Fast Design Research Articles

3D-printed microfluidic and millifluidic devices for cell culture and mechanotransduction studies: A review

The practice of conventional cell culture techniques affects the quality of biological research in mechanobiology by not replicating the cell’s microenvironment adequately. Microfluidic devices resembling 3D in vivo cell culture offer viable alternatives for mechanobiology studies and other applications including cell screening, cell separation, and point-of-care diagnostics. This review highlights recent advances in additive manufacturing (AM) to fabricate microfluidic and millifluidic devices through fast design iterations and cost reduction compared with conventional device manufacturing. Key advances in AM technologies such as fused deposition modeling (FDM), stereolithography apparatus (SLA), and digital light processing (DLP) have allowed the direct manufacture of complex microfluidic devices with master molds and sacrificial structures using multiple material components. Currently, available 3D printed devices for the precise fabrication of milli- and microfluidic devices, essential to mimicking the cellular microenvironment, while economically reducing production costs by eliminating expensive clean-room environments and reducing material waste are highlighted.

LES and RANS Modeling of an 84-Pin Hexagonal Rod Bundle with Spacer Grid and Experimental Validation

As part of an ongoing integrated research project (IRP), detailed investigations of the flow characteristics of an 84-pin hexagonal rod bundle representing a potential modular gas-cooled fast reactor design have been performed. The rod bundle features a pitch-to-diameter ratio of 1.5 and a large central guide tube, along with simple spacer grids and an outer enclosure. The primary focus of the current work is modeling and simulation of this geometry using multiple computational techniques. These include high-fidelity large eddy simulation (LES) and advanced Reynolds-averaged Navier-Stokes (RANS) approaches. The flow predictions are validated at a Reynolds number of 12 000 using matched index of refraction particle image velocimetry experimental data that were also generated by Texas A&M University as part of the IRP. The comparison data include velocity magnitude and turbulent kinetic energy (TKE) at different lateral locations and at multiple distances downstream of the spacer grid. Many characteristics of the experimental flow are replicated in the LES and RANS runs, and a general agreement between simulation and experiment is shown. Except for the immediate vicinity of the grid, LES is able to capture the velocity magnitude within a 10% error and the TKE to within the experimental uncertainty. The LES shows notably better agreement with the experimental data than RANS, particularly regarding the TKE decay behavior downstream of the grid. Both methods show significant departures from the experiment in their predictions close to the central guide tube. The experimental and high-fidelity data will be used to inform closures for novel multiscale methodologies. The long-term goal of the work is to use the high-fidelity data to yield improved multiscale thermal analysis techniques for solving fuel performance problems of direct relevance to industry.

A mechanics-based data-free Problem Independent Machine Learning (PIML) model for large-scale structural analysis and design optimization

Machine learning (ML) enhanced fast structural analysis and design recently attracted considerable attention. In most related works, however, the generalization ability of the ML model and the massive cost of dataset generation are the two most criticized aspects. This work combines the advantages of the universality of the substructure method and the superior predictive ability of the operator learning architecture. Specifically, using a novel mechanics-based loss function, lightweight neural network mapping from the material distribution inside a substructure and the corresponding continuous multiscale shape function is well-trained without preparing a dataset. In this manner, a problem machine learning model (PIML) that is generally applicable for efficient linear elastic analysis and design optimization of large-scale structures with arbitrary size and various boundary conditions is proposed. Several examples validate the effectiveness of the present work on efficiency improvement and different kinds of optimization problems. This PIML model-based design and optimization framework can be extended to large-scale multiphysics problems.

