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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 NTRS2019

High-Fidelity Multidisciplinary Sensitivity Analysis Framework for Multipoint Rotorcraft Optimization

A multidisciplinary, gradient-based sensitivity-analysis methodology is evaluated for optimization of rotorcraft configurations. The tightly coupled discipline models include physics-based fluid dynamics and rotorcraft comprehensive analysis. A discretely-consistent adjoint method accounts for sensitivities of the unsteady flow and unstructured, dynamic, over set grids, while sensitivities of structural responses to aerodynamic loads are computed using a complex-variable method. The methodology is applied to optimize the shape of UH-60A Blackhawk helicopter blades for hover and forward flight conditions. The objective of the multipoint design is to simultaneously increase the rotorcraft figure of merit in a hover flight and reduce the rotor power in a forward flight. Trimmed loose-coupling solutions for the baseline configuration are used to initiate the tight-coupling multidisciplinary analysis. Target thrust and rolling and pitching moments are enforced as optimization constraints. The optimized configuration improves the optimization metrics at both design points. The improved performance and all constraints are maintained over many revolutions beyond the optimization interval, satisfying the required flight conditions. Computational cost of the optimization cycle is assessed in a high-performance computing environment and found affordable for design of rotorcraft in general level-flight conditions.

Wang, Li↗

NASA NTRS2015

Auralization of Tonal Rotor Noise Components of a Quadcopter Flyover

The capabilities offered by small unmanned vertical lift aerial vehicles, for example, quadcopters, continue to captivate entrepreneurs across the private, public, and civil sectors. As this industry rapidly expands, the public will be exposed to these devices (and to the noise these devices generate) with increasing frequency and proximity. Accordingly, an assessment of the human response to these machines will be needed shortly by decision makers in many facets of this burgeoning industry, from hardware manufacturers all the way to government regulators. One factor of this response is that of the annoyance to the noise that is generated by these devices. This paper presents work currently being pursued by NASA toward this goal. First, physics-based (CFD) predictions are performed on a single isolated rotor typical of these devices. The result of these predictions are time records of the discrete tonal components of the rotor noise. These time records are calculated for a number of points that appear on a lattice of locations spread over the lower hemisphere of the rotor. The source noise is then generated by interpolating between these time records. The sound from four rotors are combined and simulated-propagation techniques are used to produce complete flyover auralizations.

Christian, Andrew W.↗

NASA NTRSDate not supplied

A Framework for Optimization-Based ISRU Tool Design Using Discrete Element Modeling

Novel robotic excavation technologies are needed to perform in-situ resource utilization (ISRU) tasks at levels required to sustain a long-term presence on the lunar surface. Developing and testing multiple iterations of functional hardware is time and cost prohibitive, thus slowing down the pace of progress and delaying humanity’s settlement of the Moon. High-fidelity, physics-based simulation can reduce the time and effort required to develop and deploy robotic systems [1]. We have adopted this approach to create high-fidelity models of robotic test hardware to enable rapid virtual design and optimization of excavation technologies [2]. Such models can leverage modern computational tools like Discrete Element Method (DEM) simulations that can be coupled with automated design approaches like topology optimization to reduce the amount of prototyping and physical testing needed to realize useful tools.

ISRU↗

NASA NTRS2019

Sketch-To-Solution: An Exploration of Viscous CFD with Automatic Grids

Numerical simulation of the Reynolds-averaged Navier–Stokes (RANS) equations has become a critical tool for the design of aerospace vehicles. However, the issues that affect the grid convergence of three dimensional RANS solutions are not completely understood, as documented in the AIAA Drag Prediction Workshop series. Grid adaption methods have the potential for increasing the automation and discretization error control of RANS solutions to impact the aerospace design and certification process. The realization of the CFD Vision 2030 Study includes automated management of errors and uncertainties of physics-based, predictive modeling that can set the stage for ensuring a vehicle is in compliance with a regulation or specification by using analysis without demonstration in flight test (i.e., certification or qualification by analysis). For example, the Cart3D inviscid analysis package has automated Cartesian cut-cell gridding with output-based error control. Fueled by recent advances in the fields of anisotropic grid adaptation, error estimation, and geometry modeling, a similar work flow is explored for viscous CFD simulations; where a CFD application engineer provides geometry, boundary conditions, and flow parameters, and the sketch-to-solution process yields a CFD simulation through automatic, error-based, grid adaptation.

