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At least 1,135 records · Page 63

Role of reinforcement learning for risk-based robust control of cyber-physical energy systems

Critical infrastructures such as cyber-physical energy systems (CPS-E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS-E is critical to ensure societal well-being and economic prosperity. Automated control is key for real-time operations and may be mathematically cast as a sequential decision-making problem under uncertainty. Emergence of data-driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision-making problems for risk-based robust CPS-E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS-E applications, addressing the effect of risk preferences across multiple RL methods, and development of open-source domain-aware simulation environments for RL experimentation within a CPS-E context. This article systematically analyzes the applicability of four types of RL methods (model-free, model-based, hybrid model-free and model-based, and hierarchical) for risk-based robust CPS-E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk-averse, risk-neutral, and risk-seeking) through the development and application of an open-source simulation environment. Motivating numerical simulation examples include representative single-zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

TEAMER - AquaHarmonics High Fidelity WEC Sim PTO and Control Model Validation, Test Logs and Results

Collaborative effort between AquaHarmonics, Sandia National Laboratories (SNL), and the National Renewable Energy Laboratory (NREL) to revise and validate Aquaharmonics' full wave to wire model, allowing for reduced uncertainty and increased understanding of design requirements of a utility scale wave energy converter (WEC). SNL and NREL in collaboration with AquaHarmonics, will set up and run WEC Simulator (WEC-Sim) models of the AquaHarmonics WEC, building off past model developments for inclusion of custom PTO (power take-off) dynamics. The intent is to review, update, and verify or validate a new WEC-Sim model against wave tank experimental data. Furthermore, the WEC-Sim model will be coupled to an energy storage system model to better understand the wave-to-wire functionality. This data set is described in the "Test Log" excel file. Please refer to that document for details on each specific test date/time, constraint parameters and model hardware setup details. Sim model can be found in the associated MHKDR link below.

16 TIDAL AND WAVE POWER↗

Estimation of High-Altitude Electromagnetic Pulse Signal Leakage into Power Generation Facilities: Simulations and Measurements

The coupling of early-time high-altitude electromagnetic pulse signals into a power generation facility was investigated. This study encompasses both electromagnetic simulation and on-site experimental efforts that are relevant to shielding effectiveness estimation with a passive technique. As an initial step in understanding the attenuation models involved, simulations were carried out as a function of frequency, building properties, and signal incidence angle. Testing was then performed at the US Department of Energy’s Oak Ridge National Laboratory campus to identify the equipment needed for signal collection and to fine-tune the experimental procedure. Finally, as an application of the models and methods presented, measurements were conducted at a power generation plant to derive the field transfer functions and coupled high-altitude electromagnetic pulse waveforms for various locations within the facility.

42 ENGINEERING↗

Understanding the Capabilities and Limitations of the Controls and Operations of High Voltage Direct Current (HVdc) Converters in Interconnected Electric Power Systems

This project will seek to build a fundamental understanding, and capability, to model and simulate the controls and operations of high voltage direct current (HVdc) converter stations in an electromagnetic simulation environment. While HVdc stations have been operated in the United States for over 50 years, these are typically simple two terminal point-to-point systems. Recently multi-terminal systems have begun to be deployed. The challenge with these new multi-terminal systems is that they often use different control schemes on the different terminals. The interactions of existing and new controls, and the fact that HVdc systems are rates in the 1,000’s of MWs means that small control instabilities can have dramatic impacts to bulk power systems. Despite these challenges, the operational capabilities of HVdc make them an attractive option for the transfer of the large amounts of renewable electricity that will be necessary for decarbonization of the nation’s electrical infrastructure and other sectors.

42 ENGINEERING↗

Sienna Modeling Framework [Slides]

NREL's Sienna modeling framework effectively builds, solves, and analyzes the scheduling problems and dynamic simulations of quasi-static infrastructure systems. It uses a modular framework to answer different questions about future energy systems, fundamentally advancing the nation's ability to model individual and integrated infrastructure systems at a range of spatial and temporal scales. This presentation will include NREL power grid researcher Clayton Barrows.

