Using Advanced Design Methods to Optimize the Mars Ascent Vehicle First Stage Solid Rocket Motor
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Hetterich, Robert↗
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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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Practical Considerations of Waste Heat Reuse for a Mars Mission Advanced Life Support System
Energy conservation is a key issue in design optimization of Advanced Life Support Systems (ALSS) for long-term space missions. By considering designs for conservation at the system level, energy saving opportunities arise that would otherwise go unnoticed. This paper builds on a steady-state investigation of system-level waste heat reuse in an ALSS with a low degree of crop growth for a Mars mission. In past studies, such a system has been defined in terms of technology types, hot and cold stream identification and stream energy content. The maximum steady-state potential for power and cooling savings within the system was computed via the Pinch Method. In this paper, several practical issues are considered for achieving a pragmatic estimate of total system savings in terms of equivalent system mass (ESM), rather than savings solely in terms of power and cooling. In this paper, more realistic ESM savings are computed by considering heat transfer inefficiencies during material transfer. An estimate of the steady-state mass, volume and crewtime requirements associated with heat exchange equipment is made by considering heat exchange equipment material type and configuration, stream flow characteristics and associated energy losses during the heat exchange process. Also, previously estimated power and cooling savings are adjusted to reflect the impact of such energy losses. This paper goes one step further than the traditional Pinch Method of considering waste heat reuse in heat exchangers to include ESM savings that occur with direct reuse of a stream. For example, rather than exchanging heat between crop growth lamp cooling air and air going to a clothes dryer, air used to cool crop lamps might be reused directly for clothes drying purposes. When thermodynamically feasible, such an approach may increase ESM savings by minimizing the mass, volume and crewtime requirements associated with stream routing equipment.
Levri, Julie↗
Recent Advances in Multidisciplinary Analysis and Optimization, part 1
This three-part document contains a collection of technical papers presented at the Second NASA/Air Force Symposium on Recent Advances in Multidisciplinary Analysis and Optimization, held September 28-30, 1988 in Hampton, Virginia. The topics covered include: helicopter design, aeroelastic tailoring, control of aeroelastic structures, dynamics and control of flexible structures, structural design, design of large engineering systems, application of artificial intelligence, shape optimization, software development and implementation, and sensitivity analysis.
Barthelemy, Jean-Francois M.↗
Recent Advances in Multidisciplinary Analysis and Optimization, part 2
This three-part document contains a collection of technical papers presented at the Second NASA/Air Force Symposium on Recent Advances in Multidisciplinary Analysis and Optimization, held September 28-30, 1988 in Hampton, Virginia. The topics covered include: helicopter design, aeroelastic tailoring, control of aeroelastic structures, dynamics and control of flexible structures, structural design, design of large engineering systems, application of artificial intelligence, shape optimization, software development and implementation, and sensitivity analysis.
Barthelemy, Jean-Francois M.↗
Recent Advances in Multidisciplinary Analysis and Optimization, part 3
This three-part document contains a collection of technical papers presented at the Second NASA/Air Force Symposium on Recent Advances in Multidisciplinary Analysis and Optimization, held September 28-30, 1988 in Hampton, Virginia. The topics covered include: aircraft design, aeroelastic tailoring, control of aeroelastic structures, dynamics and control of flexible structures, structural design, design of large engineering systems, application of artificial intelligence, shape optimization, software development and implementation, and sensitivity analysis.
