Design Variables Research Articles

Comparative analysis of space layouts and facade systems for office retrofit design in hot climate

ABSTRACT Worldwide, office buildings account for almost one-third of global energy consumption. Therefore, the energy retrofit design of office buildings could reduce total energy consumption. Considering the benefits of advanced facade systems, retrofit facade design is an effective strategy for energy conservation while improving daylight performance, occupant well-being, and productivity. This paper compares the daylight and energy performance of single and double-skin facade systems used in a typical office building in a hot climate with different space layouts. The main objective is to optimize the daylight and energy performance of various facade systems in combination with a wide range of design variables. Simulations were conducted in DesignBuilder software using a multi-objective optimization approach. Eleven design variables and 103 simulations were considered with the objective of enhancing daylight performance while reducing cooling and lighting loads. Based on the results, single-skin facade configurations could enhance daylight performance significantly compared to double-skin facades, while double-skin facades performed better by reducing the risk of glare and annual energy consumption. Using single and double-skin facade systems could reduce annual total energy consumption by 50% and 60% compared to the existing building.

Geometrically exact beam theory for gradient-based optimization

Decades of research have progressed geometrically exact beam theory to the point where it is now an invaluable resource for analyzing and modeling highly flexible slender structures. Large-scale optimization using geometrically exact beam theory remains nontrivial, however, due to the inability of gradient-free optimizers to handle large numbers of design variables in a computationally efficient manner and the difficulties associated with obtaining smooth, accurate, and efficiently calculated design sensitivities for gradient-based optimization. To overcome these challenges, this paper presents a finite-element implementation of geometrically exact beam theory which has been developed specifically for gradient-based optimization. A key feature of this implementation of geometrically exact beam theory is its compatibility with forward and reverse-mode automatic differentiation. Another key feature is its support for both continuous and discrete adjoint sensitivity analysis. Other features are also presented which build upon previous implementations of geometrically exact beam theory, including a singularity-free rotation parameterization based on Wiener-Milenković parameters, an implementation of stiffness-proportional structural damping using a discretized form of the compatibility equations, and a reformulation of the equations of motion for geometrically exact beam theory as a semi-explicit system. Several examples are presented which verify the utility and validity of each of these features.

Robust topology optimization of multi-material structures based on the material-field series-expansion method

The performance of composite structures can be dramatically improved through the synergy between different constituent materials. However, the topology optimization of multi-material structures poses a heavy computational burden, which becomes more significant when load uncertainty is considered. This article proposes an efficient method to address the problem of robust topology optimization of multi-material structures. Employing the property that the material-field series-expansion method can significantly reduce the number of design variables, the proposed method improves the computational efficiency of the robust optimization problem. Meanwhile, the modification of the original multi-material interpolation model improves the quality of the optimized designs. Monte Carlo simulation is used to treat the load uncertainty, but it is decoupled from the optimization process based on linear elasticity theory to reduce computation cost. The effectiveness of the proposed method is demonstrated by numerical examples, which show that robust design results can be efficiently generated by this method.

Multi-objective optimization of expansion-contraction micromixer using response surface methodology: A comprehensive study

This study provides a comprehensive analysis of the optimization of micromixers, highlighting their critical role in improving the effectiveness of chemical reactions and fluid mixing processes at a small scale. Employing OpenFOAM, we optimized the geometry of a micromixer comprised of multiple expansion-contraction segments across a Reynolds number range from 0.1 to 100. We established three design variables, P1, P2, and P3, to modify the geometry of each contraction-expansion segment to maximize the overall performance of the micromixer. Our experimental design utilized the optimal space-filling method, followed by the application of the genetic aggregation method to the generated sample to yield response surfaces. Two distinct optimization strategies were implemented following the verification of the response surfaces’ accuracy. The first focused on maximizing the mixing index, while the second aimed to increase the mixing index and reduce the pressure difference simultaneously. A subsequent sensitivity analysis revealed significant relationships between the design variables and objective parameters. Key findings include achieving maximum MI increases of 26.26 % and 23.06 % at Re values of 10 and 100, respectively, and the role of recirculation zones and chaotic convection in enhancing mixing, especially at Re = 100. Furthermore, a comparative analysis with other micromixer optimization studies demonstrated the superiority of the present study, particularly in reducing pressure difference, thereby enhancing overall mixer performance.

