Selection Of Components Research Articles

Performance optimization of nanofluid-cooled photovoltaic-thermoelectric systems: A study on geometry configuration, steady-state and annual transient effects

In this study, we explored Photovoltaic-Thermoelectric (PV-TE) systems in-depth, addressing complexities in both steady-state and annual transient performance under realistic conditions. The analysis involved twelve distinct PV-TE configurations featuring diverse thermoelectric designs incorporating various semiconductors, multi-staging, non-uniform cross-sections, and material segmentation. Model 6, the PV-TE system with a multi-stage segmented rectangular design, emerged as the top performer, exhibiting a remarkable 12% increase in electric power output during peak sunlight compared to the base model, despite a marginal decrease in efficiency. Furthermore, this research delved into the potential of advanced nanofluid cooling, investigating options such as distilled water, titanium oxide, aluminum oxide, iron oxide, and graphene. The results underscores graphene nanofluid's superiority, demonstrating a significant enhancement in thermal management at an optimal flow velocity of 2 m/s. This improvement in thermal management refers to the effective heat dissipation and temperature control within the PV-TE system. This study underlines the critical role of strategic system design and component selection in optimizing the performance of PV-TE systems. By providing a comprehensive foundation for future developments in effective and sustainable energy solutions, this research contributes to advancing the understanding and implementation of PV-TE technology.

A new fault component selection strategy based on statistical detection for slewing bearing weak signal de-noising

Slewing bearing is a critical transmission component in large-size construction machinery due to its low-speed and heavy-load conditions. Fault prognostics and health management of slewing bearing are crucial for ensuring their high availability and profitable operation. However, the presence of background noise in construction machinery signals restricts the applicability of existing signal processing approaches in prognostics and health management. To address this challenge, a novel signal de-noising method is proposed based on adaptive decomposition, along with a new strategy for recognizing fault components using statistic detection through kernel principal component analysis (KPCA). First, robust local mean decomposition is utilized to adaptively decompose the fault and normal vibration signal over the entire service life. Then, product functions (PFs) decomposed by fault and normal vibration signal are used for KPCA anomaly detection. Finally, the fault PFs are reconstructed to obtain the de-noised signal. The effectiveness of the proposed method is validated through the use of both simulated and experimental vibration signals obtained from a slewing-bearing life-cycle test. The results illustrate that the proposed method has superior de-noising capability and decomposition efficiency, making it an effective signal preprocessing technique for prognostics and health management.

Optical information hiding for different surface images.

Optical hiding often requires the selection of specific artificial optical components as carriers, which results in poor versatility of the carriers and high costs for the hiding system. To conceal secret information on different surfaces such as metal, wood, and paper, we propose an optical information hiding method. In this method, we use images of surfaces, whose grayscale histograms have the characteristic of symmetric distribution. Based on this characteristic, we first scramble the surface image, and then adjust part of the gray value of the surface image to the complementary value to embed the secret information into a scrambled surface image to generate a key image. In the extraction process, a projector is used to reproduce the scrambled surface image and the key image, which are then incoherently superimposed to extract the secret information using the human visual system. The extraction process does not require complex optical knowledge and is simple and feasible. Simulation experiments and optical experiments indicate that this method is applicable in practice and possesses good security and imperceptibility. Furthermore, we prove the reliability of this method by embedding secret information in different surface images, demonstrating the potential application of more surface images in the field of optical information hiding. Finally, we discuss the applicability of surface information images and analyze the imperceptibility of key images.

Designing Sustainable Hydrophilic Interfaces via Feature Selection from Molecular Descriptors and Time-Domain Nuclear Magnetic Resonance Relaxation Curves.

