Multidisciplinary Optimization Research Articles

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.

Multidisciplinary Design and Optimization of High-Speed Hybrid Excitation Starter Generator for Aircraft Power System

Multidisciplinary Design and Optimization of High-Speed Hybrid Excitation Starter Generator for Aircraft Power System

Blended-Wing-Body Regional Aircraft Optimization with High-Fidelity Aerodynamics and Critical Design Requirements

Conventional tube-and-wing and proposed blended-wing-body airliners must satisfy several design requirements, but the latter configuration is tightly integrated and sensitive to these requirements. In this work, blended-wing-body regional aircraft are investigated using a gradient-based mixed-fidelity multidisciplinary optimization framework centered on a Reynolds-averaged Navier–Stokes solver. In addition to sizing and cruise trim, design requirements considered include one-engine-inoperative directional trim, takeoff rotation ability, takeoff field length, initial climb performance, low-speed trim and static margin, and top-of-climb rate of climb. Results show how optimal design features vary and performance is overpredicted if critical design requirements are excluded and how key elements of geometric freedom help realize the potential of the configuration. A 4.8% block fuel burn benefit is enabled by pivot-piston variable-length landing gear even with low-mounted engines and high design freedom. The off-design constraints penalize block fuel burn by 3.2% if variable-length landing gear is considered, but this value reaches 7.6% if lower geometric freedom that inhibits tight cabin contouring and the formation of a novel forebody ridge is removed. Leading-edge carving is found to be optimal. The high design freedom and high-fidelity aerodynamics model help efficiently satisfy the design requirements, resulting in a cruise lift-to-drag ratio of 21.7 at 36,000 ft and Mach 0.78.

Surrogate-Based Multidisciplinary Optimization for the Takeoff Trajectory Design of Electric Drones

Electric vertical takeoff and landing (eVTOL) aircraft attract attention due to their unique characteristics of reduced noise, moderate pollutant emission, and lowered operating cost. However, the benefits of electric vehicles, including eVTOL aircraft, are critically challenged by the energy density of batteries, which prohibit long-distance tasks and broader applications. Since the takeoff process of eVTOL aircraft demands excessive energy and couples multiple subsystems (such as aerodynamics and propulsion), multidisciplinary analysis and optimization (MDAO) become essential. Conventional MDAO, however, iteratively evaluates high-fidelity simulation models, making the whole process computationally intensive. Surrogates, in lieu of simulation models, empower efficient MDAO with the premise of sufficient accuracy, but naive surrogate modeling could result in an enormous training cost. Thus, this work develops a twin-generator generative adversarial network (twinGAN) model to intelligently parameterize takeoff power and wing angle profiles of an eVTOL aircraft. The twinGAN-enabled surrogate-based takeoff trajectory design framework was demonstrated on the Airbus A3 Vahana aircraft. The twinGAN provisioned two-fold dimensionality reductions. First, twinGAN generated only realistic trajectory profiles of power and wing angle, which implicitly reduced the design space. Second, twinGAN with three variables represented the takeoff trajectory profiles originally parameterized using 40 B-spline control points, which explicitly reduced the design space while maintaining sufficient variability, as verified by fitting optimization. Moreover, surrogate modeling with respect to the three twinGAN variables, total takeoff time, mass, and power efficiency, reached around 99% accuracy for all the quantities of interest (such as vertical displacement). Surrogate-based, derivative-free optimizations obtained over 95% accuracy and reduced the required computational time by around 26 times compared with simulation-based, gradient-based optimization. Thus, the novelty of this work lies in the fact that the twinGAN model intelligently parameterized trajectory designs, which achieved implicit and explicit dimensionality reductions. Additionally, twinGAN-enabled surrogate modeling enabled the efficient takeoff trajectory design with high accuracy and computational cost reduction.

