Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.
The article analyzes the principal interpretations of quantum mechanics from the standpoint of an original conception of informational realism. It is shown that these interpretations can be unified within an informational ontology in which fundamental status is accorded to informational invariants of quantum constraints that are common to all empirically equivalent interpretations of quantum mechanics. A distinction is proposed between two informational layers: epistemic information (measurement data and the updating of knowledge on their basis) and ontic information (invariant structures and constraints). The first layer varies across interpretations, whereas the second is preserved, which explains their experimental equivalence. The proposed informational ontology is compared with other realist approaches, and its advantages are formulated and argued for. It is shown that all the interpretations considered, despite often incompatible philosophical foundations and worldviews, are equally dependent on a certain set of informational invariants, which prove to be primary with respect to any particular interpretation. This can be explained either by the fact that a unified descriptive model of real physical processes at the quantum level is still unknown, or – as argued in the present article – by the claim that the fundamental level consists of informational constraints, and that different descriptions are equivalent, with their differences explained by distinct perspectives. The novelty and contribution of the article consist in proposing an operational definition of a falsifiable class of invariants and constraints on operations that are stable under changes of perspective; providing a unified translational vocabulary for interpretations in terms of the two informational layers; showing that their empirical equivalence is explained by the preservation of the same ontic layer; and formulating a criterion for distinguishing interpretations from alternative theories. Taken together, this allows informational ontology to be regarded as a meta-position with respect to these interpretations.
In this study, in a three-phase reactor with a rectangular cross-section, the effects of liquid circulation rates and solid particle concentration on gas holdup and bubble size distribution (BSD) were investigated. Air, water, and glass beads were used as the gas, liquid, and solid phases, respectively. Different liquid circulation velocities and different solid loads were applied. The results demonstrate that increasing solid content from 0% to 6% can decrease gas holdup by 50% (due to increased slurry phase viscosity and promotion of bubble coalescence). Also, increasing the liquid circulation rate showed a weak effect on gas holdup, although a slight incremental effect was observed due to the promotion of bubble breakup and the extension of bubble residence time. The gas holdup in counter-current slurry bubble columns (CCSBCs) was predicted using a novel correlation that took into account the combined effects of solid concentration and liquid circulation rate. These findings are crucial for the design and optimization of the three-phase reactors used in industries such as mining and wastewater treatment.
Thermodynamics, Descriptive and experimental mechanics
Luigi Piccinini, Luca Emanuele Molteni, Daniele Panzeri
et al.
<b>Background:</b> Gait assessment is a complex task involving locomotion and balance control across all body segments, requiring a global analysis in the event of motor disorders. Among these are spinal disorders, where an understanding of spinal kinematics during walking is important to improve treatment decisions and outcomes. The technique of stereophotogrammetric motion analysis is currently the gold standard in this context. A new integrated protocol for whole-body kinematic gait analysis is proposed in this study, which takes into account the movements of the spine. <b>Methods:</b> A new protocol with 30 passive markers was developed to analyze gait. Of these markers, 22 implemented the Davis protocol for gait measurement, while the other 8 were placed onto the spine to record spinal movements. The protocol’s accuracy was assessed through comparisons of the constructive angles of a manikin replicating the human body and the angles measured with the optoelectronic system. An assessment of intra- and inter-operator repeatability and protocol usability was carried out by recruiting and applying the protocol in a population composed of ten subjects (mean age 17.36—SD 10.12) without any history of spine pathology. <b>Results:</b> The protocol was validated successfully. The validation accuracy was more than satisfactory: the measured RMSE was 1.2 ± 1° for the data collected with the optoelectronic system with respect to the manikin. The intra-operator repeatability was also good in the sagittal and frontal planes (average ICC > 0.867), and the inter-operator repeatability was moderate or good in all planes (average ICC > 0.77). The usability score obtained using the System Usability Scale was satisfactory (mean 74.75, SD 5.88). <b>Conclusions:</b> This study proposes a new protocol to assess total body kinematics, including the spine in its three main segments, during gait. The successful validation of this protocol in terms of reliability and usability allows for its subsequent clinical application.
