Long-Term Thermal Stability of Aerogel and Basalt Fiber Pipeline Insulation Under Simulated Atmospheric Aging

Abstract

Thermal insulation materials used in power and industrial systems must maintain high performance under extreme environmental conditions. Among such materials, aerogel and basalt fiber are widely applied due to their low thermal conductivity and ease of installation. However, over time, these materials are susceptible to degradation, which can significantly impair their insulating efficiency and increase energy losses. Despite their importance, the long-term behavior of these materials under realistic climatic stressors has not been analyzed enough. This study investigates the degradation of thermal insulation performance in aerogel and basalt fiber materials subjected to complex atmospheric stressors, simulating long-term outdoor exposure. Aerogel and basalt fiber mats were tested under accelerated aging conditions using an artificial weather chamber equipped with xenon lamps to replicate full-spectrum solar radiation, high humidity, and elevated temperatures. The results show that the thermal conductivity of aerogel remained stable, indicating excellent durability under environmental stress. In contrast, basalt fiber insulation exhibited a deterioration in thermal performance, with a 9–11% increase in thermal conductivity, corresponding to reduced thermal resistance. Computational modeling using COMSOL Multiphysics confirmed that aerogel insulation outperforms basalt fiber, especially at temperatures exceeding 200 °C, offering better heat retention with thinner layers. These findings suggest aerogel-based materials are more suitable for long-term thermal insulation of high-temperature pipelines and industrial equipment.

1. Introduction

The power engineering and industrial sectors have increasingly become a focal point for improving energy efficiency, driven by rising energy costs and the urgent need to mitigate their environmental impacts [1,2,3]. Specifically, the processes of pipe installation and insulation in these sectors are recognized as both technically demanding and economically intensive [4].

In this context, reducing thermal losses in piping networks is a key strategy for enhancing the energy efficiency of both building services and power generation systems and lowering the carbon footprint of these systems [5,6,7]. Thermal insulation, in particular, plays a crucial role in this process, serving as an essential mechanism for reducing energy consumption in both buildings and industrial facilities [8]. It effectively minimizes thermal losses during the distribution of heating loads, thereby enhancing the overall energy performance and reducing unnecessary energy waste. Consequently, insulation is increasingly recognized as a key component in promoting environmental sustainability and optimizing energy efficiency in contemporary urban infrastructure and complex energy systems [9,10].

Numerous studies emphasized the significant contribution of thermally optimized piping infrastructure, particularly when integrated with high-performance insulation materials and advanced energy management systems, to reducing carbon footprints [9,11].

Thermal insulation improves energy preservation and plays a pivotal role in condensation control, acoustic insulation, enhanced fire resistance, and improved operational safety [4,12]. Therefore, optimizing insulation performance is essential for maintaining thermal stability within the pipe, minimizing heat exchange with the surrounding environment, and preserving the structural and functional integrity of the system under variable thermal loads [11,13].

Furthermore, insulation is crucial in preventing surface condensation during ambient temperature fluctuations. If left unmitigated, this can lead to external corrosion and compromise the durability of the pipeline [14,15]. From a safety perspective, insulation helps to keep surface temperatures within safe limits, thereby reducing the risk of thermal injuries from accidental contact with hot pipes [16].

A key factor of thermal insulation performance is represented by the thermal conductivity of the insulation material, which determines the thickness of the thermal insulation layer and heat loss from the insulated object, influencing the insulation material’s ability to address these aspects [17,18].

Despite advancements in materials science, the real-world performance of insulation materials often deviates from laboratory predictions due to the aging effects of environmental stressors [19]. The thermal performance of insulated piping systems may deteriorate over time as a result of material degradation, corrosion of pipe surfaces, and mechanical damage to the thermal insulation layer. These factors collectively contribute to increased thermal conductivity and reduced insulation effectiveness, thereby compromising the system’s overall energy efficiency [5,14]. Additionally, continuous exposure to harsh environmental conditions can exacerbate this degradation, further diminishing the material’s thermal insulation performance and long-term effectiveness in operational settings [20,21].

Studies by Liu et al. [22] and Sun et al. [23] underscore the operational importance of maintaining effective insulation in steam and oil pipeline systems, highlighting how the insufficient thermal insulation performance occurring during the long-term life analysis of the insulation increases the temperature losses per unit length of the pipeline.

As piping systems age, their thermal insulation performance declines progressively due to material degradation, mechanical wear, and prolonged exposure to environmental stressors, resulting in increased heat loss and reduced energy efficiency [16]. Empirical studies have demonstrated that the deterioration of insulation materials and physical aging contribute to a measurable decrease in thermal resistance (R-value), thereby amplifying heat transfer to the surrounding environment and elevating the energy consumption of heating systems [5,24].