Surface modification using hydrophilic polymer coatings is a sustainable approach for preventing membrane clogging due to foulant adhesion to water treatment membranes and reducing membrane-replacement frequency. Typically, both molecular descriptors and time-domain nuclear magnetic resonance (TD-NMR) data, which reveal physicochemical properties and polymer-chain dynamics, respectively, are required to predict the properties and understand the mechanisms of hydrophilic polymer coatings. However, studies on the selection of essential components from high-dimensional data and their application to the prediction of surface properties are scarce. Therefore, we developed a method for selecting features from combined high-dimensional molecular descriptors and TD-NMR data. The molecular descriptors of the monomers present in polyethylene terephthalate films were calculated using RDKit, an open-source chemoinformatics toolkit, and TD-NMR spectroscopy was performed over a wide time range using five-pulse sequences to investigate the mobility of the polymer chains. The model that analyzed the data using the random forest algorithm, after reducing the features using gradient boosting machine-based recursive feature elimination, achieved the highest prediction accuracy. The proposed method enables the extraction of important elements from both descriptors of surface properties and can contribute to the development of new sustainable materials and material-specific informatics methodologies encompassing multiple information modalities.

An analysis of polymer material selection and design optimization to improve Structural Integrity in 3D printed aerospace components

This paper presents an analysis of material selection and design optimization techniques to enhance the structural integrity of 3D printed aerospace components. The study highlights the importance of considering material characteristics and design factors such as shape, orientation, and support structures in order to achieve reliable and high-performance components. Various materials, including metals and polymers, commonly used in aerospace applications are evaluated, along with their properties and limitations in the context of 3D printing. Furthermore, the impact of different printing parameters on the structural integrity of the components is discussed. The study identifies optimization strategies such as topology optimization, lattice structures, and infill patterns, which can significantly improve the strength and durability of 3D printed parts. The results demonstrate the potential of these techniques to optimize the design and material selection of aerospace components, leading to lighter, more efficient, and reliable parts for air and space vehicles.

Design of ultra-efficient and automatically temperature-variable cycle (TVC) Sieverts apparatus for testing sorption properties of hydrogen storage materials

The evaluation of hydrogen sorption properties in materials is crucial for their widespread applications. However, existing volumetric apparatuses often suffer from low accuracy and efficiency due to inadequate calibration techniques, temperature gradients, and limited automation. To address these limitations, a Temperature-Variable Cycle (TVC) Sieverts apparatus has been developed. Besides conventional tests, the device is capable of efficiently performing TVC tests, such as TVC Pressure-Composition-Temperature (PCT) isotherm and kinetics, through the integration of RS485 serial communication and automatic dosing techniques. The instrument's component selection and configuration were described in detail. Furthermore, novel methods for volume calibration and anti-thermal gradient were presented, where a continuous function was proposed to overcome temperature gradients across the entire range of test temperatures. Finally, a series of tests were conducted using La0.5Ce0.5Ni4Co alloy and Mg material as benchmark materials to validate the setup and methods. The accuracy of volumes and the feasibility of the methods were confirmed through null PCT tests and data comparisons of PCT with kinetic of La0.5Ce0.5Ni4Co alloy, respectively. By employing these methods, the accuracy of null tests is improved by nearly tenfold. The efficiency and robustness of the unit were demonstrated through Temperature-Programmed Desorption/Absorption (TPD/TPA), TVC PCT, and TVC kinetics tests on MgH2 powder. The results produced by the TVC system align well with the reliable data in literature.

Low-cost reversible tandem lens mesoscope for brain imaging in rodents.

The development of imaging systems that are cost-efficient and modular is essential for modern neuroscience research. In the current study, we designed, developed, and characterized a low-cost reversible tandem lens mesoscope for brain imaging in rodents. Using readily available components, we assembled a robust imaging system that is highly efficient and cost-effective. We developed a mesoscope that offers high-resolution structural and functional imaging with cost-effective lenses and CMOS camera. The reversible tandem lens configuration of the mesoscope offers two fields of view (FOVs), which can be achieved by swapping the objective and imaging lenses. The large FOV configuration of provides a spatial resolution up to , and the small FOV configuration of provides a resolution of up to . We demonstrate the efficiency of our system for imaging neuronal calcium activity in both rat and mouse brains in vivo. The careful selection of the mesoscope components ensured its compactness, portability, and versatility, meaning that different types of samples and sample holders can be easily accommodated, enabling a range of different experiments both in vivo and in vitro. The custom-built reversible FOV mesoscope is cost-effective and was developed for under US$10,000 with excellent performance.

