Name of Project Leader: Professor SLIMI Khalifa

Names of the involved researchers:      

• JBARA Abdessalem
• BELKACEM Ines

Names of PhD students:

• HENI Leila
• KOMTI Ahmed

1.Objectives:

This research project focuses on studying the negative externalities of road traffic through the development of traffic flow models. Given the rising traffic volumes and limited infrastructure capacity, the project emphasizes the need for efficient solutions to manage these externalities by optimizing existing transport systems for sustainable development. Key objectives include:

• Evaluating the impact of varying traffic conditions on fuel consumption, vehicle emissions, and noise in urban areas.
• Enhancing a two-dimensional traffic model by integrating the Generic Second Order Macroscopic (GSOM) model to estimate traffic variables with minimal data, which will be coupled with predictive models for emissions estimation.
• Developing statistical models using artificial intelligence to predict traffic-related impacts.
• Exploring innovative solutions to mitigate road transport impacts, such as intelligent transport systems, sustainable infrastructure, eco-mobility, renewable energy integration, CO2 capture, and effective urban planning.
• Future research may also involve modelling nanoparticles from road traffic and analysing their physicochemical compositions to assess their effects on human health.

2.Methodology:

The research methodology is divided into two parts: experimental and numerical.

Experimental Part: This component involves conducting measurement campaigns in selected urban areas using traffic and pollution sensors. The data collected will include:

• Information on the national vehicle fleet (category, age, fuel type, pollution standards).
• Characteristics of the road network and surrounding buildings.
• Traffic volume counts at different periods.
• Meteorological data (wind speed and direction, temperature, humidity, atmospheric stability).
• Driving cycles in various traffic situations.
• Emissions from exhaust and non-exhaust sources, as well as the physicochemical composition of nanoparticles generated by traffic.

Numerical Part: This component includes:

• Development of a two-dimensional model (BIDIM) to model traffic at intersections and within an anisotropic urban network.
• Simulation of a large-scale traffic network by integrating the BIDIM model with the GSOM model.
• Microscopic modelling of fuel consumption based on traffic conditions.
• Assessment of pollutant emissions and noise in urban areas with high traffic intensity.
• Creation of digital maps for atmospheric and noise pollution.
• Application of artificial intelligence algorithms (artificial neural networks, SVM, random forests) to develop predictive models for energy consumption and its impacts.
• Modelling of nanoparticles using artificial intelligence.

3.Cooperation

This research program is carried out in collaboration with:

• Gustave Eiffel University
• Higher Institute of Transport and Logistics of Sousse (ISTLS)
• Tunisian Ministries : Transport, Environment, Industry, Mines and Energy, and Interior.
• Technical Agency for Land Transport (ATTT).
• National Agency for Energy Conservation (ANME).
• National Agency for Environmental Protection (ANPE).
• Municipalities.

4. Research results

A sensitivity study on spatial resolution was conducted to determine the optimal resolution for the SIRANE model. The results showed that the model’s performance remained stable for grid resolutions up to 2.5 × 3.02 meters with a 4000 × 4000 cell mesh. The relative concentration deviations for PM10 and PM2.5 were only 0.16% and 0.41%, respectively, demonstrating the model’s excellent accuracy. The COPERT model results were integrated into SIRANE to evaluate pollutant dispersion, including the predefined background concentration. This approach allowed, among other things, the generation of concentration maps for different pollutant species, particularly average particle concentrations, as shown in Figure III-1-1.

The measured pollution levels are:

• PM10 concentrations: 52.83 – 49.33 – 44.08 µg/m³
• PM2.5 concentrations: 27.69 – 24.46 – 21.58 µg/m³

These values exceed the EU regulatory limits (40 µg/m³ for PM10 and 25 µg/m³ for PM2.5).

Limitations of the SIRANE model:

• Inability to capture spatial heterogeneity within streets (concentration averaged over each street canyon segment);
• Assumption of a homogeneous wind field above rooftops, which is sometimes unrealistic in real urban settings;
• No distinction between single-sided and double-sided street canyons;
• Inability to account for recirculation effects due to building clusters or intersections.

To overcome these limitations, a microscale study of the three identified hotspots was conducted using the GRAL model.

Figure III-1: (a) PM10 concentrations during peak hours (b) PM2.5 concentrations during peak hours  (c) PM10 concentrations during off-peak hours  (d) PM2.5 concentrations during off-peak hours

Name of Project Leader: Professor GHEITH Ramla

Names of the involved researchers:        

• MZALI FOUED

• GHEITH Ramla

Names of PhD students:

• ABAIED Soumaya

1. Objectives:

The primary goal of this research is to optimize new materials for the building sector, focusing on thermal, mechanical, and acoustic characterization of composites. The project encompasses five key objectives:

• Characterization of Properties: Analyze the thermophysical properties of locally available materials (natural, industrial, waste) to assess their suitability for insulation.