Kleb, William L.↗

NASA NTRS2021

Analysis of Nonlinear Shrinkage for the Bound Metal Deposition Manufacturing using Multi-scale Approach

We consider problem of nonlinear shrinkage of the metal part during bound metal deposition manufacturing on the ground and in zero-G. To analyze this problem we developed multi-scale physics-based approach that spans atomistic dynamics at the scale of nanoseconds and the full part shrinkage at the time scale of hours. Using this approach we estimated the key parameters of the problem including grain boundary width, coefficient of surface diffusion, initial redistribution of particles during debinding stage, micro-structure evolution from round particles to densely packed grains and corresponding change of the total and chemical free energy, and sintering stress. The introduced method was used to predict shrinkage at the level of two particles, filament cross-section, sub-model, and the whole green, brown, and metal parts. To further improve accuracy and reliability of the shrinkage predictions we propose concept of intelligent additive manufacturing of metal powders in space that combines the strengths of both physics-based and data-driven methods of analysis of AM.

bound metal deposition↗

NASA NTRS2010

Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight

The Integrated Health Management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. The complexity of modern aeronautic and aircraft systems (including e.g. the power distribution, flight control, solid and liquid motors) dictates employment of hybrid models and high-level reasoners for analysing mixed continuous and discrete information flow involving multiple modes of operation in uncertain environments, unknown state variables, heterogeneous software and hardware components. To provide the information link between key design/performance parameters and high-level reasoners we rely on development of multi-physics performance models, distributed sensors networks, and fault diagnostic and prognostic (FD&P) technologies in close collaboration with system designers. The main challenges of our research are related to the in-flight assessment of the structural stability, engine performance, and trajectory control. The main goal is to develop an intelligent IHM that not only enhances components and system reliability, but also provides a post-flight feedback helping to optimize design of the next generation of aerospace systems. Our efforts are concentrated on several directions of the research. One of the key components of our strategy is an innovative approach to the diagnostics/prognostics based on the real time dynamical inference (DI) technologies extended to encompass hybrid systems with hidden state trajectories. The major investments are into the multiphysics performance modelling that provides an access of the FD&P technologies to the main performance parameters of e.g. solid and liquid rocket motors and composite materials of the nozzle and case. Some of the recent results of our research are discussed in this chapter. We begin by introducing the problem of dynamical inference of stochastic nonlinear models and reviewing earlier results. Next, we present our analytical approach to the solution of this problem based on the path integral formulation. The resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. In the following Section the strengths of the algorithm are illustrated illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. Next, we discuss a number of recent results in application to the development of the IHM for aerospace system. Firstly, we apply dynamical inference approach to a solution of classical three tank problems with mixed unknown continuous and binary parameters. The problem is considered in the context of ground support system for filling fuel tanks of liquid rocket motors. It is shown that the DI algorithm is well suited for successful solution of a hybrid version of this benchmark problem even in the presence of additional periodic and stochastic perturbation of unknown strength. Secondly, we illustrate our approach by its application to an analysis of the nozzle fault in a solid rocket motor (SRM). The internal ballistics of the SRM is modelled as a set of one-dimensional partial differential equations coupled to the dynamics of the propellant regression. In this example we are specifically focussed on the inference of discrete and continuous parameters of the nozzle blocking fault and on the possibility of an application of the DI algorithm to reducing the probability of "misses" of an on-board FD&P for SRM. In the next section re-contact problem caused by first stage/upper stage separation failure is discussed. The reaction forces imposed on the nozzle of the upper stage during the re-contact and their connection to the nozzle damage and to the thrust vector control (TVC) signal are obtained. It is shown that transient impact induced torquean be modelled as a response of an effective damped oscillator. A possible application of the DI algorithm to the inference of damage parameters and predicting fault dynamics ahead of time using the actuator signal is discussed. Finally, we formulate Bayesian inferential framework for development of the IHM system for in-flight structural health monitoring (SHM) of composite materials. We consider the signal generated by piezoelectric actuator mounted on composite structure generating elastic waves in it. The signal received by the sensor is than compared with the baseline signal. The possibility of damage inference is discussed in the context of development of the SHM.

