Speed Enhancement Research Articles

Scaling transition of turbulent flame speed for thermodiffusively unstable flames

This work presents an experimental set of Bunsen flames characterized by a moderate Reynolds number and a variable turbulence intensity. Ten lean hydrogen-enriched methane–air mixtures at three turbulence levels are investigated, ranging from methane–air to hydrogen–air mixtures. Such mixtures are selected to have an almost constant laminar flame speed while inducing the onset of thermal-diffusive (TD) instability by gradually increasing the hydrogen content of the blend. The flames' global consumption speed, stretch factor, and flame surface area are investigated and discussed as functions of the effective Lewis number of the mixture. As the interplay between TD instability and turbulence enhances the overall flame propagation, below a transitional Lewis number, flames are observed to be particularly sensitive to external turbulent forcing. This synergistic interaction is discussed in terms of Karlovitz and Lewis numbers. A parameterization of the turbulent flame speed is thus proposed, based on a functional form depending, concurrently, on both Karlovitz and Lewis numbers. The proposed form is shown to fit the experimental results at different turbulence levels and to capture the flame speed enhancement across the transitional Lewis number.

The differences effects of lunges vs. squats exercise programs on the swimming speed 50-meters butterfly style: a quasi-experimental study

Objective: This study aims to investigate the impact of lunges vs. squats exercise on the swimming speed of the 50-meter butterfly stroke among athletes aged 10-13 in KU III and IV categories. Approach: An experimental method employing a two-group pre-test and post-test design was utilized. The study population consisted of athletes affiliated with the SeaRia Aquatic swimming association. Purposive sampling was employed, selecting male swimmers aged 10-13 who were members of the Searia Aquatic swimming club and willing to participate in training during the study. Twenty athletes met the inclusion criteria. The speed test was conducted using the 50-meter butterfly stroke. Statistical data analysis was performed using t-test with SPSS 25 software, employing a significance level of 0.05. Results: The analysis revealed significant effects: (1) lunge training demonstrated a significant improvement in the swimming speed of the 50-meter butterfly stroke among athletes aged 10-13 at the SeaRia Aquatic swimming association (t-count = 6.046, t-table = 2.23, P < 0.05); (2) similarly, squat training led to a significant enhancement in the swimming speed of the 50-meter butterfly stroke among the same age group at the SeaRia Aquatic swimming association (t-count = 6.086, t-table = 2.23, P < 0.05). Collectively, both lunges and squats positively influenced swimming speed in the 50-meter butterfly stroke among athletes aged 10-13 affiliated with the Searia Aquatic swimming association. Conclusion: This study underscores the beneficial effects of incorporating lunges vs. squats into training regimens for young athletes aiming to improve their swimming performance, particularly in the butterfly stroke. This study found that squats are more effective than lunges in improving performance in the butterfly swimming stroke. These findings offer valuable insights for coaches, trainers, and athletes interested in enhancing their competitive edge in swimming, particularly in butterfly style.

I see you: teacher analytics with GPT-4 vision-powered observational assessment

This preliminary study explores how GPT-4 Vision (GPT-4V) technology can be integrated into teacher analytics through observational assessment, aiming to improve reflective teaching practice. Our study develops a Video-based Automatic Assessment System (VidAAS) powered by GPT-4V. This approach uses Generative Artificial Intelligence (GenAI) to provide detailed insights into classroom dynamics. Our study encompasses various methods with multiple steps: a comprehensive literature review, prototype development of the VidAAS, and usability testing with in-service teachers. The study findings reveal that VidAAS demonstrates high accuracy in evaluating skills in the behavioral (psychomotor) domain and offers comprehensive explanations for each assessment. While showing promise in these areas, the system also indicates potential for further enhancement in processing speed and refinement in assessing cognitive and affective domains. We discuss how VidAAS supports teachers’ reflection-in-action and reflection-on-action, emphasizing the need to balance AI-driven insights and human judgment. Our study findings also guide future research avenues for VidAAS design, implementation, and integration in teacher analytics, underscoring GPT-4V’s potential for real-time, scalable feedback and a deeper classroom understanding.

