Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Complex Systems”

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.

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

At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

RAVEN and Dynamic Probabilistic Risk Assessment: Software overview

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7 [], currently under development at the Idaho National Laboratory. Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism has been employed by providing Application Programming Interfaces (APIs). These interfaces are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable to investigate the system response, investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The paper presents an overview of the software capabilities and their implementation schemes followed by some application examples.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Extremely accurate sequential verification of RELAP5-3D

Large computer programs like RELAP5-3D solve complex systems of governing, closure and special process equations to model the underlying physics of nuclear power plants. Further, these programs incorporate many other features for physics, input, output, data management, user-interaction, and post-processing. For software quality assurance, the code must be verified and validated before being released to users. For RELAP5-3D, verification and validation are restricted to nuclear power plant applications. Verification means ensuring that the program is built right by checking that it meets its design specifications, comparing coding to algorithms and equations and comparing calculations against analytical solutions and method of manufactured solutions. Sequential verification performs these comparisons initially, but thereafter only compares code calculations between consecutive code versions to demonstrate that no unintended changes have been introduced. Recently, an automated, highly accurate sequential verification method has been developed for RELAP5-3D. The method also provides to test that no unintended consequences result from code development in the following code capabilities: repeating a timestep advancement, continuing a run from a restart file, multiple cases in a single code execution, and modes of coupled/uncoupled operation. In conclusion, mathematical analyses of the adequacy of the checks used in the comparisons are provided.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Back to Basics: First Principles of HRA

Human Reliability Analysis (HRA) continues to suffer from a myriad of technical gaps in its foundations. Core issues have been widely discussed but remain largely unaddressed, although the path taken to this point was paved with good intentions. Now, while this characterization may seem dramatic it is not intended to throw doubt onto HRA as a discipline or to diminish the value it has on increasing safety and resilience in nuclear power. Rather, it is intended to communicate that HRA has some serious challenges and is desperately in need of a critical evaluation. To quote Dougherty [1], “the misrepresentations that will undoubtedly arise may serve to provoke the necessary creativity.” What bears noting is that in the last thirty years since Alan Swain declared that “no adequate HRA had ever been done,” there has been little effort to correct the issues he highlighted. Human factors practice is still poorly implemented, many complex systems are fundamentally incompatible with reliable human performance, and there remains poor foundational support for many of the assumptions within HRA. We propose three categories of issues in HRA, philosophical, theoretical, and empirical and potential solutions to these issues.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Machine learning enabled lineshape analysis in optical two-dimensional coherent spectroscopy

Optical two-dimensional (2D) coherent spectroscopy excels in studying coupling and dynamics in complex systems. The dynamical information can be learned from lineshape analysis to extract the corresponding linewidth. However, it is usually challenging to fit a 2D spectrum, especially when the homogeneous and inhomogeneous linewidths are comparable. We implemented a machine learning algorithm to analyze 2D spectra to retrieve homogeneous and inhomogeneous linewidths. The algorithm was trained using simulated 2D spectra with known linewidth values. The trained algorithm can analyze both simulated (not used in training) and experimental spectra to extract the homogeneous and inhomogeneous linewidths. Finally, this approach can be potentially applied to 2D spectra with more sophisticated spectral features.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Implementation of Creep-Damage Model for Concrete Fracture in MOOSE

Numerical implementation of an isotropic creep-damage model for concrete in multiphysics object-oriented simulation environment (MOOSE) finite element framework is presented in this paper. The constitutive model considers the combined effect of instantaneous and delayed strains on damage propagation. The implementation considers creep using generalized Maxwell or Kelvin-Voigt models. Using strain splitting assumptions, the total mechanical strains are split into elastic and creep components. Damage is considered to evolve as a function of the elastic and creep strains. This work considers damage as a function of fracture energy using the characteristic length of each finite element. This approach preserves the energy release rate of each element and avoids vanishing energy dissipation as the mesh is refined. Further, a creep-damage parameter is used to quantify the effect of creep strain on damage. The model is tested against published results on notched three-point bending specimens involving non-linear creep and predicts that about a third of the creep strain contributes towards damage for the experiments simulated. Results show that the proposed framework has predictive capabilities, and the model can be extended for more complex systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Machine-learning interatomic potentials for interfaces in all-solid-state batteries: Perspectives on training data, model selection, and validation

