Results for “Complex Systems” — Page 59

DOE OSTI2016

RAVEN User Manual, Revision 4

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 (INL). 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 includes providing Application Programming Interfaces (APIs). These APIs 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 of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Active Subspace Methods for Data-Intensive Inverse Problems (Final Report)

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

DiaMonD: An Integrated Multifaceted Approach to Mathematics at the Interfaces of Data, Models, and Decisions

The major goal of the project was to invent, analyze, implement, and apply new algorithms to speed up the computational simulation of complex systems in settings in which multiple simulations are needed. The main efforts we undertook are as follows: 1. Develop novel reduced order methods to reduce the costs of simulations, especially in settings (e.g., UQ, inference, optimization and control) for which numerous simulation are needed and also for nonlocal problems (e.g., fractional derivative, anomalous diffusion, nonlocal mechanics) for which lack of sparsity results in more expensive full-order simulation than encountered in partial differential equations settings; rigorous analyses as well as computational tests were carried out, 2. Develop multifidelity methods based on the usage of a variety of models having different fidelities and different costs to accelerate simulations again in settings where numerous simulations are needed or are especially expensive; rigorous analyses as well as computational tests were carried out, 3. Develop ensemble methods to reduced the costs of simulations again in settings where numerous simulations are needed or are especially expensive; rigorous analyses as well as computational tests were carried out, and 4. Analyze a model for Richardson pair dispersion in turbulent flows and develop discretization methods for the efficient and accurate approximations of the solution of the model; rigorous analyses as well as computational tests were carried out.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Ten Principles for Power Sector Transformation in Emerging Economies

Access to reliable, affordable electricity is a key driver of economic growth in modern economies. The electric power sector, once a predictable and slow-moving industry, is now a complex system undergoing rapid transformation. Countries are in various stages of reforming and transforming their power sectors to better incorporate modern technologies, assure reliability and affordability, reduce harmful air emissions, meet a wide range of environmental goals, and achieve critical developmental objectives. This document offers action-oriented principles for emerging economy decision makers to consider while pursuing power system transformation (PST) pathways. They are organized into three categories: I. Foundations of Power System Transformation - important framework conditions for charting PST pathways; II. Accelerating Power System Transformation -- common strategies and approaches for accelerating PST efforts; and III. Moving Forward Inclusively with Power System Transformation -- considerations for fairness and equity in PST efforts.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

New Directions in Strong-Field Control

The thrust of our AMOS-supported research was initially the problem of strong-field alignment in complex systems. Over the years, this research was generalized to strong field coherent control, including alignment, three-dimensional alignment, torsional control and molecular focusing in moderately intense laser fields. A second, related theme that was added to our AMOS-supported research was the physics, theory and potential applications of high harmonic generation from aligned molecules. This second thrust was motivated by the intense interest of the AMOS Program in attosecond science and rescattering electrons physics, and has been carried out in collaboration with AMOS experimentalists colleagues. This research led to 60 peer refereed publications.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2019

ERL Timing and optimized CBETA models

This project develops a method of finding instrument settings that minimize the power load on accelerating radio-frequency (RF) cavities in an energy recovery linac (ERL). A general symmetry theory, which has been developed in detail and was previously tested in simple ERL models, will be used to optimize more detailed models of the Cor nell Brookhaven ERL Test Accelerator (CBETA). CBETA specifications are detailed in Fig. 1. Due to the high overlap with previous work, this report briefly summarizes the relevant outcomes of older ERL symmetry models; however, the reader is encouraged to consult for a more comprehensive coverage of these topics. This report will focus primarily on extensions of symmetry modeling toward more complex systems, including multi particle beams or CBETA-specific cavity tracking.

