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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 NTRS1987

Digital robust active control law synthesis for large order systems using constrained optimization

This paper presents a direct digital control law synthesis procedure for a large order, sampled data, linear feedback system using constrained optimization techniques to meet multiple design requirements. A linear quadratic Gaussian type cost function is minimized while satisfying a set of constraints on the design loads and responses. General expressions for gradients of the cost function and constraints, with respect to the digital control law design variables are derived analytically and computed by solving a set of discrete Liapunov equations. The designer can choose the structure of the control law and the design variables, hence a stable classical control law as well as an estimator-based full or reduced order control law can be used as an initial starting point. Selected design responses can be treated as constraints instead of lumping them into the cost function. This feature can be used to modify a control law, to meet individual root mean square response limitations as well as minimum single value restrictions. Low order, robust digital control laws were synthesized for gust load alleviation of a flexible remotely piloted drone aircraft.

Mukhopadhyay, Vivek↗
NASA NTRS1990

A first-order Lyapunov robustness method for linear systems with uncertain parameters

A method for stability-robustness analysis based on a quadratic Liapunov function that varies linearly with uncertainty parameters is derived. Linear time-invariant systems with structured uncertainties are discussed. The Liapunov function is optimized numerically to maximize the robustness region in parameter space. Numerical results are given for four examples in which the first-order method is compared to previous Liapunov methods. While the zero-order method is slightly better than the first-order method for one example, the first-order method is clearly superior in the other three (more realistic) examples. The first-order method is especially superior for the active control of flexible structures, where robustness with respect to (1) unmodeled coupling between modeled modes and (2) unmodeled modes is important. For such applications, the first-order method is much better at detecting the increased robustness associated with increased separation between frequencies.

Leal, M. A.↗
NASA NTRS1990

Integrated identification and robust control tuning for large space structures

System identification is studied for the explicit purpose of supporting modern H-infinity robust control design objectives. In the analysis, the true plant is not assumed to be in the identification model set. An integrated identification/robust control problem is posed in which the optimal solution guarantees the best robust performance relative to the system information contained in a given experimental data set. A numerical example demonstrating an approximate solution to the problem indicates the usefulness of the approach.

Yam, Y.↗
NASA NTRS1995

A grid generation system for multi-disciplinary design optimization

A general multi-block three-dimensional volume grid generator is presented which is suitable for Multi-Disciplinary Design Optimization. The code is timely, robust, highly automated, and written in ANSI 'C' for platform independence. Algebraic techniques are used to generate and/or modify block face and volume grids to reflect geometric changes resulting from design optimization. Volume grids are generated/modified in a batch environment and controlled via an ASCII user input deck. This allows the code to be incorporated directly into the design loop. Generated volume grids are presented for a High Speed Civil Transport (HSCT) Wing/Body geometry as well a complex HSCT configuration including horizontal and vertical tails, engine nacelles and pylons, and canard surfaces.

Jones, William T.↗
NASA NTRS1995

A Grid Generation System for Multi-Disciplinary Design Optimization

A general multi-block three-dimensional volume grid generator is presented which is suitable for Multi-Disciplinary Design Optimization. The code is fast, robust, highly automated, and written in ANSI C for platform independence. Algebraic techniques are used to generate and/or modify block face and volume grids to reflect geometric changes resulting from design optimization. Volume grids are generated/modified in a batch environment and controlled via an ASCII user input deck. This allows the code to be incorporated directly into the design loop. Generated volume grids are presented for a High Speed Civil Transport (HSCT) Wing/Body geometry as well a complex HSCT configuration including horizontal and vertical tails, engine nacelles and pylons, and canard surfaces.

Jones, William T.↗
NASA NTRS1992

A reliable algorithm for optimal control synthesis

In recent years, powerful design tools for linear time-invariant multivariable control systems have been developed based on direct parameter optimization. In this report, an algorithm for reliable optimal control synthesis using parameter optimization is presented. Specifically, a robust numerical algorithm is developed for the evaluation of the H(sup 2)-like cost functional and its gradients with respect to the controller design parameters. The method is specifically designed to handle defective degenerate systems and is based on the well-known Pade series approximation of the matrix exponential. Numerical test problems in control synthesis for simple mechanical systems and for a flexible structure with densely packed modes illustrate positively the reliability of this method when compared to a method based on diagonalization. Several types of cost functions have been considered: a cost function for robust control consisting of a linear combination of quadratic objectives for deterministic and random disturbances, and one representing an upper bound on the quadratic objective for worst case initial conditions. Finally, a framework for multivariable control synthesis has been developed combining the concept of closed-loop transfer recovery with numerical parameter optimization. The procedure enables designers to synthesize not only observer-based controllers but also controllers of arbitrary order and structure. Numerical design solutions rely heavily on the robust algorithm due to the high order of the synthesis model and the presence of near-overlapping modes. The design approach is successfully applied to the design of a high-bandwidth control system for a rotorcraft.

