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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1972

Solid rocket motor cost model

A systematic and standardized procedure for estimating life cycle costs of solid rocket motor booster configurations. The model consists of clearly defined cost categories and appropriate cost equations in which cost is related to program and hardware parameters. Cost estimating relationships are generally based on analogous experience. In this model the experience drawn on is from estimates prepared by the study contractors. Contractors' estimates are derived by means of engineering estimates for some predetermined level of detail of the SRM hardware and program functions of the system life cycle. This method is frequently referred to as bottom-up. A parametric cost analysis is a useful technique when rapid estimates are required. This is particularly true during the planning stages of a system when hardware designs and program definition are conceptual and constantly changing as the selection process, which includes cost comparisons or trade-offs, is performed. The use of cost estimating relationships also facilitates the performance of cost sensitivity studies in which relative and comparable cost comparisons are significant.

Harney, A. G.↗
NASA NTRSDate not supplied

A Gaussian Process Enhancement to Linear Parameter Varying Models

Simulation and analysis for modern engineering systems now routinely requires the merging of multiple disciplines, physical-domains, time-scales, and data sets — all at ever increasing levels. These capabilities are especially needed in the domain of Advanced Air Mobility, where rapidly emerging vehicle designs are significantly more complex, while having to be both cost-effective and safe. To meet these engineering challenges, machine learning methods are an attractive option for merging models and data across multiple areas while providing uncertainty quantification and maintaining computational efficiency. This paper examines the use of Gaussian process machine learning to generalize and enhance the commonly used class of quasi-Linear Parameter Varying models for fast full-envelope simulation while also supporting control system design and analysis with model uncertainty. Gaussian process machine learning is selected because it: can fuse multiple data sets, enables an easy trade-off between data fitting and smoothing, provides model uncertainty quantification, scales well with increasing complexity, and does not generally require starting from a large training data set. To demonstrate the benefits of the approach, a robust stability analysis with Gaussian process uncertainty is shown for a NASA reference design of an electric quad-rotor air-taxi concept vehicle with motor parameter uncertainty.

Gaussian Process↗
NASA NTRS2016

JPL's Foundry Furnace: Web-Based Concurrent Engineering for Formulation

The Jet Propulsion Laboratory’s Innovation Foundry is an enterprise tasked with shepherding space mission concepts through the formulation lifecycle. It oversees a number of “virtual teams” for the various stages of formulation. Among these is Team X, which has had considerable success over its more than 20-year history. In a Team X study, domain experts (including engineers devoted to the various spacecraft subsystems) work concurrently and collaboratively over several days to arrive at a feasible point design with a reasonable cost estimate. They use a set of linked Excel workbooks, each developed and approved by a responsible “line organization” within JPL. This toolset has served Team X well over the years, and has evolved since its inception. At the same time, the Innovation Foundry’s portfolio of formulation teams has expanded, and so has the scope of the design challenges they face. The A-Team runs workshop-like architecture studies to focus science investigations, generate mission concepts, assess feasibility, and explore trade spaces. Team Xc performs rapid point design in the style of Team X, but for CubeSats and small spacecraft, using a different toolset. Proposal teams further mature concepts to the point where they can be proposed. Recognizing the importance of trade space modeling combined with new IT services for providing and integrating data, JPL is developing the Foundry Furnace web-based software infrastructure. It will support A-Team, Team Xc, and Team X, providing study management, a catalog of hardware components, a library of re-usable analyses, and a design environment. It is a modernization of JPL’s concurrent engineering infrastructure, embracing the core concepts of Model-Based Systems Engineering, and built with modern software design philosophies.

Murphy, Jonathan↗
DOE OSTI2026

Accelerating Traction Motor Optimization Design with AI Surrogate Models

The advancement of artificial intelligence systems enables the use of data-driven physics-based surrogate models to explore design spaces rapidly and deeply for engineering projects. This work presents a surrogate model workflow that accelerates electric traction motor design optimization by replacing finite element analysis (FEA) with an artificial neural network (ANN) and using this model in a genetic algorithm for design optimization. A baseline interior permanent-magnet motor is parameterized and sampled to generate FEA-labeled training data, after which a feed-forward ANN predicts key outputs (e.g., loss components and weight). The validated surrogate enables genetic-algorithm optimization and deep search over the design space without new FEA runs, producing Pareto-optimal trade-offs between weight and losses and set of optimized designs for rapid downselection of manufacturable motor designs.

