SEARCH · Engineering Papers

Results for “modeling framework”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 487 records · Page 27

All sources NASA NTRS DOE OSTI

NASA NTRS2022

Precision Landing Performance and Technology Assessments of a Human-Scale Lunar Lander Using a Generalized Simulation Framework

NASA has established goals of returning humans to Moon with an initial landing by 2024 and a subsequent sustained presence by 2028, which will require technological advances in spacecraft navigation to enable precision landing. The ability to assess the navigation performance of these new and existing technologies is critical to identifying areas of risk reduction and investment. To that end, the Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) project has demonstrated that a detailed six degree-of-freedom integrated performance simulation framework can provide information on and assessment of expected navigation performance. This framework incorporates engineering models of the on-board spacecraft guidance, navigation, and control systems at varying levels of fidelity. Recent advances in the development of this integrated performance simulation permit running these systems “in-the-loop,” rather than assuming perfect knowledge of the spacecraft states. This development, coupled with fast simulation time and modularization of the various system models, enables a wide variety of system trades to be assessed at once. This paper presents a summary of the advances in the SPLICE simulation framework, updates to the spacecraft navigation models, and an application of the framework to characterize the precision landing performance of a human-scale lunar lander. A series of trade studies examining effects of ground state update qualities shows that given all other assumptions, sufficiently accurate Deep Space Network (DSN) measurements can enable safe and precise human-scale Lunar landings.

"D'Souza, Sarah", 'Pensado, Alegandro R.↗

NASA NTRS1992

A thermal/nonthermal model for solar microwave bursts

A theoretical framework is developed for modeling high-resolution spectra of microwave bursts from the Owens Valley Radio Observatory which can account for departures from expectations based on simple thermal or nonthermal models. Specifically, 80 percent of the events show more than one spectral peak; many bursts have a low-side spectral index steeper than the maximum expected slope; and the peak frequency stays relatively constant and changes intensity in concert with the secondary peaks throughout a given event's solution. It is shown that the observed spectral features can be explained through gyrosynchrotron radiation. The 'secondary' components seen on the LF side of many spectra are nonthermal enhancements superposed upon thermal radiation, occurring between the thermal harmonics. A steep optically thick slope is accounted for by the thermal absorption of nonthermal radiation. If the coexistence of thermal and nonthermal particles is interpreted in terms of electron heating and acceleration in current sheets, a changing electric field strength can account for the gross evolution of the microwave spectra.

Benka, Stephen G.↗

NASA NTRS2007

Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques

Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition- Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation as in Particle Filters (PF), provides a powerful tool to integrate the diagnosis and prognosis of battery health. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for model identification, while the PF framework uses the learnt model, statistical estimates of noise and anticipated operational conditions to provide estimates of remaining useful life (RUL) in the form of a probability density function (PDF). This type of prognostics generates a significant value addition to the management of any operation involving electrical systems.

Saha, Bhaskar↗

NASA NTRS2013

Model-Based Diagnosis and Prognosis of a Water Recycling System

A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

Roychoudhury, Indranil↗

NASA NTRS2012

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.↗

NASA NTRSDate not supplied

Projecting the Lasting Fate of the Hunga Tonga-Hunga Ha’apai Eruption on the Stratosphere through Connecting Measurements to Models

On 15th Jan. 2022 the submarine volcano Hunga Tonga-Hunga Ha’apai (HTHH) injected approximately 0.5 Tg of sulfur dioxide into the stratosphere, but more significantly added 150-170 Tg of water vapor to the stratospheric background (over a 10% perturbation) in a matter of several hours. The sulfur dioxide rapidly converted to sulfate aerosol and along with water vapor, was transported around the Southern Hemisphere sub-tropics into midlatitudes with some transport into the Northern Hemisphere. With a much longer lifetime than sulfate aerosol, measurable water vapor anomalies are likely to persist for the remainder of the decade. Satellite measurements from limb and nadir viewing observing instruments provide the information needed to reasonably initialize the HTHH eruption in the Goddard Earth Observing System (GEOS) model using the “replay” framework coupled to the Global Modeling Initiative (GMI) stratosphere-troposphere chemical mechanism for the recent past and continue the simulations into the future with the free running chemistry climate model (CCM). Using a number of model ensemble members together with the satellite observations, we are beginning to quantify how the HTHH eruption is perturbing stratospheric composition and climate and projecting the influences to come as the enhanced water vapor continues to spread globally with only very slow removal mechanisms. We will also discuss some of the future measurement needs to understand how the atmosphere is responding to events like the HTHH eruption, large wildfires, and a changing climate.

