A Regression Model to Determine Pressure Ratings of Slotted Tubular Components With Imperfections

2021 ◽  
Author(s):  
Shravan Kumar Remma ◽  
Devaiah B.B. ◽  
Guijun Deng
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Design Space ◽  
Approximate Model ◽  
Computational Time ◽  
Design Parameters ◽  
Radial Deformation ◽  
Input Design ◽  
Effective Design ◽  
Tubular Components

Abstract The objective of the present work is to develop a prediction model to predict the pressure rating accounting for the imperfections using Design of Experiments (DoE) approach. Each imperfection is modelled in terms of a design parameter and bounded by a certain realistic variation. The DoE approach aids in identifying the most influencing imperfections and in further developing an approximate model that relates the input design parameters to the maximum radial deformation. Furthermore, the regression model is built to obtain the pressure rating of the component accounting for the imperfections. The study identifies that ovality and its orientation are by far the most influential imperfections when compared to others and provides the designer the effects of imperfection parameters on the component. The prediction model developed, serves as a ready reckoner to obtain the pressure rating of the component within the design space without any exclusive analysis. This approach leads to effective design and saves computational time.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

scholarly journals Evaluation of Static Mapping for Dynamic Space-Shared Multi-task Processing on FPGAs

2021 ◽  
Author(s):  
Umar Ibrahim Minhas ◽  
Roger Woods ◽  
Georgios Karakonstantis
Keyword(s):  
High Performance ◽  
Design Space Exploration ◽  
Design Space ◽  
System Throughput ◽  
Design Parameters ◽  
Temporal Constraints ◽  
Shared Resources ◽  
Task Processing ◽  
High Level ◽  
Performance Computing

AbstractWhilst FPGAs have been used in cloud ecosystems, it is still extremely challenging to achieve high compute density when mapping heterogeneous multi-tasks on shared resources at runtime. This work addresses this by treating the FPGA resource as a service and employing multi-task processing at the high level, design space exploration and static off-line partitioning in order to allow more efficient mapping of heterogeneous tasks onto the FPGA. In addition, a new, comprehensive runtime functional simulator is used to evaluate the effect of various spatial and temporal constraints on both the existing and new approaches when varying system design parameters. A comprehensive suite of real high performance computing tasks was implemented on a Nallatech 385 FPGA card and show that our approach can provide on average 2.9 × and 2.3 × higher system throughput for compute and mixed intensity tasks, while 0.2 × lower for memory intensive tasks due to external memory access latency and bandwidth limitations. The work has been extended by introducing a novel scheduling scheme to enhance temporal utilization of resources when using the proposed approach. Additional results for large queues of mixed intensity tasks (compute and memory) show that the proposed partitioning and scheduling approach can provide higher than 3 × system speedup over previous schemes.

Face Gear Width Prediction Using the DOE Method

2008 ◽  
Vol 130 (10) ◽  
Author(s):  
Michèle Guingand ◽  
Didier Remond ◽  
Jean-Pierre de Vaujany
Keyword(s):  
Regression Model ◽  
Simple Formula ◽  
Design Parameters ◽  
Face Gear ◽  
Main Design ◽  
Gear Design ◽  
Simple Regression Model ◽  
Numerical Tool ◽  
Influential Parameters ◽  
Simple Regression

This paper deals with face gear design. The goal is to propose a simple formula for predicting the width of the wheel as a function of the main design parameters. A specific software was used to achieve this goal. This numerical tool is able to simulate the geometry and the quasistatic loaded behavior of a face gear. The statistical method used for analyzing the influence of data is described: The design of experiments leads to a simple regression model taking into account the influential parameters and their couplings. In the last part of this paper, the results of the formulas are compared to those of the software and an optimal design is proposed based on the regression model.

