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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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NASA NTRS1990

A linear quadratic regulator approach to the stabilization of uncertain linear systems

This paper presents a linear quadratic regulator approach to the stabilization of uncertain linear systems. The uncertain systems under consideration are described by state equations with the presence of time-varying unknown-but-bounded uncertainty matrices. The method is based on linear quadratic regulator (LQR) theory and Liapunov stability theory. The robust stabilizing control law for a given uncertain system can be easily constructed from the symmetric positive-definite solution of the associated augmented Riccati equation. The proposed approach can be applied to matched and/or mismatched systems with uncertainty matrices in which only their matrix norms are bounded by some prescribed values and/or their entries are bounded by some prescribed constraint sets. Several numerical examples are presented to illustrate the results.

Shieh, L. S.↗

NASA NTRS1989

Nonlinear combustion instability model in two- to three-dimensions

A model is developed on using quasi-steady state equations for the injection, atomization, and vaporization processes that consider the instantaneous local pressures, temperatures, density, and gas velocity vector associated with a 3-D wave. The coupled sets of equations are solved to determine frequency and growth rate of the oscillations as a function of wave amplitude. Calculated results are used to explain the observed instabilities in a 40 Klb LOX/Methane combustor.

Priem, Richard J.↗

NASA NTRS1991

Cosmic-ray heating of cooling flows - A critical analysis

It is shown that a combination of MHD wave-mediated cosmic ray heating and thermal conduction could balance cooling in intracluster media and substantially reduce the rate of inflow. The appropriate system of steady state equations is solved, including a new self-consistent formulation for the cosmic-ray diffusivity. Models which can produce substantial positive temperature gradients in static configurations are found when conduction is reduced by a factor of 10 or more. These models have too-flat thermal pressure profiles compared with observations. It is found that cosmic-ray heating is unlikely either to stabilize positive density perturbations against condensation or to contribute appreciably to the powering of the optical filaments.

Loewenstein, Michael↗

NASA NTRS1991

The RAPT control method for nonlinear systems

The Rapid Advancement Preview Tracking (RAPT) control method uses both a nonlinear system model and a linearized system model to provide a form of feedback/preview control for nonlinear systems. It uses measurements of the states to calculate the current preview control signal from a local linearization of the state equations, and then iteratively calculates a perturbation correction for that control using the nonlinear equations. The corrected control signal is then applied to the system, driving the end-effector to the next position along a specified trajectory. Computer simulations of tracking control for two-link planar manipulators indicate that this algorithm may be fast enough for real-time control.

Long, T. W.↗

NASA NTRS1992

Thermal and dynamical regimes of single- and two-phase magmatic flow in dikes

The coupling between thermal and dynamical regimes of single- and two-phase magmatic flow in dikes, due to temperature-dependent viscosity and dissipation, was investigated using finite element calculations of magma flow in dikelike channels with length-to-width ratios of 1000:1 or more. Solutions of the steady state equations governing magma flow are obtained for a variety of conditions ranging from idealized plane-parallel models to cases involving nonparallel geometry and two-phase flows. The implications of the numerical simulations for the dynamics of flow in a dike-reservoir system and the consequences of dike entrance conditions on magmatic storage are discussed. Consideration is also given to an unmixing/self-lubrication mechanism which may be important for the lubrication of silicic magmas rising to the earth's surface in mixed magma ascent scenarios, which naturally segregates magma mixtures of two components with differing viscosities to minimize the driving pressure gradient.

Carrigan, Charles R.↗

NASA NTRS1993

Wave and pseudo-diffusion equations from squeezed states

We show that the probability distributions P(sub n)(q,p;y) := the absolute value squared of (n(p,q;y), which are obtained from squeezed states, obey an interesting partial differential equation, to which we give two intuitive interpretations: as a wave equation in one space dimension; and as a pseudo-diffusion equation. We also study the corresponding Wehrl entropies S(sub n)(y), and we show that they have minima at zero squeezing, y = 0.

