Multidisciplinary Optimization Research Articles

Exploring Core Knowledge in Interdisciplinary Research: Insights from Topic Modeling Analysis

Although interdisciplinary research has garnered extensive attention in academia, its core knowledge structure has yet to be systematically explored. To address this gap, this study aims to uncover the underlying core knowledge topics within interdisciplinary research, enabling researchers to gain a deeper understanding of the knowledge framework, improve research efficiency, and offer insights for future inquiries. Based on the Web of Science (WoS) database, this study collected 153 highly cited papers and employed the LDA topic model to identify latent topics and extract the knowledge structure within interdisciplinary research. The findings indicate that the core knowledge topics of interdisciplinary research can be categorized into four major areas: the knowledge framework and social impact of interdisciplinary research, multidisciplinary approaches in cancer treatment and patient care, Covid-19 multidisciplinary care and rehabilitation, and multidisciplinary AI and optimization in industrial applications. Moreover, the study reveals that AI-related interdisciplinary research topics are rapidly emerging. Through an in-depth analysis of these topics, the study discusses potential future directions for interdisciplinary research, including the cultivation and development of interdisciplinary talent, evaluation systems and policy support for interdisciplinary research, international cooperation and interdisciplinary globalization, and AI and interdisciplinary research optimization. This study not only uncovers the core knowledge structure of interdisciplinary research but also demonstrates the effectiveness of the LDA topic model as a data mining tool for revealing key topics and trends, providing practical tools for future research. However, this study has two main limitations: the time lag of highly cited papers and the dynamic evolution of interdisciplinary research. Future research should address these limitations to further enhance the understanding of interdisciplinary research.

Multi-objective optimization of the appendages of a sailing yacht using the normal boundary intersection method

In this paper, a multidisciplinary design optimization algorithm, the Normal Boundary Intersection (NBI) method, is applied to the design of some devices of a sailing yacht. The full Pareto front is identified for two different design problems, and the optimal configurations are compared with standard devices. The great efficiency of the optimization algorithm is demonstrated by the wideness and density of the identified Pareto front.

Design and uncertainty-based multidisciplinary optimization of a 3D star-shaped negative Poisson’s ratio structural battery pack

Design and uncertainty-based multidisciplinary optimization of a 3D star-shaped negative Poisson’s ratio structural battery pack

Multi-Disciplinary Optimization of UV-C Filter for Air Disinfection

Because of the recent COVID-19 pandemic, the problem of preventing and containing the diffusion of pathogens spread through air has become a main topic of research. The problem is particularly important for specific environments, such as dental or other medical practices, where the aerosol treatments in open-mouth patients, combined with closed and crowded rooms, raise the risk of infection. As an efficient countermeasure, in this study we propose a solution that is able to remove the risk at the source, through the aspiration of the aerosol and the neutralization of the bacterial load by means of a UV-C LED filter, which releases the sterilized air in the environment. To maximize the efficiency of the solution, in this study we performed a numerical multi-disciplinary optimization (MDO) of the filter, coupling numerical simulations of multiple disciplines (CFD and electromagnetics) by the process automation and optimization environment modeFRONTIER of ESTECO. Geometrical parameters of the filter are updated for each candidate solution proposed by the optimization algorithm, and their performance in terms of viral neutralization efficiency and air mass flow rate are evaluated by the simulations, until the optimal solution is found. The methodology and results of the study are presented.

Multidisciplinary Optimization of a Mixed-flow Impeller with 140 Parameters and its Shapley Additive Explanations

Abstract In recent years, we have made several improvements to the geometric parameterization method, the surrogate model method, and the sampling method, with the goal of making the traditional surrogate model-based optimization method applicable to aerodynamic optimization of hundreds of parameters with reasonable computational cost. However, increasing the number of control parameters raises two additional issues. First, the impeller geometry becomes too complex to ensure the required mechanical performance. Second, the optimization mechanism becomes difficult to understand with too many parameters. To address the first issue, this paper builds a multidisciplinary optimization platform to achieve the optimization of a large flow coefficient mixed-flow impeller under 140 control parameters, resulting in a significant improvement in both aerodynamic and mechanical performance. To address the latter, a novel machine learning interpretation tool, Shapley Additive Explanations (SHAP), is introduced in this paper. Using this methodology, the contribution of all 140 parameter values in the final optimal impeller to each aspect of the performance improvement is presented in this paper, providing the first in-depth understanding of the intricate mechanisms involved in the multidisciplinary optimization of hundreds of control parameters.

