Introduction

Date and location of the research: Jan.2024, US and Qatar.

Urbanization is a complex socioeconomic process transforming rural and undeveloped areas into urban settlements, resulting in significant environmental impacts and negative environmental phenomena such as Urban Heat Islands, global warming, air pollution, and deforestation (Rahman et al. 2022). By 2050, more than 50% of the world's population is expected to live in urban areas, with an anticipated increase in 68%, indicating that the impacts of urbanization on the environment will continue to intensify (Kookana et al. 2020). Jordan is a country significantly affected by urbanization and urban sprawl, compounded by significant migration from neighboring countries, which has contributed to the rapid growth of urban areas, exacerbating urban sprawl and adding pressure on natural resources and the environment (Alnsour 2016). This situation has increased demand for housing, infrastructure, and services, expanding urban areas and converting agricultural lands into urban settlements (Alnsour 2016). As the world’s population becomes increasingly urbanized, the negative impacts of urbanization are expected to become more severe.

To mitigate the expansion of urban areas, a viable and cost-effective urban planning and architectural approach entails adopting high-density development via multifamily residential buildings (Habibi and Asadi 2011). Multifamily housing is defined as a single building or group of buildings subdivided into multiple space units that can be used, occupied, and potentially owned individually (Okoye and Ngwu 2021). This housing system has been utilized in Jordan and globally as a means to address the environmental effects of urbanization (Habibi and Asadi 2011). With a growing demand for housing in urban areas, multifamily buildings are considered more sustainable and suitable for accommodating the increasing population. This type of housing can accommodate a larger number of individuals within a smaller building footprint, resulting in a reduced impact on the environment and more affordable pricing (potentially reducing the cost of construction and land acquisition) compared to other housing options (Okoye and Ngwu 2021). In other words, multifamily housing is also considered more cost-effective and sustainable as it optimizes land, resources, and infrastructure while promoting residents' social interactions (Ogunshakin and Osasona 1991).

In Jordan’s urban areas, multifamily residential buildings constitute around 40% of the housing types (Statistics 2015b). Although the housing system is considered a workable and affordable solution to the problem of urban sprawl at a larger scale, there are negative impacts related to the Indoor Environmental Quality (IEQ) at a microscale (Du et al. 2015; Pedersen et al. 2021). This includes poor air quality, inadequate access to natural light and outdoor views, lack of privacy, and limited control over thermal comfort for occupants (Molina and Yaguana 2018). One of the key challenges in achieving good IEQ in multi-residential buildings is the high energy consumption required to maintain adequate thermal comfort, indoor air quality, and lighting (Pedersen et al. 2021). This energy use impacts not only the environment but also the housing affordability for the residents. Indoor air quality (IAQ) is defined as “The concentration of contaminants within a building structure, in addition to other thermal and ventilation factors that could influence the health and well-being of the occupants”.

Passive cooling strategies, mainly natural ventilation, are important among passive design strategies for reducing energy consumption and improving IEQ (Hu et al. 2023). Since natural ventilation controls several related environmental factors, including humidity, air temperature, air velocity, and ventilation rates, natural ventilation can improve indoor air quality and thermal comfort (Ahmed et al. 2021). In light of the COVID-19 pandemic, a renewed focus has been on achieving sufficient natural ventilation in buildings. Various studies have highlighted the importance of natural ventilation as a key strategy in mitigating the spread of such pandemics. With this in mind, ensuring that buildings are equipped with effective natural ventilation systems has become a crucial consideration for maintaining the health and safety of occupants (Abdo et al. 2019; Navas-Martín and Cuerdo-Vilches 2023).

Achieving effective natural ventilation in multifamily residential buildings poses several challenges due to various social, environmental, and economic factors (Muhsin et al. 2016). Building design limitations can primarily hinder optimal airflow conditions, such as insufficient windows for cross-ventilation and a generic spatial typology that restricts proper ventilation strategies (Jin et al. 2016). In Jordan, multifamily buildings typically have isolated apartments with limited exposure to the surrounding environment, challenging cross-ventilation (Statistics 2015b). Multifamily buildings typically consist of four to five floors; each floor level consists of a number of apartments ranging from 2 to 4, arranged around a central staircase core (Statistics 2015b). Additionally, occupants’ behavior influences natural ventilation, with preferences for closed windows due to privacy, noise reduction, and protection from dust and dirt (The arid climate exposes buildings to dust and dirt, affecting indoor air quality) (Delzendeh et al. 2017). This behavior further decreases natural ventilation opportunities. The dense spatial layout and proximity of buildings in urban neighborhoods obstruct prevailing airflow, further limiting the effectiveness of natural ventilation (Gao and Lee 2012). Lastly, The prevalent use of energy-intensive AC units worsens the environmental impact, straining the power grid and increasing greenhouse gas emissions (Muhsin et al. 2016). However, many households find AC units unaffordable, necessitating affordable and energy-efficient alternatives.

