Application Of Design Optimization Research Articles

Singularity distance computations for 3-RPR manipulators using intrinsic metrics

Avoiding singularities is a crucial task in robotics and path planning. This paper proposes a novel algorithm for detecting the closest singularity to a given pose for nine interpretations of the 3-RPR manipulator. The algorithm utilizes intrinsic metrics based on the framework's total elastic strain energy density, employing the physical concept of Green-Lagrange strain. The constrained optimization problem for detecting the closest singular configuration with respect to these metrics is solved globally using tools from numerical algebraic geometry implemented in the software package Bertini. The effectiveness of the proposed algorithm is demonstrated on a 3-RPR manipulator executing a one-parametric motion. Additionally, the obtained intrinsic singularity distances are compared with extrinsic metrics. Finally, the paper illustrates the advantage of employing a well-defined metric for identifying the closest singularities in comparison with the existing methods in the literature, highlighting its application in design optimization.

Sensitivity analysis based on the fuzzy safety index and its application in possibility-based design optimization

The fuzzy safety index (FSI) is put forward in this article under the assumption that the fuzzy inputs are independently and identically distributed. Then, the failure possibility-based constraints in original possibility-based design optimization (PBDO) are equivalently converted to the FSI-based constraints, and the fuzzy safety index approach (FSIA) is presented for solving PBDO. Based on the definition of FSI and the relationship between FSI and the fuzzy most possible failure point (F-MPP), the analytical expression is derived for the local sensitivity of FSI (LS-FSI) with respect to the design parameters, and it is used to provide the exact gradient for FSIA. An efficient iterative method is proposed for solving the FSI, which can estimate LS-FSI as a by-product of analysing the FSI without any additional computational cost. Finally, five numerical examples are demonstrated to show the feasibility of the derived analytical expression of LS-FSI and the proposed FSIA for PBDO.

Multiscale modeling of woven composites by deep learning neural networks and its application in design optimization

Due to complex yarn geometries and multiscale nature, it is very challenging to predict mechanical properties and strength constants of woven composites in an accuracy and efficient way. In this paper, mechanics of structure genome (MSG) is employed to predict effective material properties and strength constants of woven composites. The microscale analysis is performed to compute the effective properties and strength constants of yarns based on a hexagonal packed microstructure. Then, the mesoscale analysis is performed to compute the effective material properties and strength constants of woven composites. The accuracy and efficiency of the MSG method are compared with the computational homogenization method. In addition, simulation data using the MSG model is generated to train deep learning neural networks (DNN) models. The trained models provide ultra-efficient surrogate models with high accuracy. The DNN models are further applied to a design optimization problem to tailor geometry parameters to enhance the mechanical performance of plain woven composites, and the optimization can be efficiently solved with gradients from the DNN models. The developed computational framework can be applied to general woven composites featuring complex microscale and mesoscale features.

Digital Twin-Driven Industrialization Development of Underwater Gliders

Underwater glider (UG) is the only marine equipment in the field of ocean observation that can realize unmanned autonomous, all-weather and wide-area continuous three-dimensional observation. The rapid individualized design (RID) and full lifecycle management (FLM) make two difficulties to be solved in the industrialization development of UGs since the UG system involves interdisciplinary multiphysics and there exists multi-source uncertainties in complex ocean environment. With the application of new information technologies in industry, the above problems can be effectively solved by the digital twin (DT)-driven methodology. In this paper, an architecture of DT-driven RID and FLM for UGs is first put forward based on our previous theoretical research and technique development. Then, the proposed architecture of DT-driven methodology is researched in detail in terms of its digital modeling, design optimization, virtual verification and practical application. Finally, Petrel developed by China is introduced, and a typical preliminary application of DT-driven methodology is presented based on Petrel to verify its feasibility. The architecture proposed in this paper is also appropriate for other types of autonomous underwater vehicles.

In vitro fatigue behavior and in vivo osseointegration of the auxetic porous bone screw