Adaptive Geometry Representation of Turbine Vane Frames for Use in Optimization

2021 ◽  
Author(s):  
Sebastian F. Riebl ◽  
Christian Wakelam ◽  
Reinhard Niehuis
Keyword(s):  
Design Space ◽  
Cfd Simulation ◽  
A Priori ◽  
Three Dimensional ◽  
Optimization Method ◽  
Three Dimensions ◽  
Design Parameters ◽  
Adjustment Process ◽  
Turbine Vane ◽  
Integrated Concept

Abstract Turbine Vane Frames (TVF) are a way to realize more compact jet engine designs. Located between the high pressure turbine (HPT) and the low pressure turbine (LPT), they fulfill structural and aerodynamic tasks. When used as an integrated concept with splitters located between the structural load-bearing vanes, the TVF configuration contains more than one type of airfoil with sometimes pronouncedly different properties. This system of multidisciplinary demands and mixed blading poses an interesting opportunity for optimization. Within the scope of the present work, a full geometric parameterization of a TVF with splitters is presented. The parameterization is chosen as to minimize the number of parameters required to automatically and flexibly represent all blade types involved in a TVF row in all three dimensions. Typical blade design parameters are linked to the fourth order Bézier-curve controlled camber line-thickness parameterization. Based on conventional design rules, a procedure is presented, which sets the parameters within their permissible ranges according to the imposed constraints, using a proprietary developed code. The presented workflow relies on subsequent three dimensional geometry generation by transfer of the proposed parameter set to a commercially available CAD package. The interdependencies of parameters are discussed and their respective significance for the adjustment process is detailed. Furthermore, the capability of the chosen parameterization and adjustment process to rebuild an exemplary reference TVF geometry is demonstrated. The results are verified by comparing not only geometrical profile data, but also validated CFD simulation results between the rebuilt and original geometries. Measures taken to ensure the robustness of the method are highlighted and evaluated by exploring extremes in the permissible design space. Finally, the embedding of the proposed method within the framework of an automated, gradient free numerical optimization is discussed. Herein, implications of the proposed method on response surface modeling in combination with the optimization method are highlighted. The method promises to be an option for improvement of optimization efficiency in gradient free optimization of interdependent blade geometries, by a-priori excluding unsuitable blade combinations, yet keeping restrictions to the design space as limited as possible.

Design Parameters Prediction of New Type Gas Turbine Based on a Hybrid GRA-SVM Prediction Model

2017 ◽  
Author(s):  
Tingting Wei ◽  
Dengji Zhou ◽  
Jinwei Chen ◽  
Yaoxin Cui ◽  
Huisheng Zhang
Keyword(s):  
Prediction Model ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Technology Development ◽  
Design Parameters ◽  
Support Vector ◽  
Performance Parameters ◽  
New Type ◽  
Equipment Manufacture ◽  
Grey Relational

Since the late 1930s, gas turbine has begun to develop rapidly. To improve the economic and safety of gas turbine, new types were generated frequently by Original Equipment Manufacture (OEM). In this paper, a hybrid GRA-SVM prediction model is established to predict the main design parameters of new type gas turbines, based on the combination of Grey Relational Analysis (GRA) and Support Vector Machine (SVM). The parameters are classified into two types, system performance parameters reflecting market demands and technology development, and component performance parameters reflecting technology development and coupling connections. The regularity based on GRA determines the prediction order, then new type gas turbine parameters can be predicted with known system parameters. The model is verified by the application to SGT600. In this way, the evolution rule can be obtained with the development of gas turbine technology, and the improvement potential of several components can be predicted which will provide supports for overall performance design.

scholarly journals Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiao-Ying Xie ◽  
Ming-Wei Wang ◽  
Zu-Ying Hu ◽  
Cheng-Jian Cao ◽  
Cong Wang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Lasso Regression

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

scholarly journals Spherical Mechanism Synthesis in Virtual Reality

1998 ◽  
Author(s):  
Todd J. Furlong ◽  
Judy M. Vance ◽  
Pierre M. Larochelle
Keyword(s):  
Virtual Reality ◽  
Design Space ◽  
Three Dimensional ◽  
Mechanism Synthesis ◽  
Kinematic Synthesis ◽  
New Approach ◽  
Spherical Mechanisms ◽  
Input Design ◽  
Previous Design ◽  
Spherical Mechanism

Abstract This paper presents a new approach to using virtual reality (VR) to design spherical mechanisms. VR provides a three dimensional design space where a designer can input design positions using a combination of hand gestures and motions and view the resultant mechanism in stereo using natural head movement to change the viewpoint. Because of the three dimensional nature of the design and verification of spherical mechanisms, VR is examined as a new design interface in this research. In addition to providing a VR environment for design, the research presented in this paper has focused on developing a “design in context” approach to spherical mechanism design. Previous design methods have involved placing coordinate frames along the surface of a constraint sphere. The new “design in context” approach allows a designer to freely place geometric models of movable objects inside an environment consisting of fixed objects. The fixed objects could either act as a base for a mechanism or be potential sources of interference with the motion of the mechanism. This approach allows a designer to perform kinematic synthesis of a mechanism while giving consideration to the interaction of that mechanism with its application environment.

