Design Space Research Articles

Paul B. Preciado’s queer hospital: healthcare architectures for pleasure, transformation and subversion

ABSTRACT This article brings the contemporary philosopher Paul B. Preciado’s work on queer sexuality and embodiment into close contact with his work on architecture, asking how Preciado’s work can help us to reconceptualize the design of clinical spaces and environments to care more effectively for queer and non-normative bodies. In Testo Junkie (2013), Preciado explores how the body is constructed in advanced capitalism as a work of both social construction and bio-technological engineering. The focus of Pornotopia (2014), meanwhile, is the specific role played by architecture in the construction of modernity’s bodies, genders and sexualities. Across both works, Preciado dialogs closely with Michel Foucault, showing how normative genders and sexualities are actively produced and policed through the complex interplays between social, biomedical, technological and architectural forces and structures. Preciado, however, goes one step further, exploring how we might “hack” these same structures and use biomedical technologies and architectural concepts to emancipate the bodies and desire from normative constraints, thus liberating the body’s true capacities for pleasure and transformation. Reading Preciado alongside interdisciplinary scholarship on queer healthcare design, this article questions investigates how “hacking” clinical architecture through queer interventions can transform the sensory landscapes of healthcare.

Functional Modules Map of Unexplored Chemical Space: Guiding the Discovery of Giant Birefringent Materials

AbstractFinding birefringent material with giant optical anisotropy is urgent for photonic applications. However, the application of existing birefringent materials is limited by restricted transparency wavelength or optical anisotropy. The process of trial and error is time‐consuming and inefficient in terms of resources, especially when compared to the vast design spaces for materials. The immense chemical space necessitates a high‐throughput approach for giant optical anisotropy materials design. Here, a full‐applied wavelength map of birefringent materials is constructed, from conventional visible expanding to ultraviolet and deep‐ultraviolet region by high‐throughput screening methods. And highlighted 579 materials birefringence exceeds the one of available commercial birefringent materials in corresponding regions. Significantly, 54 materials possess giant birefringence above 0.5 @1064 nm. Via developing a high‐throughput screening technique for microscopic structures with given coordination number, the study further characterizes a comprehensive map of functional modules featuring birefringent activeness. According to the macroscopic and microscopic dual‐functional maps, and a novel crystal Li2(HC3N3S3)∙5H2O with birefringence difference 0.532 @546 nm is designed and synthesized successfully. This study identifies desired materials with high performance in unheeded chemical spaces.

Effect of Exposure to 2D Video of Forest Environment with Natural Forest Sound on Psychological Well-Being of Healthy Young Adults

Background and Objectives: In recent years, there has been a significant increase in interest in the health benefits of contact with nature. The theoretical and practical aspects of this issue are constantly being investigated. It is worth noting, however, that there is a large group of people for whom the only available form of contact with nature can only be through images (the view outside the window, photos and films about nature), recorded sounds of nature or descriptions of nature. Research indicating how much exposure to nature in the dimension of virtual reality achieves improvements in well-being, mood and vitality is therefore urgently needed. Material and Methods: The main goal of the experiment was to examine whether watching a 20-min 2D video of forest environment or reading a textbook on forest knowledge for 20 min would bring any psychological benefits. The order of both exposures was intentionally changed (reversed) for every second participant in this study to reduce the order effect. A group of 16 volunteers, students of Warsaw University of Life Sciences, took part in the experiment. The psychological tests before and after each of the two parts of the experiment (viewing a picture with sound and reading a book) were established using five questionnaires: Profile of Mood State (POMS); Positive and Negative Affect Scale (PANAS); Restorative Outcome Scale (ROS); Subjective Vitality Scale (SVS) and State-Trait Anxiety Inventory (STAI). Results: As a result of the analyses, a 20-min viewing of a 2D video of a forest with natural sounds of nature was shown to have a positive effect on the psychological relaxation of the subjects in the form of an increase in restorativeness and vitality in the participants of the experiment, as well as a greater decrease in negative feelings, tension and anger than when reading a forest-themed book. Watching a video of a landscape can reduce tension and anger levels significantly, which can be an important guideline in relation to the interior design of public spaces and the protection of employees from the adverse effects of psychosocial working conditions, as well as the desire to stabilize anger and maintain emotional stability in everyday life.

