Effect of Thermal and Mechanical Deformation of Metamaterial FDM Components

At Lancaster University, research is currently investigating the use of rapid manufacturing (RM) to realise metamaterials, although key to the success of this project is the development of an understanding of how coated RM parts deform under thermal and mechanical stress. The research in this paper presents a comparison of the thermal and mechanical deformation behaviour of RM coated metamaterials components from a numerical context. The research uses the design of a simple metamaterial unit cell as a test model for both the experimental and finite element method (FEM). The investigation of deformation behaviour of sample Fused Deposition Modelling (FDM) parts manufactured in different orientations and simulated using commercial FEM code means that the FEM analysis can be utilized for design verification of FDM parts. This research contributes to further research into the development of RM metamaterials, specifically design analysis and verification tools for RM materials

Modeling and Optimal Design of Machining-Induced Residual Stresses in Aluminium Alloys Using a Fast Hierarchical Multiobjective Optimization Algorithm

The residual stresses induced during shaping and machining play an important role in determining the integrity and durability of metal components. An important issue of producing safety critical components is to find the machining parameters that create compressive surface stresses or minimise tensile surface stresses. In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows constructing transparent fuzzy models considering both accuracy and interpretability attributes of fuzzy systems. The new method employs a hierarchical optimisation structure to improve the modelling efficiency, where two learning mechanisms cooperate together: NSGA-II is used to improve the model’s structure while the gradient descent method is used to optimise the numerical parameters. This hybrid approach is then successfully applied to the problem that concerns the prediction of machining induced residual stresses in aerospace aluminium alloys. Based on the developed reliable prediction models, NSGA-II is further applied to the multi-objective optimal design of aluminium alloys in a ‘reverse-engineering’ fashion. It is revealed that the optimal machining regimes to minimise the residual stress and the machining cost simultaneously can be successfully located

Wavy Fin Profile Optimization Using NURBS for Air-To-Refrigerant Tube-Fin Heat Exchangers with Small Diameter Tubes

The major limitation of any air-to-refrigerant HX is the air side thermal resistance which can account for 90%, or more, of the overall thermal resistance. For this reason the secondary heat transfer surfaces (fins) play a major role in these HX’s by providing additional surface area. Many researchers extensively investigate how to improve the performance of fins. The most common passive heat transfer augmentation method applied to fins uses surface discontinuity; providing an efficient disruption-reattachment mechanism of the boundary layer. Such approach is leveraged by louvers, slits and even vortex generators. In some applications, however, these concepts are not adequate especially when there is high fouling or frosting, which is the case of many HVAC&R systems including heat pumps for cold climates. In such cases a continuous fin surface is required, which can usually be plain or wavy. The latter provides larger surface area and can induce turbulent flows improving the heat transfer. Normally the wavy fins are either a smooth sinusoidal or Herringbone profile, longitudinal to the airflow direction. In this paper we propose a novel wavy fin design method using Non-Uniform Rational B-Splines (NURBS) on both longitudinal and transverse directions. In this method the fin surface is subdivided in to 1 x n identical cells with periodic boundaries. The horizontal and vertical edges independently describe a NURBS curve on separate planes with the third spatial direction. The tools used in this work include automated CFD simulations, metamodeling and Multi-Objective Genetic Algorithm (MOGA). The analysis comprises of optimizing all wavy fin types, both the conventional ones and the novel designs presented in this paper, and compare their performance and compactness while fixing hydraulic diameter and Reynolds numbers. In conclusion, design recommendations for made for the use of the proposed novel fins.

Review of Shape and Topology Optimization for Design of Air-to-Refrigerant Heat Exchangers

Air-to-refrigerant heat exchangers (HXs) have been the topic of exhaustive research as they are fundamental components of HVAC&R systems. It has been well-established that the large airside thermal resistance dominates the HX thermal resistance, and thus significant research efforts have focused on improving the air-side performance of these heat exchangers. As HXs continue to become more compact, thermal resistance reduction is typically realized through the utilization of extended secondary heat transfer surfaces such as fins. However, past research has shown that the thermal-hydraulic trade-offs provided by fins are often not attractive enough to warrant their use, especially for small diameter tubes. Yet, the inadequate primary surface area provided by compact HXs essentially mandate the necessity of fins to meet thermal resistance requirements. In recent years, advancements in computational tools such as Computational Fluid Dynamics (CFD) and optimization algorithms, coupled with the advent of additive manufacturing technologies, have allowed engineers to expand conventional HX design ideologies to include such concepts as shape and topology optimization. This lends itself directly to primary heat transfer surface optimization and even the potential removal of finned surfaces altogether. This paper presents a comprehensive literature review investigating air-to-refrigerant HX shape and topology optimization. The fundamentals of both shape and topology optimization, model development, and experimental validations are all separately discussed. Studies featuring manufactured prototypes and/or experimentally validated optimal designs are treated with additional emphasis. This paper concludes by identifying key research gaps and proposing future research directions for HX shape and topology optimization

Computationally Efficient Optimization of a Five-Phase Flux-Switching PM Machine Under Different Operating Conditions

