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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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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Numerical studies of dilute and dense spray characteristics

Several issues involving the improvement of physical submodels and the computational efficiency in modeling dilute and dense spray combustion are discussed. First, the implementations of a dispersion width approach accounting for turbulent dispersion within each computational parcel is discussed. This is essentially a statistical transport model and the testings of this model confirm the capability of accurately representing dispersion in nearly-homogeneous and inhomogeneous turbulent flows with improved efficiency over the delta function stochastic separated flow model. To account for the dense spray effects, an existing drop collision and coalescence model and a Taylor analogy breakup (TAB) model were employed. These models were incorporated into a state-of-the-art multiphase all-speed transient flow solution procedure. Several examples including nonevaporating, evaporating, and burning dense spray cases were studied. The numerical results show reasonably good comparisons with available experimental data in terms of spray penetration, drop sizes, and overall configuration of a spray flame.

Kim, Y. M.↗
DOE OSTI2021

Challenges and Potential of Waveform Modeling for Crustal Scale Predictions

Waveform modeling is crucial to improving our understanding of observed seismograms. Forward simulation of wavefields provides quantitative methods of testing interactions between complicated source functions and the propagation medium. Here, we discuss three experiments designed to improve under standing of high frequency seismic wave propagation. First, we compare observed and predicted travel times of crustal phases for a set of real observed earthquakes with calculations and synthetic seismograms. Second, we estimate the frequency content of a series of nearly co-located earthquakes of varying magnitude for which we have a relatively well- known 1D velocity model. Third, we apply stochastic perturbations on top of a 3D tomographic model and qualitatively assess how those variations map to differences in the seismograms. While different in scope and aim, these three vignettes illustrate the current state of crustal scale waveform modeling and the potential for future studies to better constrain the structure of the crust.

58 GEOSCIENCES↗
DOE OSTI2024

Computational Modeling, Simulation, and Potential Applications of Optical Stochastic Cooling

With the rising demand for intense particles beams, much research is being conducted in the area of particle beam cooling. One of these methods, called Stochastic Cooling (SC) (developed at CERN in the 1970's), delivered a feedback method to improve the quality and lifetime of circulating proton beams by reducing their 6D phase-space and has been widely implemented in a number of hadron machines. However, traditional stochastic cooling schemes are limited by the bandwidth of microwave frequency systems. Optical Stochastic Cooling (OSC) is a promising extension of the stochastic cooling beam cooling technique. OSC instead uses optical wavelengths which allows for improved control and increased cooling but creates its own technical challenges. This lays out work conducted toward the experimental demonstration of OSC at Fermilab's Integrable Optics Test Accelerator (IOTA) storage ring. This includes the design and characterization of parts of the optical delay system, the development and validation of a high-fidelity computational model of the OSC process, and the investigations into possible applications of the OSC mechanism to advanced beam manipulations.

43 PARTICLE ACCELERATORS↗
NASA NTRS1976

Frequency domain approach for evaluation of stochastic control of elastic spacecraft

An attempt is made to develop a reduced-order filter for an elastic spacecraft. The frequency-domain approach is resorted to both for arriving at an appropriate ROF and for evaluating the resulting stochastic flight controller. The frequency-domain approach employed is contrasted to the time-domain approach previously utilized by the authors. A linearized deterministic dynamical model of a solar electric propulsion spacecraft is analyzed. Flexibility modes were incorporated profitably into the ROF model, and the stochastic controllers exhibit notching of structural resonances reminiscent of classical notch filters. Accurate estimates of modal parameters (damping, natural frequency) are deemed crucial, and use of worst-case values in the ROF design is recommended.

Larson, V.↗
DOE OSTI2020

Profiles of upcoming HPC Applications and their Impact on Reservation Strategies

With the expected convergence between HPC, BigData and AI, new applications with different profiles are coming to HPC infrastructures. Here, we aim at better understanding the features and needs of these applications in order to be able to run them efficiently on HPC platforms. The approach followed is bottom-up: we study thoroughly an emerging application from the neuroscience community (SLANT) to understand its behavior. Based on these observations, we derive a generic, yet simple, application model (namely, a linear sequence of stochastic jobs). We expect this model to be representative for a large set of upcoming applications that require the computational power of HPC clusters without fitting the typical behavior of large-scale traditional applications. In a second step, we show how one can manipulate this generic model in a scheduling framework. Specifically we consider the problem of making reservations (both time and memory) for an execution on an HPC platform. We derive solutions using the model of the first step of this work. We experimentally show the robustness of the model, even with very few data or with another application, to generate the model, and provide performance gains with regards to standard and more recent approaches used in the neuroscience community.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

