The rising power density of modern electronics requires effective thermal management to maintain performance and reliability. Phase change materials (PCMs) provide passive cooling via latent heat but are limited by low thermal conductivity, which hinders their efficiency. This study investigates the application of topology optimization for designing thermally efficient fin structures in a PCM-filled domain. The PCM numerical model is validated against experimental data, ensuring code fidelity. The optimization process is conducted within a 2D finite element framework, where fins are designed on a fixed-temperature wall while maintaining the same material volume as a baseline configuration with three rectangular fins. Three objective functions - convective heat flux, diffusive heat flux, and thermal compliance - are considered using a steady-state approach. Their influence on the melting process is systematically evaluated. Then, the best performing one is used in a multi-step approach (MSTE) that resembles the unsteady PCM melting but allows to obtain performing design through an iterative process. Results demonstrate that the choice of a convective heat flux as objective accelerates the melting process by 32.1% compared to the baseline. Then, the MSTE shows that with two iterations it is possible to generate a design that fasten the melting process by 48.9% compared to the baseline. Further analysis includes comparisons of melt fraction, Nusselt number, energy storage and mean thermal power across all cases to provide valuable insights into the advantage of the MSTE approach for optimizing thermal management systems.

The breeding blanket is a key component of tokamaks, primarily responsible for extracting heat from fusion reactions and for tritium breeding, which is essential to ensure a fusion reactor’s fuel self-sufficiency. Recent technological advancements have led to the development of Dual-Cooled Lead–Lithium (DCLL) breeding blankets, which employ a liquid metal (specifically a Lead–Lithium eutectic alloy) as a heat transfer medium and tritium breeder, while helium gas is used to cool the structural components of the reactor. The interaction between the moving electrically conducting fluid and the strong magnetic field in the tokamak environment leads to magnetohydrodynamic (MHD) effects. The latter are characterized by the induction of eddy currents within the fluid and resulting Lorentz forces generated by their interaction with the magnetic field, which cause additional pressure losses and reduce heat transfer efficiency. This work investigates the pressure drop experienced by a Lead–Lithium flow within a rectangular section conduit under the action of an external, uniform magnetic field of different intensities. An analytical model was developed to estimate the total MHD-induced pressure losses along the channel for different values of the external magnetic field intensity and then benchmarked against relative computational fluid dynamics (CFD) simulations carried out using COMSOL Multiphysics. This comparison allowed the validation of the analytical predictions as well as a better understanding of the influence of the applied magnetic field intensity on the overall pressure drop. Therefore, the aim of the analytical model is to provide analytical tools for reasonably accurate estimations of MHD pressure losses suitable for future preliminary design purposes.

The decarbonization of existing building stock, in the context of climate change, is a relevant challenge for the coming years. Technologies to deal with the increasing occurrence of heat waves need to be explored, and the opaque ventilated facades (OVFs) could be a proper building system. The operating principle is the increase in temperature of the air within the cavity – thanks to the solar radiation – which results in a stack effect phenomenon. Therefore, heat is expelled from the cavity through the air in natural convection, reducing the temperature of the internal wall and consequently the cooling energy demand. Several design parameters – among which the cavity depth, the external coating emissivity, the dimensions of the façade’s components – and boundary conditions are needed to accurately model an OVF through a computational fluid dynamics (CFD) approach. In this study, a sensitivity analysis to investigate the effects that input, and configuration parameters have on the energy performance of an OVF is proposed, facilitating the evaluation of which input are more impactful. To this end, several CFD models are realized and examined, mainly evaluating the convective heat transfer coefficient in the cavity. The proposed investigation is relevant for the development of a tool able to estimate the energy impact of an OVF through machine learning techniques.

