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Daniel Guggenheim School of Aerospace Engineering

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https://hdl.handle.net/1853/70742

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College of Engineering

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Aerospace Design Group

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Aerospace Systems Design Laboratory (ASDL)

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Air Transportation Laboratory

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Cognitive Engineering Center

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Space Systems Design Laboratory (SSDL)

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A Method for the Conceptual Design of Integrated Variable Cycle Engines and Aircraft Thermal Management Systems

(Georgia Institute of Technology, 2023-11-28) Clark, Robert Arthur

Development efforts for current and future military fighter aircraft are tasked with fulfilling strenuous requirements, many of which are at odds with each other. An increased demand for high power electronics and weapons systems has put the need for auxiliary power generation and heat dissipation on par with the more traditional military aircraft requirements of extended range and speed, stealthiness, and enhanced maneuverability. All of these requirements can be traced back in some way to the propulsion system, which is arguably the single most important subsystem on any aircraft, military or commercial. For decades, the low bypass ratio mixed flow turbofan (MFTF) has been the architecture of choice for the propulsion systems that power military fighter aircraft. However, the competing nature of modern aircraft requirements has begun to highlight the drawbacks of the fixed cycle MFTF, and has led to the development of variable cycle engines (VCEs). Variable cycle engines show promise in increasing thrust, reducing fuel consumption, and improving heat dissipation capability, all of which are critical requirements for military aircraft. There has been a further recognition that thermal management requirements need to be assessed earlier in the conceptual design phase in concert with the propulsion system, given that current aircraft such as the F-35 struggle to meet heat dissipation requirements. Unfortunately, the existing conceptual cycle design methods used to select engine cycles were not developed with variable cycle engines in mind. The objective of this research is to enhance conceptual design-level modeling methods for integrated design of variable cycle engines and thermal management systems in order to better achieve aircraft-level mission requirements. The key difference between a variable cycle engine and a traditional fixed cycle engine is the presence of variable geometry features whose positions are modulated specifically to move air between the different streams in the engine. A method of variable cycle engine design is presented that accounts for these variable geometry as a means of aiding the propulsion system designer during the conceptual design phase of the propulsion system. A series of research questions, hypotheses, and experiments that build on each other are posed in order to address the need for conceptual cycle designers to better understand how variable cycle engines impact the cycle design process, especially in the context of integrated propulsion and thermal management systems. The first research question and experiment address the need to determine optimum variable geometry positions for off-design analysis of a variable cycle engine throughout the complete flight envelope of a fighter aircraft. Existing design methods require an optimizer to determine variable geometry position targets at every off-design operating condition used during aircraft mission analysis, which, for refined mission analysis methods can be hundreds or thousands of off-design points. This results in significant cost due to the repeated use of the optimizer. This thesis develops a method for determining variable geometry schedules, which can be generated cheaply with only a small number of optimizer calls, and then used in place of the optimizer during off-design evaluation of the variable cycle engine. The use of variable geometry schedules during the off-design process is shown to significantly reduce the computational cost of off-design analysis of variable cycle engines. The second research question and experiment examine the design process for variable cycle engines and incorporate the use of the variable geometry schedules directly into the engine design process. Current design methods in the literature utilize nested optimization techniques in order to determine the optimum positions of variable geometry features during the design process. The method in this thesis takes the variable geometry schedules, shown in the first experiment to be useful for off-design analysis, and incorporates them directly into an engine design loop. The use of variable geometry schedules during the design process is shown to reduce the overall number of required engine design iterations by two orders of magnitude, relative to current design methods in the literature. The third research question and experiment address the need to assess the impact of integrating a thermal management system into the design of the variable cycle engine. The literature is sparse on how incorporating the design of a thermal management system directly into the engine design process impacts the selection of the design cycle for a variable cycle engine. This thesis demonstrates how design integration of the engine and thermal management system shifts the location of the optimal cycle within the cycle design space of a variable cycle engine. Furthermore, the utility of variable geometry schedules is demonstrated through a cycle design scenario, where schedules that minimize fuel burn or maximize heat dissipation capability for the aircraft are shown to lead the cycle designer to different locations in the optimized cycle design space. The design methods utilized for each of these experiments are synthesized into an overall conceptual design method called PREHEAT-V (Preliminary/Conceptual Design Method for Handling Heat and Aircraft Thermal Management in Variable Cycle Engines), which incorporates variable geometry optimization techniques directly into a multiple design point cycle design process. The PREHEAT-V design method allows cycle designers to evaluate large candidate variable cycle engine design spaces in a computationally efficient manner, and assess the impact of heat dissipation requirements on the optimum design cycle. The PREHEAT-V method emphasizes evaluating aircraft-level mission requirements, rather than engine-level requirements, since the ultimate barometer of success for military aircraft is mission capability, not engine capability.
An Integrated Framework for Evaluating Commercial Supersonic Aircraft Design Trade-offs and Operational Constraints

