Modeling Dynamic Behavior of Stainless Steel (SS304L) [Slides]
Abstract not provided.
36 MATERIALS SCIENCE↗
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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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Modeling the Behavior of Noble Metals During HLW Vitrification in the DM1200 Melter
The noble metals ruthenium (Ru), rhodium (Rh), and palladium (Pd) are present to various extents in defense and commercial high-level nuclear waste streams. Usually, their concentrations are higher in commercial waste. Ru, Rh, and Pd are sparingly soluble in silicate glass melts and they therefore form separate metal, oxide, or other phases. These phases are generally denser than the glass melt and tend to sediment to the bottom of the melter, where they form a "sludge" layer. Since the sludge layer has a higher electrical conductivity than the molten glass, accumulation of sludge can ultimately lead to electrical disruption of the operation of the melter. In addition, the sludge reduces the melt volume, which also changes the operational characteristics of the melter. The noble metals sludge is a highly viscous mixture of melt and noble metal phases. The concentration of noble metals in the sludge may be 10 to 100 times higher than that in the drained glass.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
A new ply model for interlaminar stress analysis
An accurate estimate of interlaminar stresses is crucial to understanding, as well as predicting, many delamination-related failures in composite materials. A new model for ply-level sublaminate analysis is presented and applied. The homogeneous plate theory developed earlier by the authors (Valisetty and Rehfield, 1983) is further refined, and the equations are reduced appropriately for the classical finite-width free-edge laminate elasticity problem and a related delamination crack growth problem. It is applied to the laminate on a ply-by-ply basis. This theory incorporates all the essential physical effects and appears to be an adequate model for predicting the behavior of individual layers in equilibrium. On the basis of the number of equations and boundary conditions required for the implementation of layer equilibrium, this theory also appears to be the simplest of its kind presented so far. The stress induced in the free-edge region of a (0,90,90,0) laminate in uniform extension and the energy release rates for the delamination between the -30 deg and 90 deg plies of a (+, -30,+, -30, 90,90)s laminate are computed using the new analysis. The results are in excellent agreement with the existing numerical solutions. The new ply behavioral model appears to be very promising; it yields stresses and displacements that are statically and kinematically compatible at interlaminar surfaces.
Rao Valisetty, R.↗
Aeroelastic Modeling and Behavior of Lifting Surfaces Incorporating Aerodynamic and Structural Nonlinearities: Volterra Series and Indicial Function Approach
Within this NASA Grant, the following points should be emphasized: 1) All the objectives stated in the proposal of the grant have been accomplished. Moreover, the activity within the project has addressed additional issues, of the linear and nonlinear aeroelasticity, not included in the objectives of the grant. 2) During the activities within the grant, we have been in a permanent contact with Dr. Walter A. Silva, the monitor of the NASA Project, to whom we have reported continuously our achievements. 3) As a result of the activities within the grant a number of papers: a. have been submitted for publication to the AIAA Journals, namely the AIAA Journal and the Journal of Guidance, Control, and Dynamics, b. have been presented at the specialized National Conferences and an International Congress, and have appeared in the proceedings of these Conferences, 4) A list of papers submitted for publication and presented at Conferences is appended herewith. 5) In all these papers, an acknowledgment to NASA Langley Research Center was included.
Librescu, Liviu↗
Aeroelastic Modeling and Behavior of Lifting Surfaces Incorporating Aerodynamic and Structural Nonlinearities: Volterra Series and Indicial Function Approach
Within this NASA Grant, the following points should be emphasized: 1) All the objectives stated in the proposal of the grant have been accomplished. Moreover. the activity within the project has addressed additional issues, of the linear and nonlinear aeroelasticity, not included in the objectives of the grant. 2) During the activities within the grant, we have been in a permanent contact with Dr. Walter A. Silva, the monitor of the NASA Project, to whom we have reported continuously our achievements. 3) As a result of the activities within the grant a number of papers: a. have been submitted for publication to the AIAA Journals, namely the AIAA Journal and the Journal of Guidance, Control, and Dynamics, and b. have been presented at the specialized National Conferences and an International Congress, and have appeared in the proceedings of these Conferences. 4) A list of papers submitted for publication and presented at Conferences is appended herewith. 5) In all these papers, an acknowledgment to NASA Langley Research Center was included.
