Search results for: Anil H. Dhanawade

Authors: Dinesh Munjurulimana, Anil Tiwari, Tingwen Li, Carlos Pereira, Sreekanth Pannala, John Waters

Abstract:

With the rapid transition to electric vehicles (EVs) across the globe, car manufacturers are in need of integrated and lightweight solutions for the battery packs of these vehicles. An integral part of a battery pack is the battery tray, which constitutes a significant portion of the pack’s overall weight. Based on the functional requirements, cost targets, and packaging space available, a range of materials –from metals, composites, and plastics– are often used to develop these battery trays. This paper considers the design and development of integrated thermoplastic-intensive battery trays, using the available packaging space from a representative EV battery pack. Presented as a proposed alternative are multiple concepts to integrate several connected systems such as cooling plates and underbody impact protection parts of a multi-piece incumbent battery pack. The resulting digital prototype was evaluated for several mechanical performance measures such as mechanical shock, drop, crush resistance, modal analysis, and torsional stiffness. The performance of this alternative design is then compared with the incumbent solution. In addition, insights are gleaned into how these novel approaches can be optimized to meet or exceed the performance of incumbent designs. Preliminary manufacturing feasibility of the optimal solution using injection molding and other commonly used manufacturing methods for thermoplastics is briefly explained. Then numerical and analytical evaluations are performed to show a representative Pareto front of cost vs. volume of the production parts. The proposed solution is observed to offer weight savings of up to 40% on a component level and part elimination of up to two systems in the battery pack of a typical battery EV while offering the potential to meet the required performance measures highlighted above. These conceptual solutions are also observed to potentially offer secondary benefits such as improved thermal and electrical isolations and be able to achieve complex geometrical features, thus demonstrating the ability to use the complete packaging space available in the vehicle platform considered. The detailed study presented in this paper serves as a valuable reference for researches across the globe working on the development of EV battery packs – especially those with an interest in the potential of employing alternate solutions as part of a mixed-material system to help capture untapped opportunities to optimize performance and meet critical application requirements.

Keywords: thermoplastics, lightweighting, part integration, electric vehicle battery packs

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Authors: Anil Saini, Jatinder Kumar Ratan

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Now a day, the key issue for the scientific community is to devise the innovative technologies for sustainable control of urban pollution. In urban cities, a large surface area of the masonry structures, buildings, and pavements is exposed to the open environment, which may be utilized for the control of air pollution, if it is built from the photocatalytically active cement-based constructional materials such as concrete, mortars, paints, and blocks, etc. The photocatalytically active cement is formulated by incorporating a photocatalyst in the cement matrix, and such cement is generally known as self-cleaning cement In the literature, self-cleaning cement has been synthesized by incorporating nanosized-TiO₂ (n-TiO₂) as a photocatalyst in the formulation of the cement. However, the utilization of n-TiO₂ for the formulation of self-cleaning cement has the drawbacks of nano-toxicity, higher cost, and agglomeration as far as the commercial production and applications are concerned. The use of microsized-TiO₂ (m-TiO₂) in place of n-TiO₂ for the commercial manufacture of self-cleaning cement could avoid the above-mentioned problems. However, m-TiO₂ is less photocatalytically active as compared to n- TiO₂ due to smaller surface area, higher band gap, and increased recombination rate. As such, the use of m-TiO₂ in the formulation of self-cleaning cement may lead to a reduction in photocatalytic activity, thus, reducing the self-cleaning, depolluting, and antimicrobial abilities of the resultant cement material. So improvement in the photoactivity of m-TiO₂ based self-cleaning cement is the key issue for its practical applications in the present scenario. The current work proposes the use of surface-fluorinated m-TiO₂ for the formulation of self-cleaning cement to enhance its photocatalytic activity. The calcined dolomite, a constructional material, has also been utilized as co-adsorbent along with the surface-fluorinated m-TiO₂ in the formulation of self-cleaning cement to enhance the photocatalytic performance. The surface-fluorinated m-TiO₂, calcined dolomite, and the formulated self-cleaning cement were characterized using diffuse reflectance spectroscopy (DRS), X-ray diffraction analysis (XRD), field emission-scanning electron microscopy (FE-SEM), energy dispersive x-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), BET (Brunauer–Emmett–Teller) surface area, and energy dispersive X-ray fluorescence spectrometry (EDXRF). The self-cleaning property of the as-prepared self-cleaning cement was evaluated using the methylene blue (MB) test. The depolluting ability of the formulated self-cleaning cement was assessed through a continuous NOX removal test. The antimicrobial activity of the self-cleaning cement was appraised using the method of the zone of inhibition. The as-prepared self-cleaning cement obtained by uniform mixing of 87% clinker, 10% calcined dolomite, and 3% surface-fluorinated m-TiO₂ showed a remarkable self-cleaning property by providing 53.9% degradation of the coated MB dye. The self-cleaning cement also depicted a noteworthy depolluting ability by removing 5.5% of NOx from the air. The inactivation of B. subtiltis bacteria in the presence of light confirmed the significant antimicrobial property of the formulated self-cleaning cement. The self-cleaning, depolluting, and antimicrobial results are attributed to the synergetic effect of surface-fluorinated m-TiO₂ and calcined dolomite in the cement matrix. The present study opens an idea and route for further research for acile and economical formulation of self-cleaning cement.