Smelyanskiy, Vadim N.↗

NASA NTRS2025

Physics-Based Modeling and Simulation of Self-Reacting Friction Stir Welding Using Computational Fluid Dynamics

A physics-based model was developed to simulate the behavior of material in a self-reacting friction stir welding (SR-FSW) process for the joining of metals. This steady-state model builds upon fundamental computational fluid dynamic (CFD) principles within Ansys Fluent to solve the discretized equations. The effective viscosity is calculated using a viscoplastic model using a Sheppard-Wright formulation of flow stress. Numerous advancements have been made in the incorporated physics including (1) temperature-dependent material properties; (2) locally adaptable flow and thermal boundary conditions; and (3) adapting material properties in nugget in response to microstructural changes. Simulation strategies to accelerate computation and improve numerical stability include adapting the mesh refinement and solver relaxation factors during simulation. The result is a highly robust and computationally efficient model capable of providing the material flow and temperature history across the domain. As material history determines the local microstructure and ultimately weld strength, an accurate and detailed physics-based model has the potential to accelerate SR-FSW process development. The model is highly adaptable to changes in process parameters, tool design, or alloy.

Process Modeling↗

NASA NTRSDate not supplied

A Multi-Disciplinary Analysis Framework for the Design of Small Launch Vehicles

Between the years of 1995 and 2014 the number of small satellites (1-500 kg) went up from 20 to 180. [1] Out of the 180 launched in 2014 66% were Nano satellites (1-10 kg). [1] With this trend of smaller satellites, one would expect a rise in number of small launch vehicles (SLVs are defined by capability to carry 1-100 kg to orbit), but this has not happened: [3] only 8% of all small satellites are launched on SLVs and the others become secondary payloads on regular launch vehicles. [1] This results in small satellites being placed into either suboptimal orbits or waiting for launch schedules to align with bigger launches, resulting in long waiting times. Dedicated SLVs could improve the responsiveness of small satellite launches, but SLV design is complicated by the large architecture space needed to be explored in order to find efficient and affordable designs. The SLV architecture trade space contains many discrete options, e.g. solid vs. liquid fuels, air launch vs. ground launch, number of stages, etc. [2][3] Performing detailed analysis for all the options at once would be prohibitive. Thus, a sizing environment/framework that is capable of providing necessary information for conceptual-level trade studies and can rapidly explore the vast SLV architecture space is necessary. The framework, illustrated in Figure 1, consists of four disciplines central to the sizing and synthesis of launch vehicles: propulsion, aerodynamics, structures, and trajectory. For the aerodynamics and trajectory disciplines, Missile DATCOM and POST2 are used, respectively. The propulsion and structures disciplines are represented in the framework with tools developed at ASDL Georgia Tech. For propulsion, the Solid Motor Analysis Code (SMAC) is a physics-based conceptual design tool for solid rocket motors. SMAC is capable of geometric burn simulation, ballistic analysis, and prediction of thrust performance. [4] For structures, Launch Vehicle Structural Analysis (LVSA) tool is a physics-based tool that focuses on structural dynamic analysis with sizing capability. These tools are integrated into the framework illustrated in Figure 1 with the corresponding connections described in Table 1. The process flow is as follows: first, SMAC sizes insulation and calculates maximum operating pressures for each vehicle stage. The MEOP and insulation thickness values from SMAC are fed to LVSA which then utilizes this information to size the motor casing. This creates a feedback loop between LVSA and SMAC that converges on the radius available for fuel, casing, and insulation thickness. Once the stage sizing is converged on SMAC creates an engine deck that is passed to POST2. Next, the data from SMAC and LVSA goes into Missile DATCOM which generates an aerodynamics database for POST2. Finally, POST2 performs a targeting optimization while maximizing the payload mass to orbit. Within the framework, POST2 is automated in order to be robust to a wide variety of possible designs by performing a Monte Carlo simulation over the initialization vector of the POST2 optimization variables. To demonstrate the capability of this framework, a sample problem of exploring the design space of an SLV capable of placing satellites into a low Earth orbit (inclination=47 deg, 196.5 by 211.3 nm) is used. This sample problem is a ground-launched SLV consisting of four in-line SRM stages. The multidisciplinary design analysis (MDA) environment is utilized to explore a design space consisting of 22 continuous and 12 discrete variables, shown by Table 