Optimization and design of a new column sequencing for crude oil distillation at Basrah refinery

Abstract The utilization of distillation stands as a predominant separation method within the chemical and petroleum industries, prominently influencing operational costs and environmental impact due to energy consumption. Enhancing energy efficiency holds paramount significance in elevating the sustainability and overall efficacy of distillation operations. Within this study, we introduce an innovative approach termed “marginal vapor flow (MVF)” to optimize the distillation column sequence for crude oil processing, focusing on the third distillation unit at the Basra Refinery. This research evaluates diverse column designs through streamlined simulations using Aspen HYSYS V11 software. The study determines total energy consumption as a benchmark, comparing it against the optimal sequence recommended by the MVF methodology. A novel application of the downward reduction equation to crude oil guides the selection of light and heavy components. Key findings from this comprehensive analysis showcase the potency of combining MVF with Aspen HYSYS for optimizing crude oil distillation column sequences. Aspen HYSYS, a widely recognized process simulation tool, accurately represents distillation processes. Simultaneously, MVF facilitates the determination of optimal column sequences based on marginal vapor flow rates. Notably, the results reveal that within the studied sequences, sequence 9 exhibits the lowest total MVF of (1393.4 kmol/h), signifying its optimality, while sequence 2 displays the highest total MVF of (4827.3 kmol/h), representing the least favorable scenario. Simulation of the optimal sequence derived through the MVF approach exhibits a remarkable 35% reduction in energy consumption compared to real-life operations. Conversely, simulating the least favorable sequence demonstrates a substantial 32% increase in energy consumption compared to actual operations. This study underscores the pivotal role of MVF methodology in optimizing distillation sequences for enhanced energy efficiency, providing actionable insights for refining operations to significantly reduce energy consumption and operational costs while advancing sustainability goals.

Software component selection methods and techniques: a systematic review

<p>Software component selection is critical in software engineering due to its vital role in reducing software development cost and time. This study analyzes software component selection research studies on methodologies, criteria, and multi-criteria decision-making (MCDM) techniques. The key study findings are: first, comprehensive standardized criteria for software component selection are lacking, with ambiguous terminology used in research. Second, current ad hoc selection processes need streamlining to reduce time, cost, and effort. Thus, an integrated approach is required to aid decision-makers. The review suggests developing automated tools or decision support systems combining multiple criteria decision methods to improve selection accuracy and efficiency. Standardized criteria catalogs can also assist software developers in the evaluation. The findings highlight that despite extensive academic research, component selection in practice remains sub-optimal. By informing future research and tool development, this review can benefit practitioners to systematically select the most appropriate software components meeting software requirements.</p>

Integration of natural selection across the life cycle stabilizes a marine mussel hybrid zone.

Hybridization among related species is now recognized as common but it remains unclear how hybrid zones persist for prolonged periods. Here, we test the hypothesis that selection in different components of the life cycle may stabilize a hybrid zone. A hybrid zone occurs in southwest England between the marine mussels Mytilus edulis and M. galloprovincialis. Previous studies have found strong directional selection against alleles from M. edulis occurs among hybrids in the adult stage. Traditional hybrid zone models argue that alleles that are selected within the hybrid zone are replaced by migration from neighboring parental population into the hybrid zone. In this system, however, migration occurs out of this hybrid zone into neighboring parental populations. This hybrid zone should therefore be unstable and dissipate, yet this zone has persisted for more than 30 years. We tested and rejected the hypothesis that differences in fecundity may select for M. edulis alleles within this hybrid zone and thus counter the selection observed against these alleles among adults. We also tested the hypothesis that selection during the larval stage may counter selection against M. edulis alleles in the adult stage. We found that selection favors M. edulis alleles during the veliger stage of larval development. The direction and strength of selection during the larval stage are sufficient to counter strong selection during the adult portion of the life cycle. This hybrid zone is stabilized by opposing forms of directional selection operating in different portions of the life cycle.

