Search UNB Scholar
About UNB Scholar
UNB Scholar is an institutional repository initiative of UNB Libraries intended to collect, preserve, showcase, and promote the open access scholarly output of the UNB community. Use UNB Scholar to explore specific collections, or search all content in the repository. Material submitted to the repository will also be freely discoverable online through Google and other major search engines.
UNB Libraries welcomes submissions to UNB Scholar. Find out more about depositing your publications!
Communities in UNB Scholar
Select a community to browse its collections.
Recent Submissions
Processing, microstructure, and mechanical properties of additively manufactured IN718 nickel-based superalloy
(University of New Brunswick, 2024-10) Hasani, Navid; Mohammadi, Mohsen
Inconel 718 (IN718) is a widely used superalloy in aerospace applications requiring high strength at elevated temperatures. This thesis comprehensively explores the processing, microstructure, and mechanical behavior of IN718 manufactured by wire arc additive manufacturing (WAAM) and via laser powder bed fusion (LPBF). First, WAAM-produced hybrid IN718-S275 components were investigated, examining interfacial characteristics and texture evolution. Notably, laves phase persistence was observed near the interface even after solution treatment. Neutron diffraction was used to validate the texture of the hybrid components where a strong texture parallel to the build direction in WAAM-IN718 was identified. Elastic-field models were utilized to understand dislocation mobility and Peierls-Nabarro stress, elucidating the role of heat treatment in modifying mechanical properties. Next, differential scanning calorimetry (DSC) was employed to optimize the solutionizing temperature for the complete dissolution of undesirable phases (δ and laves) in LPBF-IN718, with subsequent microstructural characterization. This led to the elimination of micro-segregation and significant Laves dissolution, resulting in a hardness comparable to wrought IN718 alloys. Furthermore, the dynamic deformation behavior of LPBF-IN718 was studied under various elevated strain rates. AMS 5664 heat treatment resulted in a remarkable 28% increase in ultimate compressive strength. Microstructural analysis revealed the presence of strengthening γ" and γ' phases in abundance, and high-density dislocation networks was observed. The influence of strain rate on grain size, texture, and adiabatic shear band formation was thoroughly investigated. The research presented in this thesis provides a comprehensive understanding of how processing techniques and post-fabrication treatments influence the microstructure and mechanical behavior of IN718. This knowledge contributes to optimizing manufacturing processes and developing tailored IN718 heat treatments for aerospace applications. Additionally, this work offers valuable insights into the mechanical response of additively manufactured IN718 under high-strain-rate loading conditions, enhancing the understanding of its performance in critical aerospace and engineering applications.
Machine learning approaches for estimating forest stand height using airborne LiDAR data in New Brunswick forests
(University of New Brunswick, 2024-10) Behroozi, Elham; Kershaw Jr., John A.
Accurate forest stand height estimation is crucial for effective forest management, ecological studies, and carbon stock assessment. Airborne LiDAR is an advanced remote sensing technology extensively used in forest inventory. This study investigates machine learning techniques for estimating forest stand height using airborne LiDAR data in New Brunswick, Canada, evaluating Linear Regression (LM), Random Forest (RF), Gradient Boosting Machine (GBM), and Support Vector Machines (SVM). Results show that Linear Regression and Gradient Boosting Machine models provide the highest accuracy, with R2s up to 0.76 and rMSEs as low as 1.06m. Conversely, the Random Forest model underperformed. This study demonstrates the value of combining high-resolution LiDAR data with machine learning models to improve forest stand height estimation accuracy, supporting sustainable forest management and conservation efforts.
Privacy-preserving data analytics in advanced metering infrastructure utilizing TEE
(University of New Brunswick, 2024-10) Kariznovi, Arash; Mandal, Kalikinkar
With the rise of the smart grid, modern electrical grids now support two-way communication of energy and data, enabling system optimization through data analytics. However, this also introduces cybersecurity vulnerabilities. While research has focused on using smart meter data to enhance grid performance, security and privacy concerns remain underexplored. This research proposes a secure and privacy-preserving framework for smart meter data transmission and analytics. It combines lightweight cryptography and transport layer security for end-to-end data protection, while Intel SGX ensures private data processing in the cloud. We implemented an efficient LSTM model for energy consumption prediction, demonstrating the framework’s practicality. Our approach balances security, privacy, and functionality, allowing data owners to retain control while leveraging third-party cloud resources.
Achieving secure multicast communication for IoT devices in Aging in Place system
(University of New Brunswick, 2024-10) Gui, Jinkun; Lu, Rongxing
The integration of IoT devices in Aging in Place (AiP) scenarios enhances elderly care but introduces security and privacy challenges. This thesis proposes two schemes to address these issues: an efficient multicast authenticated encryption scheme and a threshold authenticated encryption scheme. The former ensures secure and efficient communication among multiple IoT devices by utilizing Merkle Tree, prefix encoding, XOR filter, and ASCON encryption. The latter uses ElGamal threshold decryption and a binary fuse filter for secure group communication, allowing dynamic membership changes. Both schemes provide robust security, ensuring data confidentiality and integrity while being computationally efficient. These contributions enhance the security and privacy of IoT-enabled AiP systems, benefiting elderly individuals and their caregivers.
Rapid screening and optimization of adsorbents by combining molecular simulation with rapid experimental methods
(University of New Brunswick, 2024-10) Haeri Nejad, Masoud; Eić, Mladen
Adsorption has proved to be an efficient means for removal of pollutants from the atmosphere. In addition to traditional adsorbents, such as silica gel, activated carbon and zeolites, new adsorbents have been introduced which include the metal-organic frameworks (MOF’s). The number of MOF structures is enormous and therefore ranking them for any specific application requires time-saving approaches.
In this work, a novel approach dubbed “rapid screening” was introduced. It consists of (1) molecular simulation for prediction of adsorption isotherms followed by (2) the rapid experimental technique of the zero-length-column chromatography (ZLC) for isotherm confirmation. This approach was applied to the example case of screening and modifying metal-organic frameworks (MOFs) adsorbents to remove wasted inhalation anesthetic agents (IAA).
IAA adsorption isotherms for three MOFs were predicted using molecular simulation (MS). For successful simulation of these large, branched, and polar adsorbates, an all-atom force field was also developed which accounted for the flexibility of the adsorbate IAA molecules. Using Continuous Fractional Component Monte Carlo (CFCMC) algorithms proved crucial to speed up the simulations. Predictions from MS results were subsequently verified by performing experimental adsorption measurements using traditional methods followed by the faster ZLC technique, with necessary modifications for vapors. ZLC was shown to be able to replace the traditional volumetric or gravimetric adsorption methods with acceptable accuracy.
Next, the utility of rapid screening approach was verified for structural modification of a promising adsorbent for enhancing its IAA removal capability. Several structural modifications, including grafting, anion exchange and functionalization of benzene rings were proposed to the alter the pristine structure. New procedures were developed to create of new crystal information files (CIFs) for the modified structures to replace the CIF for the pristine structure as input to molecular simulations. The MS- predicted a two-fold of IAA adsorption capacity for MIL-101-Cr@NH3 (X=F-, OH-) compared with the unmodified structure. Experimental syntheses and rapid ZLC measurements confirmed this prediction.
This approach, presented as proof of concept, is applicable to a larger number of materials by procuring or performing syntheses only of those structures that molecular-simulation screening has selected as the most promising adsorbent.