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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Water foraging with dynamic roots in E3SM; The role of roots in terrestrial ecosystem memory on intermediate timescales (Final Technical Report)

Terrestrial ecosystems can show sustained responses to stress events that may last for years after the initial perturbation. This phenomenon is referred to as legacy or memory and it emerges from a set of ecosystem processes that are poorly captured by Earth System and Land Surface Models. Consequently, these models struggle to predict the impact that extreme climate events have on surface energy, carbon and hydrological exchange once a stressor such as drought has relaxed or in response to repeated exposure to stress. In this project, we set out to test how the addition of dynamic root profiles in land surface models impact the capacity for Earth System Models, such as the Department of Energy’s E3SM, to capture realistic legacy effects. Observations have shown that vegetation shifts their root profiles during stress events to forage for water and these altered root profiles may take years to relax back to the initial state. We hypothesized that the alteration of the belowground root structures may be a source of terrestrial ecosystem legacy that is missing from models. To test this idea we undertook three core activities. (1) We developed a global-scale analysis of the impact of dynamic roots on ecosystem legacy building from a recently developed dynamic root module. (2) We developed new root dynamics in the DOE’s Energy Land Model (ELM) that allowed not only root profiles to shift their profile but also simultaneously alter carbon allocation to fine root pools. We then tested the impact of these new dynamics with intensive sensitivity analysis at four long-term AmeriFlux sites. (3) We undertook detailed isotopic analysis of tree rings from these AmeriFlux sites to assess ecophysiological and ecohydrological legacy to stress events that can be used to benchmark the sensitivity experiments with ELM. From these core activities, we report the following key findings. Firstly, on a global scale, the addition of dynamic roots led to chronically water-stressed ecosystems recovering faster to climate stress while wetter ecosystem showed enhanced legacy. This is because across ecosystems, stress events were almost universally associated with water shortages that led to the development of deeper root profiles. These deeper root profiles proved beneficial for recovery from drought stress. While the root dynamics did not universally improve the modeled representation of legacy it showed complex transient dynamics that emerge from the addition of dynamic roots. Secondly, the sensitivity analysis illustrated long term shifts in rooting depths away from the prescribed default profile suggesting that initializing of root profiles could benefit from spin-up simulations that converge on locally optimized root profiles. In addition, by enabling dynamic allocation some of the sites predicted unrealistically low allocation to roots (and high allocation to leaves). While these changes did not dramatically alter modeled gross primary productivity, they illustrated that without more sophisticated root processes in models, there is little penalty to dramatically disinvest in roots. Thirdly, the isotopic analysis of tree rings showed highly distinct legacy responses across species and sites. For example, T. canadensis showed reduced transpiration the year after stress events illustrated by sustained elevated $\delta^{18}$O. In contrast, A saccharum displayed elevated $\delta^{13}$C associated with reduced stomatal conductance in response to the previous years’ stress event. These geochemical signatures of legacy were present despite tree growth returning to normal the year after the stress. The results show the importance of species-level dynamics in legacy that are absent in modeling that assumes common traits within plant functional types. In summary, the work here established new avenues to explore root dynamics in models while illustrating how additional root processes are needed before dynamic carbon allocation can be implemented. Lastly, this project provided mentorship to a postdoctoral fellow, training for an early career scientist and multiple undergraduate students recruited from a minority serving institution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Low-order aquifer thermal energy storage model for geothermal system simulation

This paper presents a low-order aquifer thermal energy storage (ATES) model for simulation of combined subsurface and above-surface energy systems. The model is included in the Modelica IBPSA Library, which is a free open-source library with basic models for building and district energy and control systems. The model uses a lumped-component method, in which the transient conductive-convective heat and mass transfer equation is radially discretized. To verify the accuracy of the model, we present an inter-model comparison from a simulation test suite. Results show that the Modelica ATES model is in good agreement, with a normalized mean bias error for yearly variation of aquifer temperatures of 1.6×10−2 and 9×10−5 at 1 m and 10 m distance from the well.

