Design Optimization Tool Research Articles

Optimization and Deep Learning Modeling of the Yield and Properties of Baobab-Derived Biodiesel Catalyzed by Waste Banana Bunch Stalk Biochar.

The integration of optimization techniques and deep learning models, which offer a promising avenue for improving the efficiency and sustainability of biodiesel production processes from baobab seed oil (BSO), is rare. This study utilized a multi-input-multioutput (MIMO) deep learning technique and the most recent central composite design (CCD) optimization tool to model and optimize the yield and properties of biodiesel produced from BSO. First, the baobab seed oil was extracted using a solvent extraction method. BSO was subsequently analyzed and converted to biodiesel by reacting CH3OH catalyzed by waste banana bunch stalk biochar activated by KOH. Multiobjective optimization and prediction of the biodiesel yield (Y) and several key fuel properties, including the cetane number (CN), kinematic viscosity (VS), and purity (P), were achieved. With better correlation coefficients of 0.9709, 0.9464, and 0.9714 for response training, response testing, and response validation, respectively, and a root-mean-square error of 0.00755, the MIMO model on the logsig transfer function accurately predicted the biodiesel yield and properties more than did the MISO and response surface methodology models. The optimum Y (96 wt %), CN (48), VS (3.3 mm2/s), and P (98.3%) were concurrently accomplished at a reaction temperature of 56 °C, a reaction time of 115 min, a CH3OH/BSO molar ratio of 15:1, a catalyst dosage of 6 wt %, and a stirring speed of 400 rpm with 98% optimal validation accuracy. CCD sensitivity analysis revealed that the CH3OH/BSO ratio was the most sensitive (50.9%) input predictor among the other input variables studied.

Optimization of an aircraft fuselage assembly to minimize radiated sound power

During the structural design of an aircraft fuselage, the acoustic performance is generally not considered. However, vibrations transmitted through the fuselage generate noise leading to passenger discomfort. Currently, the main source of noise reduction comes from the addition of damping material to the fuselage. This work investigates the use of various design optimization techniques to reduce radiated sound power by changing the fuselage structure’s geometry which consists of a skin panel, frames, and stringers. Design optimization tools, such as topology, size, and shape optimization were used to determine an optimal design for each design variable that minimized radiated sound power in the fuselage assembly. The design variables that were studied included skin panel thickness and design, frame and stringer spacing, and frame and stringer cross section. Equivalent radiated power was used as an objective function for numerical optimizations within Altair OptiStruct as an indirect method for minimizing radiated sound power numerically. To understand the source of the sound power improvements, a physics analysis was conducted for each design variable that compared sound power, equivalent radiated power, and radiation efficiency.

Computational Fluid Dynamics: Insights and Applications in the Pharmaceutical Field

Abstracts: Computational fluid dynamics (CFD) is a feasible tool to examine and troubleshoot different types of equipment utilized in the pharmaceutical industry or healthcare. As a large number of fluids are processed by unit operations, even some increments in performance and efficiency may escalate profits and reduce costs. CFD methods are primarily used in the automobile and aerospace industries, but in the current era, this technology is extensively applied in the pharmaceutical and chemical industries and has become an important tool for process design scaleup and optimization. CFD is a numerical approach utilizing CFD software to solve equations numerically. This review focuses on the diverse utilization of CFD in the pharmaceutical field and current applications in the COVID 19 pandemic, a recent health crisis that is intimidating the world.

Identifying influential architectural design variables for early-stage building sustainability optimization

Architectural design variables (ADVs) highly influence a building's sustainability performance. Thus, identifying which ADVs are most influential in a building's early stages is of great significance, especially when using computational building design optimization tools. Currently, sensitivity analysis based on computer simulations is the most commonly used means to identify which ADVs are the most influential in the early stages. However, we suggest that a stakeholder perspective should also be considered as stakeholders possess domain-specific knowledge and expertise as well as a contextual understanding that can greatly enhance the development and deployment of building design optimization tools. To explore the above, we combined a literature review with survey data from 24 architects and sustainability consultants in the Nordics. Surprisingly, we found that the influential ADVs in the literature do not always align with those of our surveyed stakeholders. For example, we found that the literature considers building plan, window-to-wall-ratio (WWR), and wall material as the most influential ADVs, which contrasts with storey number, storey height, WWR, roof material and wall material considered by stakeholders to be the most influential. We also found that the most influential ADVs differ across different sustainability optimization objectives, and that these also differ from the literature. Despite our limited survey sample, our study provides insights into influential ADVs and as such has implications for the development, use, and performance of computational building design optimization tools.