This paper investigates the comparative design optimizations of a five-phase outer-rotor flux-switching permanent magnet (FSPM) machine for in-wheel traction applications. To improve the comprehensive performance of the motor, two kinds of large-scale design optimizations under different operating conditions are performed and compared, including the traditional optimization performed at the rated operating point and the optimization targeting the whole driving cycles. Three driving cycles are taken into account, namely, the urban dynamometer driving schedule (UDDS), the highway fuel economy driving schedule (HWFET), and the combined UDDS/HWFET, representing the city, highway, and combined city/highway driving, respectively. Meanwhile, the computationally efficient finite-element analysis (CE-FEA) method, the cyclic representative operating points extraction technique, as well as the response surface methodology (in order to minimize the number of experiments when establishing the inverse machine model), are presented to reduce the computational effort and cost. From the results and discussion, it will be found that the optimization results against different operating conditions exhibit distinct characteristics in terms of geometry, efficiency, and energy loss distributions. For the traditional optimization performed at the rated operating point, the optimal design tends to reduce copper losses but suffer from high core losses; for UDDS, the optimal design tends to minimize both copper losses and PM eddy-current losses in the low-speed region; for HWFET, the optimal design tends to minimize core losses in the high-speed region; for the combined UDDS/HWFET, the optimal design tends to balance/compromise the loss components in both the low-speed and high-speed regions. Furthermore, the advantages of the adopted optimization methodologies versus the traditional procedure are highlighted

Optimization Design by Coupling Computational Fluid Dynamics and Genetic Algorithm

Nowadays, optimal design of equipment is one of the most practical issues in modem industry. Due to the requirements of deploying time, reliability, and design cost, better approaches than the conventional ones like experimental procedures are required. Moreover, the rapid development of computing power in recent decades opens a chance for researchers to employ calculation tools in complex configurations. In this chapter, we demonstrate a kind of modern optimization method by coupling computational fluid dynamics (CFD) and genetic algorithms (GAs). The brief introduction of GAs and CFD package OpenFOAM will be performed. The advantage of this approach as well as the difficulty that we must tackle will be analyzed. In addition, this chapter performs a study case in which an automated procedure to optimize the flow distribution in a manifold is established. The design point is accomplished by balancing the liquid-phase flow rate at each outlet, and the controlled parameter is a dimension of baffle between each channel. Using this methodology, we finally find a set of results improving the distribution of flow

DEVELOPMENT OF AN ADVANCED HEAT EXCHANGER MODEL FOR STEADY STATE AND FROSTING CONDITIONS

Air-to-refrigerant fin-and-tube heat exchangers are a key component in the heating, air conditioning and refrigeration industry. Considering their dominance, the industry has focused immensely on employing computer modeling in their design and development. Recently, advances in manufacturing capabilities, heat exchanger technology coupled with the move towards new environment-friendly refrigerants provide unprecedented challenges for designers and opportunities for researchers. In addition, the field of Computational Fluid Dynamics (CFD) has assumed a greater role in the design of heat exchangers. This research presents the development of an advanced heat exchanger model and design tool which aims to provide greater accuracy, design flexibility and unparalleled capabilities compared to existing heat exchanger models. The heat exchanger model developed here achieves the following. * Account for tube-to-tube conduction along fins, which is known to degrade the performance of heat exchangers, especially in carbon dioxide gas coolers * Study and develop heat exchangers with arbitrary fin sheets, which meet performance as well as packaging goals with minimal consumption of resources * Allow engineers to integrate CFD results for air flow through a heat exchanger, which the modeling tool employs to develop its air propagation sequence leading to improved accuracy over existing models which assume normal air flow propagation * Function in a quasi-steady state mode for the purpose of simulating frost accumulation and growth on heat exchangers, and completely simulate local heat transfer degradation, as well as blockage of flow passage on air side Additionally, the heat exchanger model was used to investigate gains that are enabled due to the presence of cut fins in carbon dioxide gas coolers and develop design guidelines for engineers. Finally, this dissertation analyzes the implications of minimum entropy generation on heat exchanger performance criteria of heat capacity and pressure drop, as well as evaluates the ability of entropy generation minimization as a design objective. This also serves as the first step toward an expert knowledge-based system for guiding engineers towards better designs, during the process of heat exchanger design

AMoEBA: the adaptive modeling by evolving blocks algorithm

This dissertation presents AMoEBA, the Adaptive Modeling by Evolving Blocks Algorithm. AMoEBA is an evolutionary technique for automatic decomposition of data fields and solver/descriptor placement. By automatically decomposing a numerical data set, the algorithm is able to solve a variety of problems that are difficult to solve with other techniques. Two key features of the algorithm are its ability to work with discrete data types and its unique geometric representation of the domain. AMoEBA uses genetic programming generated parse trees to define data segregation schemes. These trees also place solver/descriptors in the decomposed regions. Since the segregation trees define the boundaries between the regions, discrete representations of the data set are possible. AMoEBA is versatile and can be applied to many different types of geometries as well as different types of problems. In this thesis, three problems will be used to demonstrate the capabilities of this algorithm. For the first problem, AMoEBA used approximated algebraic expressions to match known profiles representing a steady-state conduction heat transfer problem and the fully-developed laminar flow through a pipe. To further illustrate the versatility of the algorithm, an inverse engineering problem was also solved. For this problem, AMoEBA placed different materials in the segregated regions defined by the trees and compared this to known temperature profiles. The final demonstration illustrates the application of AMoEBA to computational fluid dynamics. In this implementation, AMoEBA segregated an elbow section of pipe and placed numerical solvers in the regions. The resulting solver networks were solved and compared to a known solution. Both the time and accuracy of the networks were compared to determine if a faster solution method can be found with a reasonably accurate solution. Although AMoEBA is adapted for each application, the core algorithm of AMoEBA is unaltered in each application. This illustrates the flexibility of the algorithm