While natural ventilation is an important strategy for achieving good IEQ in multi-residential buildings, its implementation can be challenging due to building design, occupant behavior, and the surrounding urban context. Addressing these challenges through targeted interventions can help improve IEQ and reduce energy consumption. This study aims to propose and examine a modification in Jordan's typical design of multifamily residential buildings. The goal is to enhance natural ventilation using typical design elements. The specific focus is to investigate a modification that would improve the functionality of these buildings' existing structural slabs, making them multifunctional and helping achieve efficient natural ventilation throughout all apartments. Specifically, this research investigates a novel passive pipes system to be installed within the structural slabs spanning from one side of the building envelope (the windward facade) to the other (the leeward facade). The ventilation of each space will be achieved through inlet points (interior and exterior) located at the structural slab of its respective floor and outlet points (interior and exterior) situated at the ceiling (i.e., the structural slab of the upper floor).

Materials and methods

The research was conducted using two software tools: Grasshopper and Ansys. Grasshopper is a parametric design tool that allows for creating complex geometries and shapes and modifying these designs using rules and parameters (Zhang et al. 2021a, b). It is a visual programming language and a built-in plugin for Rhinoceros 3D, a 3D modeling software. It is widely used in the architecture, engineering, and construction (AEC) industries for parametric design and generative modeling (Zhang et al. 2021a, b). Parametric design is a process that utilizes algorithmic thinking to enable the generation of complex geometries and structures through the definition and manipulation of parameters and rules (Zhang et al. 2021a, b). In this research, Grasshopper was used to generate the base case model of the apartment unit and modify it to create different scenarios (system configurations) based on predefined parameters and constraints. Ansys is a multiphysics simulation software that can simulate and analyze a range of physical phenomena, including fluid flow, heat transfer, structural mechanics, electromagnetic fields, and more (Zheng et al. 2021). The specific solver of Ansys used in this research is Ansys Fluent, a computational fluid dynamic (CFD) simulation tool. CFD is a branch of fluid mechanics that uses numerical analysis and data structures to analyze and solve problems involving fluid flows, enabling the prediction of fluid behavior in various conditions and scenarios through simulation (Zheng et al. 2021). Ansys Fluent uses numerical methods to solve fluid flow and heat transfer problems and provides detailed information about the distribution of variables such as temperature, humidity, and air velocity (Zheng et al. 2021).

This research used Ansys Fluent 2023 R1 to simulate and analyze the ventilation rate and airflow inside the apartment for both the base case and modified scenarios generated using Grasshopper. In articles evaluating ventilation performance, Fluent/Ansys Fluent and Ansys CFX emerged as the predominant tools employed. These software programs are, therefore, widely recognized and validated within the research domain (Sakiyama et al. 2020). The specific versions of the software tools that were used in this research are Grasshopper inside Rhinoceros 7 and Ansys Fluent 2023 R1. These versions are the most recent ones available as of the writing of this proposal and offer the latest features and functionalities for parametric design and CFD simulations, respectively. By using the latest versions of the software tools, the research will ensure that it benefits from the latest advancements in the field of AEC and CFD.

The first step in this research is to define the base case in terms of its spatial layout configuration. This includes its spatial context, the total number of floors, the typical floor area, the number of apartments per floor, the number of rooms per apartment, and the spatial relationship among the floor apartments and within each apartment’s spaces. Also, the building construction materials, specifications, and envelope parameters, such as the window-to-wall ratio (WWR), must be identified.

The next step in the research process is to generate a base case model. This model serves as a starting point for the simulations and is based on predefined parameters and constraints, such as the apartment size, the number of windows and doors, and the location of the ventilation system. Next, the Grasshopper plugin was used to generate modified scenarios by changing the base case model. For example, they include modifying the location of the ventilation system and changing the size or number of the proposed system components.

Once the various 3D models have been generated using the Grasshopper plugin, they were imported into Ansys to simulate and analyze the ventilation rate and airflow inside the investigated space for both the base case and modified scenarios. Variables such as the area-weighted average (air velocity) and mass airflow rate were investigated. The simulation data was then analyzed and compared quantitatively and qualitatively to determine the effectiveness of various modifications of the proposed system in improving the ventilation and airflow rate inside the investigated space.

Building description and base case

Most urban residential buildings in Jordan consist of multistory structures with four floors and a basement floor (Statistics 2015a) (Abu Qadourah et al. 2022). The primary structural approach in constructing multifamily residential buildings involves reinforced concrete columns and beams. Specifically, the floor slab configuration comprises interconnected rows of hollow concrete blocks separated by secondary reinforced beams positioned at varying intervals as the structural design dictates, Fig. 1. This particular structural configuration of the floor slabs inherently facilitates the seamless integration of the proposed pipes system, offering a pragmatic avenue for its incorporation.

