This paper presents a methodology for integrating the orographic context of micro-locations into building energy modelling (BEM) for topographically diverse regions. Dynamic energy simulations rely on high-resolution weather data from weather stations that represent specific areas. When the micro-location of the simulated building differs from the weather reference site, topographic shading can trigger deviations. This study developed a three-dimensional reproduction of the micro-location’s surrounding terrain as a simplified ring model, based on horizon height data retrieved from a Digital Elevation Model (DEM). Within a 20 km radius, four Slovenian cities from the same weather-representative area were analysed and compared with a baseline calculation. When comparing solar gains (SG) at selected micro-locations using average elevated horizon angles, a clear tendency emerges: the lower the Sky-View Factor (SVF), the greater the SG deficit. By capturing the elevated horizon more precisely through the proposed methodology, the results demonstrate that—depending on azimuthal orientation and barrier height—solar gains may vary by up to 12% even for locations exhibiting nearly identical SVF values. This approach improves the precision of BEM in topographically diverse regions and can inform better design and planning decisions.
To assess the energy efficiency of wall prototypes with advanced designs, real scale experimental demonstrators are difficult and expensive projects. To address this issue, this study develops a reduced scale experimental demonstrator based on similarity laws established for a problem of transient two-dimensional heat transfer with boundary conditions varying in time and space. Based on the dimensionless formulation of the problem, scaling factors are defined to link the real model and the reduced scale demonstrator. Here, a geometry and a kinetic similarity change are applied to build a reduced experimental demonstrator using 3D printing technology. To simulate external winter climate conditions, a heat flux is imposed using a heater pad at the top of the wall. It replicates the incoming radiation flux that partially heats the sunlit part of the wall. Two wall prototypes are investigated: one with a classical planar external boundary and one with a curved external boundary. The latter results from shape optimization to enhance the energy efficiency. Measurements of temperature are used to verify the reliability of the similarity laws. A satisfactory agreement is observed between the model predictions and the experimental results. Finally, the energy efficiency of the curved and plane wall prototypes is assessed based on the experimental data.
This study numerically investigates the thermal performance of simplified multilayer roof assemblies for hot-climate applications. Six roof configurations were considered by combining aluminum or galvalume as the exposed upper reflective layer with polystyrene, polyethylene, or polyisocyanurate as the concealed middle layer and a rigid bottom substrate. A quasi-steady thermal analysis was performed in ANSYS Workbench 2020 R1 by applying time-varying solar-radiation and ambient temperature inputs as a sequence of independent quasi-steady-state calculations. The numerical model was checked using mesh-independence analysis and benchmarked against published experimental data. The results indicate that aluminum-based assemblies produced lower heat-flux transfer than galvalume-based assemblies due to the higher solar reflectance of the exposed aluminum layer. Among the tested configurations, combination-2, consisting of aluminum, polyethylene, and the rigid substrate, produced the lowest average heat flux among the aluminum-based cases, approximately 18 W/m 2 . Combination-5, consisting of galvalume, polyethylene, and the rigid substrate, showed the lowest average heat flux among the galvalume-based cases, approximately 19 W/m 2 . Since polyethylene was used as a concealed middle layer, its solar reflectance was not applied as an exposed-surface boundary condition; therefore, the observed performance should be interpreted in terms of the assigned material properties, layer arrangement, and simplified quasi-steady modeling assumptions. The findings provide a comparative assessment of selected multilayer roof assemblies and highlight the importance of exposed-surface reflectance and layer configuration in reducing roof heat gain in hot climates.
This study evaluates the indoor thermal performance and qualitative humidity trends of social housing buildings constructed with thin reinforced concrete walls in a humid tropical climate. A hybrid methodology combining dynamic simulations using DesignBuilder and field measurements using Testo 605i and EasyLog WiFi devices was applied to a four-story residential building in Santo Domingo, Dominican Republic. Operative temperature and relative humidity of the base model (BM) were compared against three passive intervention strategies (M1: EPS insulation + double glazing; M2: EPS insulation + double glazing + green roof; M3: mineral wool insulation + double glazing + green roof). Results reveal that the BM exhibited high operative temperatures (up to 34 degrees C) and extreme relative humidity (80%-99%), exceeding ASHRAE 55 comfort limits. Passive strategies reduced daily thermal variability by up to 6.8%, but humidity control remained limited, with only marginal improvements (<10%). Simulated relative humidity results were interpreted as trend indicators within the modeling scope adopted. Stratification effects were identified, with lower floors experiencing higher humidity and upper floors higher temperature peaks. The findings highlight the vulnerability of thin concrete wall housing in tropical climates and the limited role of envelope-only passive measures for humidity regulation. This study provides empirical evidence for the need to integrate passive and hybrid solutions, offering valuable insights for building codes and housing policies in tropical regions.