The deterioration of insulation in aged infrastructure necessitates more frequent maintenance and replacement interventions, which, in turn, increase operational costs and complicate energy management strategies [21]. Therefore, the selection of appropriate insulation materials is critical for mitigating the aging of piping systems [25].

Historically, several types of insulation materials have been commonly employed for thermal protection in piping systems, including mineral wool, foam rubber wool, super insulation materials, and polyurethane foam [12,26,27]. Among the most recent promising heat pipeline insulation materials, aerogel [28,29] and basalt fiber [30] mats are increasingly being used for power system applications. Several investigations have examined aerogel performance under controlled conditions to evaluate its behavior under external stress. For instance, Dai et al. [31] developed predictive models for thermal conductivity in silica aerogel under several heating tests, highlighting how the thermal insulation performance of aerogel materials is markedly influenced by their semi-transparent nature and the predicted thermal conductivity values closely align with those obtained through alternative instrumental measurements, even at temperatures reaching up to 700 °C.

Parracha et al. [32] evaluated the durability of innovative multilayer insulation materials using polystyrene foam and aerogel under hydrothermal artificial aging for one year. The results of the study showed a significant increase in capillary water absorption after accelerated aging. This was associated with a noticeable deterioration of the materials’ characteristics against the background of extensive microcracking and, in some cases, disintegration with the formation of surface voids. It has been shown that, in the case of water penetration into the cracks, there is a significant deterioration of the thermal insulation properties of materials.

Along the same line, Kovács et al. [29] conducted a comparative analysis of two commercial aerogel grades, Slentex and Pyrogel, subjected to annealing and thermal aging at 150 °C and 250 °C for 24 h. The results demonstrated significant degradation in thermal conductivity and specific heat capacity for Pyrogel, while Slentex exhibited negligible change.

Additional studies [33,34] demonstrate that, while aerogel generally exhibits excellent resistance to radiation and humidity, some deterioration can occur, particularly in granular forms or under extended exposure. Meanwhile, aerogel composites have shown promise in maintaining structural and thermal properties, especially when reinforced or combined with protective coatings [35,36].

Basalt fiber composite, produced by melting and extruding naturally occurring volcanic rock, has gained increasing attention as an advanced insulation material for pipeline systems [30]. Unlike conventional synthetic fibers, basalt fiber does not require chemical binders or hazardous additives during fabrication, rendering it non-toxic, recyclable, and aligned with green manufacturing principles. It exhibits a broad operational temperature range, typically from −260 °C to +800 °C, making it highly suitable for thermally demanding applications such as oil and gas transport, district heating, and high-temperature industrial pipelines [37]. Empirical studies have confirmed that basalt fiber composites maintain flexural stiffness and strength over extended service periods [38]. However, their in-plane shear performance may diminish upon prolonged exposure to moisture, indicating the need for appropriate protective coatings or hydrophobic treatments in humid conditions [39,40].

Although these studies provide valuable insights, they often focus on specific factors in isolation or do not extend to real-world simulation of multiple stressors.

The current lack of comparative, multi-factorial studies that assess both traditional and advanced insulation materials using accelerated weathering techniques represents a critical gap. This research aims to fill that void by applying a combined experimental and computational approach to analyze how aerogel and basalt fiber insulation materials respond to a simulated 20-year environmental exposure cycle under the influence of complex atmospheric stressors, including UV radiation, high humidity, and elevated temperatures.

A combined methodology was developed to investigate the long-term thermal behavior of the material, integrating advanced numerical simulation with accelerated laboratory testing. Finite element analysis using COMSOL Multiphysics (https://www.comsol.com/comsol-multiphysics (access date, 14 October 2024)) was employed to model heat transfer and temperature distribution within the material under various thermal loading conditions representative of prolonged service exposure. This simulation framework enabled the prediction of thermal degradation patterns and aimed to develop and apply mathematical models that allow for analyzing the heat transfer processes under different operating conditions, including the influence of humidity, ambient temperature, and characteristics of thermal insulation materials [41,42].

To validate and complement the numerical results, accelerated thermal aging tests were conducted in a climate-controlled weather chamber. The tested materials were subjected to elevated temperatures under tightly regulated conditions, replicating the thermal stress experienced during extended operational lifetimes within a condensed testing period.

This integrated approach offers significant advantages over conventional thermal aging assessments [19,43,44]. Combining high-resolution thermal simulations with empirical validation allows for faster and more accurate prediction of long-term thermal performance. Furthermore, it provides deeper insight into the underlying mechanisms of thermal degradation, thereby improving the reliability of material selection and design for thermally demanding applications [45,46].

2. Materials and Methods

2.1. Laboratory Test

This study investigated the durability and thermal performance of two types of basalt fiber mats and one aerogel-based insulation material under simulated climatic aging conditions. The aim was to quantify changes in thermal conductivity and assess material degradation due to long-term environmental exposure.

