# From the abyss to efficiency: Optimizing Sea Water Air Conditioning (SWAC) for sustainable cooling

Kanhan Sanjivy, Perceval Raybaud, Olivier Marc, Franck Lucas

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Kanhan Sanjivy, Perceval Raybaud, Olivier Marc, Franck Lucas. From the abyss to efficiency: Optimizing Sea Water Air Conditioning (SWAC) for sustainable cooling. Energy and Buildings, 2026, 352, pp.116854. 10.1016/j.enbuild.2025.116854. hal-05509827

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# From the abyss to efficiency: Optimizing Sea Water Air Conditioning (SWAC) for sustainable cooling

Kanhan Sanjivy<sup>a,b,c,*</sup> <sup>10</sup>, Perceval Raybaud<sup>a</sup>, Olivier Marc<sup>c</sup> <sup>10</sup>, Franck Lucas<sup>a</sup>

## ARTICLE INFO

### Keywords:

Sea water air conditioning (SWAC)

Sustainable cooling

Marine geothermal Energy

Energy efficiency

Sensitivity analysis

Multi-objective optimization

## ABSTRACT

As global cooling demand grows, improving air conditioning efficiency while reducing its environmental impact is an urgent challenge. Sea Water Air Conditioning (SWAC) harnesses deep seawater as a renewable cooling source and offers a promising low-carbon alternative to conventional systems. This study presents a parametric sensitivity analysis and a multi-objective optimization of a large-scale SWAC installation in French Polynesia, using a numerical model validated by experimental data and coupled with a dynamic building simulation. Key design and operational parameters, such as seawater intake temperature, chilled water loop configuration, and heat exchanger effectiveness, are identified using the Morris method and optimized through a Non-dominated Sorting Genetic Algorithm. Results show that the global coefficient of performance can be increased from 24.7 up to 85.6, nearly 24 times higher than typical values for split-system air conditioners, while preserving comfort and adequate dehumidification. Raising the seawater intake temperature from 5 °C to 8 °C significantly reduces capital costs, resulting in major economic benefits: the Levelized Cost of Cooling decreases from 0.312 $/kWh to 0.231 $/kWh, the payback period shortens from 15 to 7 years, and the Net Present Value nearly doubles. These results highlight SWAC's potential as a high-efficiency, economically viable, and climate-resilient cooling solution. Furthermore, coupling SWAC with complementary systems such as OTEC could enhance resource utilization, reinforce its contribution to net-zero targets, and broaden its applicability in tropical and coastal regions.

## 1. Introduction

The need for building cooling has been steadily increasing over the past decades. According to the IEA [1], energy consumption related to air conditioning tripled between 1990 and 2016 and is expected to triple again between 2020 and 2050. Climate change will likely accelerate this trend, with more frequent, longer, and more intense heatwaves [2]. The situation will be critical in hot climate regions, particularly ASHRAE zones 0 to 2. In tropical cities such as Singapore and São Paulo, approximately seven heatwaves were recorded annually in 2010. By 2050, projections estimate 58 and 87 heatwaves per year, respectively, rising to 136 and 172 by 2100 [3]. Along with increased frequency, heatwaves are predicted to become more intense and last longer. Under such conditions, passive cooling strategies relying solely on natural ventilation will no longer be sufficient to ensure acceptable indoor thermal comfort. Developing highly energy-efficient active cooling solutions is therefore a major challenge for the coming decades. Several

renewable and low-carbon cooling technologies, such as solar-assisted absorption systems [4], district cooling [5], and hybrid renewable systems [6], have been extensively evaluated through techno-economic and exergy-based analyses. These studies highlight the relevance of exergy and life-cycle cost metrics to assess the sustainability of emerging cooling solutions, providing a broader framework for the present work.

Sea Water Air Conditioning (SWAC) offers a low-carbon alternative, particularly for coastal and island regions. In French Polynesia, its development followed an unconventional path. Instead of progressing through TRLs [7], a full-scale SWAC system was deployed in 2006 at the Thalasso Hotel in Bora Bora directly from a TRL 3 experimental proof of concept, without prior numerical modeling or intermediate demonstrator phases. This proof of concept was initially designed to validate the principles of Ocean Thermal Energy Conversion (OTEC) at the NELHA, using a deep-sea water intake not intended for building cooling purposes [8]. Today, although several commercial SWAC installations operate successfully in French Polynesia, the intermediate stages of technological maturation (from TRL 3 to TRL 9) have not been

<footnote><sup>a</sup> GEPASUD, University of French Polynesia, Faa'a, French Polynesia</footnote>

<footnote><sup>b</sup> French Environment and Energy Management Agency, Angers, France</footnote>

<footnote><sup>c</sup> PIMENT, University of Reunion Island, Saint-Pierre, La Réunion, France</footnote>

<footnote>* Corresponding author.
E-mail address: kanhan.sanjivy@univ-reunion.fr (K. Sanjivy).</footnote>
Nomenclature

<table><tr><td>API</td><td>Application Programming Interface</td></tr><tr><td>ASHRAE</td><td>American Society of Heating, Refrigerating and Air-Conditioning Engineers</td></tr><tr><td>COP</td><td>Coefficient of Performance</td></tr><tr><td>CW/SW</td><td>Chilled Water/Sea Water</td></tr><tr><td>DOW</td><td>Deep Ocean Water</td></tr><tr><td>DPP</td><td>Discounted Payback Period</td></tr><tr><td>HDPE</td><td>High Density Polyethylene</td></tr><tr><td>IEA</td><td>International Energy Agency</td></tr><tr><td>LCOC</td><td>Levelized Cost of Cooling</td></tr><tr><td>MPC</td><td>Model Predictive Control</td></tr><tr><td>NELHA</td><td>Natural Energy Laboratory of Hawaii Authority</td></tr></table>

<table><tr><td>NPV</td><td>Net Present Value</td></tr><tr><td>NSGA</td><td>Non-dominated Sorting Genetic Algorithm</td></tr><tr><td>O&M</td><td>Operation and Maintenance</td></tr><tr><td>OAT</td><td>One At a Time</td></tr><tr><td>OTEC</td><td>Ocean Thermal Energy Conversion</td></tr><tr><td>PID</td><td>Proportional-Integral-Derivative</td></tr><tr><td>PMV</td><td>Predicted Mean Vote</td></tr><tr><td>PPD</td><td>Predicted Percentage of Dissatisfied</td></tr><tr><td>SCOP</td><td>Seasonal Coefficient of Performance</td></tr><tr><td>SWAC</td><td>Sea Water Air Conditioning</td></tr><tr><td>TRL</td><td>Technology Readiness Level</td></tr><tr><td>VFD</td><td>Variable Frequency Drive</td></tr><tr><td>VRF</td><td>Variable Refrigerant Flow</td></tr><tr><td>UH</td><td>Unmet Hours</td></tr></table>

systematically developed. This gap has left significant room for improvement and optimization of SWAC systems. Modifications based on operational feedback combined with advanced numerical modeling approaches can enhance system performance and promote wider adoption worldwide.

Recent studies highlight SWAC's global potential. Suitable bathymetric profiles, high electricity costs, and rising cooling needs make it highly relevant for tropical islands and many coastal cities within the intertropical zone [9]. Increasing the seawater intake temperature (from 5 °C to 12 °C) also increases the site eligibility, reduces pipeline lengths, and lowers investment costs, thereby expanding the applicability of SWAC to include regions like Europe and South America [10].

While this study focuses on energy performance and optimization, we acknowledge that the ecological impacts of SWAC systems, such as cold water discharge, biofouling, and potential effects on local ecosystems, are critical considerations. Dedicated environmental assessments are currently being conducted for the Tetiaroa installation, and their results will be reported separately. Existing studies nevertheless suggest that such impacts remain spatially limited and can be mitigated by appropriate design, for example by discharging below the euphotic layer [11,12]. These aspects therefore lie beyond the scope of the present paper.