Ribeiro, Pedro [ORNL] (ORCID:0009000921026641)↗
NASA NTRS2017

The Heliopause Electrostatic Rapid Transit System (HERTS) - Design, Trades, and Analyses Performed in a Two Year NASA Investigation of Electric Sail Propulsion Systems

Personnel from NASA's MSFC have been investigating the feasibility of an advanced propulsion system known as the Electric Sail (E-Sail) for future scientific exploration missions. This team initially won a NASA Space Technology Mission Directorate (STMD) Phase I NASA Innovative Advanced Concept (NIAC) award and then a two-year follow-on Phase II NIAC award in October 2015. This paper documents the findings from this three-year investigation. An Electric sail, a propellant-less propulsion system, uses solar wind ions to rapidly travel either to deep space or the inner solar system. Scientific spacecraft could reach Pluto in ~5 years, or the boundary of the solar system in ten to twelve years compared to the thirty-five plus years the Voyager spacecraft took. The team's recent focuses have been: 1) Developing a Particle in Cell (PIC) numeric engineering model from MSFC's experimental data on the interaction between simulated solar wind and a charged bare wire that can be applied to a variety of missions, 2) Determining what missions could benefit from this revolutionary propulsion system, 3) Conceptualizing spacecraft designs for various tasks: to reach the solar system's edge, to orbit the sun as Heliophysics sentinels, or to examine a multitude of asteroids.

Wiegmann, Bruce M.↗
DOE OSTI2026

Metabolic flux and resource balance in the oleaginous yeast Rhodotorula toruloides

The yeast Rhodotorula toruloides is a promising bioproduction organism due to its high lipid yields and ability to grow on cheap and abundant substrates. Quantitative, systems-level assessment of its metabolic activity is accordingly merited. Resource-balance analysis (RBA) models capture not only reaction stoichiometry but also enzyme requirements for catalysis, providing valuable tools for understanding metabolic trade-offs and optimizing metabolic engineering strategies. Here, in this work, we present systems-level measurements of R. toruloides metabolic flux based on isotope tracing and metabolic flux analysis. In combination with new proteomic measurements, these flux data are used to parameterize a genome-scale resource balance model rtRBA. We find that S. cerevisiae and R. toruloides grow at nearly indistinguishable rates using similar biosynthetic but dramatically different central metabolic programs. R. toruloides consumes one-fifth as much glucose, which it metabolizes primarily via the pentose phosphate pathway and TCA cycle unlike primarily glycolysis in S. cerevisiae . Overall, across these two divergent yeasts, protein abundances aligned more closely than metabolic flux. Resource balance modeling of these metabolic programs predicts superior theoretical yields but lower productivities in R. toruloides than S. cerevisiae for industrial chemicals, highlighting the value of rapid glucose uptake for productivity but respiratory metabolism for yields.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2016

Design and Performance of the NASA SCEPTOR Distributed Electric Propulsion Flight Demonstrator

Distributed Electric Propulsion (DEP) technology uses multiple propulsors driven by electric motors distributed about the airframe to yield beneficial aerodynamic-propulsion interaction. The NASA SCEPTOR flight demonstration project will retrofit an existing internal combustion engine-powered light aircraft with two types of DEP: small "high-lift" propellers distributed along the leading edge of the wing which accelerate the flow over the wing at low speeds, and larger cruise propellers co-located with each wingtip for primary propulsive power. The updated high-lift system enables a 2.5x reduction in wing area as compared to the original aircraft, reducing drag at cruise and shifting the velocity for maximum lift-to-drag ratio to a higher speed, while maintaining low-speed performance. The wingtip-mounted cruise propellers interact with the wingtip vortex, enabling a further efficiency increase that can reduce propulsive power by 10%. A tradespace exploration approach is developed that enables rapid identification of salient trades, and subsequent creation of SCEPTOR demonstrator geometries. These candidates were scrutinized by subject matter experts to identify design preferences that were not modeled during configuration exploration. This exploration and design approach is used to create an aircraft that consumes an estimated 4.8x less energy at the selected cruise point when compared to the original aircraft.