24 POWER TRANSMISSION AND DISTRIBUTION↗

High y + Shear-Stress Turbulence Implementation for High Flux Isotope Reactor Narrow Channel Flows

The research objective of this work was to improve the engineering predictions of the turbulence characteristics of flows in curved narrow channels. Such channel flows are commonly encountered in nuclear research and test reactors, with one of them being the high-flux isotope reactor (HFIR). Research reactors bear high heat fluxes, and the proper computing of turbulence is paramount for safe and reliable reactor operation. The study builds on the results of a previous direct numerical simulation of turbulence to inform a well-known Reynolds-averaged Navier–Stokes shear-stress turbulence model and improves its accuracy in simulating parallel channel flows. A new formulation of the loss term in the dissipation conservation equation is suggested. Combined with high wall distance computational grids, the new implementation provides a fast-running flow solution, suitable for engineering purposes. Model generalization for parallel channel flows, in a broader range of frictional Reynolds numbers, is suggested by introducing a new form of the model constants.

CFD↗

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.

59 BASIC BIOLOGICAL SCIENCES↗

CADAT network translator

Program converts cell-net data into logic-gate models for use in test and simulation programs. Input consists of either Place, Route, and Fold (PRF) or Place-and-Route-in-Two-Dimensions (PR2D) layout data deck. Output consists of either Test Pattern Generator (TPG) or Logic-Simulation (LOGSIM) logic circuitry data deck. Designer needs to build only logic-gate-model circuit description since program acts as translator. Language is FORTRAN IV.

Pitts, E. R.↗

A Boundary Condition for Simulation of Flow Over Porous Surfaces

A new boundary condition is presented.for simulating the flow over passively porous surfaces. The model builds on the prior work of R.H. Bush to eliminate the need for constructing grid within an underlying plenum, thereby simplifying the numerical modeling of passively porous flow control systems and reducing computation cost. Code experts.for two structured-grid.flow solvers, TLNS3D and CFL3D. and one unstructured solver, USM3Dns, collaborated with an experimental porosity expert to develop the model and implement it into their respective codes. Results presented,for the three codes on a slender forebody with circumferential porosity and a wing with leading-edge porosity demonstrate a good agreement with experimental data and a remarkable ability to predict the aggregate aerodynamic effects of surface porosity with a simple boundary condition.

Frink, Neal T.↗

From Interstellar PAHs and Ices to the Origin of Life

Tremendous strides have been made in our understanding of interstellar material over the past twenty years thanks to significant, parallel developments in observational astronomy and laboratory astrophysics. Twenty years ago the composition of interstellar dust was largely guessed at, the concept of ices in dense molecular clouds ignored, and the notion of large, abundant, gas phase, carbon rich molecules widespread throughout the interstellar medium (ISM) considered impossible. Today the composition of dust in the diffuse ISM is reasonably well constrained to micron-sized cold refractory materials comprised of amorphous and crystalline silicates mixed with an amorphous carbonaceous material containing aromatic structural units and short, branched aliphatic chains. In dense molecular clouds, the birthplace of stars and planets, these cold dust particles are coated with mixed molecular ices whose composition is very well constrained. Lastly, the signature of carbon-rich polycyclic aromatic hydrocarbons (PAHs), shockingly large molecules by earlier interstellar chemistry standards, is widespread throughout the Universe. The first part of this lecture will describe how infrared studies of interstellar space, combined with laboratory simulations, have revealed the composition of interstellar ices (the building blocks of comets) and the high abundance and nature of interstellar PAHs. The laboratory database has now enabled us to gain insight into the identities, concentrations, and physical state of many interstellar materials. Within a dense molecular cloud, and especially in the solar nebula during the star and planet formation stage, the materials frozen into interstellar/precometary ices are photoprocessed by ultraviolet light, producing more complex molecules. The remainder of the presentation will focus on the photochemical evolution of these materials and the possible role of these compounds on the early Earth. As these materials are thought to be the building blocks of comets and related to the carbonaceous components of micrometeorites, they are likely to have been important sources of complex organic materials on the early Earth and their composition may be related to the origin of life.