Multidisciplinary Reliability Design Optimization Modeling Based on SysML

Model-Based Systems Engineering (MBSE) supports the system-level design of complex products effectively. Currently, system design and optimization for complex products are two distinct processes that must be executed using different software or platforms, involving intricate data conversion processes. Applying multidisciplinary optimization to validate system optimization often necessitates remodeling the optimization objects, which is time-consuming, labor-intensive, and highly error-prone. A critical activity in systems engineering is identifying the optimal design solution for the entire system. Multidisciplinary Design Optimization (MDO) and reliability analysis are essential methods for achieving this. This paper proposes a SysML-based multidisciplinary reliability design optimization modeling method. First, by analyzing the definitions and mathematical models of multidisciplinary reliability design optimization, the SysML extension mechanism is employed to represent the optimization model based on SysML. Next, model transformation techniques are used to convert the SysML optimization model generated in the first stage into an XML description model readable by optimization solvers. Finally, the proposed method’s effectiveness is validated through an engineering case study of an in-vehicle environmental control integration system. The results demonstrate that this method fully utilizes SysML to express MDO problems, enhancing the efficiency of design optimization for complex systems. Engineers and system designers working on complex, multidisciplinary projects can particularly benefit from these advancements, as they simplify the integration of design and optimization processes, facilitating more reliable and efficient product development.

Multidisciplinary Design Optimization Processes for Efficiency Improvement of Aircraft: State-of-the-Art Review

Multidisciplinary Design Optimization Processes for Efficiency Improvement of Aircraft: State-of-the-Art Review

Effective green building design assessment support using sequential multidisciplinary design optimization

Green building (GB) represents a leading approach for promoting sustainable development, reducing environmental impact, and ensuring environmental responsibility across its entire life cycle. Many countries have recently developed rating systems and certification programs for GBs. Traditional GB assessment is conducted through manual and iterative procedures that entail numerous meetings and trials. Architects and engineers must thoroughly assess the interdependence of design variables to ensure compliance with assessment standards for feasible solutions in GB projects. This process could be complicated and error-prone, leading to potential cost implications. Therefore, there is a need to develop design support systems to generate feasible solutions for GBs effectively. This paper introduces an integrated assessment system for GB designs, employing a multidisciplinary design optimization (MDO) methodology to efficiently generate solutions across various disciplines, adhering to assessment standards. Practical cases in Taiwan have been employed to assess and confirm the efficiency and effectiveness of the proposed system concerning the design of building envelopes, air-conditioning systems, and artificial lighting systems. The primary advantages of the system include the rapid generation of multiple alternatives and the reduction of iterations in GB assessment by providing conflict-free solutions across various disciplines. The proposed system also offers estimated construction costs for each alternative. Designers can further explore the most cost-effective solutions through value analysis. This information can assist investors in making informed decisions. The proposed design support system shows great potential in accelerating the design process for green buildings by offering conflict-free alternatives and value-added information.

Atmospheric Aircraft Conceptual Design Based on Multidisciplinary Optimization with Differential Evolution Algorithm and Neural Networks

A methodology for selecting rational parameters of atmospheric aircraft during the initial design stages using a differential evolutionary optimization algorithm and numerical mathematical modeling of aerodynamics problems is proposed. The technique involves implementing weight and aerodynamic balance in the main flight modes, considering atmospheric aircraft with one or two lifting surfaces, applying parallel calculations, and auto-generating a three-dimensional geometric model of the aircraft’s appearance based on the optimization results. A method for accelerating the process of optimizing aircraft parameters in terms of takeoff weight by more than three times by introducing an objective function into the set of design variables is proposed and demonstrated. The reliability of mathematical models used in aerodynamics and the accuracy of the objective function calculation considering various constraints are explored. A comprehensive test of the performance and efficiency of the methodology is conducted by solving demonstration problems to optimize more than ten main design parameters for the appearance of two existing heavy-class unmanned aerial vehicles with known characteristics from open sources.