Fig. 1
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Floor slab structural configuration

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To expedite the computational work and be more efficient, we opted for a simplified model of a multifamily building for our analysis. This building comprises typical one-bedroom units, with two units per floor. The selected base case involves a five-story building, with the basement floor dedicated to parking and utilities, the ground floor, and three identical floors above. Each apartment’s approximate area is 50 m2 (538 sf), and the ceiling height measures 2.85 m. Four residential apartment units were chosen for the computational fluid dynamics (CFD) simulation: one from each floor, starting from the ground floor to the third floor, Fig. 2. In order to assess the proposed pipe system’s effectiveness, the CFD simulation focused on the living room spaces in all selected apartments, as this area tends to have the least natural ventilation. Notably, the living room is between the kitchen and bedroom and features a single sliding window measuring 1 m (height) × 2 m (width), with an operable area of up to 50% or 1 m2 of the total window area. The living room space represents the majority of spaces in the multifamily buildings with single-sided ventilation through an open window. Generally, the more apartments per floor, the more spaces with single-sided ventilation conditions. The specific dimensions of the living room are 3.40 m × 4 m with an area of 13.6 m2 and a volume of 38.75 m3. The window-to-wall ratio for the whole floor is 15%, Fig. 2.

Fig. 2
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Schematic layout of the base case

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The foundational case study is located in Jordan’s capital, Amman, at a latitude of approximately 31.9454° N and a longitude of around 35.9284° E (LatLong 2023). Within the broader context of geographical attributes, Jordan resides within the Mediterranean region, encompassing a mosaic of climatic nuances across its expanse. Amman, specifically, experiences a semi-dry climate characterized by distinct seasonal fluctuations. During the summer months, the city encounters warm and arid conditions, with average high temperatures ranging from approximately 30°C to 35°C (86°F to 95°F) (WeatherSpark 2023). Conversely, the winter season transforms Amman into a cold and relatively wet period, with average high temperatures ranging from 10°C to 15°C (50°F to 59°F) (WeatherSpark 2023). Additionally, in the warm summer months from May to October, there is a notable temperature disparity between day and night, as illustrated in Fig. 3a. This variance in temperature underscores the significance of the proposed ventilation system, especially in enhancing thermal comfort via the night flushing technique. Night flushing, also known as night ventilation, is a natural cooling method that leverages the daily temperature variations outdoors and the building’s inherent thermal mass. Enhancing outdoor airflow during the night pre-cools the structure, facilitating radiant cooling throughout the daytime when the building is in use (Landsman 2016).

Fig. 3
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Weather data in Amman (WeatherSpark 2023). a Average hourly temperature in Amman. b Wind direction in Amman. c Average wind speed in Amman

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Furthermore, the prevailing wind direction in Amman originates from the west, with an average wind speed of 3.6 m/s, Fig. 3b and c (WeatherSpark 2023). This wind pattern significantly influences the city’s climatic conditions, further shaping its architectural and environmental considerations. Expanding upon Amman’s local topography, the city is nestled within a distinctive landscape marked by undulating hills. This topographical arrangement adds a dynamic visual dimension to its urban fabric and contributes to microclimatic variations within different city districts, underscoring the significance of localized climatic investigations.

The design of the proposed pipe system and the modified cases

The conceptualization of the proposed pipe system encompasses two distinct pipe groups: the inlet pipes and the outlet pipes. These piping assemblies traverse the structural slabs of the building, constituting a fundamental innovation in natural ventilation enhancement. In specific detail, the inlet pipes course through the structural slab of the apartment’s flooring, originating from the external inlet vents on the windward façade and terminating at the designated internal inlet vents within the intended area, predominantly the living room. Conversely, the outlet pipes navigate the structural slab of the upper apartment floor, originating from the internal outlet vents within the targeted space (i.e., the living room) and concluding at the external outlet vents on the leeward façade Fig. 4.

Fig. 4
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Cross-sectional illustration with the fluid domain depicts the elements of the proposed system and how it is incorporated within the building structure. 1- external inlet, 2- internal inlet, 3- internal outlet, and 4- external outlet

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It is crucial to note that the total area of vents of the external inlet or outlet vents on the building facades precisely matches the area of the window opening in the base case, amounting to 1 m2. These vents’ dimensions, distributed across the building’s facade, are methodically diversified according to the number of pipes, collectively amassing an area of 1 m2. The strategic variance in these vent areas emanates from the distinct configurations devised for generating the modified scenarios, Fig. 4. Furthermore, each pipe within the system exhibits a distinctive conical morphology, commencing from the external vents on the facade and extending up to a distance of a third of the pipe length Fig. 5. This deliberate configuration has been ingeniously devised to increase the vent area and create an environment conducive (the Venturi effect) to increased airflow velocity within the pipes. The Venturi effect is a fluid dynamics principle that describes how the velocity of a fluid increases as it passes through a constricted section of a pipe or tube, while simultaneously the pressure within the constricted section decreases. The aim is to optimize the air movement dynamics toward the intended interior spaces (Zhang et al. 2021a, b); see Figs. 5 and 6. The value of a third of the pipe length is an initial value that could be further optimized in future research.