Allamandola, Louis J.↗

From Interstellar Polycyclic Aromatic Hydrocarbons and Ice to the Origin of Life

Tremendous strides have been made in our understanding of interstellar material over the past twenty years thanks to significant, parallel developments in observational astronomy and laboratory astrophysics. Twenty years ago the composition of interstellar dust was largely guessed at, the concept of ices in dense molecular clouds ignored, and the notion of large, abundant, gas phase, carbon rich molecules widespread throughout the interstellar medium (ISM) considered impossible. Today the composition of dust in the diffuse ISM is reasonably well constrained to cold refractory materials comprised of amorphous and crystalline silicates mixed with an amorphous carbonaceous material containing aromatic structural units and short, branched aliphatic chains. In the dense ISM, the birthplace of stars and planets, these cold dust particles are coated with mixed molecular ices whose composition is very well constrained. Lastly, the signature of carbon-rich polycyclic aromatic hydrocarbons (PAHs), shockingly large molecules by early interstellar chemistry standards, is widespread throughout the Universe. The first part of this talk will describe how infrared studies of interstellar space, combined with laboratory simulations, have revealed the composition of interstellar ices (the building blocks of comets) and the high abundance and nature of interstellar PAHs. The laboratory database has now enabled us to gain insight into the identities, abundances, and physical state of many interstellar materials. Within a dense molecular cloud, and especially in the presolar nebula, the materials frozen into the interstellar/precometary ices are photoprocessed by ultraviolet light and produce more complex molecules. The remainder of the presentation will focus on the photochemical evolution of these materials and the possible role of these compounds on the to the carbonaceous components of micrometeorites, they are likely to have been important sources of complex materials on the early Earth and their composition may be related to the origin of life.

Allamandola, Louis↗

Computational Analysis of Shock Layer Emission Measurements in an Arc-Jet Facility

This paper reports computational analysis of radiation emission experiments in a high enthalpy arc-jet wind tunnel at NASA Ames Research Center. Recently, as part of ongoing arc-jet characterization work, spectroscopic radiation emission experiments have been conducted at the 20 MW NASA Ames arc-jet facility. The emission measurements were obtained from the arc-jet freestream and from a shock layer formed in front of flatfaced models. Analysis of these data is expected to provide valuable information about the thermodynamic state of the gas in the arc-jet freestream and in the shock layer as well as thermochemical equilibration processes behind the shock in arc-jet flows. Knowledge of the thermodynamic state of the gas in arc-jet test flows and especially within the shock layer is essential to interpret the heat transfer measurements such as in surface catalysis experiments. The present work is a continuation of previous work and focuses on analysis of the emission data obtained at relatively low-pressure conditions for which the arc-jet shock layer is expected to be in thermal and chemical nonequilibrium. Building blocks of the present computational analysis are: (1) simulation of nonequilibrium expanding flow in the converging-diverging conical nozzle and supersonic jet; (2) simulation of nonequilibrium shock layer formed in front of the flat-faced cylinder model; and (3) prediction of line-of-sight radiation from the computed flowfield. For computations of the nonequilibrium flow in the conical nozzle and shock layer, multi-temperature nonequilibrium codes with the axisymmetric formulation are used. For computations of line-of-sight radiation. a nonequilibrium radiation code (NEQAIR) is used to predict emission spectra from the computed flowfield. Computed line-of-sight averaged flow properties such as vibrational and rotational temperatures, species number densities within the shock layer will be compared with those deduced from the experimental spectra. Detailed comparisons of computational and experimental spectra will also be presented.

