Tradespace Exploration Research Articles

A novel pattern language method for tradespace exploration: application to space mission architecting

With the growing complexity of systems across diverse domains, key challenges emerge in simultaneously designing networks and their underlying infrastructure, requiring resolution at concept stage. In space mission design, technological advancements and ambitious goals enable numerous mission architecture concepts due to diverse material flow options. Current design support tools function either after the initial concept design, or tend to rapidly converge toward point solutions, limiting the solution space. We introduce a digital tool for early-stage concept design, capable of generating, evaluating, and visualising a wide range of user-defined architectural options. It utilises a pattern language for space missions, incorporating building blocks like ‘launch’ or ‘electrolysis’. Validation with historic missions showcase the tool automatically identifies all four main mission architectures considered during the Apollo design and more, and accurately recommending the Lunar Orbit Rendezvous architecture which was actually selected. Predicted masses, informing costs, vary by 15% and duration by 30% from actual values, which is within expectations. Identifying missions and leveraging expert knowledge is also demonstrated in the Mars Sample Return case. This approach can help minimise the risk of overlooking effective mission concepts early on. Furthermore, the method is applicable in other domains facing concurrent infrastructure and logistics design challenges.

Development of Scenarios as Problem-Space Descriptions in Aerospace Conceptual Design

In the defense and security domain, scenarios are often descriptions of stakeholder needs, future events, and the environment. They are used for the elicitation of requirements in development of capabilities, organizations, and technical systems. In the conceptual design of aerospace applications, models of scenarios can also represent and communicate a problem-space, enabling trade-space exploration and system effectiveness robustness analysis, which provide valuable input to decision-makers. This study utilizes design science to develop a scenario framework for solution-agnostic representations of a problem-space for use in aerospace conceptual design- and trade-space exploration. A scenario ontology is developed, describing the constituent concepts of scenarios and their relationships, followed by a method for creating scenarios and evaluating their validity. Within the EU project COLOSSUS, it is demonstrated that the scenario framework has utility both for market-pull and technology-push conceptual design. Establishing an ontology for scenarios and a method for creating them as well as evaluating their validity is another step in improving the aerospace conceptual design phase.

LEVERAGING MISSION SOLUTION CONFIGURATION THROUGH MBSE AND TRADESPACE EXPLORATION

AbstractThales is a worldwide leader in innovative radar and mission solution systems used by naval ships. As the demand for personalized products increased through time, Thales shifted from a project‐oriented to a product‐oriented approach. This shift aims to capitalize on variants, minimize customization, and streamline operations. In this context, Thales established a mission solution configuration process (SCP) to facilitate the selection of product variants to compose a system during the bidding phase. However, the current SCP has limitations, constraining exploration and integration with engineering processes and system data. Consequently, the proposed systems sometimes fall short of the most optimal solution the client could get. Therefore, the objective of this work is to develop and validate an improved mission solution configuration process to streamline the creation and selection of product variants at Thales, particularly during the bidding phase, to better meet client needs and operational requirements. This method integrates Model‐Based Systems Engineering (MBSE) and Tradespace Exploration (TSE), utilizing ARCADIA as the methodology and Capella as the tool. A descriptive model is generated for analytical purposes within TSE, employing Multi‐Attribute‐Utility‐Theory (MAUT) and Pareto‐Optimization for evaluating and selecting optimal mission solution variants. Validation was conducted through a Coast Guard mission case study involving 125 solution variants, revealing Pareto‐optimal solutions balancing performance and cost. This method enhances the current configuration process by aligning client and operational needs with Thales's sales and product teams, ensuring accurate interpretation of requirements and minimizing information inconsistencies. The case study results identify technological gaps in variant designs, guiding research and development efforts towards subsystems or components with significant impact on system performance.

A tradespace exploration approach for changeability assessment from a system-of-systems perspective: application from the construction machinery industry

AbstractThe rapid development of new technologies such as electrification, autonomy, and other contextual factors pose significant challenges to development teams in balancing competing aspects while developing value-robust solutions. One approach for achieving value robustness is designing for changeability. This paper presents a tradespace exploration from a Systems-of-Systems perspective to facilitate changeability assessment during early design stages. The approach is further demonstrated on a fleet of haulers operating in a mining site.

