Multidisciplinary Optimization Research Articles

Application of topological optimization and biomechanical simulation to enhance the design of collision safety systems and injury prediction in new energy vehicles

This study explores how to establish a quantitative balance mechanism between the lightweight demand of new energy vehicles and the collision safety of occupants/batteries through multidisciplinary collaborative optimization. Integration of topology optimization and biomechanical simulation to facilitate the design and injury prediction of new energy vehicle (NEV) crash safety systems. Using extensive data from the National Highway Traffic Safety Administration (NHTSA) and the Center for Automotive Research (ARC), first, topology optimization is applied to reduce vehicle weight while maintaining crashworthiness. Subsequently, biomechanical simulations were performed using finite element analysis to simulate the human response to impact. These models are then combined to predict injury risk. Our results show that the weight of key vehicle components is substantially reduced, while the effect on structural stiffness is negligible. Biomechanical simulations provide detailed injury severity scores (ISS) for different body parts under different impact scenarios. The comprehensive model shows that compared with the unoptimized vehicle structure, the optimized vehicle structure is expected to reduce the overall weight of the new energy vehicle and reduce the damage probability of the optimized structure in the collision process by 18.2%. This study highlights the great potential of combining topology optimization and biomechanical simulation to improve the crash safety and injury prediction of new energy vehicles.

Multi-disciplinary optimization of a distributed electric propulsion aircraft under aero-propulsive effects

Multi-disciplinary optimization of a distributed electric propulsion aircraft under aero-propulsive effects

A Hybrid Approach of Adaptive Surrogate Model and Sampling Method for Reliability Assessment in Multidisciplinary Design Optimization

A Hybrid Approach of Adaptive Surrogate Model and Sampling Method for Reliability Assessment in Multidisciplinary Design Optimization

End-to-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision-makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged during the design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate multidisciplinary design optimization (MDO) incorporating UQ into the design process, leveraging derivatives through the entire optimization and machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems.

Hydrofoil Installation and Performance Optimisation for Ship Resistance Reduction in Trimaran Through Particle Swarm Optimisation Method

Abstract The trimaran vessel performs well both in calm waters and in waves. To improve its hydrodynamic efficiency, a hydrofoil is installed at the rear part of the trimaran. The primary aim of this hydrofoil is to reduce the overall resistance while the ship is moving. This study focuses on minimising the ship’s total resistance. An optimisation process is designed to determine the best position and angle of attack (AoA) for the hydrofoil at the design speed. To achieve this, a multi-disciplinary optimisation (MDO) platform is employed to conduct a computation fluid dynamics (CFD)-based automated design study. The optimisation method integrates STAR-CCM+ software with the particle swarm optimisation (PSO) algorithm as the optimiser. The flow field and wave patterns around the trimaran are analysed to assess resistance improvements. The results reveal that the optimal position for the NACA6612 hydrofoil is towards the stern and away from the midship, with the ideal AoA being 5.19 degrees at cruising speed. Comparisons indicate that the resistance of the trimaran with the optimised hydrofoil is reduced by approximately 4.49% compared to a trimaran without the hydrofoil.

A PERIOPERATIVE OPTIMIZATION PROGRAM CAN IMPROVE RESULTS AND REDUCE HOSPITAL LENGTH OF STAY IN HIP AND KNEE ARTHROPLASTY: EXPERIENCE IN CHILE.

A PERIOPERATIVE OPTIMIZATION PROGRAM CAN IMPROVE RESULTS AND REDUCE HOSPITAL LENGTH OF STAY IN HIP AND KNEE ARTHROPLASTY: EXPERIENCE IN CHILE.

