Engineering

Abstract: Transforming energy supply systems is vital for achieving a sustainable, carbon-neutral future. However, this transformation entails significant complexity, driven by the integration of carbon-free energy sources, tightly coupled technical and economic subsystems, and the need to ensure reliability, resilience, and affordability. Addressing these challenges requires advanced planning approaches capable of capturing uncertainty, multiple stakeholder perspectives, and system dynamics across temporal and spatial scales. In response, this article presents an integrated planning and operational framework that combines multi-actor, multi-criteria, and multi-stage stochastic optimization with surrogate modelling and artificial intelligence. The proposed approach enhances scalability, computational efficiency, and precision in the planning and operation of future energy systems. A case study illustrates its effectiveness in supporting high-resolution, uncertainty-aware decision-making.

Abstract: India entered the 2026 Strait of Hormuz crisis more exposed than any other major economy — its energy system characterized by deep import dependence, thin strategic reserves, and supply chains concentrated through a single maritime chokepoint. This report examines India's energy security vulnerabilities across LPG, natural gas, crude oil, and coal, and extends the analysis to aviation fuel, fertilizers, and helium. LPG emerges as the most acute vulnerability: 92% of imports transit Hormuz, against a strategic reserve of only 1.5–2 days. The crisis triggered important emergency measures — refinery stream diversion, US supply contracts, mandatory piped gas switch over — but significant structural gaps remain. The paper identifies the most important opportunities for further action: building strategic reserves across all fuel categories, installing NGL extraction units at LNG terminals to produce domestic LPG from rich imported gas, expanding non-Hormuz supply contracts, and scaling coal gasification for DME blending and synthetic gas. A key structural insight is that India's mitigations follow a cascade logic — each substitution shifts exposure toward a less Hormuz-vulnerable fuel, with domestic coal and solar-wind as the ultimate anchors. The combined strategic reserve building program would cost approximately $6.5–8.3 billion over a decade — less than India's annual LPG import bill of $12.6 billion.

Abstract: The integration of photovoltaic (PV) and wind turbine (WT) systems into modern power grids demands not only accurate component-level models but also a holistic understanding of their coordinated operation. This review bridges the gap between low-level device physics and high-level system coordination, offering a dual perspective often overlooked in existing surveys that treat generation and management separately. We systematically analyze PV models, from single-diode equivalent circuits to data-driven approaches, and WT models, ranging from aerodynamic and mechanical representations to simplified electrical equivalents suitable for stability studies. Critically, we then shift focus to the system level by examining Energy Management Systems (EMS) that enable hybrid PV–WT coordination. Unlike prior reviews that emphasize either component accuracy or dispatch strategies alone, this paper highlights the emerging synergy between hybrid PV–WT modeling and EMS architectures. By identifying mismatches between model fidelity and EMS requirements, this review maps a pathway towards more integrated hybrid renewable systems. The discussion synthesizes key trade-offs in scalability, uncertainty handling, and real-time feasibility, underscoring that true potential is unlocked only through intelligent integration of component models and control architectures.

Abstract: Wavy microchannels have been shown to enhance the heat transfer performance of microchannel heat sinks compared to straight microchannels. The present study introduces a bidirectional curved wavy microchannel design aimed at enhancing performance. Numerical simulations are conducted to investigate the thermo-hydraulic behavior of bidirectional curved and ordinary wavy microchannel under constant heat flux conditions, with Reynolds numbers ranging from 300 to 800. The results indicate that the bidirectional curved microchannel achieves optimal performance at an inlet velocity of 0.6 m/s. Compared with the ordinary wavy microchannel, the Nusselt number of the bidirectional curved wavy microchannel increases by 95.3%. The average secondary flow intensity in bidirectional curved wavy microchannels with A₂ = 2 mm and λ₂ = 12 mm is enhanced by 153%. The enhanced heat transfer is attributed to the increased main flow velocity and the secondary flow intensity due to the bidirectional curved, which promote coolant mixing.