The rising frequency and intensity of heatwaves due to climate change have amplified concerns about summertime overheating in residential buildings, particularly in regions historically characterized by mild summers. This study investigates the overheating risk and adaptation potential in a typical prefabricated concrete panel apartment in Budapest, Hungary. A calibrated multi-zone dynamic building energy model was developed based on long-term in-situ measurements, including indoor temperatures and occupant-controlled window operation. Using this model, various passive adaptation strategies, including glazing upgrades, external shading, increased natural ventilation, and facade thermal insulation, were assessed under current and future climate scenarios based on Representative Concentration Pathways (RCP 2.6, 4.5, and 8.5). Simulation results show that natural ventilation is the most effective single intervention, but its cooling potential declines under warmer conditions. Summer overheating, quantified by the ODH 26 indicator, is projected to increase up to sixfold by 2100 (RCP 8.5), highlighting the urgency of intervention. While combined passive strategies can reduce overheating by up to 66.6% under present conditions, their effectiveness drops to 21% in late-century scenarios. The study highlights the critical interaction between thermal insulation and ventilation, where insulation can worsen overheating without sufficient airflow. The novelty of this study lies in using a measurement-calibrated, multi-zone dynamic model as the foundation for all further analyses, ensuring high reliability of the results. This research builds on real-world data from an occupied apartment and systematically explores the performance of passive strategies under current and future climate conditions. The findings emphasize that passive measures alone may not guarantee thermal resilience in future climates, particularly for dense, urban housing stock prevalent across Central and Eastern Europe.
Heating, ventilation, and air conditioning (HVAC) systems offer the greatest potential for energy savings in building services. However, conventional thermostat control in offices often fails to balance comfort and efficiency. To address this issue, a model predictive control (MPC) framework is proposed to improve thermal comfort in office buildings through predictive thermostat regulation. An Extreme Learning Machine (ELM)-based predictive model is developed to forecast indoor thermal comfort conditions, which is embedded within a receding horizon optimization structure to enable real-time control decisions. To efficiently solve the underlying optimization problem, the Gray Wolf Optimizer (GWO) algorithm is adopted due to its favorable convergence characteristics. A high-fidelity Energy Plus simulation model is constructed to capture the dynamic behavior of the indoor thermal environment, from which comprehensive datasets are generated for model training and validation. The parameters of ELM model are further refined using GWO to enhance forecasting accuracy. The integrated predictive model and MPC strategy are implemented within a co-simulation environment, enabling bidirectional coupling between the MPC controller and the EnergyPlus thermal model. Furthermore, a pilot field experiment is conducted in a real-world office building to validate the applicability of the system. Simulation and experimental results demonstrate that the proposed approach significantly enhances occupant thermal comfort while maintaining energy efficiency, evidencing the effectiveness of the combined data-driven prediction and bio-inspired optimization strategy. The methodological integration of ELM-based prediction, GWO-driven optimization, and practical field validation represents a novel adaptive control framework for enhancing indoor thermal comfort.
The thermal comfort in subway carriages directly affects passengers’ travel experience and health, while optimizing it enhances energy efficiency and reduces operational costs. The overarching goal of this research is to develop a novel, metro-specific thermal comfort index that overcomes the predictive limitations of existing indices and provides a practical tool for evaluating and optimizing thermal environments in underground railway systems. To achieve this goal, this study investigated passengers’ thermal comfort across Nanjing’s six busiest metro lines through on-site thermal-humidity measurements and subjective questionnaires, yielding 1067 valid datasets. The database covers parameter ranges of air temperature (20.27°C–31.63°C), mean radiant temperature (7.92°C–35.47°C), relative humidity (45.4%–90.1%), and air velocity (0.06–3.74 m/s), with both environmental data and subjective votes exhibiting approximately normally distributed. Analysis revealed significant limitations in existing thermal comfort indices, evidenced by a 17.8% mean absolute deviation (MAD) for the best-performing existing model. To address this deficiency, dimensional analysis and least squares regression established mathematical relationships between environmental parameters and passengers’ thermal comfort responses, deriving a novel metro-specific thermal comfort index. The proposed index comprises three dimensionless parameters: relative humidity, mean radiant temperature to air temperature ratio, and air velocity water vapor partial pressure product to metabolic rate ratio, accounting for key comfort determinants. Demonstrating high predictive accuracy, it achieves a 13.2% MAD across the entire database with 88.2% predictions maintaining errors within ±30%, while showing a systematic 2.4% overprediction tendency. This empirically validated correlation provides valuable references for optimizing metro environmental design and operational strategies.
