Design Optimization Process Research Articles

Optimization of Vehicle Powertrain Mounting System Based on Generalized Inverse Cascade Method under Uncertainty

This paper presents a summary of the optimization design process for a multi-objective, two-level engineering problem, utilizing the generalized inverse cascade method under uncertainty. The primary objective is to enhance the vibration isolation performance of a mounting system, considering the influence of uncertain factors on its stiffness. The focus is on determining the value range of the design variables at the bottom layer, ensuring that the design goal is met with a specified confidence level. To illustrate the application of this methodology, the optimization design of a powertrain mount is used as a case study. A data-driven approach is adopted, establishing a quantitative mapping relationship between mount stiffness, force transmission rate, modal decoupling rate, and other design indicators. This is achieved through the development of a CRBM-DBN approximate model, which combines Conditional Restricted Boltzmann Machines (CRBMs) and a Deep Belief Network (DBN). Additionally, an intelligent optimization algorithm and interval search technology are employed to determine the optimal design interval for the mount stiffness. Simulation and experimental verification are conducted using selected parameter combinations. The results demonstrate notable improvements in the vibration isolation performance, modal decoupling rate, and vehicle NVH performance when compared to the original state. These findings provide valuable insights for the interval optimization design of similar multi-objective, as well as two-level engineering problems, serving as useful references for future research and applications.

Comparison of Sensitivity-Guided and Black-Box Machine Tool Parameter Identification

Dynamic machine tool simulation models can be used for various applications such as process simulations, design optimization, and condition monitoring. However, all these applications require that the model replicates the real system’s behavior as accurately as possible. Next to carefully building the model, the parameterization of the model, that is, determining the parameter values the model is based upon, is the most crucial step. This paper describes the application of both sensitivity-based and black-box parameter identification to a machine tool. It further provides a comparison between these two methods and the method of sequential assembly. It is shown that both methods can increase the mode shape conformity by more than 25% and significantly reduce damping deviations. However, sensitivity-based parameter identification is the most economical method, offering the chance to update a dynamic machine tool model within minutes.

Heat integration and dynamic control of reactive pressure-swing distillation for separation of tetrahydrofuran/ethanol/water

In this paper, heat integration and dynamic control of reactive pressure-swing distillation (RPSD) for separating tetrahydrofuran/ethanol/water was investigated from the aspects of energy consumption, environmental protection and practical applications. By exploring the heat exchange between separating tetrahydrofuran and ethanol distillation columns, the full heat integration process (FHI) was designed. Robust control of heat and non-heat integrated processes was realized based on the dual temperature control structure. All processes were resistant to ±20% feed flow and composition disturbances. Finally, these processes were compared from three aspects: economy, environment and dynamic performance. The results show that compared with the non-heat integration process, the FHI process can reduce the total annual cost by 24.14%, the CO2 emission by 20.21% and the SO2 and NO emission by 34.38%, meanwhile, having the best dynamic performance. This study is valuable and instructive in promoting the integrated optimal and control design of the RPSD process.

Conceptual process design and process optimization of triple-column pressure-swing distillation for the separation of ternary mixtures embedding two azeotropes with different feed composition via thermodynamic insights and genetic algorithm

