Conventional Design Research Articles

Construction of Twenty-Three Points Second Order Rotatable Design in Three Dimensions Using Trigonometric Functions

In this paper, a novel twenty-three-point second-order rotatable design is formulated utilizing trigonometric functions. Design of experiments plays a crucial role in various industries and research fields to investigate the relationship between multiple variables and their effects on a response variable. In particular, second order rotatable designs are widely used due to their ability to efficiently estimate the main, interaction, and curvature effects. Nevertheless, creating designs with a substantial number of points poses difficulties. This study concentrates on developing a second-order rotatable design with twenty-three points utilizing trigonometric functions. Trigonometric functions offer a systematic approach to distribute the points uniformly in the design space, thereby ensuring the optimal coverage of the experimental region. The proposed construction utilizes the properties of sine and cosine functions to generate a balanced and efficient design. The methodology involves dividing the design space into equidistant sectors and assigning the points using the trigonometric functions. By carefully selecting the starting angle and the angular increment, a complete and orthogonal design is achieved. The design is rotatable, meaning it can be rotated to any desired orientation without impairing the statistical properties of the design. Through this construction, the design effectively captures the main effects, interaction effects, and curvature effects. This enables reliable estimation of the model parameters, leading to accurate predictions and efficient optimization. Additionally, the design is efficient in terms of minimizing the number of experimental runs required, thereby reducing costs and time. The suggested second-order rotatable design comprising twenty-three points and employing trigonometric functions exhibits its superiority when compared to conventional designs. It offers a systematic and straightforward approach to construct a balanced and efficient design for studying the relationships between multiple variables. The design's rotatability ensures flexibility in experimental settings, making it a valuable tool for researchers and practitioners in various fields.

Design and Optimization of Heat Dissipation for a High-Voltage Control Box in Energy Storage Systems

Abstract To address the issue of excessive temperature rises within the field of electronic device cooling, this study adopts a multi-parameter optimization method. The primary objective is to explore and realize the design optimization of the shell structure of the high-voltage control box, aiming to effectively mitigate the temperature rise in internal components and enhance their thermal management efficacy without altering the fan performance, environmental conditions, or spatial layout. Initially, the study employs computational fluid dynamics methods to investigate the heat dissipation characteristics of the high-voltage control box, subsequently verifying the simulation parameters' accuracy through temperature rise tests. Building upon this foundation, the article conducts a thorough analysis of how the position and shape of the box's openings impact the device's temperature rise. The findings suggest that configuring circular openings on the front and rear sides can optimize the heat dissipation effect. Moreover, the SHERPA algorithm was employed to refine the size and distribution of the openings on the outer shell of the high-voltage control box through multi-parameter optimization, yielding locally optimal structural parameters. Post-optimization, the temperature measurement points within the high-voltage control box exhibited a maximum reduction in temperature rise of 27.16%. The pivotal contribution of this methodology is the application of a data-driven decision-making process for the enhancement of conventional heat dissipation designs. This research offers invaluable practical insights and novel perspectives on the optimization of thermal management designs for box-type electronic devices, significantly advancing the field of thermal management technology in electronic devices.

Integrated Optimization Model of Lane Function and Signal Control for Tandem Intersections

This paper presents an integrated optimal controller for the tandem intersection with lane function design and signal control, aiming to improve intersection efficiency and service reliability with a multi-objective formulation. The tandem intersection is a type of unconventional intersection that can re-organize the vehicles at entrance lanes with sorting areas, and improve intersection capacity through the coordination of pre-signals and main signals. However, most existing studies related to tandem intersection control assume that the lane functions in the sorting areas for both the through and left-turn movements are the same, and the traffic demand remains static. To fill these gaps, this paper first identifies six different tandem control modes based on the different lane functions and phase sequence schemes in the sorting area, and the corresponding delay models for each mode are derived. Furthermore, an integrated optimization model is developed to minimize the mean and semistandard deviation of the intersection delay, and the Non-dominated Sorting Genetic Algorithm-II is used to obtain the optimal solution. A case study is conducted in a real-world intersection in Melbourne, Australia, under various traffic conditions. The results show that the proposed method can decrease average delay and queue length by 19.61% and 20.94%, respectively, compared with conventional intersection design.