A Eulerian-Lagrangian Model to Simulate Two-Phase/Particulate Flows

Figure 1 shows a snapshot of liquid fuel spray coming out of an injector nozzle in a realistic gas-turbine combustor. Here the spray atomization was simulated using a stochastic secondary breakup model (Apte et al. 2003a) with point-particle approximation for the droplets. Very close to the injector, it is observed that the spray density is large and the droplets cannot be treated as point-particles. The volume displaced by the liquid in this region is significant and can alter the gas-phase ow and spray evolution. In order to address this issue, one can compute the dense spray regime by an Eulerian-Lagrangian technique using advanced interface tracking/level-set methods (Sussman et al. 1994; Tryggvason et al. 2001; Herrmann 2003). This, however, is computationally intensive and may not be viable in realistic complex configurations. We therefore plan to develop a methodology based on Eulerian-Lagrangian technique which will allow us to capture the essential features of primary atomization using models to capture interactions between the fluid and droplets and which can be directly applied to the standard atomization models used in practice. The numerical scheme for unstructured grids developed by Mahesh et al. (2003) for incompressible flows is modified to take into account the droplet volume fraction. The numerical framework is directly applicable to realistic combustor geometries. Our main objectives in this work are: Develop a numerical formulation based on Eulerian-Lagrangian techniques with models for interaction terms between the fluid and particles to capture the Kelvin- Helmholtz type instabilities observed during primary atomization. Validate this technique for various two-phase and particulate flows. Assess its applicability to capture primary atomization of liquid jets in conjunction with secondary atomization models.

Apte, S. V.↗
DOE OSTI2022

Robust Scheduling of Microgrids Considering Unintentional Islanding Conditions

This paper proposes a robust scheduling model for microgrids considering the stochastic unintentional islanding conditions. The proposed model minimizes the total operating cost of the microgrid by efficiently coordinating the supply of power from local distributed energy resources and the main grid. To capture the prevailing uncertainties in renewable generation and demand as well as unintentional islanding conditions, a two-stage adaptive robust optimization model is formulated to minimize the total operating cost under the worst realization of the modeled uncertainties. The column and constraint generation (C&CG) method is used to solve the problem in an iterative manner. The solution of the proposed scheduling model ensures robust microgrid operation in consideration of all possible realization of renewable generation, demand and unintentional islanding condition. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, two micro-turbines, a diesel generator and a battery demonstrate the effectiveness of the proposed approach.

Liu, Guodong↗
DOE OSTI2023

Experimental Validation of a Command and Control Traffic Detection Model

Network intrusion detection systems (NIDS) are commonly used to detect malware communications, including command-and-control (C2) traffic from botnets. NIDS performance assessments have been studied for decades, but mathematical modeling has rarely been used to explore NIDS performance. This paper details a mathematical model that describes a NIDS performing packet inspection and its detection of malware's C2 traffic. Here, the paper further describes an emulation testbed and a set of cyber experiments that used the testbed to validate the model. These experiments included a commonly used NIDS (Snort) and traffic with contents from a pervasive malware (Emotet). Results are presented for two scenarios: a nominal scenario and a “stressed” scenario in which the NIDS cannot process all incoming packets. Model and experiment results match well, with model estimates mostly falling within 95 % confidence intervals on the experiment means. Model results were produced 70-3000 times faster than the experimental results. Consequently, the model's predictive capability could potentially be used to support decisions about NIDS configuration and effectiveness that require high confidence results, quantification of uncertainty, and exploration of large parameter spaces. Furthermore, the experiments provide an example for how emulation testbeds can be used to validate cyber models that include stochastic variability.

mathematical model↗
NASA NTRS1983

The structure of evaporating and combusting sprays: Measurements and predictions

The structure of particle-laden jets and nonevaporating and evaporating sprays was measured in order to evaluate models of these processes. Three models are being evaluated: (1) a locally homogeneous flow model, where slip between the phases is neglected and the flow is assumed to be in local thermodynamic equilibrium; (2) a deterministic separated flow model, where slip and finite interphase transport rates are considered but effects of particle/drop dispersion by turbulence and effects of turbulence on interphase transport rates are ignored; and (3) a stochastic separated flow model, where effects of interphase slip, turbulent dispersion and turbulent fluctuations are considered using random sampling for turbulence properties in conjunction with random-walk computations for particle motion. All three models use a k-e-g turbulence model. All testing and data reduction are completed for the particle laden jets. Mean and fluctuating velocities of the continuous phase and mean mixture fraction were measured in the evaporating sprays.