Keywords: microsized-titanium dioxide (m-TiO₂), self-cleaning cement, photocatalysis, surface-fluorination

Procedia PDF Downloads 170

Authors: Anil Kumar, Rajnish Kaur, Mateusz Czyzycki, Alessandro Migilori, Andreas Germanos Karydas, Sanjiv Puri

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The radiative decay of Li(i=1-3) sub-shell vacancies produced through photoionization results in production of the characteristic emission spectrum comprising several X-ray lines, whereas non-radiative vacancy decay results in Auger electron spectrum. Accurate reliable data on the Li(i=1-3) sub-shell X-ray production (XRP) cross sections is of considerable importance for investigation of atomic inner-shell ionization processes as well as for quantitative elemental analysis of different types of samples employing the energy dispersive X-ray fluorescence (EDXRF) analysis technique. At incident photon energies in vicinity of the absorption edge energies of an element, the many body effects including the electron correlation, core relaxation, inter-channel coupling and post-collision interactions become significant in the photoionization of atomic inner-shells. Further, in case of compounds, the characteristic emission spectrum of the specific element is expected to get influenced by the chemical environment (coordination number, oxidation state, nature of ligand/functional groups attached to central atom, etc.). These chemical effects on L X-ray fluorescence parameters have been investigated by performing the measurements at incident photon energies much higher than the Li(i=1-3) sub-shell absorption edge energies using EDXRF spectrometers. In the present work, the cross sections for production of the Lk(k= γ2,3, γ4) X-rays have been measured for some compounds of 66Dy, namely, Dy2O3, Dy2(CO3)3, Dy2(SO4)3.8H2O, DyI2 and Dy metal by tuning the incident photon energies few eV above the L1 absorption-edge energy in order to investigate the influence of chemical effects on these cross sections in presence of the many body effects which become significant at photon energies close to the absorption-edge energies. The present measurements have been performed under vacuum at the IAEA end-station of the X-ray fluorescence beam line (10.1L) of ELETTRA synchrotron radiation facility (Trieste, Italy) using self-supporting pressed pellet targets (1.3 cm diameter, nominal thicknesses ~ 176 mg/cm2) of 66Dy compounds (procured from Sigma Aldrich) and a metallic foil of 66Dy (nominal thickness ~ 3.9 mg/cm2, procured from Good Fellow, UK). The present measured cross sections have been compared with theoretical values calculated using the Dirac-Hartree-Slater(DHS) model based fluorescence and Coster-Kronig yields, Dirac-Fock(DF) model based X-ray emission rates and two sets of L1 sub-shell photoionization cross sections based on the non-relativistic Hartree-Fock-Slater(HFS) model and those deduced from the self-consistent Dirac-Hartree-Fock(DHF) model based total photoionization cross sections. The present measured XRP cross sections for 66Dy as well as for its compounds for the L2,3 and L4 X-rays, are found to be higher by ~14-36% than the two calculated set values. It is worth to be mentioned that L2,3 and L4 X-ray lines are originated by filling up of the L1 sub-shell vacancies by the outer sub-shell (N2,3 and O2,3) electrons which are much more sensitive to the chemical environment around the central atom. The present observed differences between measured and theoretical values are expected due to combined influence of the many-body effects and the chemical effects.

Keywords: chemical effects, L X-ray production cross sections, Many body effects, Synchrotron radiation

Procedia PDF Downloads 132

Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

Procedia PDF Downloads 42