Engineering Commons^™

A Nonlinear Viscoelastic 3d Bellows Microspring Fabricated With Two-Photon Polymerization, Harris J. Hall, Tyler Grunzke, Samuel Campbell, Hengky Chandrahalim

Faculty Publications

In the past decade, two-photon polymerization (TPP) based 3D printing has gained popularity for prototype fabrication of micro and nanoscale structures and mechanical metamaterials. However, practical considerations including response uniformity, nonlinear behaviors, and material aging effects remain challenges for device maturation and are seldom reported. In this work, a pair of cylindrical microscale bellows springs (~ 100 μm diameter), fabricated with a commercial TPP 3D printer, are shown to exhibit nonlinear stiffness profiles under compression (up to ~ 7%), with distinct linear and nonlinear regions, that change significantly after ~ 9–10 months of open air exposure in ambient lab conditions. …

Integrating Gis With Interim Payment Valuation In Road Construction Projects: A Conceptual Framework, Abdulrahman I. Iro, Juma M. Matindana, Julian Ijumulana

Tanzania Journal of Engineering and Technology (TJET)

Abstract

Interim Payment Valuation is a critical process in road construction contract administration, yet conventional valuation practices remain heavily dependent on manual measurements, fragmented documentation, spreadsheets, and professional judgement. These limitations can affect measurement accuracy, transparency, traceability, and the timeliness of payment certification. Although Geographic Information Systems have increasingly been applied to construction planning, quantity measurement, progress monitoring, infrastructure management, and decision support, their integration with contractual and financial processes for interim payment valuation remains insufficiently explored. This study therefore develops a conceptual framework for integrating GIS with Interim Payment Valuation in road construction projects. A PRISMA-guided structured literature review …

Voltage-Related Power Quality Issues And Impacts On Distribution Networks With Sensitive Loads - A Review, Godwin Elinazi Mnkeni, Jackson Justo, Aviti Thadei Mushi, Bakari M. M. Mwinyiwiwa

Tanzania Journal of Engineering and Technology (TJET)

Voltage disturbances are the most important power quality (PQ) complications that customers and power utilities face in this smart era. The growing adoption of sophisticated electronic equipment and integration of renewable energy sources (RES) into power grids has increased the susceptibility of power distribution networks (PDNs) to voltage sags, swells, interruptions, flicker, and voltage imbalance. These disturbances, mainly caused by upstream faults, switching operations, and RES integration, compromise voltage PQ and system reliability. Consequently, they accelerate equipment degradation, increase electronic waste (e-waste), raise reactive power demand and maintenance costs, increase power losses, and impose substantial economic losses on customers and …

Ai-Driven Biomarker Discovery & Progression Modeling For Precision Diagnosis Of Glaucoma, Cheng Huang

Computer Science and Engineering Theses and Dissertations

This dissertation presents a comprehensive study on the integration of artificial intelligence (AI) for glaucoma diagnosis and retinal image analysis. Leveraging multimodal imaging data including fundus photography, Optical Coherence Tomography Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA), the research develops a suite of deep learning frameworks designed to detect early glaucomatous changes with high precision, robustness, and interpretability. A series of novel architectures are introduced, spanning vessel segmentation networks, biomarker discovery pipelines, and multimodal fusion models, all designed to enhance diagnostic accuracy and generalizability across diverse populations. To facilitate reproducible and scalable ophthalmic AI research, this work …

Analysis And Design Of Multi-Winding Current Ballasted Magnetics For Integrated Dc-Dc Conversion, Kishalay Datta

Dartmouth College Ph.D Dissertations

Power conversion circuits have become a bottleneck in terms of reducing the size and cost of modern electronic systems in a vast number of applications ranging from personal devices, to automotive systems, to data centers. This has motivated designs at higher frequencies where smaller passive components can be used. Unfortunately, magnetic components scale poorly to smaller dimensions. In addition, the AC resistance of magnetic components deteriorates due to current crowding effects at high frequencies. This has resulted in fully- integrated power converters with planar magnetic components having efficiencies lower than what may be theoretically achieved. In this work, we investigate …

A Graph Attention Network-Based Framework For Reconstructing Missing Lidar Beams, Khalfalla Awedat, Mohamed Abidalrekab, Mohammad El-Yabroudi