Shuen, J. S.↗
NASA NTRS2009

Biophysics Representation of the Two-Hit Model of Alzheimer's Disease for the Exploration of Late CNS Risks from Space Radiation

A concern for long-term space travel outside the Earth s magnetic field is the late effects to the central nervous system (CNS) from galactic cosmic ray (GCR) or solar particle events (SPE). Human epidemiology data is severely limited for making CNS risk estimates and it is not clear such effects occur following low LET exposures. We are developing systems biology models based on biological information on specific diseases, and experimental data for proton and heavy ion radiation. A two-hit model of Alzheimer s disease (AD) has been proposed by Zhu et al.(1), which is the framework of our model. Of importance is that over 50% of the US population over the age of 75-y have mild to severe forms of AD. Therefore we recommend that risk assessment for a potential AD risk from space radiation should focus on the projection of an earlier age of onset of AD and the prevention of this possible acceleration through countermeasures. In the two-hit model, oxidative stress and aberrant cell cycle-related abnormalities leading to amyloid-beta plaques and neurofibrillary tangles are necessary and invariant steps in AD. We have formulated a stochastic cell kinetics model of the two-hit AD model. In our model a population of neuronal cells is allowed to undergo renewal through neurogenesis and is susceptible to oxidative stress or cell cycle abnormalities with age-specific accumulation of damage. Baseline rates are fitted to AD population data for specific ages, gender, and for persons with an apolipoprotein 4 allele. We then explore how low LET or heavy ions may increase either of the two-hits or neurogenesis either through persistent oxidative stress, direct mutation, or through changes to the micro-environment, and suggest possible ways to develop accurate quantitative estimates of these processes for predicting AD risks following long-term space travel.

Cucinotta, Francis A.↗
NASA NTRS2020

Modeling Uncertainty in Large Natural Resource Allocation Problems

The productivity of the world's natural resources is critically dependent on a variety of highly uncertain factors, which obscure individual investors and governments that seek to make long-term, sometimes irreversible investments in their exploration and utilization. These dynamic considerations are poorly represented in disaggregated resource models, as incorporating uncertainty into large-dimensional problems presents a challenging computational task. This study introduces a novel numerical method to solve large-scale dynamic stochastic natural resource allocation problems that cannot be addressed by conventional methods. The method is illustrated with an application focusing on the allocation of global land resource use under stochastic crop yields due to adverse climate impacts and limits on further technological progress. For the same model parameters, the range of land conversion is considerably smaller for the dynamic stochastic model as compared to deterministic scenario analysis. The scenario analysis can thus significantly overstate the magnitude of expected land conversion under uncertain crop yields.

Cai, Yongyang↗
NASA NTRS1992

A probabilistic fracture mechanics approach for structural reliability assessment of space flight systems

A probabilistic fracture mechanics approach for predicting the failure life distribution due to subcritical crack growth is presented. A state-of-the-art crack propagation method is used in a Monte Carlo simulation to generate a distribution of failure lives. The crack growth failure model expresses failure life as a function of stochastic parameters including environment, loads, material properties, geometry, and model specification errors. A stochastic crack growth rate model that considers the uncertainties due to scatter in the data and mode misspecification is proposed. The rationale for choosing a particular type of probability distribution for each stochastic input parameter and for specifying the distribution parameters is presented. The approach is demonstrated through a probabilistic crack growth failure analysis of a welded tube in the Space Shuttle Main Engine. A discussion of the results from this application of the methodology is given.

Sutharshana, S.↗
DOE OSTI2022

Yield Estimation of the August 2020 Beirut Explosion by Using Physics‐Based Propagation Simulations of Regional Infrasound

Abstract The August 2020 Beirut explosion is the largest single‐fired ammonium nitrate explosion documented in history. The massive explosion excited loud infrasound in the atmosphere, and clear waveforms were recorded by a regional infrasound array at an epicentral distance of 100 km, allowing for accurate measurements of explosion energy. We estimate the explosion size based on the infrasound waveform inversion. Unlike conventional inversions using empirical models, we perform full 3‐D finite‐difference simulations to obtain a physics‐based propagation model for the inversion. Accurate numerical modeling of infrasound is challenging as the propagation is substantially affected by the turbulent atmosphere. Instead of a single deterministic prediction, we provide a range of waveform predictions by running multiple simulations with stochastic weather forecast models, which allows for comprehensive uncertainty analysis of numerical modeling and estimated yields. Finally, we expand the yield estimation technique for seismoacoustic analysis and demonstrate the substantial advantage of the joint approach.