Vansteenwyk, Brett↗
NASA NTRS2017

Robust Acoustic Objective Functions and Sensitivities in Adjoint-Based Design Optimizations

The multidisciplinary design of aircraft typically includes considerations of performance, weight, fuel burn, and noise, among other factors. An objective function is applied to each of these considerations in order to weight the influence of trade-offs between different designs. Higher-order optimization exercises have utilized an adjoint approach to reach an optimal set of objective functions, such as maximum lift and reduced drag. Taking advantage of the adjoint approach significantly reduces the computational time required to find an optimal configuration. Including acoustics in the set of objective functions during an adjoint-based design optimization requires the sensitivity of the acoustic objective function. This document will present an approach for defining the sensitivity of several acoustic metrics and operations that can fill the role of the acoustic objective function. This includes time-integrated metrics such as effective perceived noise level (EPNL) and frequency-integrated metrics such as overall sound pressure level (OASPL). A demonstration case, validation, and details on the implementation in the second generation Aircraft NOise Prediction Program (ANOPP2) are also shown.

Lopes, Leonard V.↗
NASA NTRS2001

Multidisciplinary Design Optimization of a Full Vehicle with High Performance Computing

Multidisciplinary design optimization (MDO) of a full vehicle under the constraints of crashworthiness, NVH (Noise, Vibration and Harshness), durability, and other performance attributes is one of the imperative goals for automotive industry. However, it is often infeasible due to the lack of computational resources, robust simulation capabilities, and efficient optimization methodologies. This paper intends to move closer towards that goal by using parallel computers for the intensive computation and combining different approximations for dissimilar analyses in the MDO process. The MDO process presented in this paper is an extension of the previous work reported by Sobieski et al. In addition to the roof crush, two full vehicle crash modes are added: full frontal impact and 50% frontal offset crash. Instead of using an adaptive polynomial response surface method, this paper employs a DOE/RSM method for exploring the design space and constructing highly nonlinear crash functions. Two NMO strategies are used and results are compared. This paper demonstrates that with high performance computing, a conventionally intractable real world full vehicle multidisciplinary optimization problem considering all performance attributes with large number of design variables become feasible.

Yang, R. J.↗
NASA NTRS2004

On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications

Differential Evolution (DE) is a simple and robust evolutionary strategy that has been provEn effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. Several approaches that have proven effective for other evolutionary algorithms are modified and implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for standard test optimization problems and for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.

Madavan, Nateri K.↗
NASA NTRS2004

On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications

Differential Evolution (DE) is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. These approaches are implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.

Madavan, Nateri K.↗
NASA NTRS2004

Towards Risk Based Design for NASA's Missions

This paper describes the concept of Risk Based Design in the context of NASA s low volume, high cost missions. The concept of accounting for risk in the design lifecycle has been discussed and proposed under several research topics, including reliability, risk analysis, optimization, uncertainty, decision-based design, and robust design. This work aims to identify and develop methods to enable and automate a means to characterize and optimize risk, and use risk as a tradeable resource to make robust and reliable decisions, in the context of the uncertain and ambiguous stage of early conceptual design. This paper first presents a survey of the related topics explored in the design research community as they relate to risk based design. Then, a summary of the topics from the NASA-led Risk Colloquium is presented, followed by current efforts within NASA to account for risk in early design. Finally, a list of "risk elements", identified for early-phase conceptual design at NASA, is presented. The purpose is to lay the foundation and develop a roadmap for future work and collaborations for research to eliminate and mitigate these risk elements in early phase design.