In contrast to previous studies that mainly focused on technology feasibility or steady-state performance evaluations, the present work develops and applies an experimentally validated dynamic model of a real-scale SWAC installation, coupled with a building thermal simulation. This integrated framework combines parametric sensitivity analysis, multi-objective optimization, and exergy assessment to identify the most influential design and operational parameters governing both energy efficiency and indoor thermal comfort. In parallel, a technology-economic assessment based on life-cycle indicators demonstrates that design optimization significantly improves both thermodynamic and economic performance. Applied to the Tetiaroa installation, this methodology increases the global Coefficient of Performance (COP) from 24.7, already an exceptional value compared to typical split systems with COPs around 3.5 [1], to 85.6. This represents a COP nearly 24 times higher than conventional systems, leading to a proportional reduction in electricity consumption and CO₂ emissions, while also achieving shorter payback periods. Such a methodology has not been previously applied to SWAC systems and represents a step forward toward their systematic optimization and large-scale deployment.

## 2. Sea water air conditioning (SWAC) process

SWAC is an energy system that harnesses naturally cold deep ocean water (DOW) to provide space cooling for buildings. Unlike conventional vapor-compression cooling systems, SWAC involves no refrigerants or phase-change processes, and operates outside the typical

constraints of thermodynamic cycles limited by Carnot efficiency. As a result, SWAC systems can reach energy performance levels up to an order of magnitude higher than those of conventional air conditioning technologies.

A typical SWAC system consists of three main parts: a primary loop that draws seawater from the depths, a secondary loop that circulates chilled water to buildings, and in between, a technical area including the centrifugal pumps with Variable Frequency Drive (VFD) of both loops and the heat exchangers. The SWAC system installed on the island of Tetiaroa, French Polynesia, follows this architecture and supplies cooling to the entire “The Brando” resort (Fig. 1a), located on Onetahi islet, 53 km north of Tahiti. The primary seawater loop (SW) consists of a high-density polyethylene (HDPE) intake pipe that reaches 960 m depth, where the water temperature is around 5 °C all year round. After passing through the heat exchangers, the seawater is discharged back into the ocean at a shallower depth of 30 m, typically at 12 °C. The secondary loop (CW) distributes chilled water between 7 °C (supply) and 12 °C (return) to meet the cooling demand of the connected buildings. Heat transfer occurs via titanium plate heat exchangers for corrosion resistance, operating with inlet/outlet temperatures of 6 °C/11 °C on the seawater side and 7 °C/12 °C on the chilled water side. The full installation is described in the Methods section, and is designed for flexible operation, with one or two pumps operating at variable speed, the third pump being available for redundancy during maintenance or failure.

## 3. Energy performance under operating conditions

To evaluate the energy performance of the SWAC system and compare it with conventional air conditioning technologies, two performance indicators are used: the primary COP and the global COP, which are defined in the methods section. The primary COP excludes the energy consumption of the secondary chilled water distribution network, while the global COP includes all electrical consumption. This difference helps to compare SWAC's COP to centralized or split systems respectively.

The operation of the Tetiaroa SWAC installation over a three-year period is illustrated in Fig. 2. The deep seawater intake temperature remained stable between 5.5 °C and 6.5 °C, while the chilled water inlet was consistently regulated between 7.5 °C and 8 °C. Outlet temperatures varied with cooling demand, reaching 9 °C to 10 °C for seawater and 11 °C to 13 °C for chilled water. As the building load increased and secondary flow rate decreased, the temperature differences across the loops gradually converged toward the nominal 5 °C target, improving thermal exchange effectiveness. These variations are directly linked to flow rate control strategies. The primary flow rate followed the cooling demand closely, as it was regulated to maintain the chilled water inlet temperature within its setpoint band. This control strategy ensured a suitable seawater flow rate despite fluctuating loads. The secondary
<figure><img src="image_4.png" /><figcaption>Fig. 1. The sea water air conditioning system. a) Schematic of the SWAC installation operating in Tetiaroa, French Polynesia. b and c) Photographs of the drawing and reject pipeline (made of high-density polyethylene). d) Sea water centrifugal pump. e) Titanium plate heat exchanger. f) Chilled water centrifugal pump [13].</figcaption></figure>

loop, by contrast, is either manually adjusted by the operator or regulated based on a pressure setpoint rather than the return chilled water temperature. This control strategy, implemented due to the large size of the district cooling network, results in flow rate variations that are less aligned with the actual cooling load. The cooling power delivered to the district cooling ranged from 550 kW to 900 kW, considerably below the installation's 2.4 MW design capacity. This apparent oversizing reflects the fact that the SWAC was dimensioned for a future expansion of the hotel complex, which had not yet occurred during the study period. Seasonal variations are also evident, with peak loads occurring between November and February, corresponding to the local warm season. Pump electrical consumption evolved accordingly. The seawater pumps operated near 5 kW in the early part of the period (with one pump running), increasing to approximately 10 kW in the later period when two pumps were used simultaneously. However, dual-pump operation was not technically required given the observed flow rates, resulting in a noticeable drop in both primary and global COP during this period. Chilled water pump consumption decreased progressively from 35 kW

to 26 kW and then to 18 kW, following the implementation of strategies to optimize secondary loop operation. The evolution of the primary and global coefficients of performance (COP) over time, derived from cooling production and pump electricity consumption, is shown in Fig. 2e. The primary COP ranges from 75 to 200, while the global COP varies between 15 and 38. Over the full monitoring period, the system achieved seasonal COPs (SCOP) of 127 for the primary and 24.7 for the global system, highlighting the outstanding performance of the Tetiaroa SWAC installation. These values are substantially higher than those typically observed in conventional vapor-compression systems, where the best-performing unitary air conditioners rarely exceed a COP of 5, and centralized systems reach up to 12 under optimal conditions [1].

These experimental data confirm the strong energy performance of the SWAC system under real-world operating conditions. At the same time, they reveal potential areas for improvement, particularly regarding chilled water flow control and overall system configuration. To investigate these opportunities, a detailed numerical model of the Tetiaroa installation was developed and validated, coupled with a
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Operational data of the Tetiaroa SWAC, averaged by week over three years. (a) Seawater and chilled water loop temperatures. (b) Seawater and chilled water flow rates (c) Cooling power exchanged in each loop. (d) Electrical consumption of primary and secondary pumps. (e) Primary and global Coefficients of Performance.</figcaption></figure>

building model, enabling parametric sensitivity analysis and multi-objective optimization targeting both energy efficiency and thermal comfort within the air-conditioned building.

## 4. Sensitivity analysis for parameter screening

The optimization approach is based on the validated numerical model of the Tetiaroa SWAC system, coupled with a dynamically sized building model. Full details of the modeling framework are provided in the Methods section. Based on this model, a parametric sensitivity analysis was carried out using the Morris method to identify the influence of various design and operational parameters on overall energy performance and thermal comfort. This initial screening approach is particularly suited for highlighting the most influential parameters at a relatively low computational cost [14]. The parameters considered in the sensitivity analysis included:

(i) Temperature regimes: seawater intake temperature ($T_{source}$) and chilled water loop temperature difference ($\Delta T_{CW}$).

(ii) Material properties: thermal conductivity, density, and specific heat capacity of the primary and secondary piping ($\lambda_{SW}, \lambda_{CW}, \rho_{CW}, c_{p_{CW}}$).

(iii) Pump characteristics: nominal flow rates, heads, and electrical powers of the primary and secondary pumps ($\dot{V}_{SW}, \dot{V}_{CW}, \Delta H_{SW}, \Delta H_{CW}, \dot{W}_{SW}, \dot{W}_{CW}$).