1 by blocks 1,2,3. Running a full factorial design of experiments (DOE) would have been prohibitively expensive even with this reduced design space, thus a space filling design with 3,502 and then additional expansion of 2,602 cases was used. The first DOE consisted of 3,502 cases, and all of the variables were varied. These input variables are listed in blocks 1, 2, and 3 in Table 1. Most of the variables are propulsion related with stage lengths determining the delta-V split of the SLV. The expansion consisted of 2,602 cases, and the continuous variables were set to be equal to the most promising designs from the sizing of first DOE. For each set of continuous variables, the discrete variables (grain type, star points, and propellant) were to varied. The results of the DOE can be seen in Figure 2; each of the points in this plot represents a closed launch vehicle that reaches the targeted orbit. For each of the cases, there is information on flown trajectory, structural, and propulsion properties of the SLV. For example, Figure 3 shows changes in altitude and velocity with time for a particular case. The right side of Figure 3 clearly shows the coasting (slow decrease) and burning phases (sharp increase) of the SLV mission. LV mass is positively correlated with the optimized payload mass to orbit because heavier LVs carry more fuel and thus have more stored chemical energy. Furthermore, for any given payload mass to orbit, the most efficient design would result in the smallest LV. The results as visualized in Figure 2 shows this tradeoff, and the Pareto frontier of the efficient designs can be seen along the dotted line. Figure 2 can be divided into regions with the lowest mass vehicles corresponding to the minimum bound on radius, and the highest mass vehicles corresponding to the maximum bound on radius. Within a mass region, the discrete variables, such as propellant type and propellant grain arrangement, have the most effect on payload mass. This paper presents an MDA framework that can perform an automated physics-based sizing of SLV designs and a corresponding methodology to utilize the MDA to explore the design space of SLVs. The proposed methodology was applied to a perform a design space exploration for a four stage SLV. The outputs show the expected pareto frontier forming and provide detailed information about the SLV performance and staging. Using the produced data, it will be possible to select a set of pareto optimal designs that can then be further explored in subsequent design cycles. This demonstration shows that automated design space exploration should be used in the early phase design of future SLV concepts.

Nikita S Birbasov↗

NASA NTRS2005

A Method for Aircraft Concept Selection Using Multicriteria Interactive Genetic Algorithms

The problem of aircraft concept selection has become increasingly difficult in recent years as a result of a change from performance as the primary evaluation criteria of aircraft concepts to the current situation in which environmental effects, economics, and aesthetics must also be evaluated and considered in the earliest stages of the decision-making process. This has prompted a shift from design using historical data regression techniques for metric prediction to the use of physics-based analysis tools that are capable of analyzing designs outside of the historical database. The use of optimization methods with these physics-based tools, however, has proven difficult because of the tendency of optimizers to exploit assumptions present in the models and drive the design towards a solution which, while promising to the computer, may be infeasible due to factors not considered by the computer codes. In addition to this difficulty, the number of discrete options available at this stage may be unmanageable due to the combinatorial nature of the concept selection problem, leading the analyst to arbitrarily choose a sub-optimum baseline vehicle. These concept decisions such as the type of control surface scheme to use, though extremely important, are frequently made without sufficient understanding of their impact on the important system metrics because of a lack of computational resources or analysis tools. This paper describes a hybrid subjective/quantitative optimization method and its application to the concept selection of a Small Supersonic Transport. The method uses Genetic Algorithms to operate on a population of designs and promote improvement by varying more than sixty parameters governing the vehicle geometry, mission, and requirements. In addition to using computer codes for evaluation of quantitative criteria such as gross weight, expert input is also considered to account for criteria such as aeroelasticity or manufacturability which may be impossible or too computationally expensive to consider explicitly in the analysis. Results indicate that concepts resulting from the use of this method represent designs which are promising to both the computer and the analyst, and that a mapping between concepts and requirements that would not otherwise be apparent is revealed.

