Process Modules Research Articles

A novel MOF/RGO-based composite phase change material for battery thermal management

Despite being an advanced material widely used in solar energy harvesting, indoor thermal environment of buildings and other fields, the phase change material (PCM) still suffers from the low thermal conductivity and high melt flow rate in practical applications in battery thermal management system. Against this background, a novel anti-leakage and thermally induced high thermal conductivity composite phase change material (CPCM) was proposed and prepared, and it was filled into a lithium-ion battery pack, and then the thermal management effects of the metal organic framework (MOF) based CPCM in the battery module under different conditions of harsh working environment were experimentally studied. The research results indicate that the new CPCM achieves better thermal management performance than natural convection under all test conditions. For example, under the worst test condition of 40 °C ambient temperature and 3 C discharge rate of battery module, the maximum temperature (Tmax) and maximum temperature difference (ΔTmax) in CPCM cooling module were 63.53 °C and 5.33 °C respectively, which are 23.47 °C and 6.67 °C lower than that of natural air cooling module. Interestingly, when the discharge rate was constant at 3 C, the ΔTmax in the discharge process of the battery module decreased with the increase of the test temperature, which was completely opposite to that of the natural air cooling module. It was helpful to improve the temperature consistency of the battery module. Finally, the novel CPCM has achieved better thermal management performance after comparison analysis.

Blast wave propagation characteristics in FPSO: Effect of cylindrical obstacles

The propagations of blast waves on Floating, Production, Storage, and Off-loading unit (FPSO) are influenced by the shapes, dimensions and arrangements of obstacles. In this paper, the effects of cylindrical obstacles on the characteristics of explosion pressures in the process modules of FPSO are studied by using computational fluid dynamic (CFD) method. Defining an effect coefficient η to express the changes of explosion pressures due to the existence of obstacles. It indicates that only the explosion pressures in the limited region adjacent obstacles can be affected by cylindrical obstacles. The increase of obstacle diameter only improve the η values front of the obstacles while increasing obstacle height can significant decrease the explosion pressures behind obstacle. When blast waves propagate multiple obstacles, the explosion pressures adjacent front obstacle are not affected by the rear obstacle, however, the explosion pressures in the gap of two obstacles are relieve due to the existence of front obstacle. The comparisons of effect of cubical and cylindrical obstacles reveal that cubical obstacles have more excellent pressure reduction ability as cylindrical obstacles can provide a larger pressure reduction region.

Remote and in-person research education for people with Parkinson's disease and their care partners.

TeleDREAMS, a distance learning version of the Developing a Research Participation Enhancement and Advocacy Training Program for Diverse Seniors (DREAMS) program, provides remote clinical research process and advocacy education to older adults with Parkinson's disease (PD) and their care partners. Participants engaged in remote learning, reading eight weekly clinical research process and advocacy education modules. They also had weekly half hour phone discussions with staff about each module. Participants (PD: n = 28, care partner: n = 15) were tested on health literacy, quality of life, depression, research involvement, and advocacy measures. People with PD improved on health literacy postintervention. PD participants who participated with care partners improved more on health literacy than those without care partners. PD participants' attrition rates were lower for PD participants in TeleDREAMS than those of the similar, in-person program DREAMS program studied before TeleDREAMS. Most participants reported research involvement and patient advocacy for older adults with PD 6 to 9 months postprogram. TeleDREAMS may improve health literacy in participants with Parkinson's and their care partners. If increased advocacy and health and research literacy translates to increased research involvement, then TeleDREAMS could be an important strategy for researchers interested in increasing participation. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

ADS-detector: An attention-based dual stream adversarial example detection method