Methacrylic Acid Co-Polymers: Crucial agents for the Colon Targeted Oral Drug Delivery System

The objective of this research was to formulate an Ornidazole tablet capable of delivering the drug intact to the colon for the treatment of IBD, ulcerative colitis, and Crohn's disease. In the current investigation, pH-independent and pH-dependent polymers were used to target the Ornidazole tablet to the colon. This study evaluated two time-dependent (sustained release) polymers (Eudragit RS and Eudragit RL) and one pH-dependent polymer (Eudragit S100). The influence of the two formulation variables of the colon drug delivery system on the two response variables were examined simultaneously using a statistical 32-Full factorial design. X1 Ratio of Eudragit RS 100 to Eudragit RL 100 and X2 % weight gain with Eudragit S 100 were treated as independent variables in a factorial design, while Y5 and Y12 were treated as dependent variables. Through this procedure, a total of nine F9 samples were produced. Y5 in-vitro release should not exceed 10%, whereas Y12 should surpass 85%. Based on these results, batch B8 is considered the finest of all cohorts. According to the results, the enhanced formulation provides optimal intact drug delivery to the targeted region.

The Optimization of the Geometry of the Centrifugal Fan at Different Design Points

The optimization of the geometry of a centrifugal fan is performed at maximum power and high-efficiency design points (DPs) to improve impeller efficiency. Two design variables defining the shape of fan blade are selected for the optimization. The optimal values of the geometry parameters of the impeller blades are identified by employing virtual flow simulations. The results of virtual experiments indicate the influence of the parameters of the blade geometry on its efficiency. With the optimization of impeller blade geometry, the efficiency of the fan is improved with respect to the reference model, as confirmed by comparing the performance curves. Herein, we discuss the results obtained in virtual tests by identifying the influence of DPs on the performance characteristics of centrifugal fans.

Optimization of operating conditions in the stage of steam turbine by black-box method

From the point of view of performance and economical in power plants, it is necessary to pay attention to the measurement of wet steam. The objective of this research is to employ the multi-objective black-box technique to minimize thermodynamic losses related to wet losses and improve the efficiency of the steam turbine, disregarding the process of film water deposition on the blade surface. For this purpose, a complete model of the steam turbine stage consisting of a stator-rotor is used to study wet steam. The simultaneous effects of two design variables on the performance of the steam turbine stage have been analyzed. The numerical model of wet steam flow has been used for calculating the values of the optimization criteria, including integral of wetness fraction (IWF), integral of mean droplet radius (IDR), turbine stage efficiency (TSE), condensation loss rate (Lr), and mass flow rate (Mr). According to the results, the temperature and pressure ratio were suggested to be 339.44 (K) and 0.51 (−), respectively, which improved the IWF by 30% and the IDR by about 7.3%. Also, the optimal mode increased TSE by about 1.3% and led to the reduction of Lr and Mr, by about 29%, and 1.3%, respectively.

Stress-driven generative design and numerical assessment of customized additive manufactured lattice structures

The rise of additive manufacturing (AM) has positioned lattice infilling as a pivotal strategy for creating lightweight, customized engineering components. This study presents a generative method that enables the conformal design and stiffness prediction of complex gradient strut-node lattice structures. A stress-driven Multi-Agent System (MAS) is introduced for the parametric optimization of lattice material distribution, incorporating geometric limitations, stress factors, and AM constraints. A beam element model simplifies the numerical analysis of the structure linear stiffness. By applying the Response Surface Method (RSM), a numerical model is established, not only conducting a quantitative analysis on the sensitivity of MAS design variables but predicting mechanical performance. This method is validated by designing a supporting component, demonstrating that the optimized lattice design can achieve a linear stiffness 1.4 times greater than that of conventional uniform lattice infills for the same mass. This research provides a comprehensive framework for the efficient design and analysis of irregular lattice structures at a macroscopic scale.