Piecewise-scheduled thrust command control for in-service thrust performance improvement of gas turbine aero-engines: A hybrid fast design approach

Gas turbine aero-engines including turbofans, as the dominant powerplant for modern civil aircraft, convert the fossil energy in the jet fuel to propulsive forces via the thermo-dynamic cycle. Unfortunately, thrust regulation capabilities of gas turbine aero-engines are inevitably affected by uncertainties in measurements and gas path degradation during the life cycle, while quantification efforts for these uncertainties by traditional random analysis methods are usually considerable. In this paper, a piecewise-scheduled thrust command controller is proposed based on the improvement of the industrial baseline controller and current measurement levels, aiming at enhancing in-service thrust performance within a tolerable computational burden for engine fleets against these uncertainties. The proposed controller is equipped with a bank of embedded thrust maps for different flight cycle segments, which is fulfilled by a hybrid fast design approach incorporating a random analysis part and an analytical design part, as a new uncertainty quantification method. An industrial baseline controller with the identified thrust mode is also designed as the comparison basis. Simulations are carried out on a validated aero-thermal turbofan engine model with publically accessible uncertainty statistics. Simulation time for constructing the embedded thrust maps of the proposed controller is decreased by 99.8% on a desktop computer, compared to Monte-Carlo approach. Meanwhile, simulation results show that the proposed controller owns a bounded and tight thrust distribution for both new and severely degraded engine fleets at the take-off state within the permitted safety margin of the engine, which mitigates the significant under-thrust consequences from the baseline controller. Hence, the uncertainty control benefits of the proposed controller and its design efficiency are guaranteed.

Addendum: A graph placement methodology for fast chip design.

Addendum: A graph placement methodology for fast chip design.

Fast inverse design of microwave and infrared Bi-stealth metamaterials based on equivalent circuit model

This work proposed a fast inverse design method for microwave and infrared (IR) bi-stealth metamaterials based on the equivalent circuit model (ECM). Using this method, we designed a microwave and IR bi-stealth metamaterial by deploying a multilayered structure of the indium tin oxide (ITO) film based metasurface. First, the IR emissivity of the ITO film was calculated in the framework of the ECM. Then, an ITO metasurface was proposed to implement low IR emission and high microwave transmission simultaneously. Based on the ECM of the square patch, the ECM of the whole metamaterial was established at the microwave band. An inverse design program was built by incorporating the ECM with genetic algorithm (GA). Structure parameters of the metamaterial were optimized by GA to achieve the broadest microwave stealth bandwidth for the given thickness. Finally, the sample of the optimized bi-stealth metamaterial was prepared and tested. The calculated, simulated, and measured results are in good agreement, showing that such a metamaterial has an IR emissivity of 0.18 in the band from 3 to 14 μm and an efficient microwave stealth band from 4.8 to 17 GHz with a thickness of 4.9 mm. The proposed method will benefit the design and application of microwave and IR bi-stealth metamaterials.

Fast rough mode decision algorithm and hardware architecture design for AV1 encoder

Fast rough mode decision algorithm and hardware architecture design for AV1 encoder

A bionic soft actuator for gripper based on pneumatic metastructure and design method for it

In order to achieve fast and convenient design of fully flexible actuators suitable for special gripping modes, a soft actuator for gripper based on pneumatic metastructure is proposed inspired by the pulvinus of Mimosa pudica. The hyperelastic material is used to fabricate the soft actuators. Finite element models are built to analyze and predict the deformation of both the single unit cell and entire metastructures. Finally, the automatic design method is presented. The actuators designed corresponding to the modes are fabricated and tested. The experimental results show that the gripper achieves a higher grasping stability in special gripping modes.

Sealing rubber ring design based on machine learning algorithm combined progressive optimization method

In this study, a progressive optimization method combining machine learning and optimization method is proposed and applied to seal structure design. The method is divided into two stages to select the optimal design gradually. So as to find the best design scheme meeting the design requirements. For optimal design, numerical calculation method is commonly used, but hard to evaluate the optimal solution. In this work, a series of numerical model considering the effect of super elastic material about O-ring study the waterproof performance behavior of a rubber seal. K nearest neighbors (KNN) of machine learning algorithms applied to the simulation data to predict the appropriate bolt pretension classification. Furthermore, use TOPSIS method to optimize the groove depth of 30 N bolt pretension classification. By using the TOPSIS method to consider the stress of the rubber component, optimization analysis is conducted to find the optimal design. Results show that the dual optimization method can quickly predict the best design scheme. Through the experiment, a prototype test under the condition of IPX7 verify the method. The design scheme selected by this method meets the waterproof grade requirements. There are no water stains on the surface of the O-ring and inside the motor. This paper provides a fast optimization design method for the design of sealing structure.

