Designable Parameters Research Articles

Customizable metamaterial design for desired strain-dependent Poisson’s ratio using constrained generative inverse design network

Inverse design of metamaterial structures with customized strain-dependent Poisson’s ratio has significant potential across various applications. However, achieving precise control over these mechanical properties presents a challenge due to the complex relationship between geometry and mechanical performance. Here, we present a novel data-driven approach utilizing a constrained generative inverse design network (CGIDN) to address this challenge. The CGIDN uses backpropagation to efficiently navigate the design space and achieve target mechanical properties with high accuracy. Our method starts by generating a comprehensive dataset of Poisson’s ratio-strain curves for various geometries incorporating cuts. These curves are then compressed using principal component analysis (PCA) to reduce dimensionality while preserving essential features. A deep neural network (DNN) is then trained to map input geometric parameters to these principal components, with the architecture optimized using grid search. The CGIDN facilitates the inverse design process by recommending geometric parameters for unit cell designs that match specified target Poisson’s ratio-strain curves. We validated the effectiveness of our approach through Finite Element Analysis (FEA) and experimental verification. The FEA results for the designed unit cells showed high agreement with the target and predicted curves, demonstrating the accuracy of the CGIDN model. Further, tensile tests on specimens confirmed that the inverse-designed structures reproduced the desired mechanical behavior upon scale-up. Our method, which enables efficient and accurate design of metamaterials with tailored mechanical properties, holds promise for applications in wearable devices, soft robotics, and advanced sensor systems

Numerical Analysis and Design of New Exhaust Section Downstream of Constant Volume Combustor

Abstract Pressure gain combustors (PGC) exploit either their isochoric or detonative combustion increasing the theoretical thermal efficiency of a gas turbine cycle. On this basis, a constant-volume combustor (CVC) is developed operating with rotary valves while the chamber is fed with mixture of air and liquid iso-octane. This work describes the numerical design of a new exhaust section after the CVC. First, the design parametrization of the transition duct and the resulting design of experiments (DOE) of 81 samples are introduced. Every case is numerically tested and the election of the best sample is based on the pressure losses and the oscillations characterization. In the last part, the LS89-VKI vane is added at the aft part of the best transition duct and the ensemble exhaust system is analyzed with the help of transient CFD analysis. Every component is evaluated in terms of pressure losses and oscillations, while the operation of the vane is investigated in details. The results of the stator's performance are discussed and compared with steady experimental data. During a CVC period, the cycle average outlet flow angle remains close to the outlet metal angle denoting promising results for the future numerical analysis of the subsequent rotor.

Designs in finite classical polar spaces

AbstractCombinatorial designs have been studied for nearly 200 years. 50 years ago, Cameron, Delsarte, and Ray-Chaudhury started investigating their q-analogs, also known as subspace designs or designs over finite fields. Designs can be defined analogously in finite classical polar spaces, too. The definition includes the m-regular systems from projective geometry as the special case where the blocks are generators of the polar space. The first nontrivial such designs for $$t > 1$$ t > 1 were found by De Bruyn and Vanhove in 2012, and some more designs appeared recently in the PhD thesis of Lansdown. In this article, we investigate the theory of classical and subspace designs for applicability to designs in polar spaces, explicitly allowing arbitrary block dimensions. In this way, we obtain divisibility conditions on the parameters, derived and residual designs, intersection numbers and an analog of Fisher’s inequality. We classify the parameters of symmetric designs. Furthermore, we conduct a computer search to construct designs of strength $$t=2$$ t = 2 , resulting in designs for more than 140 previously unknown parameter sets in various classical polar spaces over $$\mathbb {F}_2$$ F 2 and $$\mathbb {F}_3$$ F 3 .

Body mass index associated with respiration predicts motion in resting-state functional magnetic resonance imaging scans.