Modeling of flexible bevel gear rotor systems: Modal and dynamic characterization

Bevel gears, a crucial part of aircraft transmission systems, are more susceptible to wheel body vibration issues as lightweight requirements rise. In the past, the gear wheel body was often considered rigid while assessing the dynamic properties of gear systems. Constructing a bevel gear model with a thin-walled structure might not be appropriate for this modeling technique; this research suggests a dynamic model that considers the flexibility of bevel gears. The web and teeth structure of the gear are preserved by realistic modeling using three-dimensional hexahedral components. The component mode synthesis (CMS) approach reduces the model order for increased computational efficiency. To confirm the accuracy of the modeling method, the modal frequencies and vibration shapes of the proposed model single-gear shaft are compared with results from experiments and ABAQUS. By contrasting ABAQUS system-level meshing modal frequencies and vibration shapes, the accuracy of the system model assembly is further confirmed. The model examines the impact of bevel gear flexibility on the modal frequencies, modal vibration shapes, and system dynamic properties. The mode shape diagram at dangerous modal frequencies found using the modal strain energy technique and the vibration displacement cloud maps at resonance speeds computed using the Newmark-β method are mutually verified, and this demonstrates that lightweight bevel gears are more susceptible to excitation due to nodal diameter type vibrations. Proposed and traditional models anticipate substantially different resonance speeds since the traditional model assumes that the gear, being a rigid disk, can only excite the shaft's bending mode of vibration. The proposed model provides a more comprehensive description of the dynamic characteristics of bevel gear systems, providing a theoretical basis and optimization tool for the high-performance design, vibration reduction, and noise control of high-speed lightweight gear transmission systems.

Utilizing Machine Learning Tools for Calm Water Resistance Prediction and Design Optimization of a Fast Catamaran Ferry

The article aims to design a calm water resistance predictor based on Machine Learning (ML) Tools and develop a systematic series for battery-driven catamaran hullforms. Additionally, employing a machine learning predictor for design optimization through the utilization of a Genetic Algorithm (GA) in an expedited manner. Regression Trees (RTs), Support Vector Machines (SVMs), and Artificial Neural Network (ANN) regression models are applied for dataset training. A hullform optimization was implemented for various catamarans, including dimensional and hull coefficient parameters based on resistance, structural weight reduction, and battery performance improvement. Design distribution based on Lackenby transformation fulfills all of the design space, and sequentially, a novel self-blending method reconstructs new hullforms based on two parents blending. Finally, a machine learning approach was conducted on the generated data of the case study. This study shows that the ANN algorithm correlates well with the measured resistance. Accordingly, by choosing any new design based on owner requirements, GA optimization obtained the final optimum design by using an ML fast resistance calculator. The optimization process was conducted on a 40 m passenger catamaran case study that achieved a 9.5% cost function improvement. Results show that incorporating the ML tool into the GA optimization process accelerates the ship design process.

Utilization of the Adams system for simulating collisions of wagons equipped with elastomeric buffers

The article introduces an innovative method for modeling elastomer buffers using a rheological structure. This approach entails a detailed mathematical description of buffer properties, seamlessly integrated into the ADAMS/Rail computer code for simulating impacts between two wagons. The simulation within ADAMS/Rail provides insights into the dynamic behavior of elastomer buffers during wagon impacts, considering forces, deformations, and overall responses. Serving as a valuable optimization tool for railway system design, this methodology contributes to improved safety and efficiency. The study’s comprehensive yet concise insights bridge the gap between theoretical modeling and practical application, enhancing our understanding of elastomer buffer dynamics and their pivotal role in railway operations.