Librescu, Liviu↗
Kennedy Space Center (KSC) Launch Complex 39 (LC-39) Gaseous Hydrogen (GH2) Vent Arm Behavior Prediction Model Review Technical Assessment Report
The NESC Assessment Team reviewed a computer simulation of the LC-39 External Tank (ET) GH2 Vent Umbilical system developed by United Space Alliance (USA) for the Space Shuttle Program (SSP) and designated KSC Analytical Tool ID 451 (KSC AT-451). The team verified that the vent arm kinematics were correctly modeled, but noted that there were relevant system sensitivities. Also, the structural stiffness used in the math model varied somewhat from the analytic calculations. Results of the NESC assessment were communicated to the model developers.
Wilson, Timmy R.↗
Integrated simulation of U-10Mo monolithic fuel swelling behavior
Here, a separate computational branch has been implemented within the DART (Dispersion Analysis Research Tool) computational code to simulate the swelling behavior of U-10Mo monolithic fuel under the operating conditions of high-power research and test reactors (RTRs). The monolithic branch of the DART code implements a mechanistic rate-theory-based fission-gas-behavior model for the calculation of fission gas swelling, as well as a suite of thermal, physical, and mechanical models to take into account various processes occurring in RTR fuels during irradiation. In order to accurately simulate and eventually predict U-10Mo monolithic fuel irradiation behavior, the code uses materials properties calculated with lower length-scale computational methods, such as gas atom diffusivity and U-Mo surface energy from atomic simulations and grain-morphology-specific recrystallization kinetics (recrystallized fuel volume fractions vs. fission density) predicted using the phase-field method. The remainder of fission gas behavior parameters used in the model were calibrated with measured intergranular bubble size distributions. With this integrated simulation approach, the swelling behavior of U-10Mo monolithic fuel was simulated for various initial grain sizes at different operating conditions and compared with measured data. Furthermore, because limited experimental data exist for parameter calibration detailed sensitivity studies for the important parameters used in the fission gas behavior model were performed in order to examine their impact on both intergranular gas bubble morphology at low fission density, and on total porosity at high fission density.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Cognitive Modeling of Social Behaviors
The driving theme of cognitive modeling for many decades has been that knowledge affects how and which goals are accomplished by an intelligent being (Newell 1991). But when one examines groups of people living and working together, one is forced to recognize that whose knowledge is called into play, at a particular time and location, directly affects what the group accomplishes. Indeed, constraints on participation, including roles, procedures, and norms, affect whether an individual is able to act at all (Lave & Wenger 1991; Jordan 1992; Scribner & Sachs 1991). To understand both individual cognition and collective activity, perhaps the greatest opportunity today is to integrate the cognitive modeling approach (which stresses how beliefs are formed and drive behavior) with social studies (which stress how relationships and informal practices drive behavior). The crucial insight is that norms are conceptualized in the individual &nd as ways of carrying out activities (Clancey 1997a, 2002b). This requires for the psychologist a shift from only modeling goals and tasks - why people do what they do - to modeling behavioral patterns-what people do-as they are engaged in purposeful activities. Instead of a model that exclusively deduces actions from goals, behaviors are also, if not primarily, driven by broader patterns of chronological and located activities (akin to scripts). This analysis is particular inspired by activity theory (Leont ev 1979). While acknowledging that knowledge (relating goals and operations) is fundamental for intelligent behavior, activity theory claims that a broader driver is the person s motives and conceptualization of activities. Such understanding of human interaction is normative (i.e., viewed with respect to social standards), affecting how knowledge is called into play and applied in practice. Put another way, how problems are discovered and framed, what methods are chosen, and indeed who even cares or has the authority to act, are all constrained by norms, which are conceived and enacted by individuals.