Electrical and Computer Engineering Faculty Publications and Presentations

Vertical beam dropout in spinning LiDAR sensors -triggered by hardware aging, dust, snow, fog, or bright reflections - removes entire vertical slices from the point cloud and severely degrades 3D perception in autonomous vehicles. This paper proposes a Graph Attention Network (GAT)-based framework that reconstructs these missing vertical channels using only the current LiDAR frame, with no camera images or temporal information required. Each LiDAR sweep is represented as an unstructured spatial graph: points are nodes and edges connect nearby points while preserving the original beam-index ordering. A multi-layer GAT learns adaptive attention weights over local geometric neighborhoods and directly …

Graphene Oxide–Driven In Situ Bismuth Reduction Enables Highly Efficient Electroreduction Of Co2 To Formic Acid, Hao Feng, Bohong Zhang, Jie Huang, Xinhua Liang

Electrical and Computer Engineering Faculty Research & Creative Works

To enable large scale efficient electrochemical CO₂ reduction reaction (CO₂RR) to formic acid (HCOOH), it is important to develop catalysts that can be operated in a wide potential window with good stability. Herein, we successfully synthesized Bi₂O₃ catalyst supported on graphene oxide (GO) and graphene (G) and found that Bi₂O₃/GO catalyst had a better overall performance than Bi₂O₃/G. The Bi₂O₃/GO catalyst demonstrated an outstanding CO₂RR performance with a greater than 90% faradaic efficiency (FE) across a wide applied potential window …

Thermo-Mechanical Behaviour Of Metal-Coated Optical Fibers For Distributed High-Temperature Sensing: From Laboratory Characterization To Industrial Case Validation, Koustav Dey, Rony Kumer Saha, Bohong Zhang, Laura Bartlett, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Reliable distributed temperature sensing in high-temperature environments remains a significant challenge due to the thermal and mechanical limitations of conventional optical fibers. In particular, polymer-coated fibers degrade above ∼300 °C due to coating failure, mechanical fragility and hydrogen ingress. Metal-coated optical fibers offer a robust alternative for harsh environments such as Electric Arc Furnaces (EAFs), aerospace engines, nuclear systems, and oil and gas wells, owing to their superior mechanical strength and hermetic sealing. In this work, a first comprehensive experimental investigation of the thermo-mechanical behavior of metal-coated optical fibers for distributed high temperature sensing is presented over a wide temperature …

Ph-Compensated Hydrogen Peroxide Quantification Using A Dual-Modal Fiber-Optic Probe, Homayoon Soleimani Dinani, Bohong Zhang, Maryam Karimi, Rex E. Gerald, Shelley D. Minteer, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

We report a dual-modal fiber-optic probe that integrates electrochemical quantification of hydrogen peroxide (H₂O₂) with co-localized fluorescent pH sensing for pH-indexed interpretation of the H₂O₂ response. H₂O₂ is a reactive oxygen species involved in oxidative stress, inflammation, and cellular signaling, and local pH modulates both its production and electrochemical response. Many electrochemical H₂O₂ sensors exhibit pH-dependent sensitivity, creating ambiguity unless pH is measured and used for compensation, which is difficult in small, heterogeneous, or rapidly changing microenvironments. A three-electrode configuration—working (WE), counter (CE), and Ag/AgCl pseudo-reference (pRE) electrodes—is fabricated directly on the cylindrical surface of a 710-µm-diameter optical fiber using …

Disturbance-Learning Inertia Estimation Using Artificial Neural Networks For Power System Stability, Sospeter Igaanja Gabriel, Francis Mwasilu, Peter Makolo Dr.

Tanzania Journal of Engineering and Technology (TJET)

ABSTRACT

Power systems are progressively shifting towards low inertia as a result of incorporating significant amounts of intermittent and converter-based renewable energy sources, such as wind and solar power, into the current power grid network. This integration poses considerable problems to inertia and frequency control within the network due to a reduction in the proportion of synchronous generators. Furthermore, rapid frequency deviations occur due to the disparity between supply and demand during contingencies, complicating the maintenance of frequency stability within the power system. The disturbance-learning inertia estimation method for power system stability is presented. The simulation analysis is performed using …

Surrogate-Guided Conditional Generative Adversarial Network For Generative Design Of 3d-Printable Terahertz Holographic Metasurfaces, Manxu Lv, Zhikai Hu, Zhichuan Xie, Huizhen Feng, Dongping Zhang, Manna Gu, Chee Leong Tan, Bo Fang, Yongxing Jin, Ying Tian, Xufeng Jing