Mechanics of engineering. Applied mechanics, Descriptive and experimental mechanics
Chandra Pasapula, Nicolas Yanguma, Brayan David Solorzano
et al.
<b>Introduction:</b> Management strategies for stage II tibialis posterior tendon dysfunction are centered on tendon transfers and osteotomies. One of the most commonly used tendon transfers is flexor digitorum longus (FDL) tendon to navicular, but its superiority over transfers to other locations or transfers of other tendons, along with the role of spring ligament and tibialis posterior tendons, have not been objectively evaluated. <b>Aims:</b> We aimed to quantify both the location and magnitude of secondary stresses that develop as a consequence of the initial pathology. <b>Methods:</b> In this study, we used a computational model to study flat foot development and evaluate the effects of various tendon transfers and failures of passive structural elements, as well as their effect on the biomechanics of the foot. <b>Results:</b> We found that both FDL and FHL transfers have biomechanical advantages and disadvantages. Neither of these transfers decrease the stress on the tibialis posterior tendon if the underlying pathologies such as spring ligament failure are not addressed. <b>Conclusions:</b> Of the tendon transfers evaluated, FDL transfer to the navicular had the most profound effect on reducing the stresses on the spring ligament.
Mechanics of engineering. Applied mechanics, Descriptive and experimental mechanics
This study investigates the effect of integrating the Project-Based Learning model with Artificial Intelligence (PjBL-AI) on university students' descriptive writing skills. Using a quasi-experimental pretest–posttest control group design, the research involved third-semester students of the Indonesian Language and Literature Education Department at Universitas Negeri Makassar. Two classes were selected: the experimental group, which received instruction through the PjBL-AI model, and the control group, which was taught using conventional methods. Data were collected through a descriptive writing test and a student perception questionnaire on AI use, and analyzed using normality, homogeneity, Paired Sample t-test, and Independent Sample t-test analyses. The results showed that the experimental group achieved a significant 25.5% improvement in writing scores, compared to an 8.5% insignificant increase in the control group. The PjBL-AI model effectively enhanced students' content development, text organization, vocabulary, grammar, and writing mechanics. AI integration facilitated cognitive and metacognitive processes through real-time feedback, supporting the stages of planning, drafting, revising, and publishing. This study reinforces constructivist and sociocognitive theories in writing instruction, demonstrating that PjBL-AI can serve as an innovative and adaptive pedagogy that promotes digital literacy and higher-order thinking. Overall, this research introduces a novel approach to AI-assisted project-based learning and provides empirical evidence for its effectiveness, offering valuable insights for future studies and curriculum innovation in language education.
Razieh Hajizadeh, Hashem Piri, Nader Naserpour
et al.
Fatigue decreases muscle strength and functional capacity, disrupting neuromuscular coordination by impairing load control. This negatively impacts the kinetics and kinematics of the ankle, knee, and hip joints, resulting in reduced performance and an increased risk of injury, particularly to the anterior cruciate ligament (ACL). This study aimed to compare the effects of fatigue protocols for lower extremity and core muscles on landing mechanics and performance of female athletes. This study used a cross-sectional, comparative, pretest-posttest design with a control group. A total of 105 female athletes, aged 11 to 49, were selected via convenience and purposive sampling. Participants were divided into three groups: core muscle fatigue, lower extremity muscle fatigue, and a control group. Data were gathered using the Landing Error Scoring System (LESS), Y-Balance, and 45-degree trunk flexion tests. Data were analyzed using descriptive statistics, Shapiro-Wilk test, Levene's test, one-way ANCOVA, and the Bonferroni post hoc test. A P of 0.05 or lower was considered statistically significant. ANCOVA results showed significant differences among the groups for the LESS (P=0.001) and 45 ° trunk flexion test (P=0.001). There was no significant difference between the two experimental groups regarding the LESS (P=1.00). However, a significant difference was observed between the two experimental groups in the trunk flexion test (P=0.001). Fatigue had a greater effect size on landing mechanics (ηp²=0.209) than on the trunk flexion test (ηp²=0.143). However, no significant difference was observed between the groups regarding the Y-Balance Test (P=0.996). The study revealed that fatigue protocols targeting lower extremity and core muscles had a negative impact on kinematic parameters associated with ACL injuries during jump-landing in female athletes. Additionally, core muscle fatigue significantly impacted the 45º trunk flexion test, while lower extremity muscle fatigue had no significant effect on it.