Gradient Bi-layer coating for reducing energy consumption and improving electromagnetic wave dissipation: Elucidating the role of interfaces and interfacial polarization

The implementation of smart and logical design for absorbers, along with careful selection of components, has proven to be a successful strategy in creating efficient electromagnetic wave (EMW) absorbers. By utilizing novel nanocomposite materials that integrate various loss mechanisms, including enhanced magnetic and dielectric loss, multiple scattering and reflection, and polarization loss, the fabrication of a gradient bi-layer absorber holds great promise for superior EMW absorption. By employing chemical methods, a novel dielectric-magnetic compound comprising of Co2FeAl Heusler Alloy and MWCNTs@NiSe2 was produced. Subsequently, a gradient bi-layer absorber was developed using these components to optimize impedance matching and bolster low-frequency absorption. Abundant hetero-interfaces within this ternary bi-layer nanocomposite absorber give rise to Schottky junctions and heightened interfacial polarization between its layers, components, and matrix. Notably, the utilization of Co2FeAl Heusler Alloy nanoparticles has been shown to lead to the manifestation of magnetic loss phenomena due to their inherent magnetic properties. Through the strategic utilization of the unique layer structure, in conjunction with the incorporation of multiple loss mechanisms, the ternary bi-layer nanocomposite was able to achieve an impressive reflection loss value of -29 dB, despite having a mere 1.4 mm total thickness. Our study offers a fresh framework for creating innovative bilayer gradient absorbers suitable for low frequency applications.

Overview of safety challenges associated with integration of hydrogen-based propulsion systems for climate neutral aviation

Electrification through hydrogen-based fuel cells as well as hydrogen combustion in gas turbines is a key strategy in aviation for achieving substantial reduction of emissions. However, this transition presents multifaceted challenges. Besides the development and improvement of technologies required for such hydrogen-fuelled aero engines, the safety of hydrogen storage and distribution systems on aircraft is paramount. Challenges associated with hydrogen in terms of its material properties, the design and selection of components for the conditioning and distribution, as well as the system design are being presented and discussed in this work. This includes the consideration of high diffusivity, flammability and reactivity of hydrogen and the consequences of these traits: hydrogen embrittlement, hydrogen-induced cracking and leakage, for instance. The challenges elaborated in this work are pertinent to both hydrogen fuel cell-based propulsion systems and hydrogen combusting gas turbines. Design considerations were derived and are being outlined in this work. These are transferable to applications in other industries such as automotive and stationary power plants. The need for novel rigorous safety protocols to enable a sustainable future in aviation is being highlighted.

Tandem clustering with invariant coordinate selection

For multivariate data, tandem clustering is a well-known technique aiming to improve cluster identification through initial dimension reduction. Nevertheless, the usual approach using principal component analysis (PCA) has been criticized for focusing solely on inertia so that the first components do not necessarily retain the structure of interest for clustering. To address this limitation, a new tandem clustering approach based on invariant coordinate selection (ICS) is proposed. By jointly diagonalizing two scatter matrices, ICS is designed to find structure in the data while providing affine invariant components. Certain theoretical results have been previously derived and guarantee that under some elliptical mixture models, the group structure can be highlighted on a subset of the first and/or last components. However, ICS has garnered minimal attention within the context of clustering. Two challenges associated with ICS include choosing the pair of scatter matrices and selecting the components to retain. For effective clustering purposes, it is demonstrated that the best scatter pairs consist of one scatter matrix capturing the within-cluster structure and another capturing the global structure. For the former, local shape or pairwise scatters are of great interest, as is the minimum covariance determinant (MCD) estimator based on a carefully chosen subset size that is smaller than usual. The performance of ICS as a dimension reduction method is evaluated in terms of preserving the cluster structure in the data. In an extensive simulation study and empirical applications with benchmark data sets, various combinations of scatter matrices as well as component selection criteria are compared in situations with and without outliers. Overall, the new approach of tandem clustering with ICS shows promising results and clearly outperforms the PCA-based approach.

