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This research introduces an innovative framework to engineering design to tackle the challenges of robustness against manufacturing deviations and holistic optimization simultaneously in a multi-disciplinary, multi-subsystems context. The methodology is based on an application of ensemble artificial neural networks, which significantly accelerates computational processes. Coupled with the non-dominated sorting genetic algorithm III, this approach facilitates efficient multi-objective optimization, yielding a comprehensive Pareto front and high-quality design solutions. Here, the framework is applied to the design of gas-bearing-supported turbocompressors. These systems are challenging due to their sensitivity to manufacturing variations, particularly in the gas-bearing geometry, which can lead to rotordynamic instability. Additionally, the interdependencies between the subsystems, such as axial and journal bearings, rotor, compressor impellers, and magnets, necessitate a multidisciplinary approach that spans aerodynamics, structural dynamics, rotordynamics, mechanics, loss analyses, and more. A clear tradeoff between system efficiency, mass-flow range, and robustness has been identified for the compressor design. Higher nominal compressor mass-flows, i.e., increased nominal power, is suggested to decrease the hypervolume of feasible manufacturing deviations. Hence, there is a sweet power spot for gas-bearing supported turbomachinery. Further, the framework's computational efficiency is on par with that of a university cluster, while only employing a desktop computer equipped with a consumer-grade graphics card. This work demonstrates a significant advancement in the design of complex engineering systems and sets a new standard for speed and efficiency in computational engineering design. [ABSTRACT FROM AUTHOR]

Cable-driven serial robots have emerged with high potential for wide applications due to their compact size and low inertia properties. However, developing this type of robot encounters a motion coupling issue that the movement of one joint leads to the motion of other joints, resulting in complex control. In this paper, we proposed a novel approach for motion decoupling based on a noncircular pulley. The length change of the driving cable caused by the motion coupling problem is resolved by using the noncircular pulley. The calculation process of the profile for the noncircular pulley is illustrated in detail. An optimization process based on the brute force method is presented to identify the optimal parameters to minimize the compensation error. A cable-driven serial robot based on the decoupling method is prototyped for assessments. Experiments are conducted to evaluate the performance of the proposed motion decoupling method. The results reveal that the proposed method can effectively resolve the motion coupling issue by maintaining almost constant cable length with a maximum accumulative error only as 0.086 mm, demonstrating the effectiveness of the method. [ABSTRACT FROM AUTHOR]

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. • MDAO procedure for wing shape optimization of hypersonic passenger transport vehicle. • Conducted evolutionary multi-objective optimization for key wing performance metrics. • Trajectory optimization of pareto front vehicles to identify best compromise of metrics. • Found configurations capable of avoiding sonic booms over populated areas on various routes. [ABSTRACT FROM AUTHOR]

The vital role of extensive information exchange among stakeholders across diverse sectors and the interconnection of various scientific fields with nonhomogeneous technology readiness levels has created a new form of a complex engineering problem in the climate change era. Comprehensive sustainability assessment to enable the realization of needs requires transdisciplinary thinking to achieve systematic solutions that bridge the gap between multiple collaborative systems in a portfolio. Although the principal aim of dedicated sustainability regulations is to force companies to move toward sustainability development, general and non-engineered metrics that have not defined clear thresholds for evaluation have encountered severe challenges regarding implementation and economic viability. Therefore, adopting a transdisciplinary systems engineering approach can address multifaceted challenges like sustainability by overcoming collaboration barriers, and traditional disciplinary limits. This paper systematically reviews sustainability-dictated regulations from a transdisciplinary perspective. Different standards are compared, raised opportunities and challenges are discussed, and future remarks are highlighted. The sustainability problem is analyzed from a transdisciplinary systems engineering lens. Finally, a two-level resilient system sustainability assessment framework is proposed to effectively handle and enhance the resilience of companies' sustainability development roadmaps by enabling decision makers to find robust and highly reliable solutions regarding sustainable system design. The impact of this research is to create a new insight into addressing climate change which not only assesses the current situation but also considers uncertainty sources that affect decision making for the future. [ABSTRACT FROM AUTHOR]

A versatile framework is introduced for determining optimal steady‐state operating points for wind turbine control. The framework is based on solving constrained optimization problems at fixed wind speeds and allows for systematically studying required trade‐offs and parameter sensitivities. It can be used as a basis for many control approaches, for example, to automatically compute optimal schedules for control inputs, steady‐state operating points for model linearization, or reference values for tracking. Steady‐state simulation results are obtained using full nonlinear models to consider complex effects caused by couplings from aerodynamics, structural dynamics, and possibly also hydrodynamics in the case of floating wind turbines. Focusing only on the steady‐state response allows a fast and numerically robust optimization, which makes it especially attractive for co‐design studies. The effectiveness of the framework is demonstrated on two offshore extreme‐scale wind turbines, one floating and one fixed bottom. [ABSTRACT FROM AUTHOR]

The document from the Journal of Engineering Design discusses the importance of managing margins in engineering design to address uncertainties and risks. It highlights the need for intentional identification and management of margins to create engineered systems that deliver value throughout their lifecycle. The document also emphasizes the significance of margins in balancing decisions about system architecture properties and managing risks in a disruptive world. Through workshops and curated papers, the document explores various perspectives, models, and valuations of margins in engineering design, aiming to establish a new design paradigm where margins are essential for system value and resilience. [Extracted from the article]