Buonanno, Michael↗

NASA NTRS2010

Excavator Design Validation

The Excavator Design Validation tool verifies excavator designs by automatically generating control systems and modeling their performance in an accurate simulation of their expected environment. Part of this software design includes interfacing with human operations that can be included in simulation-based studies and validation. This is essential for assessing productivity, versatility, and reliability. This software combines automatic control system generation from CAD (computer-aided design) models, rapid validation of complex mechanism designs, and detailed models of the environment including soil, dust, temperature, remote supervision, and communication latency to create a system of high value. Unique algorithms have been created for controlling and simulating complex robotic mechanisms automatically from just a CAD description. These algorithms are implemented as a commercial cross-platform C++ software toolkit that is configurable using the Extensible Markup Language (XML). The algorithms work with virtually any mobile robotic mechanisms using module descriptions that adhere to the XML standard. In addition, high-fidelity, real-time physics-based simulation algorithms have also been developed that include models of internal forces and the forces produced when a mechanism interacts with the outside world. This capability is combined with an innovative organization for simulation algorithms, new regolith simulation methods, and a unique control and study architecture to make powerful tools with the potential to transform the way NASA verifies and compares excavator designs. Energid's Actin software has been leveraged for this design validation. The architecture includes parametric and Monte Carlo studies tailored for validation of excavator designs and their control by remote human operators. It also includes the ability to interface with third-party software and human-input devices. Two types of simulation models have been adapted: high-fidelity discrete element models and fast analytical models. By using the first to establish parameters for the second, a system has been created that can be executed in real time, or faster than real time, on a desktop PC. This allows Monte Carlo simulations to be performed on a computer platform available to all researchers, and it allows human interaction to be included in a real-time simulation process. Metrics on excavator performance are established that work with the simulation architecture. Both static and dynamic metrics are included.

Pholsiri, Chalongrath↗

NASA NTRS2021

Medium-Sized Helicopter Noise Abatement Flight Test Data Report

Helicopter noise has consistently hindered operations within urban areas and surrounding communities. To combat this, an extensive flight test campaign, described herein, was conducted to gather acoustic data to support helicopter noise research (e.g., physics-based modeling, operational planning, etc.). Acoustic measurements were collected from four helicopters in the medium-sized weight class with maximum takeoff gross weights between 7,400 and 14,200 lb: a Bell 205 “Huey”, a Sikorsky S-76D, a Leonardo AW139, and a US Coast Guard MH-65 Dolphin (a variant of the commercial Airbus AS365 Dauphin). Each aircraft per- formed a subset of level flyovers, steady descents, turns with various initial conditions, and noise abatement approaches. A distributed ground microphone array spanning approximately 2,000 by 4,000 ft collected measurements of each flyover event. Additionally, a dedicated semicircular microphone array was used for gathering acoustic measurements of static conditions (e.g., hover). This report provides an overview of the flight test methodology, conditions tested, equipment used, and details of the accompanying distribution dataset. Figures of sample results generated for each test point are included to enable comparison and selection of the data at the discretion of the user. Specifically, source noise hemispheres of the steady flight conditions illustrate directivity of over- all levels. Noise contours over the array provide insight into the ground levels associated with approaches and turning events. These data figures are derived from the original acoustic pressure-time histories and vehicle tracking data that are provided in the distribution dataset.

