Fast Design Research Articles

Sign of slant receiver for skewed alpha-stable noise shift keying-based random communication system

Purpose In contrast to traditional communication systems, slower data rate has always remained a weak link for non-traditional random communication systems (RCSs), which use alpha-stable (a-stable) noise as a carrier. This paper aims to introduce a fast receiver for skewed a-stable noise shift keying (SkaSNSK)-based RCSs. Design/methodology/approach The introduced receiver is based on the sign of slant estimator (SoSE), which provides rapid estimation of the skewed a-stable random noise signals (RNSs) received from the additive white Gaussian noise channel. The SoSE-based receiver minimizes the number of samples required to extract the encoded information from the received RNSs. This is achieved by manipulating the antipodal properties of the slant/skewness parameter of the a-stable carrier. Hence, a high data rate with relatively low complexity is guaranteed. Findings In comparison with the previously introduced sinc, logarithmic and modified extreme value method-based receivers, the proposed SoSE-based receiver also achieves improved bit error rate (BER) along with the better covertness values so that the essence of security provided by SkaSNSK-based RCSs remains intact. Research limitations/implications Because of the selected range of the associated parameters of the a-stable noise as a carrier, the BER vs MSNR results are may lack applicability for the complete range of values. Therefore, further research is required to produce results in different ranges. Practical implications The study includes implications for the hardware development based on the proposed communication scheme. Originality/value It can be seen that the paper fulfils the desired need of a fast receiver design for RCS.

Fast Design of Ultrawideband Absorbers Based on Equivalent Circuit Model

Fast Design of Ultrawideband Absorbers Based on Equivalent Circuit Model

Development of a machine learning model for wind turbine fatigue and ultimate loads based on static loads

A machine learning model is trained on HAWC2 time-domain aeroelastic load simulations in order to provide a load surrogate model from static rotor loads input to lifetime wind turbine design loads, to be utilized in fast design loads evaluation for design optimization. The simulations are performed on the IEA-3.4-130-RWT with a range of design variations for tip-speed-ratio, pitch-ramp settings, and blade length scaling. The surrogate model is shown to provide accurate predictions of lifetime blade and turbine ultimate and fatigue loads for design variations within the training design space.

Internal-multiport-model-based fast inverse design of an antireflective artificial metastructure in a waveguide system

Internal-multiport-model-based fast inverse design of an antireflective artificial metastructure in a waveguide system

CorTAF-LBE: A full scale subchannel-level thermal-hydraulic characteristics analysis code for LBR core based on OpenFOAM

The lead-bismuth cooled fast reactor core adopts box-type design, and the fuel rod bundle is fixed by wrapping wires. The complex geometric structure of the fuel assemblies significantly affects the flow and heat transfer status of the liquid metal coolant. Building upon the open-source CFD platform, OpenFOAM, this work established conservation equations for liquid metal coolant, fuel rod thermal characteristic models, inter-wrapper coolant analysis models, and coupled heat transfer models, and developed the subchannel level thermal-hydraulic analysis code CorTAF-LBE for whole-core analysis of LBR core using the finite volume method. The wire-wrapped rod bundles internal flow and heat transfer experiments of lead-bismuth, carried out based on the THEADES and KYLIN-II circulation loops, were selected to perform verification and validation. The results match well with the experimental data and CFD fine-modeling prediction. The steady-state numerical simulation of three wire-wrapped rod bundle assemblies with inter-wrapper flow was carried out under normal operation condition. The temperature and pressure distributions of the coolant in each subchannel and inter-wrapper flow region, the temperature of cladding and other key thermal-hydraulic parameters were obtained. The role of the inter-wrapper coolant in heat removal from the fuel assemblies was revealed. The work in this paper has referential value for the optimization of lead-bismuth cooled fast reactor design and safety assessment during operation.