Barthelemy, Jean-Francois M.↗
FAD-Toolset (Floating Array Design Toolset) [SWR-26-056]
The Floating Array Design (FAD) Toolset is a collection of tools for modeling and designing arrays of floating offshore structures. It was originally designed for floating wind systems but has applicability for many offshore applications. A core part of the FAD Toolset is the floating array model, which serves as a high-level library for efficiently modeling a floating array, such as a floating wind array. It combines site condition information and a description of the floating array design, and contains functions for evaluating the array's behavior considering the site conditions. For example, it combines information about site soil conditions, mooring line loads, and an array's anchor characteristics to estimate the holding capacity of each anchor. The library works in conjunction with the tools RAFT, MoorPy, and FLORIS to model floating platforms, wind turbines, mooring systems, power cables, and array wakes respectively. Layered on top of the floating array model is a set of design tools that can be used for algorithmically adjusting or optimizing parts of the a floating array. Specific tools existing for mooring lines, shared mooring systems, dynamic power cables, static power cable routing, and overall array layout. These capabilities work with the design representation and evaluation functions in the floating array model, and they can be applied by users in various combinations to suit different purposes. In addition to standalone uses of the FAD Toolset, a coupling has been made with Ard, (https://github.com/NLRWindSystems/Ard) a sophisticated and flexible wind farm optimization tool. This coupling allows Ard to use certain mooring system capabilities from FAD to perform layout optimization of floating wind farms with Ard's more advanced layout optimization capabilities. The FAD Toolset works with the IEA Wind Task 49 Ontology (https://github.com/IEAWindTask49/Ontology), which provides a standardized format for describing floating wind farm sites and designs. See example use cases in our examples folder (https://github.com/NLRWindSystems/FAD-Toolset/blob/main/examples/README.md) For working with the library, it is important to understand the floating array model structure, which is described more here: https://github.com/NLRWindSystems/FAD-Toolset/blob/main/fad/README.md.
Sirkis, Leah [National Laboratory of the Rockies (↗
Outlook towards deployable continual learning for particle accelerators
Particle accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires addressing many challenges including design, optimization and control, anomaly detection and machine protection. With recent advancements, machine learning (ML) holds promise to assist in more advance prognostics, optimization, and control. While ML based solutions have been developed for several applications in particle accelerators, only few have reached deployment and even fewer to long term usage, due to particle accelerator data distribution drifts caused by changes in both measurable and non-measurable parameters. In this paper, we identify some of the key areas within particle accelerators where continual learning can allow maintenance of ML model performance with distribution drifts. Particularly, we first discuss existing applications of ML in particle accelerators, and their limitations due to distribution drift. Next, we review existing continual learning techniques and investigate their potential applications to address data distribution drifts in accelerators. By identifying the opportunities and challenges in applying continual learning, this paper seeks to open up the new field and inspire more research efforts towards deployable continual learning for particle accelerators.
43 PARTICLE ACCELERATORS↗
Orbit transfer vehicle engine study, phase A extension. Volume 2A: Study results
Engine trade studies and systems analyses leading to a baseline engine selection for advanced expander cycle engine are discussed with emphasis on: (1) performance optimization of advanced expander cycle engines in the 10 to 20K pound thrust range; (2) selection of a recommended advanced expander engine configuration based on maximized performance and minimized mission risk, and definition of the components for this configuration; (3) characterization of the low thrust adaptation requirements and performance for the staged combustion engine; (4) generation of a suggested safety and reliability approach for OTV engines independent of engine cycle; (5) definition of program risk relationships between expander and staged combustion cycle engines; and (6) development of schedules and costs for the DDT&E, production, and operation phases of the 10K pound thrust expander engine program.
Source record↗
Plant Reload Optimization (prlo)
The PRLO framework is built on a modular and extensible architecture that tightly couples advanced evolutionary optimization algorithms with nuclear fuel depletion solvers (i.e., nuclear physics neutronics code). It supports exploring complex, high-dimensional design spaces constrained by user-specified operational, safety, and economic constraints. Objectives such as minimizing fresh fuel enrichment, flattening radial and axial power distributions, and maximizing discharge burnup are evaluated. PRLO’s equilibrium cycle optimization capability enables the identification of core configurations that maintain fuel cycle sustainability over extended planning horizons. Its integration with the RAVEN platform facilitates optimization of loading patterns or fuel shuffling schemes across multiple cycles. The interface with SIMULATE, a licensed industry-standard nodal code developed by Studsvik, ensures accurate neutronic and thermal-hydraulic feedback for reactor core design. PRLO’s automated workflow engine supports iterative design refinement, enabling utilities to streamline core design processes and meet evolving performance and regulatory targets.