Rotorcraft↗

NASA NTRS2022

Multi-Scale Modelling of the Bound Metal Deposition Manufacturing of Ti6Al4V

Nonlinear shrinkage of the metal part during manufacturing by bound metal deposition, both on the ground and under microgravity, is considered. A multi-scale physics-based approach is developed to address the problem. It spans timescales from atomistic dynamics on the order of nanoseconds to full-part shrinkage on the order of hours. This approach enables estimation of the key parameters of the problem, including the widths of grain boundaries, the coefficient of surface diffusion, the initial redistribution of particles during the debinding stage, the evolution of the microstructure from round particles to densely-packed grains, the corresponding changes in the total and chemical free energies, and the sintering stress. The method has been used to predict shrinkage at the levels of two particles, of the filament cross-section, of the sub-model, and of the whole green, brown, and metal parts.

Nonlinear shrinkage↗

NASA NTRS2014

Training Knowledge Bots for Physics-Based Simulations Using Artificial Neural Networks

Millions of complex physics-based simulations are required for design of an aerospace vehicle. These simulations are usually performed by highly trained and skilled analysts, who execute, monitor, and steer each simulation. Analysts rely heavily on their broad experience that may have taken 20-30 years to accumulate. In addition, the simulation software is complex in nature, requiring significant computational resources. Simulations of system of systems become even more complex and are beyond human capacity to effectively learn their behavior. IBM has developed machines that can learn and compete successfully with a chess grandmaster and most successful jeopardy contestants. These machines are capable of learning some complex problems much faster than humans can learn. In this paper, we propose using artificial neural network to train knowledge bots to identify the idiosyncrasies of simulation software and recognize patterns that can lead to successful simulations. We examine the use of knowledge bots for applications of computational fluid dynamics (CFD), trajectory analysis, commercial finite-element analysis software, and slosh propellant dynamics. We will show that machine learning algorithms can be used to learn the idiosyncrasies of computational simulations and identify regions of instability without including any additional information about their mathematical form or applied discretization approaches.

Samareh, Jamshid A.↗

NASA NTRS2010

Template Matching Approach to Signal Prediction

A new approach to signal prediction and prognostic assessment of spacecraft health resolves an inherent difficulty in fusing sensor data with simulated data. This technique builds upon previous work that demonstrated the importance of physics-based transient models to accurate prediction of signal dynamics and system performance. While models can greatly improve predictive accuracy, they are difficult to apply in general because of variations in model type, accuracy, or intended purpose. However, virtually any flight project will have at least some modeling capability at its disposal, whether a full-blown simulation, partial physics models, dynamic look-up tables, a brassboard analogue system, or simple hand-driven calculation by a team of experts. Many models can be used to develop a predict, or an estimate of the next day s or next cycle s behavior, which is typically used for planning purposes. The fidelity of a predict varies from one project to another, depending on the complexity of the simulation (i.e. linearized or full differential equations) and the level of detail in anticipated system operation, but typically any predict cannot be adapted to changing conditions or adjusted spacecraft command execution. Applying a predict blindly, without adapting the predict to current conditions, produces mixed results at best, primarily due to mismatches between assumed execution of spacecraft activities and actual times of execution. This results in the predict becoming useless during periods of complicated behavior, exactly when the predict would be most valuable. Each spacecraft operation tends to show up as a transient in the data, and if the transients are misaligned, using the predict can actually harm forecasting performance. To address this problem, the approach here expresses the predict in terms of a baseline function superposed with one or more transient functions. These transients serve as signal templates, which can be relocated in time and space against the signal background. One then has the ability to reconstruct a signal regardless of the precise timing of the transients. During operation, one applies the actual start times of spacecraft activities as they occur, and produces a reconstructed, accurate predict in real-time. This general approach is valid under two important conditions. First, the transients themselves must be time-invariant. Second, the transients must be reasonably consistent with respect to different operating points. Both of these assumptions are generally valid, but in the case of a complicated system with numerous types of overlapping transients, this approach may not be effective. Fortunately, there tend to be few transients in spacecraft telemetry of sensor quantities or low-level health and status information because these signals rarely reflect multiple different types of operation. Furthermore, if the predict is at least reasonably close to actual operation, the shift either in time, or in the operating point when the transient occurs - is likely to be small. The proposed approach considers three ways to recognize a transient. The first and most reliable is to use a different signal that identifies operating mode - often the transient will be correlated to a change in operating mode, and this change can usually be detected positively from discrete signals in spacecraft telemetry. If there is no useful mode signal, the second option is to identify the transient template by hand, and detect the onset of the transient using a curve-fitting approach. Finally, if one elects not to choose by hand, there is an option for automatic selection. This technique has been applied to sensor data from several JPL missions and industrial applications, demonstrating an improvement in accurate prediction of future behavior and early detection of system problems. This technique is applicable to practically any time-varying, quantitative sensor measurement.

