Design Space Research Articles

Fatigue performance and damage characterisation of ultra-thin tow-based discontinuous tape composites

Tow-cased discontinuous composites are an attractive alternative material to conventional continuous composites as they offer in-plane isotropy, enhanced manufacturability allowing to achieve complex 3D shapes with high curvatures and local reinforcement in critical areas, while also maintaining high strength and stiffness, therefore expanding the design space significantly. In addition, the use of ultra-thin tapes and optimised manufacturing methods can increase the mechanical properties even further and change the damage mechanisms. Fatigue, however, could be a limiting design factor, as the fatigue behaviour of these materials has not been fully characterised. This work presents a complete study on the fatigue response of ultra-thin tow-based discontinuous composites: fatigue S-N curves are measured, and the damage and failure mechanisms are characterised utilising optical and scanning electron microscopy. Finally, a critical interpretation of the results is also presented by comparing the performance of ultra-thin tow-based discontinuous composites against other similar fibre reinforced composites and metals. It is shown that the optimised manufacturing methods combined with low tape thickness leads to enhanced quasi-isotropic fatigue performance. In addition, the fatigue limit was raised significantly compared to other discontinuous composites, and the tow-based discontinuous composites outperformed their metal counterparts when the results were normalised with density.

FrameGraph: A Scalable Performance Evaluation Method for Frame Structure Designs Using Graph Neural Network

Abstract Multilayer perceptron (MLP) and convolutional neural network (CNN) encounter a critical scalability issue when applied to the performance evaluation task for frame structure designs. Specifically, a model of MLP or CNN is limited to structures of a particular topology type and fails immediately when applied to other topology types. In order to tackle this challenge, we propose a scalable performance evaluation method (called FrameGraph) for frame structure designs using graph neural network (GNN), offering applicability to a wide range of topology types simultaneously. FrameGraph consists of two main parts: (1) Components and their connections in a frame structure are denoted as edges and vertices in a graph, respectively. Subsequently, a graph dataset for frame structure designs with different topologies is constructed. (2) A well-defined GNN design space is established with a general GNN layer, and a controlled random search approach is employed to derive the optimal GNN model for this performance evaluation task. In numerical experiments of car door frames and car body frames, FrameGraph achieved the highest prediction precisions (96.28% and 97.87%) across all structural topologies compared to a series of classical GNN algorithms. Furthermore, the comparison with MLP and FEM highlighted FrameGraph's significant efficiency advantage. This verifies the feasibility and optimality of FrameGraph for the performance evaluation task of frame structures with different topologies.

Terms of entanglement: a posthumanist reading of Terms of Service

ABSTRACT Contemporary connected things entail ongoing relations between producers, end users, and other actors characterized by ongoing updates and production of data about and through use. These relations are currently governed by Terms of Service (ToS) and related policy documents, which are known to be mostly ignored beyond the required interaction of ticking a box to indicate consent. This seems to be a symptom of failure to design for effectively mediating ongoing relations among multiple stakeholders involving multiple forms of value generation. In this paper, we use ToS as an entrance point to explore design practices for democratic data governance. Drawing on posthuman perspectives, we make three posthuman design moves exploring entanglements, decentering, and co-performance in relation to Terms of Service. Through these explorations we begin to sketch a space for design to engage with democratic data governance through a practice of what we call revealing design that is aimed at meaningfully making visible these complex networked relations in actionable ways. This approach is meant to open alternative possible trajectories that could be explored for design to enable genuine democratic data governance.

Sequential good lattice point sets for computer experiments

Sequential Latin hypercube designs have recently received great attention for computer experiments. Much of the work has been restricted to invariant spaces. The related systematic construction methods are inflexible while algorithmic methods are ineffective for large designs. For such designs in space contraction, systematic construction methods have not been investigated yet. This paper proposes a new method for constructing sequential Latin hypercube designs via good lattice point sets in a variety of experimental spaces. These designs are called sequential good lattice point sets. Moreover, we provide fast and efficient approaches for identifying the (nearly) optimal sequential good lattice point sets under a given criterion. Combining with the linear level permutation technique, we obtain a class of asymptotically optimal sequential Latin hypercube designs in invariant spaces where the $L_1$-distance in each stage is either optimal or asymptotically optimal. Numerical results demonstrate that the sequential good lattice point set has a better space-filling property than the existing sequential Latin hypercube designs in the invariant space. It is also shown that the sequential good lattice point sets have less computational complexity and more adaptability.

