Faculty Publications (Engineering and Computer Science)
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Item Heat and mass transfer across the vapor–liquid interface: A comparison of molecular dynamics and the Enskog–Vlasov kinetic model(International Journal of Heat and Mass Transfer, 2025) Homes, Simon; Frezzotti, Aldo; Nitzke, Isabel; Struchtrup, Henning; Vrabec, JadranDue to the intricacies of the interface between vapor and liquid, evaporation and condensation processes are not fully understood. The small spatial extent of the interface renders experimental studies on this subject challenging so that computational investigations are indispensable. For two heat and mass transfer scenarios across a vapor–liquid interface, molecular dynamics simulation is compared with the direct simulation Monte Carlo solution of the Enskog–Vlasov kinetic equation. A heat flux from the vapor to the liquid in a closed system as well as classical evaporation into an open half-space are considered. In both scenarios, temperature and one-dimensional driving gradients are widely varied, sampling systems containing 5 ⋅ 105 molecules. Since the two simulation methods rest on different potential models for the molecular interactions, a meaningful transformation between the truncated and shifted Lennard-Jones fluid and the Sutherland fluid is proposed. Spatially resolved density, temperature and velocity profiles from these simulation methods are consistent, except for the interface width. Consequently, particle flux and downstream pressure match as well. The good agreement between the results reinforces the validity of these approaches. The study is accompanied by successful comparisons of these simulations to kinetic gas theory with respect to macroscopic property variations at the interface.Item Harnessing community science to support implementation and success of nature-based solutions(Sustainability, 2024) Cabling, Ludwig Paul B.; Dubrawski, Kristian L.; Acker, Maleea; Brill, Gregg C.Community science (CS), a type of community-based participatory research, plays a crucial role in advancing wide-reaching environmental education and awareness by leveraging the collective power of volunteer participants who contribute to research efforts. The low barriers of entry and well-established methods of participatory monitoring have potential to enable community participant involvement in applications of nature-based solutions (NbS). However, a better understanding of the current state of community-based approaches within NbS could improve feasibility for researchers and practitioners to implement community-based approaches in NbS. Based on the current literature, we discern five community science approaches that support NbS: (1) Environmental monitoring to determine baseline conditions; (2) Involvement of participants in NbS development and planning through discussions and workshops (i.e., co-design of NbS); (3) Using existing CS databases to support NbS design and implementation; (4) Determining the impacts and measuring effectiveness of NbS; and (5) Participation in multifunctional activities. While there are various avenues of participation, we find that CS-driven environmental monitoring (i.e., actions that involve observing, measuring, and assessing environmental parameters and conditions over time) emerges as a cornerstone of planning, implementing, and maintaining the success of NbS. As the proliferation of NbS implementation continues, future work to integrate community-based monitoring studies in NbS applications has potential, albeit far from guaranteed, to improve place-based and local societal and ecological outcomes.Item Conformational stability at low temperatures using single protein nanoaperture optical tweezers(The Journal of Physical Chemistry B, 2025) Letwin, Keiran; Peters, Matthew; Gordon, ReuvenNanoaperture optical tweezers allow for trapping single proteins and detecting their conformational changes without modifying the protein, i.e., being free from labels or tethers. While past works have used laser heating as a way to vary the local temperature, this does not allow for probing of lower temperature values. Here we investigate the lower temperature dynamics of individual Bovine Serum Albumin (BSA) proteins with the help of a custom Peltier cooling stage. The BSA transitions between the normal (N) and fast (F) states. The normal form of BSA has a maximum occupancy at 21 ± 1 °C, which is interpreted as its maximum stability point for the compact N form with respect to the F form. In this way, it is possible to find the relative thermodynamic parameters of single proteins without requiring any modifications to the intrinsic structure.Item Temperature dependent Korteweg stress coefficient from the Enskog-Vlasov equation(Physics of Fluids, 2024) Bhattacharjee, Rahul; Struchtrup, Henning; Rana, Anirudh SinghEnskog-Vlasov equation—a nonlinear