Optimal Design Methodology Research Articles

Self-acting optimal design of spindle speed variation for regenerative chatter suppression based on novel analysis of internal process energy behavior

Spindle speed variation (SSV) is a well-known effective and flexible technique to suppress chatter vibration by disrupting regenerative effect. In most research, the SSV is properly designed by a large number of complex stability simulations including identification of machine dynamics with time consumption or costly experiments while varying design parameters; hence, this approach cannot be on-line or integrable in the machine tools for autonomous self-suppression of chatter vibration. This paper presents a programmable optimal design methodology for sinusoidal spindle speed variation (SSSV), which only requires measuring the chatter frequency and can incorporate the machine constraints into the design procedure. The design concept is based on the minimization of kinematic internal process energy balance during SSSV cycle for efficient chatter energy dissipation. For this purpose, the modulation index, which is originally defined for frequency modulation (FM) technology in radio communication engineering, is introduced into SSSV as a novel index. The novel design criteria in terms of SSSV frequency are also proposed for robust chatter suppression without a momentary destabilization during SSSV cycle, called as beat vibration. A series of time-domain SSSV simulation and boring tests are carried out in depth for verification. It can be concluded that, the parameter candidates suggested by the proposed design procedure can optimally dissipate the chatter with little beat vibration.

High-Performance Megahertz-Frequency Resonant DC–DC Converter for Automotive LED Driver Applications

This paper introduces a megahertz-frequency resonant dc–dc converter that inherently achieves a load-independent output current while maintaining high efficiency across a wide output voltage range. These properties make the proposed converter well-suited for automotive light-emitting diode (LED) driver applications, where a varying number of LEDs need to be driven with a constant current. The proposed converter achieves load-independent output current by utilizing an LCL -T resonant network, and achieves high efficiency using a comprehensive design optimization methodology. This LCL -T resonant converter is also capable of regulating its output current at any desired value by utilizing phase-shift control. The performance of the LCL -T resonant converter is theoretically and experimentally compared with an LC 3 L and an LCLC resonant converter. For the experimental comparison, prototypes LCL -T, LC 3 L , and LCLC resonant converters are designed to operate at 2 MHz and across an output voltage range of 3.3–49.5 V while supplying a constant 0.5 A output current to the LEDs. The LCL -T resonant converter prototype achieves a peak efficiency of 91.1%, which is 0.6% and 1.8% higher than the peak efficiency of the LC 3 L and the LCLC converter prototypes, respectively. Furthermore, the LCL -T converter prototype maintains 0.8% and 1.6% higher average efficiency over its 15:1 output voltage range relative to the LC 3 L and the LCLC converter prototypes, respectively.

A stochastic MPEC approach for grid tariff design with demand-side flexibility

As the end-users increasingly can provide flexibility to the power system, it is important to consider how this flexibility can be activated as a resource for the grid. Electricity network tariffs is one option that can be used to activate this flexibility. Therefore, by designing efficient grid tariffs, it might be possible to reduce the total costs in the power system by incentivizing a change in consumption patterns. This paper provides a methodology for optimal grid tariff design under decentralized decision-making and uncertainty in demand, power prices, and renewable generation. A bilevel model is formulated to adequately describe the interaction between the end-users and a distribution system operator. In addition, a centralized decision-making model is provided for benchmarking purposes. The bilevel model is reformulated as a mixed-integer linear problem solvable by branch-and-cut techniques. Results based on both deterministic and stochastic settings are presented and discussed. The findings suggest how electricity grid tariffs should be designed to provide an efficient price signal for reducing aggregate network peaks.