volcano↗

NASA NTRSDate not supplied

Projecting the Lasting Fate of the Hunga Tonga-Hunga Ha’apai Eruption on the Stratosphere through Connecting Measurements to Models

On 15th Jan. 2022 the submarine volcano Hunga Tonga-Hunga Ha’apai (HTHH) injected approximately 0.5 Tg of sulfur dioxide into the stratosphere, but more significantly added 150-170 Tg of water vapor to the stratospheric background (over a 10% perturbation) in a matter of several hours. The sulfur dioxide rapidly converted to sulfate aerosol and along with water vapor, was transported around the Southern Hemisphere sub-tropics into midlatitudes with some transport into the Northern Hemisphere. With a much longer lifetime than sulfate aerosol, measurable water vapor mass anomalies have persisted with only small losses over the past almost 2 years and are likely to continue above background for the remainder of the decade. Satellite measurements from limb and nadir viewing observing instruments provide the information needed to reasonably initialize the HTHH eruption in the Goddard Earth Observing System (GEOS) model using the “replay” framework coupled to the Global Modeling Initiative (GMI) stratosphere-troposphere chemical mechanism for the recent past and continue the simulations into the future with the free running chemistry climate model (CCM). Using a number of model ensemble members together with the satellite observations, we quantify how the HTHH eruption is perturbing stratospheric composition and climate and projecting the influences to come as the enhanced water vapor continues in the stratosphere with only very slow removal mechanisms. HTHH eruption provides a useful test of chemistry climate models and an opportunity for observation-based process understanding, which we will highlight.

Hunga Tonga-Hunga Ha’apai↗

NASA NTRSDate not supplied

Observing and Projecting the Lasting Fate of the Hunga Eruption on Atmospheric Water Vapor and Hydroxyl Radical

On 15th Jan. 2022 the submarine Hunga volcano erupted injecting approximately 0.5 Tg of sulfur dioxide into the stratosphere, but more significantly added 150-170 Tg of water vapor to the stratospheric background (over a 10% perturbation) in a matter of several hours. The sulfur dioxide rapidly converted to sulfate aerosol and along with water vapor, was transported around the Southern Hemisphere sub-tropics into midlatitudes and beyond with some transport into the Northern Hemisphere. With a much longer lifetime than sulfate aerosol, measurable water vapor mass anomalies have persisted with only small losses over more than 2 years since the eruption and are likely to continue above background for the remainder of the decade. Satellite measurements from limb and nadir viewing observing instruments provide the information needed to reasonably initialize the Hunga eruption in the Goddard Earth Observing System (GEOS) model using the “replay” framework coupled to the Global Modeling Initiative (GMI) stratosphere-troposphere chemical mechanism for the recent past and have continued the simulations into the future with the free running chemistry climate model (CCM). Using a number of model ensemble members together with the satellite observations, we quantify how the Hunga eruption is perturbing atmospheric composition and climate and projecting the influences to come as the enhanced water vapor continues in the stratosphere with only very slow removal mechanisms. Modeling suggests that the water rich eruption enhanced stratospheric hydroxyl radiacal (OH) and is supported by the speed of the observed conversion of SO2 into sulfate aerosol. These simulations also suggest a significant reduction in tropospheric OH over the mid-high latitude Southern Hemisphere, which has implications for numerous species that are impacted by this radical. The Hunga eruption provides a useful test of chemistry climate models and an opportunity for observation-based process understanding and in making new connections.

