Design Of Systems Research Articles

Auto-DSM: Using a Large Language Model to generate a Design Structure Matrix

The Design Structure Matrix (DSM) is an established method used in dependency modelling, especially in the design of complex engineering systems. The generation of DSM is traditionally carried out through manual means and can involve interviewing experts to elicit critical system elements and the relationships between them. Such manual approaches can be time-consuming and costly. This paper presents a workflow that uses a Large Language Model (LLM) to support the generation of DSM and improve productivity. A prototype of the workflow was developed in this work and applied on a diesel engine DSM published previously. It was found that the prototype could reproduce 357 out of 462 DSM entries published (i.e. 77.3%), suggesting that the work can aid DSM generation. A no-code version of the prototype is made available online to support future research.

Application of Computer Simulation Technology in the Development of Tractor Transmission Systems

Tractors are the most important agricultural power machinery. With the development of tractors toward large-scale and electrification, the design of modern tractor transmission systems increasingly relies on computer simulation technology. However, tractor transmission systems, especially power-shift transmissions and CVTs, are highly complex industrial products that involve specialized knowledge from multiple disciplines. Engineers and researchers find it difficult to choose the correct mathematical model and grasp the trend of technological development when applying simulation technology. To address this issue, we conducted a systematic review of the field and carried out the following work: First, the types and basic principles of tractor transmission systems were introduced; Second, the modeling methods and applications of computer simulation technology in the structural optimization, power-shift control, and energy saving of tractor transmission systems were reviewed; Finally, the method of obtaining simulation parameters through system identification was introduced. Although computer simulation technology has been applied in the development of all tractor transmission systems, there are still some issues that need attention, such as the lack of established shift quality evaluation indicators and driving cycles suitable for tractors, which are crucial for the reliability of simulation results but are rarely studied. These issues need to be addressed in future work.

Integration of extended reality and CAE in the context of industry 4.0

ABSTRACT Simulation plays a major role in the development of new products and is considered one of the nine technological pillars of Industry 4.0. By encapsulating a plethora of digital tools, simulation has transitioned towards what is known as Computer Aided Engineering (CAE), which further enables engineers to validate the design and ensure product performance. The development of cutting-edge digital technologies, such as Extended Reality (XR), has also gained attention aiming at the facilitation of human-machine interface (HMI) and the creation of communication channels for improved remote collaboration. The contribution of this study lies in the design and development of a cloud-based framework for the integration of CAE tools with Mixed Reality (MR), specifically, Computational Fluid Dynamics (CFD), focusing on industrial refrigeration systems design and editing applications. Furthermore, the developed solution provides augmented 3D visualizations overlaid on the existing installations. The proposed framework supports the virtual co-existence of engineers during the design phase, thus enabling collaboration and mentoring between experienced and young engineers. Additionally, it can be realized as a Cloud platform, which supports the distribution of several services such as storage of CAD files, simulation results, and the XR application for the setup and visualization of simulation results.

Design and Evaluation of Microemulsion-Based Drug Delivery Systems for Biofilm-Based Infection in Burns

Normal skin is the first line of defense in the human body. A burn injury makes the skin susceptible to bacterial infection, thereby delaying wound healing and ultimately leading to sepsis. The chances of biofilm formation are high in burn wounds due to the presence of avascular necrotic tissue. The most common pathogen to cause burn infection and biofilm is Pseudomonas aeruginosa. The purpose of this study was to create a microemulsion (ME) formulation for topical application to treat bacterial burn infection. In the present study, tea tree oil was used as the oil phase, Tween 80 and transcutol were used as surfactants, and water served as the aqueous phase. Pseudo ternary phase diagrams were used to determine the design space. The ranges of components as suggested by the design were chosen, optimization of the microemulsion was performed, and in vitro drug release was assessed. Based on the characterization studies performed, it was found that the microemulsion were formulated properly, and the particle size obtained was within the desired microemulsion range of 10 to 300 nm. The I release study showed that the microemulsion followed an immediate release profile. The formulation was further tested based on its ability to inhibit biofilm formation and bacterial growth. The prepared microemulsion was capable of inhibiting biofilm formation.Graphical

Design of MEMS Pressure Sensor Anti-Interference System Based on Filtering and PID Compensation.