Electrical and Computer Engineering Faculty Publications and Presentations

Terahertz metasurface holography requires unit cells with prescribed complex transmission responses and sufficient compatibility with practical fabrication processes. For elementary low-bit phase quantization at 0.1 THz, conventional parameter sweeping of simple geometries remains an effective and reliable approach. However, when the design objective is extended from a single phase state to simultaneous consideration of complex transmission matching, transmission amplitude, structural connectivity, and fabrication-related geometric constraints, the design problem becomes a constrained multi-objective inverse-design task. Here, we propose a generative-model-assisted inverse-design framework for 3D-printable terahertz metasurface unit cells. The framework combines a VGG-based surrogate model with a modified WGAN-GP generator. The …

Fast Sparse Image Reconstruction Models In Through-The-Wall Radars: A Review, Aude Kileo, Hashimu U. Iddi, Abdi Abdalla

Tanzania Journal of Science

Through-the-Wall Radar Imaging (TWRI) is a modern technology that uses electromagnetic waves to detect objects behind walls, with key applications in surveillance, rescue operations, and reconnaissance. Achieving high resolution in both down-range and cross-range requires ultra-wideband signals and long apertures, resulting in large data volumes, increased acquisition time, and high memory demands. TWRI employs Compressive Sensing (CS) to reduce computational time, which has proved its significance in many recent TWRI applications. However, in CS, image reconstruction approaches shift the computational burden from the sensing stage to the recovery stage, which prolongs the reconstruction times, making it unsuitable in time-sensitive applications. …

Context Matters: Evaluating Llm-Generated Knowledge Graph Schemas, Ritvik Garimella, Riju Marwah, Atishay Jain, Khusham Bansal, Amit Sheth

Publications

Knowledge graph (KG) schema engineering is labor-intensive and resists automation at scale. We investigate whether LLMs can generate domain-specific KG schemas of sufficient quality for downstream symbolic reasoning. We propose a tiered contextual framework that varies domain context richness across four levels: zero context, domain scope, task requirements, and data distribution. Generated schemas are evaluated intrinsically on BioRED (600 PubMed abstracts, multi-type entities and relations), where automated tiered schemas match an established KG construction baseline at 79.9% EC, with edge conformance rising from 47.5% at L1 to a stable 78–80% from L2 onward. Extrinsic evaluation on a 50-record MedHop controlled …

Dense Platinum Nanoparticles Confined In Mn-N-C Nanocages For Robust Heavy-Duty Pemfcs, Lei Zhao, Zhen-Min Cao, Jia-Yu Zuo, Ming-Liang Yang, Hong-Yan Qiao, Jun-Song Chen, Rui Wu

Journal of Electrochemistry

High-loading Pt cathodes are essential for heavy-duty proton exchange membrane fuel cells but suffer from a critical tradeoff between ionomer sulfonate poisoning and nanoparticle instability. Herein, we report a spatial confinement strategy to encapsulate dense Pt nanoparticles (~51.8 wt%) within Mn/N-co-doped mesoporous carbon nanocages (denoted as Pt-MnNC). This architecture excludes bulky ionomers to create an ionomer-shielded environment against sulfonate poisoning, while Mn-N_x-mediated strong metal-support interactions anchor the Pt nanoparticles to prevent agglomeration and further boost durability. In the 5 × 5 cm² membrane electrode assembly tests, the Pt-MnNC catalyst delivers an exceptional power density of 1.26 W·cm …

Engineering Small-Sized Cations With Strong Solvation For High-Voltage Supercapacitors, Tong Huo, Pan Liu, Hao Chen, Peng Zhang, Zhen-Lei Chen, Guo-Fu Sun, Zhi-Qiang Shi, Jing Wang

Journal of Electrochemistry

Supercapacitors (SCs) have attracted much attention in the field of energy storage due to their high power density and long cycle life. However, their relatively low energy density limits their application in a wider range of fields. Methods to increase energy density include enhancing specific capacitance and extending operating voltage window. Herein, we report a novel electrolyte salt, tetramethylammonium bis(fluorosulfonyl)imide (TMA-FSI), the resulting electrolyte formed with propylene carbonate (PC), designated as TMF, exhibited a wide electrochemical stability window (ESW) of 5.09 V. On the one hand, we found that the small size of TMA⁺ enables it to enter the …