Fig. 5
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Components and dimensions of the system inlet pipes, including the conical morphology for Venturi effect

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Fig. 6
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The effect of conical morphology on wind velocity

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This study formulated the altered scenarios by manipulating a single key variable: the number of pipes in the inlet and outlet pipe assemblages. Irrespective of the number of pipes employed, a consistent prerequisite entailed maintaining an aggregate external vent area of 1 m2. The overall count of distinct system configurations totals nine, as visually depicted in the illustrative Table 1. Notably, it should be highlighted that the study encompassed solely the four apartment units situated on one facet of the building, given the symmetrical nature of apartments on both sides of the building. The quantity of pipes within each pipe group adheres to a range between three as the minimum and five as the maximum. This constraint is attributed to the prevalent structural slab thickness dimension within Jordan, a consideration indispensable for upholding the structural soundness of the system. In cases involving three pipes, the diameter of the external vents assumes a dimension of 33.3 cm, representing the uppermost threshold compatible with the 35 cm slab thickness. Conversely, the upper limit of five pipes aligns with the maximum permissible allocation within the prescribed slab area, aligning harmoniously with its underlying structural configuration. However, an additional configuration of 6 outlet pipes (5 × 6) was simulated to discuss the results.

Table 1 The nine system configurations with different combinations of inlet and outlet pipes number
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CFD setup and simulation

The CFD simulation of all modified scenarios and the base case was conducted using the known CFD simulation- software, Ansys workbench, version 2023 R1 (Sakiyama et al. 2020). Specifically, the Ansys solver, Fluent, was used to examine the air flow rate entering the targeted space through the different configurations of the proposed system and the base case. The 3D models created inside the Rhino 3D and Grasshopper were imported into the SpaceClaim software to prepare for the CFD simulation. The different Boolean operations of the pipe system and the hosting building were done inside the SpaceClaim software, and then the fluid domain was generated using the specialized Enclosure tool. The computational domain dimensions and boundary conditions were set according to best practices identified by (Abu-Zidan et al. 2021). The dimensions of the computational domain are determined by four distinct parameters: lu, ld, b, and h, Fig. 7. In this context, lu signifies the extent of the upstream domain, encompassing the distance from the inlet to the windward building facade.

Fig. 7
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The computational domain dimensions Lu = 120, ld = 30, b = 45, and h = 60 (h include the building height)

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Conversely, ld denotes the length of the downstream domain, spanning from the leeward building facade to the outlet plane (Abu-Zidan et al. 2021). The variable b represents the lateral clearance on both sides of the building, while h encapsulates the comprehensive height of the domain (Abu-Zidan et al. 2021). The values used in this study are (lu = 2H), (ld = 8H), (b = 3H), and (h = 4H), where H is the building height. Figure 7 illustrates the values of these parameters used in the CFD simulation setups for all simulated cases according to the height of the building (H), which is 15 m.

A mesh sensitivity analysis was conducted before executing the final computations within the Fluent solver. The computational domain was subjected to successive meshing iterations, and the air velocity values (average weighted area) were observed across an internal plane situated at a height of 1.7 m from the floor level. This step was undertaken to ensure that the diverse outcomes of the CFD simulation remained impervious to variations in mesh quality. Specifically, five distinct scenarios were devised, each corresponding to various mesh elements (Fawwaz Alrebei et al. 2022). These scenarios entailed mesh element numbers of 1,357,732, 1,721,920, 2,106,627, 3,335,412, 5,345,909, and 12,216,479. Notably, the results exhibited a consistent trend of stabilization, becoming independent of mesh quality as the mesh element count reached 12.2e + 6, Fig. 8. However, for heightened confidence and precision, a further refined mesh featuring 13.1 e + 6 elements was selected (Fawwaz Alrebei et al. 2022). Furthermore, it has been observed that as the mesh size is reduced, the relative error corresponding to the chosen mesh size diminishes, as illustrated in Fig. 9. The velocity measurements stabilized within a relative error threshold of 1%. Table 2 lists the meshing configurations employed for the computational domains across all modified scenarios and the base case, while Fig. 9 provides sample visualizations of the resulting mesh.

Fig. 8
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Mesh sensitivity analysis

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Fig. 9
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Mesh of the computational domain using Ansys Fluent

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Table 2 Mesh Sensitivity Analysis
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After completing the mesh generation phase for the extracted computational domains and assigning names to the inlet, outlet, and wall components, the subsequent phase involved initiating the calculation process within the Fluent solver. This step required configuring several aspects within the Ansys Fluent solver before commencing the solver iterations. These configurations encompassed the physics setup, solver setup, boundary conditions, initial conditions, and solution initializations (Obeidat et al. 2023).

The chosen solver setup was configured to utilize parallel processing. It employed four CPUs on the local machine and used double precision to enhance accuracy. The solver type was specified as pressure-based, and the simulation was set to a steady-state time frame. The viscosity model adopted for turbulence was the K-epsilon model, known for its accurate performance in pressure-based steady-state simulations (Obeidat et al. 2023). The near-wall treatment employed was the standard approach with standard wall functions (Obeidat et al. 2023).