Werayoot Lahamornchaiyakul, Warit Jittham, Songklod Sriwattanawarunyoo
et al.
The primary objectives of this study were (1) to analyze and compare the outcomes of manual calculations and computational fluid dynamics (CFD) simulations in determining the forces exerted by water flow and (2) to evaluate the effectiveness of a mathematical model as a pedagogical tool for teaching fluid mechanics to undergraduates. The sample included 30 mechanical engineering undergraduates from the Faculty of Engineering, Rajamangala University of Technology Lanna, Phitsanulok, divided into two groups of 15 students each. Research instruments comprised (1) commercial CFD software for fluid force calculations, (2) pre- and post-tests assessing students’ understanding of momentum and energy equations in water jet impact, and (3) a questionnaire measuring satisfaction with software integration in the learning process. Data were analyzed using descriptive statistics (mean and standard deviation). The findings showed that both groups achieved higher post-test scores than pre-test scores, indicating a positive impact on knowledge development. The quality evaluation of instructional media in terms of numerical modeling for design calculation yielded a mean score of 4.59 with a standard deviation of 0.52. Likewise, instructional worksheets and exercises received mean scores of 4.60, with standard deviations of 0.42 and 0.50, respectively. An experimental trial with 15 students in Group 2 revealed that the average accuracy in completing worksheets and exercises (E1) was 80.13%, while the average post-test score (E2) was 82.77%. Efficiency analysis confirmed that all values exceeded the 80% criterion, and post-test scores were significantly higher than pre-test scores at the 0.05 level. Students’ satisfaction with the instructional approach was also rated as “very high.” Overall, the results demonstrate that integrating CFD software with practice-based learning effectively enhances students’ understanding of fluid mechanics and improves academic performance.
This study investigated the effects of Computer-Assisted Instruction (CAI) using tutorial videos on English essay writing performance among Form Three students in public secondary schools in Bomet County, Kenya. Motivated by concerns about graduates’ inadequate communicative skills in English, as observed in job applications and interviews, the research addressed three questions: What effect does CAI focused on coherence, grammar, and writing mechanics have on essay writing performance in public secondary schools in Bomet County, Kenya? Guided by Social Learning Theory, a quasi-experimental nonequivalent control group design was employed, involving 300 students (160 males, 140 females) from 15 public secondary schools with computer laboratories, selected via stratified random sampling. The intervention group (n=150) received CAI through tutorial videos targeting coherence, grammar, and mechanics over 8 weeks, while the control group (n=150) received traditional instruction. Data were collected using essay writing tests and classroom observation schedules. Instrument reliability was confirmed (Cronbach’s alpha = 0.87), and ethical standards, including informed consent and approval from the Bomet County Education Board, were observed. Data were analyzed using the Statistical Package for the Social Sciences, with descriptive statistics (means, frequencies) and independent-sample t-tests to test hypotheses. Findings revealed significantly higher post-test essay scores in the intervention group (M=31.8) compared to the control group (M=26.3, p<0.001), with notable improvements in coherence (M=8.5 vs. 7.0, p<0.001), grammar (M=8.2 vs. 6.8, p<0.001), and mechanics (M=8.3 vs. 6.9, p<0.001), rejecting the null hypotheses. Infrastructural challenges, including limited devices and power outages, were noted as barriers. These results demonstrate CAI’s efficacy in enhancing English essay writing in resource-constrained settings, informing strategies for educational technology integration in Kenya. Article visualizations:
Misconceptions in vibration and wave topics remain a common problem in junior high school science learning, particularly due to the abstract nature of the concepts and their limited direct observability. This study aimed to analyze students' conceptual shifts on vibration and wave concepts through the implementation of a Deep Learning approach supported by a Mechanical KIT. The study employed a one-group pretest–posttest experimental design involving three classes, comprising one experimental class and two replication classes, with a total of 69 eighth-grade students serving as research participants. A three-tier diagnostic test consisting of ten multiple-choice items was used to identify students' conceptual categories, including guessing, lack of conceptual understanding, misconception, and sound conceptual understanding. The data were analyzed descriptively using SPSS to examine the direction and quality of students' conceptual shifts before and after the intervention. The results indicated that the Deep Learning approach, assisted by the Mechanical KIT, effectively facilitated positive conceptual shifts, as evidenced by a dominant transition from misconceptions, a lack of understanding, and guessing toward sound conceptual understanding across all classes. The most substantial conceptual improvements were observed in indicators related to the relationships among frequency, period, and amplitude, while relatively lower shifts occurred in concepts requiring higher levels of mathematical reasoning. These findings suggest that integrating a Deep Learning approach with concrete instructional media, such as a Mechanical KIT, is effective in promoting meaningful conceptual reconstruction and reducing misconceptions in vibration and wave learning.
