Research data for “Assessing the value of sizing and energy-management co-optimization for hybrid-electric distributed-propulsion aircraft”
Description
This dataset contains computational results supporting the manuscript “Assessing the value of sizing and energy-management co-optimization for hybrid-electric distributed-propulsion aircraft”. It includes results for 24 aircraft configurations evaluated using four strategies: a conventional powertrain reference (C), sequential design with rule-based energy management (S-Rule), sequential design with dynamic-programming energy management (S-DP), and multidisciplinary dynamic system design optimization (MDSDO). The data cover equivalent mass objectives, hybrid powertrain capacities, component mass breakdowns, constraint activity, battery-capacity adjustment studies, representative mission power and energy-management histories, and supplementary numerical verification studies. Data are organized in CSV and MATLAB MAT formats, with a README, a data dictionary, source information, and mappings to the manuscript figures and tables. Access to aircraft-specific parameters is restricted by project confidentiality requirements.
Files
Steps to reproduce
1. Download the dataset and extract the ZIP archive if applicable. Read README.md, DATA_DICTIONARY.csv and MAT_FILES.md for file organization, variable definitions and units. 2. Load the CSV tables using software that supports UTF-8 encoding. Use accepted24.csv to identify the 24 accepted configurations and the flags distinguishing the 22-case positive-coupling subset. 3. From four_strategy_results.csv, calculate the objective reductions for each configuration: hybridization gain = J(C) − J(S-Rule), fixed-hardware energy-management gain = J(S-Rule) − J(S-DP), and coupling gain = J(S-DP) − J(MDSDO). Calculate the medians across the 24 accepted configurations. 4. Use component_masses.csv and coupling_decomposition24.csv to examine component contributions. Follow the accounting flags to distinguish component details from subtotals. Use activity24.csv to summarize constraint activity. 5. Use the battery-capacity tables to compare accepted search points with their corresponding reference points, retaining the feasibility flags. Use the mission-history tables to inspect power allocation, SOC and fuel consumption. Cumulative fuel consumption is the initial remaining fuel minus the remaining fuel at each time. 6. Load the representative MAT files in MATLAB or Python with SciPy to inspect the saved result structures and model inputs. Compare configuration rankings and DP-grid results using the corresponding CSV tables. These steps reproduce the reported comparisons from the archived numerical results. The optimization methods and settings are described in the associated manuscript; the complete solver implementation is not included in this dataset.
Institutions
- National University of Defense TechnologyHunan, Changsha