Three types of thermal insulation mats were evaluated: two composed of basalt fiber and one incorporating aerogel [37,47]. The first basalt fiber sample (BF1) was fabricated from superfine basalt fibers supplied by Korda Ltd. (St. Petersburg, Russia) and subjected to thermal aging at 250 °C, while the second (BF2) underwent aging at 1100 °C. Both basalt-based specimens consisted of randomly oriented fibers bonded through intrinsic interlocking forces, forming a nonwoven structure without the use of chemical binders.

The third sample (AG) was an aerogel-based mat aged at 250 °C, specifically the Alison Aerogel Blanket (Guangdong Alison Hi-Tech Co., Ltd., Yingde, China), which consists of a roll-to-roll flexible thermal insulation composite designed for high-temperature applications. This material incorporates silica-based aerogel embedded within a protective thermal sleeve to enhance mechanical stability and handling performance. All samples were exposed to a combination of simulated adverse environmental conditions using a TBT-XLW-150A artificial weathering chamber (Nanjing T-Bota Scietech Instruments & Equipment Co., Ltd., Nanjing, China). This apparatus was equipped with xenon arc lamps that provide full-spectrum solar radiation, enabling the assessment of material performance under accelerated aging scenarios that mimic natural weathering processes (Figure 1).

To evaluate the long-term performance characteristics of thermal insulation samples, a comprehensive series of laboratory experiments employing accelerated aging techniques was conducted. These controlled experiments enabled the extrapolation of the material’s long-term properties within a significantly reduced timeframe compared to natural aging processes. A critical component of the experimental design involved the careful selection of aging conditions and the scheduling of periodic measurements. The applied stressors in each accelerated aging regime were deliberately intensified to hasten the degradation mechanisms within a practical duration of testing. However, these stress levels were meticulously calibrated to remain within thresholds that preserve the relevance and validity of the results.

Specifically, the conditions were chosen to avoid inducing failure modes or chemical alterations that would not plausibly occur under normal service environments—for instance, unrealistically high ultraviolet (UV) radiation levels might prompt chemical reactions that are not representative of actual long-term exposure scenarios. The theoretical basis of the calculations was provided by the Arrhenius equation, the Coffin–Manson relation, and the Peck model [48,49]. These models provided a framework on which to base the description of material degradation kinetics and fatigue behavior under accelerated aging conditions.

The calculation of the equivalent service life was based on general equations for complex aging, such as Equation (1):

$$A{F}_{UV+t/RH}={\displaystyle \frac{1}{3}}\left(A{F}_{T1}\times A{F}_H\right)+{\displaystyle \frac{2}{3}}\left(A{F}_{T2}\times A{F}_{UV}\right)$$

(1)

where $A{F}_{UV+t/RH}$ is the combined acceleration factor of ultraviolet (UV) radiation, temperature, and humidity;

$A{F}_{T1}$ is the temperature acceleration factor during the 4 h test cycle when only high temperature and humidity were observed;

$A{F}_H$ is the moisture acceleration coefficient;

$A{F}_{T2}$ is the temperature acceleration coefficient during an 8 h test cycle when only temperature and UV radiation were applied;

$A{F}_{UV}$ is the UV acceleration coefficient.

In order to establish the relationship between the aging test conditions and typical operating conditions, the acceleration coefficient was calculated using the Arrhenius equation (Equation (2)) [50,51]. This equation relates the holding time at the experimental temperature and the temperature of the operating condition as follows:

$${AF}_T=e^{-{\displaystyle \frac{E_A}{K}}·\left({\displaystyle \frac{1}{T_A}}-{\displaystyle \frac{1}{T_U}}\right)}$$

(2)

where AF_T is the acceleration factor due to the stressed temperature, E_A is the activation energy of the failure mechanism, K is the universal gas constant (8314 J/(mol∙K)), T_A is the accelerated temperature in Kelvin, and T_U is the use condition temperature in Kelvin.

The acceleration coefficient to combined aging for high temperature and RH levels was calculated based on the Peck model. This is a two-factor model based on temperature and relative humidity. Therefore, the effect of each of the stresses on the properties of the samples is investigated, and the aging test is modeled as a combination of two different aging processes following Equation (3):

$${AF}_{TH}={AF}_T·{AF}_H$$

(3)

where AF_H is the humidity acceleration factor (Equation (4)):

$${AF}_H={\left({\displaystyle \frac{{RH}_A}{{RH}_U}}\right)}^m$$

(4)

where RH_A is the relative humidity of the test (100%), RH_U is the relative humidity in natural outdoor climate conditions (40%), and m is a humidity constant, which assumed the value of 2.66.