Research on fresh image recognition algorithms based on machine learning

The identification of fresh images tackles issues related to accurate classification, speed and flexibility enhancement, and perhaps superior food safety evaluation. In this work, the type and freshness identification (TFI) system is based on ML (machine learning). The research suggests ML techniques for identifying various meats (pork, chicken, beef, etc) flaws and differentiating between fresh and decomposing meats to decrease labour expenses, manufacturing time, and worker effort. An efficient TFI system is suggested in this work using machine learning (ML) techniques. We gather various meat samples to effectively identify the type and freshness of the meat. Pre-processing of raw images is conducted to standardize the raw data samples. In the feature extraction process, features from the normalized data are extracted to confirm the quality of the data. The retrieved data is divided into categories for fresh meat and non-fresh meat. The suggested approach is used to evaluate TFI efficiency using a Python program. In conclusion, it was discovered that this study outperformed in improving the TFI performance

Control-Oriented Modeling of a Solid Oxide Fuel Cell Affected by Redox Cycling Using a Novel Deep Learning Approach

Abstract A solid oxide fuel cell (SOFC) is a multiphysics system that involves heat transfer, mass transport, and electrochemical reactions to produce electrical power. Reduction and re-oxidation (Redox) cycling is a destructive reaction that can occur during SOFC operation. Redox induces various degradation mechanisms, such as electrode delamination, nickel agglomeration, and microstructural changes, which should be mitigated. The interplay of these mechanisms makes a post-Redox SOFC a nonlinear, time-varying, nonstationary dynamic system. Physics-based modeling of these complexities often leads to computationally expensive equations that are not suitable for the control and diagnostics of SOFCs. Here, a data-driven approach based on dilated convolutions and a self-attention mechanism is introduced to effectively capture the dynamics underlying SOFCs affected by Redox. Controlled Redox cycles are designed to collect appropriate experimental data for developing deep learning models, which are lacking in the current literature. The performance of the proposed model is validated on diverse unseen data sets gathered from different fuel cells and benchmarked against state-of-the-art models, in terms of prediction accuracy and computation complexity. The results indicate 31% accuracy improvement and 27% computation speed enhancement compared to the benchmarks.

Dynamic counterpropagating all-normal dispersion (DCANDi) fiber laser

The fiber single-cavity dual-comb laser (SCDCL) is an emerging light-source architecture that opens up the possibility for low-complexity dual-comb pump-probe measurements. However, the fundamental trade-off between measurement speed and time resolution remains a hurdle for the widespread use of fiber SCDCLs in dual-comb pump-probe measurements. In this paper, we break this fundamental trade-off by devising an all-optical dynamic repetition rate difference (Δf rep ) modulation technique. We demonstrate the dynamic Δf rep modulation in a modified version of the recently developed counterpropagating all-normal dispersion (CANDi) fiber laser. We verify that our all-optical dynamic Δf rep modulation technique does not introduce excessive relative timing jitter. In addition, the dynamic modulation mechanism is studied and validated both theoretically and experimentally. As a proof-of-principle experiment, we apply this so-called dynamic CANDi (DCANDi) fiber laser to measure the relaxation time of a semiconductor saturable absorber mirror, achieving a measurement speed and duty cycle enhancement factor of 143. DCANDi fiber laser is a promising light source for low-complexity, high-speed, high-sensitivity ultrafast dual-comb pump-probe measurements.

Fast calculation of a cylindrical hologram by a preloaded stochastic gradient descent with skip connection

Cylindrical holography has received widespread attention due to its unique 360° viewing zone. To achieve commercial quality requirements, introducing stochastic gradient descent (SGD) is a potential approach for computer-generated cylindrical holography (CGCH). However, SGD applied to CGCH suffers from both slow convergence speed and unstable convergence, severely impacting its application. To address these issues, a preloaded SGD method with skip connection is proposed for fast calculation of cylindrical holograms in this paper. Preloaded-SGD (PSGD) exhibits a significant enhancement in convergence speed compared to the conventional SGD. Furthermore, the skip connection prevents oscillations from occurring by directly connecting the input and output, which is highly beneficial for obtaining high-quality holograms in the later stages of convergence. Numerical simulations demonstrate the effectiveness of our proposed method. PSGD with skip connection(SC-PSGD) achieves a 6.3-fold acceleration over conventional SGD. Notably, our proposed method has broad application prospects in cylindrical holographic displays and 3D displays.

