Improvement Of Design Process Research Articles

Radiation Damage Mechanisms and Research Status of Radiation-Resistant Optical Fibers: A Review.

In recent years, optical fibers have found extensive use in special environments, including high-energy radiation scenarios like nuclear explosion diagnostics and reactor monitoring. However, radiation exposure, such as X-rays, gamma rays, and neutrons, can compromise fiber safety and reliability. Consequently, researchers worldwide are focusing on radiation-resistant fiber optic technology. This paper examines optical fiber radiation damage mechanisms, encompassing ionization damage, displacement damage, and defect centers. It also surveys the current research on radiation-resistant fiber optic design, including doping and manufacturing process improvements. Ultimately, it summarizes the effectiveness of various approaches and forecasts the future of radiation-resistant optical fibers.

Application of Simulation-Based Metrics to Improve the Daylight Performance of a Secondary School, An Approach for Green Building Designers and Architects

Visual comfort affects the quality of classrooms as well as student learning. A practice-oriented approach discovers how the gap between academic research and its application in building design can be addressed. Nevertheless, the physical characteristics design of daylighting systems, including window sizes, shapes, dimensions, and materials, are considered fundamental challenges for their practicability. In this study, the physical characteristics design of daylighting systems, including window sizes, shapes, dimensions, and materials, are considered in a designed sample school, and the daylight metrics were analyzed to achieve more trustworthy and applicable daylighting systems. Grasshopper (Honeybee-Ladybug), as a parametric control method, was applied to simulate the daylighting quality for various educational spaces in a secondary school in Sanandaj City, based on average ‘Daylight Factor’, ‘Daylight Autonomy’, ‘Useful Daylight Illuminance’, and ‘Annual Sunlight Exposure’. These metrics were examined to discover the relationship between window size and positions on visual comfort. The results indicate daylighting assessments are a solid approach to revising the architectural design mistakes at the primary designing phase. Architects and other building designers or energy consumption assessors can apply the design improvement process to present more sufficient and successful architectural details. This is a fundamental step toward the implementation of green buildings.

Performance benchmark of different cell clones in discontinuous and continuous bioprocesses reveals critical impact of cellular diameter

Therapeutic proteins such as monoclonal antibodies are usually manufactured either in continuous processes such as steady-state perfusion or in discontinuous processes like fed-batch (FB). Thereby, both process formats have fundamentally different requirements for the utilized cell factories. This poses a problem as common cell line development programs are designed to select cell clones to perform well in FB cultivation. The aim of this study was to identify critical and easy to access cell line attributes for each process format and with that for the transfer of clones from fed-batch to perfusion. As a result, increased cell-volume specific productivity could be identified as beneficial within the FB not only impacting performance but as well diminishing media utilization and host-cell-proteins level. Within the perfusion, a stable cell diameter was identified as major beneficial characteristic, positively influencing cell viability, metabolite and glycan profile, as well as equipment utilization. Consistent with this, our data suggest that the inclusion of cell volume in the screening parameters is important for clone selection for both process formats and can lead to improved process design and robust cell line process transfer. Overall, this work gives valuable new insights in cell behaviour across discontinuous and continuous process formats to improve clone cell selection for both process strategies and streamline the process transfer from discontinuous towards continuous.

Design improvement for suction shape in rotary compressor

Rotary compressors applied to heating and cooling cycles accounts for a large proportion of the total energy consumed in the system. Therefore, improving compressor efficiency is crucial in order to reduce energy consumption. Design review and improvement efforts have primarily focused on compression and discharge parts with high pressure. However, due to the limitations in design improvements for the compression and discharge parts, enhancements for other components are necessary to further increase compressor efficiency. In this study, the design of the suction part of the rotary compressor was modified using FSI (Fluid-Structure Interact) analysis scheme. In order to improve the design of the suction shape, new suction shape types were devised as alternatives to the existing through type. Both the newly devised C-type and E-type suction shapes presented an increase in cooling capacity due to reduced suction resistance, with the E-type demonstrating greater improvement compared to the C-type. Consequently, a verification of the effect based on the detailed design dimensions was further conducted for the E-type suction shape. The final selected improved model increased the suction flow rate by approximately 2.5% compared to the base model. This indicates that significant improvement for the compressor efficiency can be achieved through modifying designs in parts other than the compression and discharge sections. In addition, a design improvement process was established to analyze internal flow using FSI analysis.

