Two-level Optimization Strategy Research Articles

Two-level optimization strategy for vehicle speed and battery thermal management in connected and automated EVs

The performance of the battery is affected by temperature, and the battery thermal management (BTM) system consumes considerable energy to maintain the temperature in the suitable range. The unnecessary acceleration and deceleration of electric vehicles (EVs) during driving causes higher energy consumption in the powertrain. The emergence of connected and automated vehicle (CAV) technology provides an opportunity for predictive control of thermal and energy management. To explore the coordination optimization between battery thermal and vehicle energy management, this article proposes a two-level optimization framework for the speed and BTM of EVs, which improves energy efficiency and battery safety. Each level consists of a sequential optimization of speed and battery thermal. In the upper layer, speed planning based on iterative dynamic programming (IDP) is first proposed to reduce powertrain energy consumption using intelligent traffic information. Then, based on the BTM system and the battery thermodynamics features, the long-term optimal trajectory of the battery temperature is derived according to optimized speed. In the lower layer, the model predictive controllers (MPC) are designed to track reference speed and temperature trajectories in real-time and enforce energy saving. Meanwhile, to improve the prediction accuracy of the system model, we integrate the Gaussian process (GP) model in the MPC and build the learning-based MPC strategy. Simulation results verify the performance of the proposed method which reduces the powertrain energy consumption by 20.95%. In the high and low temperature environment, compared with normal MPC, PID-based and Rule-based, it reduces BTM energy consumption by up to 15.69%, 29.68% and 38.73%.

EOSA-GAN: Feature enriched latent space optimized adversarial networks for synthesization of histopathology images using Ebola optimization search algorithm

Generative adversarial networks (GAN) represent two deep learning (DL) models positioned in an adversarial manner to generate and evaluate images. This area of research promises to address several issues associated with medical image analysis using deep learning architectures and has been applied to medical image synthesis. The histopathology image samples are the gold standard for detecting and staging cancer since they contain rich latent information. However, this medical image modality is highly problematic: imbalanced class distribution in datasets, the rareness of publicly accessible pathologic findings, the burdensome task of image annotation, the increasing need to anonymize pathology samples, segmentation of regions of interest, and the demand for high-quality with super-resolution histopathology images. In this paper, we present a highly optimized, locally attenuated and two-level optimization strategy to improve the performance of GAN. First, a novel feature space-to-latent space mapping mechanism is designed to enrich the latent space of input to the generator. We applied a DL model to extract discriminant features and used dimensionality reduction to match the number of features to latent space. Secondly, a new metaheuristic algorithm, the Ebola optimization search algorithm (EOSA), optimizes the EOSA-GAN architecture and is experimentally applied to benchmark datasets. Results obtained showed that the quality of generated samples achieved an impressive outcome when evaluated using the Feature Similarity Indexing Method (FSIM), Peak Signal to Noise Ratio (PSNR), Structured Similarity Indexing Method (SSIM) and others. The finding from this study demonstrates the impact of optimization algorithms in stabilizing and speeding up GANs to convergence.

Field-Coupled Nanocomputing Placement and Routing With Genetic and A* Algorithms

Field-Coupled Nanocomputing technologies have great potential to surpass CMOS technology because of their lower power consumption and higher device concentration. To ease the burden of placement and routing (P&R) problems for FCN circuits, many delicate two-dimensional clocking schemes have been proposed, upon which algorithms can solve the P&R problems more strategically. In this paper, we propose a two-level optimization strategy by using a genetic algorithm (GA) combined with an enhanced A* algorithm. Some circuit design requirements, such as clock synchronization, layout area, etc., are cleverly designed in the fitness value function of the GA. Numerical results demonstrate the effectiveness of the hybrid algorithm. In particular, compared to current tools, such as fiction and Ropper, the proposed algorithm can achieve an optimal solution with a higher success rate and a sizeable applicable circuit scale. In addition, the concept of design rule checking (DRC) was proposed in FCN and integrated into the algorithm, making the P&R results mapping from gate-level to cell-level more smoothly. Besides, the number of cross wires is significantly reduced, and the distribution of IO ports can be more effectively controlled.