Combining Computational Fluid Dynamics (CFD) and Flow Network Modeling (FNM) for Design of a Multi-Chip Module (MCM) Cold Plate

2013 ◽  
Author(s):  
John Fernandes ◽  
Saeed Ghalambor ◽  
Akhil Docca ◽  
Chris Aldham ◽  
Dereje Agonafer ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Thermal Analysis ◽  
Computational Fluid Dynamics ◽  
Network Modeling ◽  
Computational Time ◽  
Design Parameters ◽  
Cold Plate ◽  
Original Solution ◽  
Flow Network ◽  
Computational Fluid Dynamics Cfd

The objective of the study is to improve on performance of the current liquid cooling solution for a Multi-Chip Module (MCM) through design of a chip-scale cold plate with quick and accurate thermal analysis. This can be achieved through application of Flow Network Modeling (FNM) and Computational Fluid Dynamics (CFD) in an interactive manner. Thermal analysis of the baseline cold plate design is performed using CFD to determine initial improvement in performance as compared to the original solution, in terms of thermal resistance and pumping power. Fluid flow through the solution is modeled using FNM and verified with results from the CFD analysis. In addition, CFD is employed to generate flow impedance curves of non-standard components within the cold plate, which are used as input for the Hardy Cross method in FNM. Using the verified flow network model, design parameters of different components in the cold plate are modified to promote uniform flow distribution to each active region in the chip-scale solution. Analysis of the resultant design using CFD determines additional improvement in performance over the original solution, if available. Thus, through complementary application of FNM and CFD, a robust cold plate can be designed without requiring expensive fabrication of prototypes and with minimal computational time and resources.

Vibroacoustic design optimization of curved composite shells

2021 ◽  
Vol 29 (9_suppl) ◽  
pp. S1520-S1531
Author(s):  
Rilwan K Apalowo ◽  
Dimitrios Chronopoulos
Keyword(s):  
Optimal Design ◽  
Structural Design ◽  
Composite Structures ◽  
Sensitivity Analyses ◽  
Computational Time ◽  
Design Parameters ◽  
Sandwich Shell ◽  
Acoustic Transmission ◽  
Composite Shells ◽  
Model Composite

The need to simultaneously optimize the structural design properties, and attain a satisfactory vibroacoustic performance for composite structures, has been a challenging task for modern structural engineers. This work is aimed at developing a statistical energy analysis (SEA) based numerical scheme for computing the optimal design parameters of each individual layer of layered curved shells having arbitrary complexities and layering. The main novelty of the work focuses on the computation of SEA properties for curved composite shells and derive the sensitivities of the acoustic transmission coefficient, expressed through the computed SEA properties, with respect to the structural design characteristics to be optimized. A wave finite element approach is employed to calculate the wave propagation constants of the curved shell. The calculated wave constants are then applied to compute the vibroacoustic properties for the curved shell using a SEA approach. Sensitivity analyses are conducted on the vibroacoustic properties to estimate their response to changes in the structural properties. Gradient vector is then formulated and hence the Hessian matrix, which is employed to formulate a Newton-like optimisation algorithm for optimizing the properties of the layered composite shell. The developed scheme is applied to a sandwich shell; optimal design parameters of [Formula: see text] and [Formula: see text] are obtained for the facesheet and the core of the shell whose base parameters are [Formula: see text] and [Formula: see text], respectively. This simultaneously optimizes the structure with maximum stiffness and minimum mass and attains a satisfactory dynamic performance for acoustic transmission through the sandwich shell. The principal advantage of the scheme is the ability to accurately model composite panels of arbitrary curvature at a rational computational time.

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