The incidence of screw loosening, migration, and pullout caused by the insufficient screw-bone fixation stability is relatively high in clinical practice. To solve this issue, the auxetic unit-based porous bone screw (AS) has been put forward in our previous work. Its favorable auxetic effect can improve the primary screw-bone fixation stability after implantation. However, porous structure affected the fatigue behavior and in vivo longevity of bone screw. In this study, in vitro fatigue behaviors and in vivo osseointegration performance of the re-entrant unit-based titanium auxetic bone screw were studied. The tensile-tensile fatigue behaviors of AS and nonauxetic bone screw (NS) with the same porosity (51%) were compared via fatigue experiments, fracture analysis, and numerical simulation. The in vivo osseointegration of AS and NS were compared via animal experiment and biomechanical analysis. Additionally, the effects of in vivo dynamic tensile loading on the osseointegration of AS and NS were investigated and analyzed. The fatigue strength of AS was approximately 43% lower while its osseointegration performance was better than NS. Under in vivo dynamic tensile loading, the osseointegration of AS and NS both improved significantly, with the maximum increase of approximately 15%. Preferrable osseointegration of AS might compensate for the shortage of fatigue resistance, ensuring its long-term stability in vivo. Adequate auxetic effect and long-term stability of the AS was supposed to provide enough screw-bone fixation stability to overcome the shortages of the solid bone screw, developing the success of surgery and showing significant clinical application prospects in orthopedic surgery. Statement of significanceThis research investigated the high-cycle fatigue behavior of re-entrant unit-based auxetic bone screw under tensile-tensile cyclic loading and its osseointegration performance, which has not been focused on in existing studies. The fatigue strength of auxetic bone screw was lower while the osseointegration was better than non-auxetic bone screw, especially under in vivo tensile loading. Favorable osseointegration of auxetic bone screw might compensate for the shortage of fatigue resistance, ensuring its long-term stability and longevity in vivo. This suggested that with adequate auxetic effect and long-term stability, the auxetic bone screw had significant application prospects in orthopedic surgery. Findings of this study will provide a theoretical guidance for design optimization and clinical application of the auxetic bone screw.

Data-driven methods for stress field predictions in random heterogeneous materials

Predicting full-field stress responses is of fundamental importance to assessing materials failure and has various engineering applications in design optimization, manufacturing process control, and structural health monitoring. This article develops and evaluates different data-driven methods for efficient and accurate predictions of full stress fields in random heterogeneous materials. The first approach integrates model order reduction of proper orthogonal decomposition (POD) with classical machine learning techniques (K-nearest neighbors, random forest, and artificial neural networks) to predict full-field responses based on POD-reduced coefficients. However, this strategy shows limitations in predicting full stress fields, especially for heterogeneous material inclusions of small size or being close to the domain boundary. After that, two computer vision-based deep learning approaches were developed for full-field predictions. The first one uses a Resnet-based Convolutional Neural Network (CNN), and the second is based on a modified conditional Generative Adversarial Network (cGAN). Two representative example problems were studied: a random heterogeneous material inclusion or a void varying in size and location. In contrast to POD-based classical machine learning, almost invisible differences were found between the entire stress fields in finite element simulations and computer vision-based deep learning (CNN/cGAN) predictions, with significantly reduced mean squared error (MSE) and correlation values (R2) mostly above 0.99. On the other hand, the proposed cGAN provides more accurate predictions than CNN with fewer epochs.

Risk-informed design optimization method and application in a lead-based research reactor

Risk-informed approach has been widely applied in the safety design, regulation, and operation of nuclear reactors. It has been commonly accepted that risk-informed design optimization should be used in the innovative reactor designs to make nuclear system highly safe and reliable. In spite of the risk-informed approach has been used in some advanced nuclear reactors designs, such as Westinghouse IRIS, Gen-IV sodium fast reactors and lead-based fast reactors, the process of risk-informed design of nuclear reactors is hardly to carry out when passive system reliability should be integrated in the framework. A practical method for new passive safety reactors based on probabilistic safety assessment (PSA) and passive system reliability analyze linking is proposed in this paper. New three-dimension frequency-consequence curve based on risk concept with three variables is used in this method. The proposed method has been applied to the determination optimization of design options selection in a 10 MWth lead-based research reactor(LR) to obtain one optimized system design in conceptual design stage, using the integrated reliability and probabilistic safety assessment program RiskA, and the computation resources and time consumption in this process was demonstrated reasonable and acceptable.

DENDRIMERS FOR NEURO TARGETING

Fortunately, the system is built with established principles that create a wide safety margin, making failure an occurrence with a low chance. The mechanical behaviour of the structure is numerically modelled in an effort to accurately reproduce the real response because it is difficult to assess such a probability level in an efficient manner. However, as the complexity of the numerical model is increased in an effort to increase accuracy and take specific mechanical behaviour into account, the model tends to become more time-consuming. Dendrimers are molecules with repeated branches that have a spherical three-dimensional appearance and form the centre of dendrimers. The drug molecules can be connected to the surface of the dendrimer and its sprouting branches in a structure termed a dendron, which is the dendrimer's sole functional unit. Dendrimers can be categorised by their generation number, which ranges from G0.5 to G5, depending on how they grow. Dendrimers come in a variety of forms, with some having significant uses in the administration of drugs, such as poly(amidoamine) (PAMAM) dendrimers, poly(propylene imine) (PPI) dendrimers, polyether copolyester (PEPE) dendrimers, PEGylated dendrimers, and peptide dendrimers. Dendrimers have an architecture that depends on things like sizes, forms, surface chemistries, stiffness or flexibility, architecture, or interior elemental compositions. This article provides an overview of a comprehensive and effective product design framework that supports concurrent dendrimer design optimization applications.