A methodology for the strategic selection of lightweight design tools along the product development process

The combination of progressive population growth and accelerating global warming necessitate a responsible and sustainable use of resources to guarantee the well-being of current and future generations. Therefore, lightweight design emerges as one possibility to reduce material and energy consumptions across the entire product life cycle. The resulting demand for lightweight products and systems has led to a large diversity of strategies, construction techniques and methods (summarized as tools), whose application has not yet proceeded in a systematic and target-oriented way. Therefore, this publication presents a methodology for the situation-appropriate selection of lightweight design tools along the product development process. For this purpose, requirements of lightweight design as well as generic targets and boundary conditions have first been identified, followed by the determination of their interrelationships with existing lightweight design tools based on an expert survey. This facilitates the selection of appropriate tools for optimal lightweight design through a quality-function-deployment-oriented approach. The proposed methodology is validated on the conceptual design of two bicycle trailers (cargo vs. children’s trailer) with distinct requirements. The results indicate that the methodology provides a strategic and systematic guidance for the implementation of lightweight design in products to developers and can thus reduce development efforts.

Optimized Supporting Structures for Small Artefacts: Generative Designed Prototypes

Retrievals of small and medium scale are one of the fundamental and most numerous parts of the entire museal heritage set. Their exposition represents an issue, looking for supporting structures that could be non-invasive, aesthetically pleasant, customized, and, last but not least, safe and stiff enough to avoid any damage to the artifacts. Through the usage of several engineering methods, starting from the retrieval and the exhibition requirements, optimized supporting structure can be developed.The procedure here reported and applied starts from the Reverse Engineering acquisition in order to obtain the 3D model of each retrieval, also comprehending color and texture information. After that, exhibition and preservation requirements, given by heritage experts, are translated into engineering requirements, with the usage of an immersive Virtual Reality environment. With these inputs, the developed Generative Design Method (GDM), using CAD, CAE and optimization tools synergically, can optimize the configuration of supporting interfaces with the artifact and then can generate a lightweight conceptual design through a generative design process. In this way, we can take into account all the given requirements, obtaining a lightweight support, topologically optimized, highly customized, non-invasive, possibly pleasant, able to resist in safety, and additively manufacturable.In this paper, the procedure is applied on different retrievals from Museo Civico dell’Agrocimino of Soriano nel Cimino, Italy, with different scales and materials, proving the robustness of the method.

Effects of Natural Extract Mixtures on the Quality Characteristics of Sausages during Refrigerated Storage.

Owing to the residual toxicity and adverse health effects of chemical preservatives, there is an increasing demand for using natural preservatives in food. Although many natural extracts have been evaluated, research on their antibacterial effects remains insufficient. Therefore, this study aimed to explore the possibility of developing Psidium guajava, Ecklonia cava, and Paeonia japonica (Makino) Miyabe & Takeda extracts as natural food preservatives. Further, the effect of mixing these extracts on microbial growth and quality was evaluated during the refrigeration of sausages. Optimal mixing ratios were determined based on the minimum inhibitory and bactericidal concentrations of each mixed extract against the Listeria monocytogenes, Salmonella spp. and Escherichia coli. D-optimal mixing design optimization tool was further used to obtain an optimum mixing ratio of Formulation 1 (F1). The antibacterial activity of F1 increased with increasing concentration, with similar activities at 0.5% and 1%. The sausages with synthetic or natural preservatives showed significantly lower lipid oxidation than those of the control and grapefruit extract-treated sausages after 4 wk of refrigeration. Total plate counts were observed only in the control and treatment groups stored for 3 wk, and no significant effect of ascorbic acid was observed. Compared to the other samples, sausages with added natural extracts showed the highest overall acceptability scores initially and after 4 wk. Therefore, similar amounts of grapefruit seed and natural extracts had the same effect on microbiological analysis and lipid rancidity during sausage storage. Hence, this mixture can serve as a potential natural preservative in meat products.

