The main target of this research is to allow solar PV to contribute economically to an on-grid energy-efficient building where the dust accumulation is a significant factor. Self-cleaning coatings such as hydrophobic or hydrophilic materials have recently been introduced to reduce dust deposition on building-integrated PV (BIPV) panels. The hydrophilic Nano-coated material is examined as a solution to decrease the impact of the dust on the BIPV panels and harvest more solar energy. An impartial comparison of the BIPV panels performance under natural dust conditions, manual cleaning, and hydrophilic nanomaterial coating is performed. Through an exhaustive and qualitative experimental analysis, the anti-reflection and anti-static properties of the utilized Nano-coated material are examined. The experimental results show that the hydrophilic Nano-coated material significantly improves the gathered maximum output power by 18% compared to the manually wiped panel. The calculated efficiencies of the Nano-coated, manual cleaning, and dusty panels are 11%, 9%, and 6%, respectively, which highlights the future proofing of the Nano-coated solar panel. Compared to the dusty panels, the ecological and economical results show that the BIPV carbon emissions are desirably dropped by 11% while using Nano-coated PV panels and the payback period is reduced to 3.9 years, which is approximately 12.8% faster.

The main aim of present work is to investigate the dynamics of the chaotic nonlinear distributed order Lü model (DOLM). The distributed order (DO) derivative is used for describing the viscoelasticity of various technical models and materials. The modified spectral numerical method is used to evaluate the numerical solutions for DOLM. Using nonlinear feedback control and the Lyapunov direct approach, the adaptive synchronization of two chaotic distributed order models (DOMs) is presented. We state a theorem to drive analytical controllers which are used to achieve our synchronization. The DOLM is introduced as an example of DOMs to verify the validity of our analytical results. Numerical computations are displayed to show the agreement between both analytical and numerical results. The DOMs appear in many applications in engineering and physics, e.g., image encryption and electronic circuits (ECs). Based on our proposed synchronization, the encryption and decryption of color images are studied. Information entropy, visual analysis and histograms are calculated, together with the experimental results of image encryption and decryption. We design the EC of the DOLM using the Multisim circuit simulator for the first time to our knowledge. Using electronic circuit simulation, we achieved the same results for the numerical treatment of our synchronization. Other ECs can be similarly designed for other DOMs.

The use of reclaimed asphalt pavement (RAP) represents a recycling method with environmental benefits along with cost savings. RAP in new combinations of asphalt mixtures has benefits such as lowering the amount of virgin material, reducing cost and natural resources, and causing less environmental harm. In order to improve the physical and rheological characteristics of aged asphalt binders found in RAP, rejuvenators have been used. There are many types of rejuvenators for RAP binders, such as bio-oil, waste engine oil, and waste cooking oil. Foamed and emulsified asphalt have been widely used for their energy-saving and emission-reducing properties for cold mix-in-place (CIR) production. Hot in-place (HIR) recycling does not necessitate the transportation of significant amounts of new materials to the working site, there are fewer traffic noise and delays caused by cars coming and going from the work area. Finally utilizing cement to recycle the surface, base, and subgrade (full-depth reclamation (FDR)) to enhance the structural strength and durability of pavements.

Traffic management and control applications require comprehensive knowledge of traffic flow data. Typically, such information is gathered using traffic sensors, which have two basic challenges: First, it is impractical or impossible to install sensors on every arc in a network. Second, sensors do not provide direct information on origin-to-destination (O–D) demand flows. Consequently, it is essential to identify the optimal locations for deploying traffic sensors and then enhance the knowledge gained from this link flow sample to forecast the network’s traffic flow. This article presents residual neural networks—a very deep set of neural networks—to the problem for the first time. The suggested architecture reliably predicts the whole network’s O–D flows utilizing link flows, hence inverting the standard traffic assignment problem. It deduces a relevant correlation between traffic flow statistics and network topology from traffic flow characteristics. To train the proposed deep learning architecture, random synthetic flow data was generated from the historical demand data of the network. A large-scale network was used to test and confirm the model’s performance. Then, the Sioux Falls network was used to compare the results with the literature. The robustness of applying the proposed framework to this particular combined traffic flow problem was determined by maintaining superior prediction accuracy over the literature with a moderate number of traffic sensors.

The dynamic modulus (∣E*∣) of hot-mix asphalt mixes is one of the most time-consuming and labour-intensive material metrics to evaluate in the laboratory. This study introduces a novel paradigm for assessing the ∣E*∣'s most influential elements by employing widely accepted literature models. Witczak 1-37A, Witczak 1-40D, and Modified Hirsch Models are selected for analysing the asphalt dynamic modulus. First, a thorough laboratory database of Arizona State University is used to account for all major input factors, such as mixture gradation, binder qualities, volumetric properties, and testing conditions parameters, during models' validation. Second, each model's performance is evaluated using standard measures to build confidence levels in the subsequent analysis stage. Finally, with the aid of Latin Hypercube Simulation, a comprehensive global sensitivity analysis (GSA) is performed. Three unique GSA approaches are used; namely, elementary effects, variance-based, and PAWN methods, to highlight the effect of each input variable on the magnitude of ∣E*∣. Different GSA tools are strongly recommended since there is no analytical tool for validating the findings with the complex formulations of the selected mathematical models. The GSA demonstrates that the voids ratio in total mix, binder shear modulus, viscosity, phase angle, and binder quantity are the most significant factors.