Tackling Data Scarcity Challenge through Active Learning in Materials Processing with Electrospray

Machine learning (ML) has been harnessed as a promising modelling tool for materials research. However, small data, or data scarcity, is a bottleneck when incorporating ML in studies involving experimentation. Current experiment planning methods show several disadvantages: one‐factor‐at‐a‐time (OFAT) experimentation became impractical due to limited laboratory resources; conventional design of experiments (DoE) failed to incorporate high‐dimensional features in ML; Surrogate‐based or Bayesian optimization (BO) shifted the goal to optimize material properties rather than guiding training data accumulation. The present research proposes leveraging active learning (AL) to strategically select critical data for experimentation. Two AL strategies, query‐by‐Committee (QBC) algorithm and Greedy method, are benchmarked against random query baseline on various materials datasets. AL is shown to efficiently reduce model prediction errors with minimal additional experiment data. Investigation of hyperparameters revealed benefits of applying AL at an early stage of experimental dataset construction. Moreover, AL is implemented and validated for an in‐house materials development task ‐ electrospray modelling. AL exploration as a paradigm is highlighted to guide experiment design for efficient data accumulation purposes, and its potential for further ML modelling. In doing so, the power of ML is expected to be fully unleashed to experimental researchers.

Simulation study of a new racetrack FAIMS analyzer to achieve both high-resolution and high-sensitivity

A new racetrack field-asymmetric waveform ion mobility spectrometry (r-FAIMS) analyzer was developed in this study by combining the existing planar FAIMS (p-FAIMS) and cylindrical FAIMS (c-FAIMS). The ion inlet and outlet regions of r-FAIMS were consisted of a half of c-FAIMS, respectively, and these c-FAIMS were further connected by two p-FAIMS to form a racetrack shaped FAIMS. With such FAIMS working electrode configuration, the ions entering the r-FAIMS can be focused and separated in the first c-FAIMS section, be further separated in the p-FAIMS section with high-resolution, be focused and separated again in the final c-FAIMS section and eventually enter the mass spectrometer or other analyzers for analysis. Detailed simulation by using SIMION software with the default FAIMS user program showed that the ion focusing effect in the first c-FAIMS section ensures the ions entering the following p-FAIMS section as a compact ion packet. This effectively decreases the ion loss caused by Coulomb repulsion and thermal diffusion in p-FAIMS section as compared to the ions being introduced into the p-FAIMS gap randomly in the conventional design. As a result, the ion transmission efficiency of r-FAIMS is at least 3.3-fold higher than the single p-FAIMS under the operating conditions used in this study. The ion trajectory simulation results also showed that the resolving power of r-FAIMS is about the sum of the resolving powers for its c-FAIMS and p-FAIMS sections. The resolving power of r-FAIMS is at least 3.6-fold higher than the single c-FAIMS under the operation conditions used in this study. Therefore, the r-FAIMS can realize both high-resolution and high-sensitive ion mobility separation.

Assessment of interface characteristics and durability aspects of manufactured sand (M-sand) in engineered cementitious composites