partial-integro-differential equation, provides a robust framework for analyzing liquid dynamics and phase transition. The Vlasov force expanded using Taylor series yields Korteweg stress with two constant coefficients. The first coefficient yields the van der Waals like contribution to equilibrium pressure, while the second coefficient determines the interfacial stresses/surface tension. In this article, we express the second Korteweg constant coefficient as a function of temperature. The developed model effectively captures the liquid-vapor interfacial region in equilibrium, reproducing the sharp interfacial structure predicted by the full Enskog-Vlasov equation. Additionally, we compare our results with those obtained through a particle-based approach, as studied by Frezzotti et.al (2019) [“Direct simulation Monte Carlo applications to liquid-vapor flows,” Physics of Fluids 31, 6 (2019)]. The proposed model balances simplicity with computational efficiency, comprehensively examined within the paper.Item Incorporating mooring dynamics into the control design of a two-body wave energy converter(Journal of Marine Science and Engineering, 2023) Funk, Spencer; Haider, Ali S.; Bubbar, Kush; Buckham, BradleyMooring systems are a critical component of all floating wave energy converter (WEC) systems, yet the impact of amooring system on the WEC dynamics is often neglected during the initial assessment of candidate designs. The purpose of this study was to investigate how the inclusion of mooring dynamics in the early stages of the WEC design process influences decisions regarding hydrodynamic features and control strategies. The study was executed within a mechanical circuit framework to represent the WEC response in the frequency domain. Thevenin’s theorem was applied within this framework to transform a multi-body WEC into a single-body canonical form. This work specifically focused on self-reacting point absorbers and examined how four realistic mooring designs impact WEC intrinsic mechanical impedance across a range of common wave frequencies. We show how the mooring can easily be included in this framework, and a simple approach to identifying the mooring model parameters is described. It was observed that if mooring dynamics are considered within the WEC control design process, a 40% reduction in the required range of the controller physical variable can be achieved while yielding up to 16% more useful power. These results suggest that considering the mooring system early can enhance WEC design.Item Measuring the impact of student led tutorials on first year students' learning outcomes(Proceedings of the Canadian Engineering Education Association (CEEA), 2015) Kadhum, Sohad; Buckham, Bradley; Nadler, BenENGR 141: Engineering Mechanics is a foundational course in the UVic Engineering Faculty that serves all of the engineering degree programs: biomedical, civil, mechanical, electrical, computer and software. Between the 2013 and 2014 offerings of the course, the ENGR 141 population grew dramatically, by well over 50%, necessitating changes in the course structure and methods of student assessment. In addition to addressing logistical challenges, the changes were designed to develop the students’ confidence in their ability to wield fundamental mechanical principles independently and in peer-to-peer working environments. This was done by repurposing the tutorial sections of the course to create student driven exploration, analysis and solution of complex three dimensional mechanics problems. A series of 22 problems lying outside the domain of the assignment problem sets were addressed-two in each week of the tutorials. The assignments and midterms problems were constructed so that the impact of tutorial work on students mastery of the course Intended Learning Outcomes could be extracted. Under the new tutorial format, instructors monitored group dynamics, helped troubleshoot and provided encouragement. Presentation of solution strategies were made by select student groups each week. The current work describes the motivation for the changes made, observations made at implementation and some preliminary results from analyses of the impact of the new course structure on student mastery of the course learning outcomes. Important conclusions are that the student-led tutorials should be accompanied with additional instructor contact hours that provide opportunity for students to receive tutelage on a one-to-one basis and that individual testing should stress the procedures and tools emphasized in the tutorials. In addition, students found that assessments made through multiple choice testing contradicted values and principles being stressed in the tutorial and seminar sessions.Item Nuclear and renewables in multipurpose integrated energy systems: A critical review(Renewable and Sustainable Energy Reviews, 2024) El-Emam, Rami S.; Constantin, Alina; Bhattacharyya, Rupsha; Ishaq, Haris; Ricotti, Marco