Multi-Objective Reliability-Based Partial Topology Optimization of a Composite Aircraft Wing

Reliability-based partial topology optimization (RBPTO) is a new approach for aircraft structural design; however, it brings computational complexity and makes aeroelasticity analysis quite challenging. Therefore, the present study proposed the multi-objective reliability-based partial topology optimization of a composite aircraft wing using a fuzzy-based metaheuristic (MRBPTOFBMH) approach. The objective is to obtain an optimal layout including partial topology and sizing of the aircraft wing structure. Here, an optimal aeroelastic structure is designed by taking into account the uncertain nature of material properties and the permitted transverse displacement. To increase computational efficiency in the design process, a non-probabilistic approach called a possibilistic safety index-based design optimization (PSIBDO) with fuzzy uncertainties is proposed to quantify the uncertainties due to aeroelastic and structural constraints. Various optimum partial topological shapes and sizing of aircraft wing structures with various PSI values in the range of [0.001, 1.0] have been obtained in a single optimization run. These outcomes, including deterministic and reliable optimal aircraft wing structures, demonstrate the high effectiveness of the proposed MRBPTOFBMH technique to alleviate the complexity of unconventional aircraft wing structure design. The findings also reveal the ease in cooperation of the suggested technique with a high-performance multi-objective evolutionary algorithm (MOEA) and its application in real-world multi-objective design optimization (MODO) problems with the least computational requirements against the traditional method’s multiple runs. Furthermore, the proposed methodology can generate potential aircraft wing structures in a range of m = [89.38–127.84] kg, and flutter speed = [285.61–632.78] m/s, that adhere to all the constraints requirements.

A New Framework for Efficient Sequential Sampling-Based RBDO Using Space Mapping

Abstract In engineering applications of sampling-based reliability-based design optimization (RBDO), the Monte Carlo simulation (MCS) using a surrogate model of the performance function is mainly used for the probability of failure calculation and sensitivity analysis. However, if an inaccurate surrogate model is used, the calculation result using MCS will also be inaccurate, so it is essential to improve the accuracy of the surrogate model using sequential sampling. Hence, various sampling-based RBDO methods and sequential sampling methods have been proposed and used in various fields, and space mapping may also be a new framework for sequential sampling. In this paper, sampling-based RBDO with the Gaussian process regression (GPR) and space mapping is proposed. Space mapping generally attempts to utilize high-fidelity samples to update the low-fidelity model in multi-fidelity model conditions. However, in the proposed method, it is used for sequential sampling to improve the accuracy of the existing surrogate model. The major advantage of the proposed space mapping-based RBDO is that the existing surrogate model and the finally updated surrogate model can be formulated with simple matrix and vector calculations. In particular, when there is only a surrogate model that has been built due to the loss of existing sample information since the space mapping updates the model, the accuracy of the surrogate model can be improved by sequential sampling. The proposed method is compared with sequential sampling-based RBDO using GPR, and the calculation accuracy and efficiency are demonstrated through a 2D highly nonlinear example and an engineering problem.

OptEx: An integrated framework for experimental design and combustion kinetic model optimization

Computational fluid dynamics (CFD) simulation in the design of combustion devices imposes increased demand on combustion kinetic models with acceptable uncertainties. Model optimization is often utilized to constrain the model parameters with experimental data to reduce the prediction uncertainties. Since it is unaffordable to conduct experiments under all the concerned conditions, experimental design approaches are proposed to find the most valuable experiments to be conducted. An integrated computational framework, OptEx (Optimal Experiments), is proposed to facilitate applications of experimental design, data clustering, and model optimization with optimal experimental data. Specifically, this framework integrates the functions of dimension reduction, global sensitivity analysis, forward uncertainty quantification, model-analysis-based experimental design, and model optimization. The share of data and surrogate models between different modules significantly improves the computational efficiencies of model analysis, experimental design and model optimization. Two case studies of a methanol system are utilized to demonstrate the functionalities of OptEx. First, experimental designs are performed based on sensitivity entropy and surrogate model similarity analysis to find out informative while independent experiments. Second, experimental data clustering with OptEx is demonstrated by grouping massive experiments and selecting optimal experiments for each group. For both cases, a small size of optimal experimental datasets are utilized for model optimization, yielding an optimized model with smaller uncertainty bounds. Meanwhile, the input parameter uncertainties are significantly reduced and parameter correlations are identified via the joint probability distributions of optimized parameters.

