Flexible Architecture Research Articles

A High-Power-Density Low-Temperature Heteropoly Acid Aqueous Redox Flow Battery

Aqueous redox flow batteries (ARFBs) are promising technology for safe and long-duration energy storage owing to their flexible architecture decoupling power and energy, which is the key to achieving massive utilization of intermittent renewable energies (solar and wind power)1. However, the high-power density at low temperatures is prohibited by the weak stability, sluggish kinetics, and limited solubility of active materials. These challenges not only preclude the commercialization of ARFBs in the high-power density market (e.g., electric automobile) but also make the renewable electric grid vulnerable to severe weather fluctuations.In this study, we describe a polyoxometalate-based active material with exceptionally fast redox kinetics and high electron solubility at low temperatures2. We optimize the charging process to achieve high electrochemical performance and will discuss factors that would influence solubility. We evaluate the kinetics of the polyoxometalate-based materials by rotating ring-disk electrodes measurement (RRDE) and adjust the electrolyte composition to increase the energy density and electrochemical stability of polyoxometalate-based electrolytes. We will discuss the full polyoxometalate-based ARFBs’ electrochemical performance, the influence of the supporting, and the possible degradation mechanism of the full cell (e.g., side reaction (hydrogen evolution, decomposition of the active materials) and crossover). Acknowledgment: The work is supported by a grant from the Research Grant Council (RGC) of the Hong Kong Special Administrative Region, China (project no. CUHK 14308622). Reference: (1) Yao, Y.; Lei, J.; Shi, Y.; Ai, F.; Lu, Y.-C. Assessment methods and performance metrics for redox flow batteries. Nature Energy 2021, 6 (6), 582.(2) Ai, F.; Wang, Z.; Lai, N.-C.; Zou, Q.; Liang, Z.; Lu, Y.-C. Heteropoly acid negolytes for high-power-density aqueous redox flow batteries at low temperatures. Nature Energy 2022, 7 (5), 417.

A deep scalable neural architecture for soil properties estimation from spectral information

In this paper we propose an adaptive deep neural architecture for the prediction of multiple soil characteristics from the analysis of hyperspectral signatures. The proposed method overcomes the limitations of previous methods in the state of art: (i) it allows to predict multiple soil variables at once; (ii) it is based on a flexible neural architecture capable of automatically adapting to the spectral library under analysis. As an additional feature, our method permits to backtrace the spectral bands that most contribute to the estimation of a given variable. The proposed architecture is experimented on LUCAS, a large laboratory dataset and on a dataset achieved by simulating PRISMA hyperspectral sensor. Results, compared with other state-of-the-art methods confirm the effectiveness of the proposed solution. In fact, the proposed method achieves an overall R2 of about 0.75. In detail, pHCaCl2 and CaCO3 are predicted with an R2 above 0.90, pHH2O, OC, N, clay, sand, CEC, silt are predicted with an R2 between 0.89 and 0.70, K and P with an R2 respectively of 0.54 and 0.37. The proposed method overcomes, in terms of R2, the state of the art of about 15% on the LUCAS dataset and of about 24% on the PRISMA simulated dataset.

A STUDY ON COMPARATIVE STUDYON FLAT SLAB STRUCTURE WITH AND WITHOUT SHEAR WALL

This study presents a detailed comparative analysis of flat slab structures with and without shear walls to evaluate their seismic performance, structural behavior, and overall efficiency. Flat slab structures are widely used in modern construction due to their simplified formwork and enhanced architectural flexibility. However, their seismic performance has been a subject of concern, especially in regions prone to seismic activity. This research involved numerical simulations and analytical investigations to explore the behavior of flat slab structures under seismic loads. In this work, two main factors were considered namely with shear walls and without shear walls for flat slab structure. The Structure is modelled with the ETABS software and analysis according to 1893:2002. The project work is carried out for (G+10) stories of structure and analysis for different models, Flat slab with shear and without shear partitions. Based on the assessment of various parameters, it was observed that the flat slab with shear partition exhibited decreased values of story displacement and drift, along with improved values of story stiffness and shear when compared to the flat slab with shear wall system. Key Words: Flat Slab, Shear Wall, Seismic Loads and E-TABS.

