Design Optimization Framework Research Articles

Statistical power and optimal design for randomized controlled trials investigating mediation effects.

Mediation analyses in randomized controlled trials (RCTs) can unpack potential causal pathways between interventions and outcomes and help the iterative improvement of interventions. When designing RCTs investigating these mechanisms, two key considerations are (a) the sample size needed to achieve adequate statistical power and (b) the efficient use of resources. The current study has developed closed-form statistical power formulas for RCTs investigating mediation effects with and without covariates under the Sobel and joint significance tests. The power formulas are functions of sample size, sample allocation between treatment conditions, effect sizes in the treatment-mediator and mediator-outcome paths, and other common parameters (e.g., significance level, one- or two-tailed test). The power formulas allow us to assess how covariates impact the magnitude of mediation effects and statistical power. Accounting for the potential unequal sampling costs between treatment conditions, we have further developed an optimal design framework to identify optimal sample allocations that provide the maximum statistical power under a fixed budget or use the minimum resources to achieve a target power. Illustrations show that the proposed method can identify more efficient and powerful sample allocations than conventional designs with an equal number of individuals in each treatment condition. We have implemented the methods in the R package odr to improve the accessibility of the work. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Omnidirectional soft pneumatic actuators: a design and optimization framework

IntroductionSoft pneumatic actuators (SPAs) play a pivotal role in soft robotics due to their unique characteristics of compliance, flexibility, and adaptability. There are plenty of approaches that examine the modeling parameters of SPAs, aiming to optimize their design and, thus, achieve the most advantageous responses. Current optimization methods applied to SPAs are usually performed individually for each design parameter without considering the simultaneous effect all parameters can have on the output performance. This modeling shortcoming is essential to be addressed since customized SPAs are used in a variety of applications, each with different output requirements.MethodsThis study provides a generalized design optimization framework for modeling the SPA performance for their motion profiles, the produced strain energy while being deformed, and their stiffness characteristics. Utilizing experimentally validated finite element methods, all geometrical and material parameters of the models are investigated in response surface methodology optimization using the central composite design approach.ResultsThe results showcase the entire design space of omnidirectional SPAs, along with their output performance, providing guidelines to the end user for design optimization.DiscussionThe offering of this modeling process for SPAs can be adapted to the demands of any potential application and ensure the best performance with respect to the required output responses.

Optimal design of building envelope towards life cycle performance: Impact of considering dynamic grid emission factors

Building-related carbon emissions in the construction and operation stages account for more than one-third of global energy-related emissions. Optimizing during the early design stage is an effective way to reduce building carbon emissions. However, current studies adopt a constant grid emission factor (GEF) to assess the environmental impacts of buildings while ignoring the dynamic changes of the future electricity mix. This will overestimate the operational carbon emissions of the building in the context of the increasing share of renewable energy in power generation. In this study, a dynamic life cycle assessment model based on dynamic GEF is constructed to evaluate the life cycle environmental impacts of buildings. On this basis, a building optimization design framework considering dynamic GEF is proposed, and the impact of considering future variations of GEF on the optimal design of the building envelope is explored. The study is conducted in three steps. First, dynamically changing GEFs are predicted under the future electricity mix based on scenario analysis. Second, a multi-objective optimization framework is established to minimize the life cycle carbon emissions (LCCE) of the building while optimizing its life cycle costs (LCC) and indoor discomfort hours (IDH). Finally, the proposed optimization framework is applied to a prototype office building in Shanghai, and a comparative analysis is conducted for the optimal schemes between a static and two dynamic scenarios. The result reveals that ignoring future variations will lead to overestimating the operational carbon emissions by 49.0%-64.8%, which in turn will result in an over-insulated design of the envelope (i.e., additional material consumption). In addition, compared to the static scenario, consideration of dynamic GEF results in a 38.7%-51.6% reduction in LCCE, a 5.2%-6.1% reduction in LCC, and a 5.3%-9.5% increase in IDH for the optimal solutions. This research illustrates the importance of considering dynamic GEF in identifying the optimal design parameters and can help decision-makers reasonably choose the optimal schemes during early-stage design.

Multi-Objective Robust Design Optimization for Crashworthiness Enhancement of Hybrid 2D Triaxially Braided Composite Tube Using Evolutionary Algorithms.