A forced response induced by inlet distortion and blade row interactions can lead to high-cycle fatigue failure, especially when the unsteady excitation frequency is close to the natural vibration frequency of a blade. This paper presents the study of forced response sensitivity analysis and minimization using a full-viscosity unsteady discrete adjoint method. The goal is to improve the aeroelastic performances of turbomachinery blades and simultaneously constrain/improve aerodynamic performances. The decoupled modal reduction method is used to compute the forced response, which is considered the objective function in adjoint-based design optimization. To analyze aeroforcing and aerodamping flow and adjoint fields efficiently, the harmonic balance method and its adjoint counterpart developed by an automatic differentiation tool are applied. Two cases--the NASA Rotor 67 with inlet distortion and a three-row configuration with multiple fundamental frequencies in the second row--are used to demonstrate the effectiveness of the aerodynamic and aeroelastic coupled design optimization system developed in this work. For the latter case, the Fourier-transform-based method that first decomposes and then matches the time and space modes at two sides of an interface is used for interface coupling. The almost-periodic Fourier transform method is used to determine the time instances for cases with multiple fundamental frequencies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract A versatile framework is introduced for determining optimal steady‐state operating points for wind turbine control. The framework is based on solving constrained optimization problems at fixed wind speeds and allows for systematically studying required trade‐offs and parameter sensitivities. It can be used as a basis for many control approaches, for example, to automatically compute optimal schedules for control inputs, steady‐state operating points for model linearization, or reference values for tracking. Steady‐state simulation results are obtained using full nonlinear models to consider complex effects caused by couplings from aerodynamics, structural dynamics, and possibly also hydrodynamics in the case of floating wind turbines. Focusing only on the steady‐state response allows a fast and numerically robust optimization, which makes it especially attractive for co‐design studies. The effectiveness of the framework is demonstrated on two offshore extreme‐scale wind turbines, one floating and one fixed bottom.

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. [ABSTRACT FROM AUTHOR]

The simulation of thermal–fluid–solid coupling in turbines is critically important for design optimization. Historically, most research on thermal–fluid–solid coupling has been conducted in three-dimensional, often with computational speeds that do not meet practical expectations. This study proposes a one-dimensional performance prediction and multi-objective optimization design methodology for turbines, integrating aerothermodynamics and structural strength, to facilitate rapid multidisciplinary coupling design optimization at a low-dimensional level. Initially, a multidisciplinary coupled turbine performance prediction model is established, incorporating the combined effects of turbine aerothermodynamics and structural mechanics. This model links the thermodynamics of the blade passage with the stress and strain of the blade. The predictive accuracy of this model is validated against experimental data from a four-stage axial flow turbine, showing a maximum error of 1.56% for the total temperature ratio and 1.69% for the total expansion ratio. Subsequently, using blade parameters, degree of reaction, load coefficient, and flow coefficient as optimization variables and targeting the turbine's overall isentropic efficiency and power as optimization objectives, a rapid Non-dominated Sorting Genetic Algorithm II and the Technique for Order Preference by Similarity to an Ideal Solution are employed to optimize the last stage of the four-stage axial flow turbine. The optimized turbine demonstrates an increase in overall isentropic efficiency by 1.333% and an increase in overall power by 3.329%, while satisfying structural strength requirements. The novelty of this study lies in its rapid optimization design and performance prediction method for the coupled aerothermodynamics and structural mechanics at a one-dimensional level. [ABSTRACT FROM AUTHOR]

Multirotor vehicles typically use either complex mechanisms to vary blade pitch angle or speed control to vary thrust and torque to attain stability and maneuverability. Alternatively, thrust and torque can be varied by morphing the blades without the use of complex mechanisms and without varying the rotor speed. This method could be faster to change thrust, more efficient, and it may require fewer mechanical components. This paper presents an experimentally validated aerodynamic model with an empirical static-aeroelastic correction to predict the thrust and torque coefficient of a mechanism-free rotor with Macro-Fiber Composite actuators. A variable-camber piezocomposite rotor prototype is experimentally tested on a static-thrust setup. The experiment is used to identify the so-called unknown coefficients of the model (e.g., related to the shape of the blade induced by the piezoelectric device) and to identify a correction to account for flow-induced deformations. Using the identified model, several parametric analyses are conducted to predict thrust and torque coefficients as a function of rotor speed and the electrical excitation of the piezocomposite actuators. In addition, the model is used for design optimization, leading to several optimal designs based on a limited parameter space. Three different objective functions are used: maximizing thrust, minimizing torque, and maximizing thrust-to-torque ratio. The operational aerodynamic characteristics of these designs are also presented. [ABSTRACT FROM AUTHOR]