E.Integrated energy systems for multi-purpose applications are garnering increased interest in the international nuclear energy community, energy system designers and planners and decision makers in the context of deep decarbonization and net zero targets. They are expected to reduce costs and increase flexibility in operation of nuclear reactors when coupled with intermittent renewable energy sources, while also producing various commodities such as hydrogen or potable water. Adaptive solutions must be considered for each geographical area and based on the involved components of the energy system, available infrastructure, and policy in place. This paper provides an in-depth look at the strengths, weaknesses, opportunities, and threats of such systems, while addressing different aspects related to the creation of the business case for such systems including decentralization and digitalization of future energy systems. The regulatory aspects are the ones that impose challenges on the emerging hybrid energy systems and this paper highlights some of the considerations that are needed for the couplings involved, in terms of licensing procedures and safety analysis. The potential contribution of such integrated energy systems towards achieving the United Nations Sustainable Development Goals (UN SDGs) are also discussed. Concerning the stakeholders, special attention should be paid to building social acceptance and trust as this lays the foundation for successful implementation of such projects. By focusing on areas such as research and development, integration of technologies, policy support, market development, grid integration, energy storage, efficiency improvement, system modelling and simulations, significant advances in integrated/hybrid energy systems deployment can be achieved.Item Review of ammonia production and utilization: Enabling clean energy transition and net-zero climate targets(Energy Conversion and Management, 2024) Ishaq, Haris; Crawford, CurranIn 2015, the Paris agreement was signed by 196 countries in attendance at COP21 that highlighted the need for rapid decarbonization and carbon dioxide removal (CDR) and sets ambitions to reach net zero emissions by mid-century. The production of ammonia can contribute to achieving net-zero emissions in several ways including energy storage, clean fuel, industrial applications and carbon capture and utilization (CCU) processes, if produced using renewable energy (RE) sources with very low greenhouse gas (GHG) emissions during production and utilization. This review study highlights the potential of green ammonia production pathways, utilization, ammonia storage and transport, ammonia infrastructure and economy, to serve various roles and provide potential benefits in decarbonizing industry and clean energy transitions to meet net-zero climate targets. Renewable ammonia cannot only help decarbonize existing ammonia markets by displacing fossil fuels, but can also help greening the industrial sector such as fertilizer and chemical industries and accelerate decarbonization in hard-to-abate industries, including retrofit of existing ammonia plants. Ammonia is also expected to be used in the stationary power sector as renewable fuel as the technology matures. It can also play an imperative role as a promising maritime fuel, owing to its zero-emission properties, high energy density and compliance with ever more stringent environmental regulations, transporting RE, in the shipping industry that is one of the largest GHG emitters. Moreover, as a hydrogen carrier, ammonia can deliver industrial feedstocks and enable lower-cost hydrogen imports as compared with renewable hydrogen. Encouraging green ammonia production technologies and near-zero-emission technology progress can guide desirable future pathways for the ammonia industry, including handling important safety considerations of production, storage and end-use.Item An instructive CO2 adsorption model for DAC: Wave solutions and optimal processes(Entropy, 2024) Kay-Leighton, Emily; Struchtrup, HenningWe present and investigate a simple yet instructive model for the adsorption of CO2 from air in porous media as used in direct air capture (DAC) processes. Mathematical analysis and non-dimensionalization reveal that the sorbent is characterized by the sorption timescale and capacity, while the adsorption process is effectively wavelike. The systematic evaluation shows that the overall adsorption rate and the recommended charging duration depend only on the wave parameter that is found as the ratio of capacity and dimensionless air flow velocity. Specifically, smaller wave parameters yield a larger overall charging rate, while larger wave parameters reduce the work required to move air through the sorbent. Thus, optimal process conditions must compromise between a large overall adsorption rate and low work