Leveraging Large Language Models for Tradespace Exploration

This paper proposes a method for leveraging large language models (LLMs) to improve the question-answering capabilities of artificial intelligence (AI) assistants for tradespace exploration. The method operates by querying an information space composed of fused data sources encompassing the tradespace exploration process and responding based on the gathered information. The information retrieval process is modeled as an internal dialog where an LLM-based dialog agent converses with a subquery answering agent. A case study is conducted on a next-generation soil moisture mission (SM-NG), and a generative AI assistant (named Daphne-G) is configured on it. The effect of the dialog agent and the choice of LLM are assessed by comparing the performance of three different system configurations on a validation question set. A second validation effort is conducted, comparing Daphne-G’s responses to those of a baseline template-based AI assistant, Daphne-VA. Results show that the dialog-based system is necessary for answering complex questions requiring multiple documents. Furthermore, results show that Daphne-G can correctly answer all the questions Daphne-VA can answer, while simultaneously being able to answer a greater number of questions than Daphne-VA. The results suggest that LLMs could significantly improve the outcomes of the tradespace exploration process, which may result in better and more cost-effective mission concepts being implemented.

A System of Systems Framework for Strategic Cargo Airlift Using Agent-Based Modelling

The effective transport of cargo across the globe by aircraft, termed strategic airlift, is foundational to the success of humanitarian aid/disaster relief (HA/DR) missions and even military operations. Due to the variable extremity of these events, it is essential for aircraft and operations to be designed with a high resilience, factoring in performance in a plethora is scenarios. This work aims to provide a framework that enables the coupling of aircraft, fleet and concepts of operations (CONOPS) design to a mission effectiveness in a strategic cargo airlift. Through agent-based modelling, the complex interaction and emergent behaviors of the different systems in the dynamic airlift environment is better captured and evaluated. Unexpected events, such as cargo requirement reformulation, aircraft servicing and changing airbase accessibility, are employed to emulate the dynamic and spontaneous nature of rapid cargo airlift missions. The impact of these events is stochastically modelled, promoting an analysis of a variety of scenarios. By creating a theoretical disaster relief mission, a trade-space exploration is conducted so that aircraft designs and operational objectives can be evaluated for their mission effect. The framework demonstrates the ability to evaluate aircraft and operational performance holistically, enabling a more robust design procedure for a variety of potential design scenarios and metrics.

Fan and Motor Co-Optimization for a Distributed Electric Aircraft Propulsion System

An integrated modeling approach is presented for the conceptual design of electric aircraft propulsors, with application to NASA’s hydrogen fuel-cell powered transport aircraft concept, being developed under the CHEETA (Center for High-Efficiency Electrical Technologies for Aircraft) program. The distributed boundary layer ingesting (BLI) fan module and the fan-hub embedded MgB2 based fully superconducting (SC) electric motors are modeled together using Signomial Programming (SP). An all-at-once SP optimization eliminates external design iterations between modules and provides sensitivities to design parameters as part of the optimization solution. In addition, the approach developed here is modular and component models can be easily added to study various aircraft architectures. A trade space exploration of CHEETA propulsors indicates a highly-distributed 32 wing and fuselage mounted propulsors configuration, powered by 0.5 MW and 1.6 MW motors, respectively, as the optimal, yielding 16% reduction in the aircraft electric energy consumption relative to a conventional twin electric under-wing propulsors design. However, the design selected is a 9 propulsors configuration with 2.4 MW motors (power range more suitable for fully SC architecture), with about 14% power savings. The fan-embedded motor architecture leads to fan hub-to-tip ratios of up to 0.5, which are higher than typical.