Tumer, Irem Y.↗
NASA NTRSDate not supplied

Robust and Mass Efficient Thermal Protection Systems for Future Venus Missions

Several international Venus missions, both orbiter, and in-situ probe are in the works after a prolonged absence. Future missions are expected to be long-duration lander missions and balloon missions to investigate the cloud layer. The entry mass for these in-situ missions will be significantly higher, and the aeroshell will be larger, than previous missions. Two cardinal requirements govern the selection and use of thermal protection systems (TPS), namely, robustness to assure mission safety during entry and mass efficiency so that the useful mass for science is maximized. One cannot trade mission safety for mass when it comes to TPS. The robustness of the TPS is a paramount requirement as it is a single point of system failure. At the same time, TPS mass is carried for the entire mission duration prior to entry, and any excess TPS mass is at the cost of science payload. Future missions will benefit enormously from TPS options with mass and performance benefits far beyond advanced carbon-carbon (ACC) that is currently proposed for the DAVINCI mission. NASA STMD, and SMD jointly invested in the development of 3-D woven thermal protection systems in the last decade, and as a result, the Heat-shield for Extreme Entry Environment Technology (HEEET) TPS has been matured to TRL 6. It is ready for future mission use including Venus. The HEEET project focused on developing a broad technology base, applicable for missions not only to Venus, but also to Saturn, the Ice Giants, and higher speed sample return missions such as Mars Sample Return requiring extreme robustness due to bio-hazards. The dual-layer HEEET (DL-HEEET) is proven to be extremely robust. DL-HEEET performed well in arc jet and laser testing conditions where heritage carbon-phenolic failed. HEEET also proved to be more mass efficient compared to Carbon-Phenolic. Recently, the principal author was invited to present his perspectives to the 2023-2032 Planetary Sciences Decadal Venus subcommittee on the current state of TPS for future Venus missions [1]. In addition to pointing out successful TPS and other developments in the last decade in support of entry systems, the principal author made two findings: 1) importance of and need to sustain TPS capabilities that took nearly a decade to develop and 2) the opportunity to further optimize TPS mass without sacrificing robustness to further enable future in-situ missions. This talk will focus on the above two recommendations and provide the rationale for them. The intent of the talk is to seek advocacy from the VEXAG community for the criticality of TPS sustainment and the benefits of 3D woven TPS optimization. TPS Capability Sustainment: HEEET development was necessitated by the atrophy of heritage carbon-phenolic. Atrophy has impacted other TPS materials as well. Avcoat, the TPS that successfully allowed astronauts to explore the Moon in the 1960s and 70s, took nearly a decade and $10’sM to recover in support of Orion/Artemis. One of the most used TPS materials, SLA 561V, had to be recovered prior to its use on Mars Pathfinder. Phenolic Impregnated Carbon Ablator (PICA), the TPS that replaced SLA 561V on MSL, has undergone multiple replacements and recovery due to constituent rayon changes. Recently, NASA invested in a domestic rayon replacement program and also invested in FMI to consolidate PICA capability for NASA missions, as a result of FMI’s decision to discontinue commercial FiberForm, which is needed for PICA. TPS used for planetary missions are unique and have no other commercial or defense use. In addition, low mission cadence is also a driver for TPS atrophy. Hence, NASA, as the steward, must take steps to ensure and sustain TPS capabilities. In this talk, we outline steps NASA can take to keep abreast of emerging risks and target risk mitigation steps to ensure TPS capability sustainment for Venus and other extreme environment missions. Next Generation of Mass efficient and Robust TPS: NASA invested in an alternate TPS to PICA based on felt-technology called Conformal-PICA which has the potential to save 30% - 50% mass over PICA. The development was discontinued at a Technology Readiness Level (TRL) of ~ 5. 3D Mid-Density Carbon Phenolic (3MDCP) is a single layer variant of HEEET, based on the insulating layer only (SL-HEEET) and currently baselined for the Mars Sample Return Mission Earth Entry System due to its mass efficiency (30% more mass efficient than DL- HEEET). Currently, SL-HEEET is limited to aeroshell diameters of < 1.3m. The SL-HEEET was compared to DL-HEEET in the recent ADVENT flag-ship class mission study in support of the Planetary Science Decadal. SL HEEET was the recommended TPS based on 30% mass savings for both the balloon and lander missions. Given C-PICA and SL-HEEET have superior reliability and mass efficiency, advocacy from VEXAG is sought for completing their further development to TRL 5/6 in this decade so as to enable TPS readiness for future missions.