Decreasing body mass index (BMI) reduces head motion in resting-state fMRI (rs-fMRI) data. Yet, the mechanism by which BMI affects head motion remains poorly understood. Understanding how BMI interacts with respiration to affect head motion can improve head motion reduction strategies. A total of 254 patients with back pain were included in this study, each of whom had two visits (interval time = 13.85 ± 7.81 weeks) during which two consecutive re-fMRI scans were obtained. We investigated the relationships between head motion and demographic and pain-related characteristics-head motion was reliable across scans and correlated with age, pain intensity, and BMI. Multiple linear regression models determined that BMI was the main determinant in predicting head motion. BMI was also associated with two features derived from respiration signal. Anterior-posterior and superior-inferior motion dominated both overall motion magnitude and the coupling between motion and respiration. BMI interacted with respiration to influence motion only in the pitch dimension. These findings indicate that BMI should be a critical parameter in both study designs and analyses of fMRI data.

Raman spectroscopy of atomically thin HfX2 (X=S, Se)

Abstract We investigated interlayer modes of few-layer HfX2 (X=S, Se) by using low-frequency micro-Raman spectroscopy with three excitation energies (1.96 eV, 2.33 eV, 2.54 eV) under vacuum condition (10−6 Torr). We observed interlayer modes in HfSe2 when the 2.54-eV excitation energy was used. The low-frequency Raman spectra reveal a series of shear and breathing modes (<50 cm−1) that are helpful for identifying the number of layers. The in-plane Eg and out-of-plane A1g modes of HfSe2 are located at ~150 cm−1 and ~200 cm−1, respectively. In HfS2, in-plane Eg and out-of-plane A1g optical phonons are observed at ~260 cm−1 and ~337 cm−1, respectively. The in-plane and out-of-plane force constants of atomically thin HfSe2 are obtained to be 1.87 ×1019 N/m3 and 6.55×1019 N/m3, respectively, by fitting the observed interlayer modes using the linear chain model. These results provide valuable information on materials parameters for device designs using atomically-thin layered HfX2 (X=S, Se).

Strain-independent auxetic metamaterials inspired from atomic lattice

Metamaterials with strain-independent negative Poisson's ratio (NPR) and Young's modulus are essential for the applications with extreme loading conditions. However, strain-independent auxetic metamaterials are rare, and there is a lack of design inspiration for them. In this paper, we present a novel auxetic metamaterial inspired from the atomic lattice with strain-independent NPR. The novel NPR properties due to the unique atomic lattice keep valid when scaling up by 6 orders of magnitude to macroscopic metamaterials, with the similar deformation mechanism. The NPR and Young's modulus keep constant with increasing loading strain. These strain-independent auxetic effects are robust with various designable geometric parameters in wide ranges. Furthermore, the geometric parameters optimization for the auxetic metamaterial is conducted through developing the matrix displacement method. An optimized metamaterial is achieved with 3 times enhancement of auxeticity and 2 orders of magnitude enhancement of Young's modulus, compared to the initio architectures. This work provides a wealth of inspirations from atomic lattice in nature to design strain-independent macroscale auxetic metamaterials.

Two-step printing bionic self-lubricating composite surface fabricated by selective laser melting ink-printed metal nanoparticles

The combination of self-lubricating materials and surface texture was a promising strategy to improve tribology performance. The designed parameters and manufacturing methods of the textured composite surface were pivotal in fully harnessing this synergistic effect. Inspired by natural structures such as the scapharca subcrenata shell, trimeresurus stejnegeri snakeskin, crocodile, and tree frog toe pad surface, bionic self-lubricating composite surfaces (BSCSs) with different textures were prepared on AISI 4140 steel substrate to enhance tribological properties. These BSCSs combined self-lubricating composite material (SLCM) with copper (Cu) material in bionic patterns using the selective laser melting ink-printing metal nanoparticles manufacturing method. Tribological properties and worn surfaces of the BSCSs with various design parameters were analyzed under dry sliding conditions. Results indicated that the synergy effects of printed Cu and SLCM texture effectively reduced friction coefficient and improved wear resistance of the BSCSs. The SLCM texture density and the size of array element in the BSCSs were found to influence the types of lubricating film formed during the sliding process, thereby affecting the tribological performance. This investigation provided a potential surface design strategy and a more flexible manufacturing method for surface engineering.