Maccarini, Alessandro↗
DOE OSTI2021

Forward Modeling Populations of Flares from Tidal Disruptions of Stars by Supermassive Black Holes

Detections of the tidal disruption flares (TDFs) of stars by supermassive black holes (SMBHs) are rapidly accumulating as optical surveys improve. These detections may provide constraints on SMBH demographics, stellar dynamics, and stellar evolution in galaxies. To maximize this scientific impact, we require a better understanding of how astrophysical parameters interact with survey selection effects in setting the properties of detected flares. In this study, we develop a framework for modeling the distributions of optical TDF detections in surveys across attributes of the host galaxies and the flares themselves. This model folds in effects of the stellar disruption rate in each galaxy, the flare luminosity and temperature distributions, the effects of obscuration and reddening by dust in the host galaxy, and survey selection criteria. We directly apply this model to the sample of TDFs detected by the Zwicky Transient Facility, and find that the overall flare detection rate is in line with simple theoretical expectation. The model can also reproduce the distribution of total stellar mass and redshift of the host galaxies, but fails to match all details of the detected flares, such as their luminosity and temperature distributions. We also find that dust obscuration likely plays an important role in suppressing the TDF detection rate in star-forming galaxies. While we do not find that the unusual preference of TDFs to have hosts in post-starburst galaxies in the "green valley" can be entirely explained by selection effects, our model can help to quantify the true rate enhancement in those galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Modeling of a Generic Pebble Bed High-temperature Gas-cooled Reactor (PB-HTGR) with SAM

This report presents the modeling of the core of a generic pebble-bed reactor (PBR) at the system level using the System Analysis Module (SAM) code. This work is an extension of a previous work by the authors (Ooi et al. (2021)) that used the so-called 2-D ring model approach to model the PBMR-400. With the new approach, the pebble bed of the reactor is modeled with multiple PBCoreChannel components with spherical heat structures which allows the code to calculate thermal fluid parameters with built-in closure relations. The new core-channel approach is an improvement to the 2-D ring model approach as it does not introduce geometric distortions to the model and thus reduces the uncertainties of the predictions. In addition to thermal fluid simulations, point kinetics (PKE) are included to the model. Simulations are performed under a steady-state normal operation condition and a load-following transient scenario. This particular transient scenario is chosen as it tests both the thermal fluid and neutronics aspects of the model. The predicted results from both the steady-state and transient scenarios are compared with the results by Stew- art et al. (2021) who performed similar simulations with a Griffin-Pronghorn coupled tool. Despite the differences between the codes, with SAM being a system-analysis code and Pronghorn being a porous-medium code, both sets of results compare favorably. The over- all profiles and trends of the predicted temperatures and reactivities from the SAM and Griffin/Pronghorn simulations are similar, with some differences in their predicted values. The first part of the report covers the significance of a relatively fast-running approach that is capable of modeling the pebble bed reactor at the system-level while simultaneously capturing the radial thermal behavior of the core. Then, the modeling approach used in this work is discussed in details. Lastly, the results and comparisons with the Griffin/Pronghorn simulation are presented.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Time-dependent heterogeneity leads to transient suppression of the COVID-19 epidemic, not herd immunity

Significance Epidemics generally spread through a succession of waves that reflect factors on multiple timescales. Here, we develop a general approach bridging across these timescales and demonstrate how to incorporate population heterogeneity into a wide class of epidemiological models. We demonstrate that a fragile state of transient collective immunity emerges during early, high-paced stages of the epidemic, leading to suppression of individual epidemic waves. However, this state is not an indication of lasting herd immunity: Subsequent waves may emerge due to stochastic changes in individual social activity. Parameters of transient collective immunity are estimated using empirical data from the COVID-19 epidemic in several US locations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

On the Representation of Hyporheic Exchange in Models for Reactive Transport in Stream and River Corridors