Performance and cost potential for direct-fired supercritical CO2 natural gas power plants

Direct-fired supercritical CO2 (sCO2) power cycles are being explored as an attractive alternative to natural gas combined cycle (NGCC) plants with carbon capture and storage (CCS). Therefore, understanding their performance and cost potential is important for the commercialization of the technology. This study presents detailed techno-economic optimization results of natural gas-fired, utility-scale power plants based on the direct sCO2 power cycle, which is lacking in public literature. The study considered multiple plant configurations with varying levels of thermal integration with the plant air separation unit (ASU) to shows the systematic impact of thermal integration on plant performance and economics. Several design variables for each power cycle configuration were identified and optimized to minimize the levelized cost of electricity (LCOE). The optimized direct sCO2 power plants achieved ∼48 % plant efficiency (HHV basis) which is similar or slightly higher plant efficiencies than state-of-the-art NGCC plants based on the F-class gas turbine with carbon capture and storage (CCS). The LCOE of the optimized direct sCO2 plants is 13–17 % higher than the reference NGCC plants with CCS due to high capital costs associated with the ASU and sCO2 power block, though there is significant room for improvement due to high uncertainty in component capital costs for these new plants. Based on the results, direct sCO2 power cycles have the potential to achieve >50 % efficiency (HHV basis) with >98 % CO2 capture rate which can lead to significant efficiency improvement and CO2 emissions reductions compared to NGCC plants with CCS.

Comprehensive examination of topologically optimized thermo-fluid heat sinks

This study investigates the thermal performance of topologically optimized heat sinks. The primary aims are to conduct a comprehensive parametric study, compare thermal outputs with the benchmark model, and validate numerical simulations through experimental testing. Multi-objective topology optimization is formulated based on thermal compliance and power dissipation. Selected optimized models were built and tested. The parametric study revealed promising design variables, leading to numerical convergence with interconnected flow paths. The promising variables were found under weighted thermal compliances (wh) between 0.3 and 0.9 and targeted liquid fractions (θFV) between 0.5 and 0.8. Additionally, at low operating pressures, for example, at 0.01 kPa, the temperature difference between optimized and benchmark models could be as much as 30 °C. Nonetheless, the temperature difference between both models became smaller at higher operating pressures. Furthermore, the comparison of temperature measurements, pressure drops, and thermal imaging showed reasonable agreement between experimental and numerical results. Additionally, the effect of design variables on thermal performance was confirmed through experiments. The heat sink with higher wh exhibited a lower temperature than that of the model with lower wh. In summary, this research highlights the crucial role of design variables in achieving a balance between temperature and pressure drop.

Net-zero energy management through multi-criteria optimizations of a hybrid solar-hydrogen energy system for a laboratory in Toronto, Canada

Hydrogen production and storage in hybrid systems is a promising solution for sustainable energy transition, decoupling energy generation from demand and boosting the deployment of renewable energy. Nonetheless, the optimal and cost-effective design of hybrid hydrogen-based systems is crucial to tackle existing limitations in the diffusion of these systems. The present study explores net-zero energy management via a multi-objective optimization algorithm for an outdoor test facility equipped with a hydrogen-based hybrid energy production system. Aimed at enabling efficient integration of hydrogen fuel cell system, the proposed solution attempts to maximize the renewable factor (RF) and carbon mitigation in the hybrid system as well as to minimize the grid dependency and the life cycle cost (LCC) of the system. In this context, the techno-enviro-economic optimization of the hybrid system is conducted by employing a statistical approach to identify optimal design variables and conflictive objective functions. To examine interactions in components of the hybrid system, a series of dynamic simulations are carried out by developing a TRNSYS code coupled with the OpenStudio/EnergyPlus plugin. The obtained results indicate a striking disparity in the monthly RF values as well as the hydrogen production rate, and, therefore, in the level of grid dependency. It is shown that the difference in LCC between optimization scenarios suggested by the design of experiments could reach $15,780, corresponding to 57 % of the mean initial cost. The LCOE value yielded for optimum scenarios varies between 0.389 and 0.537 $/kWh. The scenario with net-zero target demonstrates the lowest LCOE value and the highest carbon mitigation, i.e., 828 kg CO2/yr, with respect to the grid supply case. However, the LCC in this scenario exceeds $57,370, which is the highest among all optimum scenarios. Furthermore, it was revealed that the lowest RF in optimal scenarios is equal to 66.2 % and belongs to the most economical solution.