Multidisciplinary design analysis and optimization of the aerodynamic shape of the SpaceLiner passenger stage

The SpaceLiner is a futuristic concept of a suborbital space plane intended to provide ultra-fast intercontinental passenger transport, currently in pre-development at DLR-SART. Vehicles traveling at hypersonic speeds inevitably produce shock waves that are perceived at ground level as sonic booms, with associated disturbance of the overflown populations. The reduction of this disturbance, together with the decrease of the noise associated to the launch and ascent phase, is critical for a viable future operation of the SpaceLiner. The objective of this work is the redesign of the passenger stage aerodynamic shape in order to improve its atmospheric re-entry performance, in terms of a reduction of both the heat flux and the disturbance of the overflown populations. For this purpose, a MDAO methodology was developed with the tasks of (1) computing vehicle performance from a wing shape parametrization using fast estimation methods, (2) exploring the design space and (3) finding a new promising aerodynamic shape. First, a Python tool-chain was developed to compute vehicles performances from a wing shape parametrization using fast estimation methods. The tool-chain was then systematically exploited to explore the design space by means of parametric studies, whose results informed the implementation of the forthcoming optimization. Afterwards, the wing shape was optimized using a three-objective evolutionary algorithm that minimized the vehicle mass and maximized its lift-to-drag ratio and lift coefficient. Simplified trajectory optimizations were then run on the set of non-dominated solutions in order to identify the most performing configurations. Finally, multi-objective evolutionary trajectory optimizations were run for the most promising candidate along intercontinental point-to-point routes of interest. Comparisons with the previous design iteration showed a significant improvement in terms of a reduction of both reentry heat flux and population disturbance.

Multidisciplinary Collaborative Design Optimization of Electric Shovel Working Devices

The development of the open-pit mining industry has set higher performance standards for mining electric shovels. Addressing issues such as low efficiency, high energy consumption, and high failure rates in working mining electric shovel devices, this paper comprehensively considers bulk mechanics, structural mechanics, and dynamics to conduct a multidisciplinary, collaborative design optimization for electric shovels by introducing the BLISCO method, which is based on an approximated model, into the structural-optimization design process of working electric shovel devices, aiming to enhance the overall performance of electric shovels. Firstly, a dynamic model of an electric shovel is established to analyze the hoist force and crowd force during the excavation process, and an accurate load input for the dynamic analysis is provided through the bulk material mechanics model. Additionally, to ensure that the stiffness of the boom meets the requirements, the maximum stress at the most critical position of the optimized boom is considered. Subsequently, the design variables are screened through experimental design, and an approximate model is established. Focusing on the hoist force, crowd force, maximum stress at the critical position of the boom, and the angle between the dipper arm and the wire rope, a mathematical model is constructed and optimized using a two-level integrated system co-optimization framework based on an approximate model (BLISCO-AM), followed by a simulation. Finally, a test bench for the electric shovel working device is constructed to compare pre- and post-optimization performance. Experimental results show that through the optimized design, the hoist force and crowd force required in a single excavation process are reduced by 6% and 8.48%, respectively, and the maximum angle between the wire rope and the dipper arm is increased by 4%, significantly improving excavation efficiency while ensuring the safety and reliability of the equipment.