Concrete structures usually tend to crack and cause several distresses, thus resulting in regular maintenance activities. Engineered Cementitious Composites (ECC) in structural applications are becoming popular due to their cracking resistance and deformability. However, the conventional mix design of ECC with silica sand levied a negative influence on cost and drying shrinkage, thereby affecting their practical implementation. In this study, the matrix properties and interface characteristics of ECC tailored with Manufactured sand (M-sand) replacing the river sand at varying proportions were investigated. The study conducted mechanical characterization, matrix fracture toughness, and single-crack tension tests to evaluate the micro-mechanical properties and fiber-matrix interaction. Furthermore, the shrinkage characteristics and permeability properties of ECC mixtures were also evaluated at various replacement levels of M-sand. The natural river sand adhered to the finer gradation, characterized by a fineness modulus of 1.63, while the M-sand exhibits a coarser gradation, having a fineness modulus 2.74. The experimental results showed that the ECC mixtures with M-sand improved the compressive strength (up to 29 %) at 100 % replacement level and marginally influenced the tensile strength. However, the tensile ductility and fiber bridging characteristics showed a noticeable decrease as the percentage replacement increased in the mixtures. On the other hand, the drying shrinkage of all M-sand ECC mixtures showed a notable reduction irrespective of the curing ages. It is also seen that the permeability properties were slightly increased compared to that of the control mixture. In summary, the study observed a trade-off between mechanical strength and tensile ductility, highlighting the effects of fiber-matrix interface tailoring on the overall performance of ECC mixtures with M-sand for various structural applications.

Transcranial volumetric imaging using a conformal ultrasound patch.

Accurate and continuous monitoring of cerebral blood flow is valuable for clinical neurocritical care and fundamental neurovascular research. Transcranial Doppler (TCD) ultrasonography is a widely used non-invasive method for evaluating cerebral blood flow1, but the conventional rigid design severely limits the measurement accuracy of the complex three-dimensional (3D) vascular networks and the practicality for prolonged recording2. Here we report a conformal ultrasound patch for hands-free volumetric imaging and continuous monitoring of cerebral blood flow. The 2 MHz ultrasound waves reduce the attenuation and phase aberration caused by the skull, and the copper mesh shielding layer provides conformal contact to the skin while improving the signal-to-noise ratio by 5 dB. Ultrafast ultrasound imaging based on diverging waves can accurately render the circle of Willis in 3D and minimize human errors during examinations. Focused ultrasound waves allow the recording of blood flow spectra at selected locations continuously. The high accuracy of the conformal ultrasound patch was confirmed in comparison with a conventional TCD probe on 36 participants, showing a mean difference and standard deviation of difference as -1.51 ± 4.34 cm s-1, -0.84 ± 3.06 cm s-1 and -0.50 ± 2.55 cm s-1 for peak systolic velocity, mean flow velocity, and end diastolic velocity, respectively. The measurement success rate was 70.6%, compared with 75.3% for a conventional TCD probe. Furthermore, we demonstrate continuous blood flow spectra during different interventions and identify cascades of intracranial B waves during drowsiness within 4 h of recording.

Human digital twins to inform ship design

A key building block of digital twin solutions is a virtual counterpart for an asset that can be coupled to the asset throughout its lifecycle – predicting an asset’s potential performance at design and providing insight into operation during service. This paper presents the development of human digital twins that integrate human factors into conventional ship design procedures, particularly focussing on seakeeping performance assessments. A novel method for incorporating human-centric performance criteria in seakeeping analyses is proposed and initial validation thereof is detailed. Human digital twins are seen to provide a platform for informing the ship design process using data captured during vessel operation.

Magnetic Susceptibility Modeling of Magic-Angle Spinning Modules for Part Per Billion Scale Field Homogeneity

Magic-angle spinning (MAS) solid-state NMR methods are crucial in many areas of biology and materials science. Conventional probe designs have often been specified with 0.1 part per million (ppm) or 100 part per billion (ppb) magnetic field resolution, which is a limitation for many modern scientific applications. Here we describe a novel 5-mm MAS module design that significantly improves the linewidth and line shape for solid samples by an improved understanding of the magnetic susceptibility of probe materials and geometrical symmetry considerations, optimized to minimize the overall perturbation to the applied magnetic field (B0). The improved spinning module requires only first and second order shimming adjustments to achieve a sub-Hz resolution of 13C resonances of adamantane at 150 MHz Larmor frequency (14.1Tesla magnetic field). Minimal use of third and higher order shims improves experimental reproducibility upon sample changes and the exact placement within the magnet. Furthermore, the shimming procedure is faster, and the required gradients smaller, thus minimizing thermal drift of the room temperature (RT) shims. We demonstrate these results with direct polarization (Bloch decay) and cross polarization experiments on adamantane over a range of sample geometries and with multiple superconducting magnet systems. For a direct polarization experiment utilizing the entire active sample volume of a 5-mm rotor (90 µl), we achieved full width at half maximum (FWHM) of 0.76 Hz (5 ppb) and baseline resolved the 13C satellite peaks for adamantane as a consequent of the 7.31 Hz (59 ppb) width at 2% intensity. We expect these approaches to be increasingly pivotal for high-resolution solid-state NMR spectroscopy at and above 1 GHz 1H frequencies.