Robust design optimization (RDO) is a potent methodology that ensures stable performance in designed products during their operational phase. However, there remains a scarcity of robust design optimization methods that account for the intricacies of multidisciplinary coupling. In this article, we propose a multidisciplinary robust design optimization (MRDO) framework for physical systems under sparse samples containing the extreme scenario. The collaboration model is used to select samples that comply with multidisciplinary feasibility, avoiding time-consuming multidisciplinary decoupling analyses. To assess the robustness of sparse samples containing the extreme scenario, linear moment estimation is employed as the evaluation metric. The comparative analysis of MRDO results is conducted across various sample sizes, with and without the presence of the extreme scenario. The effectiveness and reliability of the proposed method are demonstrated through a mathematical case, a conceptual aircraft sizing design, and an energy efficiency optimization of a hobbing machine tool. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In China, industrial designers are usually educated with engineering background while product designers are educated with art background. Despite these educational differences, both of them are adept at tackling design-related issues. However, different educational backgrounds can affect the idea generation, especially in the early stages of the design progress. This study investigates the thinking processes of designers between engineering and art backgrounds in the early stages of the design process. Designers are encouraged to express ideas verbally or by sketching, and linkography was used to visually depict the designer's thinking process. Subsequently, with the help of vertical transformation value and the chunk, the features in the thinking of the two types of designers are explored, including: 1) In most cases, the vertical transformation value of the product designer is higher than that of industrial designer, which indicates that product designer is better at the reflection in design. 2) The chunk patterns in the linkographs show that industrial designers prefer to decompose tasks into sub-tasks and solve them individually, while product design participants tend to solve problems as a whole from a global view. These findings can enlighten the future research directions for design education and design organizations in multidisciplinary backgrounds. [ABSTRACT FROM AUTHOR]

We present a general framework for incorporating partial differential equation (PDE)-based models into gradient-based optimization of multidisciplinary systems by integrating FEniCSx with the recently developed Computational System Design Language (). CSDL is a domain-specific language designed to facilitate adjoint sensitivity analysis multidisciplinary design optimization (MDO). We use CSDL’s abstractions to link together sub-models representing different disciplines, not all of which are necessarily modeled by PDEs. For the subsystems which are modeled by PDEs, we use FEniCSx to compute partial derivatives of problem residuals, which CSDL can combine with derivatives from other disciplines using the chain rule and the adjoint method. The development of this framework is motivated by the problem of optimizing designs of electric vertical takeoff and landing (eVTOL) aircraft where, due to the relative novelty of this class of vehicle, there is currently a large, unexplored design space. Predicting the performance of eVTOL aircraft requires PDE-based modeling of various sub-problems, including electric motors, composite shell structures, and thermoelasticity and electrochemistry of battery packs. For system-level analysis and optimization, these must be coupled to non-PDE components, such as low-fidelity aerodynamic models, geometric design tools, and lumped-parameter battery and circuit models. In this work, we demonstrate the modeling flexibility and efficiency of this framework through classic optimal control and topology optimization problems with known solutions, and also challenging eVTOL-related applications including shape optimization of an electric motor, and aeroelastic coupling for gust response of an eVTOL wing. Given the generality of this framework, we expect it to facilitate research on a wide range of PDE-constrained MDO problems beyond eVTOL applications. [ABSTRACT FROM AUTHOR]

Creating functional midsoles for shoes is a challenging task that involves considering different aspects such as stability, comfort, manufacturability, and aesthetics. No single approach exists to design a midsole that meets all these objectives effectively. Therefore, this study aims to introduce a multidisciplinary optimization method to develop custom shoe midsole structures. Our approach involves utilizing generative methods to generate diverse structures and leveraging swarm intelligence to search for optimal designs. Without loss of generality, we use tetrahedral mesh generation to create midsole structures because tetrahedral structures are renowned for their exceptional strength. To enhance the swarm's exploration of the design space and discover more local optima, we developed a swarm behavior that promotes diversity. Furthermore, we created a quantitative measurement tool to evaluate various objectives. In order to test the effectiveness of our generative approach, we analyzed the midsoles generated from our design exploration that performed the best and the worst in relation to each objective. Our findings revealed a substantial difference between them, with scores differing by two to four times. Additionally, when compared to other lattice structures, the tetrahedral midsole structure created by our method demonstrated superior compliance with the foot and better redistribution of plantar stress. The multidisciplinary optimization technique we have proposed is a valuable resource for engineers and designers in the footwear industry, allowing them to develop high-performance midsole structures that meet the needs of both consumers and athletes. Furthermore, this method can be applied to optimize other complex structures in various industries, such as civil, automotive, and aerospace engineering. [ABSTRACT FROM AUTHOR]

Reliability-based control co-design (RBCCD) formulations have been developed for the design of stochastic dynamic systems. To address the limitations of their current formulations, and to enable higher-fidelity solutions for complex problems, a novel reliability-based multidisciplinary feasible (MDF) formulation of multidisciplinary dynamic system design optimization (RB-MDF-MDSDO) and a new reliability analysis method using generalized polynomial chaos (gPC) expansion for RBCCD were developed in previous work. Although the gPC expansion method was initially selected for the reliability analysis of simulation-based RBCCD, its performance against state-of-the-art, the most-probable-point (MPP) method, has not been established yet. Therefore, in this work, the first-ever MPP-based formulations of RB-MDF-MDSDO are developed, and using two engineering test problems, the new formulations' solution efficiency and accuracy are compared to those from the gPC-based formulation. Numerical results reveal that the gPC expansion method is marginally more accurate than the MPP algorithms, and therefore, it is more suitable for accuracy-sensitive applications. Conversely, the MPP algorithms are much more efficient, and thus, are more attractive for problems where solution efficiency is the priority. [ABSTRACT FROM AUTHOR]