Borer, Nicholas K.↗
NASA NTRS2016

Modeling and Simulation of Phased Array Antennas to Support Next-Generation Satellite Design

Developing enhanced simulation capabilities has become a significant priority for the Space Communications and Navigation (SCaN) project at NASA as new space communications technologies are proposed to replace aging NASA communications assets, such as the Tracking and Data Relay Satellite System (TDRSS). When developing the architecture for these new space communications assets, it is important to develop updated modeling and simulation methodologies, such that competing architectures can be weighed against one another and the optimal path forward can be determined. There have been many simulation tools developed here at NASA for the simulation of single RF link budgets, or for the modeling and simulation of an entire network of spacecraft and their supporting SCaN network elements. However, the modeling capabilities are never fully complete and as new technologies are proposed, gaps are identified. One such gap is the ability to rapidly develop high fidelity simulation models of electronically steerable phased array systems. As future relay satellite architectures are proposed that include optical communications links, electronically steerable antennas will become more desirable due to the reduction in platform vibration introduced by mechanically steerable devices. In this research, we investigate how modeling of these antennas can be introduced into out overall simulation and modeling structure. The ultimate goal of this research is two-fold. First, to enable NASA engineers to model various proposed simulation architectures and determine which proposed architecture meets the given architectural requirements. Second, given a set of communications link requirements for a proposed satellite architecture, determine the optimal configuration for a phased array antenna. There is a variety of tools available that can be used to model phased array antennas. To meet our stated goals, the first objective of this research is to compare the subset of tools available to us, trading-off modeling fidelity of the tool with simulation performance. When comparing several proposed architectures, higher- fidelity modeling may be desirable, however, when iterating a proposed set of communication link requirements across ranges of phased array configuration parameters, the practicality of performance becomes a significant requirement. In either case, a minimum simulation - fidelity must be met, regardless of performance considerations, which will be discussed in this research. Given a suitable set of phased array modeling tools, this research then focuses on integration with current SCaN modeling and simulation tools. While properly modeling the antenna elements of a system are vital, this is only a small part of the end-to-end communication path between a satellite and the supporting ground station and/or relay satellite assets. To properly model a proposed simulation architecture, this toolset must be integrated with other commercial and government development tools, such that the overall architecture can be examined in terms of communications, reliability, and cost. In this research, integration with previously developed communication tools is investigated.

Phased Array↗
NASA NTRS2003

Process-Based Cost Estimation for Ramjet/Scramjet Engines

Process-based cost estimation plays a key role in effecting cultural change that integrates distributed science, technology and engineering teams to rapidly create innovative and affordable products. Working together, NASA Glenn Research Center and Boeing Canoga Park have developed a methodology of process-based cost estimation bridging the methodologies of high-level parametric models and detailed bottoms-up estimation. The NASA GRC/Boeing CP process-based cost model provides a probabilistic structure of layered cost drivers. High-level inputs characterize mission requirements, system performance, and relevant economic factors. Design alternatives are extracted from a standard, product-specific work breakdown structure to pre-load lower-level cost driver inputs and generate the cost-risk analysis. As product design progresses and matures the lower level more detailed cost drivers can be re-accessed and the projected variation of input values narrowed, thereby generating a progressively more accurate estimate of cost-risk. Incorporated into the process-based cost model are techniques for decision analysis, specifically, the analytic hierarchy process (AHP) and functional utility analysis. Design alternatives may then be evaluated not just on cost-risk, but also user defined performance and schedule criteria. This implementation of full-trade study support contributes significantly to the realization of the integrated development environment. The process-based cost estimation model generates development and manufacturing cost estimates. The development team plans to expand the manufacturing process base from approximately 80 manufacturing processes to over 250 processes. Operation and support cost modeling is also envisioned. Process-based estimation considers the materials, resources, and processes in establishing cost-risk and rather depending on weight as an input, actually estimates weight along with cost and schedule.

