How do Aerospace Engineers use data to solve problems?

Em missões espaciais podem ser utilizados também dados coletados de sondas e rovers para avaliação das reais conjunturas dos planetas, asteróides e das luas, com o somatório dos demais dados atmosféricos, geológicos e magnéticos pelos Engenheiros Aeroespaciais! 👨🚀🌌🛰️

As a flight test engineer, I participated in the a350 validation campaign from the telemetry room. Parameters like angular speed, control stick’s position and ailerons’ position, together with pilot’s feedback, were compared and monitored in real time and gathered for future analysis. This data helps identify problems, validate design specifications, and perform on-the-fly validation to ensure the aircraft responds as the pilot desires, with the reactivity and speed necessary for each maneuver. The telemetry room serves as a central hub for live data analysis and communication with the flight crew, enabling engineers and pilots to refine the aircraft's control law for optimal performance and overall safety.

Data collection is a key part of the beginning of any Aerospace/Aviation led analysis. Taking for instance the Aircraft performance analysis. Various data sources are pivotal to this like aircraft sensor data, fuel and oil consumption, airframe and engine utilisation, pilot and cabin reports, unscheduled component removals from the aircraft, etc. The above help us calculate Aircraft and Engine health performance, reliability, fuel efficiency, Aircraft system and component reliability respectively.

Every measurement/recording/etc. shall be justified when designing an experiment, or devising a telemetry list. Sometime trade offs are required, and experience/heritage shall support tge selection. When possible, redundant but dissimilar measurements shall be envisaged (see the Hubble experience).

aerospace engineers use data at every stage of the engineering lifecycle to address challenges and optimize aerospace systems. The integration of data-driven approaches, advanced simulations, and emerging technologies like machine learning empowers engineers to design, analyze, and refine aerospace systems with a level of precision and efficiency that was previously unattainable. The continuous feedback loop of data analysis, simulation, and testing allows aerospace engineers to push the boundaries of innovation while ensuring the safety and reliability of the systems they design.

1) check to see if the overall data is making sense and trending properly; strain gauges wired incorrectly, mismatches in daq timestamps, columns mixed up - make sure your data is the right data and it is trustworthy. 2) Be curious - interrogate the data from different directions, sub calculate forces from pressures; look at positions to calculate velocities, ; Also have a think about the physics at play - I've been most successful when the logic of the underlying physics is supported with the data - though be careful to not suffer from confirmation bias and let the cart lead the horse.

As Análises de Dados pelos Engenheiros Aeroespaciais são utilizados para o entendimento na questão da evolução das variáveis e transformações em um certo período de tempo e também como prevenção de eventos futuros, bem como uma variável pode interferir em outras.

When it comes to data analysis from systems in aerospace, you often face a gigantic table of in the order of 20,30,40,50,60 columns with millions of rows, and you end up with a CSV files thats 5GB in size and takes 25 mins to load. The key is “Data Stratification”, start by selecting two-three columns, that would show how the data is progressing. Example in flight data Long, Lat, and Altitude. See the entire flight and then select where you wish to snip. Then add columns of interest vs time or any variable of interest, and continue the analysis that unfolds as you keep asking questions like “how was the roll during that maneuver”…

In my opinion, engineers analyze the data to extract meaningful information and insights. Data analysis can involve various methods and tools, such as statistics, mathematics, physics, computer science, and artificial intelligence. Data analysis can help you understand the behavior, patterns, trends, relationships, and anomalies in the data. For example, you might use data analysis to compare the performance of different designs, to identify the root causes of failures. Also data analysis helps in prediction of upcoming problems.

Os Engenheiros Aeroespaciais utilizam técnicas de Visualização de Dados como: Gráficos 2D e 3D, Mapas de Calor, Diagramas de Contorno, Imagens e Vídeos, Animações, Realidade Virtual e Realidade Aumentada, Diagramas de Seta e Vetores, Diagramas de Blocos e Fluxogramas, Plotagem de Dados Geoespaciais e Dashboards Interativos! Assim conseguem acessar os Insights preciosos e descobrindo informações relevantes para quem tem o poder de decisão e seus colegas!

Data shall be presented in a way that grants an easy and quick grasp on what data present, and what are the implications. Bar and/or pie charts are useful, but also trend lines can provide an insight if properly presented. For instance, adfing reference like average, upper/lower limits, target perfo and so forth may support in making data trends more readable.

Visualizing data is imperative for aerospace engineers to find meaningful patterns within complex analysis and clearly communicate insights. Charts spotlight key performance metrics while images reveal physical phenomena. For one of my turbine acoustics project, animated CFD models showed turbulence flows causing noise qualitatively. Frequency spectra plots then quantified blade passing tones generating the loudest acoustics. This paired visual approach combining vivid physics representations and targeted metrics enables intuitive diagnosis of design changes to resolve issues. With multifaceted analysis and interdisciplinary decisions, data visualization is an indispensable innovation catalyst.

I agree with @Fariq below. Have a read of Edward Tufte's book on displaying quantitative information - a picture tells a thousand words

A Modelagem de Dados permite que os Engenheiros Aeroespaciais acessem e entendam sistemas complexos, sendo assim realizam através da Modelagem Matemática, Simulações Computacionais, Modelos Estruturais, Modelagem de Sistemas Dinâmicos, Modelos de Propulsão, Modelagem de Controle, Modelos Estatísticos, Modelagens de Condições Ambientais, Modelagens Geoespaciais e Modelos de Confiabilidade, como recursos eficazes sobre os comportamentos!

One example of data modelling is utilizing computational fluid dynamics (CFD) simulations that allows creating virtual representations of real-world systems. For example, one of my projects utilized CFD to model the three-dimensional geometry of an axial turbine in a commercial aircraft engine in order to predict noise patterns. By post-processing the unsteady surface pressure results over time and applying Laplace transforms in MATLAB to study in the frequency domain, an acoustic profile pinpointing which components contributed most significantly to noise was generated. This combination of geometry modelling, multivariate simulation, and post-processing data analysis gave insights to redesign turbine blades for lower noise.

DO this earlier than now - I'd consider it before the collection phase to understand the expected characteristics of the system (you may not need the data collection effort).....and without it you cannot justify your physics holds true. This is exactly how we design aircraft: we design them with calculations, build them, test them and compare the data to the calculations which supports our certification basis - often there is calibration for known unknowns

É um processo onde os Engenheiros Aeroespaciais interagem ao longo de todo o processo do ciclo de vida do projeto aeroespacial, isto é, do design inicial até às operações contínuas, assim buscam garantir resultados fidedignos bem como a melhor assertividade nas decisões e sistemas com melhorias contínuas!

Nesses cenários, é fundamental empregar feedbacks contínuos para aprimorar o produto de dados mediante novas abordagens. Além disso, é importante considerar a perspectiva de criar um produto de dados reutilizável, capaz de fornecer insumos para alimentar outros modelos ou visualizações.