A rational approach for the scantling design of GRP pleasure crafts

In various fields of industrial engineering, including naval architecture, structural design is considered one of the most challenging steps by designers as it shall accommodate all other design aspects, likely already defined in earlier steps. Moreover, structures need to comply with specific requirements, i.e. limit states, while also performing in accordance with user's demands in terms of mission profile. In a goal-based design perspective, limit state criteria have to be defined accounting for both, structural robustness, functional and operational demands. Construction feasibility is an issue as well, further constraining the scantling design. Balancing construction needs, ship performances and structural aspects is not always an easy task.The environment in which a ship operates is indeed complex and verification of safety and robustness is usually carried out in accordance with classification societies and other rules, possibly applying direct calculations whenever applicable rules do not provide suitable solutions. At the same time, the asset needs to fulfill various attributes specified by the owner. As a matter of facts, the scantling design is mathematically a multivariate and over-constrained problem whose feasible solutions are in fact rather few. In the case of composite pleasure craft hull structures, while the number of design variables increases, feasibility constraints also largely reduce the design space.To address these challenges, designers may nowadays exploit new procedures able to reduce structural weight, though ensuring that the asset performs safely and adequately meeting client's requirements. To achieve this, designers may combine a more rational design philosophy with currently available numerical problem-solving applications to obtain a rational trade-off among all actually feasible solutions, which are relatively few.Thus, the objective of this study is to present a dependable numerical method for determining the scantling while reducing the weight of hull composite structures of pleasure crafts. This method aims to provide the most suitable, high-performing, and safe layout in the quickest and easiest possible manner. It turns out that counterintuitive, yet feasible and fit-for-purpose, structural lay-outs can be obtained.

Multi-objective optimisation of a metro wheel profile to reduce wheel flange wear and RCF considering the distribution density of wheel−rail contact points

In this study, the problem of severe wheel flange wear and tread rolling contact fatigue (RCF) of metro wheels with two wheel profiles, i.e. the LM-30 profile and the DIN5573-30 profile, is investigated. The contact characteristics of the two profiles are compared to analyse the causes of wheel flange wear and RCF. Indices combining the density distribution of contact points are proposed to evaluate the wheel flange wear and RCF. The optimisation region of the wheel profile is defined by eight arc parameters, which is derived from the tangency of the arcs. A back-propagation neural network is constructed as a surrogate model for the design variables and the objective function. Four intelligent optimisation algorithms are compared and used to solve the optimisation problem. The vehicle—track dynamic model and the wheel wear and RCF prediction model are used to verify the performance of the optimised profile. The results show that the critical speed of the optimised profile is between that of the LM profile and that of the DIN5573 profile, and the curving performance of the optimised profile is significantly improved. At the same time, the optimised wheel profile can reduce wheel flange wear and tread RCF.

Advancing energy efficiency in livestock building: Simplified building energy simulation tool for geometric design of pigsty

Concerns related to food security and animal welfare have led to increasing interest in analyzing the energy demands of heating, ventilation, and air conditioning (HVAC) systems in livestock buildings. However, building energy simulation (BES) model, which facilitates precise HVAC systems energy analysis, is difficult to use for non-experts, and particularly, its usability drops significantly in livestock buildings, which are often constructed without involvement of building engineers. This study, therefore, developed a tool that can analyze the energy demand of pigsties by changing geometric design variables with only several numerical inputs without separate 3D modeling. Pigsty geometric design types were classified by examining pigsty architectural design manual and web portal map search. A simplified tool, utilizing EnergyPlus and MATLAB graphical user interface, was developed to analyze energy demand in pigsties by leveraging simple user inputs for geometric design modeling. This tool simplifies exploring potential energy savings through geometric design changes in the early design stages of livestock buildings. The methodology introduced in this study lays the groundwork for future enhancements, including the integration of various design-phase variables beyond geometric design.