Kim, Junyung [Idaho National Laboratory] (00090005↗
Recent Advances of PyROS: A Pyomo Solver for Nonconvex Two-Stage Robust Optimization in Process Systems Engineering
This poster highlights uncertainty and technical risk reduction capabilities in CCSI2, with a focus on robust optimization. It presents recent advances of the two-stage robust optimization (RO) solver PyROS and applications to advanced energy systems optimization. To demonstrate the computational performance and reliability of PyROS, a benchmarking study on a library of over 8,500 small-scale RO problems is presented. Further, PyROS is used to obtain robust system designs of a MEA-based CO2 absorber under uncertainty in the thermodynamic property models for a variety of CO2 capture rate threshold requirements. Overall, the results demonstrate that the PyROS solver, including recent extensions to multi-stage RO settings, provides a reliable avenue to optimize the design and operation of advanced energy systems subject to various sources of parametric uncertainty.
Sherman, Jason↗
Recent Advances of PyROS: A Pyomo Solver for Nonconvex Two-Stage Robust Optimization in Process Systems Engineering
This poster highlights uncertainty and technical risk reduction capabilities in CCSI2, with a focus on robust optimization. It presents recent advances of the two-stage robust optimization (RO) solver PyROS and applications to advanced energy systems optimization. To demonstrate the computational performance and reliability of PyROS, a benchmarking study on a library of over 8,500 small-scale RO problems is presented. Further, PyROS is used to obtain robust system designs of a MEA-based CO2 absorber under uncertainty in the thermodynamic property models for a variety of CO2 capture rate threshold requirements. Overall, the results demonstrate that the PyROS solver, including recent extensions to multi-stage RO settings, provides a reliable avenue to optimize the design and operation of advanced energy systems subject to various sources of parametric uncertainty.
Sherman, Jason↗
Optimization with Genetic Algorithm - 2025 Advances in Nuclear Fuel Management (ANFM) Workshop
This is a workshop presentation material, introducing the Plant ReLoad Optimization (PRLO) framework capabilities and Genetic Algorithm (GA).
Genetic Algorithm↗
Automation of Laser Plasma Focused Ion Beam Microscopy for Next-Gen Energy Materials
Automation can revolutionize the use of ultrafast laser ablation and plasma-focused ion beam (PFIB) techniques for high-throughput, reproducible cross-sectioning and various sample preparation in materials characterization. As these methods become essential for analyzing complex energy materials and next-generation devices, efficient, standardized workflows are needed to minimize variability and enhance precision. This work highlights our advancements in developing automated processes for sample preparation that integrates machine learning, workflow optimization, and large-scale data acquisition to improve efficiency and scalability in applications such as electrolyzers, photovoltaic cells, and microelectronics. To streamline cross-sectioning and lamella fabrication, we have implemented fully automated workflows that standardize laser ablation and PFIB milling sequences. These workflows incorporate pre-programmed protocols for material removal, alignment, and thinning, reducing user intervention and ensuring consistency across different sample types. Machine learning algorithms further enhance automation by predicting optimal milling strategies and adapting parameters based on material properties and sectioning requirements. This approach significantly improves throughput while maintaining the structural integrity of prepared samples for high-resolution imaging and analysis, including transmission electron microscopy. Beyond sample preparation, our automation platform enables the acquisition of large, high-resolution datasets through serial sectioning, image alignment, and 3D reconstruction. These automated routines facilitate multi-scale characterization, capturing structural and compositional details from the nanoscale to the device level. By reducing variability and increasing efficiency, our automated approach enhances defect analysis, failure diagnostics, and process optimization, accelerating advancements in materials research and device engineering.
36 MATERIALS SCIENCE↗
Advanced Models for Aeroelastic Analysis of Propulsion Systems
This report describes an integrated, multidisciplinary simulation capability for aeroelastic analysis and optimization of advanced propulsion systems. This research is intended to improve engine development, acquisition, and maintenance costs. One of the proposed simulations is aeroelasticity of blades, cowls, and struts in an ultra-high bypass fan. These ducted fans are expected to have significant performance, fuel, and noise improvements over existing engines. An interface program was written to use modal information from COBSTAN and NASTRAN blade models in aeroelastic analysis with a single rotation ducted fan aerodynamic code.