Various and orderly formations in the hydrodynamic schooling of multiple flapping swimmers

The fluid mechanics underlying the collective motion of fish schools and bird flocks still lack full understanding. In this paper, the collective motion of multiple asynchronous flapping foils is numerically studied. It is found that various and orderly formations are achieved by multiple foils only via hydrodynamic interactions. Three distinct states have been verified according to the equilibrium distance between adjacent foils, i.e., the sparse state, the compact state, and the combined state. The “head goose effect” is found in the combined state, and the significant speed enhancement can be observed in both the compact and combined states, except when the first subgroup of the combined group is isolated. The obvious energy savings can be observed in most cases examined in the current work, no matter which state occurs. Moreover, for a given phase difference, the compact group has the highest propulsive efficiency, while the sparse group has the lowest. In addition, the fluid mechanics by which the multiple-foil system achieves speed enhancement and energy savings are analyzed. The results obtained here may shed some light on understanding the collective motion of fish schools and bird flocks.

Contribution of Ladder Drill and Dryland Circuit Training to 50-meter Freestyle Swimming Speed in 13-to-14 Male Swimmers Aquatic Swimming Club

The objective of this study was to assess the impact of ladder drill and dryland circuit exercises on the pace of 50-meter freestyle swimming in male swimmers aged 13-14 years at the Aquatic Swimming Club in Medan. The researchers employed an experimental approach utilising a pre-experimental design consisting of a single group with pretest and posttest measurements. The sample comprised 8 adolescent male swimmers, aged 13-14 years, who were actively involved in the club. They were recruited via purposive sampling. The study was carried out for a duration of 6 weeks, consisting of 18 training sessions, conducted 3 times a week. Measurements were obtained using 40-meter sprint tests, medicine ball throw tests, and 50-meter freestyle swimming speed tests conducted both before and after the training treatments. The data underwent analysis through normality tests, homogeneity tests, correlation tests, and regression tests. The findings demonstrated that ladder drill training made a noteworthy contribution of 59.29% to enhancing 50-meter freestyle swimming speed. Similarly, dryland circuit training had a substantial impact, accounting for 77.44% of the improvement. Furthermore, when both training methods were combined, they together contributed 62% to the enhancement of swimming speed. The study found that both ladder drill and dryland circuit training have a substantial impact on improving the 50-meter freestyle swimming speed of male swimmers aged 13-14 at the Aquatic Swimming Club in Medan. Additionally, the dryland circuit training was found to have a greater effect than the ladder drill.

Revving up performance: the impact of interval training with weighted resistance on speed enhancement in university students

This study examines the impact of interval training combined with weighted resistance on speed enhancement in university students. Thirty students with varied academic backgrounds took part in the study, , students at first and second semester level.. The intervention's impact was assessed using quantitative (paired-sample t-test) and qualitative (theme analysis) methods. Results show a notable increase in speed after the intervention (p < 0.001). Qualitative analysis identifies three primary themes: heightened self-confidence (70% of participants), improved endurance (80% of participants), and initial adaptation difficulties (20% of participants). The results indicate that interval training combined with weighted resistance improves speed, boosts psychological resilience, and helps university students acquire adaptive skills. Implications involve progress in athletic training techniques, interdisciplinary partnerships, and comprehensive student growth initiatives. For lecturers who provide athletic learning activities, it is important to pay attention to the level of difficulty faced by students in order to improve their abilities through appropriate stages according to the level and level of students' learning abilities. At the same time, the combination of various scientific disciplines that support the progress of students' abilities in each lecture meeting increases the level of self-confidence by providing students with repeated opportunities. Keywords: Psychological resilience, Adaptive skills, Transformative experiences.

Enhancement of the response speed of CIGS-based photodetector by Te-doping

Cu(In,Ga)Se2 (CIGS) based devices are promising candidates for high performance photodetector applications. The present work investigated the effects of Te-doping on the properties of CIGS layers and studied the performance of photodetectors (PDs) fabricated on these films. The films were vacuum evaporated on glass substrates and their morphological, structural and optical properties were evaluated after an annealing step at 550ºC. Morphological data indicated that undoped CIGS films had non-uniform surface features with large grains separated by nanoparticles. Addition of 6.6 % Te improved the morphology and yielded a smoother layer. Further increase in Te concentration showed appreciable reduction in surface feature size and shape. X-ray diffraction patterns showed that increasing the Te amount in CIGS caused a shift in the XRD peaks towards smaller angles pointing to replacement of Se atoms by Te. Peak splitting at higher Te samples suggested a graded structure with Se and Te amounts changing through the thickness of the film. Raman analysis demonstrated formation of CuInGa(Se,Te)2 (CIGST) compound. According to Tauc’s approximation, CIGS films with and without Te atoms possessed two energy band gaps of Eg1 and Eg2 that were in the ranges of 1.10–1.28 eV and 1.16–1.36 eV, respectively. Electrical data obtained from photodetector devices showed a responsivity of 4.44×10−1 A/W and a detectivity of 8.01×107 Jones for Te-free material, while the rise/fall times were measured as 34/148 ms. A much faster response speed of 19/20 ms was reached for devices fabricated on films with 10.2 % Te. This work demonstrated, for the first time, the fabrication low-cost and high-performance metal-semiconductor-metal (MSM) type CIGST-based PDs.