Interfaces play a pivotal role in dictating the performance and reliability of all-solid-state batteries (ASSBs), where complex electro-chemo-mechanical phenomena at grain boundaries (GBs) and interfaces can lead to degradation and failure. Traditional atomistic simulation methods, such as first-principles calculations and classical molecular dynamics, face limitations in modeling these interfaces due to either high computational cost or insufficient transferability to the diverse atomic environments evolving at interfaces. Machine-learning interatomic potentials (MLIPs) have emerged as a transformative approach, enabling large-scale, high-accuracy simulations of disordered and chemically complex systems by leveraging the predictability of machine learning models trained on first-principles data. Recent applications of MLIPs have demonstrated their ability to capture intricate behaviors at ASSB interfaces, including ion transport, interfacial evolution, and degradation mechanisms, with accuracy and efficiency unattainable by conventional methods. This prospective paper presents comprehensive analysis and practical guidance for MLIP development for GBs and interfaces in ASSBs, with a focus on three key pillars: data generation, model selection, and validation. Here, we review the current state of MLIP applications for GBs and interfaces in both general and ASSB-specific materials, highlighting best practices and challenges in constructing diverse and representative datasets, choosing appropriate machine learning architectures, and rigorously validating model performance. We also discuss emerging strategies and opportunities for improved reliability and efficiency of MLIPs to simulate realistic interfaces in ASSBs.

Energy - Storage↗
DOE OSTI2021

Exploration of multifidelity UQ sampling strategies for computer network applications.

Network modeling is a powerful tool to enable rapid analysis of complex systems that can be challenging to study directly using physical testing. Here, two approaches are considered: emulation and simulation. The former runs real software on virtualized hardware, while the latter mimics the behavior of network components and their interactions in software. Although emulation provides an accurate representation of physical networks, this approach alone cannot guarantee the characterization of the system under realistic operative conditions. Operative conditions for physical networks are often characterized by intrinsic variability (payload size, packet latency, etc.) or a lack of precise knowledge regarding the network configuration (bandwidth, delays, etc.); therefore uncertainty quantification (UQ) strategies should be also employed. UQ strategies require multiple evaluations of the system with a number of evaluation instances that roughly increases with the problem dimensionality, i.e., the number of uncertain parameters. It follows that a typical UQ workflow for network modeling based on emulation can easily become unattainable due to its prohibitive computational cost. In this paper, a multifidelity sampling approach is discussed and applied to network modeling problems. The main idea is to optimally fuse information coming from simulations, which are a low-fidelity version of the emulation problem of interest, in order to decrease the estimator variance. By reducing the estimator variance in a sampling approach it is usually possible to obtain more reliable statistics and therefore a more reliable system characterization. Several network problems of increasing difficulty are presented. For each of them, the performance of the multifidelity estimator is compared with respect to the single fidelity counterpart, namely, Monte Carlo sampling. For all the test problems studied in this work, the multifidelity estimator demonstrated an increased efficiency with respect to MC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

First-principles simulation of solvation structure and deprotonation reactions of ore forming metal ions in very nonideal solutions

Advances in algorithms and computational performance achieved in this grant period have allowed the atomic level dynamical simulation of complex nanoscale materials using interparticle forces calculated directly from an accurate density functional solution to the electronic Schrödinger equation (ab-initio molecular dynamics, AIMD). Focus of this program was on the prediction and analysis of the properties of environmentally important ions in aqueous solutions. AIMD methods have provided chemical interpretations of these very complex systems with an unprecedented level of accuracy and detail. The structure of the solvation region neighboring a highly charged metal ion (e.g., 3+) in an aqueous solution is very different from that of bulk water. The many-body behaviors (polarization, charge transfer, etc.) of the ion-water and water-water interactions in this region are difficult to capture with conventional empirical potentials. However, a large numbers of waters (up to 128 waters) are required to fully describe chemical events in the extended hydrations shells and long simulation times are needed to reliably sample the system. Taken together this makes simulation at the 1 st principles level a very large computational problem. Our AIMD simulation results using these methods agree with the measured octahedral structure of the 1 st solvation shell of Al 3+ at the 1 st shell boundary and a calculated radius of 1.937 (exp. 1.9). Our calculated average 2 nd shell radius agrees remarkably well with the measured radius, 4.093 Å calculated vs. the measured value of 4.0-4.15 Å.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Synthesis and Understanding of Novel Catalysts