Multidisciplinary design and optimization of intelligent Distributed Satellite Systems for EARTH observation

Recent advances in small, connected and intelligent satellite systems have created a wide range of opportunities for the adoption of intelligent Distributed Satellite Systems (iDSS) in communication, navigation and Earth Observation (EO) missions. iDSS are goal-oriented systems comprising of multiple satellites or modules that interact, communicate and/or cooperate with each other to accomplish the desired mission goals. The ability to mass-produce low-cost small satellites and contemporary developments in avionics/astrionics technology have spurred interest in iDSS, especially for Low Earth Orbit (LEO) satellite constellations and regional clusters. The SmartSat Cooperative Research Centre (CRC) and Australian space roadmap, as well as the landmark National Space Programme strategy and priorities, encompass EO. To date, insufficient progress and no conclusive outcome was made in terms of how contemporary Multidisciplinary Design Optimization (MDO) models and tools can be best tailored to the new capabilities and specificities of iDSS. The MDO of iDSS is challenging because it introduces new variables and highly non-linear interactions. In this context, we propose an MDO methodology to optimize an iDSS for persistent coverage over the entire Australian landmass. Several aspects of the iDSS are considered in this work, including the constellation model, subsystem models and the coupling interactions between different satellite subsystems and constellation design parameters. The constellation configuration, as well as the subsystems, are modelled using OpenMDAO, which is used to analyze and visualize the planned iDSS EO mission. The iDSS is then optimized using the Multidisciplinary Feasible (MDF) architecture approach and the iDSS interdependencies are numerically treated using the Nonlinear Block Gauss-Seidel (NLBGS) iterative solver. The resulting N2 diagrams are presented and the proposed solution is both spatially and temporally optimized, demonstrating that the proposed iDSS will enable near real-time persistent coverage over the entire Australian continent.

A practical tool for structural design optimization of steel frames: development and application for parametric analysis

A practical tool for structural design optimization of steel frames: development and application for parametric analysis

Multibody Dynamics Modeling of a Continuous Rubber Track System: Part 1—Model Description

<div>Continuous rubber track systems for farming applications are typically designed using multiple iterations on full-scale physical prototypes which is costly and time consuming. The development of numerical design tools could speed up the design process and reduce development costs while improving product performance. In this article, a rigid multibody dynamics (MBD) model of a continuous rubber track system is presented. This article is the first part of a two-part study: Part 1 focuses on the model description and part 2 describes the experimental evaluation of the MBD model. The modeling methodology is based on a track discretization as a set of rigid body elements interconnected by 6 degrees-of-freedom bushing joints. The mathematical formalism and experimental characterization of all critical subsystems such as the roller wheels, tensioner, suspensions, and contact models are also presented. Several simulation results are presented to illustrate the capability of MBD models in design activities to rapidly evaluate new concepts as a result of the relative short-solving time of the model (approximately 3 h). The MBD model is shown to be a powerful design tool to provide input boundary conditions for structural analysis or as a design space explorer and optimization tool.</div>

Heat exchanger circuitry optimization using an enhanced integer permutation-based genetic algorithm in low-GWP reversible heat pump applications

Tube-fin heat exchangers (HXs) are key components in air-conditioning and heat pump applications. Advanced heat exchanger design optimization tools are desired to adapt to the industrial transition from conventional refrigerants to low Global Warming Potential (low-GWP) refrigerants. The performance of heat exchangers is strongly influenced by the refrigerant circuitry. This research presents an enhanced integer permutation-based Genetic Algorithm (IPGA) to optimize the circuitry of tube-fin heat exchangers. To remedy the limitation of the previous IPGA, which cannot generate designs with splitting and merging of circuits, a new chromosome, as well as new generic operators, are developed. The enhanced IPGA uses effective chromosome representations and GA operators to guarantee the chromosome (genotype) can be mapped to valid heat exchanger designs (phenotype) and incorporates real-world manufacturability constraints to ensure the optimal designs are manufacturable with the available tooling. Previous circuitry optimizations focus on improving component-level performance under a specific operating condition, i.e., the heat exchanger either works as a condenser or an evaporator. The optimal circuitry obtained under air conditioning (AC) mode cannot guarantee optimal performance when used in heat pump (HP) mode. To improve the dual-mode performance of reversible heat pumps, a bi-objective optimization problem formulation is implemented to achieve optimal system performance in both AC and HP modes. A case study using six refrigerants, i.e., R410A, R452B, R454B, R32, D2Y60, and L41a, shows that optimal heat exchangers yield up to 6.1 % energy efficiency ratio (EER) improvement under AC mode and up to 8.5 % coefficient of performance (COP) improvement under HP mode. The use of this design approach can ensure a preferable performance of reversible heat pumps during both cooling and heating modes.