Multi-scale deterministic optimisation of blended composite structures: case study of a box-wing

This work presents a multi-scale design methodology for the deterministic optimisation of thin-walled composite structures integrating a global–local approach for the assessment of the buckling strength and a dedicated strategy to recover blended stacking sequences. The methodology is based on the multi-scale two-level optimisation strategy for anisotropic materials and structures. In the first step, focused on the macroscopic scale, several design requirements are included in the problem formulation: lightness, feasibility, manufacturing, blending, buckling failure, static failure and stiffness. The second step, which focuses on the laminate mesoscopic scale, deals with the recovery of blended stacking sequences, for the structure at hand, matching the optimal geometric and elastic properties determined in the first step. As a case study, the unconventional PrandtlPlane box-wing system is used to show the effectiveness of the proposed design methodology.

Optimization of patch-wise laminated composite panels for enhanced dynamic characteristics

Fiber-reinforced laminated composites are widely utilized in the transportation industries due to their superior specific stiffness and strength over conventional metals. The most widely used forms of fiber-reinforced composites are in a laminated plate panel. The inherent anisotropy of composites and the associated dynamic loading characteristics make the design process for such a structure very challenging. In particular, the composite panels used for ship structures must be lightweight and robust enough to withstand external dynamic loads such as wave loads. In this study, we present a two-level optimization strategy to improve the modal dynamic stiffness of laminated composite panels utilising lamination parameters and a patch-wise lay-up approach. Numerical results showed a significant increase in fundamental natural frequency and specific dynamic stiffness compared to the quasi-isotropic design.

Integrated design optimization of actuator layout and structural ply parameters for the dynamic shape control of piezoelectric laminated curved shell structures

Compared with static shape control, dynamic shape control is more difficult due to its time-varying nature. To improve the control precision and reduce the number of actuators needed, an integrated design optimization model of structure and control is proposed. The following three sets of design variables are considered to minimize the time domain variance between the controlled and desired dynamic shapes: the time-varying actuator voltages, the actuator layout and the structural ply parameters. To satisfy the engineering performance requirements to the greatest extent possible, constraints on the structural mass, the energy, the maximum transient voltage and the number of actuators are considered. Because the control voltages are varying in time, a two-level optimization strategy is adopted. In the inner optimization problem, the Newmark integral method is applied to derive discrete time domain expressions for the shape control equations, and then, the Kuhn-Tucker condition is introduced to calculate the optimal time-varying voltage distribution. To solve the outer optimization problem, because of the coexistence of discrete variables and continuous variables, a simulated annealing algorithm is used. To address the shape control of complicated curved shells, a finite element formulation for an eight-node laminated curved shell element with piezoelectric actuators is derived. Numerical examples show that the proposed integrated design optimization method can significantly improve the control effect and that the optimization of the structural ply parameters plays an important role. Moreover, the control system can be simplified by taking the minimum number of actuators as the objective function when the control accuracy allows.

Strength Optimisation of Variable Angle-Tow Composites Through a Laminate-Level Failure Criterion

The development of additive manufacturing techniques for composite structures brought the emergence of a new class of composite materials: the variable angle-tow composites. Additive manufacturing of reinforced polymers allows the tow to be placed along a curvilinear path in each lamina. Accordingly, optimised solutions with enhanced properties can be manufactured. In this work, the multi-scale two-level optimisation strategy for composites is exploited to optimise the strength of variable angle-tow composites subject to mechanical and manufacturing constraints. At the first step of the strategy, the laminate strength is described through a laminate-level failure criterion based on tensor invariants and on the first-order shear deformation theory. The lay-up design phase makes use of quasi-trivial solutions and integrates a check on the first-ply failure in order to ensure the integrity of the whole laminate. The effectiveness of the strategy as well as of the proposed failure criterion is proven on some meaningful test cases.

New blending constraints and a stack-recovery strategy for the multi-scale design of composite laminates

This work presents a new strategy for dealing with blending requirements in the composite structures design. Firstly, new analytical expressions of blending constraints, in the polar parameters space, are derived. Secondly, a dedicated numerical strategy for the recovery of blended stacking sequences is presented. The proposed approach is implemented in the framework of the multi-scale two-level optimisation strategy (MS2LOS) for composite laminates design. The theoretical aspects of this work are supported by the application of the proposed methodology to a numerical benchmark taken from the literature. The results obtained by means of the MS2LOS based on the polar formalism outperform those reported in the literature.