Gokcen, Tahir↗

AMEGO: Exploring the Extreme Multimessenger Universe

The All-sky Medium Energy Gamma-ray Observatory (AMEGO) is a Probe-class mission concept that will provide essential contributions to multimessenger astrophysics in the next decade. AMEGO operates both as a Compton and pair telescope to achieve unprecedented sensitivity between 200 keV and>5 GeV. The instrument consists of four subsystems. A double-sided strip silicon Tracker gives a precise measure of the first Compton scatter interaction and tracks of pair-conversion products. A novel CdZnTe Low Energy Calorimeter with excellent position and energy resolution surrounds the bottom and sides of the Tracker to detect the Compton-scattered photons which enhances the polarization and narrow-line sensitivity. A thick CsI High Energy Calorimeter contains the high-energy Compton and pair events. The instrument is surrounded by aplastic anti-coincidence detector to veto the cosmic-ray background. We have performed detailed simulations to predict the telescope performance and are currently building a prototype instrument. The AMEGO prototype, known as ComPair, will be tested at the High Intensity Gamma-Ray Source in 2021, followed by a balloon flight in Fall of 2022. In this presentation we will give an overview of the science motivation, a description of the observatory, and an update of the prototype instrument development.

Carolyn Ann Kierans↗

An Overview of Experiments and Modeling of Polysiloxane-Coated Thermal Protection Systems for Missions to Mars, Titan, and Beyond.

Phenolic Impregnated Carbon Ablator (PICA) gained heritage during the historic Stardust mission, where it successfully returned samples from a comet’s tail and has since been instrumental in delivering payloads to the surface of Mars [1-3]. Most recently, PICA enabled the safe return of samples collected from asteroid Bennu as part of the OSIRIS-REx mission. This rich legacy underscores PICA’s critical role in allowing NASA’s most ambitious exploration missions. However, the friable nature of its phenolic phase presents challenges during handling and pre-launch activities. To mitigate this issue, PICA is coated with a polysiloxane resin system, which serves to suppress particulate dispersion and thereby safeguard spacecraft components. A comprehensive understanding of the polysiloxane resin’s behavior is imperative, as it profoundly shapes the material response of PICA during atmospheric entry by influencing its thermal and oxidative stability. This influence extends to thermocouple plugs embedded within thermal protection systems. These plugs have demonstrated their significance in missions such as Mars Science Laboratory (MSL) and Mars 2020, where the MEDLI and MEDLI2 instrumentation suites delivered in-valuable insights into the performance of thermal protection systems during entry into the Martian atmosphere [4]. Looking ahead, missions such as Dragonfly, set to descend into Titan’s dense atmosphere, aim to leverage advanced sensor technologies to further refine our understanding of thermal protection response [5]. Moreover, thermocouple plugs play an essential role in validating cutting-edge material response models, such as those pioneered under NASA’s Entry Systems Modeling Project (ESM), designed, in-part, to predict the operational integrity of thermal protection systems under the extreme stresses of atmospheric entry. To achieve these modeling goals, ground-based experiments are crucial to provide the foundational data necessary for developing and refining these predictive tools. To this end, an extensive test campaign was conducted at the Hypersonic Materials Environmental Test System (HyMETS) to investigate the high-temperature behavior of the polysiloxane resin in an air environment [6]. These experiments revealed critical phenomena, including the formation of a silicon oxycarbide layer that enhances oxidation resistance, moderates surface temperatures, and alters in-depth thermal response. Building on these findings, subsequent tests were designed to simulate atmospheric entry conditions in reactive gases, such as CO2 and N2, to mimic the environments of Mars and Titan, respectively, as well as non-reactive gases representing the atmospheres of the Ice Giants (Neptune and Uranus). A heating rate dependent decomposition mechanism has been identified for the polysiloxane resin under oxidizing conditions (Fig. 1). In the initial stage, the resin and the underlying thermal protection system undergo pyrolysis, rapidly generating a thin amorphous silicon oxycarbide interwoven with carbonaceous char and residual fibers from PICA. During the second stage, the nascent oxide layer establishes a robust, oxidation-resistant thermal barrier coating, which significantly impedes heat transfer to the underlying carbonaceous char, resulting in a stagnation of the surface temperature. A key factor contributing to this thermal resistance is the low recombination efficiency of atomic oxygen (γ), which further diminishes the heat load on the material’s interior layers [7]. Moreover, as the surface temperature stagnates, the silicon oxycarbide phase separates into distinct regions of silica and free graphite. Ultimately, when the heat flux reaches a critical threshold, a third stage is triggered, leading to the breakdown of the coating through carbothermal reduction, exposing the underlying char layer. This exposure leads to a dramatic surface temperature spike, driven by highly exothermic reactions between atomic oxygen and the char layer, further accelerating material degradation. A detailed mass and heat transfer model of PICA coated with polysiloxane resin was implemented in the Porous material Analysis Toolbox based on OpenFOAM, PATO [8]. The initial stage was considered negligible in this model because the resin decomposition occurs rapidly within a thin surface layer. Instead, the coating was directly considered as an oxygen-resistant thermal barrier coating. For the second stage, the thin amorphous silicon oxycarbide was treated as a pure silica surface to simplify the thermochemical behavior. The model ac-counts for surface equilibrium processes using representative elements of the coating-environment system. For the third stage, specific boundary conditions were developed to estimate the onset and progression of the coating removal. Two-dimensional material response simulations were conducted to compare uncoated and coated PICA using boundary conditions calibrated with HyMETS data. Fig. 2 illustrates that the simulations closely align with experimental data, successfully reproducing measured temperature profiles. This work will include the latest advancements in the coating model, including the calibration of recombination of atomic oxygen at the surface during the second phase. These simulated results will be further validated against additional CO2 data points from HyMETS, reinforcing the models’ predictive capabilities. These mechanisms and their effects on thermal protection systems, including thermochemical behavior and thermocouple probe performance in extreme environments, provide crucial insights for optimizing spacecraft designs that safeguard scientific payload and ensure mission success in future planetary exploration endeavors.