In this article, we take the tetrahydrofuran (THF)/ethanol (ETOH)/methanol (MeOH) as an example to investigate the process design of the triple-column pressure-swing distillation (TCPSD) for the separation of Serafimov’s class 2.0-2b mixtures with different feed composition. Two basic topology structures (TSI, TSII) and two separation sequences (SSI, SSII) are investigated, then, process optimization with total annual cost (TAC) as objective function is conducted via genetic algorithms. Heat integration (HI) is also applied to TCPSD. From a new perspective, the separation of THF/ETOH/MeOH can be regarded as the azeotropic distillation for THF/ETOH with MeOH as azeotropic agents based on thermodynamic features of Serafimov’s class 2.0-2b mixtures. The feasible topology structure and separation sequence of TCPSD for Serafimov’s class 2.0-2b mixtures depend on the feed composition and operating pressure. The feed composition is divided into three regions according to the concentration of methanol in azeotropic distillation column. The two critical points are azeotropic points at allowed minimum and maximum operating pressure. Especially, when feed composition is below azeotropic point at allowed minimum operating pressure, TSI is not feasible due to less MeOH as azeotropic agents. Furthermore, TCPSD combining pure azeotropic agents (ATPSD) can be developed based on this new insight. Then, two feed compositions are used to illustrate the detailed conceptual design of three topology structures as well as corresponding two separation sequences and their economic performance. The results show that when feed composition is 0.4ETOH/0.25THF/0.35MeOH, TSI and TSII are feasible, the best economical configurations without and with heat integration is TSII-SSII and TSII-SSII-HI. When feed composition is 0.4ETOH/0.4THF/0.2MeOH, TSII and ATPSD are feasible, the best economical configurations without and with heat integration is ATPSD-SSI and ATPSD-SSI-HI. Furthermore, ATPSD-SSI and ATPSD-SSI-HI have 18.28% and 19.38% TAC reduction than TSII-SSI and TSII-SSI-HI, respectively. In summary, feasible and the best economic topology structure and separation sequence of TCPSD for Serafimov’s class 2.0-2b mixtures depend on the feed composition and operating pressure. In addition, the proposed ATPSD shows good economic performance for Serafimov’s class 2.0-2b mixtures.

Development of a stepwise solvent-gradient SMB process for continuous-mode purification of methoxyethyl nucleoside phosphoramidites to be used as the key building blocks of oligonucleotide drugs

One of the commercially proven oligonucleotide-based drugs that can treat a variety of life-threatening diseases through gene-expression control is 2′-O-methoxyethyl (MOE) oligonucleotide. To keep the economical production of the MOE-oligonucleotide stable, it is important to establish an efficient process for recovering its corresponding monomer (MOE nucleoside phosphoramidites) in a large quantity at high purity from the output of the phosphitylation reaction based on using MOE nucleoside as a starting component. To address this issue, we pursued the development of an efficient simulated-moving-bed (SMB) process for continuous-mode recovery of monomer from the considered reaction output. First, the intrinsic parameters of monomer and impurities present in the reaction output were obtained through an inverse method using silica gel as an adsorbent. The resulting intrinsic parameters were then applied to the optimal design of the SMB process for monomer recovery, which revealed that its optimal configuration was a four-zone open-loop SMB configuration based on the installation of regeneration and re-equilibration zones for implementing a stepwise solvent-gradient method. Under such configuration, the optimal SMB operation parameters to maximize throughput while keeping high purity stable were determined using the relevant optimization tool based on column model and genetic algorithm. It was confirmed through the experimental run that the optimized SMB process could enable the continuous recovery of monomer from the considered reaction output with 99.6% purity while keeping the monomer loss close to zero. Finally, the developed SMB process in this article was found to increase throughput by more than 33 times, and reduce solvent usage by more than 89%, compared to the plant-scale batch chromatographic process currently in operation in related industries.

Recent advances in plastic waste pyrolysis for liquid fuel production: Critical factors and machine learning applications

It is urgent to promote the technology development of plastic waste (PW) valorization to mitigate the present serious plastic pollution. Pyrolysis has been widely used to convert PW into high-value-added products, especially liquid fuels, in a sustainable manner. However, achieving a high yield of liquid fuel with good quality remains challenging due to the difficulty in optimizing pyrolysis process and synthesizing outstanding catalysts to narrow the product distribution. This work comprehensively reviewed PW pyrolysis from both technical and computational (machine learning modeling) aspects, with a critical discussion of recent challenges to find new insights for improving the conversion efficiency and promoting commercialization. Results indicated that the impacts of various factors, including PW type, process condition, catalyst, and reactor type, on the PW pyrolysis were extensively investigated by the research community. Machine learning methods have also been frequently applied to predict, interpret, and optimize the PW pyrolysis. However, more efforts can be made in the future regarding catalyst synthesis, selection of co-pyrolysis additives, mechanism of catalyst deactivation, and design of renewable energy supply system for PW pyrolysis plants. Additionally, more attention should be paid to enlarging the data size, improving model interpretability, and exploring innovative ways of machine learning application (e.g., active learning) in process optimization and catalyst design for PW pyrolysis.