Evolution of the Leader Discharge in Bi‐Directional Propagation System in Altitude‐Triggered Lightning

AbstractOn 18 June 2023, comprehensive observations were conducted to an altitude‐triggered lightning flash. Upward positive leader (UPL) and downward negative leader (DNL) in a bi‐directional development system were detected simultaneously by a high‐speed camera, together with the coordinated measurements of magnetic field and very‐high‐frequency (VHF) emissions. High‐speed images reveal, for the first time, the enhancement of the UPL's propagation speed by DNL in the bi‐directional leader system. Concretely, the upward positive leader initially originates from a suspended wire tip and propagates at a two dimensional (2D) speed of 5.32 × 104 m/s, and after about 6.3 ms, its propagation speed was enhanced to 1.12 × 105 m/s when the stepped DNL started advancing at an average speed of 1.44 × 105 m/s. Additionally, based on the evolution of channel luminosity and the variations of magnetic radiation, it is found that there is a consistency in luminosity variation between the ascending channel and the descending channel in the bi‐directional leader system, and the amplitude of the magnetic field increases when the negative discharges start at the bottom wire end with intensive VHF emissions. Those facts indicate that the DNL has an effect, may be a positive one, on the UPL's development in the early stage of altitude‐triggered lightning.

Efficient modeling of liquid splashing via graph neural networks with adaptive filter and aggregator fusion

PurposeThe purpose of this study is to advance the computational modeling of liquid splashing dynamics, while balancing simulation accuracy and computational efficiency, a duality often compromised in high-fidelity fluid dynamics simulations.Design/methodology/approachThis study introduces the fluid efficient graph neural network simulator (FEGNS), an innovative framework that integrates an adaptive filtering layer and aggregator fusion strategy within a graph neural network architecture. FEGNS is designed to directly learn from extensive liquid splash data sets, capturing the intricate dynamics and intrinsically complex interactions.FindingsFEGNS achieves a remarkable 30.3% improvement in simulation accuracy over traditional methods, coupled with a 51.6% enhancement in computational speed. It exhibits robust generalization capabilities across diverse materials, enabling realistic simulations of droplet effects. Comparative analyses and empirical validations demonstrate FEGNS’s superior performance against existing benchmark models.Originality/valueThe originality of FEGNS lies in its adaptive filtering layer, which independently adjusts filtering weights per node, and a novel aggregator fusion strategy that enriches the network’s expressive power by combining multiple aggregation functions. To facilitate further research and practical deployment, the FEGNS model has been made accessible on GitHub (https://github.com/nanjinyao/FEGNS/tree/main).

Effect of measurements on quantum speed limit

Given the initial and final states of a quantum system, the speed of transportation of state vector in the projective Hilbert space governs the quantum speed limit. Here, we ask the question: what happens to the quantum speed limit under continuous measurement process? We model the continuous measurement process by a non-Hermitian Hamiltonian which keeps the evolution of the system Schrödinger-like even under the process of measurement. Using this specific measurement model, we prove that under continuous measurement, the speed of transportation of a quantum system tends to zero. Interestingly, we also find that for small time scale, there is an enhancement of quantum speed even if the measurement strength is finite. Our findings can have applications in quantum computing and quantum control where dynamics is governed by both unitary and measurement processes.

Prospective assessment of AI-enabled cost savings, continuous support, and psychosocial enhancement (AI New Era Study).