Clancey, William J.↗
Physics of Life: A Model for Non-Newtonian Properties of Living Systems
This innovation proposes the reconciliation of the evolution of life with the second law of thermodynamics via the introduction of the First Principle for modeling behavior of living systems. The structure of the model is quantum-inspired: it acquires the topology of the Madelung equation in which the quantum potential is replaced with the information potential. As a result, the model captures the most fundamental property of life: the progressive evolution; i.e. the ability to evolve from disorder to order without any external interference. The mathematical structure of the model can be obtained from the Newtonian equations of motion (representing the motor dynamics) coupled with the corresponding Liouville equation (representing the mental dynamics) via information forces. All these specific non-Newtonian properties equip the model with the levels of complexity that matches the complexity of life, and that makes the model applicable for description of behaviors of ecological, social, and economical systems. Rather than addressing the six aspects of life (organization, metabolism, growth, adaptation, response to stimuli, and reproduction), this work focuses only on biosignature ; i.e. the mechanical invariants of life, and in particular, the geometry and kinematics of behavior of living things. Living things obey the First Principles of Newtonian mechanics. One main objective of this model is to extend the First Principles of classical physics to include phenomenological behavior on living systems; to develop a new mathematical formalism within the framework of classical dynamics that would allow one to capture the specific properties of natural or artificial living systems such as formation of the collective mind based upon abstract images of the selves and non-selves; exploitation of this collective mind for communications and predictions of future expected characteristics of evolution; and for making decisions and implementing the corresponding corrections if the expected scenario is different from the originally planned one. This approach postulates that even a primitive living species possesses additional, non-Newtonian properties that are not included in the laws of Newtonian or statistical mechanics. These properties follow from a privileged ability of living systems to possess a self-image (a concept introduced in psychology) and to interact with it. The proposed mathematical system is based on the coupling of the classical dynamical system representing the motor dynamics with the corresponding Liouville equation describing the evolution of initial uncertainties in terms of the probability density and representing the mental dynamics. The coupling is implemented by the information-based supervising forces that can be associated with self-awareness. These forces fundamentally change the pattern of the probability evolution, and therefore, lead to a major departure of the behavior of living systems from the patterns of both Newtonian and statistical mechanics. This innovation is meant to capture the signature of life based only on observable behavior, not on any biochemistry. This will not prevent the use of this model for developing artificial living systems, as well as for studying some general properties of behavior of natural, living systems.
Zak, Michail↗
Modeling Pilot Pulse Control
In this study, behavioral models are developed that closely reproduced pulsive control response of two pilots from the experimental pool using markedly different control techniques (styles) while conducting a tracking task. An intriguing find was that the pilots appeared to: 1) produce a continuous, internally-generated stick signal that they integrated in time; 2) integrate the actual stick position; and 3) compare the two integrations to issue and cease pulse commands. This suggests that the pilots utilized kinesthetic feedback in order to perceive and integrate stick position, supporting the hypothesis that pilots can access and employ the proprioceptive inner feedback loop proposed by Hess' pilot Structural Model. The Pulse Models used in conjunction with the pilot Structural Model closely recreated the pilot data both in the frequency and time domains during closed-loop simulation. This indicates that for the range of tasks and control styles encountered, the models captured the fundamental mechanisms governing pulsive and control processes. The pilot Pulse Models give important insight for the amount of remnant (stick output uncorrelated with the forcing function) that arises from nonlinear pilot technique, and for the remaining remnant arising from different sources unrelated to tracking control (i.e. neuromuscular tremor, reallocation of cognitive resources, etc.).