Due to the inherent temperature drift and lack of static stability in traditional pressure sensors, which make it difficult for them to meet the increasing demands of various industries, this paper designs a new system. The proposed system integrates temperature measurement and regulation circuits, signal processing, and communication circuits to accurately acquire and transmit pressure sensor data. The system designs a filtering algorithm to filter the original data and develops a data-fitting operation to achieve error compensation of the static characteristics. In order to eliminate the temperature drift problem of the sensor system, the system also adopts an improved PID thermostatic control algorithm to compensate for the temperature drift. Finally, it can also transmit the processed pressure data remotely. The experimental results show that the nonlinear error at 50 °C is reduced from the initial 1.82% to 0.24%; the hysteresis error is significantly reduced from 1.23% to 0.046%; and the repeatability error control is reduced from 3.79% to 0.89%. By compensating for thermal drift, the system's thermal sensitivity drift coefficient is reduced by 74.67%, the thermal zero drift coefficient is reduced by 66.24%, and the wireless communication range is up to 1km. The above significant optimization results fully validate the high accuracy and stability of the system, which is perfectly suited for demanding pressure measurement applications.

Surface Passivation of Norland Optical Adhesive Improves the Guiding Efficiency of Gliding Microtubules in Microfluidic Devices.

The microtubule-kinesin biomolecular motor system, which is vital for cellular function, holds significant promise for nanotechnological applications. In vitro gliding assays have demonstrated the ability to transport microcargo by propelling microtubules across kinesin-coated surfaces. However, the uncontrolled directional motion of microtubules has posed significant challenges, limiting the system's application for precise cargo delivery. Microfluidic devices provide a means to direct microtubule movement through their geometric features. Norland Optical Adhesive (NOA) is valued for its mold-free application in microfluidic device fabrication; however, microtubules often climb up channel walls, limiting controlled movement. In this study, a surface passivation method for NOA is introduced, using polyethylene glycol via a thiol-ene click reaction. This technique significantly improved the directional control and concentration of microtubules within NOA microchannels. This approach presents new possibilities for the precise application of biomolecular motors in nanotechnology, enabling advancements in the design of microfluidic systems for complex biomolecular manipulations.

Seismic response and damage evolution process of a bedded slope‒tunnel system via the continuum‒discontinuum element method

To investigate the failure evolution process and failure mechanism of the bedded slope‒tunnel system under earthquakes, this work examines a specific layered slope at a tunnel portal in southeast China and conducts a 2D dynamic analysis with the continuum‒discontinuum element method (CDEM). It investigates the slope's dynamic behavior and potential for instability by evaluating the wave patterns, acceleration, displacement, Hilbert‒Huang transform (HHT) spectral analysis, and equivalent crack ratio. The research results indicate that the analysis of acceleration and displacement over time provides insights into wave propagation within a slope. During minor earthquakes (< 0.3 g), the slope experiences dynamic amplification at the surface and elevation-dependent effects, which are less evident during stronger seismic events. This work also revealed a positive correlation between the tunnel's dynamic amplification and elevation. A method leveraging HHT energy analysis, in conjunction with the slope's equivalent fracture ratio, is introduced to trace the progression of seismic damage across temporal and spatial dimensions. The progression of seismic intensity is characterized by stages of local failure initiation, expansion, and eventual holistic failure. Similarly, the temporal dynamics of instability are categorized into phases of initial instability, acceleration, and eventual sliding. Furthermore, this work explores the impact of tunnels on slope stability by comparing the equivalent fracture ratios and dynamic displacement patterns of slopes both with and without tunnels, revealing the evolution process of seismic damage to slope‒tunnel systems. This work provides a reference for the seismic design of complex slope‒tunnel systems.

