MAPNA Group (Iran)

Abstract This paper introduces the Intelligent Formation Integrity & Damage Prevention System (FIDPS), an AI-native platform designed to revolutionize real-time drilling operations through proactive formation damage prevention and operational optimization. The system addresses ten comprehensive formation damage types through a multi-layered architecture integrating real-time data acquisition, machine learning ensembles, and closed-loop optimization. FIDPS demonstrates capability to reduce formation damage incidents by 60%, decrease non-productive time by 40%, and improve drilling efficiency by 25% through its intelligent prediction and mitigation framework. The platform's microservices architecture enables scalable deployment across multiple wells while maintaining 99.9% availability and sub-second processing latency.

In this study, a novel analytical model is developed to predict the service life of generator rotor fan blades under both static and fatigue loading conditions. The static model assesses failure using a failure assessment diagram (FAD) based on fracture mechanics by calculating the fracture and load ratios. In the fatigue model, forging defects are initially represented as cracks with defined geometries based on available empirical relationships. Critical regions are then identified based on the stress distribution in the blade under cthe generator’s cyclic loading condition. The blade’s service life is subsequently estimated using Paris’ law, fracture toughness, and crack growth constants, taking into account the critical crack size and the number of load cycles. The results reveal that the lowest safety factor for the blade occurs under static loading conditions, with a value of 2.02.

Multiphysics analysis of rotating electrical machines has become essential for accurately predicting their coupled electromagnetic, thermal, and structural behaviors under real operating conditions. This study presents a comprehensive finite element analysis of a medium-power induction motor designed by Pars Generator. Technical specifications were derived from electromagnetic simulations, while structural evaluations included dimensional assessments of the stator and frame assemblies. The motor was subjected to various loading conditions, including mechanical pretension in bolts, rotor unbalance forces, thermal cycling caused by electrical losses, and electromagnetic stresses under fault scenarios. Specifically, a two-phase short-circuit fault was simulated to investigate its impact on stator integrity, with mapped electromagnetic loads. To improve computational efficiency, localized mesh refinement and torque mapping were implemented. The results demonstrate that maximum stresses occur at bolted joints and critical frame regions, while the composite inner cover and stator core maintain safety margins. These findings validate the reliability of the motor design under severe operating conditions.

Random-wound stator windings are widely used in low-voltage rotating machines, where insulation integrity is critical to ensuring long-term reliability. Conventional pre-impregnation tests such as insulation resistance, polarization index, and surge tests are often insufficient for identifying hidden defects in random-wound stator ground insulation that may be temporarily covered after resin impregnation. This study investigates the application of a reduced-level DC high voltage test as a diagnostic method to detect insulation defects prior to impregnation without overstressing the insulation. Two permanent magnet synchronous motors rated at 80 kW and 46 kW were tested under controlled factory conditions. While conventional tests did not reveal any faults, the pre-impregnation DC high voltage test successfully detected hidden defects, including damaged wire enamel and misplaced wires, which were confirmed through visual inspection. After localized repair, the stators passed both pre- and post-impregnation tests, validating the effectiveness and non-destructiveness of the proposed approach. The findings demonstrated that pre-impregnation DC high voltage testing can serve as a practical and reliable in-house quality assurance method, reducing the risk of costly post-impregnation failures and improving the long-term service life of random-wound machines.

This paper proposes an advanced control architecture leveraging Model Reference Adaptive Control (MRAC) principles for Phase-Locked Loop (PLL) systems, specifically engineered for coordinated transformation in rotating frame-based control architectures of high-power converter applications. The presented methodology introduces an innovative hybrid approach that synthesizes linear control mechanisms with adaptable nonlinear control elements, underpinned by model reference adaptive control theory. The robust stability characteristics of the closed-loop system have been rigorously established through comprehensive Lyapunov stability analysis. Utilizing the theoretical framework of the Lyapunov stability theorem, the adjustment parameters for the adaptive mechanism are derived autonomously. Additionally, the proposed framework exhibits inherent self-tuning capabilities under various grid perturbations. Extensive simulation studies conducted under nominal, unbalanced, and harmonic grid conditions substantiate the efficacy of the proposed methodology.

Computed tomography (CT) is a widely used medical imaging modality which provides invaluable visual representation of various conditions ranging from neurological lesions such as haemorrhage, stroke, tumors etc. to cardiovascular disorders like calcium deposits, pulmonary embolism and many other pathologies. However, the ionizing radiation from the CT machine’s x-ray tube has to be kept in check, because over-exposure is related to elevated risks for genetic mutation or cancer development. In this work, we attempt to reduce the radiation exposure required for high-quality CT image formation by establishing rank sparsity in principal components’ domain and developing a compressed sensing framework based on a novel nonlocal and nonlinear low-rank principal component analysis technique in image denoising, which will be subsequently incorporated as a building block for a sparse-view CT image reconstruction framework under the umbrella of convex analysis. Experiments will show that the proposed strategy provides a viable solution for low-dose CT, outperforming other well-known nonlocal image restoration models in both denoising and reconstruction tasks. In particular, the proposed method will offer 4-10% improvement in root-mean-squared error relative to other nonlocal methods at little extra computational time.

This paper presents the design and simulation of an isolated bi-directional DC/DC converter tailored for battery energy storage systems (BESS) and electric vehicle (EV) applications, incorporating a novel control algorithm. The power circuit of the DC/DC converter comprises a two-stage bidirectional configuration. The first stage employs a twoquadrant chopper, while the second stage utilizes a dual active half-bridge as a bidirectional resonant converter. A unique control algorithm, designed to achieve a high conversion ratio and rapid response time, governs the entire converter. Simulation results obtained from MATLAB Simulink demonstrate the effectiveness of the proposed control algorithm.

Introduction: This study aimed to assess the relationship between occupational stress and the symptoms of anxiety, depression, and stress among the employees of an automobile manufacturing factory in Iran. Methods: This was a cross-sectional analytical study of employees in the automobile manufacturing industry. OSIPOW occupational stress and Depression Anxiety and Stress Scale 21 (DASS-21) questionnaires were used to collect data. Results: In our survey, 4565 employees with a mean age of 42.59 ± 4.45 years were evaluated. All the participants had some degree of job stress, 33.3% were in low-stress group and 66.7% were in high-stress group. White collar workers had higher job stress compared to blue collar workers. The prevalence of the symptoms of depression, anxiety, and stress symptoms was 22.6%, 24%, and 29.3%, respectively. The participants with high job stress reported higher symptoms of depression, anxiety, and stress [OR=3.40, CI 95% (2.83-4.10)], [OR=2.35, CI 95% (1.99-2.78)], [OR=2.26, CI 95% (1.94-2.63)]. In linear regression analysis, being white collar, having higher work experience, exposure to noise and stressors in the workplace, and having symptoms of depression are recognized as the factors that might be involved in creating occupational stress. Conclusion: This research provides further support regarding the negative effects of the psychosocial hazards of the work environment on the mental health of the workers which was evaluated by measuring depression, anxiety, and stress symptoms. There is a need to manage employee’s occupational stress to maintain the general health of human resources, as well as mental health.