Designing deformable Girehs based on historical authenticity

Girehs are an important esthetic tool in Islamic architecture. The introduction of deformable Girehs can be considered as a significant solution in the areas of flexibility and authenticity in contemporary architecture. By investigating the configurations of different Girehs based on their configurational authenticity, the present study presents a method to make the studied Girehs deformable. The presented methodology can be generalized for all types of Girehs. The deployment of Girehs in a constant perimeter and the generation of new authentic Gireh with deformable configuration are among the main features of the proposed method. The method is based on the PIC methodology, which uses the change in the angle of the Gireh elements in underlying generative tessellation based on the family of the Gireh (acute, median, obtuse). The fixed points of the deformable Girehs are positioned in the midpoints of the sides of the underlying polygons. Also, to address the issue of the increased lengths of the elements and integrated movement, telescope elements and scissor structure have been used.

Thermionic enhanced solar thermoradiative-photovoltaic conversion

Solar thermoradiative-photovoltaic (TR-PV) conversion is a promising power generation technique due to its flexible architecture. However, the TR-PV converter transforms energy from heat to electrons, then photons, and ultimately back to electrons, resulting in irreversible interconversion losses. This paper attempts to present a novel concept of solar thermionic intermediated thermoradiative-photovoltaic (TRTI-PV) conversion. Thermoradiative photons, as well as thermionic electrons are released and involved between the TR and PV ends, minimizing the irreversible loss of the electron-photon transition. A detailed thermodynamic model in which the nonradiative losses are taken into account is established for solar TRTI-PV conversion. The results demonstrate that the thermionic emission dominates the solar TRTI-PV conversion, with mutual restriction between the TR and PV components. The output power of the TRTI-PV converter rises to 35.54 W/cm2 at thermoradiative and photovoltaic bandgaps of 0.3 eV and 0.2 eV, respectively, despite the TR and PV component outputs being 1.67 W/cm2 and 3.63 W/cm2 respectively, substantially lower than those in TR-PVs. The TRTI-PV converter obtains a solar conversion efficiency of roughly 34.78% at a concentration ratio of 1000, which is 3.24 times that of a single TR-PV converter.

Development of the equivalent Great Britain 36-zone power system for frequency control studies

This paper presents a dynamic model of the equivalent Great Britain (GB) 36-zone power system, which can be used for reliable and realistic assessment of emerging load frequency control mechanisms. Flexible architecture of the presented dynamic test system permits a broad range of security of supply and small-signal stability studies for design of future power grids. It can be particularly useful for academic research, but also for undertaking feasibility studies in power industries. The proposed dynamic test system, which is obtained through network reduction of the original full-scale GB transmission power system developed by National Grid Electricity System Operator (NGESO) Company, provides detailed information about the GB power system. In this regard, the required data and modelling approaches to develop the 36-zone system are provided in detail. The presented dynamic test system represents the system topology, impedance characteristics and electromechanical oscillations of the original GB power system however, it is not an exact equivalent of the master GB system. Illustrative dynamic models of the key system components, including synchronous generators, automatic voltage regulators, power system stabilizers, hydro and steam turbines models along with speed governing systems are presented. Dynamic behavior of 36-zone test system in response to infeed loss contingencies is investigated. Particularly, the impact of changes in the system inertia on the system electromechanical modes is examined using the modal analysis approach. In this context, the mode shape concept is employed to determine dominant generators and contribution of different zones in the low frequency oscillations. Moreover, time-domain simulations are undertaken to validate the modal analysis results. Additionally, the condition of different zones from the viewpoint of frequency nadir and maximum rate of change of frequency for various contingencies and extreme cases are examined.