Finally, the accelerated aging coefficient for UV radiation was obtained using Equation (5), as follows:

$${AF}_{UV}={\displaystyle \frac{{\mathsf{\Phi }}_A}{{\mathsf{\Phi }}_U}}$$

(5)

where Φ_A is the total UV energy during accelerated aging, and Φ_U is the UV radiation during natural aging outdoors. To ensure accurate simulation of environmental aging, regional climatic parameters must be taken into account when determining the ultraviolet (UV) acceleration factor. As this study focuses on the operational performance of thermal insulation mats in St. Petersburg’s specific climatic conditions, the total solar radiation at the study site was considered according to

Table 1. The total solar radiation in St. Petersburg by months with account of cloudiness [MJ/m²].

	January	February	March	April	May	June	July	August	September	October	November	December
Clear sky	70	169	396	617	846	910	877	684	446	239	97	39
Cloudy sky	21	71	214	331	515	578	545	394	230	92	25	8

.

The total solar radiation received by the samples for the year, taking into account cloudiness, was 3024 MJ/m², and the average hourly exposure to solar radiation was 95.89 W/m². The results of the calculations for the coefficients of acceleration of aging under UV exposure, high temperatures, and high relative humidity can be found in

Table 2. Aging acceleration coefficients.

Coefficients	Value
AFT1	35.35
AFT2	75.54
AFH	11.44
AFUV	12.51
AFUV+t/RH	764.8

.

According to Table 2, the required testing time in the weatherometer, equivalent to 20 years of natural outdoor exposure as required by ASTM G154 [52], can be calculated as in Equation (6):

$$L_A={\displaystyle \frac{L_U}{{AF}_{UV+T/RH}}}$$

(6)

where L_A is the life at the accelerated level, and L_U is the life at the use stress level. The resulting testing time is 229.1 h, which is equal to 19.1 cycles (12 h each) in the aging test chamber. The test cycle used is shown in Figure 2.

The tests included cycles with high levels of ultraviolet radiation alternating with high temperature and humidity levels. The testing protocol was based on a 12 h cyclic exposure regimen, comprising alternating periods of simulated solar radiation at elevated temperatures and high-humidity conditions combined with thermal stress. This protocol was selected for several reasons: (i) it adheres to the standardized procedures outlined in ASTM G154; (ii) it has been employed in comparable studies by other researchers; and (iii) it enables direct comparison with previously reported results on aerogel-based insulation mats evaluated under identical conditions using the same weathering equipment [48].

Specifically, each cycle consisted of 8 h of exposure to full-spectrum solar radiation generated by xenon arc lamps, with the black panel temperature maintained at 65 ± 3 °C. This was followed by a 4 h exposure to a high-humidity environment (relative humidity ≈ 100%) at a reduced black panel temperature of 55 ± 3 °C. This 12 h cycle was repeated continuously for a total of 20 cycles, a duration that has been calibrated to approximate the equivalent of 20 years of natural environmental aging for outdoor applications.

To measure thermal conductivity at different exposure times, samples were removed from the weathering chamber, immediately wrapped in a thin plastic film to maintain humidity, and kept at room temperature for several hours to achieve a stable humidity level.

To evaluate the effect of combined climatic stressors on the thermal insulation performance of the samples, the thermal conductivity coefficient was measured both before and after the aging tests. These measurements were carried out using the ITS-1 “150” thermal conductivity measurement unit (InterPribor, Chelyabinsk Russia), which offers a measurement accuracy with an error margin of less than 5%. The thermal conductivity was measured according to the ISO 8301:1991 [53], which has established methods for determining thermal resistance and effective thermal conductivity using a device equipped with a heat meter and a device with a hot guard zone based on the steady-state heat flow method.

The mass of the specimens was also measured before the test. After the test was completed, the specimens were dried at an elevated temperature (98 ± 3 °C) until a constant mass was reached, and then, the thermal conductivity of the specimens was measured again. The mass of each sample was then measured using the BM2202 scales, which have a standard deviation lower than 15 milligrams (OKB Vesta, Saint-Petersburg, Russia). Three samples of each type were tested (Figure 3).

2.2. Numerical Simulation

To complement experimental findings, heat transfer modeling was conducted using COMSOL Multiphysics^® (Figure 4). Modeling in the COMSOL program was carried out based on data from field experiments [37,47]. Three insulation configurations were simulated: a 100 mm basalt fiber layer (BF3), a 10 mm aerogel composite, and a 12 mm aerogel-based multilayer cover with a glass fiber shell. Each was modeled under steady-state conditions at pipe surface temperatures of 100, 200, 300, and 400 °C to determine the external surface temperatures of the insulation and evaluate their thermal efficiency under operational heat loads.