An innovative optimal design framework is developed aiming at enhancing the crashworthiness while ensuring the lightweight design of a hybrid two-dimensional triaxial braided composite (2DTBC) tube, drawing insights from the mesostructure of the composite material. To achieve these goals, we first compile the essential mechanical properties of the 2DTBC using a concentric cylinder model (CCM) and an analytical laminate model. Subsequently, a kriging surrogate model to elucidate the intricate relationship between design variables and macroscopic crashworthiness is developed and validated. Finally, employing multi-objective evolutionary optimization, we identify Pareto optimal solutions, highlighting that reducing the total fiber volume and increasing the glass fiber content in the total fiber volume are crucial for optimal crashworthiness and the lightweight design of the hybrid 2DTBC tube. By integrating advanced predictive modeling techniques with multi-objective evolutionary optimization, the proposed approach not only sheds light on the fundamental principles governing the crashworthiness of hybrid 2DTBC but also provides valuable insights for the design of robust and lightweight composite structures.

A Framework for Multi-Physics Modeling, Design Optimization and Uncertainty Quantification of Fast-Spectrum Liquid-Fueled Molten-Salt Reactors

The analysis of liquid-fueled molten-salt reactors (LFMSRs) during steady state, operational transients and accident scenarios requires addressing unique reactor multi-physics challenges with coupling between thermal hydraulics, neutronics, inventory control and species distribution phenomena. This work utilizes the General Nuclear Field Operation and Manipulation (GeN-Foam) code to perform coupled thermal-hydraulics and neutronics calculations of an LFMSR design. A framework is proposed as part of this study to perform modeling, design optimization, and uncertainty quantification. The framework aims to establish a protocol for the studies and analyses of LFMSR which can later be expanded to other advanced reactor concepts too. The Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) statistical analysis tool was successfully coupled with GeN-Foam to perform uncertainty quantification studies. The uncertainties were propagated through the input design parameters, and the output uncertainties were characterized using statistical analysis and Spearman rank correlation coefficients. Three analyses are performed (namely, scalar, functional, and three-dimensional analyses) to understand the impact of input uncertainty propagation on temperature and velocity predictions. Preliminary three-dimensional reactor analysis showed that the thermal expansion coefficient, heat transfer coefficient, and specific heat of the fuel salt are the crucial input parameters that influence the temperature and velocity predictions inside the LFMSR system.

A CAD-oriented parallel-computing design framework for shape and topology optimization of arbitrary structures using parametric level set

Recently, the high-resolution topology optimization to promote engineering applicability has gained much more attentions. However, an accurate and highly-efficient design framework for implementing shape and topology optimization of engineering structures with integration of CAD model is still in demand. In the current work, the critical intention is to develop a CAD-oriented parallel-computing design framework for arbitrary structures, where the Parametric Level Set Method (PLSM) is employed for shape and topology optimization. Firstly, an implicit identification model is constructed for generating a signed distance field using the vertex and normal information from the ‘STL’ file of engineering structures. The signed distance field is combined with the compactly supported radial basis functions (CSRBFs) to solve the initial level set function with a parametrization. This method is applied to present all domains, including design domains, Neumann boundary domains, Dirichlet boundary domains, and non-design domains. Secondly, the CPU parallel strategy is considered for allocating partitions of structural stiffness matrix in finite element analysis to different CPU cores for the parallel-computing to save computation costs. Thirdly, a parallel-computing design formulation is developed for performing shape and topology optimization of arbitrary structures, in which the partitioned terms of all design variables and stiffness matrix are concurrently computed on each CPU core. Finally, several classic benchmarks and the critical engineering structure of Virtual Reality (VR) glass part with extremely complex geometries, are discussed to demonstrate the effectiveness and efficiency of the proposed design framework.