Thermal Protection Systems↗
NASA NTRS1999

Developing a Fundamental Model for an Integrated GPS/INS State Estimation System with Kalman Filtering

This work will demonstrate the integration of sensor and system dynamic data and their appropriate models using an optimal filter to create a robust, adaptable, easily reconfigurable state (motion) estimation system. This state estimation system will clearly show the application of fundamental modeling and filtering techniques. These techniques are presented at a general, first principles level, that can easily be adapted to specific applications. An example of such an application is demonstrated through the development of an integrated GPS/INS navigation system. This system acquires both global position data and inertial body data, to provide optimal estimates of current position and attitude states. The optimal states are estimated using a Kalman filter. The state estimation system will include appropriate error models for the measurement hardware. The results of this work will lead to the development of a "black-box" state estimation system that supplies current motion information (position and attitude states) that can be used to carry out guidance and control strategies. This black-box state estimation system is developed independent of the vehicle dynamics and therefore is directly applicable to a variety of vehicles. Issues in system modeling and application of Kalman filtering techniques are investigated and presented. These issues include linearized models of equations of state, models of the measurement sensors, and appropriate application and parameter setting (tuning) of the Kalman filter. The general model and subsequent algorithm is developed in Matlab for numerical testing. The results of this system are demonstrated through application to data from the X-33 Michael's 9A8 mission and are presented in plots and simple animations.

Canfield, Stephen↗
NASA NTRSDate not supplied

Interval Predictor Models for Robust System Identification

This paper proposes a framework for the identification and uncertainty quantification of plant models according to multivariable data. The only restriction imposed upon such models is for their outputs to depend continuously on their parameters. An Interval Predictor Model (IPM) prescribes the parameters of a computational model as a path-connected set thereby making each predicted output an interval-valued function of its inputs. The formulation proposed seeks the parameter set for which the predicted outputs tightly enclose the data. This set, which is modeled as a semi-algebraic set of low-degree polynomials, enables the characterization of possibly strong parameter dependencies commonly found in practice. This uncertainty characterization makes the resulting plant model amenable to robust control approaches using polynomial optimization. Furthermore, we use non-convex scenario theory to assess the reliability of the resulting IPM. This assessment yields a distribution-free upper bound on the probability that future data will fall outside the predicted intervals.

interval↗
NASA NTRS2008

Robust Path Planning and Feedback Design Under Stochastic Uncertainty

Autonomous vehicles require optimal path planning algorithms to achieve mission goals while avoiding obstacles and being robust to uncertainties. The uncertainties arise from exogenous disturbances, modeling errors, and sensor noise, which can be characterized via stochastic models. Previous work defined a notion of robustness in a stochastic setting by using the concept of chance constraints. This requires that mission constraint violation can occur with a probability less than a prescribed value.In this paper we describe a novel method for optimal chance constrained path planning with feedback design. The approach optimizes both the reference trajectory to be followed and the feedback controller used to reject uncertainty. Our method extends recent results in constrained control synthesis based on convex optimization to solve control problems with nonconvex constraints. This extension is essential for path planning problems, which inherently have nonconvex obstacle avoidance constraints. Unlike previous approaches to chance constrained path planning, the new approach optimizes the feedback gain as wellas the reference trajectory.The key idea is to couple a fast, nonconvex solver that does not take into account uncertainty, with existing robust approaches that apply only to convex feasible regions. By alternating between robust and nonrobust solutions, the new algorithm guarantees convergence to a global optimum. We apply the new method to an unmanned aircraft and show simulation results that demonstrate the efficacy of the approach.

autonomuys vehicles↗
NASA NTRS1988

Digital robust active control law synthesis for large order flexible structure using parameter optimization

A generic procedure for the parameter optimization of a digital control law for a large-order flexible flight vehicle or large space structure modeled as a sampled data system is presented. A linear quadratic Guassian type cost function was minimized, while satisfying a set of constraints on the steady-state rms values of selected design responses, using a constrained optimization technique to meet multiple design requirements. Analytical expressions for the gradients of the cost function and the design constraints on mean square responses with respect to the control law design variables are presented.

Mukhopadhyay, V.↗
NASA NTRS1989

Numerical optimization in Hilbert space using inexact function and gradient evaluations

Trust region algorithms provide a robust iterative technique for solving non-convex unstrained optimization problems, but in many instances it is prohibitively expensive to compute high accuracy function and gradient values for the method. Of particular interest are inverse and parameter estimation problems, since function and gradient evaluations involve numerically solving large systems of differential equations. A global convergence theory is presented for trust region algorithms in which neither function nor gradient values are known exactly. The theory is formulated in a Hilbert space setting so that it can be applied to variational problems as well as the finite dimensional problems normally seen in trust region literature. The conditions concerning allowable error are remarkably relaxed: relative errors in the gradient error condition is automatically satisfied if the error is orthogonal to the gradient approximation. A technique for estimating gradient error and improving the approximation is also presented.

Carter, Richard G.↗