(iv) Heat exchanger characteristics: overall heat transfer coefficient, nominal flow rates, and pressure losses on both sides of the exchangers ($UA_{HEX}, \dot{V}_{SW,HEX}, \dot{V}_{CW,HEX}, \Delta P_{SW,HEX}, \Delta P_{CW,HEX}$).

(v) Pipe dimensions: inner and outer diameters and thicknesses for primary and secondary pipes ($D_{int,SW}, D_{ext,SW}, D_{int,CW}, e_{CW}$).

(vi) Building setpoint temperature ($T_{building}$).

An ideal control strategy was applied to the secondary loop, with the chilled water flow rate dynamically adjusted to meet the building's real-time cooling demand. The two objective functions used in the analysis were the global Coefficient of Performance ($COP_{glob}$) over the simulation period, and the percentage of hours during which the indoor temperature setpoint was not achieved, referred to as "Unmet Hours" (UH). The results of the Morris screening are presented in Fig. 3 for both objectives. Each output is visualized as a scatter plot (left), with $\mu^*$ on the x-axis and $\sigma$ on the y-axis, alongside a bar chart (right) summarizing the relative influence of each parameter to each objective.

A parameter was considered influential for the global Coefficient of Performance ($COP_{glob}$) when its $\mu^*$ value increased by at least one unit. Based on this criterion, the most influential parameters were the seawater intake temperature ($T_{source}$), the chilled water loop temperature difference ($\Delta T_{CW}$), and the overall heat transfer coefficient of the heat exchangers ($UA_{HEX}$). These were followed by the nominal characteristics of the pumps ($\Delta H, \dot{V}, \dot{W}$), the insulation thickness of the secondary pipes ($e_{CW}$), and the building setpoint temperature ($T_{building}$), yielding a total of
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Morris sensitivity analysis for global COP (a) and Unmet Hours (b). The x-axis in scatter plots represents the mean of the elementary effects (μ*), while the y-axis represents their standard deviation (σ), indicating the influence and non-linearity of each parameter, respectively. Bar charts on the right summarize the elementary effects for all parameters with respect to each objective.</figcaption></figure>

11 influential parameters out of the 22 initially considered. Fig. 3b also presents the sensitivity results for thermal comfort, measured by the proportion of unmet hours. The same three parameters, *T*<sub>source</sub>, Δ*T*<sub>CW</sub>, and *UA*<sub>HEX</sub>, showed the highest influence, which can be explained by their direct impact on the cooling capacity delivered by the SWAC system. The outer diameter and insulation thickness of the secondary piping were also influential, as they affect thermal losses in the chilled water network, and thus the effective cooling power delivered to the fan coil units inside the building.

In total, 12 parameters were identified as influential across both objectives, half of which are related to the pump configurations. These pump-related parameters directly impact the efficiency of their respective pumps, and their optimal values correspond to 100 % efficiency.

Since the optimal values of these pump-related parameters correspond to maximum pump efficiency, thereby minimizing electrical consumption and maximizing the COP, they were considered trivially optimized and excluded from subsequent analysis to reduce computational costs. Therefore, six parameters are ultimately selected for multi-objective optimization.

**5. Multi-objective optimization: energy performance and thermal comfort**

Following the screening, a multi-objective optimization was performed using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), as evolutionary algorithms are standard approaches for solving
multi-objective optimization problems [15].

Fig. 4 shows the full set of configurations generated during the multi-objective optimization, each positioned according to its global COP and corresponding percentage of Unmet Hours. The points are colored by seawater intake temperature, one of the most influential parameters in SWAC systems. This parameter directly affects the required intake depth, and therefore the length of the intake pipeline, which is a key component in the initial investment cost. In the case of Tetiaroa, the results suggest that acceptable cooling performance can be achieved even with intake temperatures around 8 to 9 °C, rather than the conventional 5 °C, potentially allowing for a significantly shorter pipeline. To ensure that the building's cooling demand is fully met, only solutions with 0 % Unmet Hours were retained. Among these, the configuration with the highest global COP, reaching 52.9, was selected as the optimal solution for further simulations and evaluations.

## 5.1. Optimized configuration

The parameter values of the selected optimized configuration, aimed at improving the design of the Tetiaroa SWAC installation, are summarized in Table 1, along with their initial, pre-optimization values.

The performance of the optimized configuration was re-evaluated with a finer timestep (1 min instead of 6 min during the optimization phase), and the results are shown in Fig. 5 for a 10-day period from December 1st to 10th using hourly averages. As in the experimental study, the analysis includes temperatures, flow rates, cooling loads, electrical power consumption, as well as both primary and global COPs. Additionally, indoor and outdoor temperature and humidity conditions are reported to assess the comfort performance of the system.

In the optimized configuration, a key design change is the increase in seawater intake temperature from 5 °C to 8 °C. This adjustment allows for a shorter intake pipe and reduces primary pump energy consumption. As a result, heat exchanger inlet temperatures range from 9.5 °C to 10 °C, and outlet temperatures reach 13 °C to 14.5 °C, with a 0.5 °C rise before discharge. On the secondary side, the chilled water loop maintains a 5 °C temperature difference, but the regime shifts from 7 °C/ 12 °C to 10 °C/15 °C due to the warmer intake. Return temperatures vary by about 1 °C depending on cooling demand.

Flow rates show that the primary loop consistently delivers higher flow than the secondary loop, in line with best practices for chilled water networks described by Jangsten et al. [16]. During nighttime low-demand periods, both pumps operate at minimum flow, roughly half their nominal capacity. Estimated cooling powers confirm that building demand fluctuates daily between 400 and 1200 kW, in the same order of magnitude as the real Tetiaroa system.

<figure><img src="image_7.png" /><figcaption>Fig. 4. Scatter plot representing the objective space for global COP and Unmet Hours across all simulated configurations. Each point represents a unique simulation, with colors indicating the seawater intake temperature ($T_{source}$).</figcaption></figure>

<caption>Table 1</caption>

Comparison of parameters between the Tetiaroa installation and the optimized configuration.

<table><thead><tr><th>Parameters</th><th>Unit</th><th>Tetiaroa installation</th><th>Optimized configuration</th></tr></thead><tbody><tr><td>Drawing temperature ($T_{source}$)</td><td>°C</td><td>5</td><td>8</td></tr><tr><td>Chilled water loop temperature difference ($\Delta T_{CW}$)</td><td>°C</td><td>5</td><td>5</td></tr><tr><td>Overall heat transfer coefficient of the heat exchangers ($UA_{HEX}$)</td><td>kW.K<sup>-1</sup></td><td>186</td><td>400</td></tr><tr><td>Inner diameter of the secondary pipes ($D_{int.CW}$)</td><td>m</td><td>0.174</td><td>0.1218</td></tr><tr><td>Thickness of the secondary pipes ($e_{CW}$)</td><td>m</td><td>0.025</td><td>0.035</td></tr><tr><td>Indoor setpoint temperature ($T_{building}$)</td><td>°C</td><td>21</td><td>26</td></tr></tbody></table>

Pump electricity consumption follows flow rate trends. In the optimized case, primary pump consumption ranges from 1 to 5 kW, and secondary pump consumption from 4 to 25 kW, while meeting loads up to 1200 kW. These savings result from both parameters optimization and ideal control strategy. Energy performance reflects this improvement: the primary COP now ranges from 200 to 670, and the global COP from 36 to 127. The system reaches a mean primary COP of 422 and a mean global COP of 85.6 over the simulation period, more than three times higher than the experimentally measured seasonal COPs (127 and 24.7, respectively).

The setpoint temperature was also maintained. The indoor temperature setpoint of 26 °C was met throughout the simulation, except for a brief deviation of less than 1 °C on December 7th, due to a sudden cooling spike. This deviation was too short and minor to qualify as an Unmet Hour. Outdoor temperatures ranged from 25 °C at night to 31.5 °C during the day.