(Georgia Institute of Technology, 2023-08-25) Wen, Jiajie

Ever since the Concorde performed its final flight in 2003, the world might finally see a new commercial supersonic transport (SST) by the end of the decade. Although the COVID-19 pandemic has significantly impacted the commercial aviation industry, an SST could provide operators with the opportunity to offer unique services and differentiate themselves from competitors when the industry recovers. A civil supersonic aircraft can greatly boost the productivity of onboard passengers by significantly reducing trip time. However, this benefit comes at the expense of additional fuel consumption and en-route noise. Most countries prohibit civil supersonic overland flight due to the disturbance of sonic boom, and such restriction is not likely to be lifted for a large commercial supersonic aircraft to cruise over land at full supersonic speeds. By analyzing the performance characteristics of SSTs, as well as the commercial aviation flight network and market demand, it becomes obvious that SSTs should be regarded as specialty products. Traditional aircraft design is driven by a fixed set of design requirements. These requirements are imposed during aircraft sizing in the conceptual design stage and followed by appropriate network and operations analyses. Due to the relatively limited use cases of an SST, conducting network-level operational analysis can greatly inform the definition of design requirements (such as supersonic cruise Mach number and design range). Furthermore, operational considerations such as limitations on overland cruise Mach number and en-route sonic boom propagation can both have direct impact on the success of future commercial supersonic operations. This research does not take into account low-boom designs, as they are improbable choices for larger commercial supersonic jets. Instead, this thesis attempts to address the lack of feedback between conventional SST design requirement definition and its network as well as operations. The research consists of three main steps: • Improving the current supersonic flight routing capability (based on rasterized search algorithm) by including aircraft mission analysis and sonic boom carpet estimation. • Creating a network simplification technique that simplifies a forecasted supersonic flight network in 2050 while retaining its underlying structure. • Using the developed flight routing capability and network simplification technique to evaluate the impact of different mission performance requirements and operational constraints.
Integrated Framework for Aircraft Design and Assembly Tradeoffs