Design and Optimization of Planar Spiral Coils for Powering Implantable Neural Recording Microsystem.

This paper presents a design and optimization method utilizing inductive coupling coils for wireless power transfer in implantable neural recording microsystems, aiming at maximizing power transfer efficiency, which is essential for reducing externally transmitted power and ensuring biological tissue safety. The modeling of inductive coupling is simplified by combining semi-empirical formulations with theoretical models. By introducing the optimal resonant load transformation, the coil optimization is decoupled from an actual load impedance. The complete design optimization process of the coil parameters is given, which takes the maximum theoretical power transfer efficiency as the objective function. When the actual load changes, only the load transformation network needs to be updated instead of rerunning the entire optimization process. Planar spiral coils are designed to power neural recording implants given the challenges of limited implantable space, stringent low-profile restrictions, high-power transmission requirements and biocompatibility. The modeling calculation, electromagnetic simulation and measurement results are compared. The operating frequency of the designed inductive coupling is 13.56 MHz, the outer diameter of the implanted coil is 10 mm and the working distance between the external coil and the implanted coil is 10 mm. The measured power transfer efficiency is 70%, which is close to the maximum theoretical transfer efficiency of 71.9%, confirming the effectiveness of this method.

Optimization of brazing process and structure lightweight design for diamond microchannel

The application of diamond material significantly improves the heat dissipation capability of liquid cooling microchannels, solving the bottleneck problem of heat dissipation for highly integrated electronic components and thus promoting the development of a new generation of military electronic equipment. In this study, diamond microchannels with ultra-high heat flux density were reliably vacuum-brazed by using AgCuTi filler metal in a high vacuum furnace. Microstructure and properties of diamond brazing joints were studied and effect of brazing temperature and time was analyzed. The optimal brazing process parameters were obtained as follows: a vacuum level of <1×10−5 Pa, brazing temperature of 840°C, brazing pressure of 0.5–3 bars and holding time of 10 min. Following vacuum-brazing experiment, the brazing residual stress and working stress of the diamond microchannel were analyzed by finite element simulation. The wall thickness of the diamond microchannel was optimized. The minimum wall thickness, which was 0.4 mm, was obtained for the diamond microchannel with dimensions of 14 mm × 10 mm × 1.3 mm. On that basis, the influence of brazing pressure on the brazing filler overflow in the diamond microchannel brazing process was studied. It was showed that when the pressure was below 1 bar, the overflow of brazing filler from the brazing seam could be controlled to a minimum, especially suitable for brazing precision. The formation of residues in the diamond microchannel was thereby avoided basically, and a diamond microchannel heat sink with good performance was obtained.

A method of aeronautical electrical interface circuit design and simulation analysis based on Saber

In this paper, the idea of model-based system engineering is adopted. For the aircraft’s electrical interface circuit, Saber software is used to carry out “device-circuit” modeling and simulation, and the corresponding hardware circuits are verified and compared. Simulation and hardware verification results show that the modeling method designed in this paper can better integrate the rapidity of modeling and the accuracy of the simulation. In order to deal with the “neck jam” problem, domestic components are widely used today. This study provides a solution for optimizing process design and improving the degree of digitalization and provides model support for Model Based Systems Engineering (MBSE) in China’s aviation field.