Design of optimal velocity tracking controllers for one and two-body point absorber wave energy converters

Abstract Point absorber Wave Energy Converters (WECs) are typically operated using linear damping control in which the resistive force of the power take-off (PTO) is linearly proportional to the velocity of the floater. Such algorithms are used predominantly due to their simplicity and ease of application, however, it is known that such control is far from optimal in terms of energy capture. Previous studies in the literature have seen a number of different, more advanced control methodologies proposed, however, in the main, these have been applied to single body point absorbers. The main objective of the work presented here is to develop and implement a novel two-body WEC optimal velocity tracking controller design methodology. First, an Optimal Velocity Tracking (OVT) controller is designed using a novel controller design model and applied to a WEC-Sim model of a utility scale single-body point absorber. The methodology is then extended to the two-body point absorber WEC case and an OVT controller is designed for a utility scale two-body WEC. The increase in energy capture for a site in UK waters, based on WEC-Sim simulations and compared to typical linear damping control, is 23% for the one-body WEC and 20% for the two-body WEC.

Deep Neural Network (DNN) Optimized Design of 2.45 GHz CMOS Rectifier With 73.6% Peak Efficiency for RF Energy Harvesting

This article presents a two-stage rectifier with novel DC-boosted gate bias for RF energy harvesting. The auxiliary gate bias enables rectifier to operate when input amplitude is smaller than its transistor threshold voltage while constraining the positive gate voltage during off state to reduce the reverse leakage current. An automated design optimization methodology using Deep Neural Network (DNN) to maximize efficiency is presented. The DNN is shown to accurately model SPICE simulated response of rectifier. Hence, the design phase turnaround time is minimized with fast prediction of optimized design parameters. The proposed rectifier has been fabricated in 65 nm standard CMOS technology. A maximum power conversion efficiency of 73.6% is measured at 2.45 GHz with input power of −6 dBm. The proposed rectifier has a measured sensitivity of −12 dBm for 1 V output voltage.

Optimum Sustainable Mix Proportions of High Strength Concrete by Using Taguchi Method

In this study, mix proportion parameters of high strength concrete (HSC) were analyzed by using the Taguchi’s experiment design methodology for optimal design. For that purpose, mixtures are designed in a L27 orthogonal array with six factors, namely, ‘Silica Fume’, ‘Steel Fiber’, ‘Super-Plasticizer’, ‘Maximum Aggregate Size (AG)’, ‘Water / cementitious material (W/C) ratio’, ‘Fly Ash’. The mixtures were extensively tested to meet technical requirements of HSC. The experimental results were analyzed by using the Taguchi experimental design methodology. The best possible levels for mix proportions were determined for maximization of compressive strength at 7, 28, 56, 90 days, splitting tensile strength at 28 days, flexural strength at 28 days, and the slump. Also the best possible levels for mix proportions were determined for minimization of the production cost. It was found that steel fibers and fly ash are the most dominant factors in the process of optimization. The advantage of using steel fiber and fly ash was the reduced energy and cost associated with the raw materials which meant more sustainable concrete could be attained. It was also found that there is a necessity to apply a multi- response optimization to get the best mix proportions.

Modeling and design of tuned mass dampers using sliding variable friction pendulum bearings

An effective vibration control device, the pendulum tuned mass damper (P-TMD), can be easily realized as a mass supported on rolling or sliding pendulum bearings. While the bearings’ concavity provides the desired gravitational restoring force, the necessary dissipative force can be obtained either from additional dampers installed in parallel with the bearings or from the same friction resistance developing within each bearing between the roller/slider and the rolling/sliding surface. The latter solution may prove cheaper and more compact but implies that the P-TMD effectiveness will be amplitude dependent if the friction coefficient is kept uniform along the rolling/sliding surface, as in conventional friction bearings. In this case, the friction P-TMD will be as efficient as a viscous P-TMD only at a given vibration level, with large performance reductions at other levels. To avoid this inconvenience, this paper proposes a new type of sliding variable friction pendulum (VFP) TMD, called the VFP-TMD, in which the sliding surface is divided into two concentric regions: a circular inner region, having the lowest possible friction coefficient and the same dimensions of the slider, and an annular outer region, having a friction coefficient set to an optimal value. A similar arrangement has been recently proposed to realize adaptive seismic isolation devices, but no specific application to TMDs is reported. To assess the VFP-TMD performance, first its analytical model is derived, rigorously accounting for geometric nonlinearities as well as for the variable (in time and space) pressure distribution along the contact area, and then, an optimal design methodology is presented. Finally, numerical simulations show the influence of the main design parameters on the device behavior and demonstrate that the VFP-TMD can achieve nearly the same effectiveness of viscous P-TMDs, while considerably outperforming conventional uniform-friction P-TMDs. The proposed analytical model can be used to enhance or validate existing models of VFP isolators that assume a constant and uniform contact pressure distribution.