Biomimetic Entropy-Dominant Molecular Hinges with Picomolar Affinity.

Molecular hinges are ubiquitous in both natural and artificial supramolecular systems. A major challenge to date, however, has been simultaneously achieving high thermodynamic and kinetic stability. Here, we employ host-enhanced intramolecular charge-transfer interactions to mediate entropy-favored complexation between a flexible AB2-type guest and a macrocyclic host, forming a new type of molecular hinge with an ultrahigh picomolar binding affinity (Ka > 1012 M-1). This entropy-promoted hinge modulates photoisomerization, exhibiting a substantial preference for the E-isomer, which is further demonstrated to mirror the natural retinal-opsin cycle, promoting the sensitization of visible light. This work unveils an efficient approach to exploit entropy-dominant architectures for the design of hierarchical molecular systems.

Machine learning-driven prediction of phosphorus adsorption capacity of biochar: Insights for adsorbent design and process optimization

Phosphorus (P) pollution in aquatic environments poses significant environmental challenges, necessitating the development of effective remediation strategies, and biochar has emerged as a promising adsorbent for P removal at the cost of extensive research resources worldwide. In this study, a machine learning approach was proposed to simulate and predict the performance of biochar in removing P from water. A dataset consisting of 190 types of biochar was compiled from literature, encompassing various variables including biochar characteristics, water quality parameters, and operating conditions. Subsequently, the random forest and CatBoost algorithms were fine-tuned to establish a predictive model for P adsorption capacity. The results demonstrated that the optimized CatBoost model exhibited high prediction accuracy with an R2 value of 0.9573, and biochar dosage, initial P concentration in water, and C content in biochar were identified as the predominant factors. Furthermore, partial dependence analysis was employed to examine the impact of individual variables and interactions between two features, providing valuable insights for adsorbent design and operating condition optimization. This work presented a comprehensive framework for applying a machine learning approach to address environmental issues and provided a valuable tool for advancing the design and implementation of biochar-based water treatment systems.

Shaping corticospinal pathways in virtual reality: effects of task complexity and sensory feedback during mirror therapy in neurologically intact individuals

BackgroundRestoration of limb function for individuals with unilateral weakness typically requires volitional muscle control, which is often not present for individuals with severe impairment. Mirror therapy—interventions using a mirror box to reflect the less-impaired limb onto the more-impaired limb—can facilitate corticospinal excitability, leading to enhanced recovery in severely impaired clinical populations. However, the mirror box applies limitations on mirror therapy, namely that all movements appear bilateral and are confined to a small area, impeding integration of complex activities and multisensory feedback (e.g., visuo-tactile stimulation). These limitations can be addressed with virtual reality, but the resulting effect on corticospinal excitability is unclear.ObjectiveExamine how virtual reality-based unilateral mirroring, complex activities during mirroring, and visuo-tactile stimulation prior to mirroring affect corticospinal excitability.Materials and methodsParticipants with no known neurological conditions (n = 17) donned a virtual reality system (NeuRRoVR) that displayed a first-person perspective of a virtual avatar that matched their motions. Transcranial magnetic stimulation-induced motor evoked potentials in the nondominant hand muscles were used to evaluate corticospinal excitability in four conditions: resting, mirroring, mirroring with prior visuo-tactile stimulation (mirroring + TACT), and control. During mirroring, the movements of each participant’s dominant limb were reflected onto the nondominant limb of the virtual avatar, and the avatar’s dominant limb was kept immobile (i.e., unilateral mirroring). The mirroring + TACT condition was the same as the mirroring condition, except that mirroring was preceded by visuo-tactile stimulation of the nondominant limb. During the control condition, unilateral mirroring was disabled. During all conditions, participants performed simple (flex/extend fingers) and complex (stack virtual blocks) activities.ResultsWe found that unilateral mirroring increased corticospinal excitability compared to no mirroring (p < 0.001), complex activities increased excitability compared to simple activities during mirroring (p < 0.001), and visuo-tactile stimulation prior to mirroring decreased excitability (p = 0.032). We also found that these features did not interact with each other.DiscussionsThe findings of this study shed light onto the neurological mechanisms of mirror therapy and demonstrate the unique ways in which virtual reality can augment mirror therapy. The findings have important implications for rehabilitation for design of virtual reality systems for clinical populations.