Probabilistic design and optimization of thermal protection system with variable thickness based on non-uniform aerodynamic heating

Thermal Protection System (TPS) is a critical component of space vehicles to protect them from damage by extreme aerothermal heating conditions. The uncertainty of the aerothermal heating conditions, including approaching velocities, atmospheric density, pressure, etc., significantly affects the aerothermal heating imposed on the TPS, vastly changing the initial design conditions. In this study, we developed a new uncertainty quantitative (UQ) analysis method to comprehensively consider all the uncertainty of TPS materials, structures, and aerothermal heating conditions. To address the challenges of a massive amount of input and determined optimization parameters, we first induced the response surface method and Monte Carlo algorithm to produce a large number of analysis samples for Probabilistic Design. Then, the key affecting factors, not all the input design parameters, were chosen for optimization with sequential optimization and reliability assessment (SORA) strategy by a sensitivity analysis. With such improvements, the optimization for UQ analysis becomes practical. We used this new UQ method to analyze the effects of uncertainty of the aerothermal heating conditions on the thermal protection layer. A variable thickness design is proposed to adapt the non-uniform aerothermal heating conditions for the TPS. The results show that significant light-weighting can be achieved without the reliability penalty. The optimized structure also reduces internal temperature gradients and facilitates a more uniform temperature field distribution for the TPS. This study provides a new UQ engineering design approach for the system with many designed parameters.

Inverse machine learning framework for optimizing gradient honeycomb structure under impact loading

In this study, an inverse design framework was constructed to explore gradient honeycomb structures (HCS) with high impact resistance. By establishing the relationship between the height of the cell and the cell-wall angle, HCS with different gradient modes were designed. The developed machine learning (ML) framework consisted of a forward regression model based on neural network (NN) and an inverse optimization model based on generative adversarial network (GAN). The regression model surrogated finite element analysis (FEA) to rapidly provide corresponding mechanical properties for the generated data of GAN. To ensure that the generated data met the desired design targets, additional physical constraints were incorporated into the generator of the GAN. This ensured that the network output not only consisted of valid data but also exhibited superior impact resistance. The results demonstrated that the trained ML model optimized both specific energy absorption (SEA) and initial peak stress of HCS. It even discovered high-performance gradient designs that outperformed the original data, with a maximum energy absorption 49.5% higher than that of uniform HCS. Moreover, the gradient modes and structural parameters of superior designs were identified, which is of great significance for the gradient design of HCS under impact loading. As a general design approach, this ML framework holds significant potential for the optimization design of metamaterials in other structures or with different mechanical properties.

Experimental study on the interface characteristics of geogrid-reinforced gravelly soil based on pull-out tests

The factors influencing geogrid–soil interface characteristics are critical design parameters in some geotechnical designs. This study describes pull-out tests performed on gravelly soils commonly encountered in the Xinjiang region and reinforced with two types of geogrids. The factors affecting the geogrid–gravelly soil interface properties are investigated with different experimental loading methods (pull-out velocity, normal stress), geogrid types, and soil particle size distributions and water contents. The ultimate pull-out force increases with the normal stress and pull-out velocity. Furthermore, with increasing coarse particle content and water content, the ultimate pull-out force increases and then decreases sharply. Based on these research results, this paper provides reasonable parameters and recommendations for the design and pull-out testing of reinforced soil in engineering structures. In reinforced soil structure design, the grid depth should be increased appropriately, and the coarse particle content of the overlying soil should be between 30 and 40%. During construction, the gravelly soil should be compacted to the maximum compaction at the optimal water content, and the structure should have a reasonable waterproofing system. According to the calculation results of the interface strength parameters, the uniaxial geogrid–gravelly soil interface has a high cohesive force csg, which should not be ignored in reinforced soil structure design.

Tri-hierarchical incomplete block designs

A tri-hierarchical incomplete block (TriHIB) design is defined as an arrangement of v equi-replicated treatments in three levels of blocking such that each block of the first level contains m1 blocks of second level and that of second level contains m2 blocks of the third level. Every level of a TriHIB design, treated independently, is an incomplete block design (IBD) and ignoring any one level of blocking, results into a bi-hierarchical incomplete block (BiHIB) design. Here, two general methods of construction of TriHIB designs are proposed. In the first method, popular association schemes (viz., triangular, Latin square and rectangular) of partially balanced IBDs have been used for developing three series of designs with equal/unequal block sizes at higher levels of blocking. Second method yields another series of designs, through cyclic development of two initial sequences, with equal block sizes at first two levels and equal/unequal block sizes at the third level of blocking. A list of parameters of TriHIB designs for number of treatments up to 30 is presented, along with efficiency factors. Further, for easy accessibility and wider adaptability of these designs, an R package ‘Tri.Hierarchical.IBDs’ has been developed for the generation of the proposed designs.