58 GEOSCIENCES↗
NASA NTRS1980

Wheat forecast economics effect study

A model to assess the value of improved information regarding the inventories, productions, exports, and imports of crop on a worldwide basis is discussed. A previously proposed model is interpreted in a stochastic control setting and the underlying assumptions of the model are revealed. In solving the stochastic optimization problem, the Markov programming approach is much more powerful and exact as compared to the dynamic programming-simulation approach of the original model. The convergence of a dual variable Markov programming algorithm is shown to be fast and efficient. A computer program for the general model of multicountry-multiperiod is developed. As an example, the case of one country-two periods is treated and the results are presented in detail. A comparison with the original model results reveals certain interesting aspects of the algorithms and the dependence of the value of information on the incremental cost function.

Mehra, R. K.↗
DOE OSTI2026

Multi-Modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation

As data collection and simulation capabilities advance, multi-modal learning, the task of learning from multiple modalities and sources of data, is becoming an increasingly important area of research. Surrogate models that learn from data of multiple auxiliary modalities to support the modeling of a highly expensive quantity of interest have the potential to aid outer loop applications such as optimization, inverse problems, or sensitivity analyses when multi-modal data are available. We develop two multi-modal Bayesian neural network surrogate models and leverage conditionally conjugate distributions in the last layer to estimate model parameters using stochastic variational inference (SVI). We provide a method to perform this conjugate SVI estimation in the presence of partially missing observations. Here, we demonstrate improved prediction accuracy and uncertainty quantification compared to unimodal surrogate models for both scalar and time series data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Differential Equation Approximation Using Gradient-Boosted Quantile Regression

The operation of cyber-physical-human (CPH) systems is subject to various epistemic and aleatory uncertainties. Overall trustworthiness of CPH systems relies on the trustworthiness of its components and their interactions. It is important that computational models comprising the cyber component of CPH provide predictions accompanied by a measure of confidence in model outcomes. Uncertainty quantification (UQ) and propagation are especially important in safety critical CPH systems. Gradient-boosted trees is a modeling approach capable both of learning the dynamics of a system and performing UQ. In this paper, we devise a method for using gradient boosting to learn the dynamics of a second order differential equation and estimate uncertainty at the same time. We do this by creating a custom loss function that trains the model to approximate the second derivative of a noisy time series, and to penalize based on a parameter that corresponds to the desired quantile. The resulting gradient boosting model can simulate stochastic trajectories of the system given a single starting point, that is, it can estimate both the expected trajectory and its uncertainty. We show that the uncertainty estimation is well calibrated and that the model can learn the dynamics even in the presence of noise. We demonstrate the approach on a simple cartpole system.

Autonomous systems↗
NASA NTRS1989

Performance evaluation of automated manufacturing systems using generalized stochastic Petri Nets

The main objective of this research is to develop a generic modeling methodology with a flexible and modular framework to aid in the design and performance evaluation of integrated manufacturing systems using a unified model. After a thorough examination of the available modeling methods, the Petri Net approach was adopted. The concurrent and asynchronous nature of manufacturing systems are easily captured by Petri Net models. Three basic modules were developed: machine, buffer, and Decision Making Unit. The machine and buffer modules are used for modeling transfer lines and production networks. The Decision Making Unit models the functions of a computer node in a complex Decision Making Unit Architecture. The underlying model is a Generalized Stochastic Petri Net (GSPN) that can be used for performance evaluation and structural analysis. GSPN's were chosen because they help manage the complexity of modeling large manufacturing systems. There is no need to enumerate all the possible states of the Markov Chain since they are automatically generated from the GSPN model.

Al-Jaar, Robert Y.↗
NASA NTRS2006

Probabilistic Prediction of Lifetimes of Ceramic Parts

ANSYS/CARES/PDS is a software system that combines the ANSYS Probabilistic Design System (PDS) software with a modified version of the Ceramics Analysis and Reliability Evaluation of Structures Life (CARES/Life) Version 6.0 software. [A prior version of CARES/Life was reported in Program for Evaluation of Reliability of Ceramic Parts (LEW-16018), NASA Tech Briefs, Vol. 20, No. 3 (March 1996), page 28.] CARES/Life models effects of stochastic strength, slow crack growth, and stress distribution on the overall reliability of a ceramic component. The essence of the enhancement in CARES/Life 6.0 is the capability to predict the probability of failure using results from transient finite-element analysis. ANSYS PDS models the effects of uncertainty in material properties, dimensions, and loading on the stress distribution and deformation. ANSYS/CARES/PDS accounts for the effects of probabilistic strength, probabilistic loads, probabilistic material properties, and probabilistic tolerances on the lifetime and reliability of the component. Even failure probability becomes a stochastic quantity that can be tracked as a response variable. ANSYS/CARES/PDS enables tracking of all stochastic quantities in the design space, thereby enabling more precise probabilistic prediction of lifetimes of ceramic components.

Nemeth, Noel N.↗