Multidisciplinary Optimization of Gyroid Topologies for a Cold Plate Heat Exchanger Design

Abstract The strive to reduce the environmental impact of aviation has led to electrification and increasing demand for powerful on-board power electronic systems. These high-performance electrical components are bound to produce significant amounts of low-quality heat waste that, if not dissipated properly, will lead to malfunctioning and even permanent damage. For this reason, high-performance heat exchangers (HX) represent a key enabler for future advances in aircraft systems electrification and are vital to meet net zero goals and reduce the aviation's carbon footprint. For a given volume of the exchanger, the heat flow rate can be increased by adopting more sophisticated fluid domains. However, excessive geometrical complexity will lead to an increase in pressure losses, often resulting in inhomogeneous temperature distributions. In this paper, a novel optimization procedure is employed to maximize the efficiency of a high-performance heat exchanger, while minimizing overall pressure loss and temperature gradients. The optimization is performed with full three-dimensional high-fidelity computational flow simulations. The geometry of the fluid domain is constituted by triply periodic minimal surfaces (TPMS), with a parametrization based on thickness and aspect ratios, done by using the ntopology suite. To assess the performance gain, the topology-optimized design is compared against the datum case and a conventional serpentine design.

Automation of conceptual design and modification of aircraft type unmanned aerial vehicles using multidisciplinary optimization and evolutionary algorithms. Part 1: Methods and models

This paper proposes a method for selecting rational parameters for large-size aircraft-type unmanned aerial vehicles at the initial design stages using an optimization algorithm of differential evolution and numerical mathematical modeling of aerodynamic problems. The method assumes implementation of weight and aerodynamic balance in the main flight modes, it can consider aircraft-type unmanned aerial vehicles with one or two lifting surfaces, applies parallel calculations, and automatically generates a three-dimensional geometric model of the aircraft appearance based on the optimization results. A method for accelerating by more than three times the process of solving the problem of optimizing aircraft takeoff weight parameters by introducing the target function into the set of design variables is proposed and demonstrated. The results of assessing the reliability of the mathematical models used for aerodynamics and the correct calculation of the target function are presented, taking into account various constraints. A comprehensive check of the operability and effectiveness of the method were considered by solving demonstration problems by optimizing more than ten main design parameters of the appearance of two existing heavy-class unmanned aerial vehicles with known characteristics from open sources. Examples of using the optimization results to modify prototypes are provided.

Multi-fidelity data mining-based design optimization framework for scramjet-based aircraft

Aircraft design faces complex multidisciplinary problems and often involves numerous design variables, making direct optimization a time-consuming task. With the development of big data and machine learning technologies, using data mining methods to assist optimization has become key to addressing the inefficiency in multidisciplinary design optimization (MDO). To address the high cost of data acquisition in current data mining, this paper proposes an aircraft design optimization framework based on multi-fidelity data mining, using a large amount of inexpensive cursory estimation data to execute the MDO process and form a multidisciplinary optimization database. Then, data mining methods are used to decouple disciplinary relationships, transforming multidisciplinary coupling problems into simpler multi-objective problems. Subsequently, under viscous CFD computation conditions, the EDGE-p-BFFD deformation method is used to fine-tune the obtained configurations, further enhancing multidisciplinary performance. Before both optimizations, data mining methods were used to analyze the impact of design variables on objective functions, forming design knowledge and completing variable selection. In the multidisciplinary optimization practice of airbreathing aircraft, it can be found that the proposed framework effectively achieves disciplinary decoupling and explores the relationship between shape and performance. In the multidisciplinary optimization of the waverider based on cursory estimations, particle swarm optimization increased the aircraft's cruising range by 8.74 %. In the multi-objective optimization of the lifting body based on viscous CFD calculations, data mining was used to transform the problem into several key objectives. The MOEA/D algorithm increased the range by 2.9 %, validating the effectiveness of the data mining-based MDO framework proposed in this paper.

Multidisciplinary Design Optimization of Alignment and Whirling Vibration Characteristics of a Submarine Propulsion Shafting Using Kriging Surrogate Model

To improve the performance indexes, such as safety, reliability and acoustic stealth, of a submarine, it is significant to optimize the dynamic characteristics of its propulsion shafting. The alignment state of a shafting has a coupling effect on its whirling vibration characteristics, and the multidisciplinary design optimization (MDO) theory can fully consider the coupling relationships between different disciplines like this, which is a scientific and effective method to achieve the design optimization of shafting dynamics. However, the iterative calculation of high-precision numerical models greatly restricts the optimization efficiency of this method. Aiming at this problem, in this paper, an MDO model was established based on the coupling dynamic analysis of submarine propulsion shafting, and the Kriging surrogate model was used to predict the state variables within each subdiscipline. Along with the reduction of computational expense, the MDO of the alignment and whirling vibration characteristics of the shafting was achieved. The studied results can be applied to the design process of submarine propulsion shafting, which can provide technical and theoretical support for improving the optimization efficiency of its coupling dynamics.