Efficient Acidic H2O2 Electrosynthesis Over Co Atoms Anchored On Nitrogen-Doped Hierarchical Porous Carbon, Xiao Huang, Zi-Hao Zhan, Hao-Xu Niu, Guan-Yu Luo, Jin-Tao Huang, Bo-Xuan Jin, De-Li Wang

Journal of Electrochemistry

The two-electron oxygen reduction reaction (2e⁻ ORR) presents a promising route for the on-site production of hydrogen peroxide (H₂O₂), offering a green alternative to energy-consuming anthraquinone process. However, the high selectivity toward the competing 4e⁻ ORR over the desired 2e⁻ pathway leads to low Faradaic efficiency for H₂O₂, posing a critical challenge in catalyst design. In this work, a nitrogen-doped hollow hierarchical porous carbon with anchored Co atoms (CoN/HPC) was constructed for high-performance H₂O₂ production. The as-prepared Co-N/HPC catalyst showed excellent 2e⁻ ORR performance, achieving …

Scheduling The Charging Of Battery-Electric Vehicles Under Heterogeneous Scheduling Constraints, Justin Whitaker, Greg Droge, Mario Harper

Electrical and Computer Engineering Student Research

Adopting battery electric vehicles (EVs) for vehicle fleets requires scheduling charging alongside day-to-day operations. This problem is complicated by complex utility cost structures, limited battery capacity, and competition for shared charging resources. Existing methods that simultaneously schedule routes and charging neither address the full cost structure nor employ high-fidelity charging models, and no prior work analyzes fleets containing vehicles with heterogeneous routing constraints. This work addresses these gaps by combining a state-of-the-art flexible-schedule formulation with time-of-use (TOU) demand costs and a non-linear, variable-rate charging model as drawn from additional state-of-the-art works. The proposed method is validated against two state-of-the-art methods, …

Superiorgat: Graph Attention Networks For Sparse Lidar Point Cloud Reconstruction In Autonomous Systems, Khalfalla Awedat, Mohamed Abidalrekab, Gurcan Comert, Mustafa Ayad, Negash Begashaw

Electrical and Computer Engineering Faculty Publications and Presentations

LiDAR-based perception in autonomous systems is fundamentally limited by sparse vertical sampling and further degraded by structured beam dropout caused by occlusions, sensor faults, or reduced-cost LiDAR hardware. These degradations disrupt vertical geometric continuity and negatively affect downstream perception tasks such as object detection, localization, and scene understanding. Existing reconstruction approaches often struggle to balance reconstruction accuracy with the computational efficiency required for real-time autonomous operation. This paper presents SuperiorGAT, a graph attention–based framework for reconstructing missing elevation information in sparse LiDAR point clouds under structured beam loss. The proposed method models LiDAR scans as beam-aware graphs and enhances standard …

Multi-Solution Ternary Grover’S Algorithm For Logic-Based Quantum Machine Learning With Pseudo-Kronecker Reed–Muller Form Minimization, Sophia Lee, Ali Al-Bayaty, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

Optimization problems in machine-learning (ML) applications can be computationally challenging for classical methods, particularly when they involve large, unstructured search spaces. Quantum computing offers a promising approach to combinatorial optimization by utilizing quantum superposition and amplitude amplification. This paper presents a new methodology using logic-based quantum machine learning (QML) as a complete framework for employing a multi-solution ternary Grover’s algorithm to minimize incomplete binary functions represented by Pseudo-Kronecker Reed–Muller (PKRO) expansions, consistent with Occam’s razor principle. Unlike previous quantum approaches based on Kronecker Reed–Muller (KRO) or fixed-polarity Reed–Muller (FPRM) representations, our work introduces the first Grover-based optimization framework for PKRO …

Breaking The Build: Detecting Software Supply Chain Vulnerabilities In Ci/Cd Pipelines, Mercedes R. Wahl, Dr. Benjamin Yankson

Military Cyber Affairs

This study examines whether integrating structured DevSec- Ops security controls into CI/CD pipelines can reduce software supply chain risk by preventing vulnerable components from progressing through the software development lifecycle. Software supply chain attacks frequently originate from weaknesses or compromises within dependencies, build environments, and trusted development stages, making early detection essential. A controlled sandbox experiment compared two pipeline configurations: a baseline CI/CD pipeline with no automated security enforcement and a secure DevSecOps pipeline integrating automated vulnerability scanning, SBOM generation, and artifact integrity verification. A known vulnerable dependency, the Python requests package (version 2.19.0) associated with CVE-2018-18074, was intentionally introduced …