This article presents a novel three-dimensional topology optimization framework developed for 3D spatial packaging of interconnected systems using a geometric projection method (GPM). The proposed gradient-based topology optimization method simultaneously optimizes the locations and orientations of system components (or devices) and lengths, diameters, and trajectories of interconnects to reduce the overall system volume within the prescribed 3D design domain. The optimization is subject to geometric and physics-based constraints dictated by various system specifications, suited for a wide range of transportation (aerospace or automotive), heating, ventilation, air-conditioning, and refrigeration, and other complex system applications. The system components and interconnects are represented using 3D parametric shapes such as cubes, cuboids, and cylinders. These objects are then projected onto a three-dimensional finite element mesh using the geometric projection method. Sensitivities are calculated for the objective function (bounding box volume) with various geometric and physics-based (thermal and hydraulic) constraints. Several case studies were performed with different component counts, interconnection topologies, and system boundary conditions and are presented to exhibit the capabilities of the proposed 3D multi-physics spatial packaging optimization framework. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

During this research, aerodynamic shape optimization is conducted on the Ahmed body with the drag coefficient as the objective function and the ramp shape as the design variable, while aero-structural optimization is conducted on NACA 0012 to reduce the drag coefficient for the aerodynamic performance with the shape as the design variable while reducing structural mass with the thickness of the panels as the design variables. This is accomplished through a gradient-based optimization process and coupled finite element and computational fluid dynamics (CFD) solvers under fluid–structure interaction (FSI). In this study, DAFoam (Discrete Adjoint with OpenFOAM for High-fidelity Multidisciplinary Design Optimization) and TACS (Toolkit for the Analysis of Composite Structures) are integrated to optimize the aero-structural design of an airfoil concurrently under the FSI condition, with TACS and DAFoam as coupled structural and CFD solvers integrated with a gradient-based adjoint optimization solver. One-way coupling between the fluid and structural solvers for the aero-structural interaction is adopted by using Mphys, a package that standardizes high-fidelity multiphysics problems in OpenMDAO. At the end of the paper, we compare and discuss our findings in the context of existing research, specifically highlighting previous results on the aerodynamic and aero-structural optimization of wind turbine blades. [ABSTRACT FROM AUTHOR]

The conceptual design of unmanned aerial vehicles (UAVs) presents significant multidisciplinary challenges requiring the optimization of aerodynamic and structural performance, stealth, and propulsion efficiency. This work addresses these challenges by integrating deep neural networks with a multiobjective genetic algorithm to optimize UAV configurations. The proposed framework enables a comprehensive evaluation of design alternatives by estimating key performance metrics required for different operational requirements. The design process resulted in a significant improvement in computational time over traditional methods by more than three orders of magnitude. The findings illustrate the framework's capability to optimize UAV designs for a variety of mission scenarios, including specialized tasks such as intelligence, surveillance, and reconnaissance (ISR), combat air patrol (CAP), and Suppression of Enemy Air Defenses (SEAD). This flexibility and adaptability was demonstrated through a case study, showcasing the method's effectiveness in tailoring UAV configurations to meet specific operational requirements while balancing trade-offs between aerodynamic efficiency, stealth, and structural weight. Additionally, these results underscore the transformative impact of integrating AI into the early stages of the design process, facilitating rapid prototyping and innovation in aerospace engineering. Consequently, the current work demonstrates the potential of AI-driven optimization to revolutionize UAV design by providing a robust and effective tool for solving complex engineering problems. [ABSTRACT FROM AUTHOR]

In the fast-paced world of product design, businesses seek a competitive edge by swiftly addressing user requirements and developing precise solutions. Depending on diverse knowledge, conceptual design makes enterprises invest significantly in knowledge management. Following the philosophy of decision-based design, a knowledge graph-aided decision guidance method is proposed to streamline and enhance knowledge utilisation in product conceptual design. Firstly, the decision-making process in product conceptual design is modelled using the Concept-Decision-Knowledge (CDK) model, yielding the meta-model of CDK (mCDK). A data-augmented BERT-BiLSTM-CRF model is adopted to extract information from diverse data sources, forming a CDK knowledge graph (CDK-KG). A decision case for employing problem-solving knowledge configuration is generated to resolve specific design decision problems when new requirements arise. Validation of the method is demonstrated through a case study on the launch vehicle conceptual design of the first and second-stage separation system. The results indicate that the proposed method can automatically extract information from data resources to construct a knowledge graph. Furthermore, it can provide decision cases for design requirements through knowledge configuration, supporting decision-making. [ABSTRACT FROM AUTHOR]

This paper proposes three machine learning-based uncertainty set construction methods and a novel uncertainty quantification method for robustness-based optimization. The proposed methods are capable of capturing all possible types of uncertainties in the form of sparse point and/or interval data and efficiently quantify uncertainty in robustness-based optimization. In contrast to traditional approaches that rely on expert opinion or assumptions to construct uncertainty sets, our methods leverage machine learning algorithms to extract uncertainty patterns from historical data and effectively capture epistemic uncertainty in input variables. Another challenge in existing robustness-based optimization under epistemic uncertainty is the high computational cost associated with the iterative method of epistemic analysis. Moreover, these methods struggle with uncertainty sets containing a mixture of point and interval data. To overcome these limitations, we propose a unified probabilistic approach that utilizes maximum likelihood estimation to efficiently quantify uncertainty, regardless of the data form. Using the proposed methods, we introduce new approaches for robustness-based portfolio optimization (RPO) and multidisciplinary design optimization (RO/MDO), namely the worst-case maximum likelihood estimation (WMLE)-based single-loop RPO and WMLE-based RO/MDO approach, respectively. Illustrated through applications to minimum variance portfolios, our methods consistently demonstrate significant improvements over established formulations in terms of both return and risk. Additionally, when applied to a complex multidisciplinary engineering design, the WMLE-based RO/MDO approach showcases a computationally efficient solving technique, yielding more realistic results than the existing methodologies. [ABSTRACT FROM AUTHOR]

