Linear Covariance Analysis Framework for Aerospace Vehicle Trajectory Modeling and Parametric Design.
Abstract not provided.
Heflin, Lucas↗
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Linear Covariance Analysis Framework for Aerospace Vehicle Trajectory Modeling and Parametric Design.
Abstract not provided.
Whitten, William↗
A cross-sectional aeroelastic analysis and structural optimization tool for slender composite structures
A fully open-source available framework for the parametric cross-sectional analysis and design optimization of slender composite structures, such as helicopter or wind turbine blades, is presented. The framework—Structural Optimization and Aeroelastic Analysis (SONATA)—incorporates two structural solvers, the commercial tool VABS, and the novel open-source code ANBA4. SONATA also parameterizes the design inputs, postprocesses and visualizes the results, and generates the structural inputs to a variety of aeroelastic analysis tools. It is linked to the optimization library OpenMDAO. This work presents the methodology and explains the fundamental approaches of SONATA. Structural characteristics were successfully verified for both VABS and ANBA4 using box beam examples from literature, thereby verifying the parametric approach to generating the topology and mesh in a cross section as well as the solver integration. The framework was furthermore exercised by analyzing and evaluating a fully resolved highly flexible wind turbine blade. Computed structural characteristics correlated between VABS and ANBA4, including off-diagonal terms. Stresses, strains, and deformations were recovered from loads derived through coupling with aeroelastic analysis. The framework, therefore, proves effective in accurately analyzing and optimizing slender composite structures on a high-fidelity level that is close to a three-dimensional finite element model.
42 ENGINEERING↗
Computational Analysis for Rocket-Based Combined-Cycle Systems During Rocket-Only Operation
A series of Reynolds-averaged Navier-Stokes calculations were employed to study the performance of rocket-based combined-cycle systems operating in an all-rocket mode. This parametric series of calculations were executed within a statistical framework, commonly known as design of experiments. The parametric design space included four geometric and two flowfield variables set at three levels each, for a total of 729 possible combinations. A D-optimal design strategy was selected. It required that only 36 separate computational fluid dynamics (CFD) solutions be performed to develop a full response surface model, which quantified the linear, bilinear, and curvilinear effects of the six experimental variables. The axisymmetric, Reynolds-averaged Navier-Stokes simulations were executed with the NPARC v3.0 code. The response used in the statistical analysis was created from Isp efficiency data integrated from the 36 CFD simulations. The influence of turbulence modeling was analyzed by using both one- and two-equation models. Careful attention was also given to quantify the influence of mesh dependence, iterative convergence, and artificial viscosity upon the resulting statistical model. Thirteen statistically significant effects were observed to have an influence on rocket-based combined-cycle nozzle performance. It was apparent that the free-expansion process, directly downstream of the rocket nozzle, can influence the Isp efficiency. Numerical schlieren images and particle traces have been used to further understand the physical phenomena behind several of the statistically significant results.
Steffen, C. J., Jr.↗
A Framework for Parametric and Predictive Uncertainty Quantification in the E3SM Land Model: Assessing Site and Observable Generalizability
Quantifying parametric uncertainty using observations from individual sites provides a critical foundation for Earth system modeling, serving as a necessary first step before scaling up to regional or global applications. This study introduces a novel computational framework designed to enhance model predictability by reducing parametric uncertainty and assessing site and observable generalizability using various observational constraints. The framework integrates five components: Model Simulation, Statistical Emulation, Global Sensitivity Analysis (GSA), Model Calibration, and Model Prediction. Using the E3SM land model, we simulated site-level land-atmosphere carbon and energy fluxes from 2003 to 2007 across five evergreen needleleaf FLUXNET sites, perturbing 26 vegetation-related model parameters. Gaussian process emulators were employed to expedite GSA and model calibration. Four critical parameters that strongly influence selected land-atmosphere fluxes were identified by GSA. Bayesian approaches were used to infer parameter probability distributions leveraging synthetic data and FLUXNET observations. The results reveal that posterior parameter distributions vary significantly across different sites and observables within the same plant functional type. Probabilistic predictions indicate that parameters calibrated at one site can enhance predictive accuracy at other sites, although site heterogeneity may sometimes outweigh parametric uncertainty. Additionally, the probabilistic predictions demonstrate that calibration for one variable can also improve predictability for other variables, thereby maximizing predictive capabilities with limited observations. This framework provides a powerful approach for reducing parametric uncertainty in Earth system models and deepening our understanding of carbon dynamics and energy cycles. Its adaptability makes it a valuable tool for broader applications in Earth system modeling.