3D rotational augmented flow effects on the multidisciplinary optimization of flatback airfoils

3D rotational augmented flow effects on the multidisciplinary optimization of flatback airfoils

Improved Augmented Lagrangian Relaxation-Assisted Analytical Target Cascading for Multidisciplinary Design Optimization

Abstract Analytical target cascading (ATC) is an optimization strategy designed for multilevel and multidisciplinary design optimization (MDO) problems. Its core aim is to enhance the overall convergence of the system by reducing the inconsistency between different levels. In previous studies, although the augmented Lagrangian relaxation method can achieve rapid and accurate positioning of the optimal solution, it is highly sensitive to the penalty parameters, which can easily cause instability in numerical calculation. In contrast, the Lagrangian duality theory effectively reduces the numerical instability by adaptively adjusting the parameters, but this method is at the expense of increasing the computational time and the number of iterations. This study proposes an analytical target cascading combined with the Maclaurin series method (ATC-MAC), which aims to reduce parameter sensitivity and accelerate convergence to the optimal solution. The method uses the Maclaurin series to approximate the cross terms in the augmented Lagrangian function to reduce its interference with the main function. Meanwhile, the step size updating strategy is optimized by combining the duality theory and the multiplier method. Through the application in three numerical cases and one engineering case, the effectiveness and practicability of the ATC-MAC method are verified.

An Adaptive GPR-Based Multidisciplinary Design Optimization of Structural and Control Parameters of Intelligent Bus for Rollover Stability

An Adaptive GPR-Based Multidisciplinary Design Optimization of Structural and Control Parameters of Intelligent Bus for Rollover Stability

Thirty-day postoperative cardiopulmonary complications in sarcoidosis: Insights from a retrospective matched cohort analysis

PurposeSarcoidosis is a rare systemic granulomatous disorder characterized by lung involvement but frequently involves the heart, gastrointestinal and lymphatic organs. Few studies have investigated sarcoidosis-related postoperative cardiopulmonary complications, creating a significant knowledge gap. Using a comparative cohort analysis, the authors hypothesized that sarcoidosis would be associated with higher risk for 30-day postoperative pulmonary complications (PPC).MethodsThis retrospective study examined hospital system data between January 1, 2013, and January 1, 2022, for patients over 18 years, admitted for procedural intervention. 389 sarcoidosis patients and controls (N = 48,823) were identified. The primary endpoint of PPC, as measured by the Agency for Healthcare Research and Quality PPC composite, and secondary endpoints of major adverse cardiovascular events (MACE), PPC subcomposites, and length of stay (LOS) were analyzed. A Mahalanobis distance matching (MDM) was used to match sarcoidosis and control patients (N = 389) on clinically relevant baseline covariates.ResultsAfter MDM and adjustment for surgical time and anesthesia type, sarcoidosis diagnosis corresponded to higher composite 30-day PPC (18.5% vs. 9.3%, adjusted odds ratio [ORadj] = 3.32, 95% confidence intervals [CI] 1.8–5.8; p < 0.001), sub-composite respiratory failure/insufficiency (10.5% vs. 5.1%, ORadj = 3.31, 95% CI 1.6–6.7; p < 0.001) but not pneumonia (5.7% vs. 3.9%, ORadj = 2.0, 95% CI 0.8–4.8; p = 0.117). The sarcoidosis cohort had longer LOS (ORadj = 2.33, 95% CI 2.0–2.7; p < 0.001). Sarcoidosis diagnosis was not associated with 30-day MACE (12.3% vs. 12.9%, ORadj = 1.43, 95% CI 0.8–2.4; p = 0.192), atrial fibrillation (6.9% vs. 5.7%; p = 0.931), or congestive heart failure events (5.9% vs. 7.2%; p = 0.526).ConclusionsSarcoidosis is associated with a twofold increased risk of 30-day PPC, primarily related to an increased incidence of 30-day respiratory failure/insufficiency. This risk appears to be independent of disease staging, but is associated with the presence of sarcoidosis features on preoperative chest radiography. Postoperatively, sarcoidosis patients experience longer hospital LOS, suggesting that when complications occur, they are more resource-intensive, when compared to controls. These findings highlight opportunities to enhance preoperative multi-disciplinary optimization, and suggest that tailored perioperative care strategies for sarcoidosis patients would be beneficial.Graphical