Forecasting indoor temperature and thermal loads plays a crucial role in improving energy efficiency, enabling advanced control strategies, and supporting design optimization. However, many data-driven approaches struggle to ensure reliability when applied across different climates and operational scenarios, which limits their robustness and transferability. To overcome this limitation, this study proposes a novel training procedure for nonlinear autoregressive with exogenous input (NARX) neural networks that accounts for multiple heating and cooling setpoint scenarios to maximize prediction reliability. The approach is tested on a representative office building prototype developed by ENEA (Italian national agency for new technologies, energy and sustainable development), typical of central Italy constructions from 1946 to 1970. EnergyPlus is used to perform dynamic simulations across 52 locations covering several Italian climatic zones. The resulting datasets are employed for multi-phase training of NARX networks: first under fixed setpoints, then validated and tested under different conditions. Results show strong predictive performance, with mean square errors between 0.038 and 0.14 degrees C2 for indoor temperature, and between 10-5 and 0.33 kW2 for thermal loads, and coefficients of determination consistently close to 1. By integrating climate variability and operational scenarios into the training process, the proposed method provides a versatile and accurate forecasting tool, adaptable to other buildings and contexts.

Keeping the battery temperature below a reasonable limit of 50 °C is a primary objective of battery thermal management systems (BTMSs). Accordingly, the battery available operating time (BAOT) can be defined as the time required for the battery maximum temperature to reach 50 °C, which could be adopted as a key indicator for safe and efficient operation. BAOT can be improved through different BTMS configurations. This work focuses on passive solutions, aiming to increase BAOT without requiring pumping power. The study numerically investigates the combined use of phase change materials (PCMs) and fins to evaluate their effectiveness in terms of time percentage improvement (TPI). A preliminary analysis is conducted to assess the need for PCMs and fins at three discharge rates, namely 1C, 3C, and 5C. The results indicate that PCMs are required under all operating conditions, while the use of fins is not always advantageous; in particular, at 1C, fins lead to a reduction in BAOT. The analysis then focuses on the 3C and 5C cases, where topology-optimized fins are employed to dump temperatures under these stress conditions. Three fin arc lengths (ψfin) and eight diffusion coefficients (Rf) are examined. The optimized fin configurations increase BAOT, achieving maximum TPIs of 10.61% and 7.69% for the 3C and 5C cases, respectively, both corresponding to ψfin = 2.75 mm and Rf = 0.10 mm. At 5C, BAOT is limited to only a few seconds; therefore, configurations with PCMs arranged in series are also analyzed using different combinations of four selected PCMs. When coupled with optimized fins, the PCM-in-series solutions yield further improvements, with maximum TPIs of 22.92% for 3C and 62.50% for 5C compared to the single-PCM configuration coupled with optimized fins. The results also show that the optimal diffusion coefficient and PCM arrangement strongly depend on the discharge rate.

When lithium-ion batteries operate under demanding conditions, excessive heat generation can lead to degradation or failure. An efficient battery thermal management system (BTMS) is therefore crucial for safety and optimal performance. To keep temperatures within safe limits and reduce weight, this study investigates the design of advanced BTMS for an 18650 cylindrical lithium nickel manganese cobalt oxide (Li-NMC) battery pack subjected to charge/discharge rates up to 5C. Both air and liquid cooling methods are evaluated in different configurations-natural and forced air convection, and direct and indirect liquid cooling. The integration of phase change materials (PCMs) to enhance efficiency, and topology optimization (TO) techniques to reduce the cold plate weight are also explored. Computational fluid dynamic (CFD) approaches are employed to simulate coupled heat transfer, fluid flow, and phase change phenomena starting from Bernardi's battery electro-thermal model with temperature-and state of charge (SoC)-dependent parameters. Results show that the benchmark case with air natural convection leads to temperatures above 100 degrees C, while forced air at 1 m/s maintains temperatures below 60 degrees C, though with a severe non-uniformity. Cross-flow liquid cooling keeps cell temperatures below 30 degrees C and improves uniformity (maximum temperature difference around 4.80 degrees C) with reduced pressure drop. PCM systems stabilize temperature during melting but lose effectiveness afterward, while adding aluminum fins enhances uniformity. Finally, the cross-flow cold plate TO-based design reduces system mass by 79.5% (from 127 g to 26 g) with a minimal temperature penalty of 6 degrees C compared to the standard configuration, that does not compromise the battery pack operation.