• Experimental Design: Develop robust experiments for accurate thermal characterization, establishing reliable parameters for material performance in realistic conditions.

• Evaluation for Construction: Assess the advantages and limitations of integrating these materials into building construction, considering economic, ecological, and sustainability factors.

• Sustainable Solutions: Formulate practical recommendations for the optimal use of local materials in thermal insulation, aiming for sustainable development and energy efficiency

2. Methodology:

As part of this research project, the following methodology will be followed to achieve the expected results:

• Selection of Local Materials: This involves a thorough analysis of locally available materials in our region. Samples of these materials will be collected, appropriate cement mixtures will be formulated, laboratory tests will be conducted, and the properties of the obtained materials will be evaluated. A list of potential materials will be established based on criteria of availability, cost, and presumed thermal performance.

• Choice of Characterization Methods: Various thermal characterization methods will be tested to identify the most appropriate techniques for characterizing the thermophysical properties of the selected materials, such as thermal conductivity, heat capacity, and thermal diffusivity. Mechanical and acoustic characterization of the resulting composite material is also planned.

• Experimental Plan: A detailed experimental plan will be developed, including measurement protocols, necessary equipment, and projected timelines.

• Data Analysis: Experimental data will be analyzed to assess the performance of local materials compared to conventional thermal insulation materials to evaluate their effectiveness.

• Comparison with Existing Literature: The results obtained will be compared to existing literature, and recommendations will be formulated for the use of these materials in construction.

3. Research results

During the 2023–2024 period, several prototypes of cellular blocks were manufactured using recycled gypsum and natural hemp fibers. The process involved the preparation of recycled gypsum powder, the design and fabrication of specific molds, as well as the production of 3D-printed PLA inserts, followed by the casting of composite blocks, which required several weeks of drying.

Figure III-2-1. Steps in the manufacturing process of cellular blocks

Mechanical tests were carried out at ENIM (Fig. III-2-2) to assess the flexural and compressive properties of the gypsum/hemp composites. Flexural tests showed an increase in strength due to the controlled addition of hemp fibers (Fig. III-2-3), with a noticeable influence of fiber content on the stiffness and toughness of the materials. Under compression, an improvement in strength was also observed, although it strongly depended on the percentage of incorporated fibers.

   

Figure III-2-2. Three-point bending test on a gypsum–hemp sample

Figure III-2-3. Flexural behavior of gypsum/hemp composites in three-point bending

 

Name of Project Leader: Professor FAYALA Faten

Names of the involved researchers:        

• BENLTOUFA Sofien
• ALIBI Hamza
• HAMDI Thouraya

Names of PhD students:

• CHAKROUN Mohamed Ghaith
• DRIRA Dalel
• KALLALA Wala
• RAHMENI Maryem

1. Objectives

The specific objectives and expected outcomes of this program are:

• Improving the comfort of textile fabrics (by mastering the various components and properties related to the fabric for well-defined usage conditions).
• Studying the influence of production stages on the comfort of textile structures.
• Modeling heat and mass transfer phenomena while considering the properties of textile materials and climatic conditions (temperature and relative humidity).
• Manufacturing and characterizing materials made from textile waste for recycling purposes. These products are sourced from textile companies (spinning, clothing, second-hand shops)

2. Methodology:

To achieve the set objectives, we plan to:

• Design experimental devices for characterizing textile materials and improve existing devices.
• Optimize manufacturing processes (pre-treatments, dyeing, finishing, special treatments, mechanical and/or chemical treatments, surface state modification, etc.).
• Produce textile materials from textile waste for conventional use in clothing or technical textiles.

3. Research results:

For sportswear, a new mathematical model expressing the drying kinetics (Figure III-3-1) was developed. It is based on the Permetest device measurement principle, following ISO 11092 standards. The use of polyester in textile fabrics reduces the drying time by 65% to 95% compared to cotton. The addition of floats to Ripstop fabrics in the reinforcement area increases the drying time by approximately 20% when cotton yarns are used, and by 32% when polyester is added.

Figure III-3-1. Modified ISO 11092 procedure for measuring drying kinetics

Figure III-3-2. Drop drying kinetics

In the textile recycling domain, we developed a multilayer fabric using recycled denim fabric to enhance comfort and water vapor transfer. Figure III-3-3 shows the components of each layer, with the denim fabric considered as the top layer. A semi-permeable membrane was used due to its breathability, high water vapor permeability, and liquid water impermeability.

Figure III-3-3. Structure of a comfortable multilayer article made from recycled materials

When comparing plain denim fabric to the multilayer sample with the PU membrane, air permeability decreased to 16.34%, relative water vapor permeability increased to 47.63%, and water vapor resistance decreased by 34.37%. Based on evaporative cooling flow kinetics, it was found that the addition of a PU nano-membrane to a multilayer fabric improves the cooling sensation by about 17% compared to plain denim.