Test Procedures and Mechanical Properties of Three-Dimensional Printable Concrete Enclosing Different Mix Proportions: A Review and Bibliometric Analysis

Three-dimensional printable concrete (3DPC) has become increasingly popular in the building and architecture industries due to its low cost and fast design. Currently, there is great interest in the mix design methods and mechanical properties of 3DPC, particularly in relation to yield stress analysis. The ability to extrude and build 3D-printed objects can be significantly affected by factors such as the rate of extrusion, nozzle size, and type of pumps used. It has been observed that a yield stress lower than 1.5 to 2.5 kPa is not sufficient to maintain the shape stability of concrete, while a yield stress above this range can limit the material’s extrudability. Furthermore, the strength properties of 3DPC are influenced by factors such as changes in yield stress and superplasticiser dosages. To meet the high mechanical strength and durability requirements of 3DPC in the construction industry, it is essential to analyse the material’s early-age mechanical properties. However, the development of standardised test methods for 3DPC is still deficient. To address this issue, a bibliometric analysis was conducted to comprehensively review the diverse test methods and mechanical characteristics of 3DPC with different mix proportions. To produce high-performance concrete from various additives and waste materials, it is critical to have a basic understanding of the hydration processes of 3DPC. Moreover, a detailed analysis of the environmental impact and energy efficiency of 3DPC is necessary for its widespread implementation. This review article will highlight the recent trends, upcoming challenges, and benefits of using 3DPC. It serves as a taxonomy to navigate the field of 3DPC towards sustainable development.

Prediction of multi-layer metasurface design using conditional deep convolutional generative adversarial networks

Designing metasurfaces is a challenging task. Traditional methodologies, which primarily depend on iterative procedures, are both time-intensive and require specialized expertise. The proposed algorithm uses conditional deep convolutional generative adversarial networks (cDCGAN) to design metasurfaces. This method instantly create a 2D image of a multi-layer metasurface using the scattering parameter S11 as the input vector. The algorithm significantly reduces the size of the training dataset by applying pre-training and post-generating steps. The pre-training step involves aliasing and modifying images using a limited color palette. The post-generating step consists of separating the color channels, converting the pixels to vector based images, and fine-tuning the borders. The algorithm is evaluated for three metasurfaces that have unique features compared to the training dataset samples: a single-band metasurface unitcell, a dual-band metasurface unitcell, and a partially trained sample improved by magnetic field analysis. The results show that the proposed algorithm can accurately predict the images of these metasurface unitcells, demonstrating its potential for fast and efficient metasurface design.

Fast Recovery Performance and Design Method for High-Power Microwave Limiter Using GaN-SBD Technology

Fast Recovery Performance and Design Method for High-Power Microwave Limiter Using GaN-SBD Technology

Fast and Scaled Counting-Based Stochastic Computing Divider Design

Fast and Scaled Counting-Based Stochastic Computing Divider Design

Reconfigurable Origami/Kirigami Metamaterial Absorbers Developed by Fast Inverse Design and Low-Concentration MXene Inks.