Computational design is growing in necessity for advancing biomedical technologies, particularly for complex systems with numerous trade-offs. For instance, in tissue scaffolds constructed from repeating unit cells, the structure's porosity and topology affect biological tissue and vasculature growth. Here, we adapt curvature-based tissue growth and agent-based vasculature models for predicting scaffold mechanobiological growth. A non-dominated sorting genetic algorithm (NSGA-II) is used for dual-objective optimization of scaffold tissue and blood vessel growth with heterogeneous unit cell placement. Design inputs consist of unit cells of two different topologies, void unit cells, and beam diameters from 64 to 313 µm. Findings demonstrate a design heuristic for optimizing scaffolds by placing two selected unit cells, one that favors high tissue growth density and one that favors blood vessel growth, throughout the scaffold. The pareto front of solutions demonstrates that scaffolds with large porous areas termed channel voids or small voids improve vasculature growth while lattices with no larger void areas result in higher tissue growth. Results demonstrate the merit in computational investigations for characterizing tissue scaffold design trade-offs and provide a foundation for future design multi-objective optimization for complex biomedical systems. [ABSTRACT FROM AUTHOR]

Copyright of Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji is the property of Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Robust approach is a special scalarization method to deal with multiobjective optimization problems in the worst-case. In this paper, we propose a new non-monotone gradient type algorithm for solving unconstrained multiobjective optimization problems by the conjugate technique and the robust approach. The proposed method has a memory gradient property since the search direction is constructed by using the current descent direction and the past multi-step iterative descent directions. For this, the search direction is called a memory gradient search direction. The step-size is computed by the non-monotone linear search. A lower bound of the stepsize is presented under some mild conditions. Then the iterative sequence generated by the proposed method is proved to be convergent to a Pareto critical point of the multiobjective optimization problem under some mild conditions. Numerical experiments are reported to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we consider an inexact projected gradient method equipped with a nonmonotone line search rule for smooth constrained multiobjective optimization. In this method, a new nonmonotone line search technique proposed here is employed and the relative errors on the search direction is admitted. We demonstrate that this method is well-defined. Then, we prove that each accumulation point of the sequence generated by this method is Pareto stationary and analyze the convergence rate of the algorithm. When the objective function is convex, the convergence of the sequence to a weak Pareto optimal point of the problem is established. [ABSTRACT FROM AUTHOR]

In this research, a Multidisciplinary Design Optimization approach is proposed for the dual-spin guided flying projectile design considering external and internal parts of the body as design variables. In this way, a parametric formulation is developed. All related disciplines, including structure, aerodynamics, guidance, and control are considered. Minimum total mass, maximum aerodynamic control effectiveness, minimum miss distance, maximum yield stress in all subsystems, controllability and gyroscopic stability constraints are some of objectives/constraints taken into account. The problem is formulated in All-At-Ones Multidisciplinary Design Optimization approach structure and solved by Simulated Annealing and minimax algorithms. The optimal configurations are evaluated in various aspects. The resulted optimal configurations have met all design objectives and constraints. [ABSTRACT FROM AUTHOR]

To enhance the comprehensive performance of artillery internal ballistics--encompassing power, accuracy, and service life--this study proposed a multi-stage multidisciplinary design optimization (MS-MDO) method. First, the comprehensive artillery internal ballistic dynamics (AIBD) model, based on propellant combustion, rotation band engraving, projectile axial motion, and rifling wear models, was established and validated. This model was systematically decomposed into subsystems from a system engineering perspective. The study then detailed the MS-MDO methodology, which included Stage I (MDO stage) employing an improved collaborative optimization method for consistent design variables, and Stage II (Performance Optimization) focusing on the independent optimization of local design variables and performance metrics. The methodology was applied to the AIBD problem. Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts, thereby accelerating system-level convergence. Meanwhile, Stage II optimization markedly enhanced overall performance. These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method. [ABSTRACT FROM AUTHOR]

In Older to inherit and cany forward the traditional chinese Han dynasty brocade patterns, the traditional patterns we® integrated into modern clothing design with diversified and innovative designs with regeneration thinking of shapes, meaning and color" . The Han Dynasty brocade "Five Stars Out of the Effect to Benefit China" pattern was taken as the research object, and literature research and iconography metlrods were first used to analyz the pattern, color and cultural connotation of the " Five Star" brocade. Secondly, the regenerative thinking of " shape, meaning and color" was used to analyze layer by layer, and the transfer of " shape", the communication of "meaning" and the integration of " color" into the innovative design of traditional patterns were realized. Finally, innovative recycled patterns were applied to apparel products. The results show that the interdisciplinary research of aesthetics, design, color science, design thinking and other disciplines can provide a multidisciplinary research exploration experience for the innovative application and dissemination of traditional Chinese patterns in modern clothing design. [ABSTRACT FROM AUTHOR]