Digital twins for intelligent cities: the case study of Matera

The digital twin (DT) paradigm provides a purpose-built digital representation of a physical system. DTs are often composed by several interconnected models, which need to be specifically tailored to fit the DT purposes. The objective of this work is the development of an urban digital twin (UDT) for the city of Matera, following urban intelligence (UI) paradigm. The latter leverages the multi-disciplinary integration and optimization of the city systems and subsystems to develop purpose-driven DTs and support the decision-making process. The UDT is intended to support governance, stakeholders, and citizens, integrating morphological data for the city representation, reliable simulation tools and data-driven methods for the city state prediction (e.g., traffic, solar irradiation), optimization algorithms for planning and emergency response, sensors for vehicle and pedestrian traffic volumes and environmental monitoring (e.g., pollutant distributions), and a participatory data collection process from the citizens. A Data Lake is used to make available to the UDT modules the data produced by the morphological representation, simulations/data-driven methods, sensors, and the participatory data. The Data Lake provides a standardized approach for the aggregation and extraction of information. Finally, an Urban Sensing Engine supports the decision-making process with artificial intelligence forms of reasoning, to effectively combine the information produced by the UDT components.

Uncertainty-based multi-disciplinary multi-objective design optimization of unmanned mining electric shovel

Uncertainty-based multi-disciplinary multi-objective design optimization of unmanned mining electric shovel

Simultaneous Design and Trajectory Optimization Strategies for Computationally Expensive Models

Simultaneous design and trajectory optimization aims to find the best possible design of a dynamic engineering system, such as an aircraft, by considering the coupling between a physical system design and its trajectory. Multidisciplinary design optimization (MDO) fully considers this coupling and corresponding design trade-offs. This article discusses the computational efficiency of MDO formulations for design-trajectory optimization. Numerical studies are performed to compare two monolithic MDO architectures and two design-trajectory coupling strategies on aircraft design test problems. The test problems concurrently optimize a climb trajectory, wing design based on a low-fidelity aerostructural analysis, and aircraft sizing variables. The results indicate that surrogate-based coupling is more efficient than direct coupling when there are only a few variables coupling the trajectory and disciplinary models, whereas direct coupling is preferable otherwise. The simultaneous analysis and design (SAND) architecture outperforms the multidisciplinary feasible (MDF) architecture when using direct coupling, whereas the costs of SAND and MDF are comparable with surrogate-based coupling. The results and discussion in this paper provide general guidelines for selecting a computationally efficient approach for simultaneous design and trajectory optimization.

Zonal Safety and Particular Risk Analysis for Early Aircraft Design

Safety is paramount in aircraft design, and increasing aircraft complexity necessitates safety assessments early in design. For unconventional aircraft with novel propulsion or system technologies, it becomes even more critical to investigate safety as early as possible to avoid unfeasible configurations. In this context, the particular risk analysis (PRA) and the zonal safety analysis (ZSA) are essential to assess early, as they impact the aircraft configuration. These analyses require a three-dimensional (3D) aircraft model and substantial manual effort, limiting the ability to perform rapid iterations required to support design space exploration and multidisciplinary design optimization (MDO). To analyze many aircraft configurations and system architectures, the 3D parametric model and the PRA and ZSA require automation. This paper reviews methodologies for performing the ZSA and PRA from a systems point of view and proposes parametric zone definition, identification of risk zones, and a conceptual-level analysis of the component placement strategy. The effectiveness of the proposed approach is demonstrated with an aft equipment bay of a business aircraft for varying geometrical granularity and system electrification. Overall, the presented method is a step toward integrating system safety analysis into MDO environments, thus increasing conceptual design maturity and reducing development time.

Implementing a telemedicine-led heart failure medication regimen optimization clinic in medically underserved heart failure populations.