Modeling and operational characteristics analysis of a dual-source synergetic heat pump based on air energy and biomass waste heat

Air source heat pumps have gradually become a widely used heating solution worldwide due to their advantages of high efficiency, energy conservation, and environmental protection. However, air source heat pumps also face some challenges in practical applications, such as unstable heating and performance degradation in low-temperature environments. In order to improve these problems, based on the cascade heat technology, a new type of dual-source synergetic heat pump system is designed using air energy and biomass waste heat dual heat sources. In order to study its performance, a simulation model of the heat pump system was first constructed using distributed-parameter method. In order to verify the effectiveness of the model, an 18-kW heat pump experimental unit was constructed. By comparing the three key parameters of user side outlet water temperature, high-temperature compressor power, and system coefficient of performance (COP), the results show that the maximum error does not exceed 11 %; Secondly, in order to improve the stability and operational efficiency of the system, a three-level control strategy was designed based on considering the coordination of different grade heat sources; Finally, based on the above, the operating characteristics of the heat pump system were investigated through testing under different external input conditions. The research results indicate that by utilizing cascade heating and parallel heat exchange technology, the heat pump system can achieve stable heating even under large fluctuations in external working conditions. At the same time, compared with air source heat pumps, the COP can be improved by 69.7 % in low-temperature environments. The system results not only provide reference for the design and operation of new heat pump systems, but also provide practical and feasible solutions for the problem of decreased heat pump performance in low-temperature environments.

Optimization strategy of property energy management based on artificial intelligence

This study focuses on the design and optimization of property energy management systems, aiming to improve energy efficiency, reduce waste, and enhance user comfort and satisfaction through intelligent means. The research background is based on the urgency of energy conservation and emission reduction, and the rise of smart property management models on a global scale, especially the increasing demand for energy efficiency monitoring, predictive analysis, automated control, and user engagement. To address the urgent need for energy conservation and emission reduction, particularly in the realm of property management, this study designed and optimized a property energy management system. The core of the research is a systematic energy management framework that encompasses efficient monitoring, intelligent predictive analytics using techniques such as Long Short-Term Memory (LSTM) networks for energy consumption forecasting, automated control, user-friendly interfaces, and system safety. An empirical case study was conducted at a large-scale commercial complex, confirming the effectiveness of the system. Through intelligent transformation, specifically the optimization of air conditioning and lighting systems using advanced technologies like frequency modulation and LED lighting, a total energy saving rate of 25% was achieved. The annual economic savings exceeded 1.25 million yuan, and user satisfaction was significantly improved. During the research process, several limitations and challenges were encountered, including data quality issues and scalability concerns. These limitations were addressed through rigorous data preprocessing and validation, ensuring the robustness of the findings and their applicability to similar environments. The results demonstrate the potential of integrating artificial intelligence and machine learning techniques into property energy management systems, paving the way for more sustainable and efficient buildings. This revised abstract includes more specific details about the technologies used, such as LSTM networks, and mentions the limitations and challenges faced during the research. It also emphasizes the practical application and scalability of the system.

Эргономические проблемы создания информационных систем, включающих элементы искусственного интеллекта

The paper analyses the main problems of creating information systems containing elements of artificial intelligence. The work considers separately the problems of designing the user interface and blocks that ensure the operation of artificial intelligence elements. A methodological approach to designing systems with elements of artificial intelligence is proposed. The authors examine and analyze bottlenecks of the proposed approach. The paper formulates the proposals for ensuring effective ergonomic design of information systems for various purposes.