Surrogate- and possibility-based design optimization for convective polymerase chain reaction devices

This paper presents a surrogate- and possibility-based design optimization (SPBDO) methodology for robust design of convective PCR. Parametric computational fluid dynamics (CFD) models are built and simulated at sampled locations within the design space to capture the effect of design configurations on thermofluidic transport and convection–diffusion-reaction in the convective PCR. A support vector machine-based classifier model is trained to retain only practically relevant data for enhanced surrogate modeling accuracy. Surrogate models are constructed by Kriging interpolation and multivariate polynomial regression methods to establish the mapping between design configurations and DNA doubling time (indicative of reactor performance). Then a process to combine the sequential method of PBDO and the surrogate model is developed, and a trade study is carried out to evaluate the impact of possibility of failure ( $$\alpha$$ -value) and the balance between performance and design reliability. Our study demonstrates that the proposed SPBDO represents an effective method to consider robustness in PCR design for POC applications, especially when the uncertainty information or possibilistic characteristics of design variables is limited.

Optimization design of a honeycomb-hole submarine pipeline under a hydrodynamic landslide impact

Submarine pipelines are likely to be impacted by frequently catastrophic landslides, resulting in broken pipelines and causing irreversible economic losses; however, because pipelines located in deep-sea environments are simply laid atop the seabed, effective protective measures have not yet been proposed. In this paper, a pipeline with a uniform distribution of honeycomb holes on the surface, namely, a honeycomb-hole submarine pipeline, is discussed and optimized in detail. A computational fluid dynamics (CFD) approach is verified and improved several times and then applied to conduct 120 numerical simulations for 12 types of suspended submarine pipelines under 10 conditions covering the Reynolds number range required by the project conditions. First, the typical characteristics of the impact forces acting on pipelines in a seawater environment are systematically investigated, and the peak drag force is isolated as the most dangerous load caused by a landslide impact. Second, according to the formation and reduction mechanism of the drag force, it is proposed that a honeycomb-hole submarine pipeline can effectively delay the separation of the boundary layer, reduce the impact velocity, prolong the arrival time, and decrease the differential pressure drag force. Finally, two normalized parameters, the depth ratio and area ratio, are established to develop an optimization design methodology, and the optimal design values of honeycomb-hole pipelines are achieved under the support of limited data. This work presents a protective technology for submarine pipelines and provides new insights into the response of fluid-structure interactions.

A Complete Step-by-Step Optimal Design for LLC Resonant Converter

LLC resonant converters have been widely used in many different industrial applications. Analysis and design methodologies have great effect on the converter performance. Accordingly, a complete step-by-step optimal design methodology based on time-domain analysis has been proposed for an LLC resonant converter in this article. The proposed design methodology is implemented under the worst operation condition, and the following considerations are included to obtain the suitable design area: operation mode; voltage stress for resonant capacitor; zero voltage switching operation for primary switches; and resonant tank root-mean-square current. Then, by finding all possible design candidates and comparing them based on the power loss model, the optimized design candidate can be found. Compared with the existing design methodologies, the proposed one has the advantages of high accuracy and small computation requirement, which makes it application in industry possible. Finally, a 192-W experimental prototype was built to validate the effectiveness of the proposed design methodology. In addition, a MATLAB graphical user interference program was built based on the proposed design methodology to visualize and facilitate the design process for engineers.