Mackey, Ryan↗

NASA NTRS2010

Adaptive Numerical Algorithms in Space Weather Modeling

Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different physics in different domains. A multi-physics system can be modeled by a software framework comprising of several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solar wind Roe Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamics (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit numerical schemes. Depending on the application, we find that different time stepping methods are optimal. Several of the time integration schemes exploit the block-based granularity of the grid structure. The framework and the adaptive algorithms enable physics based space weather modeling and even forecasting.

Toth, Gabor↗

NASA NTRS2012

Accelerated Aging in Electrolytic Capacitors for Prognostics

The focus of this work is the analysis of different degradation phenomena based on thermal overstress and electrical overstress accelerated aging systems and the use of accelerated aging techniques for prognostics algorithm development. Results on thermal overstress and electrical overstress experiments are presented. In addition, preliminary results toward the development of physics-based degradation models are presented focusing on the electrolyte evaporation failure mechanism. An empirical degradation model based on percentage capacitance loss under electrical overstress is presented and used in: (i) a Bayesian-based implementation of model-based prognostics using a discrete Kalman filter for health state estimation, and (ii) a dynamic system representation of the degradation model for forecasting and remaining useful life (RUL) estimation. A leave-one-out validation methodology is used to assess the validity of the methodology under the small sample size constrain. The results observed on the RUL estimation are consistent through the validation tests comparing relative accuracy and prediction error. It has been observed that the inaccuracy of the model to represent the change in degradation behavior observed at the end of the test data is consistent throughout the validation tests, indicating the need of a more detailed degradation model or the use of an algorithm that could estimate model parameters on-line. Based on the observed degradation process under different stress intensity with rest periods, the need for more sophisticated degradation models is further supported. The current degradation model does not represent the capacitance recovery over rest periods following an accelerated aging stress period.

Celaya, Jose R.↗

NASA NTRS2012

A Comparison of Filter-based Approaches for Model-based Prognostics

Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the stateparameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.

prognostics↗

NASA NTRS2017

Invariant Imbedded T-Matrix Method for Axial Symmetric Hydrometeors with Extreme Aspect Ratios

The single-scattering properties (SSPs) of hydrometeors are the fundamental quantities for physics-based precipitation retrievals. Thus, efficient computation of their electromagnetic scattering is of great value. Whereas the semi-analytical T-Matrix methods are likely the most efficient for nonspherical hydrometeors with axial symmetry, they are not suitable for arbitrarily shaped hydrometeors absent of any significant symmetry, for which volume integral methods such as those based on Discrete Dipole Approximation (DDA) are required. Currently the two leading T-matrix methods are the Extended Boundary Condition Method (EBCM) and the Invariant Imbedding T-matrix Method incorporating Lorentz-Mie Separation of Variables (IITM+SOV). EBCM is known to outperform IITM+SOV for hydrometeors with modest aspect ratios. However, in cases when aspect ratios become extreme, such as needle-like particles with large height to diameter values, EBCM fails to converge. Such hydrometeors with extreme aspect ratios are known to be present in solid precipitation and their SSPs are required to model the radiative responses accurately. In these cases, IITM+SOV is shown to converge. An efficient, parallelized C++ implementation for both EBCM and IITM+SOV has been developed to conduct a performance comparison between EBCM, IITM+SOV, and DDSCAT (a popular implementation of DDA). We present the comparison results and discuss details. Our intent is to release the combined ECBM IITM+SOV software to the community under an open source license.

Pelissier, Craig↗