Despite the warmer seawater intake and the shift to a 10 °C/15 °C chilled water regime, humidity control remained effective. The absence of direct humidity regulation (temperature-only, control) explains nighttime humidity peaks during low demand. Still, the system maintained effective dehumidification under the new regime, with indoor humidity reaching 50 % during the day.

Thermal comfort was assessed using the Fanger model implemented in EnergyPlus, through the Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) indices. The occupant parameters were defined to represent typical hotel guests under tropical conditions, characterized by a resting activity level and light clothing. Accordingly, the schedules were set with a work efficiency of 0 (resting metabolic state), a clothing insulation of 0.3 clo, and an air velocity varying between 0.15 m·s⁻¹ at night and 0.3 m·s⁻¹ during the day. The air velocity reflects the presence of ceiling fans installed in the rooms, which enhance air movement and are commonly used in combination with air conditioning in tropical hotels. These assumptions reflect realistic indoor conditions for tourist accommodations, where occupants are mostly sedentary and lightly dressed. The resulting PMV and PPD indices were computed dynamically from the simulated indoor temperature and relative humidity. As shown in Fig. 5g, for most of the period the PMV values ranged between -0.5 and +0.5, with the corresponding PPD remaining mostly below 15 %, confirming that the optimized configuration ensured acceptable hygrothermal comfort.

While the optimized configuration shows significant gains in energy efficiency without compromising thermal comfort, it has not yet been optimized for economic objectives. Including such considerations in future work will be essential to support the broader adoption of SWAC systems, as high initial investment costs remain a major barrier to their deployment. Even though the optimization did not explicitly target economic criteria, it is still relevant to compare the optimized configuration with the existing installation, and with a conventional chiller-based system from an economic standpoint.
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Optimized configuration of the Tetiaroa SWAC, averaged hourly over ten days. (a) Seawater and chilled water loop temperatures. (b) Seawater and chilled water flow rates (c) Cooling power exchanged in each loop and at the heat exchanger and building levels. (d) Electrical consumption of primary and secondary pumps. (e) Primary and global coefficients of performance. (f) Indoor and outdoor air conditions (g) Predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) (h) Air dew point temperature.</figcaption></figure>

## 5.2. Economic implications

The economic performance of three systems, a reference chiller, the existing Tetiaroa SWAC, and the optimized configuration, was evaluated using the indicators defined in the Methods section: the Levelized Cost of Cooling (LCOC), the Discounted Payback Period (DPP), and the Net Present Value (NPV). The main results are summarized in Table 2:

The reference chiller system was modeled to provide the same nominal cooling capacity as the SWAC installation on Tetiaroa, ensuring a consistent basis for comparing investment and operating costs. Its economic parameters were derived from typical industrial benchmarks in French Polynesia for centralized chillers. The initial investment cost was estimated using a specific ratio of 1430 $/kWf, to which the same

additional costs as in the SWAC case were applied for the technical room and the chilled-water distribution network. Operation and maintenance (O&M) expenses were estimated at 54 $/kWf per year, consistent with reported values for large-scale cooling systems in French Polynesia. The system coefficient of performance was assumed to be constant at 3, representative of conventional chillers operating in tropical climates. Major equipment replacement was assumed every 15 years, corresponding to the renewal of the chiller units, while for the SWAC system the same replacement cycle was applied to the seawater and chilled-water pumps. The local electricity price on Tetiaroa, equal to 0.84 $/kWh, was used to compute the annual energy cost.

The comparison highlights the strong economic advantage of the optimized SWAC configuration. Although its initial investment remains
<caption>**Table 2**</caption>

Summary of economic indicators for the reference chiller, existing SWAC, and optimized SWAC configurations. The table compares capital expenditures (CAPEX), annual operational costs (OPEX), levelized cost of cooling (LCOC), discounted payback period, and net present value (NPV) over a 30-year project lifetime, assuming a 7.7 % discount rate [17].

<table><thead><tr><th>Parameter</th><th>Unit</th><th>Chiller</th><th>Existing SWAC</th><th>Optimized SWAC</th></tr></thead><tbody><tr><td>CAPEX (t = 0)</td><td>M$</td><td>6.31</td><td>18.31</td><td>13.84</td></tr><tr><td>CAPEX (t = 15)</td><td>k$</td><td>3432</td><td>68.72</td><td>68.72</td></tr><tr><td>Total CAPEX</td><td>M$</td><td>9.74</td><td>18.37</td><td>13.91</td></tr><tr><td>Annual OPEX (without fuel cost)</td><td>k<br/>$/year</td><td>130</td><td>260.6</td><td>260.6</td></tr><tr><td>Annual OPEX (with fuel cost)</td><td>k<br/>$/year</td><td>1843.7</td><td>472.7</td><td>332.2</td></tr><tr><td>Levelized cost of cooling (LCOC)</td><td>$/kWh</td><td>0.396</td><td>0.312</td><td>0.231</td></tr><tr><td>Discounted payback period</td><td>years</td><td>—</td><td>15</td><td>7</td></tr><tr><td>Net present value (NPV)</td><td>M$</td><td>—</td><td>7.37</td><td>13.46</td></tr></tbody></table>

higher compared to conventional chillers, the total CAPEX of the optimized SWAC is reduced by nearly 25 % compared to the existing installation (13.9 M$ vs. 18.4 M$). This reduction directly results from the shorter seawater intake pipeline made possible by increasing the intake temperature from 5 °C to 8 °C, which lowers the depth and length requirements to reach the drawing point. The reduction in CAPEX associated with this design modification was estimated based on an installed pipeline cost of 5359 $/m [10].

Operational costs, dominated by electricity consumption, show the most significant differences among systems. The annual OPEX including fuel cost reaches 1844 k$/year for the chiller, 472.7 k$/year for the existing SWAC, and only 332.2 k$/year for the optimized system. These reductions directly result from improved energy performance: the global COP increases from 24.7 for the existing installation to 85.6 in the optimized case, corresponding to a 70 % decrease in electrical consumption.

The corresponding LCOC values, representing the discounted cost of cold energy production over 30 years, are 0.396 $/kWh for the chiller, 0.312 $/kWh for the existing SWAC, and 0.231 $/kWh for the optimized configuration. Thus, the optimized SWAC provides the same cooling service at a 42 % lower levelized cost than a conventional system, and a 26 % reduction compared to the current Tetiaroa installation. The discounted payback period decreases from 15 years for the existing system to 7 years for the optimized design, demonstrating that the higher initial investment is compensated by operational savings in less than one decade. The Net Present Value (NPV), calculated relative to the reference chiller, nearly doubles, from 7.37 M$ for the existing SWAC to 13.46 M$ for the optimized configuration, confirming the strong economic potential of design and operating optimization.

Overall, these results demonstrate that beyond its exceptional energy performance, the optimized SWAC achieves both lower life-cycle costs and shorter financial payback. By shortening the primary intake pipeline and thereby reducing the cost of marine works, which typically account for about 75 % of the total investment in a SWAC project, the proposed configuration makes the system substantially more competitive. This reduction in capital expenditure directly addresses the main barrier to the wider deployment of SWAC technology.

### 5.3. Exergy analysis

The second-law performance of both SWAC configurations was evaluated using the formulation described in the Methods section. Table 3 summarizes the component-wise exergy destruction rates for the existing Tetiaroa installation and its optimized configuration. As intended, the results reveal that exergy destruction is not uniformly distributed among the system components, and that optimization leads to

<caption>**Table 3**</caption>

Component-wise exergy destruction for the Tetiaroa SWAC and the optimized configuration.