Affordable Sensors for Speditive and Accurate Documentation of Built Heritage: First Tests and Preliminary Results

Abstract. When facing unregulated human interventions, the tumultuous backdrop of wars, and the relentless impact of climate change, a significant effect is held on those structures that stand as custodians of our cultural identity and historical legacy. The need to document their status is an essential first step toward preservation and restoration. This contribution will analyze and compare diverse datasets acquired through three distinct methods: Terrestrial Laser Scanner, Apple’s iPhone 15 Pro with no additional tool, and iPhone 15 Pro using Pix4D viDoc RTK Rover to assess the quality and accuracy of point clouds, as well as to determine the feasibility of each technique for the expeditious documentation of endangered built heritage. The use of mass-distributed low-cost sensors provides a rapid and cost-effective means to capture detailed 3D models of built heritage while, at the same time, democratizing the process of documentation. Low-cost sensors not only facilitate documentation but also enhance the efficiency of conservation efforts. The precision of LiDAR sensors helps identify structural vulnerabilities, allowing conservationists to prioritize interventions and allocate resources wisely. Furthermore, detailed imagery will enable conservationists to monitor subtle structural changes over time, acting as an early warning system against potential threats. As an additional aid, the RTK Rover enables centimeter-level positioning accuracy while maintaining a compact and fast deployable design, and the direct measurement of Ground Control Points (GCPs) significantly streamlines the surveying process and equipment footprint.

High-gain TM11 mode equilateral triangular patch antenna with shorting pins and triangular short horn

Abstract Normally, the reported gain of the microstrip patch antenna is within 8 dBi. Using properly located three shorting pins on three bisectors, the present work reports a method to convert the non-radiating TM11 mode of equilateral triangular patch antennas (ETPAs) to a deformed TM11 radiating mode. The boresight gain of ETPA operating in TM11 mode is enhanced from −10.75 to 12.1 dBi at 5.43 GHz. The boresight measured gain is further enhanced to 14.2 dBi at 5.52 GHz by using a triangular surface-mounted short horn (SMSH) of about ${{\lambda }}/5$ height. The aperture efficiency of the ETPA with the shorting pins is 84.2%. The aperture efficiency is further improved to 94.2% using the SMSH. The measured boresight cross-polarization and side-lobe level are −40 and −29 dB, respectively. The nature of the electricfield and surface current distribution is analyzed, using both the characteristic mode analysis method and high-frequency structure simulator, to understand the role of shorting pin and coaxial feed in converting the non-radiating TM11 mode to the radiating mode. A systematic design process also is presented for a fast design of shorting pin-loaded ETPA on the suitable substrate at a specified frequency.

Ultra‐fast finite element analysis of coreless axial flux permanent magnet synchronous machines

AbstractLarge‐scale design optimisation techniques enable the design of high‐performance electric machines. Electromagnetic 3D finite element analysis (FEA) is typically employed in optimisation studies for accurate analysis of axial flux permanent magnet (AFPM) machines, which require extensive computational resources. To reduce the computational burden, a FEA‐based mathematical method relying on the geometric and magnetic symmetry of coreless AFPM machines is proposed to estimate the machine performance indicators using the least number of FEA solutions, thereby significantly lowering the running time. This method is generally applicable to AFPM machines with low saturation effects and cogging torque as exemplified for a printed circuit board (PCB) stator coreless AFPM machine. To further reduce the computation time, a systematically simplified equivalent 3D FEA model for planar PCB coils integrated with this machine is also proposed. The practical implementation of the introduced method is elaborated based on an example optimisation study, and an analytical method for fast design scaling is also discussed. The results of the proposed approach are compared with detailed transient FEA results, and a prototype 26‐pole PCB stator coreless AFPM machine was also used to validate the results experimentally.

Deep Learning-Based Design Method for Acoustic Metasurface Dual-Feature Fusion.