Adversarial attacks seriously threaten the security of machine learning models. Thus, detecting adversarial examples has become an important and interesting research topic facing various adversarial attacks. However, the majority of existing adversarial example detection algorithms cannot perform well in detecting adversarial examples with slight perturbations. In this paper, we propose a novel attention-based dual stream detector (ADS-Detector) that can address the detection of adversarial examples with both slight and large perturbations. Specifically, we first design a data process module to generate pixel and prediction confidence stream data from the raw image. Then, we propose an N-layer attention module to extract the channel and spatial feature weights between the pixel and prediction confidence stream data. Eventually, we feed the dual-stream data into the same subdetection model with a convolutional block attention module; then, the output results are combined to determine whether the input image is an adversarial example or not. To validate the performance, we conduct extensive experiments on three public datasets: CIFAR10, Dogs vs. Cats and ImageNet. After sufficient analysis of the simulation results, we find that our proposed method outperforms the others for the detection of adversarial attacks generated by the considered attack methods.

Experimental Measurement and Numerical Simulation on the Snow-Cover Process of Solar Photovoltaic Modules and Its Impact on Photoelectric Conversion Efficiency

The snow falling on the surface of photovoltaic modules tends to reduce the output power. In order to understand the process of snow accumulating on solar photovoltaic modules and reveal the impact of snow accumulation on photovoltaic conversion efficiency, the snow-cover process was simulated on the surface of photovoltaic modules with different tilt angles by computational fluid dynamics (CFD). On this basis, the relationship between the amount of snow and tilt angle was explored. The snow effect of photovoltaic modules on photoelectric conversion efficiency was studied by building a test platform. At the same time, a measurement platform of snow accumulation on photovoltaic modules and photoelectric conversion efficiency was constructed. Through the experiment of the relationship between snow thickness and snow sliding distance and the power generation efficiency of photovoltaic (PV) modules, the influence of snow thickness and snow area on the power generation efficiency of PV modules is discussed. The results show that the larger angle between the photovoltaic panel and the ground is adverse to the accumulation of snow on the panel. When the thickness of snow reaches 1 cm, the power generation efficiency of the entire photovoltaic module reduces to 7.1% of that as normal. At the same time, the sliding of snow on the photovoltaic panel improves the efficiency of photoelectric conversion. Through the analysis of numerical simulation and experimental results, targeted suggestions are made on how to improve the efficiency of power generation for photovoltaic power stations under snowy conditions, which may provide a reference for engineering work.

A Research on the Method of Thermal Design for Lithium-ion Battery Modules

The thermal management of lithium-ion battery modules for energy storage is of great significance to the safe operation of battery modules. In this paper, a thermal design method of lithium-ion battery modules is proposed to test the real-time heating power of lithium-ion batteries, and simulate the temperature field change during the charging and discharging process of the battery module through CFD simulation software. A 6kWh lithium-ion battery module is designed using the method. The maximum temperature difference between the simulation results and the test results of the module prototype is only 1°C, proving that the simulation method has high accuracy.

Improving the Economic, Environmental, and Safety Performance of Bio-Jet Fuel Production through Process Intensification and Integration Using a Modularity Approach

In this work, process intensification and heat integration are applied to improve a palm oil biofuel production process using modularity analysis and evaluated through techno-economic analysis and life cycle assessment (LCA). The modularity analysis integrated with the safety indicator guided the scoping of intensification options and the application of the LCA at the level of process modules that facilitate the treatment of recycles. Two alternatives are presented: the first is the intensified one only and the second is the addition of heat integration. In both cases, there was an improvement in economic terms by decreasing utility costs by 2 and 70%, respectively. In environmental terms, the global warming potential obtained is 12.8 kg CO2 eq./GJ for the intensified design and 12.2 kg CO2 eq./GJ for the intensified and integrated design, which is a considerable improvement over other studies that have been carried out. In addition, process safety was improved as the fire and explosion damage rate decreased by 15% in the separation section. In conclusion, this work demonstrated a novel application of modularity to scope intensification opportunities, implement LCA models at the modular level, and identify hot spots.

Efficient and reliable encapsulation for perovskite/silicon tandem solar modules.