<table><thead><tr><th>Component</th><th>E&#770;<sub>D</sub>(kW<sub>ex</sub>)- Tetiaroa SWAC</th><th>E&#770;<sub>D</sub>(kW<sub>ex</sub>)- Optimized SWAC</th></tr></thead><tbody><tr><td>Sea water loop</td><td>4.21</td><td>1.28</td></tr><tr><td>Sea water pumps</td><td>1.80</td><td>0.55</td></tr><tr><td>Heat exchanger (HEX)</td><td>5.23</td><td>1.43</td></tr><tr><td>Chilled water pumps</td><td>6.88</td><td>2.13</td></tr><tr><td>Chilled water loop</td><td>16.0</td><td>4.98</td></tr><tr><td>Cooling coil</td><td>29.8</td><td>30.5</td></tr><tr><td>Ocean outfall mixing</td><td>1.26</td><td>1.09</td></tr></tbody></table>

significant reductions in most of them.

The most exergy destruction occurs within the cooling coil, which accounts for 30 kW in both configurations. This dominance is expected, since the cooling coil represents the thermodynamic interface where low-temperature cooling is delivered to the building, inherently limited by the finite temperature difference between the chilled water and indoor air. Despite the optimization of system parameters, this irreversibility remains nearly constant because it is mainly driven by building-side constraints and comfort requirements. By contrast, substantial reductions in exergy destruction are observed in all hydraulic and heat exchange components. In the optimized configuration, exergy destruction in the sea water loop and sea water pumps decreases by approximately 70 %, mainly due to the higher seawater intake temperature (from 5 °C to 8 °C) which shortens the pipeline and reduces head losses. Similarly, exergy destruction in the chilled water loop drops from 16.0 kW to 4.98 kW, reflecting both lower pumping power and improved insulation. The heat exchanger losses also decrease markedly (–73 %), thanks to the increased overall heat transfer coefficient (from 186 to 400 kW·K⁻¹), which enhances thermal approach and reduces temperature-driven irreversibilities.

Overall, the total exergy destruction of the system is reduced by about one third between the existing and optimized configurations, confirming that the optimization process not only improves energy efficiency but also enhances thermodynamic performance. The remaining irreversibilities are mostly localized at the cooling coil, indicating that further improvements should focus on air-side heat exchange and indoor temperature control strategies rather than on seawater or chilled-water subsystems.

At the system level, the exergy balance presented in Table 4 provides a global view of the second-law performance of both configurations. The product exergy, which represents the useful cooling effect delivered to the building, is considerably lower in the optimized configuration (1.08 kW) than in the existing Tetiaroa SWAC (13.6 kW). This apparent decrease does not reflect a degradation of cooling performance but rather a shift in reference temperatures and flow conditions: with warmer chilled water supply and higher indoor setpoint temperature, the temperature difference between the cooling source and the conditioned space is reduced, leading to a smaller exergy content of the useful cooling while maintaining the same thermal comfort.

Conversely, both the electrical input exergy and the seawater

<caption>**Table 4**</caption>

System-level exergy metrics for the Tetiaroa SWAC and the optimized configuration.

<table><thead><tr><th>Metric (unit)</th><th>Tetiaroa SWAC</th><th>Optimized SWAC</th></tr></thead><tbody><tr><td>Product exergy,<br/>E<sub>prod</sub>(kW)</td><td>13.6</td><td>1.08</td></tr><tr><td>Electrical input,<br/>E<sub>elec</sub>(kW)</td><td>28.9</td><td>8.95</td></tr><tr><td>Seawater exergy,<br/>E<sub>resource</sub>(kW)</td><td>48.7</td><td>34.5</td></tr><tr><td>Total exergy destruction,<br/>ΣE<sub>D</sub>(kW)</td><td>65.2</td><td>41.9</td></tr><tr><td>Exergy efficiency, η<sub>II</sub> (–)</td><td>17.5 %</td><td>2.5 %</td></tr></tbody></table>
resource exergy are significantly reduced in the optimized configuration (–69 % and –29 %, respectively). These reductions confirm the global improvement in energy efficiency achieved through parameters optimization, as less mechanical work and a smaller temperature lift are required to meet the building load. The total exergy destruction, aggregating all component-level irreversibilities, decreases from 65.2 kW to 41.9 kW, in line with the trends observed in Table 3.

Interestingly, the exergy efficiency decreases from 17.5 % in the existing system to 2.5 % after optimization. While this might seem counterintuitive, it results from the much smaller product exergy associated with higher operating temperatures on both sides of the system. In other words, the optimized configuration delivers the same thermal comfort with a lower exergy demand, and therefore a lower ratio of useful-to-input exergy, despite its vastly superior first-law (energy) efficiency. This outcome illustrates the fundamental complementarity between energy and exergy analyses: the first law highlights improvements in energy utilization, while the second law reveals that these gains are achieved by reducing the thermodynamic “quality” of the energy flows involved.

To conclude, the exergy results confirm that the optimization process effectively reduces system irreversibilities and energy consumption, even if the formal exergy efficiency metric declines due to milder temperature gradients. This decrease in exergy efficiency also highlights that the cold seawater resource is not fully exploited in the optimized configuration. In other words, a smaller fraction of the available thermodynamic potential of deep seawater is converted into useful cooling. To better harness this resource, the SWAC system could be coupled with complementary technologies, such as Ocean Thermal Energy Conversion (OTEC) or aquaculture processes that can utilize the residual cooling of the seawater. Such multi-use integration would enhance the total exergy utilization of the resource, transforming the system from a single-purpose cooling solution into a cascaded energy and resource system, thereby improving both its thermodynamic and environmental performance.

## 6. Limitations and perspectives

Although this study focuses on energy optimization, it is important to acknowledge that SWAC systems may also interact with marine ecosystems. Reported impacts include localized temperature variations, entrainment of organisms, dissolved gas release, and operational noise. However, existing studies generally conclude that these effects are minor compared to the environmental benefits in terms of carbon reduction, provided that seawater intake and discharge are carefully designed [18]. A dedicated environmental assessment of the Tetiaroa installation is currently ongoing as part of the ANR MAEVA project. This complementary study aims to provide a comprehensive evaluation of the potential ecological impacts of SWAC under real operating conditions.

For the optimization stage, an ideal control strategy was applied to the chilled water loop, with flow rates dynamically adjusted to perfectly match the cooling demand. This assumption was made to isolate the influence of design and operating parameters under optimal regulation conditions. It is acknowledged that real-world control is more complex and often suboptimal; as a result, actual performance may be lower. Future work should therefore investigate realistic control approaches, including advanced predictive control strategies such as Model Predictive Control (MPC), which have the potential to closely match the ideal regulation assumed in this study. It should also be noted that the optimized configuration has not yet been implemented in the real installation. Deploying such a configuration would require substantial modifications to the seawater intake and chilled water network, implying prohibitive costs under current conditions. The optimization results should thus be interpreted as a demonstration of the achievable performance gains based on an experimentally validated model. Field validation of optimized configurations remains a key step for future large-scale projects, ideally in collaboration with industrial partners.

Beyond these limitations, this study also provides valuable insights into the broader deployment of SWAC systems beyond the specific case of Tetiaroa. Adopting higher seawater intake temperatures expands the number of coastal locations where suitable bathymetric conditions exist. This approach significantly increases the geographical range of potential SWAC applications, including regions where deep cold-water layers are less accessible [10]. Consequently, the optimization presented here may contribute to facilitating the worldwide implementation of SWAC technology.

## 7. Conclusion

This study presented a parametric sensitivity analysis and a multi-objective optimization of a Sea Water Air Conditioning (SWAC) system, based on a numerically validated model of the Tetiaroa installation coupled with a dynamic building simulation. Unlike previous SWAC studies, which mainly focused on techno-economic feasibility or steady-state performance assessment, the present work introduces an integrated methodology that combines experimental validation, dynamic modeling, and multi-objective optimization. This comprehensive approach enables the identification of the most influential design and operational parameters governing both energy efficiency and indoor thermal comfort. To our knowledge, this is the first study to apply such a methodology to a real large-scale SWAC installation, providing a generalizable framework for future system design and operational optimization.