Demonstration of a roll-to-roll-configurable, all-solution-based progressive assembly of flexible transducer devices consisting of functional nanowires on micropatterned electrodes

We demonstrate continuous fabrication of flexible transducer devices consisting of interdigitated (IDT) Ag microelectrodes interconnected by ZnO nanowires (ZNWs), created via serially connected solution-processable micro- and nanofabrication processes. On an Ag layer obtainable from the mild thermal reduction of an ionic Ag ink coating, the roll-to-roll-driven photolithography process [termed photo roll lithography (PRL)] followed by wet-etching can be applied to continuously define the IDT microelectrode structure. Conformal ZNWs can then be grown selectively on the Ag electrodes to interconnect them via an Ag-mediated hydrothermal ZNW growth that does not require high-temperature seed sintering. Given that all of these constitutive processes are vacuum-free and solution-processable at a low temperature, and are compatible with continuous processing onto flexible substrates, they can be eventually configured into the roll-to-roll-processable progressive assembly. Through parametric optimizations of processes consisting of the roll-to-roll-configurable, solution-based progressive assembly of nanostructures (ROLSPAN), a flexible transducer consisting of ZNW-interconnected, PRL-ed IDT Ag electrodes can be developed. This flexible architecture faithfully performs UV sensing as well as optoelectronic transduction. The ROLSPAN concept along with its specific applicability to flexible devices may inspire many diverse functional systems requiring high-throughput low-temperature fabrication over large-area flexible substrates.

Regulating cathode surface hydroxyl chemistry enables superior potassium storage

Potassium vanadium fluorophosphate (KVPO4F) is regarded as a promising cathode candidate for potassium-ion batteries due to its high working voltage and satisfactory theoretical capacity. However, the usage of electrochemically inactive binders and redundant current collectors typically results in inferior electrochemical performance and low energy density, thus implying the important role of rational electrode structure design. Herein, we have reported a scalable and cost-effective synthesis of a cellulose-derived KVPO4F self-supporting electrode, which features a special surface hydroxyl chemistry, three-dimensional porous and conductive framework, as well as super flexible and stable architecture. The cellulose not only serves as a flexible substrate, a pore-forming agent, and a versatile binder for KVPO4F/conductive carbon but also enhances the K-ion migration ability. Benefiting from the special hydroxyl chemistry-induced storage mechanism and electrode structural stability, the flexible freestanding KVPO4F cathode exhibits high-rate performance (53.0% capacity retention with current densities increased 50-fold, from 0.2 C to 10 C) and impressive cycling stability (capacity retention up to 74.9% can be achieved over 1,000 cycles at a rate of 5 C). Such electrode design and surface engineering strategies, along with a deeper understanding of potassium storage mechanisms, provide invaluable guidance for better electrode design to boost the performance of potassium-ion energy storage systems.

Ultra-low power logic in memory with commercial grade memristors and FPGA-based smart-IMPLY architecture

Reducing power consumption in nowadays computer technologies represents an increasingly difficult challenge. Conventional computing architectures suffer from the so-called von Neumann bottleneck (VNB), which consists in the continuous need to exchange data and instructions between the memory and the processing unit, leading to significant and apparently unavoidable power consumption. Even the hardware typically employed to run Artificial Intelligence (AI) algorithms, such as Deep Neural Networks (DNN), suffers from this limitation. A change of paradigm is so needed to comply with the ever-increasing demand for ultra-low power, autonomous, and intelligent systems. From this perspective, emerging memristive non-volatile memories are considered a good candidate to lead this technological transition toward the next-generation hardware platforms, enabling the possibility to store and process information in the same place, therefore bypassing the VNB. To evaluate the state of current public-available devices, in this work commercial-grade packaged Self Directed Channel memristors are thoroughly studied to evaluate their performance in the framework of in-memory computing. Specifically, the operating conditions allowing both analog update of the synaptic weight and stable binary switching are identified, along with the associated issues. To this purpose, a dedicated yet prototypical system based on an FPGA control platform is designed and realized. Then, it is exploited to fully characterize the performance in terms of power consumption of an innovative Smart IMPLY (SIMPLY) Logic-in-Memory (LiM) computing framework that allows reliable in-memory computation of classical Boolean operations. The projection of these results to the nanoseconds regime leads to an estimation of the real potential of this computing paradigm. Although not investigated in this work, the presented platform can also be exploited to test memristor-based SNN and Binarized DNNs (i.e., BNN), that can be combined with LiM to provide the heterogeneous flexible architecture envisioned as the long-term goal for ubiquitous and pervasive AI.