Fault Recovery For A Class Of Unmanned Autonomous Vehicles Using Gaussian Process Regression, Garrison Greenwood

Electrical and Computer Engineering Faculty Publications and Presentations

Unmanned autonomous vehicles (UAVs) must often operate reliably in remote, hostile environments for extended periods. Fault tolerance is essential, enabling continued operation despite system faults. Fault recovery is challenging due to unknown fault characteristics and no possibility of on-site human intervention. Replacing faulty hardware with operating spares is an effective recovery method, but impractical because UAVs have limited space. Reconfiguration may be the only practical option. This paper discusses correcting faulty behavior in robot arm DC servos, guided by a PID controller, by modifying the controller’s gain values. Although simple in principle, finding new gain values is difficult and time-consuming, …

Hybrid Transformer-Bilstm Model For Early Fault Detection And Multiclass Classification Of Wind Turbine Faults Using Scada Data, Hassan Y. Mkindu

Tanzania Journal of Engineering and Technology (TJET)

Early and accurate fault detection in wind turbines is essential for improving operational reliability, reducing maintenance costs, and minimizing unplanned downtime. This study proposes a Hybrid Transformer-BiLSTM deep learning model for early fault detection and multiclass fault classification using Supervisory Control and Data Acquisition (SCADA) data. The proposed architecture combines the Transformer's self-attention mechanism to capture global temporal dependencies with the Bidirectional Long Short-Term Memory (BiLSTM) network's ability to model sequential fault evolution, enabling effective learning of multivariate time-series data. The model was developed and evaluated using the recently introduced CARE SCADA dataset, classifying five operating states: No Fault, Transformer …

A Quad-Port Dual-Band Elliptical Patch Mimo Antenna For 5g And Wlan Applications, Livingstone L. Kimaro, Hashimu Uledi Iddi, Mussa M. Kissaka, Neema S. Joseph

Tanzania Journal of Engineering and Technology (TJET)

A dual-band multiple-input multiple-output (MIMO) antenna for wireless local area network (WLAN) and fifth-generation new radio (5G NR) band n104 applications is presented. Two U-shaped slots interconnected back to back are etched on an elliptical shaped patch, four identical patch elements are then arranged orthogonally to form a quad-port antenna that resonates at 5.8 GHz and 6.7 GHz. Without additional extrinsic decoupling structures, the proposed design achieves measured isolation levels above 25.13 dB and 21.31dB, realized gains of 7 dBi and 7.2 dBi and -10 dB impedance bandwidths of 155.4 MHz and 90 MHz in the lower and upper frequency …

Design Of Permanent Magnet Synchronous Motor For Railways Traction Application, Anna S. Mwang'onda, Jackson J. Justo, Francis Mwasilu

Tanzania Journal of Engineering and Technology (TJET)

This paper presents the design of surface mounted permanent magnet synchronous motor (SMPMSM) intended for traction applications in standard gauge railway (SGR) for passengers. The proposed motor is rated 350 kW with a base speed of 3000 rpm. Moreover, speed control strategy with stability analysis is incorporated in the design to validate the performance of the SPMSM. The simulation results achieved a torque of approximately 1115 Nm at a terminal voltage of 800 V. The flux linkage of 150 mVs which ensures effective electromagnetic pertinence while maintaining safe magnetic loading because the flux density is less than 1.5 T. The …

Challenges, Trends, And The Role Of Lstm In Ai-Based Predictive Maintenance Of Electrolyzers For Solar Hydrogen Systems: A Review, Yani Koerniawan Kuatno, Muhamad Zahim Sujod

Turkish Journal of Electrical Engineering and Computer Sciences

The transition toward low-carbon energy systems has increased interest in hydrogen as a clean energy carrier, with solar-driven water electrolysis emerging as a promising technology due to its high efficiency and compatibility with renewable energy sources. However, dynamic operating conditions and intermittent renewable input accelerate electrolyzer degradation, reducing reliability and system lifespan. Predictive maintenance (PdM), supported by artificial intelligence (AI), offers a data-driven approach to anticipate failures and improve operational durability. This review systematically investigates AI-based PdM approaches for electrolyzers, with an emphasis on long short-term memory (LSTM) networks and Internet of things (IoT) integration. Following PRISMA 2020 guidelines, 35 …