Thermal modeling in COMSOL Multiphysics was conducted using input parameters derived from field experimental data to ensure realistic simulation conditions and material behavior. The measurement results were evaluated by conducting experimental studies on one local control area. The values of the surface heat flux density and operational temperatures, considering a steel pipe (λ = 55 W/m·K) with a diameter of 0.20 m and a length of 0.60 m, were used as input data. Heat flux values, measured under steady-state thermal conditions, were used to calibrate the model for different materials at elevated temperatures (100 °C, 200 °C, 300 °C, and 400 °C). For the aerogel composite sample, the average heat flux values recorded by the heat flow sensor were 95 W/m² at 100 °C, 182 W/m² at 200 °C, 380 W/m² at 300 °C, and 600 W/m² at 400 °C. In comparison, the basalt superfine fiber (BSF) sample exhibited slightly higher heat flux values at corresponding temperatures, with 104 W/m² at 100 °C, 182 W/m² at 200 °C, 411 W/m² at 300 °C, and 732 W/m² at 400 °C. For the aerogel-based thermal cover, designed for insulation applications, the heat flux readings were notably lower at most temperatures, indicating better insulating performance. The average values were 86 W/m² at 100 °C, 149 W/m² at 200 °C, 344 W/m² at 300 °C, and 616 W/m² at 400 °C. The Grubbs criterion was used to eliminate gross errors. When calculating the Grubbs criteria G₁ and G₂, it is assumed that the largest x_max or smallest x_min result will be caused by gross errors, according to Equation (7):

$$G_1={\displaystyle \frac{\left|x_{\max }-\overline{x}\right|}{S}},G_2={\displaystyle \frac{\left|\overline{x}-x_{\min }\right|}{S}}$$

(7)

Table 3. G1 and G2 values for the surface heat flux density.

	Temperature
G1	100 °C	200 °C	300 °C	400 °C
Aerogel	0.3435	0.300	0.260	0.3644
BF3	0.3620	0.364	0.352	0.3529
AG Cover	0.3571	0.352	0.450	0.3893
G2	100 °C	200 °C	300 °C	400 °C
Aerogel	0.8244	2.200	2.347	1.794
BF3	10.862	20.411	36.267	64.588
AG Cover	12.142	13.147	51.600	56.427

and

Table 4. G1 and G2 values for the temperature on the surface of the thermal insulation layer.

	Temperature
G1	100 °C	200 °C	300 °C	400 °C
Aerogel	0.0477	0.0307	0.0302	0.0232
BF3	0.0438	0.0082	0.0232	0.0090
AG Cover	0.0527	0.0277	0.0273	0.0209
G2	100 °C	200 °C	300 °C	400 °C
Aerogel	0.0066	0.0006	0.0004	0.0006
BF3	0.0459	0.0032	0.0118	0.0028
AG Cover	0.0657	0.0232	0.0212	0.0147

show the values of G1 and G2 for the surface heat flux density and for the temperature on the surface of the thermal insulation layer, respectively.

To identify and eliminate potential outliers in the dataset, the Grubbs test for outlier detection was applied. Specifically, the test statistics G₁ and G₂, corresponding to the maximum (x_max) and minimum (x_min) values in the dataset, were compared against the theoretical critical value G_T derived from the Grubbs criterion at a predetermined significance level q. According to the standards, G_T should be equal to 2.549, n = 15, and q ≥ 5%. The following criteria were also applied:

-

If G₁ > G_T, the maximum value x_max is considered an outlier and is excluded from the dataset.

-

If G₂ > G_T, the minimum value x_min is considered an outlier and is excluded.

Following the removal of any detected outlier, the arithmetic mean and standard deviation of the remaining data points are recalculated. The Grubbs test is then reapplied iteratively until no further outliers are identified.

-

If N = 1, the value x_max is not classified as an outlier and is retained in the dataset.

-

If N = −1, the value x_min is not classified as an outlier and is retained.

-

If G₁ ≤ G_T, x_max is retained as it does not meet the criterion for exclusion.

-

If G₂ ≤ G_T, x_min is similarly retained.

The confidence limits (ε) of the random measurement error were calculated using the following formula:

$$\epsilon =t{S}_{\overline{x}}$$

(8)

where t is the Student’s coefficient, depending on the confidence probability P and the number of measurement results, n. The following values were used in the calculation according to the analysis: $P=0.95$, $n=15$, and $t=2144$. The confidence limit of the random error, calculated for the surface density of the heat flow, is equal to 5.678, while the one for the temperature on the surface of the thermal insulation layer is equal to 0.129 (

Table 5. Confidence limits of the random error (ε).

	Temperature
ε	100 °C	200 °C	300 °C	400 °C
Aerogel	11.702	16.08	16.437	9.558
BF3	10.362	9.558	12.149	12.149
AG Cover	7.504	12.149	7.1466	11.702

).