DLAS: A Conceptual Model for Across-Stack Deep Learning Acceleration

Deep Neural Networks (DNNs) are very computationally demanding, which presents a significant barrier to their deployment, especially on resource-constrained devices. Significant work from both the machine learning and computing systems communities has attempted to accelerate DNNs. However, the number of techniques available and the required domain knowledge for their exploration continues to grow, making design space exploration (DSE) increasingly difficult. To unify the perspectives from these two communities, this paper introduces the Deep Learning Acceleration Stack (DLAS), a conceptual model for DNN deployment and acceleration. We adopt a six-layer representation that organizes and illustrates the key areas for DNN acceleration, from machine learning to software and computer architecture. We argue that the DLAS model balances simplicity and expressiveness, assisting practitioners from various domains in tackling co-design acceleration challenges. We demonstrate the interdependence of the DLAS layers, and thus the need for co-design, through an across-stack perturbation study, using a modified tensor compiler to generate experiments for combinations of a few parameters across the DLAS layers. Our perturbation study assesses the impact on inference time and accuracy when varying DLAS parameters across two datasets, seven popular DNN architectures, four compression techniques, three algorithmic primitives (with sparse and dense variants), untuned and auto-scheduled code generation, and four hardware platforms. The study observes significant changes in the relative performance of design choices with the introduction of new DLAS parameters (e.g., the fastest algorithmic primitive varies with the level of quantization). Given the strong evidence for the need for co-design, and the high costs of DSE, DLAS offers a valuable conceptual model for better exploring advanced co-designed accelerated deep learning solutions.

Using a Technology-Enhanced Active Learning Classroom to Teach English: A Help or Hindrance?

Educators view active learning as a pedagogical enabler for preparing students for the demands of today’s workplaces and equipping students with 21st century skills such as critical thinking, problem solving, collaboration, and teamwork. However, there seems to be a noticeable incongruity between the eagerness on the development of 21st century skills and the learning spaces where these skills are intended to be acquired. The significance of learning spaces and their potential for facilitating or impeding students’ active engagement should not be overlooked. Considering the indispensability of technology in today's world, its integration becomes crucial not only in today's pedagogy and curriculum but also in the design of learning spaces. This study aims to investigate teachers’ perspectives of teaching in a technology-enhanced active learning (TEAL) classroom by exploring their experiences of using the TEAL classrooms, the factors affecting their experiences, and how these factors shape teaching experiences from the viewpoint of teacher participants. This study adopts a mixed methods approach where quantitative and qualitative data collection and analysis are incorporated to address the research questions. Findings of the study suggest that the majority of classroom users had a positive overall impression of the TEAL classrooms with some discussing the challenges they encountered. Analysis of the data shows both classroom-related and non-classroom related factors influencing teachers’ teaching experiences. Classroom-related factors encompass aspects such as classroom layout and the technology embedded within the classroom environment. On the other hand, non-classroom related factors are classified into course-related, teacher-related, and student-related factors.

Where Do Young Children in Middle-class High-rise Housing Estates Play?A critical analysis of spatial planning and design parameters across seven heterogeneous housing estates in Pune, India.

Play is fundamental to children’s physical growth, social development, and mental and emotional well-being; and how we plan, and design high-rise housing estates impacts children’s ability to access and use spaces for play, thus impacting their overall growth and development. By using Lefebvre’s Spatial Triad (1974/1991) as an analytical framework, this paper investigates (1) ‘Conceptualized Space’ or ‘play areas and materiality of the play areas as conceptualized by design professionals’; (2) ‘Actual Space’ or ‘spaces where children actually play’; and (3) ‘Experienced Space’ or ‘caregivers’ assessment of play spaces and their preferences of play materials, elements and surfaces for young children in high-rise housing estates. Comparative case-study research of seven housing estates from a baseline study of 63 high-rise housing estates was adopted to realize contrasts, patterns, or similarities across the cases. Methods included (1) semi-structured open-ended interviews with design professionals including developers (n=4), architects (n=4), landscape architects (n=2) and play equipment manufacturer (n=1); (2) In-depth field studies; and (3) semi-structured open-ended interviews with parents (n=27), grandparents (n=5) and nannies (n=4) of young children. This study generates new knowledge about design and planning considerations for designated play spaces, caregivers’ and designers ideas around nature based play, caregivers’ preferences of play elements, materials and surfaces, and details spatial factors influencing young children’s play areas in housing estates. As a way forward, the paper offers 11 guidelines to influence the design and planning of play spaces and open areas in future housing estates to fulfil young children’s play needs.