Design for and from disassembly with timber elements: strategies based on two case studies from Switzerland

When a timber building gets disassembled and its elements either are burned or biodegrade, the carbon stored in the timber structure gets released to the atmosphere as CO2. Reusing timber elements prevents this process from happening and thus delays the global warming caused by greenhouse gas emissions. Even if there is a long historic tradition of timber reuse in Switzerland, currently a low fraction of a timber building’s elements is being reused after its disassembly. In this study, strategies that could facilitate circular use of timber elements are analyzed. The focus lies on the design process, which is investigated from two perspectives: strategies at the start-of-life of buildings to enable new timber element cycles to emerge (design for disassembly, or DforD), and strategies at the end-of-life of buildings to keep existing timber elements cycles closed (design from disassembly, or DfromD). Two case studies of recently completed multi-story timber-hybrid buildings in Switzerland were analyzed from both perspectives. Regarding DforD, a scoring system was developed that assesses single elements according to their disassembly and reuse potential. Regarding DfromD, a building design optimization tool was created that takes dimensional design tolerances of a building as an input and proposes a procurement-optimized and structurally safe arrangement of reused elements, which are taken from an inventory that is based on the two case studies. It was found that connections between reinforced concrete and timber parts play a crucial role in terms of DforD and that building layouts with DfromD elements may vary widely according to the chosen optimization variable. In conclusion, both applications have the potential to scale up the competitiveness of reused elements.

A Computational Fluid Dynamics-Based Small-Scale Combustor Design Optimization Utilizing a Many-Objective Evolutionary Algorithm

Abstract This work demonstrates the capability of an open-source autonomous computational fluid dynamics (CFD) metamodeling environment (OpenACME) for optimizing small-scale combustor designs. OpenACME couples several object-oriented programing open-source codes for CFD-assisted design using a decomposition-based many-objective evolutionary algorithm. The CFD is based on steady, incompressible, three-dimensional simulations with k–ω SST RANS and flamelet/progress variable combustion model. There are five unconstrained design variables based on combustor liner dilution hole diameters. The CFD results are compared with existing experimental data in terms of combustion efficiency as a function of severity parameter. The comparison demonstrates that the CFD methods capture combustion efficiency trends. Next, more than 500 combustor designs are evaluated with OpenACME. A Pareto Frontier is generated in terms of combustion efficiency, pattern factor, and total pressure losses. Pseudo-weights are used to select a nondominated Pareto Frontier design point for future fabrication and experimental testing. OpenACME is demonstrated to be a viable tool for small-scale combustor design optimization.

Design Evaluation and Optimization of Population Pharmacokinetics Model Using an R Package PopED

The rising cost in drug development has led to continuous calls for more efficient and powerful design optimization and analysis tools for pkpd studies. More sophisticated models are increasingly used to reflect reality, and current models invariably include non-linear mixed effects models that frequently require specialized computational tools for the design and analysis of the study. Population PK analysis employs non-linear mixed effects models and evaluates designs and optimizes them via a specialized yet versatile software package called PopED (version 0.6.0) in programming language R (version 4.2.2) for pharmacometrics analyses. We demonstrate the utilities of the package when different models and statistical criteria are used in real settings to determine the optimal sampling times and optimal dose levels for the subjects. We provide two applications; the first is illustrative and the second is a new application on developing an optimal dosing scheme for a two-compartment PK model with perturbation. Our target audiences are mathematicians and statisticians who are not aware of this useful and powerful analytic tool.