Efforts to include more detailed representations of biogeochemical processes in basin-scale water quality simulation tools face the challenge of how to tractably represent mass exchange between the flowing channels of streams and rivers and biogeochemical hotspots in the hyporheic zones. Multiscale models that use relatively coarse representations of the channel network with subgrid models for mass exchange and reactions in the hyporheic zone have started to emerge to address that challenge. Two such multiscale models are considered here, one based on a stochastic Lagrangian travel time representation of advective pumping and one on multirate diffusive exchange. The two models are formally equivalent to well-established integrodifferential representations for transport of non-reacting tracers in steady stream flow, which have been very successful in reproducing stream tracer tests. Despite that equivalence, the two models are based on very different model structures and produce significantly different results in reactive transport. In a simple denitrification example, denitrification is two to three times greater for the advection-based model because the multirate diffusive model has direct connections between the stream channel and transient storage zones and an assumption of mixing in the transient storage zones that prevent oxygen levels from dropping to the point where denitrification can progress uninhibited. By contrast, the advection-based model produces distinct redox zonation, allowing for denitrification to proceed uninhibited on part of the hyporheic flowpaths. These results demonstrate that conservative tracer tests alone are inadequate for constraining representation of mass transfer in models for reactive transport in streams and rivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A hybrid porous model for full reactor core scale CFD investigation of a prismatic HTGR

Three-dimensional (3-D) Computational Fluid Dynamics (CFD) analysis of a whole nuclear reactor core is a tremendous challenge due to the large geometric volume and complex structures. Here, this research presents a hybrid porous (HP) model to simplify a prismatic High Temperature Gas-cooled Reactor (HTGR) core, so 3-D CFD investigation can be performed on a full reactor core scale. In the HP model, the prototypic small coolant channels in the nuclear fuel blocks are lumped together to form multiple equivalent large coolant channels, and then the porous medium flow model is applied to each of them. Therefore, heat transfer in fuel blocks is computed by a hybrid combination of solid energy and porous flow energy equations. The similarity between the HP model and prototypic model is achieved by deriving the porous flow permeability, inertial resistance factor, and artificial thermophysical properties. Compared with the widely used whole porous (WP) flow model, the HP model preserves more realistic geometric structures, and therefore more accurate physical processes. The General Atomics' Modular High Temperature Gas-cooled Reactor (MHTGR) design was chosen as a prototype to demonstrate the methodology. Simulations were performed using the prototypic CFD model and HP model at steady-state forced circulation, steady-state natural circulation, and transient conditions that correspond to normal operation, extended period of pressurized cool down, and short-term transients after reactor shutdown, respectively. The comparison shows good agreement between the HP model and prototypic model in the maximum fuel temperature, average solid temperature, and helium flow rate, which demonstrates the potential applicability of the HP model for a full reactor core scale simulation in the future. As a benefit, the HP model reduces the mesh quantity by a factor of 50 from a prototypic model. Correspondingly, the computation time was reduced by a factor of at least 30.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Development of the reactor core isolation cooling system model for the extended station black-out accident

In order to accurately predict the behavior of the Reactor Core Isolation Cooling System (RCIC) in the context of the FLEX strategies under the extended station black-out accident (SBO), we present a mechanistic model of the RCIC in this work. The control volume formulation of angular momentum conservation is employed to model the Terry turbine in the RCIC system. The two-phase nozzle flow model developed in the previous work is utilized to simulate the convergent-divergent nozzles in the Terry turbine. A nozzle flow table is generated to cover the operation range of the RCIC during beyond design basis accidents (BDBAs). The mechanistic RCIC turbine-pump model and the nozzle flow table are incorporated in a TRACE BWR model. Simulations of short-term and long-term SBO transients are performed to demonstrate dynamic RCIC responses. A system analysis code with the proposed mechanistic model of the RCIC system will have a better prediction of system safety response during SBO accidents. With the addition of such more realistic models, the system code will be capable of providing expanded understanding of safety margin, which can help help to form the technical basis for the successful implementation of the FLEX and SAMG measures under Extended Loss of AC Power (ELAP) conditions for BWR or PWR reactor designs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification

Focal Area(s): (2)Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models. (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge- guided AI. Science Challenge: The Energy Exascale Earth System Model (E3SM) is a fully coupled, state-of-the-science Earth system model that uses code optimized for DOE's advanced computers to address the most critical scientific questions facing our nation and society (Golaz et al., 2019). The E3SM Land model (ELM) is designed to understand how the changes in terrestrial land surfaces will interact with other Earth system components and has been used to understand hydrologic cycles, biogeophysics, and ecosystem dynamics. In spite of great successes, the ELM has several known issues that restrain rapid improvements. For example, the ELM uses equilibrium models to simulate dynamic land-climate interactions and it requires long model spin-up time to identify suitable initial conditions for transient simulations. The ELM lacks built-in uncertainty mechanisms that can improve the robustness of model predictions. The ELM is a holistic, deterministic model system with a rigid design, and in many situations, it is hard to modify the ELM system to incorporate new theory/hypothesis and new data across scales to address emerging science problems (such as predicting the impacts of water cycle extremes). In addition, The ELM is technically optimized for traditional CPU-centric computers and it cannot fully utilize the current and incoming leadership computers for model simulations and uncertainty quantification (UQ). The success of artificial intelligence (AI) has inspired scientists to use AI models to discover intrinsic features from simulation data (Chattopadhyay et al., 2020) and observational data (Reichstein et al., 2019) to gain further process understanding of Earth science problems. However, autonomous AI model training through deep learning usually requires a huge amount of annotated data. To overcome the limitations from the data and computing resources, knowledge-guided AI models are necessary where human-knowledge is ingested in model construction (Banino et al., 2018) and training process (Silver et al., 2016) for efficient learning. Herein, we present a new way that leverages the process understanding from the ELM to guide AI model development for the ELM enhancement and UQ. We hope this study can inspire further Earth and environmental system model developments and transformations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Development and Validation of SAM Multi-dimensional Flow Model for Thermal Mixing and Stratification Modeling

Thermal mixing and stratification in large pools or enclosures are very important phenomena that are critical to nuclear reactor safety. Because of the wide ranges of time and length scales associated with such phenomena, accurate modeling and simulation of thermal mixing and stratification remain as the key unresolved, challenging problems for reactor transient analyses. In traditional system analysis codes, simplified zero-dimensional (0-D) models are widely used for their high numerical efficiency, but they generally suffer from very limited prediction accuracies or range of applicability. Like traditional system analysis codes, the current version of the SAM code has implemented such simplified 0-D and one-dimensional mixing models. On the other hand, high-resolution Computational Fluid Dynamics (CFD) tools are often used to model complex thermal mixing and stratification phenomena. They are, however, generally numerically expensive, and they require large amounts of computational resources. It is therefore desirable to implement advanced and efficient thermal mixing and stratification modeling capabilities embedded in a system analysis code. This approach will improve the accuracy of reactor safety analyses when thermal mixing and stratification are involved, and also avoid using the large computational resources needed for high-resolution CFD analysis. Currently, with the support of the U.S. DOE Office of Nuclear Energy’s Nuclear Energy Advanced Modeling and Simulation program, an effort has been launched to develop and implement a multi-dimensional flow model in the system analysis code SAM, and demonstrate its applications to model thermal mixing and stratification phenomena in large enclosures. The main outcomes of this research and development activity are summarized in this report, which presents an attempt to include a built-in advanced multi-dimensional flow model in a system analysis code with the focus on overcoming the simulation challenges of thermal mixing and stratification phenomena. In this report, we start with the introduction of existing SAM code capabilities to simulate thermal mixing and stratification phenomena, which is followed by a short summary of the multi-dimensional model implemented in the SAM code, including both the physical model and the Finite Element Method code implementation. In this study, two options were implemented in the SAM code to model turbulent flows: a relatively simple built-in turbulence model and an interface to accept externally computed turbulent viscosities (e.g., from a high- resolution CFD simulation). Code validation studies on this newly added capability were then carried out to compare SAM simulation results with experimental data from the SUPERCAVNA facility, which was designed to study the complex flow recirculation and thermal stratification phenomena relevant to sodium fast reactor designs. In this study, one transient and two steady-state test cases were used for code validation. Different approaches have been used to model the complex turbulence flow fields in the SUPERCAVNA facility. A highly simplified zero-equation turbulence model was first used, but it was determined that it is too simple to capture the complex turbulence flow fields in these test cases. Subsequently, the code validation continued with the use of turbulent viscosity data from high-resolution STAR-CCM+ CFD simulations to improve the accuracy of the results. Using this approach, the SAM simulation results showed very good agreement with both the SUPERCAVNA experimental data and STAR-CCM+ simulation results. In this report, we demonstrate the development, implementation, and successful validation of a multi-dimensional flow model in the SAM code, which aims to improve the simulation accuracy for complex thermal mixing and stratification phenomena. Lessons have also been learned, including that in cases where the flow fields are not well predicted by the zero-equation model, the iinput of turbulent viscosities from an external source can enhance the overall predictive capabilities needed to accurately capture complex thermal-hydraulic phenomena. Therefore, future research will be needed to further improve the code’s capabilities, such as by developing a more efficient and robust approach to capture the turbulence effects in the SAM code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