The performance of lithium-ion batteries is strongly affected by temperature. This study presents a coupled electro-thermal model of a lithium nickel manganese cobalt oxides pouch cell battery (Li-NMC), via a 2D and 3D approach for both electrical and thermal problems, respectively, validated through an experimental analysis. In particular, the model accounts for dispersive bars, which cause heat losses in both experiments and conventional use. Different fitting equations have been formulated for closure parameters, included a piecewise fitting for entropy coefficient. Convection was modelled using heat transfer correlations, providing a reproducible model with all parameters explicitly reported. Three configurations were analyzed: (i) excluding the connection bars, (ii) including them, and (iii) considering their thermal effects via a lumped capacitance method, by using an equivalent boundary condition on the battery tabs. An experimental validation was performed via thermal imaging. The model excluding bars overestimated the maximum temperature by 8.08 degrees C root mean square error (RMSE) for a 5C discharge, whereas the model including bars reduced this to 2.16 degrees C. Lumped capacitance model accurately reproduced average and maximum temperature trends, obtaining a maximum difference between the models of 0.18 degrees C for 5C discharge on the average temperature. The methodology was extended to a multi-cell battery module. Both maximum and average temperatures were very similar when replacing the busbar domain with equivalent boundary conditions, with deviations lower than 0.85 degrees C. This confirms the validity of the proposed approach in reducing computational effort while maintaining predictive accuracy at both cell and module level.

Energy renovation in historic buildings requires balancing architectural, historical, and esthetic preservation with eco-friendly and sustainable refurbishment. This study develops a novel bio-based polyurethane (bio-PUR) foam and evaluates its thermal and mechanical properties through laboratory tests, real-scale applications, and numerical simulations. Laboratory tests show that bio-PUR has higher mechanical strength than conventional PUR and comparable thermal performance, with a thermal conductivity of 0.036 W/mK, confirmed using nitrogen thermal control. A real-scale test, conducted in a laboratory at the University of Sannio in wintertime, validated the material's performance, showing only a 4% deviation from theoretical thermal transmittance values and consistent heat flux data. Numerical simulations applied bio-PUR foam in two historical buildings in Milan and Naples, comparing it to traditional insulation materials. Primary energy demand for heating is reduced in both climates, with a slight higher efficacy for traditional PUR, because of its lower thermal conductivity. Similar trends were observed in summer season. Indoor air temperature analysis revealed improved thermal stability with bio-PUR in winter, while potential overheating in summer can occur under free-running conditions. Overall, bio-based PUR foam provides competitive thermal properties and environmental benefits, resulting in a promising solution for the green, resilient, and sustainable renovation of heritage buildings.

Abstract Lithium-ion pouch cells are widely employed in energy storage systems across automotive, renewable energy, and portable electronics sectors. Despite their advantages, they remain vulnerable to thermal runaway — a critical safety concern triggered by excessive internal heat generation and insufficient dissipation. This study presents a coupled electro-thermal model to simulate temperature evolution and assess thermal runaway risk in Nickel-Manganese-Cobalt oxides (NMC) Li-ion pouch cells. The model solves charge and energy conservation equations, incorporating Joule and reversible heat generation, where the latter accounts for entropy changes during electrochemical reactions. The heat transfer mechanisms are modelled using validated correlations for natural and forced air convection. Model validation against experimental results 4C and 5C constant-current discharges showed good agreement, with root mean square errors (RMSE) of 1.17 °C and 0.84 °C, respectively. Simulations under natural convection revealed that narrow cell spacings (1–3 mm) lead to rapid heat accumulation, triggering thermal runaway in less than 0.4 h. In contrast, increasing the spacing to 8 mm delayed runaway onset to 0.61 h. Under forced convection, airflow at 4 m/s maintained peak temperatures below 35.2 °C, effectively preventing critical conditions. A 3D temperature field analysis confirmed core overheating under natural convection, with temperatures exceeding 168 °C for 1 mm spacing. These results highlight the necessity of precise thermal modeling and proper cooling strategies for thermal safety.