(Georgia Institute of Technology, 2023-08-15) Huynh, Dat

Aircraft passenger traffic is expected to increase and lead to demand for 40,000 new aircraft by 2040. Aircraft production rates have been rising to meet this demand, but delivery backlogs are growing at even faster rates. Large backlogs can lead to missed deliveries, canceled orders, and traffic congestion due to too few planes for too many passengers. This comes at a time when the two primary aircraft manufacturers, Boeing and Airbus, are competing for dominance in a market that a third competitor, Comac, is poised to enter as well. Increased production rates to better meet customer demand would thus also allow one of them to gain the edge over the others. Aircraft production rates must be increased and done so without inducing enormous costs to meet passenger demand and to stay competitive. Changes to aircraft assembly, which constitutes up to 50% of total production time and up to 30% of total production cost, during the design process can address this. Current Design for Assembly methods addressing assembly changes during the design process range from Product Lifecycle Management techniques to various methods in Systems Engineering and have been used to great success. However, few such methods consider aircraft design in their analysis, which would enable further tradeoff capabilities and greater production rate and cost improvements. Those methods that do explicitly incorporate aircraft design analysis alongside the assembly analysis insufficiently consider several key assembly aspects such as assembly sequence planning (ASP) and more detailed assembly line balancing (ALB), which can be used to optimize the assembly line. This work establishes a better connection between the aircraft design and assembly disciplines and joins them by accounting for geometry and material factors common to both using ASP and ALB. First, the correct analysis fidelity for the aircraft design process and ASP's geometric reasoning process is determined to allow geometry data to easily flow between the two, linking them. Then, the ASP and ALB analyses are combined and augmented to account for the novel materials traded during aircraft design and the manufacturing processes used to make them. Afterwards, the most promising assembly sequences are optimized for using metrics representative of both ASP and ALB so that sub-optimal assembly sequences are not line balanced, reducing the overall problem size to explore the large design space more efficiently. From all this an integrated aircraft design and assembly framework is made that strives to obtain higher production rates, lower costs, and better tradeoffs by leveraging the additional feedback loops produced via consideration of variables common to both disciplines. Finally, this framework is tested and compared with a state-of-the-art framework on a representative aircraft's wingbox and its production system. The developed framework demonstrates it is able to obtain significantly higher throughput and lower cost values by simultaneously: sizing the aircraft to meet its performance requirements; accounting for the aircraft's geometry via ASP determining assemblability and ALB determining the consequent task time, cost, and space requirements; incorporating the aircraft's material system via usage of specialized sequences in ASP and identification of optimal line balances in ALB given the material's manufacturing process and its subsequent resource requirements; and flowing all this manufacturing information back upstream to maximize the aircraft's manufacturability during its sizing. The developed framework is thus able to make tradeoffs such as what size the aircraft should be for a given performance, throughput, and cost requirement, what the maximum production rate is given a design, material, manufacturing process, and spatial constraint, and what costs are incurred given a desired production rate. This provides the designer with a greater understanding of the problem and its constraints and allows them to see what factors can help them increase production rate as well as what the associated costs are.
A Methodology for Identifying Experiments For Uncertainty Mitigation in Complex Multi-Disciplinary Design

(Georgia Institute of Technology, 2023-08-08) Yarbasi, Efe Yamac

The design of a flight vehicle is a lengthy, expensive process spanning many years. Thanks to increasing computational capabilities, designers have been relying on computer models to make predictions about the real-life performance of an aircraft. However, the results obtained from computational tools are never exact due to a lack of understanding of physical phenomena, inadequate modeling, and abstractions in product details. The vagueness in quantities of interest is uncertainty. Because most of the cost is committed early in the design, any decision made on quantities involving significant uncertainty may result in budget overruns, schedule delays and performance shortcomings, as well as safety concerns. The goal of this thesis is to develop a systematic methodology to identify and mitigate the sources uncertainty in aircraft design, with a focus on uncertainties due to a lack of knowledge epistemic, namely model-form and parameter uncertainties. An aircraft is a complex, multi-disciplinary system that is built as integration of other intricate subsystems. To make sure that all subsystems and the integrated system meet the pre-defined requirements, Systems Engineering (SE) practices are widely adopted throughout the aerospace industry. However, SE methods fall short of accounting for the implications of using a specific modeling and simulation environment, and simulation-borne uncertainties. The first objective of this thesis is to enhance SE by providing a way to incorporate components of a modeling and simulation activity while adhering to established principles. The second research area of this thesis addresses some of the prominent issues faced in identifying critical uncertainties, so that resources can be allocated to uncertainties that would make the biggest impact on the design. After the critical uncertainties are identified, computational and/or physical experiments can be designed to create new information, so that any epistemic uncertainty can be reduced. However, real-life operational conditions cannot be exactly duplicated in tests due to many reasons such as testing facility constraints. The third and final research area addresses the identification of optimal experiment conditions for computational and physical experiments such that real-life operational conditions can be best approximated. For each focus area, the solution approaches to each research question will be demonstrated on an appropriate, self-contained problem. The cumulative output of this thesis will be a complete, four-step methodology that can be tailored for a specific application, effectively guiding it, and highlighting the pitfalls to avoid.
A Probabilistic Approach for Margin Allocation Tradeoffs Pertaining to Early Two-Stage-To-Orbit Launch Vehicle Design