Removal of nitric oxide in bioreactors: a review on the pathways, governing factors and mathematical modelling.

The constant surge in nitric oxide in the atmosphere results in severe environmental degradation, negatively impacting human health and ecosystems, and is presently a global concern. Widely used physicochemical technologies for nitric oxide (NO) removal comes with high installation and operational costs and the production of secondary pollutants. Thus, biological treatment has been emphasized over the last two decades, but the poor solubility of NO in water makes it a challenging issue. The present article reviews the various technical aspects of biological treatment of nitric oxide, including the removal pathways and reactor configurations involved in the process. The most widely used technologies in this regard are chemical adsorption processes followed by biological reactors like biofilters, biotrickling filters and membrane bioreactors that enhance NO solubility and offer the flexibility and scope of further improvement in process design. The effect of various experimental and operational parameters on NO removal, including pH, carbon source, gas flow rate, gas residence time and presence of inhibitory components in the flue gas, is also discussed along with the developed mathematical models for predicting NO removal in a biological treatment system. There is an extensive scope of investigation regarding the development of an economical system to remove NO, and an exhaustive model that would optimize the process considering maximum practical parameters encountered during such operation. A detailed discussion made in this article gives a proper insight into all these areas.

In-process monitoring and direct simulation of Argon shielding gas and vapour dynamics to control laser-matter interaction in laser powder bed fusion additive manufacturing

Laser powder bed fusion (L-PBF) additive manufacturing (AM) enables the fabrication of parts with precise dimensional control, freedom of design and material properties similar to or better than those fabricated using traditional manufacturing approaches. AM quality control depends upon the fundamental of the laser-matter interaction during metal AM using L-PBF to exploit the potential use of the materials and process control. In this work, thermal-fluid dynamics in gas chamber experimentally and computationally is used to elucidate the interplay between vapour, liquid, and solid phases in L-PBF. It is revealed that the argon (Ar) shielding gas flow with varied inlet velocities by different nozzles has a pronounced effect to minimise the laser-fume interaction, resulting in the reduction in unstable metal vapour flow and enhancing laser absorptivity. In-process monitoring via high-speed visualisation has been used to understand the simultaneous gas plume dynamics as a result of vapourisation and subsequent laser-fume interaction, backed up by thermal-fluid flow simulation. Unfavourable process dynamics associated with unwanted defects such as lack of fusion can be avoided to improve process design and enhance process stability.

Evaluating large-language-model chatbots to engage communities in large-scale design projects

Abstract Recent advances in machine learning have enabled computers to converse with humans meaningfully. In this study, we propose using this technology to facilitate design conversations in large-scale urban development projects by creating chatbot systems that can automate and streamline information exchange between stakeholders and designers. To this end, we developed and evaluated a proof-of-concept chatbot system that can perform design conversations on a specific construction project and convert those conversations into a list of requirements. Next, in an experiment with 56 participants, we compared the chatbot system to a regular online survey, focusing on user satisfaction and the quality and quantity of collected information. The results revealed that, with regard to user satisfaction, the participants preferred the chatbot experience to a regular survey. However, we found that chatbot conversations produced more data than the survey, with a similar rate of novel ideas but fewer themes. Our findings provide robust evidence that chatbots can be effectively used for design discussions in large-scale design projects and offer a user-friendly experience that can help to engage people in the design process. Based on this evidence, by providing a space for meaningful conversations between stakeholders and expanding the reach of design projects, the use of chatbot systems in interactive design systems can potentially improve design processes and their outcomes.