In the fluid domain materials, air was considered with a density of 1.225 kg/m3 and a viscosity of 1.7894e-05 kg/m·s.

The velocity inlet condition, pressure outlet condition, and wall conditions were defined for the boundary conditions. The inlet velocity magnitude was set at 3.6 m/s, with a turbulence intensity of 5% and a turbulence viscosity ratio of 10. The walls were set to be stationary, following a no-slip shear condition with a standard roughness model (Fawwaz Alrebei et al. 2022; Obeidat et al. 2023).

The comprehensive setup provided a robust foundation for the solver iterations. As a result, the simulation could be carried out accurately and efficiently (see Table 3).

Table 3 CFD simulation assumptions and setups
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The evaluation of the operational efficacy of the proposed ventilation system for all floor levels entails a quantitative and qualitative assessment of key performance metrics, including the actual-to-required ventilation ratio (n Q) and the area-weighted average (air velocity) for the base case and all modified scenarios. The n Q was calculated as a ratio of actual ventilation (Q _actual) to required ventilation (Q _required); Eq. 1 was used (Fawwaz Alrebei et al. 2022). The actual ventilation rate (Q_actual) was determined computationally within the Ansys Fluent solver. The mass airflow rate values (kg/s) were sourced from simulation outputs at the internal inlet vents for the modified configurations and from the window aperture plane for the base case. Subsequently, the actual ventilation (Q _actual) was derived employing Eq. (2). The required ventilation rate (Q_required) for the same space was ascertained utilizing Air Change per Hour (ACH), Eq. (3).

$$nQ = Q\_{\text{actual }}/Q\_{\text{required}}$$
(1)

where:

  • Q _actual signifies the empirically derived ventilation rate calculated within the Ansys Fluent solver.

  • Q _required corresponds to the essential ventilation rate mandated by the given space, computed through the ASHRAE equation defined in Eq. 3.

    $$Q\_{\text{actual }} = \, \left( {m^{.} /\rho } \right)*{1}000$$
    (2)

Where:

  • Q is the volume flow rate (in l/s or m.3/s)

  • m˙ is the mass flow rate (in kg/s)

  • ρ is the density of the air (in kg/m.3)

It is noteworthy that the Jordanian Building Code for natural ventilation determines the minimum required ventilation rate for a similar space by 50 l/s (Society 2013). However, the minimum benchmark of 10 ACH was employed to compute the necessary airflow rate. This threshold is advocated to ensure optimal indoor air quality and thermal comfort, especially during the night cooling phase, commonly referred to as night flushing (The Engineering ToolBox 2005). The required ventilation (volumetric airflow rate) for the base case was determined using the Air Change per Hour (ACH) formula, represented as Eq. (3). According to this Equation, the required airflow rate for the given space is 387 m.3/h (107.51 L/S). This value is more than the minimum required value set by the Jordanian Building Code to ensure both the optimum indoor air quality and thermal comfort. This value was compared to the actual ventilation values (Q_actual) to calculate the n Q in Eq. (1)

The equation to calculate the required ventilation rate (Q_required):

$$Q\_{\text{required }} = \, V\left( {ACH} \right)$$
(3)

where:

  • ACH is the air changes per hour.

  • V represents the volume of the space in cubic meters (m3).

  • Q signifies the volumetric flow rate of air entering or leaving the space in cubic meters per hour (m3/h).

Air velocity served as the second performance metric evaluated in this study. Velocity data was extracted from the Ansys simulation outputs across four specific planes: the vertical plane at the window for the base case, the planes at the internal inlet vents for the altered configurations, and two internal horizontal planes situated at heights of 1 m and 1.7 m from the floor, applicable to both the base case and the varied system configuration, see Fig. 9. The 1.7 m height corresponds to the breathing zone of an average-standing individual, while the 1 m mark is roughly equivalent to the breathing level of someone seated (Obeidat et al. 2023). This multi-plane approach enriched the analysis, offering granular insights into the relative merits of the proposed pipe-based configurations compared to the standard window-based setup. In the end, vector and contour visualization of velocity within the internal fluid domain for all modified cases and base cases are plotted and compared.

Results and discussion

The study was designed to critically assess the effectiveness of an innovative natural ventilation system specifically tailored for spaces in multifamily buildings. This new approach was weighed against a standard or base case that features conventional window openings as its primary means of ventilation. This research’s novelty lies in exploring diverse system configurations, each distinguished by varying numbers of inlet and outlet pipes. A constant variable across all these configurations is the total cross-sectional area of the exterior inlets or outlets, kept steady at 1 m/s.

The research rigorously evaluated multiple key performance metrics across the new system configurations and the standard window setup (the base case). These metrics include the actual-to-required ventilation ratio, denoted as n Q, and the area-weighted average [m/s], the mass airflow [kg/s], and volume flow rate [m3/s].

