of using subjective determination on the weight of modified FMEA indices. The inclusion of the waste-worsening factors and Taguchi loss functions enables the FMEA team to articulate the severity level of waste consequences appropriately over the use of ordinal scale in ranking the risk of lean waste in modified FMEA references.

When appraising the risk of lean waste criticality, ignorance on weighting of FMEA indices may be inappropriate for an accurate risk-based decision-making. This paper provides insights to scholars and practitioners and others concerned with the lean operation to understand the significance of considering the impact of FMEA indices and waste-worsening factors in evaluating criticality of lean waste risks.

The method adopted is for quantifying the criticality of lean waste and inclusion of weighting of FMEA indices in modified FMEA provides insight and exemplar on tackling the risk of lean waste and determining the most critical waste affecting performability of company operations.

Integration of the entropy and Taguchi loss function for appraising the criticality of lean waste in modified FMEA is the first in the lean management discipline. These findings will be highly useful for professionals wishing to implement the lean waste reduction strategy.

The purpose of this paper is to empirically test a new disabled service user‐specific service quality model ARCHSECRET against a modified SERVQUAL model in the context of disabled students within higher education.

The application of SERVQUAL in the voluntary sector had raised serious issues on its portability into this sector in general and its ability to measure the experience of the disabled service user in particular. In consequence, a disabled service user‐specific service quality model – ARCHSECRET – was developed which led to this research being designed to compare ARCHSECRET and a modified SERVQUAL model in terms of their ability to predict and explain the variation in the service quality experience of disabled students in higher education.

ARCHSECRET was superior to the modified SERVQUAL in terms of its overall predictive power; ARCHSECRET key drivers were different and better in predictive power than those of the modified SERVQUAL; and ARCHSECRET was found to be reliable and valid for the measurement of the disabled student experience in higher education, while acting as a diagnostic tool for the identification of service quality shortfalls.

The reported research should be regarded as a pilot study whose results are worthy of further investigation among larger samples of disabled service users.

It is held that the disabled service user‐specific ARCHSECRET model has made a positive contribution to the measurement of service quality within the context of disabled students in higher education while demonstrating its superiority over the SERVQUAL scale which did not quite “measure up”.

The use of models for detecting earnings management in the academic literature, using accrual and real manipulation, is commonplace. The purpose of the current study is to compare the power of these models in a United Kingdom (UK) sample of 19,424 firm-year observations during the period 1991–2018. The authors include artificially-induced manipulation of revenues and expenses between zero and ten percent of total assets to random samples of 500 firm-year observations within the full sample. The authors use two alternative samples, one with no reversal of manipulation (sample 1) and one with reversal in the following year (sample 2).

The authors include artificially induced manipulation of revenues and expenses between zero and ten percent of total assets to random samples of 500 firm-year observations within the full sample.

The authors find that real earnings manipulation models have lower power than accrual earnings manipulation models, when manipulating discretionary expenses and revenues. Furthermore, the real earnings manipulation model to detect overproduction has high misspecification, resulting in artificially inflating the power of the model. The authors examine an alternative model to detect discretionary expense manipulation that generates higher power than the Roychowdhury (2006) model. Modified real manipulation models (Srivastava, 2019) are used as robustness and the authors find these to be more misspecified in some cases but less in others. The authors extend the analysis to a setting in which earnings management is known to occur, i.e. around benchmark-beating and find consistent evidence of accrual and some forms of real manipulation in this sample using all models examined.

This study contributes to the literature by providing evidence of misspecification of currently used models to detect real accounts manipulation.

Based on the findings, the authors recommend caution in interpreting any findings when using these models in future research.

The findings address the earnings management literature, guided by the agency theory.

This paper aims to propose a precise turbulence model for vehicle aerodynamics, especially for vehicle window buffeting noise.

Aiming at the fact that commonly used turbulence models cannot precisely predict laminar-turbulent transition, a transition-code-based improvement is introduced. This improvement includes the introduction of total stress limitation (TSL) and separation-sensitive model. They are integrated into low Reynolds number (LRN) k-ε model to concern transport properties of total stress and precisely capture boundary layer separations. As a result, the ability of LRN k-ε model to predict the transition is improved. Combined with the constructing scheme of constrained large-eddy simulation (CLES) model, a modified LRN CLES model is achieved. Several typical flows and relevant experimental results are introduced to validate this model. Finally, the modified LRN CLES model is used to acquire detailed flow structures and noise signature of a simplified vehicle window. Then, experimental validations are conducted.