Obstacle Avoidance Path Planning of 7-DOF Redundant Manipulator Based on Improved Ant Colony Optimization

Background: Obstacle avoidance path planning is an important parameter of robot manipulators. Path planning can directly affect the working efficiency of the manipulator. Objective: This study aims to summarize the optimization design method from a large number of literature and propose a new optimization design method to make the planned obstacle avoidance path of the manipulator shorter and smoother. Methods: The forward and inverse kinematics of the redundant manipulator is solved. Secondly, the obstacle and the robot manipulator envelope are simplified for collision detection. Then, the ant colony algorithm is improved by adding an obstacle environment to the construction of the heuristic function and dynamically adjusting the heuristic function factor to make the shortest path distance planned by the algorithm. Finally, the worst ant colony is added to the pheromone update to avoid the algorithm falling into the local optimal solution. Results: Through experiments and comparative studies, the optimized design process shows that the path planned by the improved ant colony algorithm has obvious advantages of shorter path distance and smoother path distance, which verifies the rationality of the improved algorithm. Conclusion: This method optimizes the convergence speed of the ant colony algorithm and avoids the ant colony algorithm from falling into the local optimal solution, which is of great significance for improving the obstacle avoidance path planning problem of a redundant manipulator with a degree of freedom.

Thermal design and zeotropic working fluids mixture selection optimization for a solar waste heat driven combined cooling and power system

Considering the limitation of fossil fuel resources and their environmental effects, the use of renewable energies is increasing. In the current research, a combined cooling and power production (CCPP) system is investigated, the energy source of which is solar energy. Solar energy absorbs by solar flat plate collectors (SFPC). The system produces power with the help of an organic Rankine cycle (ORC). An ejector refrigeration cycle (ERC) system is considered to provide cooling capacity. The motive flow is supplied from the expander extraction in the ERC system. Various working fluids have been applied so far for the ORC-ERC cogeneration system. This research investigates the effect of using two working fluids R-11 and R-2545fa, and the zeotropic mixtures obtained by mixing these two fluids. A multiobjective optimization process is considered to select the appropriate working fluid. In the optimization design process, the goal is to minimize the total cost rate (TCR) and maximize the exergy efficiency of the system. The design variables are the quantity of SFPC, heat recovery vapor generator (HRVG) pressure, ejector motive flow pressure, evaporator pressure, condenser pressure, and entertainment ratio. Finally, it is observed that using zeotropic mixtures obtained from these two refrigerants has a better result than using pure refrigerants. Finally, it is observed that the best performance is achieved when R-11 and R245fa are mixed with a ratio of 80 to 20%, respectively and led to 8.5% improvement in exergy efficiency, while the increase in TCR is only 1.5%.

Volumetricand ultrasonic properties of thiamine hydrochloride drug in aqueous solutions of choline-based deep eutectic solvents at different temperatures

Important efforts have been made over the past years to improve the drug acts, which leads to the discovery of novel drug preparations and delivery systems. The optimal design of such processes requires a molecular-level understanding of the interactions between drug molecules and biological membranes. The thermodynamic investigation provides deep and complete knowledge of interactions and the choice of appropriate and suitable production compounds in pharmaceutical fields. Particularly, the analysis of drugs + co-solvents in aqueous media is the central issue in many types of research because they exert their impact by interacting with biological membranes. This work is aimed to measure the density and speed of sound for the thiamine hydrochloride in water + deep eutectic solvents (DESs) mixtures (choline chloride/urea, choline chloride/ethylene glycol and choline chloride/glycerol) at temperature range (293.15–308.15) K. By correlation of the evaluated parameters in some standard relations, the partial molar parameters i.e. apparent molar volumes, Vφ,m, and apparent molar isentropic compression, κs,φ,m, are calculated. In addition, apparent molar isobaric expansion, Eφ,m0, and Hepler’s constant are computed from the density and speed of sound data. For fitting the experimental Vφ,m and κs,φ,m the Redlich-Meyer equation was employed that the important quantities; standard partial molar volume, Vm0, and partial molar isentropic compression, κs,m0, were obtained. The thermodynamic analysis of the studied system also plays a crucial role in the pharmaceutical industry.