High-performance convolutional neural network emulation via FPGA-integrated memristive circuitry

Although memristors address the potential data flow and memory access bottlenecks in traditional von Neumann architectures by moving computational operations into the storage elements, thereby improving computational efficiency and reducing energy consumption, the current costs of memristors are relatively high, and they face issues such as low reliability and short lifespan. Therefore, this paper proposes a simulation circuit model based on FPGA, which can provide a low-cost solution for rapid prototyping design and exploration of the design space. The memristor simulation circuit is deployed on the Xilinx ZYNO-7000 FPGA XC7Z020 with a resource utilization rate of less than 1%, and it has been successfully integrated and verified. Furthermore, this paper also constructs a convolutional neural network for MNIST image recognition using a memristor model. With a basis of 512 parameters, it achieves an accuracy of 90.22%.

Unidirectional Transmission Metasurfaces with Topological Continuity Generated from High-dimensional Design Space.

With the growing demand for nanodevices, there is a concerted effort to improve the design flexibility of nanostructures, thereby expanding the capabilities of nanophotonic devices. In this work, a Laplacian-weighted binary search (LBS) algorithm is proposed to generate a unidirectional transmission metasurface from a high-dimensional design space, offering an increased degree of design freedom. The LBS algorithm incorporates topological continuity based on the Laplacian, effectively circumventing the common issue of high structural complexity in designing high-dimensional nanostructures. As a result, metasurfaces developed using the LBS algorithm in a high-dimensional design space exhibit reduced complexity, which is advantageous for experimental fabrication. An all-dielectric metasurface with unidirectional transmission, designed from the high-dimensional space using the LBS method, demonstrated the successful application of these design principles in experiments. The metasurface exhibits high optical performance on unidirectional transmission in measurements by a high-resolution angle-resolved micro-spectra system, achieving forward transmissivity above 90% (400-700 nm) and back transmissivity below 20% (400-500 nm) within the targeted wavelength range. This work provides a feasible approach for advancing high-dimensional metasurface applications, as the LBS design method takes into account topological continuity during experimental processing. Compared to traditional direct binary search (DBS) methods, the LBS method not only improves information processing efficiency but also maintains the topological continuity of structures. Beyond unidirectional transmission, the LBS-based design method has generality and flexibility to accommodate almost all physical scenarios in metasurface design, enabling a multitude of complex functions and applications.

Encapsulation of Transketolase into In Vitro-Assembled Protein Nanocompartments Improves Thermal Stability.

Protein compartments offer definitive structures with a large potential design space that are of particular interest for green chemistry and therapeutic applications. One family of protein compartments, encapsulins, are simple prokaryotic nanocompartments that self-assemble from a single monomer into selectively permeable cages of between 18 and 42 nm. Over the past decade, encapsulins have been developed for a diverse application portfolio utilizing their defined cargo loading mechanisms and repetitive surface display. Although it has been demonstrated that encapsulation of non-native cargo proteins provides protection from protease activity, the thermal effects arising from enclosing cargo within encapsulins remain poorly understood. This study aimed to establish a methodology for loading a reporter protein into thermostable encapsulins to determine the resulting stability change of the cargo. Building on previous in vitro reassembly studies, we first investigated the effectiveness of in vitro reassembly and cargo-loading of two size classes of encapsulins Thermotoga maritima T = 1 and Myxococcus xanthus T = 3, using superfolder Green Fluorescent Protein. We show that the empty T. maritima capsid reassembles with higher yield than the M. xanthus capsid and that in vitro loading promotes the formation of the M. xanthus T = 3 capsid form over the T = 1 form, while overloading with cargo results in malformed T. maritima T = 1 encapsulins. For the stability study, a Förster resonance energy transfer (FRET)-probed industrially relevant enzyme cargo, transketolase, was then loaded into the T. maritima encapsulin. Our results show that site-specific orthogonal FRET labels can reveal changes in thermal unfolding of encapsulated cargo, suggesting that in vitro loading of transketolase into the T. maritima T = 1 encapsulin shell increases the thermal stability of the enzyme. This work supports the move toward fully harnessing structural, spatial, and functional control of in vitro assembled encapsulins with applications in cargo stabilization.

Convolutional neural network based methodology for flexible phase prediction of high entropy alloys

ABSTRACT The advent of high entropy alloys has created a design space that is unfeasible to explore solely through experimentation, thus necessitating the use of computational methods, such as artificial neural networks. This study proposes a new architecture utilizing a convolutional layer to extract relevant elemental features from the alloy composition without limiting the model to specific elements by treating the elemental composition of the high entropy alloy in a similar manner to a row of pixels, using relevant elemental properties in lieu of color values. This convolutional model was able to predict the crystal structure of solid solutions in a high entropy alloy composition with an accuracy of 89.3% in a six-way classification and the formation of intermetallics with an accuracy of 91.4% during holdout validation, while being capable of accurately predicting the primary crystal structure of high entropy alloys containing elements the model was not trained on. Significant space still exists for further experimentation and improvement with this methodology, including augmenting the available datasets and implementation of additional convolutional layers. Due to the limited interpretability of the model architecture, care should be taken when inferring trends from the model prior to validation through experimental, or well-established computational methods.