Perovskite/silicon tandem solar cells have a tremendous potential to boost renewable electricity production thanks to their very high performance combined with promising cost structure. However, for actual field deployment, any solar cell technology needs to be assembled into modules, where the associated processes involve several challenges that may affect both the performance and stability of the devices. For instance, due to its hygroscopic nature, ethylene vinyl acetate (EVA) is incompatible with perovskite-based photovoltaics. To circumvent this issue, we investigate here two alternative encapsulant polymers for the packaging of perovskite/silicon tandems into minimodules: a thermoplastic polyurethane (TPU) and a thermoplastic polyolefin (TPO) elastomer. To gauge their impact on tandem-module performance and stability, we performed two internationally established accelerated module stability tests (IEC 61215): damp heat exposure and thermal cycling. Finally, to better understand the thermomechanical properties of the two encapsulants and gain insight into their relation to the thermal cycling of encapsulated tandems, we performed a dynamic mechanical thermal analysis. Our understanding of the packaging process of the tandem module provides useful insights for the development of commercially viable perovskite photovoltaics.

Rain Removal from Light Field Images with 4D Convolution and Multi-scale Gaussian Process.

Existing deraining methods mainly focus on a single input image. However, with just a single input image, it is extremely difficult to accurately detect and remove rain streaks, in order to restore a rain-free image. In contrast, a light field image (LFI) embeds abundant 3D structure and texture information of the target scene by recording the direction and position of each incident ray via a plenoptic camera, which has emerged as a popular device in the computer vision and graphics research communities. However, making full use of the abundant information available from LFIs, such as 2D array of sub-views and the disparity map of each sub-view, for effective rain removal is still a challenging problem. In this paper, we propose a novel network, 4D-MGP-SRRNet, for rain streak removal from LFIs. Our method takes as input all sub-views of a rainy LFI. In order to make full use of the LFI, we adopt 4D convolutional layers to build the proposed rain steak removal network to simultaneously process all sub-views of the LFI. In the proposed network, the rain detection model, MGPDNet, with a novel Multi-scale Self-guided Gaussian Process (MSGP) module is proposed to detect high-resolution rain streaks from all sub-views of the input LFI at multi-scales. Semi-supervised learning is introduced for MSGP to accurately detect rain streaks by training on both virtual-world rainy LFIs and real-world rainy LFIs at multi-scales via calculating pseudo ground truths for real-world rain streaks. We then feed all sub-views subtracting the predicted rain streaks into a 4D convolution-based Depth Estimation Residual Network (DERNet) to estimate the depth maps, which are later converted into fog maps. Finally, all sub-views concatenated with the corresponding rain streaks and fog maps are fed into a powerful rainy LFI restoring model based on the adversarial recurrent neural network to progressively eliminate rain streaks and recover the rain-free LFI. Extensive quantitative and qualitative evaluations conducted on both synthetic LFIs and real-world LFIs demonstrate the effectiveness of our proposed method.

Spatial Non-Stationary Near-Field Channel Modeling and Validation for Massive MIMO Systems

Existing deraining methods focus mainly on a single input image. However, with just a single input image, it is extremely difficult to accurately detect and remove rain streaks, in order to restore a rain-free image. In contrast, a light field image (LFI) embeds abundant 3D structure and texture information of the target scene by recording the direction and position of each incident ray via a plenoptic camera. LFIs are becoming popular in the computer vision and graphics communities. However, making full use of the abundant information available from LFIs, such as 2D array of sub-views and the disparity map of each sub-view, for effective rain removal is still a challenging problem. In this paper, we propose a novel method, 4D-MGP-SRRNet, for rain streak removal from LFIs. Our method takes as input all sub-views of a rainy LFI. To make full use of the LFI, it adopts 4D convolutional layers to simultaneously process all sub-views of the LFI. In the pipeline, the rain detection network, MGPDNet, with a novel Multi-scale Self-guided Gaussian Process (MSGP) module is proposed to detect high-resolution rain streaks from all sub-views of the input LFI at multi-scales. Semi-supervised learning is introduced for MSGP to accurately detect rain streaks by training on both virtual-world rainy LFIs and real-world rainy LFIs at multi-scales via computing pseudo ground truths for real-world rain streaks. We then feed all sub-views subtracting the predicted rain streaks into a 4D convolution-based Depth Estimation Residual Network (DERNet) to estimate the depth maps, which are later converted into fog maps. Finally, all sub-views concatenated with the corresponding rain streaks and fog maps are fed into a powerful rainy LFI restoring model based on the adversarial recurrent neural network to progressively eliminate rain streaks and recover the rain-free LFI.