Accelerating unsteady aerodynamic simulations using predictive reduced-order modeling

Unsteady computational fluid dynamics (CFD) simulations are essential in aerospace engineering because they can provide high-fidelity flow fields to better understand transient physics, such as vortex shedding. However, unsteady full-order CFD simulations must repeatedly march the flow solution with a small time step and are computationally expensive. Reduced-order modeling (ROM) is a powerful approach to alleviate the above issue by decomposing the unsteady flow solutions into spatial modes and temporal coefficients, making the unsteady flow easier to simulate. Existing ROM studies mostly focused on parametric problems that use a large number of simulation samples to train an offline model (parametric ROM). Although the trained model can quickly predict any flow fields within the parameter space, the computational cost for generating the massive unsteady simulation samples is still high, especially when the number of parameters and their ranges increase. To further address the high-cost issue, we develop an efficient predictive ROM approach to accelerate individual unsteady aerodynamic simulations. We use the Galerkin projection approach to reduce the Reynolds-averaged Navier–Stokes equations, along with the discrete empirical interpolation method (DEIM) for decreasing the computation cost for nonlinear terms. In addition, we develop an efficient ROM formulation that correlates the temporal coefficients between the momentum, pressure, and turbulence equations, allowing solving fewer ROM equations at the prediction stage. The intrusive nature of the predictive ROM enables using the first portion of unsteady flow data to accelerate the rest of the simulation; no massive offline samples are needed. We use the stalled turbulent flow over the NACA0012 airfoil as the benchmark and evaluate the predictive ROM's speed and accuracy for challenging non-equilibrium scenarios caused by a large sudden change in flow conditions. The run time ratios between CFD and ROM (including the calculation of basis vectors and projection matrices) range between 13 and 45. The pressure, drag, lift, pitching moment, and flow fields simulated by the ROM approach agree reasonably well with the CFD references at various times. The proposed predictive ROM approach is, in principle, applicable to different airfoil geometries and flow conditions and can be integrated into a design optimization process for accelerating unsteady aerodynamic simulations.

Process design and multi-objective optimization for separation of isopropyl acetate/isopropanol/water by extractive distillation using ionic liquid mixed solvents

Efficient recovery of isopropyl acetate and isopropanol from wastewater during esterification is important. An efficient separation process using ionic liquid mixed solvent extractive distillation (MSED) to separate azeotropic mixtures was proposed. The selectivity of 169 ionic liquids was calculated based on the COSMO–segment activity coefficient model for prescreening ionic liquids. The influence of the structure of the ionic liquids on the separation performance was further explored through molecular dynamics to determine 1-ethyl-3-methylimidazole acetate ([EMIM][AC]) as the best solvent. The vapor–liquid equilibrium of the system under the influence of [EMIM][AC] was measured to verify the model used for the calculation. A multi-objective genetic algorithm was used for optimizing the process parameters with the total annual cost (TAC) and gas emission as objective functions. An energy-saving heat-pump-assisted heat-integrated extractive distillation (HPHIED) process was proposed to realize heat recovery in MSED. The results showed that the TAC and gas emissions of MSED were 26.83% and 25.32%, respectively, lower than those of the single-organic solvent extractive distillation process. The TAC and gas emissions of HPHIED process were 11.02% and 35.83%, respectively, lower than those of MSED.