From desperation to best practice: Spatial decision-making in the regeneration of Gyldenrisparken

Post-war social housing estates in Europe have been undergoing wide-scale regeneration to improve the physical decay of these sites and address the concentration of vulnerable residents in these areas, which has resulted in their social segregation, marginalisation and stigmatisation. As these estates cover and affect quite large public spaces, holistic approaches have recently been adopted. Bearing in mind that each regeneration case is unique, this paper describes the collaborative approach taken in the regeneration of Gyldenrisparken in Denmark, which evolved from a desperate situation to a best practice case. The paper focuses on the spatial decision-making process — in particular, how the architectural quality of physical interventions was established and how participatory mechanisms were utilised and developed to enable liveable spaces and sustainable regeneration. Making use of a combination of qualitative documentary analysis and in-depth interviews with key actors, this study encompasses the whole regeneration process, including initiatives taken by the housing association and municipal agents, the methodology developed to collect ideas and implement them in the physical design of the public spaces, and the social effort to make the whole process sustainable and the estates liveable. It concludes that post-war estates have the potential to secure their future by embracing physical and social efforts through proactive empowerment strategies and creating new spatial identities.

Enhancing box-wing design efficiency through machine learning based optimization

The optimization of wings typically relies on computationally intensive high-fidelity simulations, which restrict the quick exploration of design spaces. To address this problem, this paper introduces a methodology dedicated to optimizing box wing configurations using low-fidelity data driven machine learning approach. This technique showcases its practicality through the utilization of a tailored low-fidelity machine learning technique, specifically designed for early-stage wing configuration. By employing surrogate model trained on small dataset derived from low-fidelity simulations, our method aims to predict outputs within an acceptable range. This strategy significantly mitigates computational costs and expedites the design exploration process. The methodology’s validation relies on its successful application in optimizing the box wing of PARSIFAL, serving as a benchmark, while the primary focus remains on optimizing the newly designed box wing by Bionica. Applying this method to the Bionica configuration led to a notable 14% improvement in overall aerodynamic efficiency. Furthermore, all the optimized results obtained from machine learning model undergo rigorous assessments through the high-fidelity RANS analysis for confirmation. This methodology introduces innovative approach that aims to streamline computational processes, potentially reducing the time and resources required compared to traditional optimization methods.

A Survey on Progressive Visualization.

Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges.

On the psychological effects of child-like space design on preschoolers under anesthesia: A qualitative study.

In this study, a pediatric hospital was selected as the study setting, to investigate the effects of the environment of a pediatric operating room child-friendly space on preschool children under anesthesia. Interviews were conducted in pediatric hospitals through anesthesia of preschool children. This study randomizing children between the ages of 1 and 7 years (seven males, five females, mean age 3.8 years, standard deviation 0.7). Twelve participants took part in this interview, which was an in-depth interview of preschoolers' perceptions of anesthesia using generalized data analysis to assess young children's perceived preferences and sensitivity to the environment. Preschoolers' preferences for feeling under anesthesia are reflected in four main areas. (1) Interesting and engaging environment. (2) Humanized design, free of fear and unfamiliarity. (3) Caring, patient, and compassionate care by healthcare professionals. (4) High cooperation of the child and good doctor-patient relationship. The key findings of this study may help designers and clinicians to design a toddler-friendly built environment where pediatric hospital child-friendly spaces are designed to benefit their health and maximize their cooperation and psychological recovery.

