How can you optimize aerospace systems to minimize costs and maximize efficiency?

Looking at the problem. its more of a multi objective Optimization problem ,with some to be maximized and some to be minimized. In addition ,can look at machine learning to recommend the decision variables. I would recommend model it using Gurobi, leveraging python.

Optimizing aerospace systems for costs and efficiency involves a delicate combination of technological advancements, streamlined processes, and strategic decision-making. One of the most important aspects is to have a Systems Engineering Organisation, with a team of skilled Systems Engineers & Systems Architects. Laying out clear and concise Development Plans supported by an efficient Systems Engineering Management Plan is a must and one of the important initial steps. Having defined clear Functions & allocated to respective Systems with a clear definition of boundaries and interfaces, keeping safety and regulations into consideration, concludes the Top Level development activities laying out a strong base for further development.

As a good designer, as per need , you can make the objective function constraint and vice versa! You can also use machine learning to input these.

In addition to defining the objectives and constraints, specific engineering techniques can be employed to optimize aerospace systems. Examples include the use of computational modeling to predict the performance of a given design, aircraft weight reduction through material selection and design optimization, and advanced manufacturing techniques such as additive manufacturing. These techniques can enhance the efficiency and effectiveness of the system, resulting in a reduction of production costs and an increase in mission success rates.

If you don’t need it don’t ask for it. Sometimes we add things in just in case. What we really mean is “we don’t want to make the decision to remove a feature from the program.” The consequence is the program becomes unaffordable , gets cancelled and the customer spends a fortune running on a system that needs to be replaced. In reality it is the program manager who needs to tell the customer that their wish list is unaffordable.

Instead of looking at each function such as performance, stress, design/ mechanism/ configuration & their specific requirements such as load cases in isolation, Accounting all the requirements at a time for optimisation using multi objective & multi disciplinary optimisation methods would yield an effective optmised solution at system level.

One of the best ways is to perform TRADE-OFF at different development levels, especially at the Systems Level. Trade-Off analysis allows us to asses, manage & define an optimized architecture providing a complete oversight of different aspects of the System. Trade-off analysis is an efficient decision-making process that involves evaluating the advantages and disadvantages of different options or choices. It allows you to find the most favourable balance between various technical, financial and managerial aspects of a complex system development contributing to efficient decision-making. planning and development, with a clear & accurate impact assessment.

When choosing an optimization method, it's essential to evaluate the problem's characteristics, such as size, complexity, linearity, continuity, and convexity of the objective and constraint functions..

Tools are very important aspects of Complex Systems Development like in the Aerospace sector. Tools selected well, not only for the technical activities but also overall project management & collaboration can save huge amounts of engineering hours & hence costs, which otherwise are wasted in a majority of the companies. Optimization of tools in engineering activities like MBSE, Requirements Management, Software Testing, etc. is a must in a highly regulated complex engineering industry. Similarly when it comes to management, like work/dependency management, collaboration, etc one shouldn't underestimate the optimization required. The key to optimization lies in building a team with the skillset & improving them with rigorous training.

All sets of data - simulation results, experimental results and real life data have own uncertainty margins. Assumption that the experimental data is better than the simulation data could be misleading. The same goes for so called real- life data. Validity of data is constrained exclusively to their method uncertainty. If uncertainty margins of three sets of data above overlap each other, we could potentially conclude that mean values are the closest to inherently unknown true values. But if they are detached from each other, a good systems engineer will make in-depth investigation before committing to customer requirements. And certainly AI soon will be an intrinsic tool helping systems engineers modelling various scenarios.

Don't lean too heavily on your simulation results. Build a test platform and record results in all environmental conditions, with some degree of testing beyond the limits of the environmental parameters, to leave some margin for variation in performance. Never overlook any performance fault. It may be pointing to a fatal design flaw. No simulator can cover all eventualities. You have to build and test.

The VALIDATION activity is a must and should be carried out at every Development Level. To carry out Validation efficiently there should be a proper Validation Plan laid out providing clear information on how the whole process needs to be carried out. It should specify the necessary criteria for Validation, various steps of validation, validation workflow, necessary roles and their respective responsibilities, completion criteria and end deliverables/results. The overall Validation is as good and efficient as the Validation Plan and its implementation.

Analyzing and validating the results of the optimization process is a crucial step to ensure that the obtained solutions meet the requirements and expectations.

Keep testing. Do not assume that if you built x number of them that you will never have an outlier. You may, and it may clue you in to a weakness in the design that should be addressed. Do not overlook an outlier.

In the INCOSE Handbook is written that the best sweet spot of cost and effort of applying system engineering process is 15% of Project budget. You should invest around 15% of your budget into system engineering to get the best Return of Investment.