Переваги залучення штучного інтелекту для оптимізації дизайнерських процесів у сфері медіа

У статті розглянуто вплив упровадження інструментів на основі штучного інтелекту на професію медіадизайнера. Цей вплив настільки потужний, що фахівці з медіавиробництва та дизайну активно обговорюють революційні зміни професійної реорганізації праці, адже сучасні моделі штучного інтелекту є дуже ефективними в генеруванні текстів, зображень, програмного коду тощо. Розглянути практику залучення штучного інтелекту у сфері сучасного медіадизайну і є метою дослідження.&#x0D; Штучний інтелект – це гіперлогічна система рішень, які приводять до значною мірою передбачуваних цілей. А це є показником ефективності медіакомунікації, адже користувачі надають перевагу медіа з низькою візуальною складністю та високою прототипічністю.&#x0D; У дослідженні узагальнено переваги залучення штучного інтелекту для оптимізації дизайн-процесу; зокрема, це здатність до аналізу та класифікації, вирішення складних завдань, швидкість виконання завдань.&#x0D; Розглянуто популярні інструменти на основі штучного інтелекту, які використовують дизайнери в основних видах своєї прикладної діяльності, зокрема інструменти для роботи з кольорами, шрифтами, сервіси для перетворення ідей на цифрові проєкти та генерації контенту.&#x0D; Здійснено огляд практики успішного застосування технології штучного інтелекту світовими компаніями, що допомогло їм отримати вигоду. Дослідивши питання залучення штучного інтелекту в індустрії медіадизайну, можна виявити головні напрями медіабізнесу, що зазнають змін внаслідок процесу інтеграції. Це сфера відеовиробництва, створення контенту та UX-дизайн.&#x0D; У результаті дослідження зроблено висновок, що на сучасному етапі розвитку штучний інтелект не спроможний перевершити чи замінити дизайнера повністю. Наразі він може лише полегшити проєктування, якщо в ньому бере участь людина, та допомогти зробити проєктування більш керованим та рентабельним.

Exact and approximation algorithms for synthesizing specific classes of optimal block-structured processes

The design of optimal business processes is practically important in many areas including: manufacturing, process mining, planning & scheduling, and parallel computing. Business processes are usually required to satisfy correctness and robustness properties. Block-structured models have certain advantages by ensuring basic correctness and robustness by design. The aim of this work is to provide an in-depth investigation of the problem of automated synthesis of optimal block-structured processes. Our results concern new theoretical insights into this rather new problem, as well as new exact and approximation algorithms for various problem variants.

Topology Optimization of Electric Train Cable Carrier

In the area of mobility (transport), great efforts are being made to optimize means of transport. While reducing their mass the main goal is to maintain or even improve the performance of the means of transport. Additive manufacturing allows the production of very complex geometric shapes of products that cannot be made by traditional production processes; therefore it has become one of the significant tools in the design of modern vehicles or their parts. Additive manufacturing, combined with appropriate computer tools for numerical design analysis, also allows optimization of product design by topology optimization. In this paper, concept of topology optimization is applied to the roof carrier of a high voltage cable of electric train. Design optimization process was conducted based on two different optimization criteria: minimized part mass and maximized part stiffness.

Virtual test bench for computer-aided design of drum dryers based on kinetic laws of the drying process

The aim of the study is to create a virtual test bench, consisting of optimal design parameters that provide the maximum approximation of the real drying process to the experimental data.Method. Methods of modeling, formalized description, optimal design and management of technological processes and productions are applied. Result. A virtual test bench has been developed that allows real-time determination of the design parameters of drum-type dryers depending on the kinetic patterns of the drying process of various grain crops. As an object of study, the design of a two-section drum dryer was used, each section of which is equipped with a profile channel nozzle that provides a transverse supply of the drying agent through the pouring movable layer of the dispersed product to be dried. An algorithm for calculating the drying process is presented, which makes it possible to determine the fields of temperature and moisture content of grain in a wide range of regime parameters. With the use of CAD, a computer model of this installation was made with the possibility of autonomous rotation and movement of the drums relative to each other. A diagram of the synchronized operation of the moving elements of the installation under conditions of operational reconfiguration of its design in accordance with changes in the kinetic curves has been drawn up.Conclusion. The presented virtual test stand adequately and with high accuracy allows simulating the process of drying materials in a drum dryer; allows you to test the operation of a drum dryer at the development stage, conduct experiments with the highest possible accuracy, select the optimal drying modes for various materials with minimal energy consumption.