Medication Optimization Protocol Efficacy for Geriatric Inpatients

There is currently no consensus on clinically effective interventions for polypharmacy among older inpatients. To evaluate the effect of multidisciplinary team-based medication optimization on survival, unscheduled hospital visits, and rehospitalization in older inpatients with polypharmacy. This open-label randomized clinical trial was conducted at 8 internal medicine inpatient wards within a community hospital in Japan. Participants included medical inpatients 65 years or older who were receiving 5 or more regular medications. Enrollment took place between May 21, 2019, and March 14, 2022. Statistical analysis was performed from September 2023 to May 2024. The participants were randomly assigned to receive either an intervention for medication optimization or usual care including medication reconciliation. The intervention consisted of a medication review using the STOPP (Screening Tool of Older Persons' Prescriptions)/START (Screening Tool to Alert to Right Treatment) criteria, followed by a medication optimization proposal for participants and their attending physicians developed by a multidisciplinary team. On discharge, the medication optimization summary was sent to patients' primary care physicians and community pharmacists. The primary outcome was a composite of death, unscheduled hospital visits, and rehospitalization within 12 months. Secondary outcomes included the number of prescribed medications, falls, and adverse events. Between May 21, 2019, and March 14, 2022, 442 participants (mean [SD] age, 81.8 [7.1] years; 223 [50.5%] women) were randomly assigned to the intervention (n = 215) and usual care (n = 227). The intervention group had a significantly lower percentage of patients with 1 or more potentially inappropriate medications than the usual care group at discharge (26.2% vs 33.0%; adjusted odds ratio [OR], 0.56 [95% CI, 0.33-0.94]; P = .03), at 6 months (27.7% vs 37.5%; adjusted OR, 0.50 [95% CI, 0.29-0.86]; P = .01), and at 12 months (26.7% vs 37.4%; adjusted OR, 0.45 [95% CI, 0.25-0.80]; P = .007). The primary composite outcome occurred in 106 participants (49.3%) in the intervention group and 117 (51.5%) in the usual care group (stratified hazard ratio, 0.98 [95% CI, 0.75-1.27]). Adverse events were similar between each group (123 [57.2%] in the intervention group and 135 [59.5%] in the usual care group). In this randomized clinical trial of older inpatients with polypharmacy, the multidisciplinary deprescribing intervention did not reduce death, unscheduled hospital visits, or rehospitalization within 12 months. The intervention was effective in reducing the number of medications with no significant adverse effects on clinical outcomes, even among older inpatients with polypharmacy. UMIN Clinical Trials Registry: UMIN000035265.

Multidisciplinary optimization of shoe midsole structures using swarm intelligence

Multidisciplinary optimization of shoe midsole structures using swarm intelligence

Analysis, optimization, and testing of a tilt-body aircraft

This paper proposes a new multidisciplinary design optimization framework for tilt-bodies to obtain the optimal combination of geometry and powertrain, achieving both long endurance and low transition power consumption at a 5 kg takeoff weight. A low-fidelity vortex lattice method is used to derive the aerodynamic coefficients. The tandem wings are trimmed using an optimization-based method, considering the effect of distributed propulsion on the trim solution through aero-propulsive interaction at low flight speed. Detailed powertrain parameters are fitted with fewer design variables to calculate power consumption. By analyzing the unique constraints of tilt-bodies, the optimization problem is established as a simultaneous analysis and design architecture, and solved with different numbers of rotors. The impact of the relative geometric relationship between the front and rear wings on aerodynamic performance and constraints is analyzed. Results demonstrate the full angle-of-attack flight capability of tilt-bodies. The optimal design can save nearly 1000 W in transition, with a pure cruise endurance of over 1 hour, which is competitive among other configurations. With an empty weight of only 2 kg, a portion of the battery can be allocated to a heavier payload. High-fidelity computational fluid dynamics corrects the errors of the vortex lattice method on non-lifting components, including the fuselage, nacelles, and landing gear. Finally, the feasibility of the optimal design and the accuracy of the framework are validated by wind tunnel tests and in-flight drag measurements on a test platform.

Multidisciplinary Optimization of Axial Turbine Blade Based on CFD Modelling and FEA Analysis

Multidisciplinary Optimization of Axial Turbine Blade Based on CFD Modelling and FEA Analysis

Multidisciplinary Design Optimization of Active Debris Removal Mission via Electric Propulsion

A design optimization framework for an active orbital debris removal mission to minimize the life cycle cost and mission time is presented. The general concept of operations consists of spacecraft capable of deorbiting 22 identical high-priority debris from sun-synchronous orbit via continuous low thrust from electrostatic thrusters. This modular subsystem framework is proposed and investigated with a fractional factorial design of experiments approach as preliminary analysis in order to inform effects of design variables as well as identify the scope of possible design and output space. The single-objective optimization minimizes the life cycle cost of the mission through a heuristic-based genetic algorithm, resulting in a 4.12-year, $354 million mission using 5.34 kW xenon Hall thrusters. Pareto fronts generated by a multiobjective weighted-sum genetic algorithm are presented to highlight the cost and time objective space for both a nominal and expensive xenon price per kilogram to show design space fluctuations with xenon price volatility. High-power 8 kW xenon Hall thrusters are proven to be in closest proximity to the utopia of the multiobjective design space with a cost of $687 million and mission time of 1 year. High-power krypton Hall thrusters prove to offer a comparable alternative to xenon systems.