Reconfigurable metamaterial absorbers (MAs), consisting of tunable elements or deformable structures, are able to transform their absorbing bandwidth and amplitude in response to environmental changes. Among the options for building reconfigurable MAs, origami/kirigami structures show great potential because of their ability to combine excellent mechanical and electromagnetic (EM) properties. However, neither the trial-and-error-based design method nor the complex fabrication process can meet the requirement of developing high-performance MAs. Accordingly, this work introduces a deep-learning-based algorithm to realize the fast inverse design of origami MAs. Then, an accordion-origami coding MA is generated with reconfigurable EM responses that can be smoothly transformed between ultrabroadband absorption (5.5-20 GHz, folding angle α = 82°) and high reflection (2-20 GHz, RL > -1.5 dB, α = 0°) under y-polarized waves. However, the asymmetric coding pattern and accordion-origami deformation lead to typical polarization-sensitive absorbing performance (2-20 GHz, RL > -4 dB, α < 90°) under x-polarized waves. For the first time, a kirigami polarization rotation surface with switchable operation band is adapted to balance the absorbing performance of accordion-origami MA under orthogonal polarized waves. As a result, the stacked origami-kirigami MA maintains polarization-insensitive ultrabroadband absorption (4.4-20 GHz) at β = 0° and could be transformed into a narrowband absorber through deformation. Besides, the adapted origami/kirigami structures possess excellent mechanical properties such as low relative density, negative Poisson's ratio, and tunable specific energy absorption. Moreover, by modulating the PEDOT:PSS conductive bridges among MXene nanosheets, a series of low-concentration MXene-PEDOT:PSS inks (∼46 mg·mL-1) with adjustable square resistance (5-32.5 Ω/sq) are developed to fabricate the metamaterials via screen printing. Owing to the universal design scheme, this work supplies a promising paradigm for developing low-cost and high-performance reconfigurable EM absorbers.

Influence of extruder geometry and bio-ink type in extrusion-based bioprinting via an in silico design tool

Planning a smooth-running and effective extrusion-based bioprinting process is a challenging endeavor due to the intricate interplay among process variables (e.g., printing pressure, nozzle diameter, extrusion velocity, and mass flow rate). A priori predicting how process variables relate each other is complex due to both the non-Newtonian response of bio-inks and the extruder geometries. In addition, ensuring high cell viability is of paramount importance, as bioprinting procedures expose cells to stresses that can potentially induce mechanobiological damage. Currently, in laboratory settings, bioprinting planning is often conducted through expensive and time-consuming trial-and-error procedures. In this context, an in silico strategy has been recently proposed by the authors for a clear and streamlined pathway towards bioprinting process planning (Chirianni et al. in Comput Methods Appl Mech Eng 419:116685, 2024. https://doi.org/10.1016/j.cma.2023.116685). The aim of this work is to investigate on the influence of bio-ink polymer type and of cartridge-nozzle connection shape on the setting of key process variables by adopting such in silico strategy. In detail, combinations of two different bio-inks and three different extruder geometries are considered. Nomograms are built as graphical fast design tools, thus informing how the printing pressure, the mass flow rate and the cell viability vary with extrusion velocity and nozzle diameter.

A high speed pipelined radix-16 Booth multiplier architecture for FPGA implementation

Since multiplication is a complex and resource-consuming operation, it is very effective on the speed performance of a processor. In this regard, fast multiplication unit design is important in digital system architectures. FPGA hardware, which is efficient for the implementation and rapid prototyping of today’s digital system architectures, is becoming widespread. Therefore, in this study it aimed to design a fast radix-16 Booth multiplier based on the FPGA architecture. Booth multiplier implementation is preferred because it is relatively simple and efficient. The sizes of the multiplexers, which have an impact on the operating period in the encoder part of the proposed multiplier, reduced in the designed architecture by using a simple algorithm. To increase the speed performance of the system, the pipeline technique is used and parallelization is preferred in the tree structure in addition of the partial products. The effects of different multiplexer sizes, bit lengths and pipeline stages on the operating period of the system and other performance metrics examined. Different designs and the results obtained presented. According to the results, the multiplier hardware architectures designed in this study exhibit effective results in terms of speed performance.

Research and development of the neutron-photon coupled transport method in the fast reactor code system MOSASAUR

In the high-fidelity reactor physics design of fast reactors, the influence of photon effect needs to be explicitly simulated with the neutron-photon coupled transport method. The neutron-photon coupled transport-burnup method was researched based on the fast reactor physics design software package MOSASAUR. Photon library, photon cross-section production, photon source and photon power calculation methods were researched and the related calculation modules were developed and verified with the RBEC-M benchmark and the MET-1000 benchmark. Numerical results showed the good accuracy of the newly-developed neutron-photon coupled transport ability with MOSASAUR. The photon effect introduced the 80% difference for the assembly power in the RBEC-M benchmark, which showed the importance of the neutron-photon coupled transport in the fast reactor design.