Optimum chain desalination process design for treatment of high TDS brine: A case assessment for future treatment of extracted brine from Shenhua CO2storage site

AbstractCarbon dioxide‐enhanced water recovery (CO2‐EWR) is a promising strategy for managing reservoir pressure build‐up and mitigating the risk of fault activation resulting from CO2injection in deep saline aquifers. CO2‐EWR can also be employed for supplying the required water for different applications after a treatment stage for the produced saline water. In this study, a brief review on CO2‐EWR technology and its necessities are first carried out. After that, the feasibilities, advantages, and challenges of various available treatment technologies that can potentially be used to treat high total dissolved solids (TDS) brine are comprehensively assessed. Based on comprehensive evaluation on technologies, a chain desalination process, consisting of pretreatment, main treatment, and post treatment, is proposed as a strategic path for the treatment of high TDS brine extracted from the Shenhua CCS site. It is concluded that coagulation‐flocculation and gravity filtration are needed as primary stages to remove suspended particles, while membrane distillation (MD) is selected as a suitable main treatment technology for high TDS Shenhua brine. Then, MD treatment is comprehensively discussed for a small‐scale treatment of extracted Shenhua brine assuming that the pretreated brine is free of suspended solids. After presenting the heat and mass transfer equations for direct contact membrane distillation (DCMD), a mathematical thermodynamic model is programmed in EES (Engineering Equation Solver) software to briefly analyze the performance parameters of DCMD. The results indicate that the designed DCMD, in the absence of auxiliary systems and considering the inherent temperature of extracted brine from different formations, has the capability of producing 15.1 kg m−2hr of freshwater from the extracted brine of the Shihezi formation layer. In the case of employing the auxiliary system of flat‐plate collector (FPC) combined with heat exchanger (HX) to heat up the extracted Shenhua brine to the desired temperature of 80°C, the amounts of produced flux are enhanced by 133%, 72%, and 45% for the brine extracted from Liujiagou, Shiqianfeng, and Shihezi formations, respectively. Using the yearly solar radiation model in TRNSYS software, the maximum solar radiation on the tilted surface at the location of Shenhua project in Inner Mongolia Autonomous Region of China reaches 3800 kJ m−2hr at 1 PM on April 1. Considering maximum solar radiation on the tilted surface, it is proved that a small‐surface FPC can supply the required energy to heat up the extracted brine from its inherent temperature to the desired temperature of 80°C. © 2023 Society of Chemical Industry and John Wiley &amp; Sons, Ltd.

A parametric design approach for multi-lobed hybrid airships

AbstractIn recent years, there has been an increasing interest in the research and development of hybrid airships for various applications. Airship design involves multiple design parameters from various disciplines that interact mutually. Existing design methodologies, however, are often limited to fixed shapes and geometry. This paper provides a comprehensive parametric design approach for the sizing of multi-lobed hybrid air vehicles for low- and high-altitude applications. The proposed design techniques are robust so that the designer has the freedom to change the number of lobes, the relative location of lobes, the envelope profile, and the optimiser for the design optimisation process. The outcomes of the proposed methodology are envelope volume, wetted surface area, length and span of the envelope, sizing and layout of the solar array, and sizing and layout of the fins. The modeling techniques highlighted in this paper are very efficient for the design and optimisation of multi-lobed airships in the conceptual design phase with a large design exploration space. The robustness of the shape generation algorithms is tested on some of the standard envelope profiles of airships. The effect of the shape and geometry of the multi-lobed envelope on added mass is demonstrated through the added mass estimation using Boundary Element Method.