Hunga volcano↗

NASA NTRS1998

Control of the NASA Langley 16-Foot Transonic Tunnel with the Self-Organizing Feature Map

A predictive, multiple model control strategy is developed based on an ensemble of local linear models of the nonlinear system dynamics for a transonic wind tunnel. The local linear models are estimated directly from the weights of a Self Organizing Feature Map (SOFM). Local linear modeling of nonlinear autonomous systems with the SOFM is extended to a control framework where the modeled system is nonautonomous, driven by an exogenous input. This extension to a control framework is based on the consideration of a finite number of subregions in the control space. Multiple self organizing feature maps collectively model the global response of the wind tunnel to a finite set of representative prototype controls. These prototype controls partition the control space and incorporate experimental knowledge gained from decades of operation. Each SOFM models the combination of the tunnel with one of the representative controls, over the entire range of operation. The SOFM based linear models are used to predict the tunnel response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal. Each SOFM provides a codebook representation of the tunnel dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the minimization of a similarity metric which is the essence of the self organizing feature of the map. Thus, the SOFM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme than selects the best available model for the applied control. Experimental results of controlling the wind tunnel, with the proposed method, during operational runs where strict research requirements on the control of the Mach number were met, are presented. Comparison to similar runs under the same conditions with the tunnel controlled by either the existing controller or an expert operator indicate the superiority of the method.

Motter, Mark A.↗

NASA NTRS2015

Calibration of Predictor Models Using Multiple Validation Experiments

This paper presents a framework for calibrating computational models using data from several and possibly dissimilar validation experiments. The offset between model predictions and observations, which might be caused by measurement noise, model-form uncertainty, and numerical error, drives the process by which uncertainty in the models parameters is characterized. The resulting description of uncertainty along with the computational model constitute a predictor model. Two types of predictor models are studied: Interval Predictor Models (IPMs) and Random Predictor Models (RPMs). IPMs use sets to characterize uncertainty, whereas RPMs use random vectors. The propagation of a set through a model makes the response an interval valued function of the state, whereas the propagation of a random vector yields a random process. Optimization-based strategies for calculating both types of predictor models are proposed. Whereas the formulations used to calculate IPMs target solutions leading to the interval value function of minimal spread containing all observations, those for RPMs seek to maximize the models' ability to reproduce the distribution of observations. Regarding RPMs, we choose a structure for the random vector (i.e., the assignment of probability to points in the parameter space) solely dependent on the prediction error. As such, the probabilistic description of uncertainty is not a subjective assignment of belief, nor is it expected to asymptotically converge to a fixed value, but instead it casts the model's ability to reproduce the experimental data. This framework enables evaluating the spread and distribution of the predicted response of target applications depending on the same parameters beyond the validation domain.

Crespo, Luis G.↗

NASA NTRS1994

Role of pressure diffusion in non-homogeneous shear flows

A non-local model is presented for approximating the pressure diffusion in calculations of turbulent free shear and boundary layer flows. It is based on the solution of an elliptic relaxation equation which enables local diffusion sources to be distributed over lengths of the order of the integral scale. The pressure diffusion model was implemented in a boundary layer code within the framework of turbulence models based on both the kappa-epsilon-(bar)upsilon(exp 2) system of equations and the full Reynolds stress equations. Model computations were performed for mixing layers and boundary layer flows. In each case, the pressure diffusion model enabled the well-known free-stream edge singularity problem to be eliminated. There was little effect on near-wall properties. Computed results agreed very well with experimental and DNS data for the mean flow velocity, the turbulent kinetic energy, and the skin-friction coefficient.

Demuren, A. O.↗

NASA NTRS1978

Demand modelling of passenger air travel: An analysis and extension. Volume 1: Background and summary

The framework for a model of travel demand which will be useful in predicting the total market for air travel between two cities is discussed. Variables to be used in determining the need for air transportation where none currently exists and the effect of changes in system characteristics on attracting latent demand are identified. Existing models are examined in order to provide insight into their strong points and shortcomings. Much of the existing behavioral research in travel demand is incorporated to allow the inclusion of non-economic factors, such as convenience. The model developed is characterized as a market segmentation model. This is a consequence of the strengths of disaggregation and its natural evolution to a usable aggregate formulation. The need for this approach both pedagogically and mathematically is discussed.