workload↗
Machine learning-enabled discovery of ionic liquid–solvent electrolytes exhibiting high ionic conductivity
Ionic liquids (ILs), which are a class of materials with versatile nature and growing popularity, are facing impediments toward widespread usage as electrolytes due to various factors such as low ionic conductivity, high viscosity, high market price etc. One of the ways these limitations can be addressed is by mixing ILs with a molecular solvent. In a combinatorial sense, there exists an immense number of specific IL–solvent combinations. An exhaustive experimental or even simulation-based investigation of the chemical space spanned by such combinations can be extremely time-consuming, expensive, and nearly impossible. An alternative approach is to employ machine learning-based models developed from available databases. Although there exists prior literature that integrates machine learning to investigate mixtures of specific solvents with ILs, these models lack generalization necessitating development of a large number of ML models to handle various solvents. To remedy this shortcoming, as a part of designing green electrolytes with high ionic conductivity that can have potential applications in next-generation batteries and solar cells, this work aims to develop a unified machine learning model to predict ionic conductivity of any IL–solvent mixture system. In this regard, three models, namely, Random Forest, extreme gradient boosting (XGBoost), and artificial neural network (ANN) were formulated using the NIST ILThermo database. The dataset contained 549 unique ionic liquids from 16 cation families and 81 unique solvents, representing a total of 23 712 datapoints. SHAPLEY additive explanation (SHAP) method was used to assess the impact of various features on model prediction and their significance was compared with literature to gain physical insight about the model behavior. Finally, using the developed models, approximately 2.5 million IL–solvent mixtures at five different compositions were screened at room temperature. The high-throughput screening yielded nearly 19 000 IL–solvent mixtures for which ionic conductivity was found to exceed the ionic conductivity of conventional Li-ion battery electrolyte.
25 ENERGY STORAGE↗
Improving Climate Projections Using "Intelligent" Ensembles
Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.
Baker, Noel C.↗
Improving Climate Projections Using "Intelligent" Ensembles
Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.
Baker, Noel C.↗
Architectural Modeling and Analysis for Safety Engineering
Model-based development tools are increasingly being used for system-level development of safety-critical systems. Architectural and behavioral models provide important information that can be leveraged to improve the system safety analysis process. Model-based design artifacts produced in early stage development activities can be used to perform system safety analysis, reducing costs and providing accurate results throughout the system life-cycle. In this report we describe an extension to the Architecture Analysis and Design Language (AADL) that supports modeling of system behavior under failure conditions. This Safety Annex enables the independent modeling of component failures and allows safety engineers to weave various types of fault behavior into the nominal system model. The accompanying tool support uses model checking to propagate errors from their source to their effect on safety properties without the need to add separate propagation specifications. The tool also captures all minimal set of fault combinations that can cause violation of the safety properties, that can be compared to qualitative and quantitative objectives as part of the safety assessment process. We describe the Safety Annex, illustrate its use with a representative example, and discuss and demonstrate the tool support enabling an analyst to investigate the system behavior under failure conditions.