Prior to optimization, the experimental assessment of Tetiaroa installation had already demonstrated remarkable energy performance. Over a three-year monitoring period, the system achieved seasonal COPs of 127 for the primary loop and 24.7 for the global system, which are well above the efficiency of conventional vapor-compression systems. These results validated SWAC as a viable high-efficiency solution for building cooling under real-world operating conditions.

Following the experimental validation, a sensitivity analysis was conducted using the Morris method to rank the influence of 22 parameters on both energy performance and thermal comfort. The seawater intake temperature, chilled water loop temperature difference, heat exchanger effectiveness, outer diameter and insulation thickness of the chilled water loop, and the indoor temperature setpoint emerged as the most influential. These six parameters were then retained for a multi-objective optimization using the NSGA-II algorithm.

The multi-objective optimization led to substantial performance improvements. The global COP increased from an average of 24.7 to 85.6, and the primary COP improved from 127 to 422. These gains were achieved while maintaining 0 % Unmet Hours, confirming that thermal comfort was fully ensured. Despite the use of a warmer seawater intake, the system also preserved effective dehumidification. While further work will be needed to validate the optimized configurations through real-world deployment, the present results already demonstrate the strong potential of SWAC as a resilient and highly efficient cooling technology.

Beyond the thermodynamic improvements, the economic analysis confirms that design optimization also brings significant financial benefits. Compared to both the reference chiller and the existing Tetiaroa installation, the optimized SWAC configuration achieves markedly lower life-cycle costs and faster return on investment. The Levelized Cost of Cooling (LCOC) decreases to 0.231 $/kWh, representing a 42 % reduction relative to a conventional chiller and 26 % compared to the current system. This improvement arises primarily from the shorter seawater intake pipeline enabled by the higher intake temperature, which substantially reduces marine works costs, the largest contributor to initial investment. As a result, the discounted payback period is shortened from 15 years to 7 years, while the Net Present Value nearly doubles, reaching 13.46 M$. These findings demonstrate that the optimized configuration not only enhances energy and exergy performance but also makes SWAC technology economically competitive, thereby
addressing one of the main barriers to its large-scale adoption.

From a second-law perspective, the exergy analysis provides complementary insights into the optimization results. While the optimized configuration achieves major reductions in component-level irreversibilities, particularly within the seawater and chilled-water loops, it also reveals that the global exergy efficiency decreases due to milder operating temperature gradients. It emphasizes that the optimization strategy reduces both energy consumption and exergy content, leading to lower exergy destruction but also to a smaller fraction of the seawater's available potential being utilized. In this sense, the analysis highlights an opportunity for further improvement through cascaded or hybrid systems that could exploit the remaining exergy of seawater. Integrating SWAC with Ocean Thermal Energy Conversion (OTEC), aquaculture, or other applications could increase the overall resource utilization, thereby enhancing both thermodynamic and environmental performance at the system scale.

In the face of more frequent and intense heatwaves, as emphasized in IEA Annex 80 on resilient cooling [19], SWAC stands out for its exceptionally low electricity demand thanks to its high COP. This makes it ideally suited for solar photovoltaic coupling, as cooling needs and solar production naturally align. Powered by the sun and deep ocean water, SWAC could operate autonomously, even during grid outages, making it a resilient cooling solution for a warming world.

# 8. Methods

## 8.1. System description and instrumentation

The SWAC installation at The Brando resort on Tetiaroa island includes one intake and one reject pipeline (Fig. 1b and c), three seawater pumps (Fig. 1d), three counter-flow titanium plate heat exchangers (Fig. 1e), three chilled water pumps (Fig. 1f), and a distribution network serving the hotel complex. The system has a nominal cooling capacity of 2.4 MW. Cold seawater is drawn from a depth of 960 m through a 2618 m HDPE intake pipeline, consisting of a 1618 m section with a diameter of 383 mm, followed by a 1000 m section with a diameter of 368 mm leading to the technical room. The reject pipeline measures 865 m and discharges seawater at a depth of 30 m. Each heat exchanger has a surface area of 207 m², a nominal thermal capacity of 1201.7 kW, and a design flow rate of 210 m³/h. The overall heat transfer coefficient is 900 W.m⁻².K⁻¹, with nominal pressure drops of 0.49 bar on the seawater side and 0.458 bar on the chilled water side. Under typical conditions, inlet/outlet temperatures are 6 °C/11 °C for seawater and 7 °C/12 °C for chilled water. The three seawater pumps (316 stainless steel housing) each deliver 210 m³/h at a nominal head of 16 m, with an electrical power of 13.29 kW and 70.5 % efficiency. The secondary chilled water pumps (cast iron housing) operate at 140 m³/h, 80 m head, 45 kW, and 76.3 % efficiency. The chilled water distribution loop extends over 3 km and supplies the entire resort, including 35 villas (30 single-bedroom, 4 two-bedroom, and 1 three-bedroom), technical areas, and staff quarters. The total conditioned floor area is approximately 7000 m². The loop is divided into two branches: one supplies the villas, the other the technical and service buildings. The system also provides deep seawater to a marine laboratory operated by the Tetiaroa Society [13].

Instrumentation includes 16 PT100 Class A temperature sensors, an ultrasonic flowmeter (Krohne Optisonic 6300) on the seawater loop, a turbine flowmeter (BMETERS GSD5) on the chilled water loop, and three-phase power meters (Schneider Electric IEM3250 and AEM3555) for all pumps. Sensors were installed in February 2021, with the following manufacturer-specified uncertainties: ±0.28 °C for the full temperature acquisition chain (including sensor, wiring, and data logger), ±3 % for ultrasonic flow, ±5 % for turbine flow, and ±1 % for electrical power [13]. Sensor locations cover all critical points: inlet and outlet temperatures on both sides of the heat exchangers, loop flow rates, and power consumption of each pump. Data are collected in real

time via a central supervisory system. Based on these specifications, the combined uncertainty on the measured temperature difference (about 5 °C) is ±0.4 °C, corresponding to a relative uncertainty of approximately ±8 %. Using first-order uncertainty propagation, the overall relative uncertainty on the calculated COP values is ±9.4 % for the global COP and ±9.8 % for the primary COP.

All pumps are equipped with VFDs and can be operated in either automatic or manual mode. The primary loop is regulated by a PID controller targeting a chilled water supply temperature of 7 to 8 °C. Frequency ramps are configured for smooth transitions between 0 and 60 Hz in less than one minute. Even during low-load periods, one pump maintains a minimum flow rate at 9 Hz. When the demand exceeds the capacity of a single pump, a second pump is activated, and both pumps are reset to 15 % of their nominal speed before increasing again if required. The secondary loop is controlled via a 4-bar pressure setpoint managed by a supply-side valve. Pump controllers can also be manually overridden to adjust speed using a potentiometer over the 0 to 60 Hz range.