Daboul, Jamil↗

NASA NTRS1994

Airfoil optimization by the one-shot method

An efficient numerical approach for the design of optimal aerodynamic shapes is presented in this paper. The objective of any optimization problem is to find the optimum of a cost function subject to a certain state equation (Governing equation of the flow field) and certain side constraints. As in classical optimal control methods, the present approach introduces a costate variable (Language multiplier) to evaluate the gradient of the cost function. High efficiency in reaching the optimum solution is achieved by using a multigrid technique and updating the shape in a hierarchical manner such that smooth (low-frequency) changes are done separately from high-frequency changes. Thus, the design variables are changed on a grid where their changes produce nonsmooth (high-frequency) perturbations that can be damped efficiently by the multigrid. The cost of solving the optimization problem is approximately two to three times the cost of the equivalent analysis problem.

Kuruvila, G.↗

NASA NTRS1997

Aircraft Pitch Control With Fixed Order LQ Compensators

This paper considers a given set of fixed order compensators for aircraft pitch control problem. By augmenting compensator variables to the original state equations of the aircraft, a new dynamic model is considered to seek a LQ controller. While the fixed order compensators can achieve a set of desired poles in a specified region, LQ formulation provides the inherent robustness properties. The time response for ride quality is significantly improved with a set of dynamic compensators.

Green, James↗

NASA NTRS1998

Overview of Sensitivity Analysis and Shape Optimization for Complex Aerodynamic Configurations

This paper presents a brief overview of some of the more recent advances in steady aerodynamic shape-design sensitivity analysis and optimization, based on advanced computational fluid dynamics. The focus here is on those methods particularly well- suited to the study of geometrically complex configurations and their potentially complex associated flow physics. When nonlinear state equations are considered in the optimization process, difficulties are found in the application of sensitivity analysis. Some techniques for circumventing such difficulties are currently being explored and are included here. Attention is directed to methods that utilize automatic differentiation to obtain aerodynamic sensitivity derivatives for both complex configurations and complex flow physics. Various examples of shape-design sensitivity analysis for unstructured-grid computational fluid dynamics algorithms are demonstrated for different formulations of the sensitivity equations. Finally, the use of advanced, unstructured-grid computational fluid dynamics in multidisciplinary analyses and multidisciplinary sensitivity analyses within future optimization processes is recommended and encouraged.

Newman, Perry A.↗

NASA NTRS1999

Overview of Sensitivity Analysis and Shape Optimization for Complex Aerodynamic Configurations

This paper presents a brief overview of some of the more recent advances in steady aerodynamic shape-design sensitivity analysis and optimization, based on advanced computational fluid dynamics (CFD). The focus here is on those methods particularly well-suited to the study of geometrically complex configurations and their potentially complex associated flow physics. When nonlinear state equations are considered in the optimization process, difficulties are found in the application of sensitivity analysis. Some techniques for circumventing such difficulties are currently being explored and are included here. Attention is directed to methods that utilize automatic differentiation to obtain aerodynamic sensitivity derivatives for both complex configurations and complex flow physics. Various examples of shape-design sensitivity analysis for unstructured-grid CFD algorithms are demonstrated for different formulations of the sensitivity equations. Finally, the use of advanced, unstructured-grid CFDs in multidisciplinary analyses and multidisciplinary sensitivity analyses within future optimization processes is recommended and encouraged.

Newman, James C., III↗

NASA NTRS1997

Aircraft Pitch Control with Fixed Order LQ Compensators

Green, James↗

NASA NTRS1995

Airfoil Design and Optimization by the One-Shot Method

An efficient numerical approach for the design of optimal aerodynamic shapes is presented in this paper. The objective of any optimization problem is to find the optimum of a cost function subject to a certain state equation (governing equation of the flow field) and certain side constraints. As in classical optimal control methods, the present approach introduces a costate variable (Lagrange multiplier) to evaluate the gradient of the cost function. High efficiency in reaching the optimum solution is achieved by using a multigrid technique and updating the shape in a hierarchical manner such that smooth (low-frequency) changes are done separately from high-frequency changes. Thus, the design variables are changed on a grid where their changes produce nonsmooth (high-frequency) perturbations that can be damped efficiently by the multigrid. The cost of solving the optimization problem is approximately two to three times the cost of the equivalent analysis problem.