Design and testing of mechanical gripping tools for On Orbit assembling

On-orbit servicing, assembly and manufacturing (OSAM) opens a new frontier for robotic systems. A gripping tool for such applications must meet several requirements of space mechanics, such as safety, precision and reliability, while functioning in space conditions. This paper presents a development cycle of such tools designed for the assembly of a small satellite antenna on the International Space Station. Two different grippers, driven by a common drive unit, are presented, conforming to a Multi-Purpose-Tool (MPT) for orbital robotic systems and meeting the requirements of a defined OSAM mission. Both design drivers and concepts and specific component selection are described. Proposed mechanical solutions for safe gripping of objects including space tribology aspects are covered. To adapted to operations not foreseen, the grippers and the drive unit can be reconfigured. The tool architecture presented promotes modularity, scalability, reusability and convertibility of designs, thus facilitating rapid integration in similar missions. A test campaign for critical requirements will be in place to ensure reliable performance of the tools for use in the space environment.

Design of Environmental Detector System (EDS) application through industrial and vocational collaboration

Politeknik Negeri Medan (POLMED) as a vocational institution and PT Berkat Solusi Teknik as a partner have the same responsibility to solve problems that exist in society. Problems in society related to pollution from industrial waste. This is due to many complaints from the public, NGO, and other organizations regarding environmental damage due to production and economic activities. Based on the problems above, this study is needed to detect and monitor environmental parameters such as CO2 level, lighting, noise, temperature, humidity, and measurement data that can be accessed and displayed in real-time via the internet. The implementation method is strengthening work organization, making EDS prototype brackets, design of an environmental detector system (EDS) prototype, prototype testing, MBKM internship, international conferences, monitoring and coordinating with partner, internal monitoring, and evaluation I and II, and preparation of final report. We innovate in the selection of components and the technology used. This collaboration is a symbiotic mutualism between POLMED and industry. The results are in average values, namely the density of dust particles is 1.418 µg/m3. Temperature 36.60oC. Humidity 53.82%RH. Light intensity 10,615 lux. These values above are still normal condition, and there is no pollution in campus. Especially for temperature and humidity. This condition above on the threshold.

Simulink and real-time implementation of the E-cycle for measuring the reliability of the model using sensors

Utilization of rechargeable battery based bicycles has been significantly increased in India. In this study, battery powered electric vehicles have been implemented in MATLAB 2013a Simulink and real-time hardware systems. In order to design and integrate a working model of electric cycle, one needs to know about the different components involved in a system, such as lithium-ion batteries, Brushless DC motor (BLDC), and controller systems. The detailed study about the component selection has been described in this study. The drive testing has been implemented in both Simulink and real time respectively. Within a single charging system, the proposed model of electric cycle has been derived up to 25 kilo-meter up to speed of 30 kilo-meter per second respectively. The characteristics of electric vehicles such as torque (N-m) versus time (s), speed (km/h) versus time (s) and distance (km) versus time (s) have been discussed in this study. Based on characteristics study, reliability of the electric vehicle model has been enhanced in electric bicycle system by physical model confirmation.

Stimulus Avoidance Assessment: A Systematic Literature Review.