Multi-Disciplinary Surrogate-Based Optimization of a Compressor Rotor Blade Considering Ice Impact

Multi-Disciplinary Surrogate-Based Optimization of a Compressor Rotor Blade Considering Ice Impact

Multidisciplinary design optimization of an autonomous underwater vehicles based on random uncertainty

Herein, discipline decomposition was performed for an autonomous underwater vehicle (AUV) based on multidisciplinary design optimization (MDO), and its design parameters were determined. Parameterized analysis of the hull-form and structure disciplines was performed, and an approximate model of these disciplines was established with a high fitting accuracy. An analysis model of propulsion, energy, and system disciplines was developed based on the formula method. Based on collaborative optimization as well as discipline and system level models, a deterministic MDO framework for AUVs was established. Subsequently, an optimized solution was obtained. By considering the random uncertainty of design variables, an MDO framework for the uncertainty of AUVs was established and optimized solutions were obtained. The distribution of solutions obtained from uncertainty optimization was more concentrated and the distribution range was smaller than that obtained using deterministic optimization. Robustness analysis was performed on the initial scheme, typical deterministic optimization schemes, and typical uncertainty optimization schemes. Results showed that fluctuations in design variables may lead to constraints that exceed boundary conditions in the deterministic optimization design scheme. Using uncertainty and objective function optimization, the robustness of the overall scheme of AUVs was improved.

Évaluation préliminaire de l'intérêt d'une cellule d'optimisation thérapeutique sur la titration des traitements de l'insuffisance cardiaque à fraction d’éjection ventriculaire gauche réduite et la qualité de vie des patients

IntroductionA multidisciplinary therapeutic optimization unit (COT) was created in January 2023 at Versailles Hospital, aimed at therapeutic optimisation of patients with chronic heart failure with reduced left ventricular ejection fraction. The objective of the study was to assess the impact of the first year of COT activity on the sequential implementation and titration of heart failure treatments, the clinical evolution, and improvement of patients’ quality of life. MethodsThis prospective study included consecutive patients treated by the COT after hospitalisation for acute heart failure, from January to December 2023. Clinical, biological, titration, and tolerance data were analysed. Quality of life was assessed at baseline and at the end of the follow-up by COT, using standardized SF-12 and EQ-5D questionnaires. ResultsWe included 90 patients (men 73%, mean age 67 years). The mean left ventricular ejection fraction was 34 ± 10 %. At final visit (median number of visits 4 ; median follow-up duration 156 days), 76.7% of patients achieved optimisation with respect to maximum individually tolerated doses, but only 13.3% with respect to theoretical maximum doses for the four therapeutic classes. At 1-year follow up, total mortality was 4.4% (4/90), and 9 patients (10%) were rehospitalised unplanned for acute heart failure. COT monitoring was associated with significant improvement in NYHA class, left ventricular ejection fraction, and SF-12 and EQ-5D-5L quality of life scores. ConclusionAlthough titration of heart failure treatments remained suboptimal, significant improvement was observed for NYHA class, left ventricular ejection fraction, and patient quality of life parameters.

Preliminary Techno-Economic Study of Optimized Floating Offshore Wind Turbine Substructure

Floating offshore wind turbines (FOWTs) are still in the pre-commercial stage and, although different concepts of FOWTs are being developed, cost is a main barrier to commercializing the FOWT system. This article aims to use a shape parameterization technique within a multidisciplinary design analysis and optimization framework to alter the shape of the FOWT platform with the objective of reducing cost. This cost reduction is then implemented in 30 MW and 60 MW floating offshore wind farms (FOWFs) designed based on the static pitch angle constraints (5 degrees, 7 degrees and 10 degrees) used within the optimization framework to estimate the reduction in the levelized cost of energy (LCOE) in comparison to a FOWT platform without any shape alteration–OC3 spar platform design. Key findings in this work show that an optimal shape alteration of the platform design that satisfies the design requirements, objectives and constraints set within the optimization framework contributes to significantly reducing the CAPEX cost and the LCOE in the floating wind farms considered. This is due to the reduction in the required platform mass for hydrostatic stability when the static pitch angle is increased. The FOWF designed with a 10 degree static pitch angle constraint provided the lowest LCOE value, while the FOWF designed with a 5 degree static pitch angle constraint provided the largest LCOE value, barring the FOWT designed with the OC3 dimension, which is considered to have no inclination.