SUPPORTING CHANGEABILITY QUANTIFICATION IN PRODUCT-SERVICE SYSTEMS VIA CLUSTERING ALGORITHM

AbstractThe design of Product-Service Systems (PSS) is challenging due to the inherent complexities and the associated uncertainties. This challenge aggravates when the PSS being considered has a longer lifespan, is expected to encounter a dynamic context, and integrates many novel technologies. From systems engineering literature, one of the measures for mitigating the risks associated with the uncertainties is incorporating means in the system to change internally as a response to change externally. Such systems are referred to as value-robust systems, and their development largely relies on Tradespace exploration and synthesis. Tradespace exploration and synthesis can be challenging and a time-consuming task due to dimensionality. In this light, this paper aims to present an approach that enables the population of the Tradespace and then, supports the synthesis of such a Tradespace using a clustering algorithm for support changeability quantification in PSS. The proposed method is also implemented on a demonstrative case from the construction machinery industry.

Multi-Objective Optimization of an Intelligent Soil-Moisture-Monitoring Satellite Constellation

In early satellite mission design, requirements are not yet fixed, cost is sometimes negotiable, and designs are relatively unconstrained. During this period of design freedom, multi-objective optimization can provide a useful lens into the design space by showing theoretical performance limits and illuminating design tradeoffs. This work optimizes a radar constellation for a potential soil moisture mission. Several different optimization cases with different variables are considered and contrasted. The optimization of the instrument and constellation parameters is considered jointly and separately to better understand the effect of coupling on the optimization performance. A science-driven optimization based on soil moisture retrieval error is compared with a performance-metric-driven optimization. Pareto analysis and association rule mining are performed on the generated designs to provide insight into driving features. Design recommendations are made for several cost caps. Results show that optimization that considers the instrument and constellation design together find superior revisit metrics than treating instrument and constellation separately. The use of the science value metric as an optimization objective shows that while cost may always be increased to improve instrument and constellation performance, the difference in science value may be negligible. These findings can inform tradespace exploration studies for similar problems.

A Techno-Economic Assessment and Tradespace Exploration of Low Earth Orbit Mega-Constellations

As part of “New Space,” low Earth orbit mega-constellations are gaining increasing popularity as the instrument for delivering broadband Internet from space. Apart from technical requirements, the necessity of lowering satellite communication costs to provide access to everyone will be the key to success. This article introduces a techno-economic assessment of the costs of constellation ownership and the costs of service delivery to the customer. Based on the tradespace exploration of our framework, we identify key performance indicators and their techno-economic relationship. We observe that the best cost-per-capacity performance was achieved by many low-complexity satellites in low orbits. Therefore, valuable insight can be offered to further understand and guide design choices in such vast and complex systems.

Development and assessment of a resilient telecoms system

Emergency telecommunication infrastructure is essential for residents and emergency responders during natural disasters to coordinate life-saving and life-preserving efforts. Ensuring resilience of the emergency telecommunication infrastructure is of critical importance for regions with an increased likelihood of natural disasters. We developed an integrated modeling framework for assessing emergency telecommunication systems. The framework used performance models to assess coverage and surge capabilities for a given system architecture. The performance models assess the telecom system value using a multiple objective decision analysis value model with stake-holder and technology performance measures. After constructing a life cycle cost model for emergency telecommunication systems, we conducted an illustrative value versus cost trade-off analysis using three alternative decision frames. The decision analysis framework allows for exploration of the system design tradespace so that decision-makers can select the best emergency telecom architecture using a defensible and transparent, performance-driven methodology.

A Parallel Tempering Approach for Efficient Exploration of the Verification Tradespace in Engineered Systems

Verification is a critical process in the development of engineered systems. Through verification, engineers gain confidence in the correct functionality of the system before it is deployed into operation. Traditionally, verification strategies are fixed at the beginning of the system’s development and verification activities (VAs) are executed as the development progresses. Such an approach appears to give inferior results as the selection of the VAs does not leverage information gained through the system’s development process. In contrast, a set-based design (SBD) approach to verification, where VAs are dynamically selected as the system’s development progresses, has been shown to provide superior results. However, its application under realistic engineering scenarios remains unproven due to the large size of the verification tradespace. In this work, we propose a parallel tempering approach (PTA) to efficiently explore the verification tradespace. First, we formulate an exploration of the verification tradespace as a tree search problem. Second, we design a parallel tempering (PT) algorithm by simulating several replicas of the verification process at different temperatures to obtain a near-optimal result. Third, We apply the PT algorithm to all possible verification states to dynamically identify near-optimal results. The effectiveness of the proposed PTA is evaluated on a partial model of a notional satellite optical instrument.