Existing research in metasurface design was based on trial-and-error high-intensity iterations and requires deep acoustic expertise from the researcher, which severely hampered the development of the metasurface field. Using deep learning enabled the fast and accurate design of hypersurfaces. Based on this, in this paper, an integrated learning approach was first utilized to construct a model of the forward mapping relationship between the hypersurface physical structure parameters and the acoustic field, which was intended to be used for data enhancement. Then a dual-feature fusion model (DFCNN) based on a convolutional neural network was proposed, in which the first feature was the high-dimensional nonlinear features extracted using a data-driven approach, and the second feature was the physical feature information of the acoustic field mined using the model. A convolutional neural network was used for feature fusion. A genetic algorithm was used for network parameter optimization. Finally, generalization ability verification was performed to prove the validity of the network model. The results showed that 90% of the integrated learning models had an error of less than 3 dB between the real and predicted sound field data, and 93% of the DFCNN models could achieve an error of less than 5 dB in the local sound field intensity.

Deep Learning-Enhanced Inverse Modeling of Terahertz Metasurface Based on a Convolutional Neural Network Technique

The traditional design method for terahertz metasurface biosensors is cumbersome and time-consuming, requires expertise, and often leads to significant discrepancies between expected and actual values. This paper presents a novel approach for the fast, efficient, and convenient inverse design of THz metasurface sensors, leveraging convolutional neural network techniques based on deep learning. During the model training process, the magnitude data of the scattering parameters collected from the numerical simulation of the THz metasurface served as features, paired with corresponding surface structure matrices as labels to form the training dataset. During the validation process, the thoroughly trained model precisely predicted the expected surface structure matrix of a THz metasurface. The results demonstrate that the proposed algorithm realizes time-saving, high-efficiency, and high-precision inversion methods without complicated data preprocessing and additional optimization algorithms. Therefore, deep learning algorithms offer a novel approach for swiftly designing and optimizing THz metasurface sensors in biomedical detection, bypassing the complex and specialized design process of electromagnetic devices, and promising extensive prospects for their application in the biomedical field.

Feature-Assisted Neuro-CMT Approach to Fast Design Optimization of Metasurfaces

Feature-Assisted Neuro-CMT Approach to Fast Design Optimization of Metasurfaces

Designing an Integrated Information System with a Fast (Sister Fani) and Attractive Interface to Improve Performance and User Experience: Case Study at SMK PGRI 1 Tangerang

The Integrated Information System (Sister Fani) is an important innovation in the educational environment of SMK PGRI 1 Tangerang. Considering the need for a fast and attractive interface, this study aims to design and implement Sister Fani that meets high standards of performance and user experience. This study adopted a qualitative approach, collecting data through observation, interviews, and documentary research. Research results show that fast and attractive interface design can improve users' efficiency in accessing and using information and provide a better user experience. The practical implication of this study is that the adoption of Sister Fani at SMK PGRI 1 Tangerang can improve the productivity and quality of educational services

Investigation of the effects of volume fraction, aspect ratio and type of fibres on the mechanical properties of short fibre reinforced 3D printed composite materials

AbstractMechanical behaviour of 3D-printed composite parts is affected by the volume fraction, aspect ratio and type of fibre reinforcement. Although in the literature experimental approaches have been used to characterise the effects of the above factors on the mechanical properties of 3D printed parts, time and cost of the manufacturing process as well as the uncertainty associated with a large number of experimental techniques are the key issues. This study aims to address these challenges by developing a methodology based on a multi-scale Finite Element (FE) analysis of representative volume element (RVE) of 3D printed composite parts to predict the effective orthotropic properties. To account for the effects of fibre features, RVEs were modelled considering variables of volume fraction, aspect ratios and type of short fibres. To study the main and interaction effects of the above variables on the mechanical properties of 3D printed composite parts, a structured approach based on the Design of Experiments is used. The FE stress analysis of the RVE provides an understanding about the potential failure modes such as interfacial debonding between fibres and matrix, interlayer and intralayer delamination that may occur in load-bearing 3D printed composite parts. The FE computed mechanical properties are validated against experimental data through a series of mechanical testing of flexure, Iosipescu, and short beam shear which were conducted in conjunction with the Digital Image Correlation technique. As a result, certainty is obtained in using the proposed approach for a fast iterative design of 3D printed composite parts prior to industrial applications.