A general isogeometric polar approach for the optimisation of variable stiffness composites: Application to eigenvalue buckling problems

This study presents a general approach for the multi-scale design of variable stiffness composites (VSCs). The first-level problem of the multi-scale two-level optimisation strategy (MS2LOS) is solved to determine the optimal distribution of the VSC stiffness properties at the macroscopic scale satisfying the requirements of the problem at hand. In this phase, the VSC laminate is modelled as an equivalent homogeneous anisotropic plate whose behaviour is described in terms of polar parameters (PPs), which vary locally over the structure. The First-order Shear Deformation Theory is used to take into account the influence of the transverse shear stiffness on the mechanical response of the VSC and Basis Spline (B-Spline) surfaces are employed to represent the PPs fields. In this background, the expression of the gradient of the buckling factor is determined analytically by exploiting the properties of the polar formalism and of the B-Spline surfaces. Moreover, the effect of the discrete variables, involved in the definition of the B-Spline surfaces, on the performances of the optimised solution is investigated. The effectiveness of the approach is proven on two benchmark problems dealing with the maximisation of the first buckling load of a VSC laminate, subject to feasibility and geometric requirements, taken from the literature. The results obtained by means of the MS2LOS based on the polar formalism outperform those reported in the literature, which are obtained through an optimisation strategy based on lamination parameters.

A general global-local modelling framework for the deterministic optimisation of composite structures

This work deals with the multi-scale optimisation of composite structures by adopting a general global-local (GL) modelling strategy to assess the structure responses at different scales. The GL modelling approach is integrated into the multi-scale two-level optimisation strategy (MS2LOS) for composite structures. The resulting design strategy is, thus, called GL-MS2LOS and aims at proposing a very general formulation of the design problem, without introducing simplifying hypotheses on the laminate stack and by considering, as design variables, the full set of geometric and mechanical parameters defining the behaviour of the composite structure at each pertinent scale. By employing a GL modelling approach, most of the limitations of well-established design strategies, based on analytical or semi-empirical models, are overcome. The GL-MS2LOS makes use of the polar formalism to describe the anisotropy of the composite at the macroscopic scale (where it is modelled as an equivalent homogeneous anisotropic plate). In this work, deterministic algorithms are exploited during the solution search phase. The challenge, when dealing with such a design problem, is to develop a suitable formulation and dedicated operators, to link global and local models physical responses and their gradients. Closed-form expressions of structural responses gradients are rigorously derived by taking into account for the coupling effects when passing from global to local models. The effectiveness of the GL-MS2LOS is proven on a meaningful benchmark: the least-weight design of a cantilever wing subject to different design requirements. Constraints include maximum allowable displacements, maximum allowable strains, blending, manufacturability requirements and buckling factor.

A multi-scale two-level optimisation strategy integrating a global/local modelling approach for composite structures

In this work, a multi-scale optimisation strategy for the preliminary design of composite structures involving design requirements at different scales, is presented. Such a strategy, denoted as GL-MS2LOS, has been formulated by integrating a dedicated global-local (GL) modelling approach into the multi-scale two-level optimisation strategy (MS2LOS).The GL-MS2LOS aims at proposing a very general formulation of the design problem, without introducing simplifying hypotheses and by considering, as design variables, the full set of geometric and mechanical parameters defining the behaviour of the composite structure at each pertinent scale. By employing a GL modelling approach, most of the limitations of well-established design strategies based on analytical or semi-empirical models are overcome.The effectiveness of the presented GL-MS2LOS is proven on a meaningful study case: the least-weight design of a composite fuselage barrel of a wide-body aircraft undergoing various loading conditions and subject to requirements of different nature. Fully parametric global and local FE models are interfaced with an in-house metaheuristic algorithm to perform the optimisation. Refined local FE models are created only for critical regions of the structure, automatically detected during the global analysis, and linked to the global one thanks to the implementation of a sub-modelling approach. The general nature of the GL-MS2LOS allows finding an optimised configuration characterised by a weight saving of 40% when compared to an optimised aluminium solution obtained through a similar GL optimisation strategy.

Reconstruction of Continuous Brachial Arterial Pressure From Continuous Finger Arterial Pressure Using a Two-Level Optimization Strategy.