Control co-design of a floating offshore wind turbine

Several control co-design (CCD) optimizations of floating offshore wind turbines are presented in this work using the newly introduced Wind Energy with Integrated Servo-Control (WEIS) framework. Three methods for parameterizing the primary tuning inputs to the Reference Open-Source Controller are presented and optimized, including a sensitivity-margin constrained controller. WEIS, a detailed, open-source floating offshore wind turbine design optimization tool is then used to conduct CCD optimizations on the International Energy Agency (IEA) 15MW wind turbine on the University of Maine VolturnUS-S semisubmersible platform. The results from optimizations are shown to reduce the levelized cost of energy (LCOE) by approximately 1% and 4% when optimizing the tower and platform, respectively. It is also found that the coupling between the tower and control system parameters is weaker than the coupling between the floating system and control system parameters, showing that CCD may not be advantageous for certain problems. Finally, a subset of operational design load cases is run to verify the optimized controller and turbine models.

A novel deep-learning-based pressure distribution prediction approach of airfoils

Pressure distribution is a crucial flow characteristic and a key consideration in supercritical airfoil design. Traditionally, obtaining the pressure distribution involves time-consuming and computationally expensive wind tunnel experiments and computational fluid dynamics calculations. This study proposes a deep-learning-based approach to directly map input geometric information to the pressure distribution output, thereby avoiding costly wind tunnel experiments and iterative computational fluid dynamics simulations based on Navier–Stokes equations to address these challenges. Conventional surrogate models typically focus on predicting simple force factors, such as lift and drag coefficients, or require the conversion of airfoil data into images for model training. The novel approach utilizes a Variational Autoencoder for pressure distribution characteristic extraction and reconstruction from feature variables. Unlike conventional models, this approach avoids image conversion and employs a radial basis function neural network for effective mapping. The model exhibits good fitting and generalization capabilities on both training and test datasets, offering a promising solution for rapid pressure distribution prediction in airfoil design. This novel deep-learning-based approach advances airfoil design methodologies, offering significant advantages in computational efficiency and performance prediction. By directly mapping geometric information to pressure distribution, it provides an innovative and promising tool for airfoil design optimization.

A Two-Dimensional Transient Computational Multi-Physics Model for Analyzing Magnetic and Non-Magnetic Particle (Red Blood Cells and E. Coli Bacteria) Dynamics in a Traveling Wave Ferro-Magnetic Microfluidic Device for Potential Cell Separation and Sorting

Abstract This paper presents the theory and development, validation, and results of a transient computational multiphysics model for analyzing the magnetic field, particle dynamics, and capture efficiency of magnetic and nonmagnetic (e.g., Red Blood Cells and E. Coli bacteria) microparticles in a traveling wave ferromagnetic microfluidic device. This computational model demonstrates proof-of-concept of a method for greatly enhancing magnetic bioseparation in ferromicrofluidic systems using an array of copper conductive elements arranged in quadrature to create a periodic potential energy landscape. In contrast to previous works, our approach theoretically uses a microfluidic device with an electronic chip platform consisting of integrated copper electrodes that carry currents to generate programable magnetic field gradients locally. Alternating currents are applied to the electrodes in quadrature (using a 90 deg phase change from the neighboring electrode) to create a periodic magnetic field pattern that travels along the length of the microchannel. Our previous work evaluated magnetic and nonmagnetic particles in a static magnetic field within the same channel geometry. This work is a phase 2 study that expands on the previous work and analyzes the dynamics of magnetic and nonmagnetic entities characterized by material magnetic susceptibility in a transient magnetic field. This is an improvement over our previous work. The model, which is described in more detail in the methods section, combines a Eulerian-Lagrangian and two-way particle-fluid coupling CFD analysis with closed-form magnetic field analysis that is used to predict magnetic separation considering dominant magnetic and hydrodynamic forces similar to our previous works in magnetic drug targeting. The model was also validated with an experimental low frequency stationary flow study on separating nonmagnetic latex fluorescent particles in a water based ferrofluid. The results from the experimental study and the developed model demonstrate that the proposed device may potentially be used as an effective platform for microparticle and cellular manipulation and sorting. The developed multiphysics model could potentially be used as a design optimization tool for traveling wave ferromicrofluidic devices.