Singh, Brijendra↗
NASA NTRS2005

Space Shuttle Hot Cabin Emergency Responses

Methods: Human thermal tolerance, countermeasures, and thermal model data were reviewed and compared to existing shuttle ECS failure temperature and humidity profiles for each failure mode. Increases in core temperature associated with cognitive impairment was identified, as was metabolic heat generation of crewmembers, temperature monitoring, and communication capabilities after partial power-down and other limiting factors. Orbiter landing strategies and a hydration and salt replacement protocol were developed to put wheels on deck in each failure mode prior to development of significant cognitive impairment or collapse of crewmembers. Thermal tradeoffs for use of the Advanced Crew Escape Suit (ACES), Liquid Cooling Garment, integrated G-suit and Quick Don Mask were examined. candidate solutions involved trade-offs or conflicts with cabin oxygen partial pressure limits, system power-downs to limit heat generation, risks of alternate and emergency landing sites or compromise of Mode V-VIII scenarios. Results: Rehydration and minimized cabin workloads are required in all failure modes. Temperature/humidity profiles increase rapidly in two failure modes, and deorbit is recommended without the ACES, ICU and g-suit. This latter configuration limits several shuttle approach and landing escape modes and requires communication modifications. Additional data requirements were identified and engineering simulations were recommended to develop more current shuttle temperature and humidity profiles. Discussion: After failure of the shuttle ECS, there is insufficient cooling capacity of the ACES to protect crewmembers from rising cabin temperature and humidity. The LCG is inadequate for cabin temperatures above 76 F. Current shuttle future life policy makes it unlikely that major engineering upgrades necessary to address this problem will occur.

Stepaniak, P.↗
NASA NTRS2017

NASA's Electric Sail Propulsion System Investigations over the Past Three Years

Personnel from NASA's MSFC have been investigating the feasibility of an advanced propulsion system known as the Electric Sail for future scientific missions of exploration. This team initially won a NASA Space Technology Mission Directorate (STMD) Phase I NASA Innovative Advanced Concept (NIAC) award and then a two year follow-on Phase II NIAC award. This paper documents the findings from this three year investigation. An Electric sail propulsion system is a propellant-less and extremely fast propulsion system that takes advantage of the ions that are present in the solar wind to provide very rapid transit speeds whether to deep space or to the inner solar system. Scientific spacecraft could arrive to Pluto in ~5 years, to the boundary of the solar system in ten to twelve years vs. thirty five plus years it took the Voyager spacecraft. The team's recent focused activities are: 1) Developing a Particle in Cell (PIC) numeric engineering model from the experimental data collected at MSFC's Solar Wind Facility on the interaction between simulated solar wind interaction with a charged bare wire that can be applied to a variety of missions, 2) The development of the necessary tether deployers/tethers to enable successful deployment of multiple, multi km length bare tethers, 3) Determining the different missions that can be captured from this revolutionary propulsion system 4) Conceptual designs of spacecraft to reach various destinations whether to the edge of the solar system, or as Heliophysics sentinels around the sun, or to trips to examine a multitude of asteroids These above activities, once demonstrated analytically, will require a technology demonstration mission (~2021 to 2023) to demonstrate that all systems work together seamlessly before a Heliophysics Electrostatic Rapid Transit System (HERTS) could be given the go-ahead. The proposed demonstration mission will require that a small spacecraft must first travel to cis-lunar space as the Electric Sail must be outside of Earth's Magnetic fields to produce thrust. The paper will outline what was done over the past three years from performing various plasma chamber tests to obtain data for the PIC model development, investigation of tether material trades, and conceptual designs of proposed spacecraft.

Wiegmann, Bruce M.↗
NASA NTRS2010

GOATS - Orbitology Component

The GOATS Orbitology Component software was developed to specifically address the concerns presented by orbit analysis tools that are often written as stand-alone applications. These applications do not easily interface with standard JPL first-principles analysis tools, and have a steep learning curve due to their complicated nature. This toolset is written as a series of MATLAB functions, allowing seamless integration into existing JPL optical systems engineering modeling and analysis modules. The functions are completely open, and allow for advanced users to delve into and modify the underlying physics being modeled. Additionally, this software module fills an analysis gap, allowing for quick, high-level mission analysis trades without the need for detailed and complicated orbit analysis using commercial stand-alone tools. This software consists of a series of MATLAB functions to provide for geometric orbit-related analysis. This includes propagation of orbits to varying levels of generalization. In the simplest case, geosynchronous orbits can be modeled by specifying a subset of three orbit elements. The next case is a circular orbit, which can be specified by a subset of four orbit elements. The most general case is an arbitrary elliptical orbit specified by all six orbit elements. These orbits are all solved geometrically, under the basic problem of an object in circular (or elliptical) orbit around a rotating spheroid. The orbit functions output time series ground tracks, which serve as the basis for more detailed orbit analysis. This software module also includes functions to track the positions of the Sun, Moon, and arbitrary celestial bodies specified by right ascension and declination. Also included are functions to calculate line-of-sight geometries to ground-based targets, angular rotations and decompositions, and other line-of-site calculations. The toolset allows for the rapid execution of orbit trade studies at the level of detail required for the early stage of mission concept development.