Board certified behavior analysts are ethically required to first address destructive behavior using reinforcement-based and other less intrusive procedures before considering the use of restrictive or punishment-based procedures (ethics standard 2.15; Behavior Analyst Certification Board, 2020). However, the inclusion of punishment in reinforcement-based treatments may be warranted in some cases of severe forms of destructive behavior that poses risk of harm to the client or others. In these cases, behavior analysts are required to base the selection of treatment components on empirical assessment results (ethics standard 2.14; Behavior Analyst Certification Board, 2020). One such preintervention assessment is the stimulus avoidance assessment (SAA), which allows clinicians to identify a procedure that is likely to function as a punisher. Since the inception of this assessment approach, no studies have conducted a systematic literature review of published SAA cases. These data may be pertinent to examine the efficacy, generality, and best practices for the SAA. The current review sought to address this gap by synthesizing findings from peer-reviewed published literature including (1) the phenomenology and epidemiology of the population partaking in the SAA; (2) procedural variations of the SAA across studies (e.g., number of series, session length); (3) important quality indicators of the SAA (i.e., procedural integrity, social validity); and (4) how the SAA informed final treatment efficacy. We discuss findings in the context of the clinical use of the SAA and suggest several avenues for future research.

Design analysis of hybrid solar-wind renewable energy systems using water strider optimization

Hybrid energy systems (HES) have the potential to reduce global warming and provide an affordable alternative to provide electricity access to remote regions. This paper proposes an optimal design of HES using water strider optimisation (WSO) with multi-objective parameters. A selection of appropriate components, sizing, radius index, pitch control, and battery bank size for a HES system is optimised with the WSO algorithm. The objective is to provide cost-efficient, dependable, and alternative energy optimisation in various load demand conditions. The designed parameters, including a battery bank of 2.5 kWh to 800 kWh, a solar photovoltaic (PV) array size of 50 kW to 300 kW in terms of load demand, a 2-axis tracking PV system with an area of 12 m2, and a wind turbine (WT) with a swept area of 22 m2 and a hub height of 12.3 m, make up an optimised HES. The single-family energy demand in India is the topic of this study, with a lifespan of 20 to 30 years.

Techno-economic configuration of an optimized resident microgrid: A case study for Afghanistan

The escalating demand for electricity and the imperative to reconstruct underprivileged areas in Afghanistan necessitates a resilient energy supply system. This study advocates for the implementation of a cost-effective and high-performing microgrid in a region situated in the northern of Kandahar City, Afghanistan. Utilizing a mix of diesel engines, batteries, wind, and solar energy sources, the microgrid aims to provide a sustainable and eco-friendly solution to meet the electricity requirements. Employing the HOMER software for comprehensive analysis, the proposed microgrid configuration comprises a 3.69 kW PV, a 1.50 kW diesel generator, and 27 units of 1 kWh LA batteries. The study investigates six different configurations to identify the optimal solution based on community-specific criteria. Factors such as energy generation, cost, and system performance are rigorously evaluated by the software, culminating in the selection of components that constitute the hybrid system. The optimal configuration includes a capital cost of 5962 $, a net present cost (NPC) of 10108$, operating and maintenance (O&M) costs amounting to 320.69$, and a cost of energy (COE) at 0.190$/kWh. These results show the viability and economic feasibility of the proposed microgrid as a sustainable energy solution for the studied community in Afghanistan.

Development and implementation of a wireless-controlled robotic arm for lifting applications with 6 DOF

This paper is centered on the design and construction of a Bluetooth-controlled robotic arm with 6 degrees of freedom. It is capable of manipulating given objects as well as lifting and conveying a payload from one point to another. Any smartphone that possesses an Android operating system can be used for remote operations. This offers a background look at robotic arms, from invention to current trend as well as simplification of design to make it more accessible to robotics enthusiasts. The design process for the robotic arm is chronicled in this paper, from the working principle to the development of the kinematic equations, as well as CAD modeling and component selection. Tests are also conducted to ascertain the robot’s strength and range of capabilities. The availability of this robotic arm would serve as an indispensable learning tool for experimenting with robotics in training institutions.