III-nitride m-plane violet narrow ridge edge-emitting laser diodes with sidewall passivation using atomic layer deposition

A novel deep-ridge laser structure with atomic-layer deposition (ALD) sidewall passivation was proposed that enhances the optical characteristics of 8-µm ridge width III-nitride violet lasers on freestanding m-plane GaN substrates. The internal loss was determined using the variable stripe length method, where the laser structure with ALD sidewall passivation showed lower internal loss compared to the conventional shallow-ridge laser design. ALD sidewall passivation plays a critical role in device improvements; compared to the lasers without ALD sidewall passivation, the lasers with ALD sidewall passivation yield improved optoelectrical performance and longer lifetime under continuous-wave operation at high current density. This work demonstrates the importance of ALD sidewall passivation to laser performance, which enables high energy efficiency.

Experimental validation of a PNS-optimized whole-body gradient coil.

Peripheral nerve stimulation (PNS) limits the usability of state-of-the-art whole-body and head-only MRI gradient coils. We used detailed electromagnetic and neurodynamic modeling to set an explicit PNS constraint during the design of a whole-body gradient coil and constructed it to compare the predicted and experimentally measured PNS thresholds to those of a matched design without PNS constraints. We designed, constructed, and tested two actively shielded whole-body Y-axis gradient coil winding patterns: YG1 is a conventional symmetric design without PNS-optimization, whereas YG2's design used an additional constraint on the allowable PNS threshold in the head-imaging landmark, yielding an asymmetric winding pattern. We measured PNS thresholds in 18 healthy subjects at five landmark positions (head, cardiac, abdominal, pelvic, and knee). The PNS-optimized design YG2 achieved 46% higher average experimental thresholds for a head-imaging landmark than YG1 while incurring a 15% inductance penalty. For cardiac, pelvic, and knee imaging landmarks, the PNS thresholds increased between +22% and +35%. For abdominal imaging, PNS thresholds did not change significantly between YG1 and YG2 (-3.6%). The agreement between predicted and experimental PNS thresholds was within 11.4% normalized root mean square error for both coils and all landmarks. The PNS model also produced plausible predictions of the stimulation sites when compared to the sites of perception reported by the subjects. The PNS-optimization improved the PNS thresholds for the target scan landmark as well as most other studied landmarks, potentially yielding a significant improvement in image encoding performance that can be safely used in humans.

Optimized multi-frequency nonlinear broadband piezoelectric energy harvester designs

Many electrical devices can be powered and operated by harvesting the wasted energy of the surroundings. This research aims to overcome the challenges of output power with a sharp peak, small bandwidth, and the huge dimensions of the piezoelectric energy harvesters relative to the output power. The aforementioned challenges motivated us to investigate the effect of nonlinearity in the shape (tapered and straight cross-section area) as well as the fixation method (the number of fastened ends) to determine the optimal design with high output power and wide working frequency. This research proposes a novel piezoelectric energy harvester array, where each beam is made up of three fixed beams that are joined together by a center mass. The proposed design produces an output power of 35 mW between 25 and 40 Hz. The output power of the proposed design is 3.24 times more than the conventional designs. The recommended approach is simulated utilizing finite element analysis FEA. Analytical and experimental methods validate the proposed FEA, which exhibits excellent agreement.