Optimization of an electric field calibration chamber for high-precision measurements for imaging applications

The DEVCOM Army Research Laboratory (ARL) electric field “cage” generates a uniform E-field over a large working volume, along the lines of the IEEE-Std 1308–1994. The end plates are spaced farther apart than the IEEE standard field source, and the fringing fields are controlled by the addition of “guard tubes.” This chamber was originally constructed to calibrate and characterize electric field sensors, and it has been redesigned to support quasi-static electric field imaging applications. A planar array of sensors forms a grounded end plate of the cage and is used to measure distortions in the uniform field generated by the cage due to objects placed inside. However, the array itself distorts this field and introduces significant errors. Several modifications were made to mitigate the errors, including adding nonfunctional “dummy” elements, a border around the array, and a back plane behind it. The parameter space for these additions is very large, since the number of nonfunctional elements, the width of the border, and the size and placement of the back plane can all be tuned independently. Extensive computer modeling was used to explore this parameter space and test thousands of possible designs. The design chosen yields modeled absolute field errors over a 1.2-m × 0.8-m sensing plane that are less than 0.5 % for a uniform ambient field (empty cage), and less than 1 % for a sphere with a 0.5-m radius in an ambient field.

A Qualitative Study on University Students’ Restorative Experience of the Library Space Environment

Currently, university students are facing high levels of stress and negativity. Creating a campus learning environment that promotes students’ physical and mental health and well-being has become a key focus of current research. There is now a wealth of research proving that the natural attributes of learning spaces are highly restorative for university students, both physically and mentally. In order to comprehensively clarify the characteristics of the library spatial environment that has a restorative effect on university students, this paper conducted semi-structured interviews with 50 university students and thematically analyzed the data obtained from these interviews. Ultimately, we identified five key themes: (1) Being away: students believe that studying alone is a way to escape social pressures, immersing themselves in their studies helps them forget everything around them, and the view from the window creates a sense of detachment; (2) Extent: students perceive the design of the library space as rich and orderly; (3) Fascination: many elements of the library spatial environment appeal to them and make them feel energized. The overall atmosphere motivates them to engage more actively in their learning; (4) Compatibility: the library spatial environment provides a variety of facilities that meet their learning needs, along with various types of spaces catering for different behaviors; and (5) Biophilia: Many interviewees mentioned the benefits of natural environments and timber, as well as their preference for spaces with a sufficient sense of enclosure and the ability to observe others. The first four themes were found to be closely related to the Attention Restorative Theory during the course of the study, while the last theme was mentioned by most of the students. The results of this study prove that library spaces can be described as restorative environments in the same way as natural environments, with both physical and mental restorative effects. Furthermore, the results of this study can also provide certain ideas for the design and renovation of the internal spatial environment of university libraries.

Training on Innovative Learning Environments: Identifying Teachers’ Interests

This article explores the training interests of non-university in-service teachers on the topic of innovative learning environments (ILEs). For this purpose, a specific questionnaire was designed and validated to collect teachers’ opinions on their current training interests. The questionnaire comprised a total of 32 items related to ILEs, grouped into four dimensions that included aspects of methodologies, digital technology, educational spaces, and specific didactic activities developed in these learning environments. The analysis of the 254 valid responses showed that the greatest interest is shown in the dimension that addresses didactic aspects of teaching in these environments, followed by aspects of the design of innovative educational spaces. Variations were also analysed according to demographic aspects such as gender, age, teaching seniority, and the educational stage at which the class is taught. The data obtained allow us to affirm that teachers have a high level of interest in the topic analysed. Furthermore, the grouping of different topics revealed by the correlational statistical study provides valuable information that allows teacher training consultants to create designs in coherence with the demands and interests expressed.