(Electrodeposition Division Research Award) Electrodeposition as an Enabling Technology in Future Semiconductors, Energy Storage, and Sustainable Metal Production

Electrodeposition is a truly fascinating process. On the one hand, it is highly complex as it entails a myriad of intricately intertwined physiochemical events (transport, kinetics, nucleation, growth, to name a few). Despite this, to a practitioner, electrodeposition can be remarkably simple, easy to control, and safe to operate. Electrodeposition is also extremely versatile, enabling fabrication of various materials (metals, alloys, semiconductors), fabrication onto conductive or resistive substrates, facilitating intricate pattern formation, and providing selectivity in growth. These advantages make electrodeposition a process of choice in a broad range of technologically-important applications: batteries, semiconductor devices, electrometallurgy, and others.1-3 Morphology control is crucial in the industrial practice of electrodeposition. During metallization of nano-scale interconnects in advanced semiconductor devices, metal deposition with near atomic precision is required.1 Similarly, during operation of lithium metal rechargeable batteries, electrodeposition must be controlled to avoid dendritic growth during battery charging.2 Electrodeposition processes in high temperature molten salts, such as those used for electrowinning rare-earth metals, favor spongy deposits which can manifest in entrapment of impurities – here too, controlling morphology is central to overall electrowinning efficacy.3 In this talk, I will discuss and demonstrate how unprecedented morphology control can be achieved in electrodeposition. Such control leverages fundamental understanding of the electrochemical processes at the electrode-electrolyte interface. Specifically, by regulating the electrolyte properties, applied current or potential waveforms, and by employing ‘additives’, the diffusion-reaction processes can be controlled yielding desirable electrodeposit morphologies. Such design of electrodeposition processes is considerably aided by mathematical modeling. I will provide a few examples of how quantitative modeling enables improved process designs in aforementioned applications.During my odyssey in electrodeposition since 2001, I have been fortunate to have exceptional colleagues, collaborators, sponsors, mentors, students and friends, who have made this journey enjoyable and enriching. I am also very thankful for my affiliation to the Electrochemical Society and its electrodeposition division. In my talk, I will provide a few perspectives and anecdotes on how the ECS community has positively impacted me personally and professionally.

Analyzing and Modeling the Municipal Sewage Sludge Drying Process Using Python

The programming language Python offers the opportunity to analyze and model the municipal sewage sludge (MSS) drying process in an illustrative chemical engineering practice. The drying process is performed on a flat plate while maintaining a uniform, parallel drying air speed. The Python program helps to analyze the digitalized weight measurements from each sample. The program enables the sorting of input data, determination of the drying critical point, and evaluation of the first and second drying periods. Moreover, the model calculates the fundamental drying parameter and forms a drying master curve to support the transfer to different drying conditions. The basic parameters calculated are mass transfer coefficient, heat transfer coefficient, and diffusion coefficient. The results are consistent with published data for those coefficients over the drying temperature range of 19.4–52.4 °C and relative humidity range of 8.2–33.6%. The findings of this study demonstrate the potential of Python as a powerful tool for analyzing experimental data and modeling chemical processes, which can lead to improved process design, optimization, and control.

Development and testing of lean simulation game on set-based design

Purpose Set-based design (SBD) is a lean tool widely adopted for improving design processes and providing value maximization to clients. The purpose of this paper is to present the development and testing of a lean simulation game that incorporated point-based and SBD principles. The objective of the game was to enhance learning of lean design management among construction students. Design/methodology/approach After a thorough and comprehensive literature review consisting of secondary data in journal papers, books, thesis references and primary data in the form of interviews with lean practitioners, the simulation game prototype was developed. The testing of the game was carried out with a study group. Data were collected during the gameplay with the help of a questionnaire survey on a confidence scale and Likert scale and assessed using the Wilcoxon signed-rank test, histogram, one-sample t-test and false discovery rate (Benjamini–Hochberg) correction method. Findings The data collected both pre- and post-simulation showed an increase in average confidence in understanding from 3.33 to 3.89, a 16.7% rise. The data was further interpreted by using Wilcoxon signed-rank test, indicating that the post-simulation learning experience was significantly better than the pre-simulation one. Promising positive results were obtained for the questions on game design, engagement and understanding of point-based design and SBD concepts. Originality/value The simulation game helps bridge the gap between knowledge building and real-life by effectively imitating the process. The game facilitates a dynamic and critical approach toward developing new educational simulation games and their successful incorporation in propagating lean principles in the construction industry.