requirements.Item Exploring the impacts of carbon pricing on Canada’s electricity sector(Energies, 2024) Arjmand, Reza; Hoyle, Aaron; Rhodes, Ekaterina; McPherson, MadeleineCanadian provinces are required to regulate their power sectors using carbon pricing systems that meet national minimum stringency standards, which are set by the federal government. A diverse set of systems has emerged as a result. However, there has been limited assessment of how different pricing mechanisms impact the evolution of Canada’s electricity system. To address this gap, we use an electricity system planning model called COPPER and a scenario-based approach to assess if, and to what extent, different policy regimes impact power sector greenhouse gas emissions and costs. Our results show that carbon pricing systems currently in place lead to significant carbon reductions over the long term, provided that free emissions allocations are reduced. However, the cost-optimal pathway for the power sector differs across provinces depending on the carbon pricing mechanism. Some provinces achieve least-cost emissions reductions by switching from high-carbon technologies to renewables, while others are better served by replacing high-carbon technologies with low-carbon fossil fuel alternatives. Further, provinces that implement cap-and-trade systems may affect the transitions of interconnected jurisdictions. Power sector climate policy design should reflect the heterogeneity of available assets, resources, and neighbouring approaches.Item Energy management in modern buildings based on demand prediction and machine learning—A review(Energies, 2024) Moghimi, Seyed Morteza; Gulliver, T. Aaron; Thirumai Chelvan, IlamparithiIncreasing building energy consumption has led to environmental and economic issues. Energy demand prediction (DP) aims to reduce energy use. Machine learning (ML) methods have been used to improve building energy consumption, but not all have performed well in terms of accuracy and efficiency. In this paper, these methods are examined and evaluated for modern building (MB) DP.Item Wind turbine damage equivalent load assessment using Gaussian process regression combining measurement and synthetic data(Energies, 2024) Haghi, Rad; Stagg, Cassidy; Crawford, CurranAssessing the structural health of operational wind turbines is crucial, given their exposure to harsh environments and the resultant impact on longevity and performance. However, this is hindered by the lack of data in commercial machines and accurate models based on manufacturers’ proprietary design data. To overcome these challenges, this study focuses on using Gaussian Process Regression (GPR) to evaluate the loads in wind turbines using a hybrid approach. The methodology involves constructing a hybrid database of aero-servo-elastic simulations, integrating publicly available wind turbine models, tools and Supervisory Control and Data Acquisition (SCADA) measurement data. Then, constructing GPR models with hybrid data, the prediction is validated against the hybrid and SCADA measurements. The results, derived from a year of SCADA data, demonstrate the GPR model’s effectiveness in interpreting and predicting turbine performance metrics. The findings of this study underscore the potential of GPR for the health and reliability assessment and management of wind turbine systems.Item Coordinated hybrid approach based on firefly algorithm and particle swarm optimization for distributed secondary control and stability analysis of direct current microgrids(Sustainability, 2024) Lasabi, Olanrewaju; Swanson, Andrew; Jarvis, Leigh; Aluko, Anuoluwapo; Goudarzi, ArmanStandalone DC microgrids can potentially influence intelligent energy systems in the future. They accomplish this by employing droop control to smoothly integrate various renewable energy sources (RESs) to satisfy energy demands. This method ensures equitable allocation of load current among RESs, promoting efficiency and smooth operation. Utilizing droop control typically leads to a reduction in the voltage of the DC bus. Hence, to uniformly distribute current among several RESs while simultaneously regulating the DC bus voltage, this research proposes a distributed secondary control technique. The proposed technique ensures fair distribution of current and eliminates bus voltage variations by integrating both current and voltage errors within the designed control loop. An innovative hybrid firefly and particle swarm optimization algorithm (FFA–PSO) is introduced to aid in parameter selection for the distributed control approach, facilitating the attainment of the intended control objectives. A DC microgrid state-space model was developed, which incorporates eigenvalue observation analysis to evaluate the impacts of the optimized secondary distributed control on the stability of the microgrid. A real-time testing setup is built using MATLAB/Simulink® R2022b