Simulation Training Using DIALux Evo Software for Estimation of Visual Comfort Level of a Building

This community outreach aims to conduct simulation training using DIALux Evo to estimate the visual comfort level in a room. Visual comfort is a crucial aspect of building design and management that can impact the occupant’s health, comfort, and productivity. However, it is often challenging for interior designers to accurately estimate the level of visual comfort. This activity involves the application of simulation technology using DIALux Evo. Participants will be provided with knowledge and skills in operating this software, covering tasks from designing rooms, placing objects, selecting building materials, and choosing artificial lighting, to analyzing, and visualizing the lighting level in a room, also will study SNI related to visual comfort. The program’s outcomes provide participants with the knowledge and skills to use DIALux Evo in estimating the visual comfort level before implementation. Finally, they can optimize space design, ensure appropriate lighting quality, and enhance the occupant’s comfort and health.

Ninguna casa sin jardín

Th. Sørensen was involved in the most important Danish collective social housing programmes between the 1930s and 1950s, in which his aim was to design community gardens designed to complete the modern habitat programme with outdoor public spaces. This article traces the landscape architect's thinking on the design of public space and studies Sørensen's six most outstanding collective gardens with the aim of highlighting the design strategies employed that have consolidated them into spaces of high civic value that are still suitable nowadays. The text is structured in three approaches that depend on the spatial pattern of the buildings and its degree of dispersion. The chapters analyse the programme and the use of topography and vegetation for the definition of a defined character, with a clear spatial order and resources to reconcile the public and human scale.

DRLAR: A deep reinforcement learning-based adaptive routing framework for network-on-chips

Adaptive routing plays a pivotal role in the overall performance of Network-on-Chips (NoCs). However, with many-core architectures supporting complex and constantly changing traffic patterns for emerging applications, this aspect presents significant challenges. Our meticulous examination and analysis of existing heuristic adaptive routing algorithms revealed three key limiting factors: single network status metric, lack of system feedback awareness, and lack of ability to customize. Reinforcement Learning (RL) methods have demonstrated promising opportunities for exploring adaptive routing design. Deep reinforcement learning (DRL) techniques, in particular, enable efficient exploration in adaptive routing design spaces where heuristic strategies may be inadequate. This paper proposes a novel deep reinforcement learning framework for adaptive routing called DRLAR that is suitable for diversified traffic patterns and resolves multi-objective optimization simultaneously. DRLAR formulates routing as an agent, which makes routing decisions based on autonomous learning using multiple network state features and system-level feedback information. We conduct extensive experiments against state-of-the-art routing algorithms to evaluate our design. The results show that DRLAR reduces packet latency by an average of 33.3%, achieving a reduction of 41.6% and 10.5% on average under heavy synthetic traffic and the PARSEC 2.1 benchmark, respectively. We also perform a cost analysis to validate the potential implementation of DRLAR on NoCs with low computational, storage, and power.

Design Space Exploration of Low-Bit Quantized Neural Networks for Visual Place Recognition

Design Space Exploration of Low-Bit Quantized Neural Networks for Visual Place Recognition

Design of Fully Spectral CNNs for Efficient FPGA-Based Acceleration.

Computing convolutional layers in the frequency domain using fast Fourier transformation (FFT) has been demonstrated to be effective in reducing the computational complexity of convolutional neural networks (CNNs). Nevertheless, the main challenge of this approach lies in the frequent and repeated transformations between the spatial and frequency domains due to the absence of nonlinear functions in the spectral domain, as such it makes the benefit less attractive for low-latency inference, especially on embedded platforms. To overcome the drawbacks in the existing FFT-based convolution, we propose a fully spectral CNN using a novel spectral-domain adaptive rectified linear unit (ReLU) layer, which completely removes the compute-intensive transformations between the spatial and frequency domains within the network. The proposed fully spectral CNNs maintain the nonlinearity of the spatial CNNs while taking into account the hardware efficiency. We then propose a deeply customized and compute-efficient hardware architecture to accelerate the fully spectral CNN inference on field programmable gate array (FPGA). Different hardware optimizations, such as spectral-domain intralayer and interlayer pipeline techniques, are introduced to further improve the performance of throughput. To achieve a load-balanced pipeline, a design space exploration (DSE) framework is proposed to optimize the resource allocation between hardware modules according to the resource constraints. On an Intel's Arria 10 SX160 FPGA, our optimized accelerator achieves a throughput of 204 Gop/s with 80% of compute efficiency. Compared with the state-of-the-art spatial and FFT-based implementations on the same device, our accelerator is 4×∼ 6.6× and 3.0×∼ 4.4× faster while maintaining a similar level of accuracy across different benchmark datasets.