This study extends numerical analyses of 3D-printed cementitious walls incorporating microencapsulated phase change materials (MPCMs). Two wall configurations are examined to assess the combined effects of conduction, natural convection, and surface radiation, with particular attention to emissivity-driven radiative heat transfer. A finite-element CFD model is developed, using thermophysical properties derived from experimental data and homogenised via a three-phase Maxwell-Eucken scheme. Natural-convection predictions are validated against benchmark measurements. A parametric analysis, varying wall geometry and MPCM content, evaluates their influence on thermal performance under Mediterranean summer conditions. Key performance indicators, including periodic transmittance, temperature damping, time lag, and latent-energy storage, are computed. Results show that MPCM content is the dominant factor governing thermal evolution in time, whereas divider geometry has only a minor impact because cavity convection remains laminar and radiative exchange controls heat transfer near peak temperatures. Increasing MPCM content yields large reductions in periodic transmittance and average heat flux (up to 50% and 40%, respectively), followed by pronounced attenuation of indoor temperature oscillations and thermal-wave delays exceeding 9 h. A maximum reduction of 2 °C in peak indoor wall surface temperature is observed under severe conditions. Melt-fraction fields reveal that phase change occurs primarily in the external wall region, while interior layers contribute minimally to latent-energy cycling. This indicates that uniform MPCM distribution is suboptimal and that concentrating MPCM in the outer part of the wall, or adopting asymmetric material grading, would further enhance thermal buffering. Results demonstrate the strong potential of MPCM-enhanced 3D-printed walls to improve dynamic thermal inertia.

The energy retrofit of existing residential buildings is crucial to achieve the decarbonization targets, however, identifying optimal envelope insulation solutions is a complex challenge due to the interplay between environmental impacts, economic performance, and the different building typology and climatic conditions.This study proposes an integrated parametric framework combining dynamic energy simulation, life cycle assessment, and economic evaluation to support multi-objective decision-making in insulation retrofit design. A fully automated workflow is developed by coupling MATLAB with EnergyPlus to conduct large-scale parametric simulations. Three representative residential building archetypes, i.e., apartment block, multi-family house, and single-family house are analyzed across Italian climatic zones B to E. The framework, aiming to create a comprehensive mapping of retrofit configurations, explores combinations of several design variables: vertical and roof insulation thicknesses for five insulation materials (expanded polystyrene, rock wool, glass wool, hemp, and cork). For each scenario, operational energy consumption, life-cycle global warming potential, and net present value over a 50-year service life are assessed, incorporating three possible policy incentive schemes (0%, 50%, and 70% investment subsidies).The results highlight the strong influence of building compactness and climatic conditions on both cost- and carbon-optimal insulation. Buildings with lower surface-to-volume exhibit reduced sensitivity to insulation thickness, while detached buildings show significantly higher benefits from enhanced envelope performance. Although bio-based insulation materials present lower embodied emissions, synthetic materials, e.g., expanded polystyrene often emerge as optimal solutions when considering embodied and operational impacts. Policy incentives significantly shift the Pareto-optimal solutions, improving the economic feasibility of deeper retrofit strategies.

This work aims to explore the potential of topology optimization in the design of forced air-cooled heat sinks for inverters equipping hybrid-electric aeronautical propulsion systems. Compared to the automotive applications that are driving the electrification of the transportation sector, the design of heat sinks in aircraft requires minimizing not only the thermal resistance and pumping power of the fans, but also the weight and volume. The challenge is further made more difficult by ambient air temperature and density that vary with the aircraft's altitude. Considering the inverter of a real aircraft equipped with a hybrid electric propulsion system, the authors first designed conventional heat sinks with finned configurations by applying semi-empirical formulations and then a free-form heat sink by exploiting topology optimization. Conventional heat sinks serve as a reference for carrying out the evaluation with heat sinks based on topology optimization. The latter prove to be characterized by superior performance as the thermal resistance is up to 7 % lower, the pumping power of the fan is reduced by 34 % at the same inlet velocity and the weight saving is around 45 %. Finally, the heat sinks were verified at the system level by having their models integrated into a completed aircraft model.