A Framework for Adaptive Façade Optimization Design Based on Building Envelope Performance Characteristics

The adaptive façades serve as the interface between the indoor and outdoor energy of the building. Adaptive façade optimization design can improve daylighting performance, the thermal environment, view performance, and solar energy utilization efficiency, thus reducing building energy consumption. However, traditional design frameworks often neglect the influence of building envelope performance characteristics on adaptive façade optimization design. This paper aims to reveal the potential functional relationship between building façade performance characteristics and adaptive façade design. It proposes an adaptive façade optimization design framework based on building envelope performance characteristics. The method was then applied to a typical office building in northern China. This framework utilizes a K-means clustering algorithm to analyze building envelope performance characteristics, establish a link to adaptive façade design, and use the optimization algorithm and machine learning to make multi-objective optimization predictions. Finally, Pearson’s correlation analysis and visual decision tools were employed to explore the optimization potential of adaptive façades concerning indoor daylighting performance, view performance, and solar energy utilization. The results showed that the optimized adaptive façade design enhances useful daylight illuminance (UDI) by 0.52%, quality of view (QV) by 5.36%, and beneficial solar radiation energy (BSR) by 14.93% compared to traditional blinds. In addition, each office unit can generate 309.94 KWh of photovoltaic power per year using photovoltaic shading systems. The framework provides new perspectives and methods for adaptive façade optimization design, which helps to achieve multiple performance objectives for buildings.

Multi-objective robust optimization design framework for low-pollution emission burners

The optimal design of low-pollution emission burners plays an important role in controlling pollutant emissions of industrial equipment, and is crucial for the sustainable development of the national economy and environmental protection. However, many uncertain factors challenge the optimal design of low-pollution emission burners. The Latin hypercube sampling (LHS) method was used to obtain sampling data representing the distribution of the uncertain variable. The training dataset was obtained using the turbulent combustion coupling model. A high-precision sparse polynomial chaos expansion (PCE) model was constructed by the degree-adaptive scheme and least angle regression (LAR) algorithm. Furthermore, the Legendre polynomial is introduced to establish a continuous robust optimization model. The model is carried out by the non-dominated sorting genetic algorithm II (NSGA-II). The results show that the excess air coefficient of 1.227 is optimal. Compared with the excess air coefficient of 1.20 under the discrete robust optimization, the optimal coefficient can further reduce pollutant emissions and bring strong robustness to the ethylene cracking furnace. It has also been proven that the continuous robust optimization scheme improves the optimization granularity. Compared with discrete robust optimization, this method reduces the number of samples by 66.7 %.

Optimization Design Framework for In-Vehicle Time-Sensitive Networking Architecture

Optimization Design Framework for In-Vehicle Time-Sensitive Networking Architecture

CPOpt: A modular framework for genetic algorithm optimization and post-optimization analysis in complex charged particle optical design

This article reports on the development of an optimization framework for charged particle optics design (CPOpt). The optimization part of CPOpt involves a custom-developed Python multi-objective genetic algorithm (MOGA), which incorporates NSGA-II to optimize for multiple objectives, such as particle transmission and beam spot size. The simultaneous tuning of different types of parameters, such as electrode voltages and geometrical parameters, is facilitated by creating isolated groups of parameters. Modeling of parameter configurations is done with the charged particle optics simulation tool SIMION and a ZeroMQ-based communication interface enables a fully automated interaction with the MOGA. The post-optimization analysis part of CPOpt provides standardized and broadly applicable methods to identify suitable solutions, based on the outcome of the optimization part. Global sensitivity analysis, based on the Delta Moment-Independent-Measure in combination with dedicated Latin hypercube sampling, allows higher-level information about parameter sensitivity and robustness to be extracted, as well as effective randomized local optimization. Demonstrated on a two-lens system with intermediate aperture, the CPOpt framework effectively optimized the two conflicting objectives of reduced beam spot size and increased transmission at the detector plane for 12 parameters, covering both electrode voltages and geometrical parameters. The optimization process was performed within less than 11h on a workstation, while all the simulations required for the post-optimization analysis took another 7h. The additional computational effort for the post-optimization analysis can contribute to deliver solutions with high performance and stability.