A Decoupling Algorithm for Efficient Estimation of Failure Probability Function Based on Statistical Moment Functions

Abstract Failure probability function (FPF) is an important index that reflects the influence of designable distribution parameters on the safety degree of a structure, and it can be used for decoupling reliability optimization models. Thus, its efficient solution is expected. A decoupling algorithm based on statistical moment functions (SMFs) of performance function is proposed to solve the FPF efficiently in this paper. The proposed algorithm first constructs an extended density weight function (EDWF), which can cover the interested region of the distribution parameters and is independent of the distribution parameters so that the statistical moment integrals corresponding to different realizations of the distribution parameters can share the same EDWF. Then, using the same EDWF, a strategy is dexterously designed to estimate the SMFs by sharing a set of integral characteristic nodes. Finally, the FPF is approximated by the SMFs, which varies with the distribution parameters in the interested design region. In addition, the proposed algorithm introduces the Box–Cox transformation of the performance function to guide the high accuracy of FPF approximated by the SMFs. The main contribution of the proposed algorithm is constructing the EDWF to decouple the dependence of solving SMFs on the realizations of the distribution parameters over the interested region and designing the strategy of estimating the SMFs by sharing the same integral characteristic nodes. Since the proposed algorithm employs a point estimation method to evaluate the FPF, it has higher efficiency than the competitive methods. Numerical and engineering examples demonstrate the superiority of the proposed algorithm.

Design and analysis of a contact-aided flexure hinge (CAFH) with variable stiffness

This paper presents a novel contact-aided flexure hinge (CAFH) with variable stiffness, which consists of a contact-aided segment, a flexible segment and a rigid part. The proposed CAFH can facilitate a compact design and provide an alternative for stiffness-variable designs under any loading conditions. With a mortise-tenon structure, the CAFH is trivially affected by friction. The design and deformation procedures of the CAFH are described in detail, followed by its theoretical kinetostatic modeling using the chained beam-constraint model. The deformation of all segments is considered in the kinetostatic model, which expands the space of design parameters for stiffness-variable designs. Then, the accuracy of the theoretical model and the variable stiffness design are verified by nonlinear finite element analysis (FEA) and experimental tests. In term of stiffness, the maximum relative errors of the theoretical model are 0.76% in Stage 1 and 0.70% in Stage 2, as compared with FEA, respectively. Further, the parameter sweep is carried out, followed by sensitivity analysis to identify the main test error sources. Finally, the multi-material scenarios are investigated preliminarily, and some outlooks are discussed.

Optimising building heat load prediction using advanced control strategies and Artificial Intelligence for HVAC system

Amid the dynamic challenges posed by the COVID-19 era, this study offers a nuanced exploration, delving into the complexities of optimising building heat load prediction. This study addresses the imperative challenge of optimising building heat load prediction by implementing advanced control strategies and integrating Artificial Intelligence (AI) into air conditioning systems. The study emphasises the need to design HVAC devices for minimal energy consumption without compromising comfort conditions. The study introduces an intelligent predictive adaptive neuro-fuzzy inference system (ANFIS) model that proves highly capable of accurately predicting HVAC performance outcomes while contributing to the development of a comprehensive dataset. The uncertainty percentage is evaluated across various membership functions, with the trapezoidal membership exhibiting the lowest error rate, followed by the Gaussian membership function. Weather parameters crucial to ventilation efficiency and heat load are examined, leading to the identification of an optimal combination involving 45 % relative humidity, 13 °C dry bulb temperature, and 5 km/h wind speed. The current system demonstrates significant efficiency improvements, with ventilation and heat load rates reaching 93 % and 97 %, respectively, compared to pre-COVID-19 conditions. The findings underscore the importance of considering these parameters in future HVAC designs, particularly in the context of COVID-19 guidelines (75 % and 79 %, respectively).