Robust optimization of an organic Rankine cycle for geothermal application

A robust design optimization (RDO) methodology for Organic Rankine Cycles (ORC) is presented, allowing to ensure an improved, stable performance over a large range of operating conditions. In contrast with classical ORC design methods, whereby all modeling hypotheses and operating conditions are considered as perfectly known, i.e. deterministic, the RDO approach allows to account for the manifold sources of uncertainty affecting the system. For geothermal ORC, the latter are related on one hand with the ill-known properties of the geothermal source and, on the other, with intrinsically random parameters, such as the condensation temperature. The proposed RDO approach selects values of the design parameters that maximize the expected (average) performance while minimizing its variance under uncertain nominal operating conditions. The optimal design delivered by the proposed strategy outperforms the one derived from the standard deterministic approach: specifically, the expected power output is increased by 1.5%, while its standard deviation is reduced by 8.5% and the surface of the heat exchangers by 34%. • Geothermal Organic Rankine cycle performance is subject to many uncertainties. • A robust optimization strategy is devised for cycle design under uncertainty. • The design maximizes average performance while minimizing the variance. • Nested Kriging surrogates are used to efficiently speed-up the design loop. • Robust optimum has +1.5% power output and smaller variance wrt standard design.

Optimum span length for a PCI-girder expressway bridge

ABSTRACT In the design of PCI-girder bridges, the application of various optimum design methodologies can result in significant cost savings and improved structural performance. However, most of the optimisation techniques focus on the individual components and the overall structural system of the superstructure of the bridge system. Limited studies are carried out in the context of longitudinal and transverse configurations of the members in a particular bridge system. This study identifies the optimum span for the PCI-girder expressway bridge system by adopting longitudinal and transverse arrangement of members as design variables while keeping the cross-section of the girder constant. Using an existing case study bridge structure located in Bangkok, selected parametric studies are carried out to achieve cost optimisation. It is observed that the optimum span range for the PCI-girder bridge is in the range of 25 m (82 ft) to 33 m (108 ft).

Surrogate based optimization of functionally graded plates using radial basis functions

This work presents an efficient methodology for optimum design of functionally graded plates. Isogeometric analysis is used to evaluate the structural responses and the material gradation is described using B-Splines to enhance design flexibility. A constraint is included in the optimization model to ensure a smooth material gradation. In order to improve the computational efficiency of the optimization process, a surrogate model based on Radial Basis Functions is used to accurately approximate the structural responses. Different methods to define the width of basis functions based on analytical and cross-validation techniques are adopted and compared. Two infill criteria based on the expected improvement technique are used to continuously improve the surrogate model accuracy by balancing both the local and global searches. The accuracy and efficiency of the proposed approaches are assessed through a set of problems involving the maximization of the buckling load and the fundamental frequency of functionally graded plates, showing excellent results.

A Methodology for Optimal Design of Transmission Lines to Protection against Lightning Surges in Presence of Arresters

In this paper, a methodology for determination of the optimal value of protection design parameters, i.e. tower footing resistance, insulation strength, and surge arresters’ rating in the planning stage of transmission lines (TLs) is presented. This method calculates the shielding failure flashover rate (SFFOR) of TLs, based on Electro-geometric model (EGM) of TLs, and the back flashover rate (BFR) of TLs, based on the Monte Carlo method, in which the accuracy of the proposed methodology has been verified by comparing the resultant results with those obtained with the use of the IEEE FLASH program. The proposed method can be directly used to achieve the minimum lightning flashover rate (LFOR) of TLs by the minimum investment cost. Also, it can be used, indirectly, for determination of the appropriate value of the footing resistance, insulation strength and arresters’ rating to satisfy any target number of LFOR that might be specified by the utilities or standards.