software. and implemented on a Speedgoat™ real-time machine to verify the practical performance of the proposed approach in real-world applications. The results showcase the robustness of the proposed control technique in achieving voltage stabilization and even current allocation within the DC microgrid. This is evidenced by minimal oscillations and undershoots/overshoots and swift response times.Item Unveiling the black box: A unified XAI framework for signal-based deep learning models(Machines, 2024) Shojaeinasab, Ardeshir; Jalayer, Masoud; Baniasadi, Amirali; Najjaran, HomayounCondition monitoring (CM) is essential for maintaining operational reliability and safety in complex machinery, particularly in robotic systems. Despite the potential of deep learning (DL) in CM, its ‘black box’ nature restricts its broader adoption, especially in mission-critical applications. Addressing this challenge, our research introduces a robust, four-phase framework explicitly designed for DL-based CM in robotic systems. (1) Feature extraction utilizes advanced Fourier and wavelet transformations to enhance both the model’s accuracy and explainability. (2) Fault diagnosis employs a specialized Convolutional Long Short-Term Memory (CLSTM) model, trained on the features to classify signals effectively. (3) Model refinement uses SHAP (SHapley Additive exPlanation) values for pruning nonessential features, thereby simplifying the model and reducing data dimensionality. (4) CM interpretation develops a system offering insightful explanations of the model’s decision-making process for operators. This framework is rigorously evaluated against five existing fault diagnosis architectures, utilizing two distinct datasets: one involving torque measurements from a robotic arm for safety assessment and another capturing vibration signals from an electric motor with multiple fault types. The results affirm our framework’s superior optimization, reduced training and inference times, and effectiveness in transparently visualizing fault patterns.Item From nature to design: Tailoring pure mycelial materials for the needs of tomorrow(Journal of Fungi, 2024) Whabi, Viraj; Yu, Bosco; Xu, JianpingModern efforts to influence materials science with principles of biology have allowed fungal mycelial materials to take a foothold and develop novel solutions for the circular bioeconomy of tomorrow. However, recent studies have shown that the value of tomorrow’s green materials is not determined simply by their environmental viability, but rather by their ability to make the polluting materials of today obsolete. With an inherently strong structure of chitin and ?-glucan, the ever-adaptable mycelia of fungi can compete at the highest levels with a litany of materials from leather to polyurethane foam to paper to wood. There are significant efforts to optimize pure mycelial materials (PMMs) through the entire process of species and strain selection, mycelial growth, and fabrication. Indeed, the promising investigations of novel species demonstrate how the diversity of fungi can be leveraged to create uniquely specialized materials. This review aims to highlight PMMs’ current trajectory, evaluate the successes in technology, and explore how these new materials can help shape a better tomorrow.Item In vitro glioblastoma model on a plate for localized drug release study from a 3D-printed drug-eluted hydrogel mesh(Cells, 2024) Chehri, Behnad; Liu, Kaiwen; Vaseghi, Golnaz; Seyfoori, Amir; Akbari, MohsenGlioblastoma multiforme (GBM) is an aggressive type of brain tumor that has limited treatment options. Current standard therapies, including surgery followed by radiotherapy and chemotherapy, are not very effective due to the rapid progression and recurrence of the tumor. Therefore, there is an urgent need for more effective treatments, such as combination therapy and localized drug delivery systems that can reduce systemic side effects. Recently, a handheld printer was developed that can deliver drugs directly to the tumor site. In this study, the feasibility of using this technology for localized co-delivery of temozolomide (TMZ) and deferiprone (DFP) to treat glioblastoma is showcased. A flexible drug-loaded mesh (GlioMesh) loaded with poly (lactic-co-glycolic acid) (PLGA) microparticles is printed, which shows the sustained release of both drugs for up to a month. The effectiveness of the printed drug-eluting mesh in terms of tumor toxicity and invasion inhibition is evaluated using a 3D micro-physiological system on a plate and the formation of GBM tumoroids within the microenvironment. The proposed in vitro model can identify the effective combination doses of TMZ and DFP in a sustained drug delivery platform. Additionally, our approach shows promise in GB therapy by enabling localized delivery of multiple drugs, preventing off-target cytotoxic effects.Item Predicting maturity of coconut fruit