Meteorological factors, specifically wind direction and magnitude, influence the dispersion of atmospheric pollutants due to road traffic by affecting their spatial and temporal distribution. In this study, we are interested in the effect of the evolution of horizontal wind components, i.e., in the plane perpendicular to the altitude axis. A two-dimensional numerical model for solving the coupled traffic flow/pollution problem, whose pollutants are generated by vehicles, is developed. The numerical solution of this model is computed via an algorithm combining the characteristics method for temporal discretization with the finite-element method for spatial discretization. The numerical model is validated through a sensitivity study on the diffusion coefficient of road traffic and its impact on traffic density. The distribution of pollutant concentration, computed based on a source generated by traffic density, is presented for a single direction and different magnitudes of the wind velocity (stationary, Gaussian, linearly increasing and decreasing, sudden change over time), taking into account the stretching and tilting of plumes and patterns. The temporal evolution of pollutant concentration at various relevant locations in the domain is studied for two wind velocities (stationary and sudden change). Three regimes were observed for transport pollution depending on time and velocity: nonlinear growth, saturation, and decrease.
Thermodynamics, Descriptive and experimental mechanics
Clément Loiseau, Stéphane Mimouni, Didier Colmont
et al.
The CFD numerical study of the flash boiling phenomenon of a water film was conducted using an Euler–Euler method, and compared to the experiments on the flashing of a water film. The water film is initially heated at temperatures ranging from 34 to 74 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C (frim 1 to 41 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C superheat), and the pressure is decreased from 1 bar to 50 mbar during the experiments. This paper shows that the experiments could not be correctly modelled by a simple liquid/bubble model because of the overestimation of the drag force above the water film (in the gas/droplet region). The generalised large interface model (GLIM), however, a multi-regime approach implemented in the version 7.0 of the neptune_cfd software, is able to differentiate the water film, where liquid/bubble interactions are predominant from the gas region where gas/droplet interactions are predominant, and gives nice qualitative results. Finally, this paper shows that the interfacial heat transfer model of Berne for superheated liquids could accurately predict the evolution of the water temperature over time.
Thermodynamics, Descriptive and experimental mechanics
Jana Korte, Thomas Rauwolf, Jan-Niklas Thiel
et al.