Complex systems design problems entail a suitable structure in which all disciplines, including their coupled relationships, have been considered and modeled at the same time. These types of design problems involve time and computational cost challenges. Multi-Disciplinary Design Optimization (MDO) methods have been developed to address these issues simultaneously. This article aims to provide a proper design structure for an uncertainty-based re-entry trajectory design optimization problem under the control restrictions and structural constraints of a Reusable Flexible Space Launch Vehicle (RFSLV) alongside the determination of optimal skin thickness and thermal protection system thickness concerning the design criteria of the flexible structure in such a way that the final design would meet the desired reliability. Trajectory, structure, aerodynamics, aeroelasticity, and thermal protection systems are considered to be involved disciplines in the design problem. The study's purpose will be to obtain an optimal trajectory to meet all the control and structure restrictions while estimating optimal body skin and thermal protection thicknesses based on structural design criteria evaluating the re-entry trajectory, which is in process. The flexible space launcher body has been considered as a free-free Bernoulli-Euler beam for bending variation and D’alembert’s principle for inertia force in static model with the aim of assessing structural design standards. The 3DOF longitudinal dynamic equations plus the first bending mode have been considered. By Chebyshev polynomial interpolation, the angle of attack scope has been achievable, and then the trajectory optimization problem has been transformed to a discrete nonlinear programming problem (NLP), which leads to numerical integration of state equation and satisfying all path constraints in Bolza optimal control problem. All highly nonlinear uncertainty-based constraints in the model have led to taking advantage of the evolutionary optimization algorithm that has been implemented here by Non-Dominated Sorting Genetic Algorithm (NSGA-II). Finally, epistemic and aleatory uncertainties have been applied through Probability Theory to estimate the reliability of constraints that had been affected by uncertainties. The result shows a 75 percent decrease through utilizing the evolutionary multi-objective technique against the gradient-based algorithm in design space optimization regarding computational cost in recalling objective functions. The other conclusion is that the sequential reliability analysis structure modeling efficiency is much better compared to the parallel one.

Blended-wing-body underwater gliders (BWBUGs) are a new generation of underwater vehicles that have been successfully applied to long-range missions. It is generally recognized that shapes with a high aspect ratio have a higher lift–drag ratio (LDR) and superior fluid performance. However, the pursuit of higher LDR cannot achieve the optimal overall performance, and there are complex design factors. It is necessary but difficult to conduct a thorough examination of shape layout, energy-carrying and other issues. In this article, the layout and energy models are established, and the causal graph is used as a multidisciplinary analysis method to show the interaction between system variables. A shape design method based on surrogate models and causal graphs is proposed that incorporates knowledge into optimization by combing the information flow and finding the relationship between variables. The results show that causal-assisted optimization can mine the characteristics of variables and discover more valuable shape designs. [ABSTRACT FROM AUTHOR]

Sequential sampling methods have gained significant attention due to their ability to iteratively construct surrogate models by sequentially inserting new samples based on existing ones. However, efficiently and accurately creating surrogate models for high-dimensional, nonlinear, and multimodal problems is still a challenging task. This paper proposes a new sequential sampling method for surrogate modeling based on a hybrid metric, specifically making the following three contributions: (1) a hybrid metric is developed by integrating the leave-one-out cross-validation error, the local nonlinearity, and the relative size of Voronoi regions using the entropy weights, which well considers both the global exploration and local exploitation of existing samples; (2) a Pareto-TOPSIS strategy is proposed to first filter out unnecessary regions and then efficiently identify the sensitive region within the remaining regions, thereby improving the efficiency of sensitive region identification; and (3) a prediction-error-and-variance (PE&V) learning function is proposed based on the prediction error and variance of the intermediate surrogate models to identify the new sample to be inserted in the sensitive region, ultimately improving the efficiency of the sequential sampling process and the accuracy of the final surrogate model. The proposed sequential sampling method is compared with four state-of-the-art sequential sampling methods for creating Kriging surrogate models in seven numerical cases and one real-world engineering case of a cutterhead of a tunnel boring machine. The results show that compared with the other four methods, the proposed sequential sampling method can more quickly and robustly create an accurate surrogate model using a smaller number of samples. [ABSTRACT FROM AUTHOR]

Machine learning algorithms can enhance the design and experimental processing of solar cells, resulting in increased conversion efficiency. In this study, we introduce a novel machine learning-based methodology for optimizing the Pareto front of four-terminal (4T) perovskite-copper indium selenide (CIS) tandem solar cells (TSCs). By training a neural network using the Bayesian regularization-backpropagation algorithm via Hammersley sampling, we achieve high prediction accuracy when testing with unseen data through random sampling. This surrogate model not only reduces computational costs but also potentially enhances device performance, increasing from 29.4% to 30.4% while simultaneously reducing material costs for fabrication by 50%. Comparing experimentally fabricated cells with the predicted optimal cells, the latter show a thinner front contact electrode, charge-carrier transport layer, and back contact electrode. Highly efficient perovskite cells identified from the Pareto front have a perovskite layer thickness ranging from 420 to 580 nm. Further analysis reveals the front contact electrode needs to be thin, while the back contact electrode can have a thickness ranging from 100 to 145 nm and still achieve high efficiency. The charge-carrier transport layers play a crucial role in minimizing interface recombination and ensuring unidirectional current flow. The optimal design space suggests thinner electron and hole transport layer thicknesses of 7 nm, down from 23 to 10 nm, respectively. It indicates a balanced charge-carrier extraction is crucial for an optimized perovskite cell. Overall, the presented methodology and optimized design parameters have the potential to enhance the performance of 4T perovskite/CIS TSC while reducing material fabrication costs. [ABSTRACT FROM AUTHOR]