Abstract: The transition to net-zero energy systems involves substantial uncertainty in exogenous conditions such as policy, fuel prices, and technology deployment. Conventional energy system optimization models, formulated as forward problems, excel at identifying a single least-cost solution but provide limited insight into the diverse configurations feasible within an acceptable cost range. This study proposes a hierarchical inverse-analysis framework integrating a genetic algorithm (GA) and linear programming (LP). The upper-level GA explores a broad space of exogenous conditions, including policy conditions, fuel prices, end-use electrification rates, and CO2 capture rates, while the lower-level LP rigorously optimizes operations for each candidate. The framework applies explainable AI (SHAP) to identify dominant cost-determining factors and their interactions, and employs k-means clustering to compress the high-dimensional feasible solution space into representative scenarios. As an illustrative demonstration, the framework is applied to a hypothetical 2050 net-zero case for the Kanto region. The results confirm diverse solution generation, identification of dominant factors, and extraction of five representative scenarios, enabling systematic distinction between common and variable elements characterizing net-zero pathways. The proposed framework extends energy system modeling beyond single-optimum solutions toward interpretable decision-support analytics for long-term net-zero planning under deep uncertainty.

Abstract: Rising demand for high-performance battery thermal management systems (BTMS) has rendered single-mode cooling insufficient for advanced lithium-ion batteries (LIBs) in new energy vehicles (NEVs), particularly under high discharge rates. This study proposes a synergistic hybrid BTMS integrating composite phase change material (CPCM)–Aluminum foam with liquid cooling to enhance thermal regulation of cylindrical battery modules under 5 C discharge conditions. Multiple liquid cooled plate (LCP) configurations, including serpentine, straight, and leaf-shaped designs, together with different flow channel topologies (FCTs), were systematically investigated and optimized. The effects of coolant flow speed (CFS) and ambient temperature are also analyzed. Results indicate that the optimized leaf-shaped LCP with FCT #2 delivers superior performance, limiting the maximum temperature to 309.98 K, reducing temperature difference by 7.6 %, and decreasing pressure drop by 88.79 % compared to the serpentine configuration. Increasing CFS improves heat dissipation and delays PCM melting, although it raises pressure losses. Furthermore, the proposed system maintains a cell-to-cell temperature difference below 0.51 K, indicating excellent thermal uniformity. Compared to a CPCM-only system, the hybrid BTMS reduces peak temperature by 8.81 K under elevated ambient conditions (309.15 K), demonstrating strong potential for reliable and efficient thermal management in demanding operating environments.

Abstract: To investigate the critical extinction criterion for fire extinguishment through acoustic oscillation and achieve the transition from empirical qualitative studies to quantitative and precise applications for acoustic fire extinguishment, this study, based on the flame-fuel cycle model proposed by Friedman, A.N., conducts modifications and extensions of several critical parameters. By modifying the Quintiere-Spalding B-number model for gaseous fuels and premixed combustion, and carrying out multi-factor extinction experiments considering combustion type, flame size, and fuel properties, a generalized acoustic extinction criterion model applicable to gaseous fuels is established, breaking through the serious limitation that the original theory was only applicable to liquid fuels with similar Prandtl numbers. Through logarithmic fitting of methane, propane and butane diffusion flames, the flame height exponent α = 0.6868 is quantitatively determined, and the flame type terms for methane and propane gas premixed flames at an equivalence ratio ϕ ≈ 1 are found to be kM = 3.7975 and kP = 2.8123, respectively. The critical extinction criterion for gaseous fuel flames is finally obtained as Θ′ A = 0.0817. Meanwhile, comprehensive universal validation of the above parameters is performed. Finally, the study reveals the dual effect of acoustic frequency on flame extinction and the phenomena of flame necking and fracture under acoustic field interference, and discovers an abnormal increase in the critical particle velocity for acoustic extinction in the relatively high-frequency regime above 90 Hz. This research provides theoretical support for the engineering application of acoustic fire extinguishing technology and the in-depth exploration of the mechanism of sound-induced flame extinction.