Multi-fidelity multidisciplinary meta-model based optimization of a slender body with fins

Multidisciplinary design optimization (MDO) involving aero-elastic simulations still proves challenging for computational cost. This paper proposes a competitive cost-effective multi-fidelity MDO strategy based on two ideas. Firstly, a new multi-fidelity fluid-structure interaction model (MF-FSI) is proposed, allowing a considerable saving of the FSI simulation cost. Secondly, the optimization cost is further reduced using a meta-model approximation of the MF-FSI computations during optimization. Therefore, the MF-FSI model is validated on an experimental case of supersonic projectile fins. Then, it is combined with a meta-model-based optimization strategy (MBO) to improve the fins design. The constrained multi-objective problem aiming to maximize the lift-to-drag ratio and minimize the fin mass is solved using both high-fidelity (HFMDO) and multi-fidelity (MFMDO). The results showed that the proposed MFMDO strategy could produce the same optimal solutions as the HFMDO one with a 52% lower cost.

Timeline of surgery in localized angiosarcoma of the breast: Improving outcome following multidisciplinary treatment optimization

Primary (PAS) and radiation-associated angiosarcomas (RAAS) of the breast are rare tumors of vascular origin with poor survival. In this retrospective cohort study, we aimed to assess the impact of multidisciplinary treatment optimization on the prognosis of patients who underwent surgery at a national referral center. Cases of operable angiosarcoma of the breast evaluated by a multidisciplinary team including surgeons, medical oncologists and radiation oncologists expert in the field and treated from January 2012 to January 2023 were retrieved from a prospectively maintained database. The outcomes of three treatment groups, defined by the timing of surgery in relation to adjuvant and neoadjuvant therapies, were compared. Fifty-nine patients with operable angiosarcomas of the breast (49 RAAS and 10 PAS) were retrospectively identified. The five-year overall survival was 85.2% (95% CI 73.9-98.2) and event-free survival was significantly better in patients with grade 1 than those with grade 2 or 3 tumors. Patients receiving neoadjuvant chemotherapy had significantly better outcomes than those treated with primary surgery. Pathological complete response was significantly higher in patients receiving neoadjuvant radiotherapy after neoadjuvant chemotherapy, and a trend towards better distant-disease-free survival was found for patients with complete response at time of surgery. Optimization of angiosarcoma treatment based on specialized, multidisciplinary assessment regarding the type and timing of surgery and the use of neoadjuvant chemoradiotherapy can improve outcomes. The findings of this study support the use of neoadjuvant chemotherapy as well as adjuvant and neoadjuvant radiotherapy in clinical practice.

A surrogate-assisted expensive constrained multi-objective global optimization algorithm and application

Expensive multi-objective optimization problems (MOPs) have seen the successful applications of surrogate-assisted evolutionary algorithms (SAEAs). Nevertheless, the majority of SAEAs are developed for costly unconstrained optimization, and costly constrained MOPs (CMOPs) have received less attention. Therefore, this article proposes a surrogate-assisted global optimization algorithm (named CTEA) for solving CMOPs within a very limited number of fitness evaluations. The proposed algorithm combines two selection frameworks, a bi-level selection framework, and an adaptive sampling framework, to enhance optimization performance. Leveraging on a constraint-improving strategy and a Pareto-based three-indicator criterion (convergence, constraint, and diversity indicators) at the different levels, the proposed bi-level selection framework can select more promising solutions. Moreover, an adaptive sampling framework is developed to prioritize objective and constraint functions and select the candidate solutions for real function evaluations according to the priority. Experimental results demonstrate that the proposed CTEA exhibits superior performance when compared with five state-of-the-art algorithms, achieving the best results in 61.9 % out of the 64 test instances. Finally, CTEA is applied to the multidisciplinary design optimization of blended-wing-body underwater gliders, and an impressive solution set is obtained.

Multidisciplinary optimization methodology for truss-braced wing aircraft using high-fidelity structure sizing

In this research, a method is developed to optimize the truss-braced wing aircraft configuration in a multidisciplinary design framework. Physics-based high-fidelity methods, that can capture the nature of the configuration changes, are employed for the disciplines where the existing classical methods are not reliable. High-fidelity geometry modeling, structure loading, structure optimization, and aeroelastic sizing methods are integrated into the aircraft multidisciplinary design and optimization. The developed algorithm is applied for the multi-objective optimization of a regional jet aircraft to minimize the cost and weight. The results demonstrate that the cost-optimum solution converges to a higher aspect ratio wing equipped with a higher bypass ratio engine, and a 7.94% reduction in the direct operating cost can be achieved. On the other hand, the weight-optimum wing planform tends to a slightly lower aspect ratio wing with a lower bypass ratio engine, while a 6.18% reduction in take-off weight is achieved. In addition to that, the findings of this study highlight the considerable effect that the engine technology has on the optimum layout, which suggests that the engine technology and its performance should also be a part of the design optimization process. The developed modular framework offers further optimization potential for the truss-braced wing aircraft, as more detailed models can be integrated.