Autotrophic Production of the Sesquiterpene α-Humulene with Cupriavidus necator in a Controlled Bioreactor.

Cupriavidus necator is a facultative chemolithotrophic organism that grows under both heterotrophic and autotrophic conditions. It is becoming increasingly important due to its ability to convert CO2 into industrially valuable chemicals. To translate the potential of C. necator into technical applications, it is necessary to optimize and scale up production processes. A previous proof-of-principle study showed that C. necator can be used for the de novo production of the terpene α-humulene from CO2 up to concentrations of 11 mg L-1 in septum flasks. However, an increase in final product titer and space-time yield will be necessary to establish an economically viable industrial process. To ensure optimized growth and production conditions, the application of an improved process design in a gas bioreactor with the control of pH, dissolved oxygen and temperature including a controlled gas supply was investigated. In the controlled gas bioreactor, the concentration of α-humulene was improved by a factor of 6.6 and the space-time yield was improved by a factor of 13.2. These results represent an important step toward the autotrophic production of high-value chemicals from CO2. In addition, the in situ product removal of α-humulene was investigated and important indications of the critical logP value were obtained, which was in the range of 3.0-4.2.

Enhancing Energy Efficiency and Reliability in Floating LNG Operation: A Hydrofluoroolefin-Based SMR Cycle with Thermo-Economic Assessment and Uncertainty Analysis

Natural gas liquefaction is the most energy-intensive technique in floating liquefied natural gas (FLNG) operations. Hence, a hydrofluoroolefin-based single mixed refrigerant (SMR) technology was proposed to achieve energy-efficient and eco-friendly operations. The study was perfomed to enhance the SMR process by substituting expansion valves with hydraulic turbines to improve process reversibility and enhance energy efficiency. The proposed process was optimized using a sine–cosine concept-based algorithm that tuned the design variables which yielded an overall compression energy consumption of 0.2501 (kWh/kg-LNG), resulting in a 21.43% saving as compared to the conventionally used mixed refrigerant-based process. This resulted in a reduction of about 38.81% in overall exergy destruction. The economic analysis results draw curtains that the process saves about 26.02% of total annualized cost as compared to conventional study. Owing to the appropriate mixed refrigerant component mixture ratio and process design improvement. To determine the process reliability under uncertain conditions the optimal compression power and minimum approach temperature were determined. Further, uncertainty analysis reveals that the methane and butane refrigerant flow rates along with evaporation, and condensation pressure, were among the most influential parameters that influence the process most that may affect the whole operation in uncertain process inlet conditions. In summary, our approach presents immense promise for achieving energy efficiency, environmental responsibility, and economic viability in FLNG operations, offering a robust solution for the challenges of offshore plants.

Automobile rear axle housing design and production process improvement using Failure Mode and Effects Analysis (FMEA)

The safety of automobile design is crucial to protect drivers, passengers, and other road users, ensuring reliable performance, accident prevention, and regulatory compliance.The rear axle housing in automobiles serves as a protective enclosure for the rear axle assembly, providing support, protection, and structural integrity for the drivetrain components. This research paper employs Failure Modes and Effects Analysis (FMEA) to comprehensively identify and assess common failures in the design and manufacturing process of rear axle housings in automobiles. The study highlights five critical failure modes, namely less durability, faulty blank, hole position out, burred edges, and bore position out. Through the application of recommended solutions, including design optimization, maintenance of shear blades, and the use of jigs, substantial reductions in Risk Priority Number (RPN) values were achieved. Specifically, the RPN values for the aforementioned failure modes were significantly decreased from 280 to 40, 256 to 48, 378 to 21, 378 to 45, and 392 to 56, respectively. Implementation of these solutions promises several benefits, such as enhanced customer satisfaction, reduced failure rates, elevated product/process quality and reliability, streamlined processes, and optimized resource utilization. This study emphasizes the significance of continuous improvement efforts in the automotive industry and demonstrates the pivotal role of FMEA as an invaluable tool for achieving and sustaining these improvements.