Assessment of grid electricity systems using the life-cycle carbon-emission model

The previous study developed a life-cycle carbon emissions (LCCE) algorithm in MS Excel. Despite improvements, a comprehensive approach is needed to conduct LCCE inventory analysis using current methods. This study diverges from existing research by assessing life-cycle carbon emissions inventory data of power generation and transmission systems on studied grids, considering component lifespans, recycling pollutants, and retirement rates. The life-cycle carbon emissions inventory analysis results improve understanding of power system environmental performance, aligning with stakeholders' objectives. This study aims to enhance the environmental performance of electric power systems in Kenya, Rwanda, and Tanzania by evaluating the life-cycle carbon emissions of power generation and transmission within their national grids. The selected grids are the right participants for the study, of non-renewable and renewable grid electricity generation mixes, due to their different environmental features, potential power trade, upcoming grid interconnection, and power transmission practises at various scales. The study applied a life cycle assessment method and simulated the learning patterns using RStudio. The data (emission factors and activity) has been collected from the reports (scientific and technical) and national utility actors. The presented results show that only Kenyan generation and transmission systems have the lifetime decarbonization performance relationship between renewable energy sources dominated power systems and non-renewable energy sources dominated power systems. A major challenge of this study has been the scarcity of primary data, leading to reliance on some secondary and external sources. Therefore, future research should consider the use of more internal and primary data sources, and the use of the most current data, including new technologies adopted from cradle-to-grave of the systems. This study's findings inform better system designs, policies, and plans for improved environmental performance in electrical power systems.

Revolutionizing Drug Discovery: The Impact of Distinct Designs and Biosensor Integration in Microfluidics-Based Organ-on-a-Chip Technology.

Traditional drug development is a long and expensive process with high rates of failure. This has prompted the pharmaceutical industry to seek more efficient drug development frameworks, driving the emergence of organ-on-a-chip (OOC) based on microfluidic technologies. Unlike traditional animal experiments, OOC systems provide a more accurate simulation of human organ microenvironments and physiological responses, therefore offering a cost-effective and efficient platform for biomedical research, particularly in the development of new medicines. Additionally, OOC systems enable quick and real-time analysis, high-throughput experimentation, and automation. These advantages have shown significant promise in enhancing the drug development process. The success of an OOC system hinges on the integration of specific designs, manufacturing techniques, and biosensors to meet the need for integrated multiparameter datasets. This review focuses on the manufacturing, design, sensing systems, and applications of OOC systems, highlighting their design and sensing capabilities, as well as the technical challenges they currently face.

Hydraulic Transport of Large Solid Particles in Inclined Pipes Under Pulsating Flow Conditions

Abstract The pressure loss due to the hydraulic transport of large solid particles should be predicted for the design of subsea mining systems. The mixture flow in a flexible jumper is expected to be unsteady during lifting operations in an actual mining system. The authors develop a one-dimensional mathematical model that predicts such pressure loss under pulsating mixture flows in a static inclined pipe assuming that the flow in the jumper is fully developed. An experiment is performed on the hydraulic transport of solid particles to obtain data for model validation. In this experiment, several kinds of solid particles are used: alumina beads, glass beads, and gravel. The experimental parameters are mixture velocity, solid concentration, pulsation period and amplitude of water velocity, and pipe inclination angle. The proposed model is validated through a comparison with experimental data. The validation confirms that the model is applicable for the prediction of the pressure loss in inclined pipes under pulsating flow conditions. Furthermore, we calculate the pressure loss due to the hydraulic transport of polymetallic sulfide ores using the proposed model. The calculation results show that the time-averaged pressure loss drastically varies with the pipe inclination angle, reaching its maximum value between the pipe inclination angles of 30 deg and 60 deg, at which the flow is inclined upward. The results also show that the amplitude of pressure loss pulsation differs little between pipe inclination angles and that the pulsation component of pressure loss should be considered in designing lifting systems.