Perovskite-based optoelectronic devices have emerged as a promising energy source due to their potential for scalable production. This study introduces "perovskene," a novel class of 2D materials derived from the ABC3-like perovskites, synthesized via a data-driven, high-throughput computational strategy. We harness machine learning and multitarget deep neural networks to systematically investigate the structure–property relations, paving the way for targeted material design and optimization in fields such as renewable energy, electronics, and catalysis. The characterization of over 1500 synthesized structures shows that more than 500 structures are stable, revealing properties such as ultra-low work function and large magnetic moment, underscoring the potential for advanced technological applications. [ABSTRACT FROM AUTHOR]

The architectural complexity of Hagia Sophia, a grand monument erected by Justinian in Constantinople, has been a topic of substantial investigation across diverse disciplines such as architecture, art history, and archaeology. Research efforts focused on elucidating the geometric relationships intrinsic to the monument's design, providing invaluable insights into the philosophical and symbolic influences that shaped its construction. However, despite significant strides, a comprehensive understanding of Hagia Sophia's complex geometric design remains elusive due to the inherent challenges in integrating diverse geometric patterns into a unified model. The current study aims to bridge this gap through a comparative analysis of the mathematical and geometric principles used in the design of Hagia Sophia with the inscribed in stone complex geometrical design found in the Octagon at the Galerious Complex in Thessaloniki and has recently been recognised as the conceptual design for the Kastas monument at Amphipolis. By exploring parallels and creating comparisons, the article deepens the understanding of the design intent and geometric foundations that underpin the enduring architectural legacy of Hagia Sophia. [ABSTRACT FROM AUTHOR]

This paper presents an efficient integrated multidisciplinary design optimization method for shaping a high-pressure cooling turbine blade in aero engines. This approach utilizes a novel regression/interpolation combination surrogate model (R/ICSM), facilitating comprehensive design optimization through collaborative coupling feature parameterization modeling and numerical simulation analysis across various disciplines. The optimized blade adjusts the load distribution on its surface, effectively eliminating flow separation at the tip and trailing edge. Notably, the optimized blade achieves a 0.69% increase in isentropic efficiency while satisfying aerodynamic, strength, and structural constraints. This highlights the effectiveness and progressiveness of the multidisciplinary design optimization method for a cooling turbine blade based on the R/ICSM in enhancing overall performance. It offers a novel and feasible approach for turbine blade design optimization and provides valuable insights for future research and applications. [ABSTRACT FROM AUTHOR]

Hydrogen aircraft have the potential to achieve more significant climate impact reductions at a lower cost than aircraft powered by biofuels or other drop-in sustainable aviation fuels. Nevertheless, even as a liquid, hydrogen requires four times more volume than kerosene to store the same energy. Companies and researchers have suggested that the blended wing body configuration is well-suited to hydrogen because it can efficiently store large fuel tanks. However, nobody has quantified this claim, at least publicly. We address this gap by comparing optimized kerosene and hydrogen versions of blended wing body and tube and wing aircraft. Our models predict that, with ambitious hydrogen tank technology assumptions, a hydrogen blended wing body has a 3.8% energy penalty compared to a kerosene blended wing body. In contrast, the energy penalty for a tube and wing is 5.1%. These results suggest that the blended wing body configuration is slightly better suited to hydrogen than a tube and wing. However, the conclusion breaks down under less optimistic scenarios where lightweight conformal liquid hydrogen tanks are unavailable. Nonetheless, the energy penalties of switching the two configurations to hydrogen have a similar order of magnitude. Any kerosene blended wing body fuel burn benefit over a tube and wing also applies to hydrogen versions. As a result, kerosene blended wing body technology developments, such as maturing the structural design of the noncircular pressurized passenger cabin, remain valuable for future hydrogen-powered aircraft. [ABSTRACT FROM AUTHOR]

The adjoint method provides an efficient way to compute sensitivities for system models with a large number of inputs. However, implementing the adjoint method requires significant effort that limits its use. The effort is exacerbated in large-scale multidisciplinary design optimization. We propose the adoption of a three-stage compiler as the method for constructing computational models for large-scale multidisciplinary design optimization to enable accurate and efficient adjoint sensitivity analysis. We develop a new modeling language called the Computational System Design Language that provides an appropriate input to the compiler front end that works well with multidisciplinary models. This paper describes the three-stage compiler methodology and the Computational System Design Language. The proposed solution uses a graph representation of the numerical model to automatically generate a computational model that computes adjoint-based sensitivities for use within an optimization framework. For two engineering models, this approach reduces the amount of user code by a factor of approximately two compared to their original implementations, without a measurable increase in computation time. This paper also includes a best-case complexity analysis that is built into the compiler implementation to allow users to estimate the memory required to evaluate a computational model and its derivatives, which is independent of the compiler back end that ultimately generates the computational model. Future compiler implementations are expected to approach the theoretical best-case memory cost and improve run time performance for both model evaluation and derivative computation. [ABSTRACT FROM AUTHOR]