This study investigates entropy generation and mixed-mode heat transfer characteristics in corner-cut enclosures filled with a dual-temperature (LTNE) porous medium and subjected to heterogeneous inner heating. A novel hybrid framework, integrating the Characteristic-Based Split Finite Element Method (CBS-FEM) with machine learning techniques, is developed to analyze complex thermal behaviors and enhance heat transfer efficiency. The CBS-FEM accurately captures coupled convection-conduction mechanisms and nonlinear temperature fields, while machine learning models are employed to predict entropy generation trends and improve computational accuracy. The effects of geometrical modifications, heating intensity variations, and dual-temperature medium properties on entropy production are systematically analyzed. Results reveal that velocity components exhibit oscillatory patterns under heterogeneous periodic heating, while uniform and linear heating cases show a steady monotonic behavior. The flow field becomes non-uniform under oscillatory temperature conditions, whereas symmetrical patterns emerge for uniform and linear heating modes. These findings provide valuable insights into thermal management and entropy minimization strategies, with potential applications in building physics, such as optimizing indoor thermal comfort, energy-efficient heating, ventilation, and cooling (HVAC) systems, and sustainable building envelope design. Overall, the study contributes to the development of energy-efficient thermal systems in advanced engineering applications.
The effect of 2 mm millimetric perforations on the performance of multi-layered breathing exterior wall systems was investigated in relation to both thermal transmittance and carbon dioxide (CO2) diffusion. The study aimed to determine whether such perforations on glass fiber coated gypsum board (GFCGB) layers could enhance gas permeability without significantly compromising thermal insulation. Three wall configurations were prepared: a reference wall with unperforated GFCGB, and two alternatives with perforations of 2 mm spaced at 5 and 1 cm intervals. These were tested in a custom-built cold box setup designed to simulate indoor and outdoor conditions and allow simultaneous measurement of steady-state U values and effective CO2 diffusion coefficients (D-EFF). Results showed that the introduction of perforations increased D-EFF value by 14% and 54% for the 5 and 1 cm intervals, respectively, compared to the unperforated reference. The measured U values of all testes systems, obtained by the cold-box tests under the steady-state laboratory boundary conditions, are within the range of 0.32-0.36 W/m(2)& centerdot;K. The findings suggest that improved gas diffusion may be achieved through carefully designed perforation patterns without substantial thermal penalty. This study represents the first application of the D-EFF parameter to multi-layered wall systems composed of industrially available materials, marking a shift from material-scale evaluations to envelope-scale performance assessment. The perforated wall system proposed here offers a low-tech, scalable approach for breathable building envelopes, particularly relevant in settings where mechanical ventilation may be limited or absent. Overall, the study shows that air permeability, when quantified and optimized, can be treated as a functional property of building envelopes, contributing to healthier and more sustainable architectural solutions.
Urban microclimates, driven by urban form and regional climate, strongly affect building thermal performance and energy demand. However, many studies focus on individual cities or specific climatic zones, neglecting broader spatial heterogeneity and the combined effects of meso- and micro-scale climatic factors. To address this gap, this study integrates Grasshopper-based energy simulation, the Local Climate Zone (LCZ) classification, and 103 in-situ meteorological observations, including air temperature, relative humidity, wind speed and direction, and precipitation, to evaluate the differences in building heating and cooling demands between the LCZ-specific in-situ observation and those derived from the Typical Meteorological Year (TMY) data across five major climatic regions in China. Results show that built-type LCZs generally exhibit higher temperatures and lower humidity than the TMY baselines, resulting in reduced heating demand and increased cooling demand. Annual energy deviations range from -47.83% to +57.69%, depending on the climate region and LCZ type. The TMY data significantly underestimate peak heating loads, particularly in the heating summer and cold winter regions, while often overestimating peak cooling loads, with a maximum deviation of 42.03%. Monthly and hourly analyses reveal notable energy discrepancies during transitional seasons and diurnal extremes. Air temperature is identified as the primary factor affecting energy demand, with humidity and wind contributing secondary effects via correlation analysis. By coupling LCZ-based microclimate inputs with building simulation, this study establishes a transferable framework for more reliable prediction of heating and cooling loads. The approach enhances thermal management, supports energy-efficient system design, and contributes to sustainable, climate-responsive urban development.