Microfluidic Metasurface with Constant Absorption Efficiency Throughout the Ku Band

AbstractMetasurfaces with designable electromagnetic parameters are ideal candidates for broadband and tunable microwave absorbers. Water‐based metasurface microwave absorbers have been demonstrated to exhibit ultra‐broadband and near‐unity absorption. However, a broadband microwave absorber with constant and designable efficiencies remains a challenge due to stringent impedance‐matching conditions over a large frequency region. In this work, a design paradigm of microfluidic metasurface absorbers is proposed to regulate absorption efficiency over a wide frequency range. As an example, microfluidic metasurface absorbers are demonstrated with constant absorption efficiency over the Ku band, which is designable from 61.8% to 99.9%. The broadband metasurface absorbers have the advantages of easy fabrication, low cost, and optical transparency, making them highly promising for applications in fields like electromagnetic shielding, active camouflage, electromagnetic pollution control, and wireless communications.

The Minimum Admissible Detuning Efficiency of MRI Receive-Only Surface Coils.

The minimum admissible detuning efficiency (DE) of a receive coil is an essential parameter for coil designers. A receive coil with inefficient detuning leads to inhomogeneous B1 during excitation. Previously proposed criteria for quantifying the DE rely on indirect measurements and are difficult to implement. To present an alternative method to quantify the DE of receive-only surface coils. Theoretical study supported by simulations and phantom experiments. Uniform spherical (100 mm diameter) and cylindrical (66 mm diameter) phantoms. Dual repetition time B1 mapping sequence at 1.5T, and Bloch-Siegert shift B1 mapping sequence at 3.0T. One non-planar (80 × 43 mm2) and two planar (40 and 57 mm diameter) surface coils were built. Theoretical analysis was performed to determine the minimum DE required to avoid B1 distortions. Experimental B1 maps were acquired for the non-planar and planar surface coils at both 1.5T and 3.0T and visually compared with simulated B1 maps to assess the validity of the theoretical analysis. None. Based on the theoretical analysis, the proposed minimum admissible DE, defined as DEthr = 20 Log (Q) + 13 dB, depended only on the quality factor (Q) of the coil and was independent of coil area and field strength. Simulations and phantom experiments showed that when the DE was higher than this minimum threshold level, the B1 field generated by the transmission coil was not modified by the receive coil. The proposed criterion for assessing the DE is simple to measure, and does not depend on the area of the coil or on the magnetic field strength, up to 3T. Experimental and simulated B1 maps confirmed that detuning efficiencies above the theoretically derived minimal admissible DE resulted in a non-distorted B1 field. 2 TECHNICAL EFFICACY: Stage 1.

On Accelerated Metaheuristic-Based Electromagnetic-Driven Design Optimization of Antenna Structures Using Response Features

Development of present-day antenna systems is an intricate and multi-step process requiring, among others, meticulous tuning of designable (mainly geometry) parameters. Concerning the latter, the most reliable approach is rigorous numerical optimization, which tends to be resource-intensive in terms of computing due to involving full-wave electromagnetic (EM) simulations. The cost-related issues are particularly pronounced whenever global optimization is necessary, typically carried out using nature-inspired algorithms. Although capable of escaping from local optima, population-based algorithms exhibit poor computational efficiency, to the extent of being hardly feasible when directly handling EM simulation models. A popular mitigation approach involves surrogate modeling techniques, facilitating the search process by replacing costly EM analyses with a fast metamodel. Yet, surrogate-assisted procedures feature complex implementations, and their range of applicability is limited in terms of design space dimensionality that can be efficiently handled. Rendering reliable surrogates is additionally encumbered by highly nonlinear antenna characteristics. This paper investigates potential benefits of employing problem-relevant knowledge in the form of response features into nature-inspired antenna optimization. As demonstrated in the recent literature, re-formulating the design task with the use of appropriately selected characteristic locations of the antenna responses permits flattening the functional landscape of the objective function, leading to faster convergence of optimization procedures. Here, we apply this concept to nature-inspired global optimization of multi-band antenna structures, and demonstrate its relevance, both in terms of accelerating the search process but also improving its reliability. The advantages of feature-based nature-inspired optimization are corroborated through comprehensive (based on three antenna structures) comparisons with a population-based search involving conventional (e.g., minimax) design problem formulation.