Strategies for Enhancing Audience Engagement in Cultural Exhibition Space Design

In this study, we explored strategies to enhance audience engagement through cultural exhibition space design. Utilizing the Evaluation Grid Method (EGM) and the Kano Two-Dimensional Quality Model, this research aims to identify key spatial design factors that influence emotional and cognitive engagement from the consumer's perspective. This includes examining how spatial layout, interactive devices, and the presentation of cultural elements can enhance the overall experience of the audience. The research methodology combined in-depth interviews and surveys, providing data that helped us analyze which design features most effectively capture and maintain audience interest and participation. The findings of this study offer specific guidance for cultural exhibition designers to improve exhibition space design, thereby attracting more audience participation and enhancing the educational and entertainment value of exhibitions. The study emphasizes the importance of innovative space design in increasing audience engagement in cultural exhibitions, providing an empirical foundation for future exhibition design practices.

Surrogate-Based Multidisciplinary Optimization for the Takeoff Trajectory Design of Electric Drones

Electric vertical takeoff and landing (eVTOL) aircraft attract attention due to their unique characteristics of reduced noise, moderate pollutant emission, and lowered operating cost. However, the benefits of electric vehicles, including eVTOL aircraft, are critically challenged by the energy density of batteries, which prohibit long-distance tasks and broader applications. Since the takeoff process of eVTOL aircraft demands excessive energy and couples multiple subsystems (such as aerodynamics and propulsion), multidisciplinary analysis and optimization (MDAO) become essential. Conventional MDAO, however, iteratively evaluates high-fidelity simulation models, making the whole process computationally intensive. Surrogates, in lieu of simulation models, empower efficient MDAO with the premise of sufficient accuracy, but naive surrogate modeling could result in an enormous training cost. Thus, this work develops a twin-generator generative adversarial network (twinGAN) model to intelligently parameterize takeoff power and wing angle profiles of an eVTOL aircraft. The twinGAN-enabled surrogate-based takeoff trajectory design framework was demonstrated on the Airbus A3 Vahana aircraft. The twinGAN provisioned two-fold dimensionality reductions. First, twinGAN generated only realistic trajectory profiles of power and wing angle, which implicitly reduced the design space. Second, twinGAN with three variables represented the takeoff trajectory profiles originally parameterized using 40 B-spline control points, which explicitly reduced the design space while maintaining sufficient variability, as verified by fitting optimization. Moreover, surrogate modeling with respect to the three twinGAN variables, total takeoff time, mass, and power efficiency, reached around 99% accuracy for all the quantities of interest (such as vertical displacement). Surrogate-based, derivative-free optimizations obtained over 95% accuracy and reduced the required computational time by around 26 times compared with simulation-based, gradient-based optimization. Thus, the novelty of this work lies in the fact that the twinGAN model intelligently parameterized trajectory designs, which achieved implicit and explicit dimensionality reductions. Additionally, twinGAN-enabled surrogate modeling enabled the efficient takeoff trajectory design with high accuracy and computational cost reduction.

Artificial intelligence-driven real-world battery diagnostics

Addressing real-world challenges in battery diagnostics, particularly under incomplete or inconsistent boundary conditions, has proven difficult with traditional methodologies such as first-principles and atomistic calculations. Despite advances in data assimilation techniques, the overwhelming volume and diversity of data, coupled with the lack of universally accepted models, underscore the limitations of these traditional approaches. Recently, deep learning has emerged as a highly effective tool in overcoming persistent issues in battery diagnostics by adeptly managing expansive design spaces and discerning intricate, multidimensional correlations. This approach resolves challenges previously deemed insurmountable, especially with lost, irregular, or noisy data through the design of specialized network architectures that adhere to physical invariants. However, gaps remain between academic advancements and their practical applications, including challenges in explainability and the computational costs associated with AI-driven solutions. Emerging technologies such as explainable artificial intelligence (XAI), AI for IT operations (AIOps), lifelong machine learning to mitigate catastrophic forgetting, and cloud-based digital twins open new opportunities for intelligent battery life-cycle assessment. In this perspective, we outline these challenges and opportunities, emphasizing the potential of innovative technologies to transform battery diagnostics, as demonstrated by our recent practice and the progress made in the field. This includes promising achievements in both academic and industry field demonstrations in modeling and forecasting the dynamics of multiphysics and multiscale battery systems. These systems feature inhomogeneous cascades of scales, informed by our physical, electrochemical, observational, empirical, and/or mathematical understanding of the battery system. Through data assimilation efforts, meticulous craftsmanship, and elaborate implementations—and by considering the wealth and spatio-temporal heterogeneity of available data—such AI-based intelligent learning philosophies have great potential to achieve better accuracy, faster training, and improved generalization.