Glass wool is a common high-performance insulation material employed in buildings. The effective thermal conductivity of such materials with large open-pore structures can drastically increase if internal natural convection takes place inside the latter. This can occur in the case of a thick insulation layer with high porosity (low density) and subjected to a large temperature gradient. Such conditions can be met for blown glass wool insulation during winter in cold-climate regions. Consequently, the accurate assessment of the criteria determining the onset of internal natural convection in porous insulation is fundamental to optimizing the thermal performance of the latter. However, the numerical simulation of such phenomena is very complex and does not yield reliable results. Experimental investigations in realistic conditions are thus necessary. This article reports the findings of a full-scale experimental replication study on the onset of internal natural convection inside a horizontal insulation layer of blown glass wool with joists. The insulation layer is heated from below with a closed boundary enclosure at the bottom and an open boundary at the top. The thickness of the insulation layer is set to 30 cm or 60 cm, with a glass wool density ranging from 11.9 to 19.7 kg/m3, a temperature difference spanning from 5.2 to 59.5 K, and an average temperature ranging from -5.3 degrees C to 26.5 degrees C. The onset of the internal natural convection is identified by the critical Rayleigh number above which the modified Nusselt number (Nu + 1) increases with increasing Rayleigh number. For this configuration, the critical Rayleigh number is found to be situated between 13 and 15. These results are in good agreement with the recommendations from standards and other similar experimental studies on this topic. This study provides additional guidance for insulation design in cold-climate buildings.
This study examines hollow brick geometry as a thermal metamaterial with shape-dependent insulation performance. Independently of normative constraints, it aims to examine the effect of internal geometry on the thermal behavior of 15 hollow bricks of different internal structures designed from the reference hollow clay brick with eight holes (HCB8), in order to select a configuration that offers the best thermal insulation performance under the conditions studied. Various operating parameters are taken into account, including outside temperature, thermal conductivity of the solid material, emissivity, and filling material. Three types of insulation (polyurethane foam [PUF], expanded polystyrene [EPS], and cardboard powder) are used as filling material inside the cavities of the bricks in order to compare their thermal behavior with air. The findings highlight that how the solid parts and air cavities are arranged inside the brick has a real impact on its thermal resistance. They also show that the performance of each configuration can shift depending on the operating parameters. Configurations with elongated cavities incorporating protuberances (D4 and D2) provide the best thermal insulation in most situations studied (outdoor temperature, emissivity, and thermal conductivity). The only exception is the case without radiation, where configuration with three elongated cavities without protuberances (C2) becomes the most effective. For filled bricks, configuration with three or four elongated cavities, delivers the best thermal performance for all three insulation materials tested (EPS, PUF, and cardboard powder). In contrast, configuration with four cavities arranged in a 2 & times; 2 pattern (B1), generally the least efficient, shows a noticeable improvement once an insulating material is added. These results highlight the importance of taking external conditions and material properties into account in thermal analysis, as they can significantly alter the performance order of geometries and guide the design of more sustainable building materials.
This review explores the innovative potential of integrating marine-based photocatalysts in the built environment, aiming to bridge the gap between sustainable material synthesis and performance-based envelope design. By analyzing 108 studies, the research addresses 10 primary questions concerning synthesis pathways, characterization techniques, and the transition from laboratory chemistry to building-relevant implementation. The analysis reveals that algae (41.7%) and seashells (38%), often combined with TiO 2 , ZnO, and Ag, serve as effective carriers (53.7%), reducing agents (36.1%), or photocatalysts themselves (10.2%). While these bio-composites demonstrate significant chemical potential, a critical disparity is identified: over 77% of current research targets wastewater treatment. This review critically evaluates the transferability of these liquid-phase findings to building physics applications, highlighting challenges in gas-phase pollutant diffusion and surface boundary layer interactions. Despite this gap, key positive findings demonstrate that marine-derived composites can achieve complete degradation of indoor pollutants, such as formaldehyde and toluene. Furthermore, bio-doping was shown to effectively reduce bandgap energies, enabling passive air purification under indoor light sources. The review concludes that future research must prioritize standardized gas-phase testing and long-term hygrothermal durability assessments to validate these materials for high-performance building envelopes.