The research took advantage of our capabilities to perform in-situ and operando Raman spectroscopy on complex systems along with our developing expertise in the synthesis of uniform, supported metal oxide materials to investigate relationships between the catalytically active oxide composition, atomic structure, and support and the corresponding chemical and catalytic properties. The project was organized into two efforts: 1) Synthesis of novel catalyst materials by atomic layer deposition (ALD). 2) Spectroscopic and chemical investigations of coke formation and catalyst deactivation. ALD synthesis was combined with conventional physical characterization, Raman spectroscopy, and probe molecule chemisorption to study the effect of supported metal oxide composition and atomic structure on acid-base and catalytic properties. Operando Raman spectroscopy studies of olefin polymerization leading to coke formation and catalyst deactivation clarified the mechanism of coke formation by acid catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Neurons to algorithms LDRD final report.

Over the last three years the Neurons to Algorithms (N2A) LDRD project teams has built infrastructure to discover computational structures in the brain. This consists of a modeling language, a tool that enables model development and simulation in that language, and initial connections with the Neuroinformatics community, a group working toward similar goals. The approach of N2A is to express large complex systems like the brain as populations of a discrete part types that have specific structural relationships with each other, along with internal and structural dynamics. Such an evolving mathematical system may be able to capture the essence of neural processing, and ultimately of thought itself. This final report is a cover for the actual products of the project: the N2A Language Specification, the N2A Application, and a journal paper summarizing our methods.

Rothganger, Fredrick H.↗
DOE OSTI2014

Petascale Hierarchical Modeling VIA Parallel Execution

The research allows more effective model building. By allowing researchers to fit complex models to large datasets in a scalable manner, our algorithms and software enable more effective scientific research. In the new area of “big data,” it is often necessary to fit “big models” to adjust for systematic differences between sample and population. For this task, scalable and efficient model-fitting tools are needed, and these have been achieved with our new Hamiltonian Monte Carlo algorithm, the no-U-turn sampler, and our new C++ program, Stan. In layman’s terms, our research enables researchers to create improved mathematical modes for large and complex systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Optimization Algorithms for Hierarchical Problems, with Applications to Nanoporous Materials

The aim of the overall project was research to formulate and solve multi-scale optimization problems for energy storage applications. More specifically, we formulated multi-scale models for optimizing the internal structure of nanoporous materials, with the goal of developing models for related energy applications associated with sustainable sources of energy. The applications include supercapacitors, hydrogen storage, catalytic beds, and filters. The optimization algorithms we have developed can be applied to general hierarchical design problems arising from complex systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Risk Management for Sodium Fast Reactors.

Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of self - correcting, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the syste m's design to manage the accident. While inherently and passively safe designs are laudable, extreme boundary conditions can interfere with the design attributes which facilitate inherent safety , thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a beyond design basis seismic event with the intend of exploring two issues : (1) can human intervention either improve or worsen the potential end states and (2) can a Bayes ian Network be constructed to infer the state of the reactor to inform (1). ACKNOWLEDGEMENTS The author s would like to acknowledge the U.S. Department of E nergy's Office of Nuclear Energy for funding this research through Work Package SR - 14SN100303 under the Advanced Reactor Concepts program. The authors also acknowledge the PRA teams at A rgonne N ational L aborator y , O ak R idge N ational L aborator y , and I daho N ational L aborator y for their continue d contributions to the advanced reactor PRA mission area.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Developing a Comprehensive Risk Assessment Framework for Geological Storage CO 2