Abstract: Both temperature and solar radiation cause variations in photovoltaic output power. Nev-ertheless, each variation has a maximum power point, which represents the photovoltaic output's maximum efficiency. Photovoltaic power must be managed at the highest point in order to achieve optimal efficiency. This can be accomplished by employing a converter to regulate the photovoltaic output voltage at the maximum power. This study proposes a quadratic boost converter (QBC) to control photovoltaic output power by using the Deep Recurrent Neural Network (DRNN) algorithm. The goal of DRNN is to decrease ripple at the maximum point and speed up time to reach the maximum power point. QBC is de-signed to obtain a higher DC output voltage than a regular boost converter, so it can elim-inate the use of a step-up transformer if the photovoltaic is connected to an inverter. The proposed method is applied to a 50 Wp solar panel with an Arduino microcontroller as the controller device. The experimental results demonstrate that the DRNN algo-rithm-based QBC has effectively controlled the solar panel output power at the maximum point with a smoother ripple and a faster response. QBC has also been able to produce higher voltage output according to its characteristics.

Abstract: This article considers several aspects of creating a combined bioreactor heating system without the use of external power grid power sources. It's known to intensify anaerobic digestion of organic waste, the bioreactor temperature is maintained within a specified range. A bioreactor heating system based on a "water jacket" , that heated by combustion of coal-water fuel has been developed. A technology for preparing and burning coal-water fuel using a radial circulation injection device is offered. Calculations are performed to determine the optimal temperature regime for the combustion process. The results obtained can contribute to the optimization of waste management technologies and ensure environmental sustainability by reducing carbon dioxide emissions and waste accumulation.

Abstract: Lithium-ion batteries (LIBs) are ubiquitous in modern technology, powering consumer electronics, electric vehicles, and energy-storage systems. As these systems age, internal structural degradation can lead to reduced performance, diminished lifetime, and increased safety risks, including thermal instability. Because many forms of degradation occur internally and are not detectable through external measurements, accurate assessment of structural health can be observed by non-destructive imaging and robust analysis techniques. In this study, a transfer learning-based deep learning framework for classifying the structural health conditions of 18650-format LIB cells using X-ray micro-computed tomography (µCT) imaging is proposed. This approach includes preprocessing that extracts radial CT slices and core-region cropping to capture localized 3D structure. The dataset is balanced and augmented with transformations and rotations, and a pretrained InceptionResNet-V2 model is fine-tuned to distinguish between various cell conditions. Modified classification layers with dropout and class weighting improve robustness. Initial results demonstrate that the model can identify internal structural differences with promising accuracy, supporting the development of automated µCT-based battery health assessment and safety diagnostics.

Abstract: Hero Ridge shale oil reservoirs are characterized by stacked pay boxes, strong vertical heterogeneity, rapid variations in lithology and in situ stress, and significant well-to-well interference during platform-scale three-dimensional development. Conventional fracturing design methods that focus mainly on single-well stimulation are insufficient to simultaneously address fracture propagation, reservoir contact and development economics. Taking the 1H platform and representative wells in the upper member of the Xiaganchaigou Formation (E₃², Boxes 5-6) as examples, this study establishes a workflow integrating reservoir-engineering dual-quality evaluation, single-well parameter optimization, platform-coordinated fracturing, dynamic pore-pressure-stress updating, and EUR-IRR response-surface analysis. Results show that Box 6 has better reservoir quality and fracability than Box 5, with average porosity, oil saturation and brittle-mineral content of 7.6%, 50.9% and 67.6%, respectively. Well 1H6-1, with a 1500 m lateral, penetrated Class I + II sweet spots for 90.6% of the horizontal interval, providing a geological basis for efficient volume stimulation. For conventional sweet-spot wells, the optimal single-well design includes eight clusters per stage, a pumping rate of 18 m³/min, a fluid intensity of 35 m³/m and a proppant intensity of 3.25 m³/m. For 200 m-spaced wells, the pumping rate and fluid intensity should be reduced to 16 m3/min and 32 m³/m, respectively, with 100 m³ of prepad gel to mitigate fracture overlap and stress interference. Further response-surface analysis based on actual EUR-IRR data shows that the highest EUR occurs at a lateral length of 4000 m and well spacing of 50 m (EUR = 566,261 m³), but the IRR is -27.1%. By contrast, the best IRR point is at a lateral length of 4000 m and well spacing of 600 m (IRR = 14.5%), with EUR of 377,500 m³. This demonstrates that the production-optimal and economics-optimal schemes are not coincident. The expanded pilot scheme has an after-tax IRR of 9.31%, after-tax NPV of RMB 131.38 million and payback period of 5.93 years. The results indicate that fracturing optimization in Hero Ridge should move from single-well engineering maximization to integrated decision-making that combines single-well design, platform coordination, lateral-length/well-spacing optimization and techno-economic evaluation.