We attempt to reconstruct brachial arterial pressure (BAP) waves from finger arterial pressure waves measured using the vascular unloading technique without arm-cuff calibration. A novel method called two-level optimization (TOP) strategy is proposed as follows. We first derive a simplified transfer function (TF) based on a tube-load model with only two parameters to be estimated, a coefficient B and a time delay ∆t. Then, at level one, two minimization problems are formulated to estimate the optimal coefficient Bopt and time delay ∆topt. Then, we can derive an optimal TF hopt(t). However, this derivation requires true (or reference) BAP waves. Therefore, at level two, we apply multiple linear regression (MLR) to further model the relationship between the derived optimal parameters and subjects' physiologic parameters. Hence, eventually, one can estimate coefficient BMLR and time delay ∆tMLR from subject's physiologic parameters to derive the MLR-based TF hMLR(t) for the BAP reconstruction. Twenty-one volunteers were recruited for the data collection. The mean ± standard deviation of the root mean square errors between the reference BAP waves and the BAP waves reconstructed by hopt(t), hMLR(t), and a generalized transfer function (GTF) were 3.46 ± 1.42mmHg, 3.61 ± 2.28mmHg, and 6.80 ± 3.73mmHg (significantly larger with p < 0.01), respectively. The proposed method can be considered as a semi-individualized TF which reconstructs significantly better BAP waves than a GTF. The proposed TOP strategy can potentially be useful in more general reconstruction of proximal BP waves.

Two-Level Optimisation and Control Strategy for Unbalanced Active Distribution Systems Management

This article proposes a new approach to the operation of unbalanced Active Distribution Systems (ADS) using an economic dispatch optimisation model for Active Distribution Systems Management (ADSM). The model proposes a two-level control strategy. The first one poses an optimisation problem with the objective of minimising total active power losses in the ADS and the second one proposes an algorithm that controls the position of the taps of three-phase on-load tap-changer (OLTC) transformers to ensure compliance with the technical constraints imposed by the Distribution System Operator (DSO). The optimisation problem is solved by MATLAB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sup> and DIgSILENT PowerFactory <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sup> for power systems static simulations. This paper includes a novel peer to peer communication framework between MATLAB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sup> /DIgSILENT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sup> . The control and optimisation strategy is validated on the IEEE 34-Node Distribution Test Feeder. This network incorporates balanced and unbalanced three-phase loads, single-phase loads in the different phases, and two-phase loads. In this scientific paper, photovoltaic (PV) and wind power generation (WT) have been integrated to test feeder operation, with the support of battery energy storage systems (BESS). The correct operation of the proposed ADSM is demonstrated using numerical simulation on five scenarios considering several configurations of the renewable generation units and the batteries. The strategy has also been validated in a more extensive distribution network, proving its good performance.

A Novel Two-Level Optimization Strategy for Multi-Debris Active Removal Mission in LEO

A Novel Two-Level Optimization Strategy for Multi-Debris Active Removal Mission in LEO

Efficient aero-structure coupled wing optimization using decomposition and adaptive metamodeling techniques

This paper proposes an efficient decomposition-based optimization framework using adaptive metamodeling for expensive aero-structure coupled wing optimization problems. First, high-fidelity aero-structure coupled analysis method is presented through spatial interpolations of distributed aerodynamic loads and structural deformation. The proposed optimization framework decomposes the original complex coupled optimization problem into 2-D airfoil optimization (i.e., Stage-I) and 3-D wing optimization (i.e., Stage-II) to alleviate the expensive computational costs. In Stage-II, a two-level optimization strategy is tailored to further decompose the 3-D wing optimization into system-level and subsystem-level for dimension reduction. An adaptive response surface method using intelligent space exploration strategy is used to perform optimization tasks involving CFD simulations (i.e., 2-D airfoil optimization and system-level optimization in Stage-II), while sequential quadratic programming is employed to optimize the massive subsystem structure sizing variables. The effectiveness of two-level optimization strategy is validated on a numerical testing problem and a simplified wing optimization case. Finally, the developed models and proposed methods are successfully applied to aero-structure coupled optimization of a high aspect ratio wing. The optimization results demonstrate the effectiveness of the developed aero-structural analysis models and efficiency of the proposed optimization methods.

