KEYVAN Aviation offering flight charts including with Hi and Low level airways charts , flight procedure charts ( SID , STAR , APPROACH) in GEO PDF format and digital format. The charts produced according to the specific standards and requirements and our team designed charts layout according to the pilot most required and interested template. Avoiding to add unnecessary data , test and graphic elements on the map will help the pilot for comfortable usage from our generated charts.

KEYVAN Aviation , also offering visualization solutions which is included with the capability to visualize the aeronautical data and charts in any kind of GIS software.

.AERO Whois Database, discover comprehensive ownership details, registration dates, and more for .AERO TLD with Whois Data Center.

The design of new aircraft involves understanding the flow physics around the configurations as well as their stability and control characteristics over the entire flight envelope. Such a coverage is extremely expensive if wind tunnel and flight tests are the primary means of understanding the aerodynamic characteristics. Hence, there has been a push towards use of simulations for understanding aerodynamics of airframes early in the design process. However, high-fidelity simulations can quickly become very expensive, so there is a need to create multi-fidelity databases for aerodynamic data. For this purpose, IAI proposes to develop probabilistic aerodynamic databases, that not only provide confidence on the fit, but also characterize the sources of uncertainty. Morever, adaptive design of experiments are performed for maximum efficiency in database creation. The proposed ProForMA tool can fill in a critical gap in design optimization and certification tools used by NASA and the industry

Backup of the now-defunct "aircraft-database.com" online database of aircraft and aircraft engines, re-published under the original Open Data Commons Attribution License.

This is a dataset for the 25% scale DrivAer model. Data was collected in the Large Wind Tunnel at Loughborough University, a 2.5m^2, closed working section, fixed ground open return tunnel.The CAD geometry for the mounting hardware and the wind tunnel are all included in the dataset as ASCII .stl files, with the units in m. The CAD geometry of the DrivAer model has not been duplicated for this dataset.Photos of the set up and some unique model dimensions are also included. The model was supplied by FKFS and is a 25% scale DrivAer model with three backs, the estate, fast and notchback variants. The model included the 5 spoke wheels, complex underbody, wing mirrors, the drivetrain, an open front grill and a porous radiator. As the model is not symmetric, in the engine bay and on the underside, it was set at a geometric 0 yaw condition as measured in the wind tunnel. This is estimated to be +/-0.1 degrees.No corrections (for example blockage) have been applied to the data. All the data is presented in SI units and all measurements are from the origin (mid-track, mid-wheelbase on the tunnel floor) with x positive downstream and z positive up, using the right hand rule to find positive y.The data is split into '_Mean' and '_Instantaneous' for each measurement type (Force, Pressure, Flow Field). All the data was taken during the same test session with a total sample time of 300 seconds typically and 100 seconds for the measurements in the stagnation region. The different data sets are not correlated with each other in time. The Force data was sampled at 300Hz, Pressure data at 260Hz and the Flow Field data at 5Hz. The data presented in the '_Mean' folders is the arithmetic mean of that presented in the '_Instantaneous' folders. All the '_Mean' folders contain Comma Separated Variable (csv) files, for ease of parsing with your desired programming language, and the same data is provided in a .dat file that is set up to be read into TecPlot. The csv format was used to reduce size and complexity for the '_Instantaneous' data. Example MATLAB code has been provided (tested in 2018a) that reads both the '_Mean' and '_Instantaneous' csv files for the pressure and flow field measurements, plotting them accordingly.

15871 Global import shipment records of Aero from United States with prices, volume & current Buyer’s suppliers relationships based on actual Global import trade database.

SPECIAL NOTE: C-MAPSS and C-MAPSS40K ARE CURRENTLY UNAVAILABLE FOR DOWNLOAD. Glenn Research Center management is reviewing the availability requirements for these software packages. We are working with Center management to get the review completed and issues resolved in a timely manner. We will post updates on this website when the issues are resolved. We apologize for any inconvenience. Please contact Jonathan Litt, jonathan.s.litt@nasa.gov, if you have any questions in the meantime. Subject Area: Engine Health Description: This data set was generated with the C-MAPSS simulator. C-MAPSS stands for 'Commercial Modular Aero-Propulsion System Simulation' and it is a tool for the simulation of realistic large commercial turbofan engine data. Each flight is a combination of a series of flight conditions with a reasonable linear transition period to allow the engine to change from one flight condition to the next. The flight conditions are arranged to cover a typical ascent from sea level to 35K ft and descent back down to sea level. The fault was injected at a given time in one of the flights and persists throughout the remaining flights, effectively increasing the age of the engine. The intent is to identify which flight and when in the flight the fault occurred. How Data Was Acquired: The data provided is from a high fidelity system level engine simulation designed to simulate nominal and fault engine degradation over a series of flights. The simulated data was created with a Matlab Simulink tool called C-MAPSS. Sample Rates and Parameter Description: The flights are full flight recordings sampled at 1 Hz and consist of 30 engine and flight condition parameters. Each flight contains 7 unique flight conditions for an approximately 90 min flight including ascent to cruise at 35K ft and descent back to sea level. The parameters for each flight are the flight conditions, health indicators, measurement temperatures and pressure measurements. Faults/Anomalies: Faults arose from the inlet engine fan, the low pressure compressor, the high pressure compressor, the high pressure turbine and the low pressure turbine.