Keith, Theo G., Jr.↗
An overview of parameter estimation techniques and applications in aircraft flight testing
Parameter estimation is discussed as it applies to aircraft flight testing, and an overview of the symposium is presented. The evolution of techniques used in flight testing is reviewed briefly, and it is pointed out how the changing character of the aircraft tested and the availability of advanced data systems have promoted this evolution. Recent advances in optimal estimation theory have stimulated widespread interest and activity in parameter estimation. The framework of these advanced techniques is outlined to set the stage for subsequent papers. The session topics are introduced and related to the requirements of flight-test research.
Rediess, H. A.↗
Optimization of the propulsion cycles for advanced shuttles. II - Performance model methodology
In 1988 computer codes from NASA and DLR were combined to analyze and optimize rocket engines for advance launch vehicles. Further effort has addressed performance propulsion parameters such as chamber pressure, cycle selection (including mixed and tripropellant cycles), nozzle extension, and new technologies. Results of the study indicate that lower dry masses can be reached with the staged combustion cycle than with the gas-generator cycle, using oxygen/hydrogen at relatively high mixture ratio (7:1) and modest chamber pressure (220 bar). For low dry masses the staged combustion cycle is only excelled by the dual-expander cycle using hydrogen and propane as fuels. The emphasis here is to explain the procedure and boundary conditions used for the analysis and the variety of configurations treated, as well as to demonstrate the methods used by presenting some of the major results.
Manski, Detlef↗
Analysis and Optimization of Test Plans for Advanced Exploration Systems Reliability and Supportability
Future crewed exploration missions beyond Low Earth Orbit (LEO) will operate farther from Earth and be logistically isolated for longer than any previous human spaceflight mission. Under these conditions, supportability and reliability willbestronger drivers of mission mass and risk than they have been in the past. Items with high failure rates, or uncertain failure rates, can result in high spares mass requirements and/or high risk on deep space missions. Testing is a critical element of system development which provides the opportunity to identify and resolve design issues, defects, or other failure modes before they cause problems during a mission. Reliability growth programs can reduce failure rates by identifying and remove failure modes via design changes, and long-duration life testing can provide valuable data to reduce failure rate estimate uncertainty and verify (to some level of confidence) that components are as reliable as expected. Testing activities take time and resources, however, and must be incorporated into program plans in order to be fully effective. This paper presents an integrated reliability test plan analysis and optimization methodology, which has been used to inform Advanced Exploration Systems (AES) Life Support Systems (LSS) ground test planning for future missions. The methodology determines the optimal number of test units to purchase and allocation of test time –split between reliability growth and uncertainty reduction testing –across a given set of items in order to minimize spares mass for a given mission under constraints on total test cost and schedule. Model outputs also include expected spares mass after testing and the expected number of modifications or refurbishments during testing, both of which can inform program planning. Discussion of the model, conclusions, and future work are also presented.
Testing↗
Analysis and Optimization of Test Plans for Advanced Exploration Systems Reliability and Supportability
Future crewed exploration missions beyond Low Earth Orbit (LEO) will operate farther from Earth and be logistically isolated for longer than any previous human spaceflight mission. Under these conditions, supportability and reliability willbestronger drivers of mission mass and risk than they have been in the past. Items with high failure rates, or uncertain failure rates, can result in high spares mass requirements and/or high risk on deep space missions. Testing is a critical element of system development which provides the opportunity to identify and resolve design issues, defects, or other failure modes before they cause problems during a mission. Reliability growth programs can reduce failure rates by identifying and remove failure modes via design changes, and long-duration life testing can provide valuable data to reduce failure rate estimate uncertainty and verify (to some level of confidence) that components are as reliable as expected. Testing activities take time and resources, however, and must be incorporated into program plans in order to be fully effective. This paper presents an integrated reliability test plan analysis and optimization methodology, which has been used to inform Advanced Exploration Systems (AES) Life Support Systems (LSS) ground test planning for future missions. The methodology determines the optimal number of test units to purchase and allocation of test time –split between reliability growth and uncertainty reduction testing –across a given set of items in order to minimize spares mass for a given mission under constraints on total test cost and schedule. Model outputs also include expected spares mass after testing and the expected number of modifications or refurbishments during testing, both of which can inform program planning. Discussion of the model, conclusions, and future work are also presented.
Testing↗