Thermal conditions represent a key component of indoor environmental quality, influencing occupants’ comfort, well-being, performance, and health. Thermal responses vary across body regions and between individuals, with gender differences being among the most widely reported. These differences often lead to discomfort. Localized heating devices have shown potential for improving comfort. In this study, gender-based differences in local and overall thermal sensation, thermal comfort, and skin temperature were examined under slightly cool indoor conditions using localized radiant heating. Custom radiant heating devices were used to conduct 270 controlled experiments. Skin temperature at multiple body regions was continuously recorded, and participants filled out questionnaires about their thermal sensation and comfort. Welch’s t -test and Cohen’s d were used to assess statistical and practical gender differences. Localized radiant heating substantially reduced gender differences in thermal responses. Most of the skin temperature differences between genders were not statistically significant. Effect sizes were negligible to small for most body parts, although moderate to large values remained for the chest, face, and upper leg. Across all body parts, both local and overall thermal sensation showed no significant gender differences, and effect sizes were small or negligible. Similar results were found for thermal comfort, with only the lower arm showing a moderate effect size in local thermal comfort. Applying localized heating to a single body part increased whole-body comfort levels for both genders. Among all tested body parts, heating the pelvis and chest produced the strongest improvements in whole-body sensation and comfort, and these regions also showed the lowest variability in comfort votes, identifying them as particularly influential targets for localized heating strategies. Overall, the findings demonstrate that localized radiant heating is an effective strategy for narrowing gender-based gaps in thermal responses, particularly under cooler conditions where women typically experience greater cool discomfort.
The design of building facades involves striking a balance between indoor comfort and efficient facade construction techniques. This study aims to develop a framework for designing efficient building facades by considering occupant requirements and adapting the Analytic Hierarchy Process (AHP) and Quality Function Deployment (QFD) methods. AHP is a Multi-Criteria Decision-Making method that simplifies complex problems by breaking them down into smaller components. Each component is then weighted to determine its priority using the AHP methodology. The QFD method is used in industry to improve the quality of products and is based on considering user requirements throughout the product design process and transferring them from the design stage to the production stage by using House of Quality matrices. In this study, AHP method is used to identify the priority of targets in the design of efficient and appropriate facades. Additionally, the QFD method is adapted as a way to improve the quality of building facade design by occupant requirements. For the design of efficient building facades, this study developed a facade design framework with four phases, namely the design targets phase, the occupant requirements phase, the design techniques phase and the facade design phase. These phases consisted of different steps based on analyzing case studies, conducting two questionnaire surveys, using four AHP matrices and developing House of Quality matrices. This study demonstrated the applicability of QFD and AHP methods in the facade design process by defining the framework for designing appropriate and efficient building facades.
In this study, we examined the impact of different approximation methods for physical property values on degradation prediction simulations of outdoor cultural properties due to moisture. We compared integral mean, weighted harmonic mean, weighted arithmetic mean, and three types of interface value evaluation methods, simulating both the water absorption process and the dry-wet cycle under outdoor environment fluctuation. The results showed that, in situations such as the water absorption process where there is rapid water penetration from dry conditions, the harmonic mean and the interface value evaluation methods, using the average chemical potential of water, significantly underestimated water transport. This presents a significant challenge when modeling rainfall-induced moisture infiltration. In the dry-wet cycle under outdoor environment, the interface value evaluation method using the average moisture content and the logarithmic mean of the chemical potential of water yielded favorable results. Given that the integral mean is the most accurate but also the highest computationally cost, it is essential to select an appropriate approximation method based on the analysis objective and the required level of precision. In addition to providing important insights into balancing accuracy and efficiency in numerical simulations for cultural property preservation, this study helps to understand trends in numerical computation errors in cases where the approximation methods cannot be freely chosen, such as with general-purpose software.