Performing the self: the construction of Casa Malaparte as living images

The paper discusses architectural design as a transdisciplinary practice that interlinks architecture, performance, and the moving image through a building example: the Casa Malaparte, the house of Italian writer Curzio Malaparte (1898–1957) built in Capri, Italy, between 1938 and 1942. Malaparte stated many times that he considered his house the best portrait of himself and he called the Casa Malaparte ‘A House Like Me’. Through theoretical, archival, and fieldwork research, the paper argues that the house performs Malaparte's autobiography. Unlike the conventional architectural design process, the Casa Malaparte was built by its owner Curzio Malaparte in collaboration with a master builder in an improvisational manner that incorporated methods of cinematic framing, material collage, and assemblage. The house is discussed as a self-portrait of its inhabitant questioning what could be architecture's role in mirroring the self and theorises Casa Malaparte's creation as a practice that works in between architecture, performance, and the visual arts. By drawing on Bertolt Brecht's ‘alienation effect’, Antonin Artaud's ‘theatre of cruelty’, and Denis Diderot discussion of the ‘tableau vivant’ which literally means ‘living pictures’, the paper provides theoretical frameworks that bring together spatial and performance theories. The Casa Malaparte as a tableau vivant is discussed as an alternative performative device that can potentially expand architectural design and render it as a living practice.

Enhancing modulation performance by design of hybrid plasmonic optical modulator integrating multi-layer graphene and TiO2 on silicon waveguides

A novel way to enhance modulation performance is through the design of a hybrid plasmonic optical modulator that integrates multi-layer graphene and TiO2 on silicon waveguides. In this article, a design is presented of a proposed modulator based on the use of the two-dimensional finite difference eigenmode solver, the three-dimensional eigenmode expansion solver, and the CHARGE solver. Leveraging inherent graphene properties and utilizing the subwavelength confinement capabilities of hybrid plasmonic waveguides (HPWs), we achieved a modulator design that is both compact and highly efficient. The electrical bandwidth f 3dB is at 460.42 GHz and it reduces energy consumption to 12.17 fJ/bit with a modulator that functions at a wavelength of 1.55 μm. According to our simulation results, our innovation was the optimization of the third dielectric layer’s thickness, setting the stage to achieve greater modulation depths. This synergy between graphene and HPWs not only augments subwavelength confinement, but also optimizes light–graphene interaction, culminating in a markedly enhanced modulation efficiency. As a result, our modulator presents a high extinction ratio and minimized insertion loss. Furthermore, it exhibits polarization insensitivity and a greater bandwidth. Our work sets a new benchmark in optical communication systems, emphasizing the potential for the next generation of chip-scale with high-efficiency optical modulators that significantly outpace conventional graphene-based designs.

Designing for Modern Living

This study delves into the evolving landscape of modern living in South Korea, which has the widespread apartment complexes that have emerged from the efficiency centric approaches of industrial capitalism. It explores the paradigm shift in the 21st-century capitalist society, which now values creativity and individual expression over functionality and uniformity. This shift has led to a noticeable disparity between the monotonous spatial composition of mass-produced housing and the dynamic, creative lifestyles of contemporary residents. The research method involves a comprehensive analysis of both lifestyle and architectural magazines, providing insights into the changing preferences and lifestyles of residents, as well as the perspectives of professionals. The study aimed to highlight the changing nature of residential spaces and the design strategies, moving away from the conventional utility-focused designs, towards environments that foster creativity and reflect the individuality of inhabitants. Key findings indicate a growing public preference for residential spaces that are versatile, creatively stimulating, and aligned with the multifaceted nature of modern lifestyles. Contrasting these views, architectural experts emphasize the fundamental values of living, advocating for spaces that connect residents with nature and enrich everyday experiences through sensory engagement. The study concludes that while there is a divergence in perspectives between the general public and architectural specialists, both recognize the necessity for sustainable housing solutions. These solutions should cater to contemporary societal changes while preserving essential life values, thereby overcoming the limitations of the prevalent apartment centric urban housing model in South Korea.