Enhanced sliding mode controller design via meta‐heuristic algorithm for robust and stable load frequency control in multi‐area power systems

AbstractThis article introduces a novel approach named HBA‐dHoSMO, which combines a continuous decentralized higher‐order sliding mode controller‐based observer (dHoSMO) with the honey badger algorithm (HBA), specifically designed for load frequency control in multi‐area power systems (MAPSs). Traditional sliding mode controllers (SMCs) employed in load frequency control of MAPSs often face challenges related to chattering and oscillations, leading to decreased robustness and stability. Additionally, tuning the parameters for these SMC designs to achieve optimal performance in MAPSs can be challenging. The HBA‐dHoSMO is proposed to address the issues of chattering and oscillations, while the optimal parameters for SMC design are obtained using HBA. The stability analysis of the entire system is conducted using linear matrix inequality and the Lyapunov stability theory, affirming the reliability and feasibility of the approach. A comprehensive set of case studies is performed under various configurations and conditions. Additionally, particle swarm optimization and tuna swarm optimization, in conjunction with SMC‐based and proportional–integral–derivative controllers, are examined for performance comparison. Simulation results demonstrate the superior performance of the proposed controller across all case studies. This is evidenced by the lowest integral time absolute error values recorded as 0.0133, 6.45 × 10−4, and 0.0167 for single‐, two‐, and three‐area power systems, respectively.

Development of a 3D printed perfusable in vitro blood-brain barrier model for use as a scalable screening tool.

Despite recent technological advances in drug discovery, the success rate for neurotherapeutics remains alarmingly low compared to treatments for other areas of the body. One of the biggest challenges for delivering therapeutics to the central nervous system (CNS) is the presence of the blood-brain barrier (BBB). In vitro blood-brain barrier models with high predictability are essential to aid in designing parameters for new therapeutics, assess their ability to cross the BBB, and investigate therapeutic strategies that can be employed to enhance transport. Here, we demonstrate the development of a 3D printable hydrogel blood-brain barrier model that mimics the cellular composition and structure of the blood-brain barrier with human brain endothelial cells lining the surface, pericytes in direct contact with the endothelial cells on the abluminal side of the endothelium, and astrocytes in the surrounding printed bulk matrix. We introduce a simple, static printed hemi-cylinder model to determine design parameters such as media selection, co-culture ratios, and cell incorporation timing in a resource-conservative and high-throughput manner. Presence of cellular adhesion junction, VE-Cadherin, efflux transporters, P-glycoprotein (P-gp) and Breast cancer resistance protein (BCRP), and receptor-mediated transporters, Transferrin receptor (TfR) and low-density lipoprotein receptor-related protein 1 (LRP1) were confirmed via immunostaining demonstrating the ability of this model for screening in therapeutic strategies that rely on these transport systems. Design parameters determined in the hemi-cylinder model were translated to a more complex, perfusable vessel model to demonstrate its utility for determining barrier function and assessing permeability to model therapeutic compounds. This 3D-printed blood-brain barrier model represents one of the first uses of projection stereolithography to fabricate a perfusable blood-brain barrier model, enabling the patterning of complex vessel geometries and precise arrangement of cell populations. This model demonstrates potential as a new platform to investigate the delivery of neurotherapeutic compounds and drug delivery strategies through the blood-brain barrier, providing a useful in vitro screening tool in central nervous system drug discovery and development.

Scaling photonic systems-on-chip production with neural networks

We describe our use of deep learning to optimize the multi-dimensional parameter space of systems-on-chip as an important step towards the scalable production of photonic solutions and their widespread integration into high-volume applications. The challenges of transitioning between prototype and volume production are highlighted, and the suitability of deep neural networks for navigating the multi-dimensional design space of today’s photonic circuits is discussed. We adopt multi-path neural network architectures to reduce the computational requirements of model training and to mitigate the risk of overfitting. We demonstrate the use of a multi-path neural network to optimize the construction parameters of photonic designs in a high-volume production environment. Lastly, we discuss the advantages of using machine learning not only as a highly capable tool for navigating the multi-dimensional design space of complex systems-on-chip but also as an effective strategy for compensating for fabrication process non-uniformities that are undetectable by standard process metrology instruments.