Salt weathering of outdoor cultural properties is a significant conservation issue caused by external climatic fluctuations and groundwater uptake, and this study focuses on poultice desalination to suppress salt weathering. In a previous study, simultaneous heat, moisture, and salt transfer equations in porous materials considering osmosis were derived. Based on this theory, this study aims to evaluate the applicability and limitations of poultice desalination utilizing osmotic flow, as well as to characterize the calculation methods and challenges involved in simulating desalination processes. To achieve these objectives, the experimental results of poultice desalination reported by other researchers were reproduced by simulation. In the simulations, kaolin clay applied between the substrate and poultice was assumed to be the source of osmosis, and three calculation approaches were compared: (a) without considering osmosis, (b) as a filtration membrane that physically blocks the solute, and (c) considering the osmotic flow caused by the surface charges on kaolin. A good agreement with the experimental results was obtained in the third scenario. The key factors of this simulation are as follows: (i) anisotropy of the reflection coefficient, (ii) the calculation methodology of osmotic pressure, and (iii) introduction of a distinct equilibrium moisture content for kaolin clay. In addition, to reproduce a prolonged period of osmosis, this study demonstrates the importance of considering bidirectional solution flow and treating solute advection as a dispersion phenomenon under conditions in which osmosis and osmotic pressure are balanced. Furthermore, for the practical application of poultice desalination using osmotic flow, the interface material must be adequately supported to prevent its collapse under the applied osmotic pressure, thereby ensuring the desired effect. This research presents a simulation method capable of reproducing poultice desalination while incorporating osmotic flow, highlighting both the potential and current limitations of this approach.
Heat, air and moisture (HAM) models are essential for predicting the hygrothermal behavior of building components. However, their outputs can vary significantly due to differences in how hygrothermal material properties are implemented. This study systematically examines the implementation strategies and related uncertainties of moisture transport properties in two widely applied HAM models, WUFI and DELPHIN, focusing on their parameterization logics governed by different driving potentials: relative humidity and capillary pressure. The first part of the study consolidates the characterization methods, data processing procedures and implementation strategies to bridge experimental data to model inputs. The second part applies this setup in two comparative simulation scenarios: one under "extreme" exposure, representing liquid-dominated transport beyond the hygroscopic range in a single loadbearing material with a finishing layer; and another under "service" conditions, involving an internally insulated concrete wall exposed to 50%-98% relative humidity (RH). Results show that simplified liquid transport formulations markedly distort predictions under over-hygroscopic conditions, while the choice between integral or separate representations of vapor and liquid transport significantly alters the coupled heat-moisture balance. This synthesis identifies inconsistencies between material characterization and its practical implementation and quantifies how these discrepancies affect hygrothermal predictions across different regimes. While the findings are specific to the tested regimes, they demonstrate how modeling assumptions and data-handling strategies shape prediction accuracy. By aligning the characterization-processing-implementation chain, the study offers a diagnostic showcase for assessing implementation uncertainty, supporting more robust and physically consistent HAM modeling.
In response to the challenges posed by climate change and growing urbanization, the construction sector is under increasing pressure to reduce its environmental footprint. Plant-based insulation materials offer a sustainable alternative to conventional solutions due to their low cost, renewability, and carbon-neutral life cycle. Among these, sesame (Sesamum indicum L.) is widely cultivated in Senegal, yet its fibrous by-products are largely underutilized. This study investigates the thermo-physical properties of sesame fibers to assess their suitability for use in eco-friendly building insulation materials. Physical characterization included particle size distribution, moisture content, water absorption, solubility, bulk density, and sorption-desorption behavior. Thermal performance was evaluated through thermal conductivity and effusivity measurements using the hot-wire method. Results show that sesame fibers are highly porous, with an average bulk density of 101.13 kg/m(3), and exhibit strong hydrophilic behavior, absorbing up to similar to 300% of their dry mass. Their moisture content ranged from 11% to 12%, and solubility was minimal (<0.05%). Thermal conductivity values ranged from 0.048 to 0.057 W.m(-1).K-1, increasing with bulk density. These values are comparable to those of other natural insulators such as hemp and Typha, based on the literature. The findings confirm the potential of sesame fibers as a low-cost, locally available, and environmentally sustainable alternative for the production of bio-composites materials.