The study employed four distinct measurement planes within the living room space to enable a more nuanced understanding of these metrics, see Fig. 10. These measurement planes are the internal inlet vents cross sections, the horizontal planes at 1 m and 1.7 m from the floor level, against the window plane for the base case.

Fig. 10
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Multiple planes for assessing the performance metrics

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Actual-to-required ventilation rate (n Q) and area-weighted average (wind velocity) at internal inlet vents planes VS vertical window plane

Statistically, regarding the first performance metric n Q, out of ten system configurations studied, seven demonstrated superior performance metrics when contrasted with the base case. More complex configurations, characterized by more inlet and outlet pipes, generally outperformed simpler ones. For instance, a system with six pipes (configured as 3 × 3) recorded the lowest n Q = 72.53%, while a configuration with 11 pipes (5 × 6) exhibited the highest n Q = 158.15%, see Fig. 11.

Fig. 11
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The actual-to-required ventilation ratio across different configurations and against the base case

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These metrics gain additional significance when considered against the base case, which underperformed on several fronts. For example, the base case reached only 85% of the required ventilation rate (107 m/s). However, this statistic may be misleading. The window plane, used for measuring the base case’s ventilation rate, primarily captures air flow near the window, as indicated by contour lines in Figs. 24 and 25. Hence, the least effective pipe configuration with n Q at 72% may still surpass the base case’s 85% when viewed in this context. In conclusion, the study underscores the promising potential of pipe-based natural ventilation systems in enhancing indoor air quality, especially when juxtaposed against traditional window-based systems.

Generally speaking, the configurations with the most pipes consistently demonstrated superior performance compared to other setups. This implies that having more pipes resulted in better outcomes in terms of pure airflow and ventilation efficiency. However, it's essential to note that as the number of pipes increases, so does the complexity of installation. This could involve intricate design adjustments, potential structural changes, and a requirement for specialized installation techniques or expertise, which could escalate costs.

Therefore, while the data suggests that having more pipes can enhance ventilation, the decision should not be based on performance metrics alone. It is crucial to adopt a holistic approach, weighing the benefits of improved performance against the potential challenges and costs of implementation. This balanced perspective ensures that the chosen configuration not only meets ventilation needs but is also feasible, cost-effective, and aligns with the broader objectives of the project.

The findings related to the second key performance metric, the area-weighted average (air velocity), exhibit similar trends. Systems with more complex arrangements, based on the number of pipes, generally display higher air velocity values. For instance, the 5 × 5 and 5 × 6 configurations clocked the maximum air velocity, registering at 2.947 m/s and 2.718, respectively. The base case displays the lowest air velocity value of 0.379 m/s, see Fig. 12.

Fig. 12
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Air Velocity ate plane of measurement one (inlet pipes cross sections and vertical window plane)

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The configuration dynamics of the ventilation system present some surprising outcomes. Notably, the simplest 3 × 3 configuration did not register the slowest air velocity. Instead, the 5 × 3 setup had the lowest recorded air velocity at 1.687 m/s, as depicted in Figs. 12 and 13. This observation underscores the complex interplay between the number of inlet and outlet pipes and how they affect performance.

Fig. 13
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Plane of measurement one (inlet pipes cross sections and vertical window plane)

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More specifically, even when the combined total of inlet and outlet pipes remains the same, the ratio between them can significantly alter the system’s efficiency. Visual representations in related figures highlight the impact of this inlet-to-outlet ratio on air velocity for configurations with an identical overall pipe count. For instance, when examining configurations with a combined eight pipes (e.g., 3 × 5, 4 × 4, and 5 × 3), the air velocity was found to be most optimal in the balanced 4 × 4 setup, recording velocities of 2.571 m/s, 2.550 m/s, and 1.687 m/s, respectively.

Clear performance patterns arise when there is an imbalance between inlets and outlets. Configurations with more outlet pipes, such as 3 × 4 (2.426 m/s), 3 × 5 (2.550 m/s), and 4 × 5 (2.927 m/s), outperform those with a higher count of inlet pipes, like 4 × 3 (2.017 m/s), 5 × 3 (1.687 m/s), and 5 × 4 (2.308 m/s). This trend is especially evident in configurations like 3 × 5 and 5 × 3, where the difference between inlets and outlets is greater than one.

While the total number of pipes in a configuration is significant, the delicate balance between inlets and outlets dictates performance. It is a nuanced factor that plays a pivotal role in determining the overall efficacy of the ventilation system (Figs. 14, 15, 16, 17).

Fig. 14
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Plane of measurement one (inlet pipes cross sections and vertical window plane)

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Key performance metrics at Planes 1 m and 1.7 m from the floor level

Besides the vertical plane at the window in the base case and the cross-sectional planes at the interior inlets vents in all system configurations, the area-weighted average and volume flow rate metrics were also assessed across two additional horizontal planes situated at 1 m and 1.7 m from the floor within the room. As previously stated, this multi-plane approach provides a comprehensive understanding of the proposed system’s performance and the overall airflow quality across the living room space, see Figs. 18, 19 and 20.