(Georgia Institute of Technology, 2023-07-30) Harris, Lea

The evolution of complex aerospace vehicle designs, traced by the progression of novel missions and state-of-the-art technology, has created a continuous need to adapt to new uncertainties in the design process. This sequence introduces more uncertainties into the decision-making process with increased sources and unknowns. Navigating early decision-making for requirements is often a trade-off between predicted uncertainties and willingness to take on risk. Less distinguishable uncertainties are difficult to address with substantive data or examples, which drives traditional design margin allocation to account for uncertainties through a deterministic proportionate value related to the risks of design variation. There are significant complications in adapting margin standards for new missions and novel technologies. Without applicable or accessible data to inform the impacts of uncertainties more effectively, the industry is driven to apply conservative margins or iterate as the design becomes more defined, which can result in costly redesigns and schedule slips. The needed quantitative and probabilistic design methods are often implemented at the disciplinary design level during the conceptual and preliminary design phases. There remains a need for quantitative methods in the systems engineering discipline to reduce uncertainties in decisions for estimated performance constraints and allocate margins. This research focuses on enabling a probabilistic design approach to tradeoffs during performance requirements and margin allocation to address underlying design uncertainties. The Probabilistic Uncertainty in Margin Allocation (PUMA) Framework developed by this research provides a foundation for decomposing the uncertain measures in a requirements decomposition and translating it into a quantitative modeling environment. The multidisciplinary and hybrid fidelity simulation environment harnesses conceptual design tools to identify drivers of uncertainty, employs model reductions for more accessible design simulations, and estimates response variability as a function of uncertainty parameters. The framework demonstration uses a single-stack, Two-Stage-To-Orbit launch vehicle concept with a mission to deliver a crewed payload to the ISS. The demonstrated scenarios for design and margin trades inform the likelihood of meeting aerodynamic, structural, and propulsion-based estimated performance measures. The tradeoff scenarios explored in this research provide an approach to effective decision-making by quantifying the performance variability due to uncertainties embedded in the design explored. The study evaluates the decisions to augment the design or adjust constraints and allocated margins, demonstrating how to effectively meet a baseline probability of success for the requirements design phase. The novel capability this framework provides is a quantitative approach to understanding uncertainty, substantiating decisions, and improving communication of uncertainty during the formulation phase of design.
Accelerated Simulation-Based Analysis of Emergent and Stochastic Behavior in Military Capability Design