Purpose: The analysis of pathological human left ventricular hemodynamics using high-resolved image-based blood flow simulations shows a major potential for examining mitral valve insufficiency (MI) under exercise conditions. Since capturing and simulating the patient-specific movement of the left ventricle (LV) during rest and exercise is challenging, this study aims to propose a workflow to analyze the hemodynamics within the pathologically moving LV. Methods: Patient-specific ultrasound (US) data of ten patients with MI in different stages were captured with three-dimensional real-time echocardiography. US measurements were performed while patients were resting and while doing handgrip exercise (2–4 min work). Patient-specific hemodynamic simulations were carried out based on the captured ventricular wall movement. Velocity and kinetic energy were analyzed for rest and exercise and for the different MI stages. Results: The results reveal a dependency of the kinetic energy over time in the ventricular volume curves. Concerning the comparison between rest and exercise, the left ventricular function reveals lower systolic kinetic energy under exercise (kinetic energy normalized by EDV; mean ± standard deviation: rest = 0.16 ± 0.14; exercise = 0.06 ± 0.05; <i>p</i>-value = 0.04). Comparing patients with non-limiting (MI I) and mild/moderate (MI II/III) MI, lower velocities (mean ± standard deviation: non-limiting = 0.10 ± 0.03; mild/moderate = 0.06 ± 0.02; <i>p</i>-value = 0.01) and lower diastolic kinetic energy (kinetic energy normalized by EDV; mean ± standard deviation: non-limiting = 0.45 ± 0.30; mild/moderate = 0.20 ± 0.19; <i>p</i>-value = 0.03) were found for the latter. Conclusion: With the proposed workflow, the hemodynamics within LVs with MI can be analyzed under rest and exercise. The results reveal the importance of the patient-specific wall movement when analyzing intraventricular hemodynamics. These findings can be further used within patient-specific simulations, based on varying the imaging and segmentation methods.
Thermodynamics, Descriptive and experimental mechanics
Camila Farinati Nadler, J. Pina, Suyanne de Quadros Schmidt
et al.
ABSTRACT Objective: to assess the impact of high-fidelity clinical simulation on undergraduate teaching, specifically in the Pediatric Nursing area. Method: a quasi-experimental study of the pre- and post-test type, developed at three public Higher Educations Institutions (HEIs) in Brazil. The participants were 93 undergraduate Nursing students, enrolled in the Pediatric Nursing academic disciplines, and randomly allocated to the control or experimental groups. The data were collected in the first half of 2017, through a structured knowledge test and the Satisfaction with Simulated Clinical Experiences Scale. The experimental group received the usual intervention (participation in the theoretical and theoretical-practical activities offered in the disciplines) and the study intervention (high-fidelity clinical simulation); the control group only received the usual intervention. The data were analyzed by means of descriptive and analytical statistics. An explanatory model was prepared by means of multiple linear regression to assess the impact of simulation on teaching. Results: the mean difference between the knowledge pre- and post-tests was 4.04 points (p=0.0004) higher among the experimental group participants, indicating a greater increase in knowledge with the simulation. The participants from University A, who performed the simulation after the theoretical activities and before the theoretical-practical activities, obtained a higher mean difference between the knowledge pre- and post-tests (by 3.89 points, p=0.0075) than that of obtained by the participants from the other institutions. In relation to the satisfaction scale, high scores were achieved (mean=9.11±0.67). Conclusion: high-fidelity clinical simulation in Pediatrics contributed to increasing the Nursing students’ knowledge and satisfaction levels.
Kari Miller, Wanda Strychalski, M. Nickaeen
et al.
Pollen, a neighbor-less cell containing the male gametes, undergoes mechanical challenges during plant sexual reproduction, including desiccation and rehydration. It was previously shown that the pollen-specific mechanosensitive ion channel MscS-like (MSL)8 is essential for pollen survival during hydration and proposed that it functions as a tension-gated osmoregulator. Here, we test this hypothesis with a combination of mathematical modeling and laboratory experiments. Time-lapse imaging revealed that wild-type pollen grains swell, and then they stabilize in volume rapidly during hydration. msl8 mutant pollen grains, however, continue to expand and eventually burst. We found that a mathematical model, wherein MSL8 acts as a simple-tension-gated osmoregulator, does not replicate this behavior. A better fit was obtained from variations of the model, wherein MSL8 inactivates independent of its membrane tension gating threshold or MSL8 strengthens the cell wall without osmotic regulation. Experimental and computational testing of several perturbations, including hydration in an osmolyte-rich solution, hyper-desiccation of the grains, and MSL8-YFP overexpression, indicated that the cell wall strengthening model best simulated experimental responses. Finally, the expression of a nonconducting MSL8 variant did not complement the msl8 overexpansion phenotype. These data indicate that contrary to our hypothesis and to the current understanding of MS ion channel function in bacteria, MSL8 does not act as a simple membrane tension-gated osmoregulator. Instead, they support a model wherein ion flux through MSL8 is required to alter pollen cell wall properties. These results demonstrate the utility of pollen as a cellular scale model system and illustrate how mathematical models can correct intuitive hypotheses.