Abstract: This project checks methods in wind power forecasting by comparing Gregorian calendar based on seasonal alignments with the vedic lunisolar calendar parallely. Rather than using timestamps like most forecasting methods, this project seeks to determine whether periodic cycles based on nature’s cosmos could reveal correlational patterns of wind activity surges and enhance accuracy. This study exploits the SOLETE dataset from SYSLAB, Denmark, which consists of 15 months of power generation alongside weather data. The dataset underwent processing with the CleanTS tool (an R package) and it was transformed into Gregorian and Vedic time frameworks. Within both time frameworks, the forecast approaches a hybrid forecasting model integrating “Variational Mode Decomposition (VMD) with Gaussian Process Regression (GPR)” was designed and assessed [11][12 ]. The Vedic forecasting approach is slightly better as it gives RMSE of 2.5519 and MAE of 2.0763, while the Gregorian forecasting approach gives RMSE of 2.6123 and MAE of 2.1424. The MAE correlation analysis over months revealed differing patterns within the two forecasting approaches with vedic giving better correlation than gregorian. This suggests that the Vedic calendar forecasting approach is better than the gregorian calendar system, which is based on natural cycles and is lunisolar, it is more accurate in capturing the chaotic signal of wind patterns than the arbitrary gregorian forecasting approach. This project helps in research, questioning the standard time representation in forecasting models which uses the gregorian timestamps and gives idea that if we put natural cycles through alternative calendar systems will it enhance the accuracy of energy predictions, potentially updating grid integration and operational planning.

Abstract: Photovoltaic (PV) systems are fundamentally limited by spectral mismatch between the solar spectrum and semiconductor band gaps, resulting in thermalization and transmission losses that reduce overall efficiency. This paper presents a critical review of spectral management approaches, focusing on solar spectrum splitting as a means to improve energy conversion. Existing strategies, including multijunction solar cells, optical spectrum splitting, dispersive and diffractive systems, luminescent solar concentrators, hybrid photovoltaic–thermal systems, and photonic filtering, are analyzed and compared. While these approaches improve spectral utilization, they are often constrained by fabrication complexity, alignment sensitivity, angular dependence, or inherent energy losses. A qualitative, integrative literature review methodology is used to evaluate performance, limitations, and implementation feasibility across these technologies. The analysis shows that no current approach simultaneously achieves high efficiency, low complexity, and robust performance under diffuse illumination. Photonic spectrum splitting combined with independently operated photovoltaic channels is identified as a promising direction. However, the absence of experimental validation remains a limitation, and future work should focus on developing compact, alignment-tolerant systems for practical applications.

Abstract: Diffusers in diffuser-augmented wind turbines (DAWTs) require high-camber airfoils operating at low Reynolds numbers (Re) and its laminar separation bubbles significantly complicate aerodynamic predictions. This study provides an experimental and numerical data for a custom-designed airfoil tested at Re = 68k–159k and angles of attack α = 0°–17.5°. Lift, drag, and pressure coefficient (C_p) distributions were measured experimentally. The XFOIL, the fully turbulent 3D k-ω SST, and the γ-Re_θ transition RANS models were validated against the experimental data using multiple quantitative metrics. The γ-Re_θ model demonstrated superior performance, achieving lift Maximum Absolute Percent Error of 1.6–3.4%, near-zero bias, and coefficient of determination > 0.99. It accurately captured the laminar separation bubble pressure plateau at mid-chord, with mean gross-averaged C_p percent errors of 8.1% and 2.1% for upper and lower surfaces, respectively. In contrast, the k-ω SST model overpredicted lift by up to +9.8% at Re = 68k and underpredicted drag by up to 66%. XFOIL showed poor reliability in transitional flow regimes. Sensitivity analyses confirmed the robustness of the γ-Re_θ model across the tested Re and α ranges. The generated experimental dataset, combined with the validated transition-sensitive RANS approach, provides a strong foundation for low-Re airfoil and DAWT diffuser design. Future work should extend experimental measurement below Re = 5x10⁴ and above 2x10⁵, including post-stall conditions and system level designing.