FTA↗
An Operational Wake Vortex Sensor Using Pulsed Coherent Lidar
NASA and FAA initiated a program in 1994 to develop methods of setting spacings for landing aircraft by incorporating information on the real-time behavior of aircraft wake vortices. The current wake separation standards were developed in the 1970's when there was relatively light airport traffic and a logical break point by which to categorize aircraft. Today's continuum of aircraft sizes and increased airport packing densities have created a need for re-evaluation of wake separation standards. The goals of this effort are to ensure that separation standards are adequate for safety and to reduce aircraft spacing for higher airport capacity. Of particular interest are the different requirements for landing under visual flight conditions and instrument flight conditions. Over the years, greater spacings have been established for instrument flight than are allowed for visual flight conditions. Preliminary studies indicate that the airline industry would save considerable money and incur fewer passenger delays if a dynamic spacing system could reduce separations at major hubs during inclement weather to the levels routinely achieved under visual flight conditions. The sensor described herein may become part of this dynamic spacing system known as the "Aircraft VOrtex Spacing System" (AVOSS) that will interface with a future air traffic control system. AVOSS will use vortex behavioral models and short-term weather prediction models in order to predict vortex behavior sufficiently into the future to allow dynamic separation standards to be generated. The wake vortex sensor will periodically provide data to validate AVOSS predictions. Feasibility of measuring wake vortices using a lidar was first demonstrated using a continuous wave (CW) system from NASA Marshall Space Flight Sensor and tested at the Volpe National Transportation Systems Center's wake vortex test site at JFK International Airport. Other applications of CW lidar for wake vortex measurement have been made more recently, including a system developed by the MIT Lincoln Laboratory. This lidar has been used for detailed measurements of wake vortex velocities in support of wake vortex model validation. The first measurements of wake vortices using a pulsed, lidar were made by Coherent Technologies, Inc. (CTI) using a 2 micron solid-state, flashlamp-pumped system operating at 5 Hz. This system was first deployed at Denver's Stapleton Airport. Pulsed lidar has been selected as the baseline technology for an operational sensor due to its longer range capability.
Barker, Ben C., Jr.↗
Conceptual models of information processing
The conceptual information processing issues are examined. Human information processing is defined as an active cognitive process that is analogous to a system. It is the flow and transformation of information within a human. The human is viewed as an active information seeker who is constantly receiving, processing, and acting upon the surrounding environmental stimuli. Human information processing models are conceptual representations of cognitive behaviors. Models of information processing are useful in representing the different theoretical positions and in attempting to define the limits and capabilities of human memory. It is concluded that an understanding of conceptual human information processing models and their applications to systems design leads to a better human factors approach.
Stewart, L. J.↗
AutoGen Version 5.0
Version 5.0 of the AutoGen software has been released. Previous versions, variously denoted Autogen and autogen, were reported in two articles: Automated Sequence Generation Process and Software (NPO-30746), Software Tech Briefs (Special Supplement to NASA Tech Briefs), September 2007, page 30, and Autogen Version 2.0 (NPO- 41501), NASA Tech Briefs, Vol. 31, No. 10 (October 2007), page 58. To recapitulate: AutoGen (now signifying automatic sequence generation ) automates the generation of sequences of commands in a standard format for uplink to spacecraft. AutoGen requires fewer workers than are needed for older manual sequence-generation processes, and greatly reduces sequence-generation times. The sequences are embodied in spacecraft activity sequence files (SASFs). AutoGen automates generation of SASFs by use of another previously reported program called APGEN. AutoGen encodes knowledge of different mission phases and of how the resultant commands must differ among the phases. AutoGen also provides means for customizing sequences through use of configuration files. The approach followed in developing AutoGen has involved encoding the behaviors of a system into a model and encoding algorithms for context-sensitive customizations of the modeled behaviors. This version of AutoGen addressed the MRO (Mars Reconnaissance Orbiter) primary science phase (PSP) mission phase. On previous Mars missions this phase has more commonly been referred to as mapping phase. This version addressed the unique aspects of sequencing orbital operations and specifically the mission specific adaptation of orbital operations for MRO. This version also includes capabilities for MRO s role in Mars relay support for UHF relay communications with the MER rovers and the Phoenix lander.
Gladden, Roy E.↗
Benefits of detailed models of muscle activation and mechanics
Recent biophysical and physiological studies identified some of the detailed mechanisms involved in excitation-contraction coupling, muscle contraction, and deactivation. Mathematical models incorporating these mechanisms allow independent estimates of key parameters, direct interplay between basic muscle research and the study of motor control, and realistic model behaviors, some of which are not accessible to previous, simpler, models. The existence of previously unmodeled behaviors has important implications for strategies of motor control and identification of neural signals. New developments in the analysis of differential equations make the more detailed models feasible for simulation in realistic experimental situations.
Lehman, S. L.↗