## 8.2. Performance indicators

The SWAC system was evaluated using performance indicators designed to enable comparison with conventional air conditioning systems. Two Coefficients of Performance (COP) were used: the primary COP and the global COP. The primary COP (COP<sub>prim</sub>) defined in Eq. (1), is the ratio of the cooling power delivered to the secondary chilled water loop to the electrical consumption of the primary seawater pumps. This indicator excludes the energy consumption of the secondary distribution network and is suitable for comparison with centralized cooling systems, such as chilled water plants or VRF systems, which also rely on a separate distribution loop. The global COP, defined in Eq. (2), includes the electrical consumption of both primary and secondary pumps and is representative of the overall performance of the system. This metric is suitable for comparison with unitary systems such as split units, which do not rely on a separate distribution loop. For Tetiaroa SWAC system, both COP values are calculated based on the cooling power delivered to the secondary chilled water loop, rather than at the building level, due to instrumentation constraints. The primary and global COP values refer to instantaneous or short-term mean performance indicators calculated over periods shorter than one year.

$$ COP_{prim} = \frac{\dot{Q}_{CW}}{\dot{W}_{SW}} \qquad (1) $$

$$ COP_{glob} = \frac{\dot{Q}_{CW}}{\dot{W}_{SW} + \dot{W}_{CW}} \qquad (2) $$

To characterize average seasonal performance, Seasonal Coefficients of Performance (SCOP) were also computed, as defined in Eqs. (3) and (4) [19]. SCOPs represent the COP over a period of at least one full year of operation, capturing system efficiency under realistic seasonal conditions. In this study, SCOP values were calculated over a three-year monitoring period. All COP and SCOP values are dimensionless.

$$ SCOP_{prim} = \frac{\int_{0}^{T} \dot{Q}_{CW} dt}{\int_{0}^{T} \dot{W}_{SW} dt} \qquad (3) $$

$$ SCOP_{glob} = \frac{\int_{0}^{T} \dot{Q}_{CW} dt}{\int_{0}^{T} \dot{W}_{SW} dt + \int_{0}^{T} \dot{W}_{CW} dt} \qquad (4) $$

In addition to energy indicators, thermal comfort in the numerical model was evaluated using the Unmet Hours (UH) metric, defined in Eq. (5) as the proportion of time during which the indoor temperature (T<sub>indoor</sub>) exceeded the setpoint temperature (T<sub>building</sub>) by more than 1 °C.

$$ UH(\%) = \frac{\sum_{t=0}^{t_{total}} \delta(T_{indoor}(t) > T_{building} + 1)_{*}}{t_{total}} \qquad (5) $$
## 8.3. SWAC numerical model

Two numerical models were developed to represent the SWAC system at Tetiaroa. The first, a decoupled model, includes a detailed representation of the SWAC system, while the cooling demand is imposed as a fixed load profile, derived from either experimental data or an external building simulation. This configuration significantly reduces simulation time and is particularly well suited for experimental validation. The second, a coupled model, integrates the SWAC system with a dynamic building simulation. It includes a detailed representation of the building's thermal behavior and accounts for heat losses along the buried chilled water pipes. This model allows for sensitivity analysis and system optimization by capturing the mutual influence between building dynamics and cooling system performance. While this coupling remains relatively weak in the case of SWAC, since outdoor conditions have limited impact on seawater temperature or pump operation, it becomes much more significant in conventional air conditioning systems, where higher outdoor temperatures directly reduce system performance.

Both models were implemented in the EnergyPlus simulation environment, in co-simulation with custom Python plugins thanks to an API. This framework allows dynamic interactions between user-defined control strategies and the system state, by executing Python scripts at predefined simulation steps. All the SWAC main components and the building are modeled using EnergyPlus [20] while Python plugins serve three main functions: (i) Heat loss estimation along the intake and reject pipelines, accounting for internal convection, external convection, and conduction through the pipe wall as described by the differential Eq. (6). (ii) Pump head calculation, based on hydraulic resistance models and defined in Eq. (7) which depend on flow rate, pipe length, diameter, and roughness. (iii) Control of chilled water flow rate, used in the coupled model for sensitivity analysis and optimization, with ideal regulation assumed to meet the cooling demand, as defined in Eq. (8).

$$ \frac{dT_{sw}}{dL} = \frac{2\pi L}{\dot{V}_{sw}c_p} U(T_{ext} - T_{sw}), \quad \text{with } U = \left(\frac{1}{h_{int}} + \frac{1}{h_{ext}} + \frac{e}{\lambda}\right)^{-1} \qquad (6) $$

$$ R = \sum_{i=1}^{n} \left( \frac{\Delta H}{\dot{V}^2} \right)_n, \quad \text{with } \Delta H = \frac{\lambda L_* v^2}{D 2g} \qquad (7) $$

$$ \dot{V}_{CW}(t) = \frac{\dot{Q}_{coll}(t)}{\rho_{CW} c_p \Delta T_{setpoint}} \qquad (8) $$

Finally, the thermodynamic characteristics of seawater depending on temperature and salinity used in the primary loop model is given by the work of Nayar [21] and Sharqawy [22].

## 8.4. Experimental validation

The SWAC model was validated using measurement data from the Tetiaroa installation collected during a continuous monitoring period in December 2021. This month was selected due to the completeness and reliability of the dataset, with no missing value. Although the detailed validation is restricted to one month of continuous monitoring, the model outputs were also confronted with three years of operational averages. The agreement between the December validation and the long-term trends provides confidence in the robustness of the modeling approach. We acknowledge, however, that extended continuous validation over multiple months would further reinforce this conclusion, and this will be pursued in future work. The available data included seawater and chilled water temperatures, flow rates in both loops, and the electrical power consumption of the pumps. Simulations and measurements were processed using an hourly timestep. For the validation, the chilled water thermal load was provided as an input to the model, replacing both the building simulation and weather data. During this period, the building's cooling demand ranged from 600 kW to 1 MW,

which is less than half the nominal capacity of the SWAC system. The installation operated under single-pump conditions throughout, making it possible to validate the model under this operational regime.

Model accuracy was evaluated by calculating residuals, defined as the difference between measured and simulated values. Validation was confirmed when residuals remained within the measurement uncertainty bounds, demonstrating that the model reliably reproduces the system's behavior under real-world conditions [23].

## 8.5. Sensitivity analysis and optimization procedure

In this part of the study, the building was simulated in "autosize" mode, allowing both the cooling coil surface area and the nominal air flow rate to adjust automatically for each tested SWAC configuration. This approach ensures that the air-side system is properly sized relative to the cooling capacity provided. Each configuration was simulated over a one-week period with a six-minute timestep, following a preliminary one-day sizing run. The optimization targeted two objectives: maximizing the global COP and minimizing the Unmet Hours. Thermal losses along the chilled water loop were also minimized to avoid oversizing the cooling coil and air flow rate in compensation for distribution inefficiencies. The parameter space explored during the screening phase covered a wide range of design and operational variables relevant to the SWAC system. The seawater intake temperature was varied from 5 °C to 12 °C, while the chilled water loop temperature difference ranged from 4 °C to 8 °C. The indoor temperature setpoint was adjusted between 24 °C and 27 °C. Material properties of the piping system, including thermal conductivity, density, and specific heat capacity, were varied by ±30 % relative to the values of the Tetiaroa installation. Pump characteristics, such as nominal flow rate, head, and electrical power, were varied within 5 % of their nominal reference values. The overall heat transfer coefficient of the heat exchangers was explored over a range of 100 to 400 kW.K⁻¹, and other heat exchangers properties such as pressure losses were also varied by ±30 % relative to Tetiaroa design. The insulation thickness of the chilled water pipes was tested between 1 cm and 4 cm, while the inner and outer diameters of both primary and secondary pipes were varied by ±30 % compared to original dimensions.

The most influential parameters were identified using the Morris method, a commonly used One-At-a-Time (OAT) screening approach. This method estimates the mean (μ*) and standard deviation (σ) of the elementary effects of each parameter, which respectively reflect its overall influence and the variability of its impact, potentially indicating non-linear behavior or interactions with other parameters. The number of required simulations is given by the Eq. (9):

$$ n_{Morris} = (k+1)*r \qquad (9) $$

In this study, the number of parameters before screening was k = 22. The number of trajectories was set to r = 20, in accordance with the recommendations of Campolongo et al. [24], resulting in a total of 460 simulations for the screening phase. The multi-objective optimization was performed on the influential parameters using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a standard evolutionary algorithm for identifying Pareto-optimal solutions. A population size of 12 was chosen to match the number of parallel simulations allowed by the available computational resources. The crossover and mutation rates were set to 100 % and 20 %, respectively. These values are consistent with best practices for small population sizes, as reported in the literature [25]. A 100 % crossover rate ensures maximum recombination between individuals, thereby avoiding premature convergence, while a 20 % mutation rate provides sufficient genetic diversity to explore the solution space without excessive randomization. Additional sensitivity checks with mutation rates between 10 and 30 % and crossover rates between 80 and 100 % confirmed that the optimization outcomes (COP and Unmet Hours) were robust to these variations. The optimization
involved 200 generations with a population size of 12, resulting in 2400 simulations. Each generation, consisting of twelve one-week simulations with a 6-minute timestep, required approximately 24 min of computation. The total computational time was around 80 h on a standard workstation (Intel i7 10th Gen, 16 GB RAM), which remained manageable thanks to parallel processing. Convergence of the Pareto front was verified by tracking the evolution of the hypervolume indicator and the stability of non-dominated solutions. After about 150 generations, improvements became negligible, confirming that the algorithm had converged.