54 ENVIRONMENTAL SCIENCES↗
Robustness Analysis and Optimally Robust Control Design via Sum-of-Squares
A control analysis and design framework is proposed for systems subject to parametric uncertainty. The underlying strategies are based on sum-of-squares (SOS) polynomial analysis and nonlinear optimization to design an optimally robust controller. The approach determines a maximum uncertainty range for which the closed-loop system satisfies a set of stability and performance requirements. These requirements, de ned as inequality constraints on several metrics, are restricted to polynomial functions of the uncertainty. To quantify robustness, SOS analysis is used to prove that the closed-loop system complies with the requirements for a given uncertainty range. The maximum uncertainty range, calculated by assessing a sequence of increasingly larger ranges, serves as a robustness metric for the closed-loop system. To optimize the control design, nonlinear optimization is used to enlarge the maximum uncertainty range by tuning the controller gains. Hence, the resulting controller is optimally robust to parametric uncertainty. This approach balances the robustness margins corresponding to each requirement in order to maximize the aggregate system robustness. The proposed framework is applied to a simple linear short-period aircraft model with uncertain aerodynamic coefficients.
Dorobantu, Andrei↗
Integrated Design Engineering Analysis (IDEA) Environment Automated Generation of Structured CFD Grids using Topology Methods
This report documents the work performed from March 2010 to March 2012. The Integrated Design and Engineering Analysis (IDEA) environment is a collaborative environment based on an object-oriented, multidisciplinary, distributed framework using the Adaptive Modeling Language (AML) as a framework and supporting the configuration design and parametric CFD grid generation. This report will focus on describing the work in the area of parametric CFD grid generation using novel concepts for defining the interaction between the mesh topology and the geometry in such a way as to separate the mesh topology from the geometric topology while maintaining the link between the mesh topology and the actual geometry.
Kamhawi, Hilmi N.↗
HelioSwarm: Relative Orbit Maintenance in Eccentric P/2 Lunar Resonant Orbit
Science missions employing multi-point measurements require planned and repeatable swarm geometries. Cis-lunar orbits enable heliophysics studies of solar wind but introduce new challenges in relative motion control. For a multi-satellite swarm in lunar-resonant orbit, insertion and maintenance maneuvers address orbit eccentricity and evolution, as well as three-body gravitational perturbations. Local Velocity-Normal-Conormal coordinates prove to be an effective framework for relative motion design and control. Parametric studies investigate maneuver placement efficiency in controlling relative motion in cis-lunar orbit.
Relative Motion↗
Multi-modality deep learning for pulse prediction in homogeneous nonlinear systems via parametric conversion
In this Letter, we introduce FusionNet, a multi-modality deep learning framework designed to predict and analyze output pulses in high-power rare-earth-doped laser systems driving parametric conversion in homogeneous guided nonlinear media. FusionNet integrates temporal, spectral, and physical experimental conditions to model ultrafast nonlinear phenomena, including parametric nonlinear frequency conversion, self-phase modulation, and cross-phase modulation in homogeneous guided systems such as gas-filled hollow-core fibers. These systems bridge physical models with experimental data, advancing our understanding of light-guiding principles and nonlinear interactions while expediting the design and optimization of on-demand high-power, high-brightness systems. Our results demonstrate a 73% reduction in prediction error and an 83% improvement in computational efficiency compared to conventional neural networks. This work establishes a new paradigm for accelerating parametric simulations and optimizing experimental designs in high-power laser systems, with further implications for high-precision spectroscopy, quantum information science, and distributed entangled interconnects.
47 OTHER INSTRUMENTATION↗
Parametric design and gridding through relational geometry
Relational Geometric Synthesis (RGS) is a new logical framework for building up precise definitions of complex geometric models from points, curves, surfaces and solids. RGS achieves unprecedented design flexibility by supporting a rich variety of useful curve and surface entities. During the design process, many qualitative and quantitative relationships between elementary objects may be captured and retained in a data structure equivalent to a directed graph, such that they can be utilized for automatically updating the complete model geometry following changes in the shape or location of an underlying object. Capture of relationships enables many new possibilities for parametric variations and optimization. Examples are given of panelization applications for submarines, sailing yachts, offshore structures, and propellers.