Design and implementation of deep learning-based object detection and tracking system

Many human tracking methods by deep learning rely on powerful computing resources. For embedded platforms with limited resources, efficient use of resources is a priority. In this paper, we design an object detection and tracking system based on deep learning methods. We propose an efficient system with software and hardware design. We apply the framework of Vitis AI and its Deep Learning Processing Unit using a hardware/software co-design approach. This approach capitalizes on a higher-level acceleration design framework, where the convolutional models can be updated more flexibly and rapidly. This design approach not only provides a fast design flow but also has good performance in terms of throughput. We facilitate the design and accelerate the object detection model YOLO v3 to achieve higher throughput and energy efficiency. Our tracking method achieves a 1.27x improvement in processing speed with the addition of a single-object tracker. Our proposed human tracking methods can achieve better performance than the others in precision with the same test sequences.

A new accelerated thermal fatigue experiment method of pistons and its application

This study proposed a new test method to take the piston instead of the internal combustion engine as the experimental object and use electromagnetic induction heating and auxiliary cooling to simulate the actual operating conditions of the piston during engine start-stop, to achieve accurate control of the temperature of the heating and cooling cycles. The experimental process can be accelerated by worsening the service conditions. In order to prove the feasibility of this scheme, the temperature field distribution, fatigue life, and failure mode of the piston are compared and analyzed using finite element simulation, theoretical calculation, and surface topography observation. The results show that the error between the simulated temperature value of the key point of the piston and the experimental value of the actual operating condition is less than 5%, the position and value of the maximum temperature of the piston under the thermal fatigue test condition are consistent with the actual engine operating condition, the error between the fatigue life test results and the theoretical calculation results is less than 10%, and the location and cracking mode of the main crack is consistent with the actual working condition. It is proved that the test method is reasonable and feasible, which can provide an important guarantee for the fast and efficient design of high-performance pistons.

Fast Prediction of Airfoil Aerodynamic Characteristics Based on a Combined Autoencoder

Aircraft airfoils are classified into two main categories: symmetrical and asymmetrical. Both types of airfoils have a significant impact on the flight performance and safety of the aircraft. The fast prediction of the aerodynamic coefficients and pressure distributions of airfoils is crucial for the design of aircraft. The traditional wind tunnel test and CFD methods have the disadvantages of high test cost and high time consumption. To solve these problems, a combined autoencoder (CAE) network is proposed in this paper, which can achieve the fast prediction of airfoil aerodynamic coefficients and pressure distributions. The network consists of an airfoil shape autoencoder (AE) network and a multilayer perceptron (MLP) network. Firstly, an autoencoder network reflecting the characteristics of the airfoil shape is established, and the effects of different latent variables on the performance of the autoencoder network are investigated. Then, the latent variables obtained from the autoencoder are concatenated with the inflow conditions such as the Reynolds number and the angle of attack to be used as inputs to the MLP network, and the aerodynamic coefficients of different airfoils in different inflow conditions are predicted. The effects of various latent variable inputs, as well as the direct input of the airfoil shape into the MLP network, on the prediction performance of aerodynamic coefficients are compared and analyzed. The optimal aerodynamic coefficient prediction network is then obtained. Finally, the CAE network is also applied to predict the pressure distributions of different airfoils in different inflow conditions and the effects of different latent variables and input conditions on the prediction performance of the pressure distributions are analyzed and compared with the advantages and disadvantages of the CAE network and the conditional variational autoencoder (CVAE) network. The results demonstrate that the proposed method is capable of accurately predicting aerodynamic characteristics in a shorter time, offering a valuable reference for the fast and efficient design of aircraft airfoils.