Jacobson, I. D.↗

NASA NTRS2012

An MBSE Approach to Space Suit Development

The EVA/Space Suit Development Office (ESSD) Systems Engineering and Integration (SE&I) team has utilized MBSE in multiple programs. After developing operational and architectural models, the MBSE framework was expanded to link the requirements space to the system models through functional analysis and interfaces definitions. By documenting all the connections within the technical baseline, ESSD experienced significant efficiency improvements in analysis and identification of change impacts. One of the biggest challenges presented to the MBSE structure was a program transition and restructuring effort, which was completed successfully in 4 months culminating in the approval of a new EVA Technical Baseline. During this time three requirements sets spanning multiple DRMs were streamlined into one NASA-owned Systems Requirement Document (SRD) that successfully identified requirements relevant to the current hardware development effort while remaining extensible to support future hardware developments. A capability-based hierarchy was established to provide a more flexible framework for future space suit development that can support multiple programs with minimal rework of basic EVA/Space Suit requirements. This MBSE approach was most recently applied for generation of an EMU Demonstrator technical baseline being developed for an ISS DTO. The relatively quick turnaround of operational concepts, architecture definition, and requirements for this new suit development has allowed us to test and evolve the MBSE process and framework in an extremely different setting while still offering extensibility and traceability throughout ESSD projects. The ESSD MBSE framework continues to be evolved in order to support integration of all products associated with the SE&I engine.

Cordova, Lauren↗

NASA NTRS1994

Operations on Graphical Models with Plates

This paper explains how graphical models, for instance Bayesian or Markov networks, can be extended to model problems in data analysis and learning. This provides a unified framework that combines lessons learned from the artificial intelligence, statistical and connectionist communities. This also offers a set of principles for developing a software generator for data analysis, whereby a learning or discovery system can be compiled from specifications. Many of the popular learning algorithms can be compiled in this way from graphical specifications. While in a sense this paper is a multidisciplinary review of learning, the main contribution here is the presentation of the material within the unifying framework of graphical models, and the observation that, as a result, the process of developing learning algorithms can be partly automated.

Buntine, Wray L.↗

NASA NTRS2003

System and Software Reliability (C103)

Within the last decade better reliability models (hardware. software, system) than those currently used have been theorized and developed but not implemented in practice. Previous research on software reliability has shown that while some existing software reliability models are practical, they are no accurate enough. New paradigms of development (e.g. OO) have appeared and associated reliability models have been proposed posed but not investigated. Hardware models have been extensively investigated but not integrated into a system framework. System reliability modeling is the weakest of the three. NASA engineers need better methods and tools to demonstrate that the products meet NASA requirements for reliability measurement. For the new models for the software component of the last decade, there is a great need to bring them into a form that they can be used on software intensive systems. The Statistical Modeling and Estimation of Reliability Functions for Systems (SMERFS'3) tool is an existing vehicle that may be used to incorporate these new modeling advances. Adapting some existing software reliability modeling changes to accommodate major changes in software development technology may also show substantial improvement in prediction accuracy. With some additional research, the next step is to identify and investigate system reliability. System reliability models could then be incorporated in a tool such as SMERFS'3. This tool with better models would greatly add value in assess in GSFC projects.