Haber, Benjamin M.↗
NASA NTRS2020

Enabling Assurance in the MBSE Environment

A number of specific benefits that fit within the hallmarks of effective development are realized with implementation of model-based approaches to systems and assurance. Model Based Systems Engineering (MBSE) enabled by standardized modeling languages (e.g., SysML®) is at the core. These benefits in the context of spaceflight system challenges can include [1]: • Improved management of complex development • Reduced risk in the development process • Improved cost management • Improved design decisions With appropriate modeling techniques the assurance community also can improve early oversight and insight into project development. NASA has shown the basic constructs of SysML in an MBSE environment offer several key advantages, within a Model Based Mission Assurance (MBMA) initiative [2, 3]. These include the following: • Model viewpoints that promote rapid and systematic assessment of requirements coverage, hazard tagging and risk management • Embedded safety assessments for launch vehicles • Deployment of model assisted development of reliability products - Failure Modes and Effects Analyses (FMEAs) and Fault Trees • Test Planning • Validation and Verification of complex functions • Support of Assurance Case development for complex systems In addition, while there are benefits to be harvested, there is a realization that these do not come without effort and cost. Enabling model-based approaches requires structure, not only in an organizational context, but in a modeling context as well. There can be a steep learning curve and costs associated to train skilled modelers. But, on the other hand, not all of the assurance community need to be modelers. Models themselves must conform to ontologies that enable assurance. This places constraints upon the models and modelers. Optimums have yet to be developed where resources and constraints on modeling must be traded off in the organization and modeling efforts for projects. A number of barriers need to be overcome, as well, which pose challenges to the developers of the software that supports MBSE/MBMA. Information and data must be made to flow seamlessly through the life cycle. Because there is a wide variety of tools used in the community, to avoid the problems of the past of silos, delays, and diverging interests, information should flow among these tools to support the “single source of truth” paradigm of MBSE. This will greatly facilitate MBMA and advancement of assurance functions.

Evans, John W.↗
DOE OSTI2026

Repowering: The Other Side of the Reliability Coin

Extreme weather, cracked backsheets, severe PID, poorly built modules, and installation flaws - all can compromise a solar plant's health and force repowering long before end of life. With more than 70% of U.S. PV capacity less than seven years old, the fleet is young, but its rapid expansion has introduced new materials and system designs that are still being tested under real-world conditions. As a result, reliability - not economics - is what most often drives repowering decisions. Repowering is frequently assumed to be an economically motivated choice, but our work shows that reliability concerns are the real trigger. Drawing from industry interviews, case studies, and modeling, we highlight the physical, electrical, and policy barriers owners face when deciding whether to repair, repower, or decommission. At the same time, repowering can create opportunities: renewed interconnection periods, improved energy yields, and strategic upgrades to extend system value. We present a quantitative framework using NLR's System Advisor Model (SAM) and PV in Circular Economy (PV ICE) tool to evaluate trade-offs across financial, material, and energy impacts. These findings provide practical guidance for navigating the realities of repowering today and underscore the critical role of reliability in shaping the future performance and sustainability of the PV fleet.

14 SOLAR ENERGY↗
NASA NTRS2007

ESAS-Derived Earth Departure Stage Design for Human Mars Exploration

The Vision for Space Exploration has set the nation on a course to have humans on Mars as early as 2030. To reduce the cost and risk associated with human Mars exploration, NASA is planning for the Mars architecture to leverage the lunar architecture as fully as possible. This study takes the defined launch vehicles and system capabilities from ESAS and extends their application to DRM 3.0 to design an Earth Departure Stage suitable for the cargo and crew missions to Mars. The impact of a propellant depot in LEO was assessed and sLzed for use with the EDS. To quantitatively assess and compare the effectiveness of alternative designs, an initial baseline architecture was defined using the ESAS launch vehicles and DRM 3.0. The baseline architecture uses three NTR engines, LH2 propellant, no propellant depot in LEO, and launches on the Ares I and Ares V. The Mars transfer and surface elements from DRM 3.0 were considered to be fixed payloads in the design of the EDS. Feasible architecture alternatives were identified from previous architecture studies and anticipated capabilities and compiled in a morphological matrix. ESAS FOMs were used to determine the most critical design attributes for the effectiveness of the EDS. The ESAS-derived FOMs used in this study to assess alternative designs are effectiveness and performance, affordability, reliability, and risk. The individual FOMs were prioritized using the AHP, a method for pairwise comparison. All trades performed were evaluated with respect to the weighted FOMs, creating a Pareto frontier of equivalently ideal solutions. Additionally, each design on the frontier was evaluated based on its fulfillment of the weighted FOMs using TOPSIS, a quantitative method for ordinal ranking of the alternatives. The designs were assessed in an integrated environment using physics-based models for subsystem analysis where possible. However, for certain attributes such as engine type, historical, performance-based mass estimating relations were more easily employed. The elements from the design process were integrated into a single loop, allowing for rapid iteration of subsystem analyses and compilation of resulting designs.