Likewise, configurations featuring a higher count of both inlet and outlet pipes registered the maximum wind velocities. However, the wind velocity values displayed fluctuations when looking at the setups with an equal total number of pipes. Unlike observations made at the cross-sectional planes of the interior inlet vents, these variations didn't adhere to any discernible pattern. This is justified since these planes are apart from the interior inlet vents. The highest wind velocity was achieved by the 5 × 6 configuration, 0.347 m/s and the lowest wind velocity was achieved by the base case, 0.124 m/s.

In most system configurations, the observed air velocity values for both horizontal planes surpassed the 0.2 m/s benchmark, as recommended for indoor air velocity by ASHRAE 55 (American Society of Heating, 2020). However, a higher air velocity is advisable to optimize indoor air quality and promote thermal comfort, particularly during night cooling. This enhanced air movement can contribute to a more comfortable perceived temperature for occupants. Importantly, all recorded air velocities remained below the 1.5 m/s threshold (DesigningBuildings 2021), ensuring occupants' comfort. An added advantage of the system is its potential for adjustability. Design modifications, such as the integration of valves, can allow for precise control of the air velocity entering the space, catering to specific needs or preferences.

As illustrated in Fig. 15 and Table 4, the rates of change for the evaluated metrics in regard to the number of outlet pipes are 0.4432, 0.0106, 0.0086, and 0.080, respectively. In terms of R2, the area-weighted average [m/s] yielded an R2 = 0.8031, indicating a strong positive correlation with the number of outlet pipes. The mass airflow, volume flow rate, and actual-to-required ventilation ratio all resulted in an R2 = 0.1035, indicating a weak association. A Pearson test was also performed on the variables of interest, and a 0.896135411 value was found for the area-weighted average [m/s], indicating a strong relationship between this variable and the number of outlets used in each configuration. With respect to the other parameters, the mass airflow, volume flow rate, and actual-to-required ventilation ratio all registered 0.3217, indicating a weak correlation.

Fig. 15
figure 15

Key performance metrics in terms of the number of outlet pipes a Number of outlet pipes and area-weighted average, b number of outlet pipes and mass airflow, c number of outlet pipes and volume flow rate, d number of outlet pipes and actual-required ratio

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Table 4 Key performance metrics across the number of outlets
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The rate of change for variables of interest in terms of the number of inlet pipes is 0.2426, 0.0146, 0.1358, and 0.0179, respectively. The R2 value, the area-weighted average, was 0.2406, while the remaining metrics yielded a value of 0.2977. The Parsons test resulted in a value of −0.490528094 for the area-weighted average parameter, indicating a negative and fairly weak correlation. On the other hand, a value of 0.545 was observed for each of the other variables, as shown in the table, suggesting a moderate positive association (Table 5).

Fig. 16
figure 16

Key performance metrics in terms of the number of inlet pipes a Number of inlet pipes and area-weighted average, b number of inlet pipes and mass airflow, c number of inlet pipes and volume flow rate, d number of inlet pipes and actual-required ratio

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Table 5 Key performance metrics across the number of inlets
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The measured rate of change for the evaluated metrics shown in Fig. 17 and Table 6 is 0.2006, 0.0285, 0.0233, and 0.2159, respectively. The R2 values for mass air flow, volume flow rate, and the actual-to-required ratio were all 0.7524, demonstrating a moderately significant positive relationship between these parameters and the total number of pipes in each setup. However, the R2 value for the area-weighted average was 0.1645, exhibiting an insignificant association with the independent variable of interest. Pearson’s test findings for the area-weighted average were 0.405607317 and 0.867388932 for the other metrics, demonstrating a rather weak association for the area-weighted average parameter, whereas displaying a significant positive relationship between the total number of pipes and the mass airflow [kg/s], volume flow rate [m3/s], and actual-to-required ventilation ratio.

Fig. 17
figure 17

Key performance metrics in terms of the number of total number pipes a Number of total pipes and Area-weighted average, b number of total pipes and mass airflow, c number of total pipes and volume flow rate, d number of total pipes and actual-required ratio

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Table 6 Key performance metrics across the number of total pipes
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The 4 × 5 arrangement had the highest volume flow rate of all configurations, with a value of 0.137, while the basic case had the lowest value of 0.085 .

Fig. 18
figure 18

Volume flow rate by system configuration

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Fig. 19
figure 19

Air velocity at 1 m plane of measurement against vertical window plane

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Fig. 20
figure 20

Air velocity at 1.7 m plane of measurement against vertical window plane

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The qualitative analysis includes plotting and comparing visual representations of the air velocity at the various internal planes. Figures 21, 22, 23 and 24 present Contour and Vector illustrations depicting air velocity across the two horizontal planes inside the living room for the base case and each system configuration. In these visual representations, the interior inlets are positioned on the left, while the interior outlets are on the right. These graphics reinforce the quantitative findings that configurations with greater complexity tend to have enhanced airflow efficiency and higher wind velocities. In contrast, the base scenario demonstrates suboptimal airflow and the least wind velocity. A notable observation from the figures is the pronounced wind velocity near the inlet vents, approximately one m/s, which diminishes as it disperses away from the vent within the room. When juxtaposed with the 3.5 m/s wind velocity from the air conditioning unit’s vent, the inlet vent’s output appears to be within an acceptable range, suggesting an efficient and comfortable airflow pattern.