Deep-turbulence phase compensation using tiled arrays.

Tiled arrays use modulo-2π phase compensation and coherent beam combination to correct for the effects of deep turbulence. As such, this paper uses wave-optics simulations to compare the closed-loop performance of tiled arrays to a branch-point-tolerant phase reconstructor known as LSPV+7 [Appl. Opt.53, 3821 (2014)10.1364/AO.53.003821]. The wave-optics simulations make use of a point-source beacon and are setup with weak-to-strong scintillation conditions. This setup enables a trade-space exploration in support of a power-in-the-bucket comparison with LSPV+7. In turn, the results show that tiled arrays outperform LSPV+7 when transitioning from weak-to-strong scintillation conditions. These results are both encouraging and informative for those looking to tackle the branch-point problem in adaptive optics.

Designing the Design Space: Evaluating Best Practices in Tradespace Exploration, Analysis and Decision-Making

<div class="section abstract"><div class="htmlview paragraph">Determining the validity of the design space early in the conceptualization of a project can make the difference between project success and failure. Early assessment of technical feasibility, project risk, technical readiness and realistic performance expectations based on models with different levels of fidelity, uncertainty, and technical robustness is a challenging mission critical task for large procurement projects. Tradespace exploration uses model-based engineering analysis, design exploration methods, and multi-objective optimization techniques to enable project stakeholders to make informed decisions and tradeoffs concerning the scope, schedule, budget, performance and risk profile of a project. As the intersection with a number of project stakeholders, tradespace studies can provide a significant impact upon the direction and decision-making in a project. Yet, the act of studying the tradespace is data intensive, subject to variability, uncertainty complexity and ambiguity (VUCA). These properties of the data in the tradespace present challenges in both conducting tradespace studies but also in presenting the results of tradespace studies to a variety of stakeholders. In this work, data collected from the literature concerning tradespace exploration, analysis and decision-making practices is reviewed and analyzed to identify the best practices and common pitfalls experienced by organizations conducting tradespace studies. Using a series of interviews conducted with members of a tradespace study group, the authors then proceed to use these best-practices and common issues to evaluate the tradespace study group to reveal avenues for improvement and areas of excellence within that group. Through this paper, other organizations can perform similar self-assessments to improve their capabilities with respect to tradespace studies.</div></div>

Federated Space Systems’ Trade-Space Exploration for Strategic Robustness

Motivated by the growth of the commercial space economy and renewed focus on the disaggregation of military space systems, this work develops a method for conceptual design of federated satellite systems as a collaborative system of systems (SOS). Objectives seek to improve the likelihood of successful SOS formation and pursue constituent system utility robustness. The proposed metaheuristic optimization trade-space exploration method accounts for technical and economic design variables and multi-decision-maker strategy dynamics. Constituent system designs are ranked on their simulated net present value. A game-theoretic measure of risk dominance is used in concert with the net present value to assess the robustness and utility of candidate SOS designs. The method is validated with a notional application case that assesses potential collaboration between Earth-observing and telecommunications systems. The proposed methodology reduces the threshold probability of partner collaboration for which SOS participation is economically rational by up to 18% for the most efficient designs as compared to a typical conceptual design method, thereby increasing the likelihood of successful SOS formation. The results highlight the importance of accounting for strategy dynamics when designing systems for collaboration.