(Georgia Institute of Technology, 2023-07-25) Braafladt, Alexander

In military capability design, the United States Air Force (USAF) is working to modernize to be ready to succeed in future operations. During the process, high-fidelity military simulation is used iteratively to build up understanding of complex military scenarios and consider technology and concept alternatives. While high-fidelity simulation is critical to the analysis, it is often expensive and time consuming to work with. In addition, the required pace for analysis needs to be accelerated as technology and threats rapidly evolve. In response to these challenges, the research in this thesis focuses on accelerating two central parts of simulation-based analysis in capability design. The first part focuses on improving methods for searching for emergent behavior, which is critical for building up understanding with simulation. The second part focuses on including stochastic responses from simulation in parametric models used during tabletop design exercises, which are critical for comparing alternatives. To accelerate simulation-based analysis of emergent behavior, a specific definition of emergent behavior is synthesized from the literature that prompts optimization approaches to be used for searching more quickly than with brute-force Monte Carlo Simulation (MCS). This definition also allows formulation of the new ENFLAME (Exploration of Nonlinear and stochastic Future behavior under Lack of knowledge using simulation-based Analysis to Manage Emergent behavior) framework for structuring activities working to manage emergent behavior with simulation-based analysis. Specifically in this work, the new LANTERN (Low-cost Adaptive exploratioN to Track down Extreme, Rare events using Numerical optimization) methodology for searching for emergent behavior as rare, localized and stochastic extreme events is developed that accelerates the process using novel Bayesian Optimization (BO) techniques that adaptively query the simulation to find rare events. In experiments with test problems based on the behavior expected with an Agent-Based Modeling (ABM) simulation approach, the new BO techniques show significant improvement over MCS. For accelerating analysis of stochastic behavior during tabletop design exercises, the ECDF-ROM surrogate modeling approach that uses Reduced-Order Modeling (ROM) techniques combined with a new field representation is developed. The surrogate modeling approach is shown to work effectively with distributions like those expected with military simulation, allowing parametric, interactive queries of distributions. A final demonstration of the techniques was completed using two scenarios developed in simulation with the Advanced Framework for Simulation, Integration, and Modeling (AFSIM). First, a Suppression of Enemy Air Defenses (SEAD) scenario was used to demonstrate the effectiveness of the new techniques at searching for rare, localized extreme events. Second, a four vs. four air combat scenario was used to demonstrate the effectiveness of the new technique for searching for rare, stochastic extreme events, and to demonstrate the new distribution surrogate modeling approach. The results together show that the LANTERN methodology accelerates the search for emergent behavior effectively for iterative simulation-based analysis of military scenarios and the ECDF-ROM approach enables parametric models of stochastic outcomes.
A Flexible Methodology for Analysis and Optimization of Unconventional Wing Structural Geometries Using a Computationally Efficient Aeroelastic Model

(Georgia Institute of Technology, 2023-05-19) Solano Sarmiento, Heriberto David

Aerospace research and industry have been focused on pushing boundaries and designing next-generation aircraft to meet the needs of the aviation sector and reducing its impact on climate change. During the early stages of design, it is important to design the structure to sustain loads specified by 14-CFR regulatory authorities while keeping the weight, sizes, and costs low. Unconventional designs, such as the Truss-Braced Wing (TBW) design and the Parallel Electric-Gas Utilization Scheme (PEGASUS), promise great structural and aerodynamic efficiency but require additional dynamic load considerations, and more accurate physical structural models. This work centers around the design and optimization of unconventional wing structures. A methodology is developed to best decide which model fidelity and tools to use during design space exploration to maximize exploration performance, with respect to the number of configurations considered, inter-model bias and correlation, and confidence of optimum. In addition, a multi-engine configuration structural model will be developed and tested to assess the ability of the lower-fidelity part of the methodology to assess different wing layout configurations based on diverse sets of structural constraints, as well as rib mass surrogate models to further improve its accuracy and lower its biases. For applications where high-fidelity models are not convenient, a process is developed that enables the inclusion of pre-trained stress field surrogates to better represent the stress of the structure when a beam model is used. Furthermore, a computationally efficient model of the truss-braced concept is developed, which has multiple components joined to one another as the primary structure. The model will be shown to have well-conditioned low-order physics, allow for dynamic loads, and have an improved fidelity thanks to the inclusion of strength and buckling considerations for all the individual structural components. To test the methodology, five sets of experiments will be carried out: 1) demonstrate the methodology of choosing appropriate model fidelity by tracking the number of feasible alternatives explored and fitness of solution tracked; 2) demonstrate the accuracy of the developed lower-fidelity model by comparing to a higher-fidelity model with regards to structure layout sizing; 3) demonstrate that by adding stress surrogates from a higher-fidelity source into a lower-fidelity model, it is possible to increase the amount of accurate information at early stages of design and aid in structural sizing; 4) demonstrate that the lower-fidelity model can properly analyze and size a complex multi-member structure; 5) demonstrate that the developed conditioning procedure lowers the condition number of the differential-algebraic equation system and improves its run-time, under varying conditions. Finally, the capabilities developed will be demonstrated to perform a study on the effects of engine placement and layout when addressing gust loads on the PEGASUS configuration, as well as a design space exploration of the TBW, with regards to key design variables for the structural assessment of novel configurations such as airfoil thickness, structural joint locations, and rib spacing, in both static and dynamic scenarios, with the dynamic scenarios looking at the strut placement as the main variable of interest. The resulting methodology provides a multi-fidelity, fully modular, and flexible approach for the analysis and sizing of unconventional wing structural designs at the conceptual phase, allowing designers to assess potential strengths and pitfalls of different layouts and configurations before committing to more computationally expensive efforts in the latter stages of design.
A Methodology for the Inclusion of Uncertainty in Space Logistics Campaign Planning and Optimization