e13615 Background: Cancer patients (pts) need extensive primary care across their treatment journey, yet the world's primary health systems struggle with patient volume and resource scarcity. This study aims to assess the effectiveness of Artificial Intelligence (AI) medical service model in primary care and clinical service pre-screening, as well as their potential impact on improving the quality of medical services and reducing medical costs. Methods: A total of 17,856 medical consultation inquiries from 7,522 pts with various types of cancer have been collected and categorized. The study employed a novel AI-driven medical service model (based on large language model), amalgamating clinical guidelines with a local oncology database, to prospectively field questions from 454 cancer-afflicted pts. Clinicians evaluated the accuracy and comprehensiveness of the responses to basic medical issues, while pts assessed the intelligibility and satisfaction. Results: Treatment-related inquiries constituted 34.44% (n = 6,149) of the total, with 81.53% of pts asking such questions. Basic medical issues were addressed in 65.56% (n = 11,707) of inquiries, raised by 59.78% of pts. Specific concerns within basic medical issues included test report explanations (20.45%), adverse reaction consultations (19.82%), and medication information requests (17.66%). In light of the prevalence of basic medical issues, the study prospectively explores the potential of AI technology to revolutionize medical models, driving the advent of a new era in healthcare research. As result, the AI model exhibited high performance across several metrics: accuracy (96.87 ± 10.29), comprehensiveness (97.40 ± 9.91), intelligibility (95.72 ± 12.62), and satisfaction (96.34 ± 11.03). Notably, the model demonstrated a proficiency in identifying queries necessitating physician expertise, with a 95.60% accuracy rate upon review. A survey of 50 outpatients indicated an enhancement in service speed (7.78%) and patient satisfaction (14.29%). This increase in satisfaction was primarily ascribed to pts' heightened sense of control and safety. When benchmarked against GPT-4, the AI model's overall performance was significantly superior (95.08 ± 10.05 vs 89.70 ± 12.85, p<0.01), particularly in domestic drug knowledge and gene report analysis. Conclusions: Largely fueled by the advanced capabilities of large language model, AI has the potential to profoundly reshape the entire patient care journey. Our AI medical service model efficiently meets key healthcare needs, addressing over half of clinical issues and cutting costs. It also guides pts to the right specialists for timely care. Early surveys indicate better primary care and improved doctor-patient relations. Further research is needed to understand AI's effects on fairness, access, and outcomes across varied demographic and socioeconomic strata.

Predicting Gait Speed Improvement in Idiopathic Normal Pressure Hydrocephalus Patients: The Role of Evans Index and Ventricular Volume.

Objective This study aims to investigate the association between specific imaging parameters, namely, the Evans index (EI) and ventricular volume (VV), and the variation in gait speed observed in patients with idiopathic normal pressure hydrocephalus (iNPH) before and after cerebrospinal fluid (CSF)removal/lumbar drain (LD). Furthermore, it seeks to identify which imaging parameters are the most reliable predictors for significant improvements in gait speed post procedure. Methods In this retrospective analysis, the study measured the gait speed of 35 patients diagnosed with idiopathic normal pressure hydrocephalus (iNPH) before and after they underwent CSF removal. Before lumbar drain (LD), brain images were segmented to calculate the Evans index and ventricular volume. The study explored the relationship between these imaging parameters (the Evans index and ventricular volume) and the improvement in gait speed following CSF removal. Patients were divided into two categories based on the degree of improvement in gait speed, and we compared the imaging parameters between these groups. Receiver operating characteristic (ROC) curve analysis was employed to determine the optimal imaging parameter thresholds predictive of gait speed enhancement. Finally, the study assessed the predictive accuracy of these thresholds for identifying patients likely to experience improved gait speed post-LD. Results Following CSF removal/lumbar drain, the participants significantly improved in gait speed, as indicated by a paired sample t-test (p-value = 0.0017). A moderate positive correlation was observed between the imaging parameters (EI and VV) and the improvement in gait speed post-LD. Significant differences were detected between the two patient groups regarding EI, VV, and a composite score (statistical test value = 3.1, 2.8, and 2.9, respectively; p-value < 0.01). Receiver operating characteristic (ROC) curve analysis identified the optimal thresholds for the EI and VV to be 0.39 and 110.78 cm³, respectively. The classification based on these thresholds yielded significant associations between patients displaying favorable imaging parameters and those demonstrating improved gait speed post-LD, with chi-square (χ²) values of 8.5 and 7.1, respectively, and p-values < 0.01. Furthermore, these imaging parameter thresholds had a 74% accuracy rate in predicting patients who would improve post-LD. Conclusion The study demonstrates that ventricle volume and the Evans index can significantly predict gait speed improvement after lumbar drain (LD) in patients with iNPH.