Multi-objective optimization of engineered cementitious composite based on machine learning and generative adversarial network

This study aims to establish a novel framework for mixture design optimization of engineered cementitious composite (ECC) by first collecting two original datasets of ECC's tensile stress and strain from the extensive and credible literature. The datasets comprise a wide range of variables including cementitious ingredients, 9 types of fiber and characteristics, admixtures, and experimental conditions. The data augmentation is then performed using a tuned constraints-modified Conditional Tabular Generative Adversarial Network (Tuned-CTGAN) to increase the model accuracy and generalizability. The fitness functions of tensile stress and strain are established based on four machine learning models with the hyperparameters tuned by the Hunger Games search (HGS) algorithm. After the data augmentation, the values of R2 in their test sets are increased from 0.874 to 0.925 and from 0.772 to 0.889, respectively. Subsequently, the third objective function (cost) is computed by polynomials and four classes of constraints (Min-max, volume, ratio, and fiber) are set up to define the variable's search space. A non-dominated sorting genetic algorithm based on reference-point strategy (NSGA-III) is introduced to optimize the mixture proportions of ECC by simultaneously optimizing tensile stress, tensile strain, and cost. This paper combines the results of data augmentation, model prediction, and multi-objective optimization for complex ECC design, which aims to provide a basis for practical application.

Cost-effective framework for design optimization using metamodel and evolutionary algorithm: with a case study of bio-filtration system

Cost-effective framework for design optimization using metamodel and evolutionary algorithm: with a case study of bio-filtration system

Broaden sustainable design and optimization of decarbonized campus Energy systems with scope 3 emissions accounting and social ramification analysis

A holistic approach that considers economic, environmental, and social dimensions is crucial for informed and responsible decision-making in energy system decarbonization. Integrating techno-economic, environmental, and social perspectives, we propose a multi-objective optimization framework for the sustainable design of decarbonized campus energy systems, resonating with the tri-fold principles of Corporate Social Responsibility. This comprehensive approach reveals the techno-economic intricacies inherent in adopting renewable solutions to accommodate cooling, heat, and power demand on university campuses. Our proposed model endeavors to minimize both total annualized costs and the broader spectrum of emissions (Scope 1, 2, and 3) in pursuit of environmental justice. Meanwhile, by merging life cycle assessment (LCA) with economic input-output analysis based on established social LCA databases, we can gauge the potential social impacts associated with these sustainability initiatives. The results serve as a roadmap for designing and operating truly sustainable campus energy systems, pinpointing environmental concerns, suggesting remedial strategies, and spotlighting the resultant social consequences. To showcase the tangible applicability of this holistic modeling framework, we have used real-world data from the main campus of Cornell University, Ithaca, New York State, to develop a case study. Our results show that with a plentiful biogas supply, it is possible to reduce yearly GHG emissions by more than 70%. Even when procurement emissions are left out, Scope 3 emissions still represent 69–82% of the total GHG emissions, dominated by the transportation sector. Turning to sustainable transport options, such as electric vehicles (EVs), can markedly lower these figures. On the socio-economic front, ramping up geothermal drilling could intensify negative social consequences, such as risk of forced labor, conflicts, accidents, and natural disasters, by 29%. These are primarily due to safety issues and the labor demands of earth source heat site preparation, encompassing risks such as forced labor and accidents. As the push for energy decarbonization grows stronger, it is imperative to weigh these downsides against the broader social benefits.

Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty

We consider optimal experimental design (OED) for Bayesian nonlinear inverse problems governed by partial differential equations (PDEs) under model uncertainty. Specifically, we consider inverse problems in which, in addition to the inversion parameters, the governing PDEs include secondary uncertain parameters. We focus on problems with infinite-dimensional inversion and secondary parameters and present a scalable computational framework for optimal design of such problems. The proposed approach enables Bayesian inversion and OED under uncertainty within a unified framework. We build on the Bayesian approximation error (BAE) approach, to incorporate modeling uncertainties in the Bayesian inverse problem, and methods for A-optimal design of infinite-dimensional Bayesian nonlinear inverse problems. Specifically, a Gaussian approximation to the posterior at the maximum a posteriori probability point is used to define an uncertainty aware OED objective that is tractable to evaluate and optimize. In particular, the OED objective can be computed at a cost, in the number of PDE solves, that does not grow with the dimension of the discretized inversion and secondary parameters. The OED problem is formulated as a binary bilevel PDE constrained optimization problem and a greedy algorithm, which provides a pragmatic approach, is used to find optimal designs. We demonstrate the effectiveness of the proposed approach for a model inverse problem governed by an elliptic PDE on a three-dimensional domain. Our computational results also highlight the pitfalls of ignoring modeling uncertainties in the OED and/or inference stages.

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.