Multistable compliant linkages with multiple kinematic paths separated by energy barriers

Multistable morphing structures can reconfigure between different stable states that are separated by energy barriers, and one-degree-of-freedom (1-DOF) mechanisms have many merits, like simple actuation. This paper combines the two and proposes a new family of reconfigurable compliant linkages with many (2−6) 1-DOF kinematic paths that are separated by energy barriers. This new type of design is an extension of multistable structures, where each stable state corresponds to not just one configuration but a 1-DOF configuration space, i.e., a kinematic path. Components of the linkages are made elastically compliant, therefore enabling the switch between two isolated compatible paths with multi-stability. A generation-selection hybrid design algorithm to follow prescribed reconfigurable paths is proposed, and a minimum energy path (MEP) finding method to guide actuation to switch between different kinematic paths is developed. Four design examples with 2–3 reconfigurable paths and their experiments are presented, and the effectiveness of this method is verified. This work provides a fresh perspective to design the single-DOF reconfigurable mechanisms with larger design space, more reconfigurable kinematic paths, and easier reconfiguration actuation.

A Survey of Designs for Combined 2D+3D Visual Representations.

We examine visual representations of data that make use of combinations of both 2D and 3D data mappings. Combining 2D and 3D representations is a common technique that allows viewers to understand multiple facets of the data with which they are interacting. While 3D representations focus on the spatial character of the data or the dedicated 3D data mapping, 2D representations often show abstract data properties and take advantage of the unique benefits of mapping to a plane. Many systems have used unique combinations of both types of data mappings effectively. Yet there are no systematic reviews of the methods in linking 2D and 3D representations. We systematically survey the relationships between 2D and 3D visual representations in major visualization publications-IEEE VIS, IEEE TVCG, and EuroVis-from 2012 to 2022. We closely examined 105 articles where 2D and 3D representations are connected visually, interactively, or through animation. These approaches are designed based on their visual environment, the relationships between their visual representations, and their possible layouts. Through our analysis, we introduce a design space as well as provide design guidelines for effectively linking 2D and 3D visual representations.

Infrared thermography techniques for boundary layer state visualisation

The rapid decarbonisation of the power generation and aviation sectors will require a move away from incremental development, exposing designers and researchers to the risk of unexpected results from uncertainty in boundary layer state. This problem already exists for parts developed with fully turbulent assumptions, but in novel design spaces the risk increases for both real components, where previous knowledge of similar designs may be inapplicable, and particularly in experimental testing of scaled models, where reducing Reynolds number can result in a drastic change in flow topology that skews the conclusions of a test. Computational methods struggle to reliably predict boundary layer state so experimental techniques for diagnosing boundary layer state are needed. Infrared thermography (IR) is a non-invasive technique that offers simple, fast visualisation of boundary layer state with no additional instrumentation. IR is relatively uncommon in the literature and there is minimal information available on the best practices for its use. This paper aims to encourage the adoption of IR as a diagnostic tool by demonstrating routes for optimisation and pointing out pitfalls to avoid. A low-order model is developed and used to predict how the signal-to-noise ratio (SNR) of an IR visualisation changes depending on the thermal design of the test piece. It is shown that in low-speed flows with active heating from the surface the SNR is maximised through a suitable choice of surface insulation, while in high-speed flows, where passive temperature differences are used, there is a crossover between heat transfer and recovery temperature effects that results in an SNR of zero, an effect that can arise in both steady-state and transient experiments. Experimental validation of the 1D model in both flow regimes is shown alongside two case studies on the use of IR in sub-scale testing where uncertainty in boundary layer state results in critical differences from the full-scale flow.

Optimal performance and preliminary parameter matching for hydrogen fuel cell powertrain system of electric aircraft

Fuel cells are true net-zero carbon emission power sources for aircraft, which is highly sensitive to weight. In the initial phase of adapting hydrogen fuel cell systems for aircraft powertrains, preliminary design parameter matching remains premature. An explicit method for the performance optimization of aircraft hydrogen fuel cell powertrain systems and a process of preliminary parameter matching are proposed to address this problem. Performance and weight models of the fuel cell stack and its auxiliaries, the cathode air compressor subsystem, and the cooling subsystem are designed, and system performance at various altitudes and power output levels is calculated. The aircraft flight mission performance is synthesized and considered in the optimization process. The optimization result of system performance and the corresponding design parameters are then graphically illustrated as tern plots. Unlike the traditional iterative preliminary system parameter matching and optimization method, which explores the design space non-directionally and converges to a single local optimal point, the proposed explicit method sweeps the design space globally and obtains a group of design points with acceptable optimality. The system design process is boosted by a compact iterative loop. In the optimization practice, the cruise powertrain specific energy is improved by 6.5%. The relationship between specific system design parameters and system performance is displayed globally by the resulting tern plots. Multiple design guidelines are observed and proposed, and design scenarios are directly obtained from the graphs for further engineering processes.