Kuruvila, G.↗

NASA NTRS2006

Convective Weather Forecast Quality Metrics for Air Traffic Management Decision-Making

Since numerical weather prediction models are unable to accurately forecast the severity and the location of the storm cells several hours into the future when compared with observation data, there has been a growing interest in probabilistic description of convective weather. The classical approach for generating uncertainty bounds consists of integrating the state equations and covariance propagation equations forward in time. This step is readily recognized as the process update step of the Kalman Filter algorithm. The second well known method, known as the Monte Carlo method, consists of generating output samples by driving the forecast algorithm with input samples selected from distributions. The statistical properties of the distributions of the output samples are then used for defining the uncertainty bounds of the output variables. This method is computationally expensive for a complex model compared to the covariance propagation method. The main advantage of the Monte Carlo method is that a complex non-linear model can be easily handled. Recently, a few different methods for probabilistic forecasting have appeared in the literature. A method for computing probability of convection in a region using forecast data is described in Ref. 5. Probability at a grid location is computed as the fraction of grid points, within a box of specified dimensions around the grid location, with forecast convection precipitation exceeding a specified threshold. The main limitation of this method is that the results are dependent on the chosen dimensions of the box. The examples presented Ref. 5 show that this process is equivalent to low-pass filtering of the forecast data with a finite support spatial filter. References 6 and 7 describe the technique for computing percentage coverage within a 92 x 92 square-kilometer box and assigning the value to the center 4 x 4 square-kilometer box. This technique is same as that described in Ref. 5. Characterizing the forecast, following the process described in Refs. 5 through 7, in terms of percentage coverage or confidence level is notionally sound compared to characterizing in terms of probabilities because the probability of the forecast being correct can only be determined using actual observations. References 5 through 7 only use the forecast data and not the observations. The method for computing the probability of detection, false alarm ratio and several forecast quality metrics (Skill Scores) using both the forecast and observation data are given in Ref. 2. This paper extends the statistical verification method in Ref. 2 to determine co-occurrence probabilities. The method consists of computing the probability that a severe weather cell (grid location) is detected in the observation data in the neighborhood of the severe weather cell in the forecast data. Probabilities of occurrence at the grid location and in its neighborhood with higher severity, and with lower severity in the observation data compared to that in the forecast data are examined. The method proposed in Refs. 5 through 7 is used for computing the probability that a certain number of cells in the neighborhood of severe weather cells in the forecast data are seen as severe weather cells in the observation data. Finally, the probability of existence of gaps in the observation data in the neighborhood of severe weather cells in forecast data is computed. Gaps are defined as openings between severe weather cells through which an aircraft can safely fly to its intended destination. The rest of the paper is organized as follows. Section II summarizes the statistical verification method described in Ref. 2. The extension of this method for computing the co-occurrence probabilities in discussed in Section HI. Numerical examples using NCWF forecast data and NCWD observation data are presented in Section III to elucidate the characteristics of the co-occurrence probabilities. This section also discusses the procedure for computing throbabilities that the severity of convection in the observation data will be higher or lower in the neighborhood of grid locations compared to that indicated at the grid locations in the forecast data. The probability of coverage of neighborhood grid cells is also described via examples in this section. Section IV discusses the gap detection algorithm and presents a numerical example to illustrate the method. The locations of the detected gaps in the observation data are used along with the locations of convective weather cells in the forecast data to determine the probability of existence of gaps in the neighborhood of these cells. Finally, the paper is concluded in Section V.

Chatterji, Gano B.↗

NASA NTRS2007

Cosmological Inflation: A Personal Perspective

Approximately twenty five years ago a novel proposal was made to explain two of the outstanding cosmological conundrums, namely those of the Horizon Problem and the Flatness Problem of the Universe. These are the fact that widely separated parts of the sky that have never been in causal contact during the evolution of the Universe have apparently the same CMB temperature and the fact that the mean density of the Universe is very close to the critical one, i.e. very close to the density that separates the closed and open models. These coincidences implied that the corresponding initial condition of the Universe must have been set to exquisite accuracy. This novel proposal posted that at these very early times, the energy density of the Universe was dominated by a fluid which had the equation state attributed to the vacuum (i.e. dominated by tension rather than pressure) and that this led to an exponential expansion of the Universe which was "inflated" by many orders of magnitude of its original size. It was then shown that this "inflation" could provide a resolution of the above outstanding problems. The talk will cover the speaker's personal perspective and contributions to this idea and the subsequent developments over the following 25 years since its inception.