Fine-Tuning of the Pore Aperture and Framework Flexibility of Mixed-Metal (Zn/Co) Zeolitic Imidazolate Framework-8: An In Situ Positron Annihilation Lifetime Spectroscopy Study under CO2 Gas Pressure.

The mixed-metal (Zn/Co) strategy has been used to enhance the gas separation selectivity of zeolitic imidazolate framework-8 (ZIF-8)-based membranes. The enhancement in selectivity has been attributed to possible modifications in the grain boundary structure, pore architecture, and flexibility of the frameworks. In the present study, we used in situ positron annihilation lifetime spectroscopy (PALS) under varying CO2 pressure to investigate the tuning of the pore architecture and framework flexibility of mixed-metal (Zn/Co) ZIF-8 frameworks with varying Co contents. The random distribution of Zn and Co metal nodes within the highly crystalline frameworks having an SOD topology was established using electron microscopy, Fourier transform infrared spectroscopy, and Raman spectroscopy. The inherent aperture as well as cavity size of the frameworks, and the pore interconnectivity to the outer surface, were observed to vary with the Co content in ZIF-8 due to the random distribution of Zn and Co metal nodes in the frameworks. The aperture size is reduced with the incorporation of an additional metal (Zn or Co) in ZIF-67 or ZIF-8, respectively. The aperture size remains the smallest for a lower Co content (∼0.20) in ZIF-8. The framework flexibility determined by in situ PALS measurements under CO2 pressure continuously reduces with increasing Co content in ZIF-8. A smaller aperture size as well as low flexibility of ZIF-8 with a low Co content is seen to be directly correlated to a higher separation selectivity of membranes prepared with this mixed-metal composition.

Software Defined Networking Architecture for Energy Transaction in Smart Microgrid Systems

A decentralized power distribution network consisting of smart microgrids introduces opportunities to trade with energy called transactive energy. However, research studies in the existing literature suggest that several standardized information models for TE do not meet the network architecture’s reliability, flexibility, and security requirements. This limitation is mainly due to the static nature of traditional IP infrastructure. To achieve these requirements in the network architecture, this study investigates the optimized application of software-defined network architecture for transactive energy in smart microgrid systems. Through literature research, unique design approaches in an SDN architecture are identified that improve the reliability, flexibility, and security of the SDN architecture. These design approaches include a decentralized controller network layout, redundant link configuration, a mesh network topology, and data encryption. The proposed solution uniquely combines these design approaches into a single optimized SDN solution for TESMS. To validate the improvements of the findings from the literature research, each design approach is simulated in this study using Mininet SDN emulator and AnyLogic system simulation software. The proposed solution is then applied to a use-case scenario that shows the improvements required for TESMS. The use-case scenario shows significant improvement in the data path uptime. An improvement of 0.27% is achieved, which equates to a 2 h per month increase in the data path uptime. The results of the simulation show that the proposed SDN architecture improves the reliability and flexibility of a traditional SDN network. Furthermore, enabling encryption between the nodes improves the security of the SDN architecture.