Network-level Design Space Exploration of Resource-constrained Networks-of-Systems

Driven by recent advances in networking and computing technologies, distributed application scenarios are increasingly deployed on resource-constrained processing platforms. This includes networked embedded and cyber-physical systems as well as edge computing in mobile applications and the Internet of Things (IoT). In such resource-constrained Networks-of-Systems (NoS), computation and communication workloads need to be carefully co-optimized yet are tightly coupled. How to optimally partition and schedule application tasks among an appropriately designed NoS architecture requires a simultaneous consideration of design parameters from applications and processing platforms all the way to network configurations. Traditionally, however, systems and networks are designed in isolation and combined in an ad hoc manner, which ignores joint effects and optimization opportunities. To systematically explore and optimize NoS design spaces, a higher level of design abstraction on top of traditional system and network design is required. In this article, we propose a novel network-level design methodology for resource-constrained NoS optimization and design space exploration. A key component in such a design flow is fast yet accurate network/system co-simulation to rapidly evaluate NoS parameters with high fidelity. We first introduce a novel NoS simulator (NoSSim) that integrates source-level simulation models of applications with a host-compiled system simulation platform and a reconfigurable network simulation backplane to accurately capture system and network interactions. The co-simulation platform is further combined with model generation tools and a multi-objective genetic search algorithm to provide a comprehensive and fully automated NoS design space exploration framework. Finally, we apply our network-level design flow on several state-of-art IoT/mobile design case studies. Results show that NoSSim can achieve more than 86% simulation accuracy on average as compared to a real-world edge device cluster, where sensitivities to various design parameters are faithfully captured with high fidelity. When applying our network-level design space exploration methodology, design decisions are automatically optimized, where non-obvious NoS configurations are discovered outperforming manually designed solutions by more than 45%.

Optimal IMC-PID controller design for large-scale power systems via EDE algorithm-based model approximation method

In this paper, the authors propose an optimal IMC-PID controller design for the Load Frequency Control (LFC) of large-scale power system via model approximation method. The model approximation method uses the Enhanced Differential Evolution (EDE) algorithm to determine an optimal Reduced Order Model (ROM) for the considered large-scale power system by minimizing the performance measure called Integral Square Error (ISE) between their step responses. Later, the LFC design is carried out using an optimal ROM instead of processing with the large-scale power system model. Thus, this simplifies the design, reduces the computational efforts and also helps in determining the lower order controller. An optimal IMC design methodology is proposed by minimizing ISE between the actual output and the reference input responses of the large-scale power system using EDE algorithm. Further, PID controller gains are acquired by least square model matching with the optimal IMC transfer function. The proposed IMC-PID controller design allows a satisfied reference input tracking performance, robustness in disturbance rejection and improves the dynamic stability of the power system. The proposed method is validated by applying it to a single area power system of third-order SISO model and also extended to a centralized two-area thermal–thermal non-reheated power system of a seventh-order MIMO model. The simulation results and the comparison of error performance indices show the efficacy of the proposed method over the significant methods available in the literature.

Generic techno-economic optimization methodology for concurrent design and operation of solvent-based PCC processes

A techno-economic equation-based methodology is developed for optimal design and operation of integrated solvent-based post-combustion carbon capture (PCC) processes using a rate-based model for the interaction of gas and liquid. The algorithm considers a wide range of techno-economic design and operation parameters such as number of absorber/desorber columns, height of columns, diameter of columns, operating conditions (P, T) of columns, pressure drop, packing type, percentage of CO2 mitigated, captured CO2 purity, amount of solvent regeneration, flooding velocities of columns, and number of compression stages. A case study is conducted to showcase two common objective-functions i) minimizing total capital investment, and ii) minimizing levelized capture costs, both for a 300 MW coal-power plant in Australia. The former objective leads to the lowest possible total capital cost of $312.4 M corresponding to levelized carbon capture cost of 58.1 $/tonne−CO2. For objective (ii), however, the lowest levelized carbon capture cost is found to be around ten percent lower (52.8 $/tonne−CO2), though it leads to a higher total capital cost ($325.2 M). The results indicate that the design and operation variables are markedly interactive, and no unique optimal design exists which can deliver all desired outcomes at once. Therefore, decisions on the selection of right variables become dependent on the decision-makers techno-economic objectives.