METAL: Methodology for liquid metal fast reactor core economic design and fuel loading pattern optimization

The design of Liquid Metal-cooled Fast Reactor (LMFR) cores encompass two levels of design. The first is the physical core design, which is concerned with the design of the fuel assembly geometry in the context of a full core. The second is the fuel loading design, which is an in-core fuel management problem concerned with the design of fuel enrichment and core loading pattern. In this paper, an optimized fuel design search is developed to improve core loading patterns. As part of the implementation, the multiphysics simulation suite LUPINE has been enhanced to allow for fuel shuffles and an in-core fuel management optimization methodology has been added with the objective of reducing the fuel cost. The design methodology is referred to as Methodology for Economical opTimization of Applied LMFR (METAL), and this methodology is demonstrated using the popular Super Power Reactor Innovative Small Modular (SPRISM) sodium-cooled fast reactor (SFR) as a reference core. One challenge currently facing the deployment of LMFRs is the availability of TRansUranium (TRU) and high assay low enriched uranium (HALEU) driver fuel. The two forms of fuel are expensive and not widely available, which poses a challenge on the startup of LMFR cores. To address the availability of driver fuel, and to lower fuel costs, low enriched uranium (LEU) blankets are investigated as replacements to the natural uranium or depleted uranium blankets typically used. The advantage of this solution is the wide availability of LEU and its ability to lower the amount of TRU or HALEU driver fuel needed. The design objectives, constraints, and optimization algorithm for a SFR are identified. Then, METAL is applied to design a SFR core fueled by TRU driver fuel assemblies and LEU blanket assemblies. To demonstrate the advantage of introducing LEU blankets, METAL is also used to design another SFR core following the same objectives and constraints, but using naturally enriched blankets. By comparing the two cores, it was found that the introduction of LEU blankets results in a ∼28% reduction in the driver fuel mass requirements. Upon development of a levelized fuel cycle cost (LFCC) model, the 28% reduction in driver fuel mass corresponds to a 10% decrease in the LFCC. Additional advantages of using LEU blankets include reducing the core conversion ratio (CR), and improving the assembly radial power peaking factor (RPPF), which enhances the safety and non-proliferation performances of the SFR core.

Low-complexity reconfigurable FIR lowpass equalizers for polynomial channel models

This paper introduces realizations of a reconfigurable finite-impulse-response (FIR) filter for simultaneous equalization and lowpass filtering. The main advantage of the proposed solutions is computational complexity reduction compared to existing solutions for a given performance, which leads to reduced hardware complexity. The proposed structures employ properties of both a variable bandwidth (VBW) filter and a variable equalizer (VE) with variable coefficients. The overall transfer function of the proposed reconfigurable lowpass equalizer (RLPE) is a weighted linear combination of fixed subfilters where the weights are directly determined by the bandwidth and one or several parameters of the channel needed to be equalized. The paper provides design procedures based on minimax optimization and introduces a fast design method for the filter with several variable parameters that can substantially decrease the design time. Filter order estimation expressions as well as complexity expressions are presented for all proposed realizations. Design examples include comparison of the RLPE structures and a common approach of using a regular FIR equalization filter requiring online redesign when the bandwidth or channel characteristics are changed. It is shown that the number of general multiplications can be reduced up to 91% using the proposed RLPE.

Optimizing code allocation for hybrid on-chip memory in IoT systems

With the increasing application of IoT devices, the memory subsystem, as the performance and energy bottleneck of IoT systems, has received a lot of attention. One of the keys is on-chip memory which can bridge the performance gap between the CPU and main memory. While many off-the-shelf embedded processors utilize the hybrid on-chip memory architecture containing scratchpad memories (SPMs) and caches, most existing literature ignores the collaboration between caches and SPMs. This paper proposes static SPM allocation strategies for the architecture mentioned above in IoT systems, which try to minimize the overall instruction memory subsystem latency and/or energy consumption. We capture the intra- and inter-task cache conflict misses via a fine-grained temporal cache behavior model. Based on this cache conflict information, we propose an integer linear programming (ILP) algorithm to generate an optimal static function level SPM allocation for system performance. Furthermore, to improve the scalability of the proposed allocation scheme for an enormous task set, we offer the interference factor to calculate the interference impact quantitatively. Then, based on the interference factor, we present two approximate knapsack based heuristic algorithms to provide near optimal static allocation schemes at both function- and basic block-level granularities, which favors fast design space exploration. The experiment results demonstrate that the proposed solution achieves a 30.85% improvement in memory performance, and up to 31.39% reduction in energy consumption, compared to the existing SPM allocation scheme at the function level. In addition, the proposed basic block level allocation algorithm shows better performance than our function level allocation algorithm and other basic block level allocation algorithm.