(Georgia Institute of Technology, 2023-05-19) Downs, Chloe

Human space flight exploration is moving towards long-duration, sustainable campaigns that are much more complex than previous missions. Modern campaigns consist of multiple launches, in-space rendezvous, in-situ resource utilization (ISRU), and other factors that require more support than past missions and thus complicate campaign-level planning. Therefore, there is a desire to automate campaign planning to allow designers to examine more potential options faster and produce better-informed results for decision makers. Space flight logistics planning is a field that deals with planning these long-duration campaigns. Several methods of performing planning tasks for these types of campaigns exist, however, the existing literature does not sufficiently handle uncertainty considerations and calculations in this planning. The goal of this work is to develop a new method of space flight logistics planning that uses methods of optimization under uncertainty to better account for these uncertainties and allow more informed decision making. To achieve this goal, a taxonomy is created to classify the types of uncertainties that affect campaigns. This allows for identification of the major sources of uncertainty that affect campaigns as well as their impacts and potential mitigation techniques. From this, a methodology is developed which incorporates elements of probabilistic modeling as well as scenario-based uncertainty. This methodology will help designers examine more potential campaign options in detail earlier in the planning process, producing campaigns that are more robust to the impacts of uncertainties.
A Set-Based Methodology for Aircraft Design Space Exploration Enriched with Higher Fidelity Data

(Georgia Institute of Technology, 2023-05-18) Kallou, Evanthia

The aircraft design process is constantly evolving and subject to changes in political, industrial, and economic environments, which can affect performance and cost requirements. Decisions made early in the design process have downstream effects on the total project cost and design decisions. Additionally, novel configurations lack historical data, which makes early decisions uncertain. As the design progresses from conceptual to preliminary, more variables are needed to describe the design, making it difficult to perform optimization. Additionally, during preliminary design, there are challenges such as organizational barriers, uncertainty propagation, and the need to choose appropriate model fidelity. To address these challenges, a novel multi-level Set-Based Design Space Exploration (SBDSE) method is proposed, which uses classification and supervised dimensionality reduction methods to define and communicate design sets between disciplines and subsystems. The objective of this methodology is to establish an effective framework that facilitates both horizontal and vertical design communication and integration, while addressing the challenges inherent in design space exploration for preliminary design through the use of the SBDSE. The benefits of using the SBDSE methodology will be demonstrated through a multi-level aircraft design problem. Furthermore, the situations in which each communication scenario is preferable are identified. The consideration and documentation of physics assumptions, the computational budget, and the analysis code’s accuracy are crucial, while setting up the MDAO for each system and subsystem. The first research area regards the comparison between the multi-level communication of a point design versus a design set with hypercubic bounds. The second research area aims to tackle the issue of increased dimensionality in the input design space when analyzing lower-level subsystems and components. Dimensionality reduction methods can assist in identifying critical design variables and directions to sample from. If the dimensionality of the input design space for a subsystem is too computationally complex, the design of experiments (DoE) should be for the latent space. Dimensionality reduction should be based on the MDAO outputs to establish correlations and should occur before generation of approximation models. The number of design samples required for training approximation models is determined by the dimensionality of the design space. Finally, the third research area highlights the importance of selecting suitable analysis tools for each design group within a decomposition level, based on factors such as the complexity of the design, the type of analysis required and the level of fidelity needed. The expected contributions of this thesis are to provide a framework for more efficient design communication and integration, tackle challenges in design space exploration, demonstrate the benefits of SBDSE on a multi-level aircraft design problem, and emphasize the importance of documenting and considering physics assumptions, computational budget, and analysis code accuracy in an MDAO setup.
Assessing An Aerospace Application Of Digital Twins For Multi-Agent Dynamic Decision Making