G. H. Lamanepa, C. M. Maing, Maria Ursula Jawa Mukin
et al.
This study aims to highlight the importance of visual representation skills in classroom practice, especially in classical mechanics physics, by discussing (1) free-body diagram representation, (2) resultant force system, (3) types and directions of quantities, (4) representation of results and units. This research was conducted on 30 students in second grade at Beringin Kupang High School. The research method was carried out experimentally using a special image representation test tool for classical mechanical materials. The research data were analyzed by descriptive method. The results showed that (1) more than 50% of students did not draw FBD before solving the problem, (2) the success rate of students in representing the resultant force system was the highest at 33%, (3) determining the type and direction of motion the success rate was 67%, and (4) representation results and units with a success rate of 40%. The results of this study become a reference for increasing the role of visual representation in learning physics.
Although recent advances of four-dimensional (4D) flow magnetic resonance imaging (MRI) has introduced a new way to measure Reynolds stress tensor (RST) in turbulent flows, its measurement accuracy and possible bias have remained to be revealed. The purpose of this study was to compare the turbulent flow measurement of 4D flow MRI and particle image velocimetry (PIV) in terms of velocity and turbulence quantification. Two difference flow rates of 10 and 20 L/min through a 50% stenosis were measured with both PIV and 4D flow MRI. Not only velocity through the stenosis but also the turbulence parameters such as turbulence kinetic energy and turbulence production were quantitatively compared. Results shows that 4D flow MRI velocity measurement well agreed with the that of PIV, showing the linear regression slopes of two methods are 0.94 and 0.89, respectively. Although turbulence mapping of 4D flow MRI was qualitatively agreed with that of PIV, the quantitative comparison shows that the 4D flow MRI overestimates RST showing the linear regression slopes of 1.44 and 1.66, respectively. In this study, we demonstrate that the 4D flow MRI visualize and quantify not only flow velocity and also turbulence tensor. However, further optimization of 4D flow MRI for better accuracy might be remained.
Thermodynamics, Descriptive and experimental mechanics
The constant growth of urban agglomerations with the development of transport networks requires the optimal use of energy and new ways of storing it. Energy efficiency is becoming one of the main challenges of modern engineering. The use of phase change materials in construction expands the possibilities of accumulating and storing solar energy, as well as reducing energy consumption. In this study, we consider the problem of the effect of natural convection on heat transfer in a building block containing a phase change material. Heat transfer, taking into account melting in brick, was analyzed at various temperature differences. The mathematical model was formulated in the form of time-dependent equations of conjugate natural convection using non-dimensional stream function, vorticity, and temperature. The equations describing melting, taking into account natural convection, were solved using the finite difference method. Smoothing parameters were used to describe phase transitions in the material. As a result of calculations, local characteristics of heat and mass transfer at various points in time were obtained, as well as changes in temperature profiles on the side surfaces. It is shown that with a large volume of melt, natural convection increases heat loss by more than 10%.
Thermodynamics, Descriptive and experimental mechanics
Greenwater (splashing of water on the deck) loading is a classical problem faced by designers of ship-shaped vessels, which becomes even worse when the vessel operates in harsh weather conditions for an extended period of time. Installation of breakwaters on the deck can play a crucial role in minimizing this impact. However, research on the design and optimization of the breakwater is still in its infancy, and this study aims at shedding further light on this area by proposing and analysing the effectiveness of three breakwater designs on a fixed box-shaped vessel. The commercial CFD software ANSYS Fluent is used for this investigation. The design model (without breakwater) was validated at first against experimental results of greenwater splashing, before performing the actual simulations with the proposed breakwater design. A vertical plate is used as the deck structure, and the greenwater pressure at several locations on that plate is measured to compare the effectiveness of various breakwater designs. Overall, breakwaters with openings (perforations, grillages, etc.) were found to be more effective in minimizing the pressure generated by the greenwater. Nevertheless, there is significant room for improvement on breakwater designs, and some topics for further research are also suggested in this regard.
Thermodynamics, Descriptive and experimental mechanics