Fig. 21
figure 21

Area-weighted average (Velocity) contour profiles at 1 m above floor level

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Fig. 22
figure 22

Area-weighted average (Velocity) vector profiles at 1 m above floor level

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Fig. 23
figure 23

Area-weighted average (Velocity) contour profiles at 1.7 m above floor level

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Fig. 24
figure 24

Area-weighted average (Velocity) vector profiles at 1.7 m above floor level

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In configurations with higher complexity, there is a noticeable uniformity in wind velocity throughout the room. The highest air movement tends to cluster near the walls, while the central area of the living room, often the primary occupant space, experiences a gentler wind flow. This pattern is advantageous as it ensures that inhabitants of the room are not subjected to strong drafts, enhancing comfort. The design seems to strike a balance, ensuring efficient air circulation while maintaining a pleasant environment in areas where people are most likely to spend their time. This thoughtful distribution could improve indoor air quality and occupant satisfaction.

When evaluating the modified scenarios against the base case, a distinct improvement in airflow distribution within the room is evident. In the base case, regions such as the room's center, corners, and areas close to the walls experience suboptimal air circulation. In contrast, the rooms equipped with the proposed system in the modified configurations demonstrate a more uniform and effective air distribution, ensuring these previously underserved areas receive better ventilation and air movement, see Figs. 21, 22, 23 and 24.

This research furnishes a detailed evaluation of the proposed ventilation system, setting a solid benchmark for future explorations. While the primary focus has been gauging the system's airflow efficiency, a vast realm of further study remains untapped. Future research can widen its lens to delve into aspects such as indoor air quality, the thermal comfort of occupants, and the potential impact of external factors, including climatic variations, architectural design intricacies, and the nuances of urban settings on the system’s operation. Moreover, there is a compelling need to investigate and optimize the design aspects of the pipes, along with the internal and external inlets. Undertaking these comprehensive studies would provide a holistic understanding of the system and ensure that it not only boosts airflow but holistically enhances the indoor ambiance, ensuring the comfort and well-being of its occupants. Figure 25 illustration of air movement path lines within the room, highlighting the trajectory from internal entry points to the exit through internal outlet locations.

Conclusion

The primary objective of this research was to evaluate the efficiency of a novel natural ventilation system in a living room environment, and the findings have provided valuable insights into its performance compared to traditional systems. The proposed ventilation system’s operational efficacy was thoroughly examined using a combination of computational analysis and performance metrics.

The findings highlighted that proposed systems, especially configurations with greater complexity with an increased count of inlet and outlet pipes, notably demonstrated enhanced performance. Notably, most configurations achieved air ventilation rates surpassing the established minimum thresholds. These thresholds include the 50 L per second criterion set by the Jordanian Building Codes (Society 2013) and the 107 L per second standard according to air changes per hour (ACH) Eq. 3 (The Engineering ToolBox 2005). Such results indicate the proposed system’s potential to significantly improve air quality and thermal comfort within the parameters of recognized benchmarks. Notably, the relationship between the number of inlets and outlets emerged as a crucial factor influencing the system’s efficiency (Figs. 25).

Fig. 25
figure 25

Illustration of air movement path lines within the room, highlighting the trajectory from internal entry points to the exit through internal outlet locations

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From a practical standpoint, these findings hold significant implications for architectural and engineering practices. With its enhanced airflow capabilities, the proposed ventilation system promises improved ventilation and the potential to augment indoor air quality and thermal comfort for occupants.

However, like all research endeavors, this study had its limitations. While the primary focus was on airflow efficiency, indoor air quality and occupants’ thermal comfort warrant deeper exploration. Moreover, external factors such as climatic conditions or specific architectural nuances might have influenced the results and should be considered in future studies.

Looking ahead, there is a rich avenue for further research. A more comprehensive analysis of indoor air quality, thermal comfort, and the impact of external elements would provide a holistic understanding of the proposed system’s performance. Additionally, the design nuances of the pipes and inlets deserve further exploration and optimization to maximize efficiency.

In conclusion, this research has laid a robust foundation for understanding the proposed ventilation system’s capabilities. As the quest for sustainable and efficient architectural solutions continues, systems like the one proposed in this study could play a pivotal role in shaping future indoor environments, ensuring efficiency and occupant well-being. Overall, the research is expected to contribute to the knowledge and understanding of sustainable architectural design and urban planning in Jordan and other similar urban contexts facing similar challenges.