(Georgia Institute of Technology, 2023-05-02) Marks, Ian

The concept of Dynamic Decision Making (DDM) is essential for achieving an overall goal by adapting to the results of previous decisions and unexpected environmental changes. Example applications of DDM in aerospace vary from individual predictive maintenance to multi agent tasking . When making dynamic decisions in a multi-agent scenario, the goal is to minimize uncertainty for future actions by predicting consequences for both the individual asset and the group. In a squadron with vehicles of the same type, it is expected that performance (e.g., fatigue rate and structural health ) vary form one vehicle to the next. Infusing individual performance capabilities and their uncertainties can overwhelm the decision maker. One approach to improve the decision-making process for multiple agents is by using Digital Twins, an authoritative virtual representation of a connected physical system. The digital twin’s aspects of computational, physical, and communications limits impact their overall utility. Furthermore, the aspects of fidelity, runtime, latency, and proximity (due to the physical requirements) need to be assessed to determine the value within multi-agent DDM. A vision for Digital Twins is to enable real time operational decision making by predictive and proactive measures while mitigating potential anomalies. This thesis seeks to evaluate the infusion of Digital Twins in a multi agent DDM architecture, the challenges with the infusion, and a comparison to historically deterministic decision-making processes for a relevant aerospace scenario to trade overall mission effectiveness. To that end, three steps are required: a method of evaluating different decision-making architectures, digital twin selection, and scenario definition. A structured decision-making process was developed such that both twinned and twinless multi agent DDM methods could be interchanged. The digital twin selected for evaluation was the airframe prognostic health of a remote-control aircraft. The digital twin determined how tightly a turn can be performed ( or ) as a function of health status mid-mission. A field surveillance/survey mission scenario was implemented with area surveilled as a metric. During the mission, each aircraft (twinned or twinless) defines their turn load, while a multi-agent coordinator modifies waypoints for agents. To ensure multi-agent interactions with DDM, a perturbance (treated as a gust event) occurs leading to one aircraft leaving the mission early and requiring the remaining aircraft to adapt their missions to mitigate the unexplored areas. Each aircraft leaves the mission area upon mission completion, digital twin health assessments or crashing. The assessment for permitting aircraft to leave the mission area is traded between the multi agent commander and by agents; both traded as a function of latency. Each agent has unique variations in both airframe life and digital twin architectures (instance vs aggregate) and are traded. The design of experiments enables trades across the agents factors of the digital twin fidelity (fit error with sensor to loads), initial health, and overall system latency. From the data generated, surrogate models were fit and analyzed to determine variable significance via ANOVA as well as a comparison between a turn only (treated as a twinless/human baseline) and various digital twin fidelities. Sensitivity analysis revealed that airframe life had the greatest impact on overall mission effectiveness among both digital twin-infused dynamic decision-making methods. Following closely was the influence of overall system latency, with digital twin fidelity being least important of the three. Additionally, the digital twin comparisons to human baseline show that digital twins significantly increase mission performance by longevity in the field as the entire fleet significantly ages. A simplified axiom for the digital twin’s infusion into multi agent dynamic decision making is as follows: 1) Having information is good (digital twin usage) 2) Having accurate information is better (digital twin fidelity) 3) Having information on time to make decisions is critical (data communication)