The difference in COP value displayed between the optimized configuration obtained after the selection process and the subsequent detailed simulation is mainly due to the simulation timestep. During the optimization stage, a six-minute timestep was used to reduce computational time, whereas the optimized solution was later re-simulated with a one-minute timestep. This finer temporal resolution directly affects the pump control response time, leading to a higher COP in the detailed simulation.

Simulations were managed using the jPlus and jPlus + EA tools developed by Zhang et al. [26], which interface with EnergyPlus and enable advanced parametric analyses. jPlus + EA integrates the Morris sensitivity analysis and the NSGA-II algorithm, which both operates via the JEA engine on the ENSIMS web service platform.

## 8.6. Economic performance indicators

The economic analysis of the three cooling systems, the reference chiller, the existing Tetiaroa SWAC, and the optimized configuration, was conducted over a 30-year lifetime using standard economic indicators: the Levelized Cost of Cooling (LCOC), the Discounted Payback Period (DPP), and the Net Present Value (NPV). A discount rate of 7.7% was applied, consistent with Lazard's LCOE methodology [17]. The Levelized Cost of Cooling (LCOC) represents the discounted cost of cooling energy production over the project lifetime and is defined as:

$$LCOC = \frac{\sum_{t=0}^{n} \frac{I_t + O_t + F_t}{(1+r)^t}}{\sum_{t=0}^{n} \frac{E_t}{(1+r)^t}} \quad (10)$$

where $I_t$ is the investment cost at year t, $O_t$ the operation and maintenance cost, $F_t$ the electricity (fuel) cost, $E_t$ the cooling energy delivered, r the discount rate, and n the project lifetime (years). The Discounted Payback Period (DPP) corresponds to the time required for the cumulative discounted net cash flow to become positive, and is given by:

$$DPP = min \left\{ t \mid \sum_{i=0}^{t} \frac{R_i - C_i}{(1+r)^i} \geq 0 \right\} \quad (11)$$

where $R_i$ represents the annual savings generated by the system and $C_i$ the cumulative costs (investment and operation). Finally, the Net Present Value (NPV) quantifies the total discounted benefit over the project lifetime compared to a reference system, expressed as:

$$NPV = \sum_{t=0}^{n} \frac{R_t - C_t}{(1+r)^t} \quad (12)$$

A positive NPV indicates that the investment is economically viable compared to the reference case. These three metrics allow for a consistent comparison of cooling technologies, capturing both the investment effort and long-term operating performance.

## 8.7. Exergy balance

We assess second-law performance of the SWAC at steady state. The environment (dead state) is fixed at 26.5 °C ($T_0$). Liquids are treated as incompressible with constant heat capacity, kinetic and potential energy changes are neglected. Control volume includes seawater (SW) loop,

primary and secondary pumps, heat exchanger (HEX), chilled water (CW) loop, cooling coil, and ocean outfall mixing. The useful effect is defined at the conditioned zone as in Eq. (13); the electrical input is defined in Eq. (14); and the deep seawater resource exergy is calculated using Eqs. (15) and (16).

$$\dot{E}_{prod} = \dot{Q}_{coil} \left( \frac{T}{T_{indoor}} - 1 \right) \quad (13)$$

$$\dot{E}_{elec} = \dot{W}_{sw} + \dot{W}_{cw} \quad (14)$$

$$b(T) = c_p [(T - T_0) - T_0 \ln \left( \frac{T}{T_0} \right)] \quad (15)$$

$$\dot{E}_{resource} = \dot{m}_{sw} [b(T_{sw,in}) - b(T_{sw,out})] \quad (16)$$

All the component irreversibilities are estimated with the relations below. The finite temperature heat exchange in the heat exchanger is quantified by Eq. (17). Pump power is split into internal losses with Eq. (18) and hydraulic work defined in Eq. (19) dissipated as pipe friction. Finally, the ocean outfall is represented by a conservative mixing term in Eq. (20) which is small here because ocean surface temperature is close to the environment temperature.

$$\dot{E}_{D,HEX} = T_0 \dot{Q}_{CW} \left( \frac{1}{T_{cold}} - \frac{1}{T_{hot}} \right) \quad (17)$$

$$\dot{E}_{D,pump} = (1 - \eta_{pump}) \dot{W}_{pump} \quad (18)$$

$$\dot{E}_{D,hyd} = \eta_{pump} \dot{W}_{pump} \quad (19)$$

$$\dot{E}_{D,mix} = \dot{Q}_{reject} \left( 1 - \frac{T_0}{T_{ocean}} \right) \quad (20)$$

The cooling coil is treated explicitly to account for both sensible and latent effects. First, the exergy decrease of the chilled water stream across the coil is evaluated with Eq. (21), which represents the water-side exergy made available to cool and dehumidify the air. Second, the exergy exported with the liquid condensate is computed by Eq. (22). Finally, the total coil irreversibility is obtained from the coil control-volume exergy balance in Eq. (23): it is the portion of exergy supplied to the coil that is neither delivered as useful product for cooling the conditioned space nor carried away by the condensate.

$$\dot{E}_{CW,drop} = \dot{m}_{CW} c_p \cdot CW [(T_{out} - T_{in}) - T_0 \ln \left( \frac{T_{out}}{T_{in}} \right)] \quad (21)$$

$$\dot{E}_{cond} = \dot{m}_{cond} c_p \left[ (T_{cond} - T_0) - T_0 \ln \left( \frac{T_{cond}}{T_0} \right) \right] \quad (22)$$

$$\dot{E}_{D,coil} = \dot{E}_{CW,drop} - \dot{E}_{prod} - \dot{E}_{cond} \quad (23)$$

To conclude the second-law analysis, we verify the system exergy balance and define the overall exergy efficiency at the system boundary. The sum of components irreversibilities equals the net exergy input minus the product as in Eq. (24), and the exergy efficiency is estimated by Eq. (25). Together, these provide a balance-closure check on the component estimates and a single metric to compare the Tetiaroa SWAC to the optimized configuration on a second-law basis.

$$\dot{E}_{fuel} + \dot{E}_{resource} - \dot{E}_{prod} = \sum \dot{E}_{D} \quad (24)$$

$$\eta_{II} = \frac{\dot{E}_{prod}}{\dot{E}_{fuel} + \dot{E}_{resource}} \quad (25)$$
CRediT authorship contribution statement

**Kanhan Sanjivy:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Perceval Raybaud:** Writing – review & editing, Writing – original draft, Visualization. **Olivier Marc:** Writing – review & editing, Supervision.
**Franck Lucas:** Writing – review & editing, Supervision, Project administration, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the French Environment and Energy Management Agency (ADEME), the University of French Polynesia. The authors would like to thank the Pacific Beachcomber group and especially its CEO Richard Bailey, its manager Bruno Chevallereau and Clément Capel, for allowing us to exploit data from The Brando SWAC installation. Its instrumentation was funded as a part of the COPSWAC project by the ADEME and the Polynesian government, which we thank as well.

Data availability

The authors do not have permission to share data.

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