The operational risks for CCS projects include: risks of capturing, compressing, transporting and injecting CO₂; risks of well blowouts; risk that CO 2 will leak into shallow aquifers and contaminate potable water; and risk that sequestered CO 2 will leak into the atmosphere. This report examines these risks by using information on the risks associated with analogue activities such as CO 2 based enhanced oil recovery (CO 2 -EOR), natural gas storage and acid gas disposal. We have developed a new analysis of pipeline risk based on Bayesian statistical analysis. Bayesian theory probabilities may describe states of partial knowledge, even perhaps those related to non-repeatable events. The Bayesian approach enables both utilizing existing data and at the same time having the capability to adsorb new information thus to lower uncertainty in our understanding of complex systems. Incident rates for both natural gas and CO 2 pipelines have been widely used in papers and reports on risk of CO 2 pipelines as proxies for the individual risk created by such pipelines. Published risk studies of CO 2 pipelines suggest that the individual risk associated with CO2 pipelines is between 10 -3 and 10 -4 , which reflects risk levels approaching those of mountain climbing, which many would find unacceptably high. This report concludes, based on a careful analysis of natural gas pipeline failures, suggests that the individual risk of CO 2 pipelines is likely in the range of 10-6 to 10-7, a risk range considered in the acceptable to negligible range in most countries. If, as is commonly thought, pipelines represent the highest risk component of CCS outside of the capture plant, then this conclusion suggests that most (if not all) previous quantitative- risk assessments of components of CCS may be orders of magnitude to high. The potential lethality of unexpected CO 2 releases from pipelines or wells are arguably the highest risk aspects of CO 2 enhanced oil recovery (CO2-EOR), carbon capture, and storage (CCS). Assertions in the CCS literature, that CO 2 levels of 10% for ten minutes, or 20 to 30% for a few minutes are lethal to humans, are not supported by the available evidence. The results of published experiments with animals exposed to CO 2 , from mice to monkeys, at both normal and depleted oxygen levels, suggest that lethal levels of CO 2 toxicity are in the range 50 to 60%. These experiments demonstrate that CO 2 does not kill by asphyxia, but rather is toxic at high concentrations. It is concluded that quantitative risk assessments of CCS have overestimated the risk of fatalities by using values of lethality a factor two to six lower than the values estimated in this paper. In many dispersion models of CO 2 releases from pipelines, no fatalities would be predicted if appropriate levels of lethality for CO 2 had been used in the analysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Advance Liquid Metal Reactor Discrete Dynamic Event Tree/Bayesian Network Analysis and Incident Management Guidelines (Risk Management for Sodium Fast Reactors)

Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of self-correcting, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the system's design to manage the accident. Inherently and passively safe designs are laudable, but nonetheless extreme boundary conditions can interfere with the design attributes which facilitate inherent safety, thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a beyond design basis seismic event with the intent of exploring two issues: (1) can human intervention either improve or worsen the potential end states and (2) can a Bayesian Network be constructed to infer the state of the reactor to inform.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Parameter Estimation and Model Validation of Nonlinear Dynamical Networks

In the performance period of this work under a DOE contract, the co-PIs, Philip Gill and Henry Abarbanel, developed new methods for statistical data assimilation for problems of DOE interest, including geophysical and biological problems. This included numerical optimization algorithms for variational principles, new parallel processing Monte Carlo routines for performing the path integrals of statistical data assimilation. These results have been summarized in the monograph: “Predicting the Future: Completing Models of Observed Complex Systems” by Henry Abarbanel, published by Spring-Verlag in June 2013. Additional results and details have appeared in the peer reviewed literature.

42 ENGINEERING↗
DOE OSTI2015

V&V Framework

A Verification and Validation (V&V) framework is presented for the development and execution of coordinated modeling and experimental program s to assess the predictive capability of computational models of complex systems through focused, well structured, and formal processes. The elements of the framework are based on established V&V methodology developed by various organizations including the Department of Energy, National Aeronautics and Space Administration, the American Institute of Aeronautics and Astronautics, and the American Society of Mechanical Engineers. Four main topics are addressed: 1) Program planning based on expert elicitation of the modeling physics requirements, 2) experimental design for model assessment, 3) uncertainty quantification for experimental observations and computational model simulations, and 4) assessment of the model predictive capability. The audience for this document includes program planners, modelers, experimentalist, V &V specialist, and customers of the modeling results.

97 MATHEMATICS AND COMPUTING↗