Design Flexibility in Islamic Architecture as a Methodology for Treating Design Deficiencies in Contemporary Governmental Housing Projects in Egypt

Design Flexibility in Islamic Architecture as a Methodology for Treating Design Deficiencies in Contemporary Governmental Housing Projects in Egypt

Flexible FPGA 1D DCT hardware architecture for HEVC

In this work, we propose a flexible 1D DCT hardware design with a constant throughput of 32 pixels per cycle for all transform block (TB) size modes. The design supports all TB sizes defined in the HEVC standard. Flexibility is achieved by multiplexing only the outputs, which results in lower data path delays compared to other proposed designs. Also, the additional partial butterfly units are transferred to the output of transformation cores. The transformation operation is done in a single stage to minimize the latency of the design and reduce hardware usage. The highly parallel input reduces the need for a very high operational frequency, which is suitable for low-power FPGA designs. Four different reusable transformation cores are used that are designed using parallel Multiple Constant Multiplication (MCM) units to further reduce the calculation time. The design was implemented on the Virtex UltraScale + device. The implementation has hardware usage of 21,818 LUTs, and it can reach the maximal throughput of 4.90 Gsps at the working frequency of 153 MHz which is enough to support the video resolutions of up to 8192 × 4320@60fps. Comparison with the other works shows that DCT FPGA implementation without DSPs can reach the performance of the ASICs with trade-offs in power consumption.

A New Policy Toolbox for Semiconductor Supply Chains

Instead of relying on production subsidies to shore up fragile semiconductor supply chains, policymakers should explore alternative approaches, including common design platforms and flexible architectures.

Physics-Informed Neural Networks for Bingham Fluid Flow Simulation Coupled with an Augmented Lagrange Method

As a class of non-Newtonian fluids with yield stresses, Bingham fluids possess both solid and liquid phases separated by implicitly defined non-physical yield surfaces, which makes the standard numerical discretization challenging. The variational reformulation established by Duvaut and Lions, coupled with an augmented Lagrange method (ALM), brings about a finite element approach, whereas the inevitable local mesh refinement and preconditioning of the resulting large-scaled ill-conditioned linear system can be involved. Inspired by the mesh-free feature and architecture flexibility of physics-informed neural networks (PINNs), an ALM-PINN approach to steady-state Bingham fluid flow simulation, with dynamically adaptable weights, is developed and analyzed in this work. The PINN setting enables not only a pointwise ALM formulation but also the learning of families of (physical) parameter-dependent numerical solutions through one training process, and the incorporation of ALM into a PINN induces a more feasible loss function for deep learning. Numerical results obtained via the ALM-PINN training on one- and two-dimensional benchmark models are presented to validate the proposed scheme. The efficacy and limitations of the relevant loss formulation and optimization algorithms are also discussed to motivate some directions for future research.

Влияние социально-культурных потребностей человека на архитектуру и планировочные решения коливингов

Architectural solutions should always be aimed at meeting the socio-cultural needs of a person. Le Corbusier, Ralph Erskine, Jane Jacobs, Kevin Lynch, Richard Sennett are devoted to the study of the impact of social development on urban planning and residential development. The need for housing to a large extent depends on the period of a person's life. In this regard, Le Corbusier advocated the creation of a flexible architecture that changes depending on the phase of life. Housing should correspond to the social status and personal needs of each family member. One way to fulfill this need is coliving. It is attractive to people at different stages of life. Young people can choose co-living to save on rent and live closer to work and play. Young families can use it to share space with other parents and children. Coliving is also handy during life transitions and allows you to make new friends and have more opportunities for social interaction. Architects who have worked on coliving projects include Olafur Eliasson, Norman Foster, Jeannette Mardini and David Chipperfield. The paper proposes the layout of coliving for housing graduate students and teachers of higher education.

Bendable Silicene Membranes.

Due to their superior mechanical properties, two-dimensional (2D) materials have gained interest as active layers in flexible devices co-integrating electronic, photonic, and straintronic functions altogether. To this end, 2D bendable membranes compatible with the technological process standards and endowed with large scale uniformity are highly desired. Here, we report on the realization of bendable membranes based on silicene layers (the 2D form of silicon) by means of a process in which the layers are fully detached from the native bulk substrate and then transferred onto arbitrary flexible substrates. The application of macroscopic mechanical deformations induces a strain-responsive behavior in the Raman spectrum of silicene. We also show that the membranes under elastic tension relaxation are prone to form microscale wrinkles displaying a local generation of strain in the silicene layer consistent with that observed under macroscopic mechanical deformation. Optothermal Raman spectroscopy measurements reveal a curvature-dependent heat dispersion in silicene wrinkles. Finally, as compelling evidence of the technological potential of the silicene membranes, we demonstrate that they can be readily introduced into a lithographic process flow resulting in the definition of flexible device-ready architectures, e.g. piezoresistor, and thus paving the way to a viable advance in a fully silicon-compatible technology framework. This article is protected by copyright. All rights reserved.