For cylindrical objects, the coefficient of thermal conductivity of the material under study was determined by Equation (9):

$$\lambda ={\displaystyle \frac{q_L\cdot \mathit{ln}\frac{d_2}{d_1}}{2\pi \cdot \left(t_1-\left.t_2\right)\right.}}$$

(9)

where $q_L$ is the linear heat flux density [W/m]; $d_1$ the outer diameter of the pipe [m]; $d_2$ is the outer diameter of the pipe with the sample [m]; $t_1$ is the temperature on the surface of the pipe, measured using thermocouples [°C]; and $t_2$ is the temperature on the surface of the thermal insulation layer, measured using thermocouples [°C]. The recalculation of heat losses from 1 m² of the thermal insulation surface per one running meter of the length of the cylindrical layer of this insulation was performed according to Equation (10):

$$q_L=q_F\cdot \pi \cdot d_2$$

(10)

where $q_F$ is the surface heat flux density [W/m²].

Table 6. Surface heat flux density q_F and the linear heat flux q_L.

	Temperature
ql	100 °C	200 °C	300 °C	400 °C
Aerogel	64.93	125.72	262.50	414.48
BF3	72.53	125.72	283.91	505.66
AG Cover	58.71	102.92	237.63	425.53
qF	100 °C	200 °C	300 °C	400 °C
Aerogel	94	182	380	600
BF3	105	182	411	732
AG Cover	85	149	344	616

shows the surface heat flux density $q_F$ and the linear heat flux $q_L$ for each sample based on different evaluated temperatures.

The resulting thermal conductivity of the three tested samples is shown in

Table 7. Thermal conductivity λ [W/mK] for each sample class based on the operational temperature.

Thermal Conductivity	100 °C	200 °C	300°C	400 °C
Aerogel	0.015	0.014	0.017	0.020
BF3	0.016	0.036	0.079	0.193
Cover	0.014	0.014	0.015	0.020

.

These experimental results were incorporated into the COMSOL simulation as input data to model and compare the thermal performance of each material under identical thermal loading conditions (Figure 4). This approach provided a reliable basis for evaluating temperature-dependent heat transfer characteristics and for validating the numerical model against real-world behavior.

3. Results

3.1. Laboratory Test

The first phase of this study assessed the degradation of thermal performance in different insulation materials following exposure to simulated climatic stressors. The results in

Table 8. Results of thermal conductivity measurements of samples.

Sample	Aged T	Mass of the Sample Before Testing	Thermal Conductivity Coefficient (λ) Before Testing	Mass of the Sample After Testing	Thermal Conductivity Coefficient (λ) After Testing	Thermal Conductivity Coefficient (λ) Modification
		[gr]	[W/(m × K)]	[gr]	[W/(m × K)]	[%]
BF1	250 °C	78.73	0.0430 ± 0.002	78.76	0.0469 ± 0.002	9.07%
BF2	1100 °C	143	0.0393 ± 0.002	143.13	0.0436 ± 0.002	10.94%
AG	250 °C	83.07	0.0199 ± 0.001	83.15	0.0199 ± 0.001	0.00%

indicate that both basalt fiber-based samples (BF1 and BF2) experienced a measurable increase in thermal conductivity after the aging process, while the aerogel-based insulation (AG) maintained its thermal performance.

Specifically, sample BF1, aged at 250 °C, showed an increase in thermal conductivity from 0.043 W/(m·K) to 0.0469 W/(m·K), corresponding to a 9.07% degradation. Sample BF2, aged at a more extreme temperature of 1100 °C, exhibited a more pronounced increase of 10.94%, from 0.0393 W/(m·K) to 0.0436 W/(m·K). In contrast, the aerogel mat (AG) exhibited complete stability, with no detectable change in thermal conductivity (0.0199 W/(m·K) before and after testing).

This suggests a significantly higher resistance of aerogel-based materials to environmental aging compared to basalt fiber. The increase in thermal conductivity for basalt samples can be attributed to microstructural changes such as fiber embrittlement, formation of voids, and potential loss of inter-fiber bonding due to prolonged thermal and moisture exposure.

The results of the thermal conductivity coefficient make it possible to calculate the resistance of heat protection mats to heat transfer R, in (m²∙K)/W, according to Equation (11):

$$R={\displaystyle \frac{\delta }{\lambda }}$$

(11)

where $\delta$ is the thickness of the thermal insulation mat, in m. The calculation of heat transfer resistance for 13 mm thick thermal insulation mats is presented in

Table 9. Calculation of heat transfer resistance of insulating mats after exposure to climatic factors.

Sample	R Before Testing (m2 × K)/W	R After Testing (m2 × K)/W	Increase in Thermal Protection Properties
BF1	0.302	0.277	8.30%
BF2	0.331	0.298	10.00%
AG	0.653	0.653	0.00%

.

The heat transfer resistance (R-value) of each material, calculated using a standardized 13 mm thickness, further supports these findings (Table 9). After exposure, the R-values of BF1 and BF2 decreased by 8.30% and 10.00%, respectively, indicating a reduction in insulating performance.

The aerogel sample again demonstrated no decline, maintaining an R-value of 0.653 (m²·K)/W. This stability underscores the superior durability and long-term thermal insulation capability of aerogel materials, particularly in environments where sustained thermal cycling and moisture ingress are prevalent.