No robot is an island - what properties should an autonomous system have in order to be resilient?

Autonomous systems are inherently designed to be self-sufficient only under particular circumstances, for specific activities. The environment needs to be tailored to align with these design boundaries, ensuring that technical systems operate as intended. This paper proposes that insights from resilience engineering can enhance the self-sufficiency of so-called autonomous systems, consequently bolstering their adaptive capacity in unforeseen or unpredicted scenarios. Two cases of autonomous systems are examined to illustrate how this enhancement can be achieved. The conclusion drawn is that the resilience envelope of a system can be augmented by enabling the system to adapt to multiple design cases, by defining and prioritizing the core values of the system, and by designing the system to possess a framework for situations rather than being confined to a singular situational frame. The implications for the design of human-machine systems are also discussed.

A comprehensive review of rectangular duct solar air heaters featuring artificial roughness

Abstract Solar air heaters (SAHs) are widely used solar thermal systems with applications in diverse sectors. However, its effectiveness is restrained by low convective heat transfer (HT) coefficients at the absorber plate, leading to inefficient HT, and the elevated temperature of the absorber plate causes significant heat losses, reducing thermal efficiency. This study addresses these challenges by introducing ribs or roughness on the absorber plate creating turbulence in the airflow, resulting in significant improvements. The research investigates various rib configurations, the influence of rib parameters, performance methods, and arrangements to evaluate their HT and friction characteristics. Among these rib configurations, a comparative analysis is done on various factors such as the Nusselt number ratio, thermal enhancement factor, friction factor ratio, and thermal efficiency to optimize distinct roughness parameters and rib arrangement patterns. This study also provides valuable recommendations from existing literature, offering insights into the effective design, prospects, and implementation of SAH systems.

Terahertz quasi-optics imaging systems with easy zoom based on beam-shaping devices and a freeform lens.

Terahertz (THz) waves show outstanding application value in the nondestructive detection of hidden targets that are impenetrable to visible light. However, the uncertain location of hidden targets puts a higher demand on the zoom function of the THz quasi-optical systems, especially when the object is immovable. This paper proposes a continuous zoom system working in the THz band consisting of a negative axicon disk, a segmented axicon, and a freeform lens. The negative axicon disk and segmented axicon shape THz waves into annular beams with adjustable diameters, which are respectively focused at different positions by the zone-designed freeform lens. Both simulation and experimental results indicate that the zoom range of the system can reach 82 mm while maintaining an imaging resolution of 6 mm. Notably, continuous zooming is achieved by merely rotating the negative axicon disk, instead of the lens moving. Moreover, all devices in the system can be fabricated by 3D printing or machining. This approach offers the advantages of simple adjustment and low cost, providing, to our knowledge, a novel perspective for the design and application of THz quasi-optical imaging systems.

AlphaFold2 Predicts Alternative Conformation Populations in Green Fluorescent Protein Variants.

Artificial intelligence-based protein structure prediction methods such as AlphaFold2 have emerged as powerful tools for characterizing proteins sequence-structure relationship offering unprecedented opportunities for the molecular interpretation of biological and biochemical phenomena. While initially confined to providing a static representation of proteins through their global free-energy minimum, AlphaFold2 has demonstrated the ability to partially sample conformational landscapes, providing insights into protein dynamics, which is fundamental for interpreting and potentially tuning the function of natural and artificial proteins. In this study, we show that targeted column masking of AlphaFold2's multiple sequence alignment enables the characterization and estimation of the population ratio of the two main conformations of engineered green fluorescent proteins with alternative β-strands. The possibility of quickly estimating relative populations through AlphaFold2 predictions is expected to speed-up the computational design of related systems for sensing applications.