Recently, there has been a growing interest in mixed-categorical metamodels based on Gaussian Process (GP) for Bayesian optimization. In this context, different approaches can be used to build the mixed-categorical GP. Many of these approaches involve a high number of hyperparameters; in fact, the more general and precise the strategy used to build the GP, the greater the number of hyperparameters to estimate. This paper introduces an innovative dimension reduction algorithm that relies on partial least squares regression to reduce the number of hyperparameters used to build a mixed-variable GP. Our goal is to generalize classical dimension reduction techniques commonly used within GP (for continuous inputs) to handle mixed-categorical inputs. The good potential of the proposed method is demonstrated in both structural and multidisciplinary application contexts. The targeted applications include the analysis of a cantilever beam as well as the optimization of a green aircraft, resulting in a significant 439-kilogram reduction in fuel consumption during a single mission. [ABSTRACT FROM AUTHOR]

The declarative approach is key to efficiently finding optimal solutions to various types of NP-hard real-world combinatorial optimization problems. Most work on practical declarative solvers—ranging from classical integer programming to finite-domain constraint optimization and maximum satisfiability (MaxSAT)—has focused on optimization under a single objective; fewer advances have been made towards efficient declarative techniques for multi-objective optimization problems. Motivated by significant recent advances in practical solvers for MaxSAT, in this work we develop BiOptSat, an exact declarative approach for finding Pareto-optimal solutions to bi-objective optimization problems, with propositional logic as the underlying constraint language. BiOptSat can be viewed as an instantiation of the lexicographic method. The approach makes use of a single Boolean satisfiability solver that is incrementally employed throughout the entire search procedure, allowing for finding a single Pareto-optimal solution, finding one representative solution for each non-dominated point, and enumerating all Pareto-optimal solutions. We detail several algorithmic instantiations of BiOptSat, each building on recent algorithms proposed for single-objective MaxSAT. We empirically evaluate the instantiations compared to recently-proposed alternative approaches to multi-objective MaxSAT solving on several real-world domains from the literature, showing the practical benefits of our approach. [ABSTRACT FROM AUTHOR]

Two problems exist in the study of the trajectory optimization problem of powered hypersonic gliding vehicles (HGVs) due to insufficient consideration of the overall design constraints as well as the strong couplings among relevant disciplines: (1) the engine and thrust models are not compatible with the existing HGV; (2) configuration parameters of the HGV are not included as design variables during trajectory optimization (i.e., propulsion discipline is decoupled in the process of the HGV configuration design), thus failing to fully explore the effect of power to improve the performance of the HGV. Therefore, the application of multidisciplinary design optimization (MDO) in the overall design of powered HGVs should be investigated. First, a MDO task analysis and a multidisciplinary model analysis are carried out for the powered HGV. Second, the multidisciplinary optimization problem is defined, and the couplings between disciplines of the powered HGV are analyzed so that a six-discipline model is established that is suitable for the overall design process, including the parameterized configuration geometry, aerodynamics, propulsion, mass properties, trajectory, and aerodynamic heat/thermal protection system (TPS). Finally, a surrogate model is used to replace the time-consuming accurate model, and numerical optimization examples verify the effectiveness of the method. The optimization results show that the method has a good convergence speed, which increases the gliding range of the optimized vehicle by 8.37%. In addition, by decoupling the propulsion discipline, the validation shows that the coupled propulsion discipline during the overall design can increase the range of the powered HGV by 3.87% compared to the powered HGV optimized with the decoupled propulsion discipline. The work done in this paper provides a new design idea for the overall design of a powered HGV. [ABSTRACT FROM AUTHOR]

System design is a challenging and time-consuming task which often requires close collaboration between several multidisciplinary design teams to account for complex interactions between components and sub-systems. As such, there is a growing demand in industry to create better performing, efficient, and cost-effective development tools to assist in the system design process. Additionally, the ever-increasing complexity of systems today often necessitates a shift away from manual expertise and a movement towards computer-aided design tools. This work narrows the scope of the system design process by focusing on one critical design aspect: the packaging of system components. The algorithm presented in this paper was developed to optimize the packaging of system components with consideration of practical, system-level functionalities and constraints. Using a dynamic acceleration methodology, the algorithm packages components from an initial position to a final packed position inside of a constrained volume. The motion of components from initial to final positions is driven by several acceleration forces imposed on each component. These accelerations are based on physical interactions between components and their surrounding environment. Various system-level performance metrics such as center of mass alignment and rotational inertia reduction are also considered throughout optimization. Results of several numerical case studies are also presented to demonstrate the functionality and capability of the proposed packaging algorithm. These studies include packaging problems with known optimal solutions to verify the efficacy of the algorithm. Finally, the proposed algorithm was used in a more practical study for the packaging of an urban air mobility nacelle to demonstrate the algorithm's prospective capabilities in solving real-world packaging problems. [ABSTRACT FROM AUTHOR]