Development and validation of structured training module for healthcare workers involved in managing pediatric patients during COVID-19 pandemic using "Objective Structured Clinical Examination" (OSCE).

COVID-19 (Coronavirus disease-19) is an ongoing pandemic. COVID vaccine administration in adults has provided some degree of protection from infection but children are still susceptible So, we have to be prepared to handle COVID-19 infection in children by training our healthcare workers by updating both their knowledge and skills. We developed a training module to train our healthcare workers in all domains of learning and also planned related assessment methods to know the effectiveness of the module. This was a quasi-experimental study with pre- and post-intervention conducted at a tertiary-level teaching medical college in southern India from July to September 2021. The training module was developed as per the "ADDIE" model of the development process module. It was further validated by five experts before implementation. In addition to the quasi-experimental method of evaluation like pre- and post-test, Observed Skill clinical examination (OSCE) had been also used as an assessment tool at the completion of training. A total of 92 participants have been trained as per this module in our tertiary-level care hospital. The association between continuous and categorical variables was assessed using an independent t-test and ANOVA, and paired t-test was used for comparing the difference between pre- and post-test scores. Pre-test scores had no association with years of experience (P = 0.803) and previous training status of participants (P = 0.350). The mean difference of pre- and post-test scores was 3.8 and it was statistically significant (P value < 0.001) A weak positive correlation between pre- and post-test was present by the Spearmen correlation test (r = 0.337). The correlation between post-test score and OSCE score does not have a significant correlation. Structured training module was effective in training the participants. Multimode assessment method (Pre-test, Post-test, and OSCE) is an important step to evaluate any training program as compared to only the pre- and post-test methods of evaluation.

Design of matrix production systems: A skill-based systems engineering approach

A high changeability of value creation enables manufacturing companies to constantly adapt to new situations in Industry 4.0 such as changing product portfolios or supply chain disruptions. Matrix manufacturing systems provide a high changeability of value creation enabled by individually plannable process modules, interconnected through a flexible material flow. Higher maturity levels of matrix manufacturing systems integrate transportation and value creation tasks and are facilitated by location-independent equipment that merges into new resources according to dynamically changing manufacturing demands, i.e., a fluid production. The efficacious design of matrix manufacturing systems is still a complex challenge for research and practice. For this purpose, the authors propose a novel design approach specifically scoped for matrix manufacturing systems. This approach aims at offering a more practical guideline for the design of matrix manufacturing systems. Skill-based systems engineering provides the methodological foundation. The application opportunity for novel generative AI tools is exemplified. An experiment, addressing the manufacturing of a toy action figure, validates and improves the conceptualized design approach.

Application of Reconfiguration Process for Matrix Manufacturing System in an Industrial Use Case

The increasing need for changeable and, at the same time, productive production systems in variant-rich series production has generated the concept of so-called matrix manufacturing systems (MMS). A MMS is a cyber-physical modular production system composed of autonomous process modules. Flexible interlinking results in order-specific material flows, which enable the simultaneous production of different products and variants in the same production system. Cyber-physical elements continuously optimize the material flows, ensuring high productivity.. Beyond this flexibility aspect, modularity enables deeper changes via so-called reconfigurations. Reconfiguration requirements arise from the need to change the range of functions or capacities as well as from changes in the arrangement or the replacement of working methods. This way, the four goals of production variability, quality, speed, and profitability can be optimized. In order to use the possibility of recurring reconfigurations, the so-called reconfiguration process for MMS was developed in previous work. Now, the process is applied in an industrial use case. The effort to apply the process to the use case is estimated, as well as the additional effort to enable reconfiguration capabilities of the system. Then, the reconfiguration process is run through two scenarios. This allows a statement about the resulting efforts and benefits. The interpretation of the results shows that recurring reconfigurations are possible via the process and that the resulting benefits exceed the efforts. The paper concludes with the outlook that further research should work out ways to automate functions of the reconfiguration process step by step. These can be designing, decision-making, and execution functions. This could lay the foundation for the autonomous execution of reconfigurations aimed at so-called fluid production systems.