Abstract: After leakage from buried hydrogen-blended natural gas pipelines, gas may seep through soil into enclosed spaces and form buoyancy-driven non-uniform combustible clouds. The effect of ignition delay on such clouds remains insufficiently understood, especially regarding the relationship between visible flame behavior and local thermal response. In this study, 44 soil-seepage combustion experiments were conducted in a 1.5 m × 1.5 m × 1.5 m enclosure. Methane and hydrogen concentrations at three heights, flame evolution, and transient temperatures were measured using gas sensors, high-speed imaging, and thermocouples. The ignition delay ranged from 27 s to 5429 s, with hydrogen blending ratios of 10–30 vol% and ignition positions at the floor, middle, and ceiling. The results show that longer ignition delays generally weakened visible flame luminosity and propagation extent. However, the peak temperature measured at the central thermocouple did not decrease accordingly. For the long-delay subset with td > 307 s, the central peak temperature increased with ignition delay, with R² = 0.74. Concentration measurements indicate that preferential hydrogen migration and slower methane redistribution continuously reconfigured the local flammability state before ignition. These findings suggest that, in enclosed soil-seepage HBNG scenarios, prolonged ignition delay may weaken visible flames but does not necessarily reduce local thermal exposure.

Abstract: The fractional flow function in the Buckley--Leverett equation is conventionally assumed to be S-shaped. Rastegaev recently established a sufficient condition for this property based on monotonicity of \(m''/m'\), and showed that strict convexity of the phase mobilities alone is not sufficient. This note demonstrates that Rastegaev's criterion is not necessary, by exhibiting an explicit one-parameter polynomial family of strictly convex mobility functions, \[ m_\alpha(s)=s^2(1+\alpha s^4)=s^2+\alpha s^6,\qquad \alpha\ge 0, \] for which the symmetric fractional flow function \[ f_\alpha(s)=\frac{m_\alpha(s)}{m_\alpha(s)+m_\alpha(1-s)} \] retains its S-shape for every \(\alpha\ge 0\): \(f_\alpha''>0\) on \((0,1/2)\), \(f_\alpha''(1/2)=0\), and \(f_\alpha''<0\) on \((1/2,1)\). The mobility lies outside Rastegaev's class once \(\alpha>(5-2\sqrt 5)/15\approx 0.0352\). The proof reduces the sign of \(f_\alpha''\) to four explicit polynomial inequalities on \([0,1/4]\), certified by the convex-hull property of Bernstein coefficients. The same Bernstein certificate applies, without modification, to \(m_\alpha(s)=s^2(1+\alpha s^q)\) for every integer \(q\in\{2,3,4,5,6\}\); at \(q=7\) the certificate just fails.

Abstract: This research gives a region-based technology, economic, and environmental assessment of biomass power production in Yalova, Türkiye. The assessment includes an inventory of biomass resource data, how well the materials can be converted to electricity, assumptions regarding the transportation cost of biomass, and a financial analysis of the biomass project. The study will assess if a utility size biomass facility can be constructed in Yalova. In addition to the revised feasibility framework, a MATLAB-based optimization layer was introduced to determine the feedstock blend that minimizes delivered feedstock cost per unit of electricity under regional availability constraints. The compiled inventory indicates a total biomass potential of 610,498 t/year in Yalova, equivalent to 55,040 toe/year, with forestry residues forming the dominant resource class. The configurations of mixed waste stream (forest residues) resulted in the highest yield of electricity. Based in part on this data and optimizing the feedstock allocation by an annual period (to favour chicken litter/forest residues), the potential for generating electricity from a 220,000-tonne-per-annum biomass facility in Yalova is 40.37 GWh/year; approximately 0.184 MWh is produced for each tonne of feedstock delivered at a cost of 286.8 USD/MWh. As evidenced by these results, biomass energy is a technologically feasible means of contributing to emissions reductions in Yalova through implementing data-supported feedstock allocation methodologies that enhance the reliability of investment and operational planning.