Comparison of nature‐inspired algorithms in finite element‐based metaheuristic optimisation of laminated shells

AbstractThis work presents a unique technique for optimising composite laminates used as structural components, which is critical for situations where failure might result in disastrous effects. Unlike traditional surrogate‐based optimisation approaches, this methodology combines the accurate modelling capabilities of finite element (FE) analysis with the iterative refining capacity of metaheuristic algorithms. By combining these two methodologies, our method intends to improve the design process of laminated shell structures, assuring robustness and dependability is crucial. Compared to existing benchmark solutions, the current FE shows a <1% error for cylindrical and spherical shells. The prime objective of this study is to identify the optimum ply angles for attaining a high fundamental frequency. The problem is NP‐hard because the possible ply angles span a wide range (±90°), making it difficult for optimisation algorithms to find a solution. Seven popular metaheuristic algorithms, namely the genetic algorithm (GA), the ant lion optimisation (ALO), the arithmetic optimisation algorithm (AOA), the dragonfly algorithm (DA), the grey wolf optimisation (GWO), the salp swarm optimisation (SSO), and the whale optimisation algorithm (WOA), are applied to and compared on a wide range of shell design problems. It assesses parameter sensitivity, discovering significant design elements that influence dynamic behaviour. Convergence studies demonstrate the superior performance of AOA, GWO, and WOA optimisers. Rigorous statistical comparisons assist practitioners in picking the best optimisation technique. FE‐GWO, FE‐DA, and FE‐SSA methods surpass the other techniques as well as the layerwise optimisation strategy. The findings obtained, employing the GWO, DA, and SSA optimisers, demonstrate ~3% improvement over the existing literature. With respect to conventional layup designs (cross‐ply and angle‐ply), the current optimised designs are better by at least 0.43% and as much as 48.91%.

Innovative approach to estimate structural damage using linear regression and K-nearest neighbors machine learning algorithms

Conventional structural design methodologies often utilize elastic analysis techniques, such as the equivalent static force method and the response spectrum method. While these methods are known for their simplicity and computational efficiency, they prove inadequate in capturing the extent of structural damage caused by seismic forces. Additionally, employing nonlinear dynamic analysis to estimate structural damage represents a challenging and intricate task, posing difficulties for many structural designers. Consequently, the objective of this paper is to present an innovative methodology for evaluating seismic structural damage of moment-resisting frame structures. This involves the utilization of machine learning algorithms, which have been trained and tested on a large data set generated using a newly developed and numerically efficient simulation procedure. The machine learning algorithms employ both linear regression and K-nearest neighbors approaches to accurately replicate the Park-Ang structural damage index.

Transfer-Learning-Enhanced Regression Generative Adversarial Networks for Optimal eVTOL Takeoff Trajectory Prediction