Flaherty, Kevin↗
DOE OSTI2024

Advances on CHP District Energy and Microgrids Deployment: Simplified Tool for Rapidly Deploying Feasibility Analytics for the Non-Technical User (Final Technical Report)

Community energy systems have proven to have the potential to improve cost efficiency, resilience, and decarbonize. However, investing in community energy systems such as community microgrids or district energy systems is a complex decision due to the high initial investment and the uncertainties associated with the long development time and lifecycle of the project. Tools that make feasibility assessments accessible to non-technical users like investors, policymakers, and other stakeholders will result in more feasibility analyses completed, more candidate projects identified, and more community energy systems deployed. The pilot tool developed under this award is named Energy Fellow. Energy Fellow allows technical and non-technical users to complete feasibility analyses for district energy systems and community microgrids. This is the first software tool of its kind designed for non-technical users and available at no cost. Its scope was adjusted to a 25x25-mile region within the Houston area in Texas to make its development compatible with the funding available. However, the findings and models developed make this pilot tool easily scalable to the US. The lessons learned during the design, implementation, and testing stages have helped find trade-off solutions to software and hardware challenges related to implementing 3D models in online tools. Green software strategies has been successfully applied to the design and operations of the tool, and the team has researched the aspects of the (non-technical) user experience that will make commercial developments of this tool even more impactful.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Analyses Made to Order: Using Transformation to Rapidly Configure a Multidisciplinary Environment

Aerospace problems are highly multidisciplinary. Four or more major disciplines are involved in analyzing any particular vehicle. Moreover, the choice of implementation technology of various subsystems can lead to a change of leading domain or reformation of the driving equations. An excellent example is the change of expertise required to consider aircraft built from composite or metallic structures, or those propelled by chemical or electrical thrusters. Another example is in the major reconfiguration of handling and stability equations with different control surface configuration (e.g., canards, t-tail v four-post tail). Combinatorial problems are also commonplace anytime that a major system is to be designed. If there are only 5 attributes of a design to consider with 4 different options, this is already 1024 options. Adding just 5 more dimensions to the study explodes the space to over one million. Even generous assumptions like the idea that only 10% of the combinations are physically feasible can only contain the problem for so long. To make matters worse, the simple number of combinations is only the beginning. Combining the issue of trade space size with the need to reformulate the design problem for many of the possibilities makes life exponentially more difficult. Advances in software modeling approaches have led to the development of model-driven architecture. This approach uses the transformation of models into inferred models (e.g. inferred execution traces from state machines) or the skeletons for code generation. When the emphasis on transformation is applied to aerospace, it becomes possible to exploit redundancy in the information specified in multiple domain models into a unified system model. F1urther, it becomes possible to overcome the combinatorial nature of specifying integrated system behavior by manually combining the equations governing a given component technology. Transformations from a system specification combined with a system-analysis mapping specification enable one-click combination of domain analyses. This is a flexibility that has been missing from many engineering codes, which often entangle design specification and physical examination much more than is required to conduct the analysis. This capability has been investigated and cultivated within the DARPA F6 program by a team of JPL and Phoenix Integration engineers building the Adapatable Systems Design and Analysis (ASDA) framework. By embracing system modeling with SysML and the Query-View-Transformation (QVT) language, the ASDA team has been able to build a flexible, easily reconfigurable framework for building up and solving large tradespaces. Examples of application and lessons learned in building the framework will be described in this paper. In addition, the motivation will be laid for various tool vendors to develop open model description standards while being able to maintain competitive advantage through proprietary algorithms and approaches. These standards will also be compared to the underpinnings of model-driven architecture and the OMG standards of the Meta-Object Facility (MOF), SysML, and QVT.

Cole, Bjorn↗