UAV動力システムにおけるn-オクタノール添加が燃焼性能と排出に及ぼす影響
Impact of n-Octanol Addition on Combustion Performance and Emissions in UAV Power Systems (原題)
Maria Căldărar, R. Mirea, M. Dombrovschi, G. Badea, Flavia-Elena Blaga, Răzvan Roman
🤖 gxceed AI 要約
日本語
本研究は、UAV用ハイブリッド推進システムにおいて、Jet-A燃料へのn-オクタノール添加(10%、20%、30%)が燃焼性能と排出挙動に与える影響を実験的に調査した。アイドル時にはO10でCO排出が約9.9%削減され、排気温度も低下したが、高負荷ではオクタノール比率が高いと熱的制約が生じる。O10が最もバランスの取れた性能を示し、小規模ハイブリッドUAV推進の移行戦略として有望である。
English
This study experimentally investigates the effect of n-octanol addition to Jet-A fuel on combustion and emissions in a hybrid UAV power system. At idle, O10 reduced CO by ~9.9% and lowered exhaust temperature, but at high load, higher octanol fractions introduced thermal constraints. O10 provided the most balanced performance, suggesting moderate blending as a transitional strategy for small-scale hybrid UAV propulsion.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、UAVや小型航空機の脱炭素化はまだ初期段階であり、本研究成果は国産の小型ハイブリッド推進システムにおける代替燃料の可能性を示す点で参考になる。ただし、実用化にはさらなる試験と規制対応が必要。
In the global GX context
Globally, this research contributes to the limited literature on alternative fuels for small-scale aviation, particularly UAVs. It provides empirical data on n-octanol blending that could inform future sustainable aviation fuel (SAF) strategies for niche applications, though broader applicability requires further study.
👥 読者別の含意
🔬研究者:Provides experimental data on n-octanol blending in micro-turboprop engines, useful for alternative fuel research in small-scale aviation.
🏢実務担当者:May inform UAV manufacturers or operators exploring drop-in fuel alternatives to reduce emissions, though practical adoption is not immediate.
📄 Abstract(原文)
The present study experimentally investigates the influence of n-octanol addition to Jet-A fuel on the combustion performance and emission behavior of a micro-turboprop-based hybrid UAV (“Unmanned Aerial Vehicle”) power system. The experiments were conducted on a dedicated hybrid propulsion test bench equipped with a KingTech micro-turboprop engine mechanically coupled to a T-Motor electric generator and supplying a regulated 48 V DC bus. The system is capable of delivering approximately 3 kW of continuous electrical power, with peak values reaching 3.5 kW. Jet-A and three n-octanol/Jet-A blends containing 10%, 20%, and 30% n-octanol by volume, denoted O10, O20, and O30, respectively, were tested under four operating regimes ranging from idle to 2500 W electrical load. Exhaust gas temperature, carbon monoxide, sulfur dioxide, nitrogen oxides, electrical output, and near-field pollutant dispersion were evaluated. The results show that n-octanol addition affects engine behavior in a strongly load-dependent manner. At idle, the O10 blend reduced CO concentration from approximately 2520 ppm for Jet-A to approximately 2270 ppm, corresponding to a reduction of about 9.9%. At the same operating condition, O10 reduced exhaust gas temperature from approximately 498.3 °C to 463.2 °C, while O20 and O30 produced stronger cooling effects. At intermediate regimes, the oxygenated molecular structure of n-octanol contributed to lower CO formation in selected cases, indicating improved combustion-completeness behavior. At high load, however, exhaust gas temperatures converged toward or exceeded those of Jet-A, particularly for O30, showing that higher octanol fractions may introduce additional thermal constraints. Among the tested fuels, O10, corresponding to 10% n-octanol by volume, provided the most balanced behavior across the investigated operating range, from idle to 2500 W electrical load. The dispersion measurements performed at 30 m from the source further showed that ambient pollutant concentrations are strongly influenced by wind speed, wind direction, and plume transport. These findings support moderate n-octanol blending as a promising transitional strategy for small-scale hybrid UAV propulsion systems, while highlighting the need for future repeated testing, direct fuel-flow measurement, and numerical dispersion modeling.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/fuels7030058first seen 2026-09-09 05:21:36 · last seen 2026-09-22 04:54:09
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