Letcher, John S., Jr.↗
LaSDI: Parametric Latent Space Dynamics Identification
Enabling fast and accurate physical simulations with data has become an important area of computational physics to aid in inverse problems, design-optimization, uncertainty quantification, and other various decision-making applications. This paper presents a data-driven framework for parametric latent space dynamics identification procedure that enables fast and accurate simulations. The parametric model is achieved by building a set of local latent space model and designing an interaction among them. An individual local latent space dynamics model achieves accurate solution in a trust region. By letting the set of trust region to cover the whole parameter space, our model shows an increase in accuracy with an increase in training data. Herein we introduce two different types of interaction mechanisms, i.e., point-wise and region-based approach. Both linear and nonlinear data compression techniques are used. We illustrate the framework of Latent Space Dynamics Identification (LaSDI) enable a fast and accurate solution process on various partial differential equations, i.e., Burgers’ equations, radial advection problem, and nonlinear heat conduction problem, achieving 0 (100)x speed-up and 0 (1)% relative error with respect to the corresponding full order models.
97 MATHEMATICS AND COMPUTING↗
SPRITE - A computer code for the optimization of space based heat pipe radiator systems
An integrated analytical tool has been developed for use in designing optimized space-based heat pipe radiator systems. This code, SPRITE-1, incorporates the thermal, structural, and reliability aspects of the radiator into a single framework from which a physically consistent design can be obtained. A parametric study of the integral heat pipe panel radiator was performed using SPRITE-1, and a preliminary minimum mass design was obtained. The radiator design is summarized, and the mass minimization method and results are presented.
Buksa, John J.↗
Hard and Soft Constraints in Reliability-Based Design Optimization
This paper proposes a framework for the analysis and design optimization of models subject to parametric uncertainty where design requirements in the form of inequality constraints are present. Emphasis is given to uncertainty models prescribed by norm bounded perturbations from a nominal parameter value and by sets of componentwise bounded uncertain variables. These models, which often arise in engineering problems, allow for a sharp mathematical manipulation. Constraints can be implemented in the hard sense, i.e., constraints must be satisfied for all parameter realizations in the uncertainty model, and in the soft sense, i.e., constraints can be violated by some realizations of the uncertain parameter. In regard to hard constraints, this methodology allows (i) to determine if a hard constraint can be satisfied for a given uncertainty model and constraint structure, (ii) to generate conclusive, formally verifiable reliability assessments that allow for unprejudiced comparisons of competing design alternatives and (iii) to identify the critical combination of uncertain parameters leading to constraint violations. In regard to soft constraints, the methodology allows the designer (i) to use probabilistic uncertainty models, (ii) to calculate upper bounds to the probability of constraint violation, and (iii) to efficiently estimate failure probabilities via a hybrid method. This method integrates the upper bounds, for which closed form expressions are derived, along with conditional sampling. In addition, an l(sub infinity) formulation for the efficient manipulation of hyper-rectangular sets is also proposed.
Crespo, L.uis G.↗
Scalable phononic metamaterials: Tunable bandgap design and multi-scale experimental validation
Phononic metamaterials offer unprecedented control over wave propagation, making them essential for applications such as vibration isolation, waveguiding, and acoustic filtering. However, achieving scalable and precisely tunable bandgap properties across different length scales remains challenging. This study presents a user-friendly design framework for phononic metamaterials, enabling ultra-wide bandgap tunability (B/ω c ratios up to 172 %) across multiple frequency ranges and scales. Using finite element simulations of a Yablonovite-inspired unit cell, we establish a comprehensive parametric design space that illustrates how geometric parameters, such as sphere size and beam diameter, controls bandgap width and frequency. The scalability and robustness of the framework are validated through experimental testing on additively manufactured structures at both macro (10 mm) and micro (80 µm) scales, fabricated using Stereolithography and Two-Photon Polymerization. Transmission loss measurements, conducted with piezoelectric transducers and laser vibrometry, closely match simulations in the kHz and MHz frequency ranges, confirming the reliability and consistency of the bandgap behavior across scales. This work bridges theory and experiments at multiple scales, offering a practical methodology for the rapid design of phononic metamaterials and expanding their potential for diverse applications across a broad range of frequencies.