Electric vertical takeoff and landing (eVTOL) aircraft represent a crucial aviation technology to transform future transportation systems. The unique characteristics of eVTOL aircraft include reduced noise, low pollutant emission, efficient operating cost, and flexible maneuverability, which in the meantime pose critical challenges to advanced power retention techniques. Thus, optimal takeoff trajectory design is essential due to immense power demands during eVTOL takeoffs. Conventional design optimizations, however, adopt high-fidelity simulation models in an iterative manner resulting in a computationally intensive mechanism. In this work, we implement a surrogate-enabled inverse mapping optimization architecture, i.e., directly predicting optimal designs from design requirements (including flight conditions and design constraints). A trained inverse mapping surrogate performs real-time optimal eVTOL takeoff trajectory predictions with no need for running optimizations; however, one training sample requires one design optimization in this inverse mapping setup. The excessive training cost of inverse mapping and the characteristics of optimal eVTOL takeoff trajectories necessitate the development of the regression generative adversarial network (regGAN) surrogate. We propose to further enhance regGAN predictive performance through the transfer learning (TL) technique, creating a scheme termed regGAN-TL. In particular, the proposed regGAN-TL scheme leverages the generative adversarial network (GAN) architecture consisting of a generator network and a discriminator network, with a combined loss of the mean squared error (MSE) and binary cross-entropy (BC) losses, for regression tasks. In this work, the generator employs design requirements as input and produces optimal takeoff trajectory profiles, while the discriminator differentiates the generated profiles and real optimal profiles in the training set. The combined loss facilitates the generator training in the dual aspects: the MSE loss targets minimum differences between generated profiles and training counterparts, while the BC loss drives the generated profiles to share analogous patterns with the training set. We demonstrated the utility of regGAN-TL on optimal takeoff trajectory designs for the Airbus A3 Vahana and compared its performance against representative surrogates, including the multi-output Gaussian process, the conditional GAN, and the vanilla regGAN. Results showed that regGAN-TL reached the 99.5% generalization accuracy threshold with only 200 training samples while the best reference surrogate required 400 samples. The 50% reduction in training expense and reduced standard deviations of generalization accuracy achieved by regGAN-TL confirmed its outstanding predictive performance and broad engineering application potential.

ECHO: Energy-Efficient Computation Harnessing Online Arithmetic—An MSDF-Based Accelerator for DNN Inference

Deep neural network (DNN) inference demands substantial computing power, resulting in significant energy consumption. A large number of negative output activations in convolution layers are rendered zero due to the invocation of the ReLU activation function. This results in a substantial number of unnecessary computations that consume significant amounts of energy. This paper presents ECHO, an accelerator for DNN inference designed for computation pruning, utilizing an unconventional arithmetic paradigm known as online/most significant digit first (MSDF) arithmetic, which performs computations in a digit-serial manner. The MSDF digit-serial computation of online arithmetic enables overlapped computation of successive operations, leading to substantial performance improvements. The online arithmetic, coupled with a negative output detection scheme, facilitates early and precise recognition of negative outputs. This, in turn, allows for the timely termination of unnecessary computations, resulting in a reduction in energy consumption. The implemented design has been realized on the Xilinx Virtex-7 VU3P FPGA and subjected to a comprehensive evaluation through a rigorous comparative analysis involving widely used performance metrics. The experimental results demonstrate promising power and performance improvements compared to contemporary methods. In particular, the proposed design achieved average improvements in power consumption of up to 81%, 82.9%, and 40.6% for VGG-16, ResNet-18, and ResNet-50 workloads compared to the conventional bit-serial design, respectively. Furthermore, significant average speedups of 2.39×, 2.6×, and 2.42× were observed when comparing the proposed design to conventional bit-serial designs for the VGG-16, ResNet-18, and ResNet-50 models, respectively.

Effect of patterned condensation surface on the performance of a newly designed basin-type solar desalination unit

Evaporative solar desalination systems present a cost-effective solution for generating clean water from brine solutions or seawater. This study investigates the impact of condensation surface inclination, surface pattern, and wettability on the performance of a newly proposed basin-type solar desalination unit using computational fluid dynamics (CFD) based simulations. The proposed design of the system shows better performance in terms of water production compared to previously developed conventional units. It was observed that the wettability-based patterning with different arrangements on the condensation surface has a significant effect on the rate of water condensation. Five different cases of surface patterning on the condensation surface were considered. In one case, coatings of alternating hydrophobic and hydrophilic strips were considered (termed as Pattern-I, Pattern-II and Pattern-II for different cases based on the hydrophilic and hydrophobic coverages). In another case, small rectangular patches of hydrophilic (or hydrophobic) layers were coated on a hydrophobic (or hydrophilic) base substrate (termed as checkerboard Pattern-IV or V). The result shows 75.0% more efficiency for a checkerboard-based surface (Pattern-IV) and 150% more efficiency for a strip-based (Pattern-II) pattern than the pristine wettability surface of conventional design. Furthermore, the validation of the surface characteristics was performed based on available literature which implied satisfactory performance.