Research on the Application of Wireless Sensor in the Design of Curtain Automatic Adjustment System

With the development of science and technology and the progress of wireless sensor network technology, a variety of intelligent electronic products are used more and more widely in people's lives. High technology and intelligent technology have entered residential home users from intelligent buildings, and home intelligent technology has become a cutting-edge technology topic. The development of mobile Internet provides a new platform for home decoration, which is of great significance in promoting the formation of smart families and smart communities. Curtain is an important element in interior decoration design. It is of great practical value to develop a mobile virtual display system that can manage curtains and provide curtain decoration effects. The design idea of modularization is adopted, and the design process of wireless communication module, relay module, key module, power protection and level conversion module of the system is emphatically analyzed. The basic working principle and operation method of the system are introduced, and the server and client software based on TCP communication protocol are designed to realize the remote control of curtains through the Internet or personal mobile devices. The actual results show that the scheme can be applied to the home or office environment, and has the characteristics of high stability and simple operation.

Investigation of Isolation Approaches and the Stoichiometry of SiNx Passivation Layers in “Buffer‐Free” AlGaN/GaN Metal–Insulator–Semiconductor High‐Electron‐Mobility Transistors

Critical process modules for the fabrication of metal–insulator–semiconductor high‐electron‐mobility transistors (MISHEMTs) based on a novel ‘buffer‐free’ AlGaN/GaN heterostructure grown with metal–organic chemical vapor deposition (MOCVD) are presented. The methods of isolation and passivation for this type of heterostructure are investigated. Utilizing nitrogen implantation, it is possible to achieve off‐state destructive breakdown voltages (BVs) of 2496 V for gate–drain distances up to 25 μm, whereas mesa isolation techniques limit the BV below 1284 V. The stoichiometry of the SiN x passivation layer displays a small impact on the static and dynamic on‐resistance. However, MISHEMTs with Si‐rich passivation show off‐state gate currents in the range of 1–100 μA mm−1 at voltages above 1000 V, which is reduced below 10 nA mm−1 using a stoichiometric SiN x passivation layer. Destructive BVs of 1532 and 1742 V can be achieved using gate‐integrated and source‐connected field plates for MIHEMTs with stoichiometric and Si–rich passivation layers, respectively. By decreasing the field plate lengths, it is possible to achieve BVs of 2200 V. This demonstrates the implementation of MISHEMTs with high‐voltage operation and low leakage currents on a novel “buffer‐free” heterostructure by optimizing the SiN x stoichiometry.

Digitalisation of chemical processes as graphs and applications of modular decomposition to process design and analysis

The digital transformation of chemical processes requires methods that support the modelling and analysis tools suitable to decompose complex systems into manageable components at all stages of process development, starting with conceptual design and analysis. This paper presents examples on how the digitalisation of chemical processes as graphs can facilitate their study and analysis for developing alternative process designs. In a process graph, inputs, outputs and unit operations are represented as nodes and streams are edges which can be weighted using process variables such as flow rate. Once a process is represented as a graph, community detection algorithms can be applied to identify process modules and obtain their modularity. The novel approach presented is based on modular decompositions to identify highly related unit operations in a flowsheet which can be analysed for insightful directions to improve a chemical process. The approach showed to be helpful in analysing a biofuel production process by adopting a combined economic and greenhouse gas emissions analysis at a modular level for hot spot analysis and identification of opportunities for decarbonisation. In an application to process integration, CO2 capture and utilisation networks are analysed in terms of its modularity and efficiency in processing CO2. In both cases, the improved designs had higher modularity values suggesting a correlation between modularity and technical performance which needs to be investigated further. Finally, prospective applications of modularity and modular decomposition approaches to realise several digitalisation tasks are presented.