3.2. Numerical Simulation

In the second part of this study, computational simulations using COMSOL Multiphysics^® provided additional insights into the thermal efficiency of different insulation configurations at operational temperatures ranging from 100 °C to 400 °C. The models simulated steady-state heat flux through basalt fiber (BF3), an aerogel composite, and an aerogel-based multilayer thermal insulation cover (

Table 10. Results of thermal insulation modeling.

Thermal Insulation	Thickness [mm]	Pipe Temperature [°C]	Surface T of the Thermal Insulation [°C]
BF3	100	100	46
		200	112
		300	177
		400	253
Aerogel composite	10	100	42.9
		200	69.91
		300	98.7
		400	135.26
Aerogel-based thermal insulation cover	12	100	41.6
		200	66.35
		300	93.5
		400	125.5

).

Across all temperature conditions, the aerogel systems, especially the multilayer insulation, demonstrated better surface temperature reduction relative to the basalt fiber. At a pipe temperature of 400 °C, the surface temperature of the BF3 sample reached 253 °C, whereas the aerogel composite and aerogel cover exhibited significantly lower surface temperatures of 135.26 °C and 125.5 °C, respectively. This indicates more effective thermal barrier performance and reduced heat loss with thinner aerogel-based layers.

Furthermore, the thermal advantage of aerogel materials becomes increasingly evident at higher operating temperatures, confirming their suitability for high-temperature pipeline applications where thermal efficiency and compact insulation profiles are critical.

Based on the simulation results, it can be concluded that aerogel-based thermal insulation promotes better thermal energy storage than traditional basalt fiber-based thermal insulation.

A comparison of the results of thermal insulation modeling in COMSOL Multiphysics software to the results obtained experimentally is presented in

Table 11. Comparison of modeling results with experimental data.

	Thermal Insulation
	Basalt Fiber (BF3)				Aerogel Composite				Aerogel-Based Thermal Cover
	Operational Temperature [°C]
	100	200	300	400	100	200	300	400	100	200	300	400
	Temperature on the Surface of the Insulation Layer [°C]
Experimental data	45.75	111.8	177.4	253	42.8	69.56	98.6	135	41.8	66.25	93.67	125.7
Calculated data	46	112	177	253	42.9	69.91	98.7	135.2	41.6	66.35	93.5	125.5
Value difference	0.25	0.2	0.43	0.0	0.1	0.35	0.1	0.26	0.2	0.1	0.17	00.28

. Across all tested temperatures, the temperature differences between the experimental and simulated values were minimal, with the largest deviation being only 0.43 °C. This tight correlation confirms the accuracy and reliability of the simulation model and, by extension, its utility in predictive performance analysis for real-world applications.

This validation is particularly important as it underscores the value of computational modeling in reducing the time, cost, and complexity associated with physical prototyping, especially for long-term performance studies. The calculation of the temperature on the insulation surface when the pipe is heated at 100, 200, 300, and 400 °C showed that aerogel-based thermal insulation is more efficient than basalt fiber and provides better heat retention with a smaller insulation layer thickness. However, comparing the heat flux and temperature at the insulation surface, it can be concluded that aerogel shows better performance than basalt at temperatures above 200 °C. Therefore, the use of thermal insulation based on aerogel composites is reasonable for high-temperature heat pipes and equipment.

4. Discussion

The findings from both experimental aging and numerical simulations reinforce the strategic advantages of aerogel insulation in long-term, high-temperature applications. While basalt fiber remains a cost-effective and mechanically resilient material, its vulnerability to thermal and environmental degradation must be addressed, especially in systems exposed to severe operational conditions. The negligible degradation and superior thermal performance of aerogel-based systems, despite thinner profiles, suggest substantial benefits in reducing heat loss, improving energy efficiency, and extending service life—key priorities for industrial and urban heat distribution networks. Additionally, the alignment of simulation results with laboratory measurements validates the use of numerical modeling as a reliable tool for insulation system design and optimization.

After 20 cycles of exposure to complex climatic factors (elevated temperature, high humidity, and sunlight), the thermal conductivity of aerogel mats did not change. This coincides with the previous results of tests of aerogel mats presented in [54]. The thermal conductivity of basalt fiber mats increased by about 10% for both samples due to stress factors destroying their structure.

The unchanged mass of all samples before and after testing indicates negligible material loss due to volatilization or erosion; yet, the increase in thermal conductivity in basalt samples suggests that internal degradation (e.g., microcracking and moisture wicking) is more critical than surface wear. This is an important consideration for insulation designers, who often rely on mechanical robustness as a proxy for performance longevity [55,56].