Small satellite synthetic aperture radar (SAR) design: A trade space exploration model

As small satellites are finding increased applicability in a number of space missions, the community is actively exploring the feasibility of missions employing active instruments on small spacecraft. In particular, synthetic aperture radars are of interest as they prove to be versatile tools for Earth Observation purposes.This paper proposes a trade space exploration model to identify designs for space-borne satellite synthetic aperture radars that address the desired instrument requirements. Two trade space analysis approaches are proposed, based on a multivariate analysis with two evaluation metrics and using parallel coordinate plots. The paper demonstrates the utility of the trade space model by considering two case studies. The first study looks at radar instruments on a broad range of small satellite platforms. This study narrows down a trade space of 1265 feasible radar designs to less than 44 Pareto optimal designs at different center frequencies (C- and X-band), elicits conditions for L-band SARs feasibility, and discusses the feasibility bounds and technical constraints on associated instrument and spacecraft requirements. The second study considers radars for 3U CubeSat platforms. The study identifies 44 Pareto optimal designs within 12,928 feasible designs and sheds light on the operational constraints required for the development of such innovative miniaturized radars.

MBSE Enabled Trade‐Off Analyses

AbstractThis paper examines the potential to use Model‐Based Systems Engineering (MBSE) tools to perform trade‐off analysis of alternative systems decisions in the system life cycle from the concept stage to the retirement stage. Specially, we searched for integrated models that automate the simultaneous evaluation of the performance, effectiveness, stakeholder value, and cost of multiple alternative system designs. We used the Web of Science to perform a literature search to identify published papers that describe the use of MBSE tools to support automated analysis of alternatives and trade‐off analyses. We found very few papers that claimed to use MBSE to provide analysis of design alternatives or tradespace exploration. Based on the literature search insights, we identify and describe the required and desired capabilities to perform automated trade‐off analyses of performance, effectiveness, stakeholder value, and cost for multiple system design alternatives using integrated models.

Guided architecture trade space exploration: fusing model-based engineering and design by shopping

Guided architecture trade space exploration: fusing model-based engineering and design by shopping

Multi-attribute assessment of a river electromobility concept in the Amazon region

In this study, a water electro-mobility concept has been analyzed in the Amazon using the case study of the native communities in the Yarinacocha district in Peru. Multi-Attribute Tradespace Exploration (MATE) methodology is applied to identify the various stakeholders' objectives, generate the designs, and evaluate them. This methodology captured the intention of assessing not only technical aspects but also environmental and economic sustainability. Six monohull designs for each of the three navigation routes identified, plus an aluminum catamaran, resulted from the combination of the three materials (wooden, fiber reinforced plastic, and aluminum) of canoe-type monohulls with two types of electric propulsion systems: outboard pod propeller and long-tail shaft coupled with permanent magnet brushless DC motors. It was found that the Amazon canoe hulls studied require propulsion units of 3 kW to navigate in calm waters and 10 kW in rivers. The silicon-based solar photovoltaic systems are equipped with lithium iron phosphate storage batteries. Each design is valued according to the project's stakeholders' preferences, guiding the decision-maker to focus on those that offer higher value at a lower expense. Most solar boats on the two long routes had higher internal rates of return than the current design. The best options generate rates of 29.62% versus 11% and 10.79% versus 5.26%. It could be demonstrated that electric propulsion is competitive to fossil fuel-based propulsion for this case study.

Quantitative Set-Based Design to Inform Design Teams

System designers, analysts, and engineers use various techniques to develop complex systems. A traditional design approach, point-based design (PBD), uses system decomposition and modeling, simulation, optimization, and analysis to find and compare discrete design alternatives. Set-based design (SBD) is a concurrent engineering technique that compares a large number of design alternatives grouped into sets. The existing SBD literature discusses the qualitative team-based characteristics of SBD, but lacks insights into how to quantitatively perform SBD in a team environment. This paper proposes a qualitative SBD conceptual framework for system design, proposes a team-based, quantitative SBD approach for early system design and analysis, and uses an unmanned aerial vehicle case study with an integrated model-based engineering framework to demonstrate the potential benefits of SBD. We found that quantitative SBD tradespace exploration can identify potential designs, assess design feasibility, inform system requirement analysis, and evaluate feasible designs. Additionally, SBD helps designers and analysts assess design decisions by providing an understanding of how each design decision affects the feasible design space. We conclude that SBD provides a more holistic tradespace exploration process since it provides an integrated examination of system requirements and design decisions.