Each AeroCube-6 vehicle carries three dosimeters measuring electrons with energies from about 43 keV to about 830 keV and protons with energies ranging from 370 keV to 12 MeV. The dataset manager, Dr. Paul O Brien, can be reached at paul.obrien@aero.org. The data are described in AeroCube-6 Dosimeter Data README (v3.0), Aerospace Report No. TOR-2016-01155, The Aerospace Corporation, March 4, 2016, El Segundo, CA. Also see AeroCube-6 Dosimeter Equivalent Energy Thresholds and Flux Conversion Factors, Aerospace Report No. TOR-2017-02598, The Aerospace Corporation, July 1, 2019, El Segundo, CA.

Non-traditional data signals from social media and employment platforms for AERO.SW stock analysis

The Aerospace Data Recording Systems market is experiencing robust growth, driven by increasing demand for enhanced flight safety, stringent regulatory compliance, and the rising adoption of advanced aircraft technologies. The market, estimated at $1.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the ongoing need for comprehensive flight data analysis to improve aircraft maintenance, reduce operational costs, and enhance overall safety is a significant driver. Secondly, the expanding commercial aviation sector, coupled with the increasing adoption of sophisticated data analytics tools, is further boosting market expansion. Finally, the integration of data recording systems into next-generation aircraft designs, including unmanned aerial vehicles (UAVs), is expected to contribute significantly to market growth over the forecast period. Several challenges remain, however. High initial investment costs associated with implementing these systems, coupled with the complexities of data management and analysis, could pose constraints on market growth. Furthermore, the stringent regulatory landscape and the need for continuous system upgrades to accommodate evolving data requirements can also present hurdles. Despite these challenges, the long-term outlook for the Aerospace Data Recording Systems market remains positive, driven by the continuous need for improved flight safety and operational efficiency across the aerospace industry. Leading companies like Honeywell, Airbus, and Teledyne Technologies are actively involved in developing and supplying advanced systems, shaping the market's competitive landscape and fostering innovation. The market segmentation, while not explicitly provided, can be reasonably inferred to encompass various system types, aircraft platforms (commercial, military, general aviation), and geographic regions, further contributing to the overall market complexity and growth potential.

Aeronautical Data Quality Management Market Outlook

According to our latest research, the global aeronautical data quality management market size reached USD 1.19 billion in 2024, reflecting a robust demand for advanced data management solutions in the aviation sector. The market is expected to grow at a CAGR of 8.7% during the forecast period, reaching USD 2.51 billion by 2033. This impressive growth is primarily driven by the increasing need for real-time, accurate, and secure data to enhance flight safety, streamline air traffic management, and comply with stringent regulatory standards worldwide. The integration of innovative technologies, such as artificial intelligence and machine learning, further accelerates the adoption of aeronautical data quality management systems across various aviation stakeholders.

One of the key growth factors propelling the aeronautical data quality management market is the rapid digital transformation within the aviation industry. Airlines, airports, and air navigation service providers are increasingly leveraging sophisticated software and services to ensure the integrity and reliability of aeronautical data. The proliferation of connected aircraft, the expansion of unmanned aerial vehicles (UAVs), and the growing complexity of airspace management have created a pressing need for robust data quality frameworks. These frameworks not only help in maintaining data accuracy but also facilitate seamless data exchange between multiple stakeholders, thereby optimizing flight operations and minimizing risks associated with data discrepancies. The adoption of digital platforms is further encouraged by regulatory mandates from organizations such as the International Civil Aviation Organization (ICAO) and the Federal Aviation Administration (FAA), which require the implementation of stringent data quality standards.