A Parameter Optimization Design Method for Single-Phase Dual Active Bridge AC-DC Converter

The single-stage dual active bridge (DAB) AC-DC converter has the advantages of high power density, low cost, and simple control; it has a broad potential for application in the field of onboard chargers (OBC). However, the lack of fast and accurate quantitative parameter optimization design methods in single-stage DAB AC-DC converters limits the overall efficiency of the converter. Based on the above problem, in order to improve the overall operating efficiency of the converter by optimizing the parameter transformer ratio and power inductance, this paper proposes a parameter design method considering a multi-timescale strategy by combining the steady-state analysis model of the converter in the line cycle and switching cycle and step-by-step reducing its design space through the constraints on the parameters. The first step is to obtain a safe design space for the parameters under the converter’s transmitted power and current stress constraints. The second step obtains the optimization design space of the parameters under the optimization of conduction loss and switching loss of the converter. Finally, the optimal parameters are determined by the loss analysis model. The proposed parameter optimization method entirely takes into account the steady-state characteristics of the DAB AC-DC converter during the line cycle, and the step-by-step constraints greatly accelerate the parameter design process. In addition, the proposed parameter optimization design method applies to all types of single-stage DAB AC-DC converters, which can be well applied to engineering practice.

Improving data-efficiency of deep generative model for fast design synthesis

Improving data-efficiency of deep generative model for fast design synthesis

Investigation of innovative designs of high-velocity channels for damping kinetic energy of flows.

In our contemporary world, demanding sustainable resource management, the study focuses on innovative fast flow channel designs. It investigates their efficacy in reducing flow kinetic energy, aiming to optimize water and energy management and diminish flood risks. Employing diverse methodologies, it analyzes and develops these designs, proving their substantial impact on stream energy management. These innovations not only enhance energy efficiency but also mitigate risks associated with excess kinetic energy, promoting safer stream management. This research significantly contributes to fluid dynamics and engineering, deepening the understanding of kinetic energy control in flows and offering potential solutions for water supply, environmental sustainability, and infrastructure safety challenges.

NRX-101 (D-Cycloserine + Lurasidone) Is Active against Drug-Resistant Urinary Pathogens In Vitro.

D-Cycloserine (DCS) is a broad-spectrum antibiotic that is currently FDA-approved to treat tuberculosis (TB) disease and urinary tract infection (UTI). Despite numerous reports showing good clinical efficacy, DCS fell out of favor as a UTI treatment because of its propensity to cause side effects. NRX-101, a fixed-dose combination of DCS and lurasidone, has been awarded Qualified Infectious Disease Product and Fast Track Designation by the FDA. In this study, we tested NRX-101 against the urinary tract pathogens Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii in cation-adjusted Mueller-Hinton broth (caMHB) and artificial urine media (AUM). Several strains were multidrug resistant. Test compounds were serially diluted in broth/media. Minimum inhibitory concentration (MIC) was defined as the lowest concentration of the test compound at which no bacterial growth was observed. DCS exhibited antibacterial efficacy against all strains tested while lurasidone did not appreciably affect the antibacterial action of DCS in vitro. In AUM, the MICs ranged from 128 to 512 mcg/mL for both DCS and NRX-101. In caMHB, MICs ranged from 8 to 1024 mcg/mL for NRX-101 and 32 to 512 mcg/mL for DCS alone. Our data confirm that DCS has antibacterial activity against reference and drug-resistant urinary pathogens. Furthermore, lurasidone does not interfere with DCS's antimicrobial action in vitro. These results support the clinical development of NRX-101 as a treatment for complicated urinary tract infections.