Improved process design for monoclonal antibody charge variants separation with multicolumn counter-current solvent gradient purification

The multicolumn counter-current solvent gradient purification (MCSGP) method has proven effective in addressing the issue of elution profile overlap for difficult-to-separate proteins, leading to improved purity and recovery. However, during the MCSGP process, the flow rate and proportion of loaded proteins undergo changes, causing a significant discrepancy between the elution profiles of batch process design and the actual MCSGP process. This mismatch negatively impacts the purity and recovery of the target protein. To address this challenge, an improved process design (reDesign) was proposed with the first-run MCSGP to mimic the actual continuous process and obtain elution profiles that closely resemble the real ones. The reDesign was demonstrated with both a model protein mixture and a sample of monoclonal antibody (mAb) with charge variants. For model protein mixture, the reDesign-based MCSGP process (reMCSGP) showed a remarkable improvement in recovery, increasing from 83.6% to 97.8% while maintaining a purity of more than 95%. For mAb sample, the recovery of reMCSGP improved significantly to 93.9%, surpassing the performance of normal MCSGP processes at a given purity level of more than 84%. In general, the new process design strategy developed in this work could generate a more representative elution profile that closely mirrors actual conditions in continuous processes, which enhances the separation performance of MCSGP.

From understanding the rate limitations of bioleaching mechanisms to improved bioleach process design

While bioleaching has become established technology for the leaching of refractory gold concentrates and in the heap leaching of copper sulfide minerals, many other applications, although demonstrated at the laboratory scale, have not found significant uptake in industrial biomining. The reasons for this are manifold, but generally relate back to engineering constraints in operating processes at large scale while remaining economically viable.The paper unpacks acidophile sulfide bioleaching mechanisms within bioreactors into various sub-processes and analyses them in terms of their rate limitations, such as gas-liquid mass transfer of oxygen and CO2, diffusion constraints between and within mineral particles, competing side reactions etc. It becomes clear that microbial metabolic reactions are generally not what limits the kinetics of bioleaching reactions overall but rather the reaction environment. Improved process design to enhance the economic performance of bioleaching therefore needs to target the slowest sub-process in the bioleaching mechanism, which is rarely the biological one.Conventional tank and heap bioleaching processes are discussed in this context and routes taken towards their improvement discussed. Further, various promising novel concepts in biomining, such as leaching with biogenic lixiviants, phytomining and tailings leaching are evaluated within this framework, in each case identifying the potentially limiting sub-processes. It becomes clear that the relative ‘slowness’ of these sub-processes prohibits exploitation through designed active industrial processes; they can, however, still be facilitated through suitably designed ‘passive’ processes in which the time taken for slow processes is less of an economic constraint. While this is an obvious approach in bioremediation, it can also be employed for the economic recovery of value from otherwise sub-economic mineral deposits and waste materials through heap bioleaching. This way it can also play a role in the drive towards a circular economy.

Experiment investigation on mix proportion optimization design of anti-cracking stone filled with cement stabilized macadam

Semi-rigid base asphalt pavement is widely used in China for asphalt pavement construction. However, the presence of reflection cracks remains a significant issue associated with semi-rigid bases. Large Stone Filled with Cement Stabilized Macadam (LSFC) has been identified as an effective solution for inhibiting reflection cracks. However, the construction of LSFC using large particle size coarse aggregates can be challenging. Additionally, the existing mix design process lacks clarity and results in low strength. This paper aims to optimize the design method for anti-cracking Stone Filled with Cement Stabilized Macadam (SFC) by selecting appropriate materials and improving design process. Initially, a mix proportion for SFC is designed using coarse aggregates with common particle sizes employed in engineering applications. The filling proportion of the coarse aggregates, cement dosage, and mass proportion of the cement mortar are gradually determined through compaction and 7-day unconfined compressive strength tests, resulting in the complete mix proportion for SFC. Subsequently, the strength performance, fatigue performance, dry shrinkage performance, and temperature shrinkage performance of SFC are evaluated and compared with common Cement Stabilized Macadam (CSM). The results indicate that while the strength and fatigue properties of SFC are slightly inferior to those of common CSM, it exhibits superior dry shrinkage performance. Moreover, SFC demonstrates better overall crack resistance compared to common CSM, despite having slightly inferior temperature shrinkage performance.