Toward Practical Quantum Embedding Simulation of Realistic Chemical Systems on Near-term Quantum Computers

Quantum computing has recently exhibited great potentials in predicting chemical properties for various applications in drug discovery, material design, and catalyst optimization. Progress has been made in simulating small molecules, such as LiH and hydrogen chains of up to 12 qubits, by using quantum algorithms such as variational quantum eigensolver (VQE). Yet, originating from limitations of the size and the fidelity of near-term quantum hardware, how to accurately simulate large realistic molecules remains a challenge. Here, integrating an adaptive energy sorting strategy and a classical computational method, the density matrix embedding theory, which effectively finds a shallower quantum circuit and reduces the problem size, respectively, we show a means to circumvent the limitations and demonstrate the potential toward solving real chemical problems. We numerically test the method for the hydrogenation reaction of C6H8 and the equilibrium geometry of the C18 molecule, with basis sets up to cc-pVDZ (at most 144 qubits). The simulation results show accuracies comparable to those of advanced quantum chemistry methods such as coupled-cluster or even full configuration interaction, while the number of qubits required is reduced by an order of magnitude (from 144 qubits to 16 qubits for the C18 molecule) compared to conventional VQE. Our work implies the possibility of solving industrial chemical problems on near-term quantum devices.

Toward practical quantum embedding simulation of realistic chemical systems on near-term quantum computers.

Quantum computing has recently exhibited great potential in predicting chemical properties for various applications in drug discovery, material design, and catalyst optimization. Progress has been made in simulating small molecules, such as LiH and hydrogen chains of up to 12 qubits, by using quantum algorithms such as variational quantum eigensolver (VQE). Yet, originating from the limitations of the size and the fidelity of near-term quantum hardware, the accurate simulation of large realistic molecules remains a challenge. Here, integrating an adaptive energy sorting strategy and a classical computational method—the density matrix embedding theory, which respectively reduces the circuit depth and the problem size, we present a means to circumvent the limitations and demonstrate the potential of near-term quantum computers toward solving real chemical problems. We numerically test the method for the hydrogenation reaction of C6H8 and the equilibrium geometry of the C18 molecule, using basis sets up to cc-pVDZ (at most 144 qubits). The simulation results show accuracies comparable to those of advanced quantum chemistry methods such as coupled-cluster or even full configuration interaction, while the number of qubits required is reduced by an order of magnitude (from 144 qubits to 16 qubits for the C18 molecule) compared to conventional VQE. Our work implies the possibility of solving industrial chemical problems on near-term quantum devices.

Synthesis and structure design of I–III–VI quantum dots for white light-emitting diodes

The synthesis and structural design of I–III–VI quantum dots are summarized and WLED applications are highlighted. The routes to improve the performance of WLED devices and the challenges of design optimization and practical application are discussed.

A detailed insight into the optimization of plate and frame heat exchanger design by comparing old and new generation metaheuristics algorithms

The voluminous utilization and application of plate and frame heat exchangers (PFHE) in many industries has accelerated the consumer and designer both to optimize exchanger total cost. Over the last few years, several old and new generation algorithms were employed and exploited to optimize PFHE cost. This study explores the application and performance of three new-generation algorithms Big Bang-Big Crunch (BBBC), Grey Wolf Optimizer (GWO), and Water Evaporation Optimization (WEO) in designing optimally PFHE. Besides, this study also compares the performance of three well-established old generations algorithms namely genetic algorithm (genetics and natural selection), particle swarm optimization (animals behaviour), and differential evolution (population-based) with the above three new algorithms in the optimization of PFHE.Seven design factors are chosen for PFHE optimization: exchanger length on hot and cold sides, height and thickness of fin, length of the fin-strip, fin frequency, and the number of hot side layers. The applicability of the suggested algorithms is assessed using a case study based on published research. Though DE performs the best in this study of design optimization concerning total cost and computational time, the three new-generation meta-heuristic algorithms BBBC, GWO, and WEO also provide the novel scope of application in heat exchanger design optimization and successfully finding the cost of the heat exchanger. According to this study, capital costs increase by 19.5% for BBBC, 24% for GWO, and 7.6% for GWO, but operational costs fall by 9.5% for BBBC and GWO when compared to the best performing algorithm (DE). On the other hand, WEO shows an increase of 32.6% in operational costs. Aside from that, a full analysis of the computing time for each algorithm is also provided. The DE has the quickest run time of 0.09 ​s, while the PSO takes the longest at 33.97 ​s. The rest of the algorithms have nearly identical values. As a result, a good comparison is established in this study, offering an excellent platform for designers and customers to make selections. Additionally, the three new generations algorithms mentioned here were not used earlier for optimization of PFHE and the comparative study illustrates that each of them possesses eat potential for cost optimization and also solving other complex problems.