HashGrid: An optimized architecture for accelerating graph computing on FPGAs

Large-scale graph processing poses challenges due to its size and irregular memory access patterns, causing performance degradation in common architectures, such as CPUs and GPUs. Recent research includes accelerating graph processing using Field Programmable Gate Arrays (FPGAs). FPGAs can provide very efficient acceleration thanks to reconfigurable on-chip resources. Although limited, these resources offer a larger design space than CPUs and GPUs.We propose an approach in which data are preprocessed in small chunks with an optimized graph partitioning technique for execution on FPGA accelerators. The chunks, located on the host, are streamed directly into a customized memory layer implemented in the FPGA, which is tightly coupled with the processing elements responsible for the graph algorithm execution. This improves application memory access latency, which is crucial in large-sale graph computing performance.This work presents a hardware design that, combined with graph partitioning, enables us to achieve high-performance and potentially scalable handling of large graphs (i.e., graphs with millions of vertices and billions of edges in current scenarios) while using popular graph algorithms. The proposed framework accelerates performance 56 times compared with CPU (multicore with 16 logical cores in our reference experiments), 2.5 times and 4 times faster compared to state-of-the-art FPGA and GPU solutions (FPGA has 15 compute units, and GPU reference has 128 streaming-multiprocessors in our experiments), respectively, when using the PageRank algorithm. For the Single-Source-Shortest-Past (SSSP) algorithm, we achieve speedups of up to 65x, 26x, and 18x compared to CPU, GPU, and FPGA works, respectively. Lastly, in the context of the Weakly Connected Component (WCC) algorithm, our framework achieves a speedup of up to 403 times compared to the CPU, 7.4x against the GPU, and it is faster than the FPGA alternatives up to 10.3x.

Analysis of the Forces and Moments in Canine Bodily Movement with Different Clear Aligners’ Extraction Space Designs

This study aimed to optimize space closure efficiency by comparing the forces and moments exerted by different designs of clear aligners (CAs) during the movement of maxillary canines into the premolar extraction space. The forces and moments were measured using a multi-axis force/moment transducer on the maxillary right canine. The CAs were fabricated from thermoformed polyethylene terephthalate glycol. The following four edentulous space designs were tested: the edentulous space was left intact (Group 1); the edentulous space was replaced with a premolar pontic (Group 2); the edentulous space was replaced with a half-sized premolar pontic (Group 3); and the edentulous space was replaced with a rectangular column beam (Group 4). The maxillary right canine was moved 0.25 mm distally. All groups experienced buccodistal and intrusive forces; compared with the other groups, Group 1 showed significantly greater intrusive and smaller distal forces, and Group 4 showed significantly greater distal forces. All groups experienced distal tilting, lingual inclination, and mesial rotational moments. These findings suggest that modifying the thickness and extent of the adjacent teeth in the edentulous area of the CA can improve local stiffness, thereby reducing the tipping of the teeth into the edentulous space. This study emphasizes the importance of the CA design in controlling forces and moments for effective orthodontic treatment.

Rational Design of 3D Space Connected Donor–Acceptor System in Covalent Organic Frameworks for Enhanced Photocatalytic Performance

AbstractHerein, a rational strategy is presented to reduce the energy barrier of singlet ground state to singlet excited state transitions, whilst simultaneously reducing energy losses in populating triplet excited states. The approach relies on constructing 3D space connected donor–acceptor systems in COFs. The 3D space connected D–A system in 8‐connected 3D COFs (denoted as COF‐1 and COF‐2) allows the efficient transfer of electrons, overcoming the traditional electron transport limitations of 2D COFs and significantly boosting the solar energy utilization efficiency under visible light irradiation. COF‐2, possessing an extended π‐conjugated structure relative to COF‐1, demonstrated high selectivity for the photocatalytic generation of H2O2 (6.93 mmol g−1 h−1) in natural seawater without the need for sacrificial reagents, exceeding the performance of most previously reported COF‐based photocatalysts. The 3D space connected D–A system reported in this work offers a new approach for optimizing electron and energy transfer in COF‐based photocatalysts for H2O2 production and other applications.