The influence of dynamic pitching on the aerodynamic performance of large horizontal axis wind turbines

To delve into the aerodynamic changes of wind turbines during pitching, this study employs the user-defined function (UDF) and dynamic mesh technique. The dynamic aerodynamic performance of a 3.3 MW turbine blade during variable pitch is investigated via numerical simulation. The results show that the optimal pitch angle increases with increasing incoming wind speed. At the initial and final stages of pitch change, the wind turbine experiences the influence of impact loads, leading to minor fluctuations in torque and thrust coefficients. Within the dynamic pitch change process, the pitch angle notably affects the aerodynamic performance of airfoils at blade x/ c = 0~0.6 c cross sections. The primary stress area of the airfoil contracts, with the blade root experiencing comparatively less impact. In the constant-wind-speed pitching scenario, the low-speed region in the immediate wake of the wind turbine diminishes, resulting in a 10.7% enhancement in the average wind speed at 2.5D wake.

Accelerating handwritten number classifier with pipelined Multiply-Accumulate units in neural networks

This research project explores the development and assessment of a Handwritten Number Classifier based on Neural Networks, employing the widely used MNIST dataset. The primary objective of this study is to enhance computational efficiency, and to achieve this, it concentrates on integrating and contrasting non-pipelined and pipelined structures within the Multiply-Accumulate (MAC) unit. The initial phase involved conducting MATLAB simulations, which yielded promising results regarding the accuracy of weight calculations. Subsequently, Hardware Description Language (HDL) testing was carried out to further validate the classifiers performance. In the HDL testing phase, the classifier incorporating the pipelined MAC unit demonstrated a substantial 42.9% enhancement in processing speed when compared to its non-pipelined counterpart. These results highlight the potential advantages of employing pipeline processing in neural network architectures, emphasizing its effectiveness in achieving faster and more efficient image classification, particularly when dealing with extensive datasets. In conclusion, this research project not only presents valuable insights into improving the efficiency of neural network-based image classifiers but also lays the groundwork for potential future endeavors. These future directions may include adapting the classifier to handle more complex datasets and addressing emerging challenges.

Enhancing Speaker Recognition Models with Noise-Resilient Feature Optimization Strategies

This paper delves into an in-depth exploration of speaker recognition methodologies, with a primary focus on three pivotal approaches: feature-level fusion, dimension reduction employing principal component analysis (PCA) and independent component analysis (ICA), and feature optimization through a genetic algorithm (GA) and the marine predator algorithm (MPA). This study conducts comprehensive experiments across diverse speech datasets characterized by varying noise levels and speaker counts. Impressively, the research yields exceptional results across different datasets and classifiers. For instance, on the TIMIT babble noise dataset (120 speakers), feature fusion achieves a remarkable speaker identification accuracy of 92.7%, while various feature optimization techniques combined with K nearest neighbor (KNN) and linear discriminant (LD) classifiers result in a speaker verification equal error rate (SV EER) of 0.7%. Notably, this study achieves a speaker identification accuracy of 93.5% and SV EER of 0.13% on the TIMIT babble noise dataset (630 speakers) using a KNN classifier with feature optimization. On the TIMIT white noise dataset (120 and 630 speakers), speaker identification accuracies of 93.3% and 83.5%, along with SV EER values of 0.58% and 0.13%, respectively, were attained utilizing PCA dimension reduction and feature optimization techniques (PCA-MPA) with KNN classifiers. Furthermore, on the voxceleb1 dataset, PCA-MPA feature optimization with KNN classifiers achieves a speaker identification accuracy of 95.2% and an SV EER of 1.8%. These findings underscore the significant enhancement in computational speed and speaker recognition performance facilitated by feature optimization strategies.

A review of noteworthy progress and opportunities in swimsuit design

Swimsuits represent an interdisciplinary research field of textiles and clothing, materials, bionics, fluid mechanics, and other disciplines. The aesthetic style, comfort design, speed performance, and additional functions of swimsuits have been studied extensively. However, due to the diversity of research topics, swimsuit-related research is scattered in various research branches. An overview of research progress and frontiers in this whole field is hard to find. Given that a comprehensive application of technologies is required to meet consumers’ increasing demands for more functional and professional sports products, it is necessary to have knowledge of the latest progress in each research area. This study covers the aesthetics, comfort, speed enhancement, and other additional functions of swimsuits, summarizes the research progress in each relevant area, and predicts potential hotspots based on analysis. It is predicted that smart textiles and technologies will be applied more widely to swimsuits for real-time monitoring and instruction, intelligent drag reduction, energy harvesting, etc. With the upgrade of each function, efforts will be needed to maintain the delicate balance between the multiple functions integrated into swimsuits, and a more refined classification will be required to meet the specialized demands of swimmers.