36 MATERIALS SCIENCE↗
Hybrid Symbolic-Numerical Modeling and Parametric Stability Analysis of DC–AC Power Systems
Hybrid DC-AC power systems integrating diverse inverter-based resources (IBRs) and multi-terminal high-voltage direct current (MTDC) networks represent a promising architecture for future power grids, while introducing challenges for modeling, stability analysis, and control design. This paper develops a hybrid symbolic-numerical modeling framework and tool to characterize the parametric small-signal stability of DC-AC coupled power systems. The proposed approach constructs parametric state-space models to enable efficient representation of system dynamics under varying control parameters and network configurations, with target parameters retained as symbolic variables and the remainder treated numerically. The stability analysis framework covers eigenvalue, sensitivity, and stability boundary and region characterization. Enhanced linear matrix inequality (LMI) techniques are proposed to directly certify small-signal stability over regions of parameter space while also reducing the conservativeness and computational burden. The resulting tools and frameworks enable rapid parametric model construction across diverse grid conditions, thereby facilitating stability-informed control and operation in complex DC–AC power systems.
DC–AC power systems↗
An Overview of NASA's Integrated Design and Engineering Analysis (IDEA) Environment
Historically, the design of subsonic and supersonic aircraft has been divided into separate technical disciplines (such as propulsion, aerodynamics and structures), each of which performs design and analysis in relative isolation from others. This is possible, in most cases, either because the amount of interdisciplinary coupling is minimal, or because the interactions can be treated as linear. The design of hypersonic airbreathing vehicles, like NASA's X-43, is quite the opposite. Such systems are dominated by strong non-linear interactions between disciplines. The design of these systems demands that a multi-disciplinary approach be taken. Furthermore, increased analytical fidelity at the conceptual design phase is highly desirable, as many of the non-linearities are not captured by lower fidelity tools. Only when these systems are designed from a true multi-disciplinary perspective, can the real performance benefits be achieved and complete vehicle systems be fielded. Toward this end, the Vehicle Analysis Branch at NASA Langley Research Center has been developing the Integrated Design and Engineering Analysis (IDEA) Environment. IDEA is a collaborative environment for parametrically modeling conceptual and preliminary designs for launch vehicle and high speed atmospheric flight configurations using the Adaptive Modeling Language (AML) as the underlying framework. The environment integrates geometry, packaging, propulsion, trajectory, aerodynamics, aerothermodynamics, engine and airframe subsystem design, thermal and structural analysis, and vehicle closure into a generative, parametric, unified computational model where data is shared seamlessly between the different disciplines. Plans are also in place to incorporate life cycle analysis tools into the environment which will estimate vehicle operability, reliability and cost. IDEA is currently being funded by NASA?s Hypersonics Project, a part of the Fundamental Aeronautics Program within the Aeronautics Research Mission Directorate. The environment is currently focused around a two-stage-to-orbit configuration with a turbine-based combined cycle (TBCC) first stage and a reusable rocket second stage. IDEA will be rolled out in generations, with each successive generation providing a significant increase in capability, either through increased analytic fidelity, expansion of vehicle classes considered, or by the inclusion of advanced modeling techniques. This paper provides the motivation behind the current effort, an overview of the development of the IDEA environment (including the contents and capabilities to be included in Generation 1 and Generation 2), and a description of the current status and detail of future plans.
Robinson, Jeffrey S.↗
Parametric Model of an Aerospike Rocket Engine
A suite of computer codes was assembled to simulate the performance of an aerospike engine and to generate the engine input for the Program to Optimize Simulated Trajectories. First an engine simulator module was developed that predicts the aerospike engine performance for a given mixture ratio, power level, thrust vectoring level, and altitude. This module was then used to rapidly generate the aerospike engine performance tables for axial thrust, normal thrust, pitching moment, and specific thrust. Parametric engine geometry was defined for use with the engine simulator module. The parametric model was also integrated into the iSIGHT multidisciplinary framework so that alternate designs could be determined. The computer codes were used to support in-house conceptual studies of reusable launch vehicle designs.
Korte, J. J.↗
Parametric Model of an Aerospike Rocket Engine
A suite of computer codes was assembled to simulate the performance of an aerospike engine and to generate the engine input for the Program to Optimize Simulated Trajectories. First an engine simulator module was developed that predicts the aerospike engine performance for a given mixture ratio, power level, thrust vectoring level, and altitude. This module was then used to rapidly generate the aerospike engine performance tables for axial thrust, normal thrust, pitching moment, and specific thrust. Parametric engine geometry was defined for use with the engine simulator module. The parametric model was also integrated into the iSIGHTI multidisciplinary framework so that alternate designs could be determined. The computer codes were used to support in-house conceptual studies of reusable launch vehicle designs.
Korte, J. J.↗