Multi-objective shape-section optimization of free-form latticed shells using the RBF-NSGA-II algorithm

In this study, a novel RBF-NSGA-II optimization framework based on the response surface methodology (RSM) and the constrained NSGA-II algorithm is proposed to simultaneously improve the static, nonlinear stability and economic performance of free-form latticed shells. The bending stiffness of joints is innovatively considered one of the design variables for shape-member section coupled optimization. The augmented radius basis functions (RBFs) are adopted to develop the metamodels of both the objectives and constraints. The improved minimum distance selection method (TMDSM) is used to select the optimal solution from the Pareto optimal solution set. Moreover, three alternative solutions are presented as supplements to meet various engineering requirements. The proposed RBF-NSGA-II algorithm is validated to have high accuracy for multi-objective optimization problems with high nonlinearity. Compared with those of the initial structures, the strain energy and steel consumption of the optimal structure decreased by 82.23 % and 17.97 %, respectively, while the buckling load considering both the geometric nonlinearity and material nonlinearity increased by 1.2 times. The involvement of the joint stiffness is demonstrated to have a great influence on the multi-objective optimization results. It is necessary to consider the joint stiffness as one of the design variables during shape-member section coupled optimization.

A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings

Traditional methods of evaluating the performance of journal bearings, for example thermal-elastic-hydrodynamic- lubrication theory, are limited to simplified conditions that often fail to accurately model real-world components. Numerical models that include additional phenomena such as cavitation and fully coupled effects like deformation, temperature, pressure and viscosity can be more accurate but require a large amount of computational overhead, making analysis slower and more costly. To address this limitation, a novel machine learning-driven approach is developed to predict the 2D distribution of surface deformation, film thickness, temperature, and pressure across the bearing surface as a function of design variables such as load and speed. The training dataset, generated using a fully coupled Reynolds’ Equation solver implemented in OpenFOAM, contains a significantly extended range of conditions than in previous studies with approximately 39000000 points encompassing 4925 different test cases. Modelled bearing speeds range from 2000 to 10000rpm, while load values are varied between 1 and 30kN. Predicting surface deformation, film thickness, temperature and pressure across the bearing surface results in a mean absolute percentage error below 0.4% or better. The work also demonstrates that the trained models have a strong ability to generalise the prediction beyond the original training data range with only a 1% error at up to 200% of the highest trained speed. This work also demonstrates that machine learning-based processes are a practical alternative to physics-based numerical modelling, especially in cases where rapid performance evaluation is desired as real-time calculation is possible with significantly reduced computational cost. This has the potential to enable development of rapid design optimisation tools and real-time performance monitoring at high resolution and with low latency. Using consumer hardware, it is found that the neural network-based approach is faster than the existing numerical modelling technique by a factor of over 10000, enabling real-time predictions of lubrication systems.

Experimental optimization of butterfly-shaped film cooling hole configuration with a 30 degree injection angle

Film cooling is one of the external cooling techniques that is widely applied to the hot-gas path components of a gas turbine, forming a thin film of a coolant on the surface to prevent the direct convective heat transfer between combustion gas and components. The film cooling effectiveness (FCE) is significantly influenced by the geometry of the injection hole. Therefore, it is important to adjust the shape parameters in order to derive the optimal hole configuration under given flow conditions. In a previous study, a butterfly-shaped film cooling hole configuration (BSHC), which combines the advantages of fan-shaped holes and anti-vortex holes, was proposed. In this study, an investigation into the optimal shape design of the BSHC was performed using the design of experiments. The forward expansion angle, lateral expansion angle, and twisted angle were defined as design variables. Before the optimization, the effect of each design variable on local and averaged FCE was analysed. Generally, a high FCE could be obtained when the forward expansion angle was small within a range of 7⁰ - 15⁰. However, at high blowing ratios, high effectiveness was also observed even with a large forward expansion angle. Both the lateral expansion angle and the twisted angle had high FCE near the centre point, 11⁰ and 20⁰, respectively. The response surface of the overall averaged FCE was predicted using the Kriging model. The optimized BSHC under a density ratio of 2.0 and blowing ratio of 2.0 was determined and named OPT BSHC. The OPT BSHC showed 8.6 % and 10.5 % higher FCE respectively, compared to two types of the optimized fan-shaped holes that were optimized under the same conditions.