Rapid Prototyping of Variable Angle-Tow Composites

The recent development of new manufacturing techniques of composite structures, e.g., additive manufacturing (AM) techniques, allows for going beyond the classical design rules, thus leading the designer to find innovative and more efficient solutions like the variable angle-tow (VAT) composites. VAT composites allow taking advantage from the benefits related to the curvilinear fibre path in the most effective way, though their utilisation unavoidably implies an increased complexity of the design process. In fact, VAT composites are characterised by a large number of design variables involved at different scales. Accordingly, a dedicated multi-scale optimisation approach able to integrate both mechanical and technological requirements (to ensure the manufacturability of the solution) must be developed. In this work, the multi-scale two-level (MS2L) optimisation strategy for VAT laminates is used, for the first time, to design a VAT laminate by taking into account the manufacturing requirements related to the fused filament fabrication (FFF) and continuous filament fabrication (CFF) processes. To show the effectiveness of the MS2L strategy in terms of manufacturability of the optimised solutions, a prototype of the VAT plate is realised by developing an ad hoc CAD model interfaced with the CFF machine. The realised prototype matches very well the optimum fibre path resulting from the MS2L strategy and the importance of including technological constraints within the design process is highlighted.

Slew path planning of agile-satellite antenna pointing mechanism with optimal real-time data transmission performance

This paper investigates the mission planning problem of an agile satellite equipped with a two-dimensional antenna pointing mechanism. Multiple relay satellites and ground stations are included into the scenario to ensure the real-time data downlink. Therefore, to achieve the optimal real-time data transmission performance, two optimization modes are proposed in this paper to generate the slew path for both the satellite base and the antenna pointing mechanism. First, a baseline attitude coordinate system is introduced to efficiently handle the constraint of the problem. Second, the decoupled method is presented based on the two-level optimization strategy, which combines the genetic algorithm and the artificial potential method. Third, the dynamic programming concept is introduced to generate the optimal slew path by multi-body control approach. Simulation results show that the proposed algorithms can properly circumvent the complex mission planning problem, and that the maximum real-time data downlink can be established while ensuring the complete coverage of the target area.

Blending constraints for composite laminates in polar parameters space

This work proposes a new formulation of blending constraints, in the framework of the multi-scale two-level (MS2L) optimisation strategy for composites. This approach aims to optimise simultaneously both geometrical and mechanical parameters of the laminate at each characteristic scale (macroscopic and mesoscopic ones). In particular, this study deals with the first level of the MS2L optimisation strategy which focuses on the laminate macroscopic scale. At this scale, the behaviour of the laminate is described in terms of the polar parameters of each constitutive stiffness matrix in the framework of the First-order Shear Deformation Theory (FSDT). Therefore, blending constraints are formulated as equivalent mechanical requirements to be imposed to both polar and geometric parameters of the laminate within the first-level problem. The effectiveness of the proposed approach is tested on a meaningful benchmark: the least-weight design of a composite wing-box subject to constraints of different nature. The optimised solutions provided by the MS2L design strategy are characterised by a weight saving of about 12% (when compared to the reference solution taken from the literature) by meeting the full set of feasibility, manufacturing and mechanical requirements.

Formation of the adaptive fuzzy model of the rock geological structure for exploratory drilling

The authors suggest an approach aimed at improving the technology, increasing energy efficiency and quality of controlling borehole drilling in mining various mineralogical-technological types of iron ore through applying a geological structure model based on data of measuring drilling parameters of a drilling rig (drilling rate, torque, energy characteristics) and rock properties on the basis of studying regularities of changing parameters of the specifically formed ultrasonic field. In rapidly changing conditions of borehole drilling, it is offered to apply a two-level adaptive optimization strategy implying a simultaneous investigation into drilling and improvement of control over this process by means of the adaptive fuzzy system.

A general multi-scale two-level optimisation strategy for designing composite stiffened panels

This work deals with the problem of the least-weight design of a composite stiffened panel subject to constraints of different nature (mechanical, geometrical and manufacturability requirements). To face this problem, a multi-scale two-level (MS2L) design methodology is proposed. This approach aims at optimising simultaneously both geometrical and mechanical parameters for skin and stiffeners at each characteristic scale (mesoscopic and macroscopic ones). In this background, at the first level (macroscopic scale) the goal is to find the optimum value of geometric and mechanical design variables of the panel minimising its mass and meeting the set of imposed constraints. The second-level problem focuses on the laminate mesoscopic scale and aims at finding at least one stacking sequence (for each laminate composing the panel) meeting the geometrical and mechanical parameters provided by the first-level problem. The MS2L optimisation approach is based on the polar formalism to describe the macroscopic behaviour of the composites and on a special genetic algorithm to perform optimisation calculations. The quality of the optimum configurations is investigated, a posteriori, through a refined finite element model of the stiffened panel making use of elements with different kinematics and accuracy in the framework of the Carrera’s Unified Formulation (CUF).