Design of optimal low-pass filter by a new Levy swallow swarm algorithm

The swallow swarm optimization (SS) is a challenging method of optimization, which has a quicker convergence speed, not getting caught in the local extreme points. However, the SS suffers from a few shortcomings—(1) the movement speed of particles is not controlled suitably during the search due to the requirement of an inertia weight and (2) the less flexibility of variables does not permit to maintain a balance between the local and the global searches. To solve these problems, a new Levy swallow swarm optimization (SSLY) algorithm with the exploitation capability is proposed. This article also provides an optimal design methodology for the low-pass filter using the suggested SSLY technique. A new objective function is introduced to achieve the maximally flat frequency response, which is another important contribution to the field. The firefly algorithm (FA), the sine cosine algorithm (SCA) and the standard global optimizers—real coded genetic algorithm (GA), conventional particle swarm optimization (PSO), cuckoo search (CS) and SS, are considered for a comparison. The proposed SSLY outperforms the FA, SCA, GA, PSO, CS and SS algorithms. Results authenticate suitability of the proposed algorithm for solving the filter design problems in the FIR domain.

Reliability-based design optimisation of structural systems using high-order analytical moments

Reliability-based design optimisation paradigm has become increasingly popular to achieve economical yet safer structural designs. However, within the iterative optimisation procedure, it is a challenging problem to simultaneously satisfy both, accuracy and computational efficiency of reliability analysis, particularly for nonlinear or large design problems. Addressing the shortcomings of the mainstream reliability estimation methods, this paper presents a new reliability-based design optimisation method that combines an analytical high-order moment-based uncertainty evaluation with an efficient response surface modelling. The proposed framework allows for the precise calculation of reliability through accurate high-order moments. The fast and accurate reliability estimation in combination with efficient sampling, in turn, reduces the total number of finite element analysis in achieving the final design. The proposed moment-based design optimisation methodology was tested on problems ranging from the design of a simple cantilever to a three-dimensional multistorey steel structure. It outperforms the mainstream methods with higher accuracy and lower computational burden especially when applied to a highly nonlinear numerical problem.

A generalized methodology for multidisciplinary design optimization using surrogate modelling and multifidelity analysis

The advantages of multidisciplinary design are well understood, but not yet fully adopted by the industry where methods should be both fast and reliable. For such problems, minimum computational cost while providing global optimality and extensive design information at an early conceptual stage is desired. However, such a complex problem consisting of various objectives and interacting disciplines is associated with a challenging design space. This provides a large pool of possible designs, requiring an efficient exploration scheme with the ability to provide sufficient feedback early in the design process. This paper demonstrates a generalized optimization framework with rapid design space exploration capabilities in which a Multifidelity approach is directly adjusted to the emerging needs of the design. The methodology is developed to be easily applicable and efficient in computationally expensive multidisciplinary problems. To accelerate such a demanding process, Surrogate Based Optimization methods in the form of both Radial Basis Function and Kriging models are employed. In particular, a modification of the standard Kriging approach to account for Multifidelity data inputs is proposed, aiming to increasing its accuracy without increasing its training cost. The surrogate optimization problem is solved by a Particle Swarm Optimization algorithm and two constraint handling methods are implemented. The surrogate model modifications are visually demonstrated in a 1D and 2D test case, while the Rosenbrock and Sellar functions are used to examine the scalability and adaptability behaviour of the method. Our particular Multiobjective formulation is demonstrated in the common RAE2822 airfoil design problem. In this paper, the framework assessment focuses on our infill sampling approach in terms of design and objective space exploration for a given computational cost.