Topology optimization (TO) is a design algorithm providing the optimal material layout within a design domain to minimize/maximize an objective function. In thermal science, for instance, it can be used to optimize the design of heat sinks to minimize thermal compliance, entropy generation, average temperature, etc. Recently, classical TO frameworks have been enhanced in multi-material TO in order to include more materials, thereby enhancing the degrees of freedom of the system, and thus ensuring better thermal performance. This work implements both TO and multi-material TO (MMTO) to address a benchmark heat conduction problem, i.e., the cooling of a circular heat generating volume through heat conduction paths. The heat generation is uniform in the disc, the rim is adiabatic, while the centre is set at a fixed temperature - Dirichlet boundary condition - and serves as heat sink. In TO the choice is between void and high- conductivity material, while in MMTO variable-porosity metal foams are integrated. The interpolation of the materials' thermal conductivity is conducted via an ordered solid isotropic material penalization (SIMP) algorithm. The distinction between materials is attained by setting different thresholds in the interpolation and projection functions. The dimensionless global thermal resistance and domain average temperature are alternately addressed as objective functions to be minimized at equal weight of the system. The findings unveil that MMTO outperforms TO, which outperforms constructal tree networks, considering in the latter case different configurations of different complexity.

Accurately estimating a building’s energy demand is fundamental for optimizing energy management and promoting energy efficiency. This study focuses on an in-house developed method for annual energy demand estimation, which relies on 30 key input parameters to predict energy consumption. While this approach is effective for long-term assessments, it lacks the temporal resolution required for applications that demand a more dynamic analysis, such as the creation of energy communities and smart energy systems. To address this limitation, an alternative methodology has been developed by scaling down an in-house annual estimation method and adapting it for hourly calculations, specifically for heating demand. In addition to comparing the results of the annual and hourly approaches, this study further investigates each individual contribution to heating demand—namely, heat transmission, ventilation losses, internal heat gains, and solar gains—by disaggregating them and scaling them on an hourly basis. This detailed analysis allows for a more precise representation of short-term variations in energy demand and highlights the limitations of aggregated annual estimates, to support the development of renewable energy communities, based on hour-ahead thermal and electric loads’ estimation. The findings demonstrate the advantages of a higher-resolution approach and represent the first step in developing a flexible and robust tool capable of estimating both annual and hourly energy needs. Such a tool can be particularly useful for integrating renewable energy sources, optimizing demand-side management strategies, and supporting the development of energy communities, where precise energy profiling is crucial for improving efficiency and sustainability.

This study presents a comparative Life Cycle Assessment (LCA) and economic analysis of three insulation materials — expanded polystyrene (EPS), rockwool, and hemp — applied to residential buildings in four Italian climatic zones. Environmental impact is evaluated through Global Warming Potential (GWP), while economic performance is assessed via Net Present Value (NPV) over the life cycle. An automated simulation workflow using EnergyPlus enables the assessment of multiple configurations by varying material properties, thickness, and climatic conditions. Results highlight trade-offs between environmental and economic performance. Compliance with regulatory thresholds can be achieved with varying insulation thickness depending on climate and material. Bio-based solutions like hemp may offer greater environmental benefits, although in most climates they require incentives to be cost-effective. This integrated approach provides useful insights for designers, policymakers, and stakeholders aiming to balance cost-effectiveness with sustainability in building retrofit and construction.

The development of bioclimatic technologies for energy retrofitting of building envelopes is crucial for achieving energy efficiency and sustainability in the built environment. Beyond addressing these challenges, opaque ventilated façades (OVFs) can contribute to reduce cooling energy demand, being a solution toward decarbonized buildings. The system operates by absorbing solar radiation on the external coating, which heats up and warms the air, creating a stack effect in the cavity. This natural convection phenomenon expels heat from the cavity, lowering the temperature of internal walls and subsequently reducing the building’s cooling loads. The studies on OVFs have overlooked the presence of obstructions within the cavity, which are essential. Indeed, structural connections are necessary for the mechanical anchor of the coating to the massive wall, while horizontal metallic frame serves as a support for the external cladding façade, guaranteeing stability. These obstructions introduce additional pressure drops, reducing the mass flow rate within the cavity, weakening the stack effect, so diminishing the heat expelled. Consequently, this affects the heat flux entering the building wall. This study aims to evaluate the impact of these obstructions on the thermal performance of ventilated façades using three-dimensional computational fluid dynamics (CFD) analyses. The necessity of multi-dimensional analyses arises from the need for a representation of the stack effect, for an accurate assessment of the outgoing heat flow from the cavity. Specifically, a comparison between configurations with and without obstructions in terms of cooling load and energy expelled from the cavity is presented.