Collaborative optimization of two-dimensional convergent divergent exhaust system based on Kriging model

In order to enhance the performance of the two-dimensional exhaust system during high-speed cruising and enhance aircraft survivability against infrared-guided weaponry, this study undertakes a systematic optimization of the geometric and thermodynamic parameters governing the two-dimensional exhaust system. The optimization objectives encompass augmenting both the discharge coefficient and the thrust coefficient of the nozzle while concurrently mitigating the infrared radiation intensity emanating in the tail direction. Imposing limitations on the infrared radiation intensity across diverse detection angles, the study further imposes constraints on the thrust efficiency and deflection efficiency of the thrust vectoring nozzle subsequent to a 15° deflection. Such measures ensure the maintenance of optimal stealth capabilities across all detection angles while preserving the unhampered thrust vectoring performance of the nozzle. This study employs the optimal Latin hypercube method and Kriging surrogate models in conjunction with collaborative optimization techniques to address multidisciplinary design optimization challenges. Comparative analyses with the initial design revealed significant enhancements: up to a 2.88% increase in the discharge coefficient, a maximum 0.53% increase in the thrust coefficient, and a notable reduction of up to 17.09% in tail direction dimensionless infrared radiation intensity, validating the effectiveness of the optimized exhaust system design.

Multidisciplinary Design Optimization of Underwater Vehicles Based on a Combined Proxy Model

Multidisciplinary Design Optimization of Underwater Vehicles Based on a Combined Proxy Model

Collaborative optimization of a composite pressure cylindrical shell based on dynamic penalty functions

This article reports the design and optimization of pressure cylindrical shells comprising carbon/epoxy and glass/epoxy composite materials. It presents a method for analyzing the pressure cylindrical shells of composite materials, analyzes the strength and buckling of composite pressure cylindrical shells with different rib numbers, and investigates the influence of thickness and ply angles ([±θ]10 and [903(±θ)2903]) on the critical buckling and strength-failure stresses. An approximate model of composite pressure cylindrical shells and collaborative optimization based on dynamic penalty functions are combined into a multidisciplinary optimization framework of composite pressure cylindrical shells. The optimization aims to maximize the critical buckling stress while minimizing the buoyancy coefficient. At a buoyancy coefficient similar to that of the initial scheme, the critical buckling stresses of the carbon/epoxy and glass/epoxy cylindrical shells increased by 21.85% and 9.5%, respectively, verifying the effectiveness of the collaborative optimization framework based on dynamic penalty functions.

Multi-disciplinary optimization of single-stage hybrid rockets for lunar ascent

This study aims to determine the optimal design and ascent trajectory for a single-stage hybrid rocket in the context of a lunar ascent mission, with the objective of reaching a 100 km circular orbit on the Moon equatorial plane. A multi-disciplinary optimization problem is formulated, integrating flight design variables and launch vehicle design variables to achieve the best mission scenario. A zero-dimensional internal ballistics model is employed to estimate key parameters such as thrust, mass flow rate, and gross mass of the hybrid rocket, while a three-degree of freedom dynamical model is utilized to simulate the ascent trajectory. The resulting integrated optimization problem is solved using an in-house heuristic algorithm that combines particle swarm and genetic algorithms. Two propellant combinations, liquid oxygen/paraffin-wax and hydrogen peroxide (90%)/high-density polyethylene, are evaluated, considering both their performance in the integrated system and the feasibility of the proposed mission. After conducting a sensitivity analysis to investigate the influence of particle count in the optimization process, results for both propellant combinations are presented. While paraffin and oxygen provide the highest payload ratio, hydrogen peroxide and polyethylene entail a more feasible and flexible design for a lunar mission.