Dynamic Focal Loss Adjustment For Railway Defect Detection, Mehmet Koç, Ridvan Özdemi̇r, Ömer Gerek

Turkish Journal of Electrical Engineering and Computer Sciences

Railway infrastructure is critical to the safe and efficient operation of transportation systems, and the early detection of defects is essential for preventing catastrophic failures. Automated defect detection methods are therefore crucial for maintaining continuous safety while reducing maintenance costs. Although Focal Loss is widely used in object detection under class-imbalanced conditions, its fixed α parameter may limit its effectiveness in detecting rare defects. In this study, we propose an adaptive α-tuned Focal Loss approach that dynamically adjusts class weights based on average precision (AP) values. By iteratively optimizing α without relying on gradient-based optimization, the proposed method improves the …

Relative Rate Observer-Based Online Tuning Mechanism For Single-Input Interval Type-2 Fuzzy Pid Controllers, Oqba Aldreiei, Cenk Ulu, Mert Can Kurucu, Müjde Güzelkaya

Turkish Journal of Electrical Engineering and Computer Sciences

The characteristics of the footprint of uncertainty (FOU) in interval type-2 membership functions (IT2-MFs) are crucial to the performance and robustness of interval type-2 fuzzy controllers (IT2-FCs). However, existing IT2-FC design approaches mostly use fixed FOU structures. This study proposes an online membership function (MF) adjustment mechanism for a single-input interval type-2 fuzzy PID controller (SIT2-FPID)  that adjusts the FOU of the antecedent MFs and weights of the consequent MFs, respectively, to achieve high performance and robustness. The proposed online adjustment mechanism consists of a relative rate observer (RRO), a two-input rule-base adjustment system, and a first-order smoothing filter. The …

Cuip-X25: A Real-World Network Intrusion Dataset For Next-Generation Ai-Driven Security, Arshad Iqbal, Sohail Asghar

Turkish Journal of Electrical Engineering and Computer Sciences

The efficacy of artificial intelligence (AI) in intrusion detection systems (IDS) is critically dependent on high-fidelity training data. However, as detailed in the manuscript's literature review, existing benchmark datasets are predominantly synthetic, outdated, or imbalanced and fail to capture the complexity of the contemporary threat landscape. To bridge this gap, this study introduces CUIP-X25, a novel real-world cyber-attack dataset captured over a four-month period using a dionaea honeypot deployed on a public network. Unlike synthetic alternatives, this dataset provides an authentic representation of modern adversarial tactics, techniques, and procedures, encompassing 3.16 million real events across ten distinct attack categories, including …

Robust Load Frequency Control For Multiarea Electrical Power Systems Via Analytical Proportional-Integral-Derivative Plus Second Order Derivative Controller Design, Yavuz Güler, Mustafa Nalbantoğlu, Ibrahim Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

This research presents a proportional-integral-derivative plus second order derivative (PIDD2) controller design based on the Direct Synthesis Method (DSM) for load frequency control (LFC) of interconnected power systems. The parameters of the proposed PIDD2 controller are determined using the DSM, which offers an analytical approach for tuning. The design approaches have been developed specifically for single, two, and three-area power systems, encompassing nonreheated and reheated thermal turbines. In the proposed design method, the best values of PIDD2 controller parameters were found by using a multicriteria objective function that includes the integral of absolute error (IAE) and settling time. In response …

Boundary Layer Sliding Mode Control Strategy For Variable-Speed Compressor In Deep Freezers: Experimental Validation Of Energy Efficiency And Freezing Capacity, Sertan Aksoy, Kami̇l Çeti̇n, Sezai̇ Taşkin

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents the design and experimental validation of a nonlinear sliding mode controller developed for a deep freezer equipped with a variable-speed compressor. The proposed control strategy aims to minimize energy consumption while maintaining rapid and stable cooling performance under varying ambient conditions. A detailed thermal model of the deep freezer was established using an equivalent resistance–capacitance network representation, enabling precise analysis of temperature dynamics. The sliding mode-based control algorithm dynamically adjusts the compressor’s operating frequency according to temperature deviation, ambient conditions, and time-dependent factors, providing robust performance without requiring parameter retuning for different models. Beyond theoretical-based analysis, a …