Estimating Regression Predictive Distributions with Sample Networks

Estimating the uncertainty in deep neural network predictions is crucial for many real-world applications. A common approach to model uncertainty is to choose a parametric distribution and fit the data to it using maximum likelihood estimation. The chosen parametric form can be a poor fit to the data-generating distribution, resulting in unreliable uncertainty estimates. In this work, we propose SampleNet, a flexible and scalable architecture for modeling uncertainty that avoids specifying a parametric form on the output distribution. SampleNets do so by defining an empirical distribution using samples that are learned with the Energy Score and regularized with the Sinkhorn Divergence. SampleNets are shown to be able to well-fit a wide range of distributions and to outperform baselines on large-scale real-world regression tasks.

SEPT: Towards Scalable and Efficient Visual Pre-training

Recently, the self-supervised pre-training paradigm has shown great potential in leveraging large-scale unlabeled data to improve downstream task performance. However, increasing the scale of unlabeled pre-training data in real-world scenarios requires prohibitive computational costs and faces the challenge of uncurated samples. To address these issues, we build a task-specific self-supervised pre-training framework from a data selection perspective based on a simple hypothesis that pre-training on the unlabeled samples with similar distribution to the target task can bring substantial performance gains. Buttressed by the hypothesis, we propose the first yet novel framework for Scalable and Efficient visual Pre-Training (SEPT) by introducing a retrieval pipeline for data selection. SEPT first leverage a self-supervised pre-trained model to extract the features of the entire unlabeled dataset for retrieval pipeline initialization. Then, for a specific target task, SEPT retrievals the most similar samples from the unlabeled dataset based on feature similarity for each target instance for pre-training. Finally, SEPT pre-trains the target model with the selected unlabeled samples in a self-supervised manner for target data finetuning. By decoupling the scale of pre-training and available upstream data for a target task, SEPT achieves high scalability of the upstream dataset and high efficiency of pre-training, resulting in high model architecture flexibility. Results on various downstream tasks demonstrate that SEPT can achieve competitive or even better performance compared with ImageNet pre-training while reducing the size of training samples by one magnitude without resorting to any extra annotations.

FlexiArch: A computational tool to assess the longevity of buildings through flexibility

The term “Flexibility” in architecture refers to the capability of a building to continuously alter its spatial arrangement to accommodate evolving demands. Flexibility is a crucial aspect of sustainability as it reduces the need for costly structural renovations, relocations, and demolitions. Currently there are limited tools available to evaluate architectural Flexibility during the design process, and the available tools are unwieldy and/or limited in the range of parameters that they consider. The current project was conducted to help fill this gap, by (1) developing a new computational tool, “FlexiArch,” to consolidate and implement Flexibility factors from the prior research literature; and (2) conducting a practitioner survey (n = 237) to assess similarities or differences between working architects’ perceptions of floorplan Flexibility vs. the literature-based computational scoring. We found that the aggregated architects were unable to differentiate literature-based Flexibility features in the floorplans, rating all designs essentially the same. The computational approach, in contrast, demonstrated a much higher level of discrimination. We interpret this result to mean that there is not a strong agreement between current working architects and the academic literature on the topic of floorplan Flexibility. While additional research is needed to fully ascertain the reasons for this discrepancy, we feel it is likely that architectural designers may be, on average, insufficiently trained in understanding the importance of Flexibility and how to achieve it. Tools such as FlexiArch can potentially assist in filling this training/knowledge gap by providing robust quantitative feedback and encouraging designers to consider Flexibility issues in their work.