Active Oxidation↗

Removing Rotational Variations from Shoulder Thermocouples in Friction Stir Welding

Thick plate and lower temperature friction stir welding results in good post-weld properties by welding at very low tool rotational speeds. Low speeds cause large variations in the measured shoulder temperature, making temperature control difficult. This paper describes a computationally light method to correct for oscillating measured temperatures. Data is collected over the previous two revolutions and used to build a compensation table and calculate a derivative. In simulation, measured temperature variations are reduced by a factor of five to ten. Temperature data is preserved and not cornered, nor is any time delay induced by this method, unlike with filtering methods.

Friction Stir Welding, Temperature↗

EFT Workshop at Notre Dame

The LPC EFT workshop was held April 25-26, 2024 at the University of Notre Dame. The workshop was organized into five thematic sessions: "how far beyond linear" discusses issues of truncation and validity in interpretation of results with an eye towards practicality; "reconstruction-level results" visits the question of how best to design analyses directly targeting inference of EFT parameters; "logistics of combining likelihoods" addresses the challenges of bringing a diverse array of measurements into a cohesive whole; "unfolded results" tackles the question of designing fiducial measurements for later use in EFT interpretations, and the benefits and limitations of unfolding; and "building a sample library" addresses how best to generate simulation samples for use in data analysis. This document serves as a summary of presentations, subsequent discussions, and actionable items identified over the course of the workshop.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Simulation and Experimental Measurements of Inductively Coupled CF4 and CF4/Ar Plasmas

The recently developed code SEMS (semiconductor equipment modeling software)is applied to the simulation of CF4 and CF4/Ar inductively coupled plasmas (ICP). This work builds upon the earlier nitrogen, transformer coupled plasma (TCP) SEMS research by demonstrating its accuracy for more complex reactive mixtures, moving closer to the realization of a virtual plasma reactor. Attention is given to the etching of and/or formation of carbonaceous films on the quartz dielectric window and diagnostic aperatures. The simulations are validated through comparisons with experimental measurements using FTIR (Fourier Transform Infrared) and UV absorption spectroscopy for CFx and SiFx neutral radicals, QMS (quadrupole mass spectrometry) for the ions, and Langmuir probe measurements of electron number density and temperature in an ICP GEC reference cell.

Hash, D. B.↗

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