Reinforcement learning control of multi-spectral LED systems for rendering optimal lighting performance in indoor environment

Light-emitting diodes (LEDs) have superseded traditional light sources due to their superior qualities. However, achieving desired spectral distribution in indoor environment still remains a challenge, particularly for optimizing multiple lighting parameters, which are nonlinear functions of the light spectra, including correlated color temperature (CCT), color rendering index (CRI) and circadian action factor (CAF). Existing studies have been mainly devoted to seeking optimal solutions, by means of various optimization methods, e.g. genetic algorithms. However, these methods can only result in fixed recipes to achieve pre-assigned reference parameters, and cannot meet the need of dynamic varying lighting environment. Therefore, this paper proposes a novel framework for optimal spectrum design and control in multichannel LED lighting systems. It surpasses traditional methods by employing reinforcement learning (RL) to generate an entire optimal spectral power distribution (SPD) model, comprehensively considering CCT, CRI, CAF, and other relevant parameters. This framework integrates the RL-based optimal SPD generator with a corresponding real-time controller, bridging the gap between simulations and real-world applications. The effectiveness of this new framework has been verified by an experimental prototype, showcasing both the optimal SPD and the achieved control performance. This work paves the way for high-quality, user-centric lighting with dynamic CCT and spectral control, and enables the creation of lighting environments that optimize human well-being, productivity, and overall experience.

Concurrent multiscale topology optimization for size-dependent structures incorporating multiple-phase materials

The significant progress in additive manufacturing has resulted in the development of well-engineered microstructures with innovative topological configurations, showcasing exceptional performance across diverse fields. Topology optimization is widely recognized as an advanced design tool that provides engineers with new insights for creating lightweight structures. As integration devices continue to trend towards miniaturization, the size effect in small-scale structures remains a persistent challenge. The absence of characteristic parameters to describe this size effect hinders proper explanation through traditional multiscale topology optimization based on classical mechanics. Therefore, we propose a size-dependent topology optimization framework for concurrent multiscale design involving multiple-phase materials. The modified couple stress theory is proposed to account for the size effect in small-scale structures. The numerical findings suggest that the size effect is significantly influenced by the ratio of elastic modulus, volume fraction, and width-to-length ratio of the representative volume element as its characteristic size approaches the length of the material. These results highlight the importance of considering size-dependent effects in topology optimization for multiscale design and provide valuable insights for engineers and researchers in this field.

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.

Risk-informed integrated design optimization for offshore wind farm electrical systems

The present paper develops and presents a risk-informed framework for the design of electrical systems of offshore wind farms. The framework provides a consistent theoretical and methodical basis for the treatment of uncertainties affecting service life performances and accounts for both the direct and indirect consequences related to system failure events. The risk of electrical systems is represented through a probabilistic systems modeling approach in which the states (direct failure, available, and unavailable) of the components and the resulting consequences are duly accounted for. The paper further proposes a risk-informed design optimization framework for offshore wind farm electrical systems (collection and transmission systems). Two risk indicators, the scenario-based net present value, and the robustness index, are formulated and quantified to support decision-making. The proposed framework and associated methods are illustrated through an application example considering an OWF comprised of 57 10MW wind turbines with HVAC transmission.

Deep learning-driven optimization design for the configuration of lazy-wave umbilical cables

Umbilical cables are essential components of subsea production systems for deep sea oil and gas exploration. Umbilical cables are often set to specific configurations that compensate for the motion of the floating body resulting from harsh environmental loads during operation to ensure structural safety and proper functioning. However, the optimization design of the umbilical cable configuration is a challenging problem owing to highly nonlinear relationship between loads and structural responses as well as the huge computational cost of evaluating numerous sea state combinations. This study proposes a deep learning-driven optimization design framework based on the residual artificial neural network to address the configuration design of lazy-wave umbilical cables. A parallel accelerated computation method based on Adam optimizer was established for the generation of the database. The transfer learning technique was also introduced into the deep learning model to build a configuration design prediction model based on small samples, which significantly reduced the cost of model building and training. Based on the above optimization design framework, a combination of tensile and bending optimization for a lazy-wave umbilical cable configuration was achieved, verifying the effectiveness and practicality of the proposed approach. This study provides an important reference for the rapid engineering design of umbilical cables.