Wallace, Dolores↗

NASA NTRS2008

Intelligent Integrated Health Management for a System of Systems

An intelligent integrated health management system (IIHMS) incorporates major improvements over prior such systems. The particular IIHMS is implemented for any system defined as a hierarchical distributed network of intelligent elements (HDNIE), comprising primarily: (1) an architecture (Figure 1), (2) intelligent elements, (3) a conceptual framework and taxonomy (Figure 2), and (4) and ontology that defines standards and protocols. Some definitions of terms are prerequisite to a further brief description of this innovation: A system-of-systems (SoS) is an engineering system that comprises multiple subsystems (e.g., a system of multiple possibly interacting flow subsystems that include pumps, valves, tanks, ducts, sensors, and the like); 'Intelligent' is used here in the sense of artificial intelligence. An intelligent element may be physical or virtual, it is network enabled, and it is able to manage data, information, and knowledge (DIaK) focused on determining its condition in the context of the entire SoS; As used here, 'health' signifies the functionality and/or structural integrity of an engineering system, subsystem, or process (leading to determination of the health of components); 'Process' can signify either a physical process in the usual sense of the word or an element into which functionally related sensors are grouped; 'Element' can signify a component (e.g., an actuator, a valve), a process, a controller, an actuator, a subsystem, or a system; The term Integrated System Health Management (ISHM) is used to describe a capability that focuses on determining the condition (health) of every element in a complex system (detect anomalies, diagnose causes, prognosis of future anomalies), and provide data, information, and knowledge (DIaK) not just data to control systems for safe and effective operation. A major novel aspect of the present development is the concept of intelligent integration. The purpose of intelligent integration, as defined and implemented in the present IIHMS, is to enable automated analysis of physical phenomena in imitation of human reasoning, including the use of qualitative methods. Intelligent integration is said to occur in a system in which all elements are intelligent and can acquire, maintain, and share knowledge and information. In the HDNIE of the present IIHMS, an SoS is represented as being operationally organized in a hierarchical-distributed format. The elements of the SoS are considered to be intelligent in that they determine their own conditions within an integrated scheme that involves consideration of data, information, knowledge bases, and methods that reside in all elements of the system. The conceptual framework of the HDNIE and the methodologies of implementing it enable the flow of information and knowledge among the elements so as to make possible the determination of the condition of each element. The necessary information and knowledge is made available to each affected element at the desired time, satisfying a need to prevent information overload while providing context-sensitive information at the proper level of detail. Provision of high-quality data is a central goal in designing this or any IIHMS. In pursuit of this goal, functionally related sensors are logically assigned to groups denoted processes. An aggregate of processes is considered to form a system. Alternatively or in addition to what has been said thus far, the HDNIE of this IIHMS can be regarded as consisting of a framework containing object models that encapsulate all elements of the system, their individual and relational knowledge bases, generic methods and procedures based on models of the applicable physics, and communication processes (Figure 2). The framework enables implementation of a paradigm inspired by how expert operators monitor the health of systems with the help of (1) DIaK from various sources, (2) software tools that assist in rapid visualization of the condition of the system, (3) analical software tools that assist in reasoning about the condition, (4) sharing of information via network communication hardware and software, and (5) software tools that aid in making decisions to remedy unacceptable conditions or improve performance.

Smith, Harvey↗

NASA NTRS2011

Model-Based Diagnostics for Propellant Loading Systems

The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are necessary to quickly identify when a fault occurs, so that recovery actions can be taken or an abort procedure can be initiated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physical processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their effects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which describes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the influence of different faults on this process. The model takes into account the main physical processes such as highly nonequilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydrogen and vapor flows inside the system in the presence of helium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based diagnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and perform fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.

Daigle, Matthew John↗

NASA NTRSDate not supplied

Uncertainty Quantification using Deep Ensembles for Decision Making in Cyber-Physical-Human Systems

In this paper and its companion, Differential Equation Approximation Using Gradient-Boosted Quantile Regression, Robison et al., we examine an approach to quantifying model uncertainty with the aim of increasing the trustworthiness of computational models in human-machine interactions. In Differential Equation Approximation Using Gradient-Boosted Quantile Regression, we focus on gradient-boosted decision trees, while in this one, we give more details about deep ensembles. Uncertainty quantification is crucial for building trustworthy autonomous decision-making agents in human-machine teams. There are two types of uncertainties: aleatoric and epistemic. The former is related to the inherent stochasticity (noise) of the process, whereas the latter is associated with the lack of knowledge or representation capability of models, such as neural networks. By lack of knowledge, we mean the model’s inability to accurately predict outputs for all possible inputs. The aleatory uncertainty can be estimated fairly easily with, for example, filters, whereas epistemic uncertainty is challenging to compute. This paper uses deep ensembles to quantify both aleatory and epistemic uncertainty. It can act as an uncertainty-aware surrogate transition model for decision-making frameworks. "Uncertainty-aware" means that the surrogate transition model should make predictions along with confidence in those predictions. In the context of decision-making, the transition models are ordinary differential equations (ODEs). Since ODEs can be simulated to make one-step or multi-step predictions, a good surrogate model for them should perform reasonably well in both modes. In a multi-step approach, the trajectory sampling method TS∞ was used to propagate uncertainty over multiple steps. The cartpole dynamical system was selected to demonstrate the ability of deep ensembles as good surrogate transition models for decision-making frameworks. The deep ensembles modeled the dynamics of cartpole ODEs and made uncertainty-aware predictions in single-step and multi-step transition modes.

CPH systems↗