Impact of smart health systems on the behavior of older adults under community healthcare.

With the trend of world population aging, a good community health care system will determine whether the elderly can get good medical conditions. How to improve the community health care system can study how the behavior of the elderly affects it. This paper is based on the analysis of the current situation of population aging at home and abroad. On the premise of analyzing the demand and behavior of elderly people seeking medical treatment and the function of community health service institutions. Literature research was conducted to analyze the influencing factors of community health care needs and elderly people's medical seeking behavior at home and abroad. Then the elderly in Tianjin were investigated by issuing questionnaires, and the law of medical treatment behavior of the elderly in Tianjin was calculated. Combined with the results of relevant investigations abroad, the common phenomenon is summarized. Finally, the analysis method of intelligent medical system is proposed, and the design process of system acquisition module and user usage mode are given. The smart medical system can bring great convenience to the elderly and community healthcare. It emphasizes the powerful functions of smart health systems and their future importance for the health care of the elderly.

Characteristics of gas leakage and typical dispersion modes for FPSO's process modules

Gas cloud explosion caused by flammable gas leakage is the most serious accidents which threat to structures and humans on floating production, storage, and offloading (FPSO) production process. However, the shape, position and concentration of leakage gas cloud are irregular in the process modules. Some concepts and standards are proposed to convert irregular gas cloud into rectangular cuboid cloud, but neglect the actual sizes and position of cuboid cloud in the modules. The main purposes of this paper are to summarize the gas dispersion modes based on the gas leakage analyses and propose a new method to determine the position and sizes of cuboid cloud. Considering the effect of leak parameters, the gas dispersion characteristics are analyzed in free field and actual process module and eleven modes can be summarized to represent the gas dispersion, the methods for determining the cuboid cloud characteristics of each mode are proposed. The summary of gas dispersion characteristics and modes can be used to determine the spatial distribution of leakage gas cloud and evaluate the leakage accidents consequence. The determination method for cuboid cloud characteristics are applied to be the input information to assess the consequence of flammable gas explosion.

Failure analysis on the premature delamination in the power module of the inverter for new energy vehicles

For the inverter in the new energy vehicle, the existence of the power module provides it have the function of converting direct current (DC) in the battery pack into three-phase alternating current (AC) driving the motor. Therefore, the reliability of the power module is significant to ensure the stable operation of vehicles. In this paper, a failure case about premature delamination between the copper bridge and epoxy molding compound (EMC) in the power module during power cycling tests was reported. To find out the causes of premature delamination, a series of characterization methods including thermogravimetric analysis (TGA), inductively coupled plasma-mass spectrometry (ICP-MS), ion chromatography (IC), three-dimensional stereo microscope (3D-SM), metallurgical microscope (MM), scanning electron microscope (SEM) and energy dispersive spectrometer (EDS) were adopted. The analysis objects contained the EMC, circulating water, copper strips in different stages, copper bridges and decapped power modules, which were all covered in the manufacturing process of power modules. By comprehensive analysis, over-corrosion on the copper strip surface and overlapping skins on the copper bridge surface were determined as the root causes of premature delamination. Based on the conclusions, corresponding countermeasures were also proposed. This paper gives a kind of pre-analysis method to the possible failure of the components, so it is meaningful for how to effectively prevent the failure of the components during assembly process.

Design of mini airborne emergency power supply based on super capacitor

In order to solve the problem that it is difficult to install emergency battery with limited space on some flight planes, a micro emergency power supply based on super capacitor was designed in combination with the good energy storage characteristics of super capacitor. The selection calculation of capacitor and the design process of main control module and charge and discharge module are discussed in detail. In the laboratory environment, a small airborne test system was built to carry out verification experiments. The results show that the device meets the basic requirements of the system for backup power supply, and has the advantage of taking up less space and avoiding maintenance, which provides a new choice for the backup power supply selection of the future in-flight test system.