Conceptualizing demand-side technological and social innovations in modeling pathways to carbon neutrality

Achieving mid-century carbon neutrality goals requires drastic energy and carbon dioxide (CO2) emissions reductions that can be enabled by transformative demand-side technological and social innovations. These innovations can significantly reduce energy demand and related emissions to achieve global low energy demand (LED) pathways that rely less on uncertain CO2 removal technologies to meet climate targets. Many existing decarbonization pathway studies focus more on supply-side innovations with less attention on LED innovations, and those that do often have limited focus on industry, freight transport, and broader cross-sector strategies. In this perspective, we apply the “avoid-shift-improve” framework to assess the technical potential and deployment feasibility (in terms of adoption, implementation and response) barriers of demand-side innovations. We observed that smart, integrated building systems improve energy performance, with further reductions possible through design, occupant behavior changes, and social interactive programs. Improved design processes, higher quality products, and circular economy strategies can reduce material demand and associated industrial energy use. Shared mobility systems face uncertain net energy impacts, but smart freight and logistics, and aviation to rail shift can be deployed quickly. Sustainable food, fashion and lifestyle changes are needed beyond technological transformations. Our work illuminates potential impacts and factors that affect realization of the technical potential of individual LED innovations to support their inclusion in future global and national LED scenarios and climate policy development. Additional research is needed to ensure successful integration of LED innovations into a broader mix of climate actions to provide greater flexibility, speed, and lower costs for decarbonization.

MARINER 4.0: INTEGRATING SEAFARERS INTO A MARITIME 4.0 ENVIRONMENT

Digitalisation of systems and processes in the maritime industry are spurring Maritime 4.0, a digital transformation promising enormous gains, including improved design processes and reduced operational costs. Gleaning inter-disciplinary proficiency from Operator 4.0 in manufacturing, the present work seeks to introduce the Mariner 4.0 – a seafaring passenger or crew member with technologically augmented skills. This research aims to realise Mariner 4.0 through a human digital twin solution – a virtual representation of the state and behaviour of a unique individual over time – for seafarers on South Africa’s polar research and supply vessel, the SA Agulhas II. This paper presents human-related challenges identified within seafaring vessels, the expected value of human digital twin solutions for the maritime industry, the development of a human digital twin solution to integrate seafarers into a Maritime 4.0 environment (i.e. the development of Mariner 4.0), and an illustrative use case focussing on human comfort monitoring and management.

Design knowledge graph-aided conceptual product design approach based on joint entity and relation extraction

Design knowledge is critical to creating ideas in the conceptual design stage of product development for innovation. Fragmentary design data, massive multidisciplinary knowledge call for the development of a novel knowledge acquisition approach for conceptual product design. This study proposes a Design Knowledge Graph-aided (DKG-aided) conceptual product design approach for knowledge acquisition and design process improvement. The DKG framework uses a deep-learning algorithm to discover design-related knowledge from massive fragmentary data and constructs a knowledge graph for conceptual product design. The joint entity and relation extraction model is proposed to automatically extract design knowledge from massive unstructured data. The feasibility and high accuracy of the proposed design knowledge extraction model were demonstrated with experimental comparisons and the validation of the DKG in the case study of conceptual product design inspired by massive real data of porcelain.

A novel model-based technique to improve design processes for microstrip antennas

The present work aims to prove the concept of a novel approach to designing microstrip antennas with desired radiation patterns without time-consuming trials and simulations. While it is pretty straightforward to design a microstrip antenna operating at a specific frequency, it requires repetitive trials to design an antenna with a specific radiation pattern. For this purpose, a unique model-based design technique is applied using the cavity model expressions in the present work. The behaviors and mutual influences of various parameters in the intended design problem are described by two graph models. The “employer model” oversees the limitations and freedoms of the antenna's parameters, while the “employee model” uses graph theory and machine learning to define the relationships between graph nodes. These two models have a dynamic structure that changes every calculation step to minimize design error. After two models are constructed, these models suggest physical parameter values according to the cavity model for the desired antenna radiation pattern. Then, the results for examples are demonstrated, and the validity of the proposed technique is proven. Finally, the developability of the method and its further works are discussed.