Application of Structural Design Optimization in Building Structural Design

Application of Structural Design Optimization in Building Structural Design

Transient temperature‐pressure field model of supercritical CO2 fracturing wellbore with tubing and annulus co‐injection

AbstractThe physical parameters of supercritical carbon dioxide (SC‐CO2) fracturing fluid are sensitive to temperature and pressure. Accurate prediction of wellbore temperature and pressure during injection is critical for the fracturing efficiency. In this paper, a transient pressure‐field model of SC‐CO2 fracturing wellbore with tubing and annulus co‐injection was established, which was adjusted to consider the Joule–Thomson effect, axial heat conduction, expansion/compression heat, and friction heat. The model predicted the variation patterns of wellbore temperature and pressure, comparing them with those under tubing injection conditions. Effects of tubing‐annulus injection ratios and endothermic mode on bottom‐hole temperature (BHT) and wellhead pressure (WHP) were analyzed in detail. Compared with pure tubing injection, co‐injection provided higher BHT and lower WHP. At constant BHP, WHP dropped with time in the processes of tubing injection or co‐injection, while the former provided a larger WHP drop. With an increase in tubing injection ratio, the difference between tubing and annulus BHT values slightly grew. When the tubing and annulus injection displacements were allocated according to their cross‐sectional area ratio, their WHP was minimal. If CO2 was assumed to absorb all the friction heat, overestimation of BHT and WHP would be observed, which was more pronounced at larger injection displacements. This study's findings are considered instrumental in the design optimization and field application of SC‐CO2 fracturing in unconventional reservoirs. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd.

Quantum dots-hydrogel composites for biomedical applications

Quantum dots-hydrogel composites are promising new materials that have attracted extensive attention due to their incomparable biocompatibility and acceptable biodegradability, leading to enormous potential applications for various fields. This review summarizes the recent advances in quantum dots-hydrogel composites with a focus on synthesis methods, including hydrogel gelation in quantum dots (QDs) solution, embedding prepared QDs into hydrogels after gelation, forming QDs in situ within the preformed gel and cross-linking via QDs to form hydrogels. In particularly, biomedical applications as bioimaging, biosensing and drug delivery are also reviewed, followed by a discussion on the inherent challenges of design optimization, biocompatibility and bimodal applications and the prospect of the future development. These results will guide the development of quantum dots-hydrogel composites and provide critical insights to inspire researchers in future.

Explore artificial neural networks for solving complex hydrocarbon chemistry in turbulent reactive flows

Global warming caused by the use of fossil fuels is a common concern of the world today. It is of practical importance to conduct in-depth fundamental research and optimal design for modern engine combustors through high-fidelity computational fluid dynamics (CFD), so as to achieve energy conservation and emission reduction. However, complex hydrocarbon chemistry, an indispensable component for predictive modeling, is computationally demanding. Its application in simulation-based design optimization, although desirable, is quite limited. To address this challenge, we propose a methodology for representing complex chemistry with artificial neural networks (ANNs), which are trained with a comprehensive sample dataset generated by the Latin hypercube sampling (LHS) method. With a given chemical kinetic mechanism, the thermochemical sample data is able to cover the whole accessible pressure/temperature/species space in various turbulent flames. The ANN-based model consists of two different layers: the self-organizing map (SOM) and the back-propagation neural network (BPNN). The methodology is demonstrated to represent a 30-species methane chemical mechanism. The obtained ANN model is applied to simulate both a non-premixed turbulent flame (DLR_A) and a partially premixed turbulent flame (Flame D) to validate its applicability for different flames. Results show that the ANN-based chemical kinetics can reduce the computational cost by about two orders of magnitude without loss of accuracy. The proposed methodology can successfully construct an ANN-based chemical mechanism with significant efficiency gain and a broad scope of applicability, and thus holds a great potential for complex hydrocarbon fuels.

Calculation of Deflection and Stress of Assembled Concrete Composite Beams under Shrinkage and Creep and Its Application in Member Design Optimization

Calculation of Deflection and Stress of Assembled Concrete Composite Beams under Shrinkage and Creep and Its Application in Member Design Optimization