Abstract: Long-term disposal and management of nuclear waste is one of the major hurdles for nuclear energy. Deep geological disposal, an idea originating in the 1970s, is currently the preferred path being followed in many countries. However, acceptance of this approach is hindered by societal concerns around the safety of the “one-million years” geological disposal and passing the waste burden to future generations. iMAGINE, an integrated nuclear system based on molten salt fast reactor technology with self-sustained iso-breeding, online clean-up and reverse reprocessing, has been proposed as an innovative way to eliminate the demand for a long-term high-level waste disposal. In this work, we investigate the use of iMAGINE as a technological approach for reimagining high-level nuclear waste management by overcoming the challenges of classical partitioning and transmutation (P&T). After discussing the current status and limitations of P&T along with an overview of the nuclear waste classification and management framework existing in major nuclear power producing countries, we demonstrate the potential impact of iMAGINE in simplifying the disposal of high-level waste. The results indicate that among the key long-lived radionuclides, many lie well below the criteria for requiring deep geological disposal. Besides Finland, India and Russia where most (or all) of the important long-lived fission products exceed current high-level waste thresholds, only two to three fission products (generally with half-lives from 30 to 100 years) exceed the national limits in Canada, China, France, Germany, Japan, and South Korea, while concrete conclusions can’t be drawn for the UK and US based on available information. The results presented here do not aim to make inflated claims but should be seen as the scientific basis for deeper discussions amongst all nuclear waste disposal stakeholders to explore a novel technological solution for rethinking high-level waste management and possibly eliminating the need for a “one-million years” geological disposal.

Abstract: The downtime and maintenance associated with the failure of a wind turbine gearbox can be significant, leading to high repair costs. Currently, when warning signals are received through the condition-monitoring system, wind farms typically perform maintenance on the gearbox to ensure continued operation. However, reducing power not only leads to an imbalance between the life of the transmission system and the amount of electricity generated, but also reduces revenue; Moreover, it faces the dilemma of being unable to accurately grasp the health status of the gear transmission system, which increases the difficulty of life extension. To address the above issues, this study proposes a gearbox life extension strategy based on wind turbine control methods. This approach breaks through the limitation of traditional methods where damage assessment is decoupled from operating conditions, and transforms the previous research status where life and power generation optimization were treated as separate entities. And the effectiveness of the life extension strategy was validated using actual operating data from China. The results demonstrated that the proposed strategy could extend the gearbox's life and enhance total power generation.

Abstract: We present TADI (Tool-Augmented Drilling Intelligence), an agentic AI system that transforms drilling operational data into evidence-based analytical intelligence. Applied to the Equinor Volve Field dataset, TADI integrates 1,759 daily drilling reports, selected WITSML real-time objects, 15,634 production records, formation tops, and perforations into a dual-store architecture: DuckDB for structured queries over 12 tables with 65,447 rows, and ChromaDB for semantic search over 36,709 embedded documents. Twelve domain-specialized tools, orchestrated by a large language model via iterative function calling, support multi-step evidence gathering that cross-references structured drilling measurements with daily report narratives. The system parses all 1,759 DDR XML files with zero errors, handles three incompatible well naming conventions, and is backed by 95 automated tests plus a 130-question stress-question taxonomy spanning six operational categories. We formalize the agent's behavior as a sequential tool-selection problem and propose the Evidence Grounding Score (EGS) as a simple grounding-compliance proxy based on measurements, attributed DDR quotations, and required answer sections. The complete 6,084-line, framework-free implementation is reproducible given the public Volve download and an API key, and the case studies and qualitative ablation analysis suggest that domain-specialized tool design, rather than model scale alone, is the primary driver of analytical quality in technical operations.