Implementing a telemedicine-led heart failure medication regimen optimization clinic in medically underserved heart failure populations.

A Chebyshev interval iteration-based multidisciplinary interval reliability optimization

A Chebyshev interval iteration-based multidisciplinary interval reliability optimization

Credibility-Based Multidisciplinary Design Optimization of Hybrid and Fully Electric Aircraft

Aircraft designs for future electric aircraft often have large discrepancies in the underlying assumptions for highly influential design parameters. More optimistic assumptions on these parameters could result in better aircraft performance but include higher risks in the realization of the design in the defined timeline. In this work, the novel concept of design credibility is introduced to be used for quantifying the effect of assumptions on the underlying technologies on the design performance and the risks. For a range of highly important parameters, probability curves have been created that can predict a range of assumed future performance by 2035. A credibility-based multidisciplinary design optimization framework is created, which evaluates the maximum achievable mission ranges for hybrid electric aircraft under credibility constraints. The optimizations are performed using a surrogate-based global optimization routine, the efficient global optimization. With the full framework, the multiple aircraft designs are optimized under a range of credibility limits to create range-credibility Pareto curves. The results show that battery gravimetric energy density is the most dominant factor for the optimization. Motor and airframe parameters have a smaller effect on the achievable range. Their influence on the optimal design depends on the desired credibility level. For high credibilities, the motor is found to be more relevant. For low credibilities, airframe technology improvements are dominant. The shape of the underlying credibility distributions of secondary parameters has a strong effect on their relevance for the optimal configuration.

Reliability-based multidisciplinary design optimization method under original probability model for wind turbine structure

Reliability-based multidisciplinary design optimization method under original probability model for wind turbine structure

USING FRAILTY SCREENING TO INFORM PREOPERATIVE MANAGEMENT OF HIGH-RISK SURGICAL PATIENTS

Abstract Frailty is a clinical syndrome of age-related decline in physiologic reserve and increased vulnerability to stressors. Frail older adults undergoing surgery are at higher risks for postoperative complications and mortality and increased health care costs. Thus, frailty has emerged as a robust predictor of poor surgical outcomes in vulnerable older patients. There have been immense interest and research for bench-to-bedside translation and integration of preoperative frailty screening into clinical practice. These efforts have centered on the development of preoperative frailty screening as a surgical risk-stratification tool and its application to guide preoperative management aimed to mitigate surgical risk in older adults. The Veterans Health Administration (VHA) has been recognized for its Surgical Pause initiative in which: (1) routine frailty screening is ascertained in surgical patients using the Risk Analysis Index (RAI) in order to identify those at the highest risk of postoperative complications, loss of independence, and mortality; and (2) multidisciplinary care strategies are implemented to reduce frailty-associated risks to enable reduction of potential complications before surgery in those identified as frail. In this symposium, we will provide a state-of-the-art look at the VHA experiences in: (1) validation of RAI as a preoperative risk-stratification tool in older adults; (2) implementation and integration of RAI screening in clinical practice; and (3) evaluation of RAI screening and multidisciplinary optimization board prior to surgery.

Automation of conceptual design and modification of aircraft-type unmanned aerial vehicles using multidisciplinary optimization and evolutionary algorithms. Part 2: results and analysis

An example of practical application of the methodology of multidisciplinary optimization of the concept parameters of aircraft-type flight vehicles is considered in the form of solving the problem of modifying two existing aircraft with characteristics presented in open sources. The convergence of the optimization problem was investigated. The results of validation of mathematical models of aerodynamics are presented by comparison with the experiment results. An assessment of the reliability of the calculation of the objective function was carried out using the example of solving the direct problem of calculating the take-off weight and flight performance characteristics of prototype aircraft and comparing the results with their real characteristics. A method for longitudinal trim of aircraft-type flight vehicles with two lifting surfaces with parameter optimization is proposed.