The effect of geometrical parameters on dimensional deviation in LPBF produced TPMS lattices: a numerical simulation based study

Triply periodic minimal surface (TPMS) lattices have drawn great attention both in academic and industrial perspective due to their outstanding mechanical behaviours. Additive manufacturing (AM) modalities enable the production of these lattices very easily. However, dimensional inaccuracy is still one of the problems that AM still faces with. Manufacturing of these lattices with AM modalities, then measuring the critical dimensions and making design changes accordingly is a costly process. Therefore, it is necessary to predict the dimensional deviation of TPMS lattices before print is a key topic. This study focused on prediction of dimensional deviation of laser powder bed fusion (LPBF) produced gyroid, diamond, primitive, IWP and Fisher-Koch lattices by using thermomechanical simulations. TPMS type, unit cell size, volume fraction, functional grading and part orientation were selected as design variables. Results showed that all the design inputs have effects on dimensional accuracy of LPBF produced parts and TPMS type has the most critical factor. Based on analysis of variance analysis, an optimum lattice configuration was proposed to obtain the lowest dimensional deviation after LPBF build.

Reliability-Based Design Optimization of the PEMFC Flow Field with Consideration of Statistical Uncertainty of Design Variables

Recently, with the fourth industrial revolution, the research cases that search for optimal design points based on neural networks or machine learning have rapidly increased. In addition, research on optimization is continuously reported in the field of fuel cell research using hydrogen as fuel. However, in the case of optimization research, it often requires a large amount of training data, which means that it is more suitable for numerical research such as CFD simulation rather than time-consuming research such as actual experiments. As is well known, the design range of fuel cell flow channels is extremely small, ranging from hundreds of microns to several millimeters, which means the small tolerance could cause fatal performance loss. In this study, the general optimization study was further improved in terms of reliability by considering stochastic tolerances that may occur in actual industry. The optimization problem was defined to maximize stack power, which is employed as objective function, under the constraints such as pressure drop and current density standard deviation; the performance of the optimal point through general optimization was about 3.252 kW/L. In the reliability-based optimization problem, the boundary condition for tolerance was set to 0.1 mm and tolerance was assumed to occur along a normal distribution. The optimal point to secure 99% reliability for the given constraints was 2.918 kW/L, showing significantly lower performance than the general optimal point.

Size Optimization of Truss Structures Based on Genetic Algorithm by Developing Single-point Crossover with Bit-wise Mutation Using FORTRAN Programming

A truss is a collection of axially loaded structural elements connected through pin joints. Optimization of truss structures indicates the determination of best possible conditions that are necessary for achieving the most economical design in terms of lowest possible weights of the truss elements. According to recent studies, Size, Topology and Shape based optimizations are the three possible independent optimization scopes for finding the optimum weight of a truss. The current study focuses on the size optimization of truss structure and aims to identify the most cost-effective sections of the truss using a powerful evolutionary optimization technique named as Genetic Algorithm (GA). Here single point cross over method and bit wise mutation operator are adopted for expanding the search space with the assigned displacement and stress constraints. Fortran based sub routines are developed for each individual steps of GA such as fitness evaluation, selection, cross-over and mutation. Finaly the complete program is run for validation of its outcome. While comparing with the solution of an existing literature regarding a minimization of objective function having two design variables the outcomes tend to show a good correlation. Further, the study is extended for the weight minimization of two widely used truss having 10 members and 17 members respectively with standard Finite Element Method of analysis. Minimum weight of each truss is obtained via convergence plot and compared with the existing literatures. With this simple but efficient global optimization technique optimum weight of truss structure can easily be achieved.