Current results indicate that the modified LRN CLES model is capable of achieving acceptable accuracy in prediction of various types of transition at various Reynolds numbers. And, the ability of this model to simulate the vehicle window buffeting noise is greater than commonly used models.

Based on the TSL idea and separation-sensitive model, a modified LRN CLES model concerning the laminar-turbulent transition for the vehicle window buffeting noise is first proposed.

This paper aims to propose an automatic and direct method to manipulate global parameters of the object for prototyping and simulation, given an STL mesh model of a thin-walled object. Proposed method is useful in rapid prototyping, where changing the global parameters such as thickness, scaling local features or draft of walls of an STL mesh is often required. Presently, user needs to iterate over the cycle of modification of the computer-aided design (CAD) model and tessellating it to change the global parameters. The proposed algorithm eliminates the need for CAD model while manipulating those global properties, as it works directly with the mesh model.

Proposed algorithm automatically identifies walls and its thickness, and then, it extracts mid-surface from each wall. Global parameters are then modified by using these mid-surfaces.

Mesh directly modified and the mesh obtained by tessellating modified CAD model has same global properties; proposed method can also allow multiple parameters to be modified at the same time.

Input STL model is assumed to be error-free, where models containing errors like self-intersection will lead to incorrect mid-surfaces. Present algorithm assumes that the mid-surface represent of the input STL model is a manifold surface.

A novel algorithm of directly manipulating global parameters of a thin-walled object in its STL mesh model is proposed. The paper also presents a novel method of extracting mid-surface representation from a thin-wall STL mesh.

This paper aims to provide designers/engineers, in engineering structural design and analysis, approaches to freely and accurately modify structures (geometric and/or material), and then quickly provide real-time capability to obtain the numerical solutions of the modified structures (designs).

The authors propose an isogeometric independent coefficients (IGA-IC) method for a fast reanalysis of structures with geometric and material modifications. Firstly, the authors seamlessly integrate computer-aided design (CAD) and computer-aided engineering (CAE) by capitalizing upon isogeometric analysis (IGA). Hence, the authors can easily modify the structural geometry only by changing the control point positions without tedious transformations between CAE and CAD models; and modify material characters simply based on knots vectors. Besides, more accurate solutions can be obtained because of the high order degree of the spline functions that are used as interpolation functions. Secondly, the authors advance the proposed independent coefficients method within IGA for fast numerical simulation of the modified designs, thereby significantly reducing the enormous time spent in repeatedly numerical evaluations.

This proposed scheme is efficient and accurate for modifying the structural geometry by simply changing the control point positions, and material characters by knots vectors. The enormous time spent in repeated full numerical simulations for reanalysis is significantly reduced. Hence, enabling quickly modifying structural geometry and material, and analyzing the modified model for practicality in design stages.

The authors herein advance and propose the IGA-IC scheme. Where, it provides designers to fasten and simple designs and modify structures (both geometric and material). It then can quickly in real-time obtain numerical solutions of the modified structures. It is a powerful tool in practical engineering design and analysis process for local modification. While this method is an approximation method designed for local modifications, it generally cannot provide an exact numerical solution and its effectiveness for large modification deserves further study.

The standard market models assume that all investors are rational with the same level of risk aversion, whereas investors in the real world are neither rational nor homogeneous. This contrast makes these models inappropriate for evaluating manager skill. The purpose of this paper is to attempt to bridge the gap between model assumption and fund investment practice.

This study proposes a series of modified models using the excess return of peer funds to estimate fund alpha. In these models, the market excess return in the standard market models is replaced with the average excess return of bootstrapped funds. In addition, the author examines the reasons for the difference between the modified models and the standard models.

The modified models better explain the variation of fund returns, and they exhibit that a considerably higher percentage of funds can earn positive alpha, thus the skill of fund managers is underestimated based on the standard market models.

The proposed models provide a more reliable method for investors to identify skilled fund managers, and they can also serve as an objective benchmark in evaluating fund performance and in designing manager compensation packages.