Additionally, the results clearly demonstrate that material selection must consider both initial thermal performance and long-term stability, especially in outdoor, high-temperature, and moisture-prone environments such as steam pipelines or chemical processing equipment. Aerogel’s minimal aging response and superior insulating efficiency suggest that while the initial cost may be higher, lifecycle performance and operational energy savings make it a cost-effective solution in the long term.

While this study provides a reliable assessment of thermal performance under controlled laboratory conditions, there remain several critical limitations that should be addressed in future research to enhance the robustness and applicability of the findings. One important consideration is the impact of freeze–thaw cycles and subzero temperatures, which are commonly encountered in real-world pipeline and industrial insulation applications, particularly in colder climates. These conditions can significantly affect both the mechanical integrity and thermal performance of fibrous insulation materials, potentially leading to microcracking, delamination, and moisture retention—factors that were not captured in the current study.

Additionally, the aging model employed to estimate the equivalent service life of insulation materials is based on the assumption of linear degradation in response to the average solar radiation. This simplification likely overlooks the complex, and often nonlinear, nature of material aging, which is influenced by dynamic environmental variables such as diurnal and seasonal radiation fluctuations, temperature cycles, and humidity gradients. To improve predictive accuracy, future models should integrate time-dependent degradation kinetics and incorporate more granular climatic data, allowing for a more realistic simulation of long-term environmental exposure. To enhance the understanding of insulation material degradation, future studies should incorporate scanning electron microscopy (SEM) for detailed microcrack analysis. While this study relied on conventional imaging and surface evaluation techniques, these methods are limited in their ability to detect and characterize micro-scale cracks, which can critically impact the material’s performance. SEM offers high-resolution imaging capable of revealing fine crack networks, interfacial defects, and microstructural changes that precede macroscopic failure. Integrating SEM into future investigations would allow for a more comprehensive assessment of crack initiation and propagation mechanisms, ultimately contributing to improved material design, reliability analysis, and lifetime prediction.

Finally, the limited dimensions of the climatic chamber (300 × 300 mm) constrain the spatial representativeness of the experimental setup. This small-scale testing environment restricts the ability to replicate real-world stress distributions, structural constraints, and material behaviors that emerge in full-scale applications. To address this, future studies should consider the scaling up of sample sizes or the implementation of field-based accelerated aging protocols, which would significantly enhance the ecological validity and generalizability of the experimental results. Integrating these considerations will be essential for developing more comprehensive and reliable assessments of insulation material durability and performance under practical service conditions.

5. Conclusions

This study comprehensively examined the long-term thermal performance and aging behavior of aerogel and basalt fiber insulation materials under accelerated climatic conditions that simulated 20 years of outdoor exposure to high temperatures, humidity, and solar radiation. A combined methodology incorporating experimental weatherometer tests and numerical simulations via COMSOL Multiphysics enabled a robust comparative analysis of the materials’ thermal stability and degradation mechanisms under complex environmental stressors. The experimental findings showed that aerogel-based insulation kept a consistent thermal conductivity of 0.0199 W/(m·K) throughout the whole testing period, suggesting remarkable resistance to thermal aging and environmental degradation. By contrast, basalt fiber samples exhibited measurable deterioration, with the thermal conductivity of the BF1 and BF2 specimens increasing by 9.07% and 10.94%, respectively. This degradation was attributed to internal microstructural changes, such as fiber embrittlement and void formation, which led to an 8–10% reduction in thermal resistance (R-value). This implies a decline in insulation performance and increased energy losses over time. The mass stability of all samples confirmed that internal degradation rather than material erosion was the dominant factor affecting performance.

Numerical modeling further validated these observations, demonstrating that aerogel insulation significantly outperforms basalt fiber in limiting surface temperatures across operational ranges. At a pipe temperature of 400 °C, the aerogel-based thermal cover had a surface temperature of just 125.5 °C, compared to 253 °C for basalt fiber insulation, even though the aerogel layer was much thinner. These results confirm the superior thermal efficiency and compactness of aerogel, particularly in high-temperature applications where space constraints and thermal demands are critical.

The strong correlation between the results of the experiments and the simulations confirms the reliability of the methodology adopted for predicting the behavior of materials under real-world conditions. Aerogel composites are a highly effective solution for long-term, high-efficiency thermal insulation, particularly in industrial and energy infrastructure exposed to combined climatic stressors. Although basalt fiber offers short-term mechanical resilience, it is susceptible to thermal degradation caused by aging, which limits its suitability for an extended service life without additional protective measures.

To improve future evaluations, this study highlights the need to refine aging models to consider nonlinear responses to solar radiation and to incorporate additional environmental factors, such as freeze–thaw cycles and subzero temperatures. Integrating microscopic characterization techniques, such as scanning electron microscopy, would improve our understanding of degradation mechanisms. Additionally, a comprehensive lifecycle assessment and cost–benefit analysis would inform the strategic selection of insulation materials in sustainable infrastructure design.