Kazanas, D.↗

NASA NTRS2002

Predicting System Accidents with Model Analysis During Hybrid Simulation

Standard discrete event simulation is commonly used to identify system bottlenecks and starving and blocking conditions in resources and services. The CONFIG hybrid discrete/continuous simulation tool can simulate such conditions in combination with inputs external to the simulation. This provides a means for evaluating the vulnerability to system accidents of a system's design, operating procedures, and control software. System accidents are brought about by complex unexpected interactions among multiple system failures , faulty or misleading sensor data, and inappropriate responses of human operators or software. The flows of resource and product materials play a central role in the hazardous situations that may arise in fluid transport and processing systems. We describe the capabilities of CONFIG for simulation-time linear circuit analysis of fluid flows in the context of model-based hazard analysis. We focus on how CONFIG simulates the static stresses in systems of flow. Unlike other flow-related properties, static stresses (or static potentials) cannot be represented by a set of state equations. The distribution of static stresses is dependent on the specific history of operations performed on a system. We discuss the use of this type of information in hazard analysis of system designs.

Malin, Jane T.↗

NASA NTRS2012

Development of a High Fidelity Dynamic Module of the Advanced Resistive Exercise Device (ARED) Using Adams

NASA's Digital Astronaut Project (DAP) implements well-vetted computational models to predict and assess spaceflight health and performance risks, and enhance countermeasure development. DAP provides expertise and computation tools to its research customers for model development, integration, or analysis. DAP is currently supporting the NASA Exercise Physiology and Countermeasures (ExPC) project by integrating their biomechanical models of specific exercise movements with dynamic models of the devices on which the exercises were performed. This presentation focuses on the development of a high fidelity dynamic module of the Advanced Resistive Exercise Device (ARED) on board the ISS. The ARED module, illustrated in the figure below, was developed using the Adams (MSC Santa Ana, California) simulation package. The Adams package provides the capabilities to perform multi rigid body, flexible body, and mixed dynamic analyses of complex mechanisms. These capabilities were applied to accurately simulate: Inertial and mass properties of the device such as the vibration isolation system (VIS) effects and other ARED components, Non-linear joint friction effects, The gas law dynamics of the vacuum cylinders and VIS components using custom written differential state equations, The ARED flywheel dynamics, including torque limiting clutch. Design data from the JSC ARED Engineering team was utilized in developing the model. This included solid modeling geometry files, component/system specifications, engineering reports and available data sets. The Adams ARED module is importable into LifeMOD (Life Modeler, Inc., San Clemente, CA) for biomechanical analyses of different resistive exercises such as squat and dead-lift. Using motion capture data from ground test subjects, the ExPC developed biomechanical exercise models in LifeMOD. The Adams ARED device module was then integrated with the exercise subject model into one integrated dynamic model. This presentation will describe the development of the Adams ARED module including its capabilities, limitations, and assumptions. Preliminary results, validation activities, and a practical application of the module to inform the relative effect of the flywheels on exercise will be discussed.

Humphreys, B. T.↗

NASA NTRS2023

An Observation-Based, Reduced-Form Model for Oxidation in the Remote Marine Troposphere

The hydroxyl radical (OH) fuels atmospheric chemical cycling as the main sink for methane and a driver of the formation and loss of many air pollutants, but direct OH observations are sparse. We develop and evaluate an observation-based proxy for short term, spatial variations in OH (Proxy OH ) in the remote marine troposphere using unprecedented and comprehensive measurements from the NASA Atmospheric Tomography (ATom) airborne campaign. Proxy OH is a reduced form of the OH steady-state equation representing the dominant OH production and loss pathways in the remote marine troposphere, according to box model simulations of OH constrained with ATom observations. Proxy OH comprises only eight variables that are generally observed by routine ground- or satellite-based instruments. ProxyOH scales linearly with in situ [OH] spatial variations along the ATom flight tracks (median r 2 = 0.90, interquartile range = 0.80 – 0.94 across 2 km altitude by 20° latitudinal regions). We deconstruct spatial variations in Proxy OH as a first-order approximation of the sensitivity of OH variations to individual terms. Two terms modulate within-region Proxy OH variations—water vapor (H 2 O) and, to a lesser extent, nitric oxide (NO). This implies that a limited set of observations could offer a novel avenue for observation-based mapping of OH spatial variations over much of the remote marine troposphere. Both H 2 O and NO are expected to change with climate, while NO also varies strongly with human activities. We also illustrate the utility of Proxy OH as a process-based approach for evaluating inter-model differences in remote marine tropospheric OH.

Colleen B. Baublitz↗

DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