Another significant driver is the increasing emphasis on flight safety and operational efficiency. With the global air travel sector rebounding post-pandemic, the volume of flights and complexity of airspace operations are rising, necessitating advanced data management solutions. Aeronautical data quality management systems enable real-time monitoring and validation of critical information, such as flight paths, weather conditions, and navigational data. This not only enhances situational awareness for pilots and air traffic controllers but also reduces the likelihood of incidents caused by erroneous or outdated data. Furthermore, the integration of predictive analytics and machine learning algorithms allows for proactive identification and correction of data anomalies, thereby improving the overall safety and efficiency of aviation operations.

Technological advancements and the growing adoption of cloud-based solutions are further catalyzing market expansion. Cloud deployment models offer scalability, flexibility, and cost-effectiveness, making them increasingly attractive to both large enterprises and smaller aviation stakeholders. The ability to access real-time data from anywhere, coupled with robust cybersecurity measures, ensures that sensitive aeronautical information remains secure and compliant with international standards. Additionally, the emergence of big data analytics and the Internet of Things (IoT) is enabling more comprehensive data collection, validation, and dissemination, supporting the evolution of smarter and more connected aviation ecosystems. These technological trends are expected to continue shaping the market landscape, driving further investments in data quality management solutions.

From a regional perspective, North America leads the aeronautical data quality management market, owing to its advanced aviation infrastructure, high adoption of digital technologies, and presence of major industry players. Europe follows closely, driven by stringent regulatory frameworks and significant investments in airspace modernization initiatives. The Asia Pacific region is witnessing the fastest growth, fueled by rapid expansion of air travel, modernization of airport infrastructure, and increasing government focus on aviation safety and efficiency. Latin America and the Middle East & Africa are also emerging as promising markets, supported by ongoing investments in aviation infrastructure and growing awareness of the importance of data quality in ensuring safe and efficient airspace operations.

This dataset is designed to assist in optimizing the dynamic mechanical behavior of aerospace structures, with a focus on aircraft materials and design parameters. It simulates data related to the design, material properties, and environmental conditions of aerospace structures, as well as the optimization process using quantum computing techniques. The dataset contains 300 rows and a variety of features that represent different design variables and their impact on the dynamic mechanical behavior of aircraft structures.

The target variables in the dataset represent the outcome of the optimization process, which includes metrics related to vibration damping, computational time, weight efficiency, and durability of the structures under various operational conditions.

Key Features:

Material Type: The type of material used in the construction of the aerospace structure (e.g., Aluminum, Titanium, Carbon Fiber). E (GPa): Young's modulus, a measure of stiffness of the material in gigapascals. ν: Poisson's ratio, which relates lateral strain to axial strain in the material. ρ (kg/m³): Density of the material in kilograms per cubic meter. Tensile Strength (MPa): The maximum tensile strength (in megapascal) the material can withstand. Young’s Modulus: A specific measure of the material's elasticity. Altitude (m): The altitude at which the aerospace structure is intended to operate (in meters). Temperature (°C): The operational temperature range for the aerospace structure (in degrees Celsius). Pressure (Pa): The operational pressure on the structure (in pascals). Operational Life (years): The expected operational life of the structure in years. Wing Span (m): The span of the wings in meters. Fuselage Length (m): The length of the fuselage in meters. Structural Thickness (mm): Thickness of the aerospace structure’s material (in millimeters). Structural Shape: The shape of the aerospace structure (e.g., Cylindrical, Rectangular, Tapered). Load Distribution: Type of load distribution used in the structural analysis (e.g., Uniform, Point load, Distributed). Quantum Algorithm Type: The quantum computing optimization method used (e.g., Chaotic Quantum Genetic, Shark Optimizer). Number of Iterations: The number of iterations run in the quantum algorithm during optimization. Optimization Time (sec): The time (in seconds) taken to complete the optimization process. Target Variables:

Vibration Damping: Categorized as Low, Moderate, or High, indicating the level of vibration damping achieved for the structure. Computational Time: The categorization of computational time required for optimization, represented as Short, Medium, or Long. Weight Efficiency: Categorized as Poor, Good, or Excellent, indicating the weight efficiency of the aerospace structure. Durability: Categorized as Low, Medium, or High, reflecting the durability of the structure under operational conditions.