from acoustic signal with applications of deep learning(Biology and Life Sciences Forum, 2024) Sattar, FarookThis paper aims to develop an effective AI-driven method to predict the maturity level of coconut (Cocos nucifera) fruits using acoustic signals. The proposed sound-based autonomous approach exploits various deep learning models, including customized CNN pretrained networks, i.e., the ResNet50, InceptionV3, and MobileNetV2, models for maturity level classification of the coconuts. The proposed study also demonstrates the effectiveness of various deep learning models to automatically predict the maturity of coconuts into three classes, i.e., premature, mature, and overmature coconuts, for inspecting the coconut fruits by using a small amount of input acoustic data. We use an open-access dataset containing a total of 122 raw acoustic signals, which is the result of knocking 122 coconut samples. The results achieved by the proposed method for coconut maturity prediction are found to be promising, which enables producers to accurately determine the yield and product quality.Item On the multidisciplinary design of a hybrid rocket launcher with a composite overwrapped pressure vessel(Journal of Composites Science, 2024) Souza, Alain; Gonçalves, Paulo Teixeira; Afonso, Frederico; Lau, Fernando; Rocha, Nuno; Suleman, AfzalA multidisciplinary design optimisation (MDO) study of a hybrid rocket launcher is presented, with a focus on quantifying the impact of using composite overwrapped pressure vessels (COPVs) as the oxidiser tank. The rocket hybrid propulsion system (RHPS) consists of a combination of solid fuel (paraffin) and liquid oxidiser (NOx). The oxidiser is conventionally stored in metallic vessels. Alternative design concepts involving composite-based pressure vessels are explored that could lead to significant improvements in the overall performance of the rocket. This design choice may potentially affect parameters such as total weight, thrust curve, and maximum altitude achieved. With this eventual impact in mind, structural considerations such as wall thickness for the COPV are integrated into an in-house MDO framework to conceptually optimise a hybrid rocket launcher.Item Micro- and macro-scale topology optimization of multi-material functionally graded lattice structures(Journal of Composites Science, 2024) Santos, Jerónimo; Sohouli, Abdolrasoul; Suleman, AfzalLattice structures are becoming an increasingly attractive design approach for the most diverse engineering applications. This increase in popularity is mainly due to their high specific strength and stiffness, considerable heat dissipation, and relatively light weight, among many other advantages. Additive manufacturing techniques have made it possible to achieve greater flexibility and resolution, enabling more complex and better-performing lattice structures. Unrestricted material unit cell designs are often associated with high computational power and connectivity problems, and highly restricted lattice unit cell designs may not reach the optimal desired properties despite their lower computational cost. This work focuses on increasing the flexibility of a restricted unit cell design while achieving a lower computational cost. It is based on a two-scale concurrent optimization of the lattice structure, which involves simultaneously optimizing the topology at both the macro- and micro-scales to achieve an optimal topology. To ensure a continuous optimization approach, surrogate models are used to define material and geometrical properties. The elasticity tensors for a lattice unit cell are obtained using an energy-based homogenization method combined with voxelization. A multi-variable parameterization of the material unit cell is defined to allow for the synthesis of functionally graded lattice structures.Item Transceiver optimization for multiuser multiple-input multiple-output full-duplex amplify-and-forward relay downlink communications(Telecom, 2024) Shao, Yunlong; Gulliver, Thomas AaronThis paper considers the transceiver design in a multiuser multiple-input multiple-output (MIMO) full-duplex (FD) amplify-and-forward (AF) relay downlink communication system, where users simultaneously transmit data via an FD relay node. The design incorporates an imperfect loop interference (LI) cancellation which results in a residual LI. Linear precoders are employed at the sources and relay, and minimum mean-squared-error (MMSE) combiners are employed at the destinations to mitigate the effect of the residual LI. The corresponding design problem is highly nonconvex, so a closed-form solution is intractable. Thus, an iterative method is developed to solve this optimization problem. Simulation results are presented which show that the proposed iterative algorithm provides better performance than the corresponding half-duplex (HD) solution in terms of the achievable rate under residual LI.