43 PARTICLE ACCELERATORS↗

DOE OSTI2019

Precision Local Positioning System

The primary goal of the project is to lay the groundwork for a new system design, meaning that at the end of this project’s effort, a traditional LLNL project could begin. For this goal to be met, the overarching needs of potential sponsors and stakeholders must be analyzed and understood, and a clear statement of work should be able to be drafted from the finding in this report. More specifically, the results from this project should immediately feed into the drafting of requirements, an implementation plan, and a budget plan for a future project. In order to accomplish this goal, the team set a broad framework for work to be completed. First, conduct current market research and select reference technologies to decompose. From the decompositions, analyze shortcomings, possible pain-points and form metrics and architectural decisions. Next, analyze the potential market area by interviewing/researching stakeholders and understanding their needs. As an additional project goal, attempt to capture as large a market area and scope as reasonably possible by incorporating more than the traditional LLNL sponsors. From the information gathered, study stakeholder salience and understand the relationships between stakeholders, mission areas, mission critical functions, and system metrics. Further analyze the relationships to focus on critical system aspects and to group stakeholders and mission together in order to reduce system complexity while maintaining wide marketability. Finally, begin brainstorming concepts from the established architectural decision and use the found metrics to create tradespaces to down select the most viable architectures. This document presents the team’s findings as it progressed through the project’s strategy.

42 ENGINEERING↗

DOE OSTI2016

Magnetic Smart Tags (MaST) for Arms Control and Treaty Verification

The ability to track nuclear material is a challenge for resiliency of complex systems, e.g., harsh environments. RF tags, frequently used in national security applications, cannot be used for technological, operational, or safety reasons. Magnetic Smart Tags (MaST) is a novel tag technology based on magnetoelastic sensing that circumvents these issues. This technology is enabled by a new, cost-effective, batch manufacturing electrochemical deposition (ECD) process. This new advancement in fabrication enables multi-frequency tags capable of providing millions of possible codes for tag identification unlike existing theft deterrent tags that can convey only a single bit of information. Magnetostrictive 70% Co: 30% Fe was developed as the base alloy comprising the magnetoelastic resonator transduction element. Saturation magnetostriction, λ S , has been externally measured by the Naval Research Laboratory to be as high as 78 ppm. Description of a novel MEMS variable capacitive test structure is described for future measurements of this parameter.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2019

Active Subspace Methods for Data-Intensive Inverse Problems

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Computer Simulation of Proton Transport in Fuel Cell Membranes (Final Report)

This DOE-supported research grant focused on understanding the nature of proton transport in complex systems such as proton exchange membranes (PEMs). The most unique aspect of the research was the development and implementation of a novel multiscale reactive molecular dynamics (MS-RMD) methodology. In this approach, covalent bonds can dynamically break and form, allowing one to accurately treat the proton hopping process essential to capturing the physics of proton transport. The Voth group applied this method to proton exchange membrane systems, providing insight into their proton transport mechanism. They found that protons can diffuse most rapidly in the water-rich regions, but that protons actually spend so little time in such regions that transport along the hydrophobic – hydrophilic interface controls the membrane performance. In addition, the group worked to increase understanding of acidic solutions, developing methods for simulating and interpreting experimental infrared vibrational spectroscopy for excess protons (acidic solutions). The group also implemented novel tools for developing proton transport reactive MD models using a relative entropy minimization scheme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Post-Irradiation Fracture Toughness Characterization of Generation II FeCrAl Alloys

Currently, the handbook on FeCrAl material properties contains only limited data regarding the fracture toughness properties of any FeCrAl alloy, let alone alloys currently under investigation within the Advanced Fuels Campaign (AFC) such as the Oak Ridge National Laboratory (ORNL) derived FeCrAl alloys. Unirradiated datasets developed both by ORNL and previously within literature using Charpy V-notch specimens to determine the ductile to brittle transition temperature (DBTT) showed key findings including (i) increasing Al and/or Cr content can raise the DBTT of FeCrAl alloys, (ii) DBTT values can typically reside at or above room temperature for FeCrAl alloys, and (iii) grain size and residual strain can have an effect on the fracture properties. These results suggest that the fracture toughness of FeCrAl alloys may be a factor in its deployment for nuclear power applications, but no studies have been completed prior regarding the DBTT or fracture toughness of FeCrAl alloys after neutron irradiation, especially for the leaner Cr content alloys currently under investigation as candidate alloys for light water reactor (LWR) cladding. Taken in isolation, embrittlement of a cladding alloy during service has no bearing on possible licensure. Hydriding of zirconium cladding alloys is well understood to result in significant embrittlement. Furthermore, mechanical failure of irradiated cladding containing fuel pellets occurs at far higher stresses than defueled cladding samples because the presence of the fuel toughens the fuel rod. Despite this absence of direct relevance to licensure, radiation embrittlement is a fundamental structure-property evolution that should be understood to guide further development of FeCrAl alloys. Licensure of FeCrAl incorporates far more complex system behaviors that will not be possible until larger quantities of fueled irradiated rodlets are available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Machine-Learning Enabled Evaluation of Probability of Piping Degradation In Secondary Systems of Nuclear Power Plants

The transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. Furthermore, of the operations and maintenance costs in U.S. plants, approximately 80% are labor costs. To address the issue of rising operating costs and economic viability, technologies used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants (NPPs) are under evaluation. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real time measurements, rather than at a pre-determined inspection interval thus reducing the maintenance cost. This paper describes distributed high-temperature stable fiber sensors fabricated in optical fibers through a roll-to-roll laser direct writing process using femtosecond lasers. Using phase-sensitive optical time domain reflectometry, distributed acoustic and vibration sensors can be developed and deployed to critical components and systems in NPPs to perform active measurements with spatial resolution down to 0.5-meter throughout the piping systems. Complex acoustic and vibration signatures harnessed by distributed sensors are registered and analyzed by artificial intelligence algorithms for degradation detection and flaw identification. Piping elbows with machined-in flaws were instrumented with fiber sensors. High-spatial-resolution data were used to develop and validate machine learning algorithms, including both linear and nonlinear regression, and classification. Additionally, classification and sensor analysis were also performed for data analysis. The paper concludes with recommendations and future work on applications of machine learning enabled high-resolution fiber sensors for piping degradation monitoring in current or future NPPs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

Electric Grid Market-Control Structure

Markets and controls are frequently portrayed as being polar opposites, incompatible alternative paradigms, or even competing philosophies for organizing the activities of large complex systems. In the electricity industry in the US prior to the 1990s, the dominant organizational structure was the vertically-integrated utility, characterized by a well-defined geographic and electrical service territory and the ownership or control of most generating facilities, a transmission network, local distribution systems and a monopoly over retail electric service for most if not all of the end-users in the territory. A vertically-integrated utility, many of which still exist today, operates mostly on controls with very little scope for markets or market transactions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

An Integrated Two-Factor Authentication Solution Using Pulse Connect Secure and Apache HTTP Server

Complex system requirements can often lead to a situation wherein no single product or platform can provide a solution. In these cases, a solution integrating the features of multiple products and platforms is needed. Recently, a program sponsor required discontinued use of a third-party Security Assertion Markup Language Identity Provider that was being used to implement a two-factor authentication (2FA) solution that fully integrated with Pulse Connect Secure (PCS). PCS alone could not provide the features necessary to meet all the requirements of a new 2FA solution. The challenge was to leverage the features of additional platforms to design an integrated solution to meet all requirements. This paper presents a solution that integrates the features of PCS, Apache HyperText Transfer Protocol Server, and a custom application that meets all requirements, including a requirement to defend against Uniform Resource Locator manipulation as means to gain unauthorized access to backend applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Modeling Water Vapor Transport at Liquid/Membrane Interfaces for Applications in Liquid Desiccant Air Conditioners (CRADA CRD-17-679 Final Report)

Conventional compressor-based cooling has undergone incremental changes over the past 100 years. Further improvements in efficiency require evermore complex systems, especially under humid conditions. Meso-porous membranes offer unique opportunities for efficient humidity control in buildings using an absorbent desiccant solution, but membranes are not designed or optimized for this purpose. This project will use molecular dynamic simulations to determine optimal membrane properties for these air conditioning applications, focusing on the membrane properties at the membrane/liquid/air interface. Optimal membrane designs will enable smaller, more durable, and less expensive designs of these membrane air conditioning systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17–18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17-18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Uncertainty Quantification in Scientific ML

The intricate interactions between data sampling, model selection and the inherent randomness in complex systems strongly emphasize the need for a rigorous characterization of ML algorithms. In conventional statistics, uncertainty quantification (UQ) provides this characterization by measuring how accurately a model reflects the physical reality and by studying the impact of different error sources on the prediction.

97 MATHEMATICS AND COMPUTING↗