Multidisciplinary Optimization of Aircraft Aerodynamics for Distributed Propulsion Configurations

The combination of different aerodynamic configurations and propulsion systems, namely, aero-propulsion, affects flight performance differently. These effects are closely related to multidisciplinary collaborative aspects (aerodynamic configuration, propulsion, energy, control systems, etc.) and determine the overall energy consumption of an aircraft. The potential benefits of distributed propulsion (DP) involve propulsive efficiency, energy-saving, and emissions reduction. In particular, wake filling is maximized when the trailing edge of a blended wing body (BWB) is fully covered by propulsion systems that employ boundary layer ingestion (BLI). Nonetheless, the thrust–drag imbalance that frequently arises at the trailing edge, excessive energy consumption, and flow distortions during propulsion remain unsolved challenges. These after-effects imply the complexity of DP systems in multidisciplinary optimization (MDO). To coordinate the different functions of the aero-propulsive configuration, the application of MDO is essential for intellectualized modulate layout, thrust manipulation, and energy efficiency. This paper presents the research challenges of ultra-high-dimensional optimization objectives and design variables in the current literature in aerodynamic configuration integrated DP. The benefits and defects of various coupled conditions and feasible proposals have been listed. Contemporary advanced energy systems, propulsion control, and influential technologies that are energy-saving are discussed. Based on the proposed technical benchmarks and the algorithm of MDO, the propulsive configuration that might affect energy efficiency is summarized. Moreover, suggestions are drawn for forthcoming exploitation and studies.

Prediction of perceived annoyance caused by an electric drone noise through its technical, operational, and psychoacoustic parameters.

Electric drones serve diverse functions, including delivery and surveillance. Nonetheless, they encounter significant challenges due to their annoying noise emissions. To address this issue, a sound database was created from experiments conducted in a hover-test-bench and real flights operated indoors. These experiments involved a wide range of parameter variations and operational conditions. A global digital user study involving 578 participants was conducted to assess drone noise annoyance. Furthermore, correlations between annoyance levels, psychoacoustic metrics, sociocultural factors, and technical/operational parameters were analyzed. The effects of implementing acoustic optimization modifications on the drone's performance were quantified with a conceptual design tool. The findings indicate that reducing the levels of loudness, sharpness, tonality, and roughness or fluctuation strength led to an improvement in annoyance. Differences in variable importance of psychoacoustic metrics dependent on the specific model were found. Sociocultural factors did not affect annoyance. Technical and operational parameters impacted annoyance, especially when reducing blade tip speed. A 20% reduction in tip speed showed potential through tool application as it maintained acceptable drone performance while beneficially targeting annoyance. A multi-disciplinary optimization is recommended to maintain operational efficiency. Last, psychoacoustic metrics were validated as an effective measure to evaluate a design solution.

Online ultrasonic monitoring of FDM nozzle temperature based on metamaterial buffer rod

Abstract Fused deposition modeling (FDM) has been widely used for fabricating parts with complex geometries. However, during the printing process the nozzle temperature fluctuates which may cause nozzle blockage, inter-filament delamination, over-melting, etc. Therefore, it is of significance to develop an online and in-situ nozzle monitoring system which could overcome such drawbacks for quality assurance. In this work, a multifunctional system is designed by incorporating a traditional ultrasonic probe and a metamaterial buffer rod to online monitor the nozzle temperature and ensure a correct extrusion force and speed. A parametric simulation model is first constructed implicitly based on thermal-acoustic metamaterial structures for the buffer rod. Secondly, since the high-fidelity calculation is computationally expensive, an efficient surrogate model with kriging approach is constructed based on the dataset drawn from limited trials for rapid prediction. Thirdly, multidisciplinary optimization is performed by the NSGA-III algorithm considering the thermal insulation performance, ultrasonic attenuation and structure integrity. The rod is fabricated according to the best parameters decided. A cross-correlation algorithm is calibrated with thermocouple measurement after waveform recording at different temperatures. The online and in-situ temperature measurement can be realized during 3D printing successfully eventually. This will help to improve the printing quality with potential feedback strategies.