Rural activity spaces integration with creative market models

AbstractAs urbanization advances and societal shifts unfold, the integration of rural development and activity spaces has become a focal point in academic discourse. This study aims to explore the synergistic effects of the creative market model on rural activity spaces, dissecting its role in shaping these spaces, enhancing community engagement, and generating socio-economic benefits. Empirical findings highlight the affirmative role of the creative market model in this fusion. The research on community participation and spatial design reveals how creative markets enhance residents' sense of involvement and explores the influence of activity space design on community interaction. This study, by thoroughly examining the integration of the creative market model and rural activity spaces, offers theoretical and empirical support for advancing rural development.

Shape optimization of non-matching isogeometric shells with moving intersections

While shape optimization using isogeometric shells exhibits appealing features by integrating design geometries and analysis models, challenges arise when addressing computer-aided design (CAD) geometries comprised of multiple non-uniform rational B-splines (NURBS) patches, which are common in practice. The intractability stems from surface intersections within these CAD models. In this paper, we develop an approach for shape optimization of non-matching isogeometric shells incorporating intersection movement. Separately parametrized NURBS surfaces are modeled using Kirchhoff–Love shell theory and coupled using a penalty-based formulation. The optimization scheme allows shell patches to move without preserving relative location with other members during the shape optimization. This flexibility is achieved through an implicit state function, and analytical sensitivities are derived for the relative movement of shell patches. The introduction of differentiable intersections expands the design space and overcomes challenges associated with large mesh distortion, particularly when optimal shapes involve significant movement of patch intersections in physical space. Throughout optimization iterations, all members within the shell structures maintain the NURBS geometry representation, enabling efficient integration of analysis and design models. The optimization approach leverages the multilevel design concept by selecting a refined model for accurate analysis from a coarse design model while maintaining the same geometry. We adopt several example problems to verify the effectiveness of the proposed scheme and demonstrate its applicability to the optimization of the internal stiffeners of an aircraft wing.

Evaluation and quantification of sound intelligibility (SdI): Metro commercial spaces as an example

Sound intelligibility (SdI) is investigated in the context of modelling aural comfort in the design of metro commercial spaces. The term describes an individual's clear aural perception of their surrounding environment. The present paper describes the key objective variables used to evaluate and quantify SdI for inclusion in models predicting aural comfort. In-situ and laboratory questionnaire surveys were conducted to measure SdI in various spatial forms of metro commercial spaces. The significantly influencing factors were studied using Pearson correlation and linearity analysis. The results show that acoustic parameters such as sound levels, sound source, and reverberation time (RT) are not key factors in the evaluation of SdI, but the variables of spatial form and sound absorptions are. Based on the findings of analyzing the key influencing factors, prediction models for quantifying SdI were developed. The results show that there is no holistic approach to quantifying SdI for all kinds of metro commercial space, due to the distinct spatial form of each type of the spaces.

A full-scale experimental investigation of natural gas explosion in a 710-m long utility tunnel with multiple pipelines

Natural gas poses significant risks to the safe operation of utility tunnels, which house multiple critical infrastructure systems, including natural gas pipelines. To date, no full-scale gas explosion tests in utility tunnels have been published, and there is ongoing debate about whether pipelines can withstand such explosions. This study presents the first-ever 1:1-scale experiments conducted in a 710-meter-long tunnel with cross-sectional dimensions of 3.2 m × 2.6 m. Three pipelines, each approximately 25 m in length and constructed to industrial standards, were used to investigate potential damage. The experiments comprehensively captured the development of flow fields, including flame images, flame signals, temperature, and overpressure, as well as pipeline responses. Results revealed that variations in overpressure within the combustion region were driven by both positive and negative feedback loops. A significant pressure drop was also observed as airflow passed through a local expansion. The experiments further demonstrated that while pipelines within utility tunnels are unlikely to be directly damaged by gas explosions, accessories such as racks are at high risk of severe destruction. These findings offer essential insights for improving the safety design, construct, and operation of tunnel-like spaces involving flammable gases.