An experimental and kinetic modeling study of the influence of NOx (i.e. NO2, NO and N2O) addition on the ignition behavior of methane/‘air’ mixtures is performed. Ignition delay time measurements are taken in a rapid compression machine (RCM) and in a shock tube (ST) at temperatures and pressures ranging from 900–1500 K and 1.5–3.0 MPa, respectively for equivalence ratios of 0.5–2.0 in ‘air’. The conditions chosen are relevant to spark ignition and homogeneous charge compression ignition engine operating conditions where exhaust gas recirculation can potentially add NOx to the premixed charge. The RCM measurements show that the addition of 200 ppm NO2 to the stoichiometric CH4/oxidizer mixture results in a factor of three increase in reactivity compared to the baseline case without NOx for temperatures in the range 600–1000 K. However, adding up to 1000 ppm N2O does not show any appreciable effect on the measurements. The promoting effect of NO2 was found to increase with temperature in the range 950–1150 K, while the sensitization effect decreases at higher pressures. The experimental results measured are simulated using NUIGMech1.2 comprising an updated NOx sub-chemistry in this work. A kinetic analysis indicates that the competition between the reactions ĊH3 + NO2 ↔ CH3Ȯ + NO and ĊH3 + NO2 (+M) ↔ CH3NO2 (+M), the former being a propagation reaction and the latter being a termination reaction governs NOx sensitization on CH4 ignition. Recent calculations by Matsugi and Shiina (A. Matsugi, H. Shiina, J. Phys. Chem. A. 121 (2017) 4218–4224) for the nitromethane formation reaction CH3 + NO2 (+M) ↔ CH3NO2 (+M), together with the recently calculated rate constants for HONO/HNO2 reactions significantly improve ignition delay time predictions in the temperature range 600–1000 K. Furthermore, the experiments with NO addition reveal a non-monotonous sensitization impact on CH4 ignition at lower temperatures with NO initially acting as an inhibitor at low NO concentrations and then as a promoter as NO concentrations increase in the mixture. This non-monotonous trend is attributed to the role of the chain-termination reaction ĊH3 + NO2 (+M) ↔ CH3NO2 (+M) and the impact of NO on the transition to the chain-branching steps CH2O + HȮ2 ↔ HĊO + H2O2, H2O2 (+M) ↔ ȮH + ȮH (+M), HĊO ↔ CO + Ḣ followed by CO + O2 ↔ CO2 + Ö and Ḣ + O2 ↔ Ö + ȮH. NUIGMech1.2 is systematically validated against the new ignition delay measurements taken here together with species measurements and high temperature ignition delay time data available in the literature for CH4/oxidizer mixtures diluted with NO2/N2O/NO and is observed to accurately capture the sensitization trends.

New ignition delay time (IDT) data for stoichiometric natural gas (NG) blends composed of C1–C5 n-alkanes with methane as the major component were recorded using a high pressure shock tube (HPST) at reflected shock pressures (p5) and temperatures (T5) in the range 20–30 bar and 1000–1500 K, respectively. The good agreement of the new IDT experimental data with literature data shows the reliability of the new data at the conditions investigated. Comparisons of simulations using the NUI Galway mechanism (nuigmech1.0) show very good agreement with the new experimental results and with the existing data available in the literature. Empirical IDT correlation equations have been developed through multiple linear regression analyses for these C1–C5 n-alkane/air mixtures using constant volume IDT simulations in the pressure range pC = 10–50 bar, at temperatures TC = 950–2000 K, and in the equivalence ratio (φ) range 0.3–3.0. Moreover, a global correlation equation is developed using nuigmech1.0 to predict the IDTs for these NG mixtures and other relevant data available in the literature. The correlation expression utilized in this study employs a traditional Arrhenius rate form including dependencies on the individual fuel fraction, TC, φ, and pC.

Allene and propyne are important intermediates in the pyrolysis and oxidation of higher hydrocarbon fuels, and they are also a major source of propargyl radical formation, which can recombine into different C6H6 isomers and finally produce soot. In a prior work (Panigrahy et al., “A comprehensive experimental and improved kinetic modeling study on the pyrolysis and oxidation of propyne”, Proc. Combust. Inst 38 (2021)), the pyrolysis, ignition , and laminar flame speed of propyne were investigated. To understand the kinetic features of initial fuel breakdown and oxidation of the two C3H4 isomers, new measurements for allene pyrolysis and oxidation are conducted in the present paper at the same operating conditions as those studied previously for propyne. Ignition delay times of allene are measured using a high-pressure shock tube and a heated twin-opposed piston rapid compression machine in the temperature range 690–1450 K at equivalence ratios of 0.5, 1.0 and 2.0 in ‘air’, and at pressures of 10 and 30 bar. Pyrolysis species measurements of allene and propyne are also performed using a gas chromatography integrated single-pulse shock tube in the temperature range 1000–1700 K at pressure of 2 and 5 bar. Furthermore, laminar flame speeds of allene are measured at elevated gas temperatures of 373 K at pressures of 1 and 2 bar for a wide range of equivalence ratios from 0.6 to 1.5. A newly updated kinetic mechanism developed for this study is the first model that can well reproduce all of the experimental results for both allene and propyne. It is observed that in the pyrolysis process, allene dissociates faster than propyne. Both isomers exhibit similar ignition delay times at high temperatures (>1000 K), while, at intermediate temperatures (770–1000 K) propyne is the faster to ignite, and at lower temperatures (< 770 K) allene becomes more reactive. Furthermore, laminar flame speeds for propyne are found to be slightly faster than those for allene under the conditions studied in this work.