Health management (HM) technologies have been employed for safety critical system for decades, but a coherent systematic process to integrate HM into the system design is not yet clear. Consequently, in most cases, health management resorts to be an after-thought or ‘band-aid’ solution. Moreover, limited guidance exists for carrying out systems engineering (SE) on the subject of writing requirements for designs with integrated vehicle health management (IVHM). It is well accepted that requirements are key to developing a successful IVHM system right from the concept stage to development, verification, utilization, and support. However, writing requirements for systems with IVHM capability have unique challenges that require the designers to look beyond their own domains and consider the constraints and specifications of other interlinked systems. In this paper we look at various stages in the SE process and identify activities specific to IVHM design and development. More importantly, several relevant questions are posed that system engineers must address at various design and development stages. Addressing these questions should provide some guidance to systems engineers towards writing IVHM related requirements to ensure that appropriate IVHM functions are built into the system design

ADFS is a web server system that integrates a database of allergenic proteins for food safety. This allergen database for food safety was launched as a project of the Novel Foods and Immunochemistry of National Institute of Health Sciences, and this project was partly supported by a grant from the Ministry of Health, Labor and Welfare. To survey the sequence homology in assessing a potential of allergenicity of a protein in the food, the database has been constructed to include known allergens and B-cell epitope sequences. This database includes 13 (aero animal, aero fungi, aero insect, aero mite, aero plant, contact, food animal, food fungi, food plant, gliadin, protozoan, venom/salivary, and worm) categorized allergens based on allergen type in AllergenOnline, with their accession numbers, epitope information, 3D-structure information, and sugar-containing information . This site also provides sequence search tools for obtaining the sequence homology of a certain protein or peptide relating to allergens (BLAST, epitope(peptide) search). Furthermore, this site provides allergenicity prediction tools of a certain protein (FAO/WHO method, Motif-based method).

SAM2_AERO_PRF_NAT data are Stratospheric Aerosol Measurement (SAM) II - Aerosol Profiles in Native (NAT) Format which measure solar irradiance attenuated by aerosol particles in the Arctic and Antarctic stratosphere.The Stratospheric Aerosol Measurement (SAM) II experiment flew aboard the Nimbus 7 spacecraft and provided vertical profiles of aerosol extinction in both the Arctic and Antarctic polar regions. The SAM II data coverage began on October 29, 1978 and extended through December 18, 1993, until SAM II was no longer able to acquire the sun. The data coverage for the Antarctic region extends through December 18, 1993, and has one data gap for the period of time from mid-January through the end of October 1993. The data coverage for the Arctic region extends through January 7, 1991, and contains data gaps beginning in 1988 that increase in size each year due to an orbit degradation associated with the Nimbus-7 spacecraft.

Aero Design Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

This repository contains the data compilation, gridded datasets, model output, model source code changes and model inputs for the paper: “AERO-MAP: A data compilation and modelling approach to understand the fine and coarse mode aerosol composition “.

There are two subdirectories as tar files:

collectoutputfiles.zip: which contains the detailed data descriptions in a csv files, gridded data in netcdf and model output in netcdf format. More details in the README file in that zipped directory.

modelfiles.zip: which contains the Source code changes and input files needed to reproduce the simulations in the paper. More details in the README file in that zipped directory.

Mexico No. of Passengers: Domestic: Mexican Airlines: Aero Calafia data was reported at 28,830.000 Person in Dec 2017. This records an increase from the previous number of 26,423.000 Person for Nov 2017. Mexico No. of Passengers: Domestic: Mexican Airlines: Aero Calafia data is updated monthly, averaging 0.000 Person from Jan 1992 (Median) to Dec 2017, with 312 observations. The data reached an all-time high of 32,759.000 Person in Jul 2017 and a record low of 0.000 Person in Oct 2014. Mexico No. of Passengers: Domestic: Mexican Airlines: Aero Calafia data remains active status in CEIC and is reported by Secretary of Tourism. The data is categorized under Global Database’s Mexico – Table MX.TA007: Number of Passengers: Domestic Flights.

Aero Fight S A Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

This RWTH dataset contains numerical data for aerodynamics and acoustics of configuration B1, which was defined in the H2020 ENODISE project (https://www.vki.ac.be/index.php/about-enodise). In this configuration, three 6-bladed XPROP-S propellers are installed side-by-side on the leading edge of a wing with an airfoil shape of NLF-Mod22(B).

The investigated operating point is:

• The incoming flow velocity U∞ = 30 m/s, the wing angle of attack AoA = 2 deg, the propeller advance ratio J = 0.8, and the relative blade phase angle between propellers ∆Φ = 0.

Wall-resolved Large eddy simulations (LES) of the configuration are performed based on the multiphysics flow solver m-AIA of RWTH. The far-field noise is predicted by the Ffowcs-Williams and Hawkings (FW-H) method. The data set contains the time-averaged aerodynamic results predicted by the LES simulation and the aeroacoustic results calculated by the FW-H method. It should be noted that the aeroacoustic data is only a preliminary result and it will be updated soon.

More details of the simulation and measurement setups are explained in the attached document ENODISE_B1_RWTH.pptx.