H2Fills™ [SWR-19-67]

H2FillS: Hydrogen Filling Simulation The Hydrogen Filling Simulation (H2FillS) software is a thermodynamic model designed to track and report on the transient change in hydrogen temperature, pressure, and mass flow when filling a fuel cell electric vehicle (FCEV). H2FillS simulates gas flow from the hydrogen station to the FCEV storage system. Using empirical fueling data sets, the model has been validated over a range of fueling conditions to match common light-duty FCEV fill profiles. Overall, it provides significant benefits to the light-duty fueling market and fill knowledge gaps of the interaction between a hydrogen station and an FCEV. Capabilities Use the comprehensive H2FillS model to: Safely design and operate a hydrogen fueling station Support code refinement with readily available data Develop system and operational improvements to reduce capital or operating costs at hydrogen stations or on-board FCEVs. How It Works H2FillS uses a "drag-and-drop" graphical user interface to simulate station and vehicle systems with preset parameters for common hydrogen station components as default values. When needed, users can define their own parameters to create their own station or vehicle components. Download H2FillS in Two Steps Read the terms of the end user software license agreement. Register to download the software. There are two versions of the model: A full-station model starts the simulation at high-pressure ground storage, runs through a dispenser, and ends at a vehicle storage system. A partial-station model starts at the dispenser breakaway and consists solely of the dispenser components and the vehicle storage system. H2FillS will automatically output fill performance data from the vehicle by tracking pressure and temperature throughout the fill. Users can input their own fill profiles into the model to run a variety of simulations.

Peters, Michael↗
DOE OSTI2025

PSA 2025 Presentation: "Modeling and Sensitivity Analysis of a Generation IV Pebble Bed Reactor Using MELCOR 2.2"

Accompanying the advancement of reactor technologies is the need for computational modeling and simulation to predict their behavior under normal operating conditions and accident scenarios. New Generation IV reactor designs which employ non-conventional fuel have a particular need for modeling the behavior and release of radionuclides and other material from the fuel. In this work, MELCOR version 2.2, a system-level safety and accident scenario code developed by Sandia National Laboratories, was used to model a 200-MWth pebble bed modular reactor and calculate the inventories of circulating and deposited graphite, metal dust, and elemental components released from the fuel elements. A base case modeling the reactor under standard operating conditions was calculated using MELCOR and the inventories were extrapolated to 30 years of operation time using a logarithmic regression fit. A sensitivity analysis was also performed in which several key parameters for the base case model were modified to explore the effect of these changes on the inventories calculated by MELCOR. A set of transient scenario simulations for a depressurized loss of forced cooling (DLOFC) accident were also performed. The results of the sensitivity analysis and transient simulations are reported and discussed in relation to the modeling techniques used for this study.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Coupled Neutronics - Thermal-Hydraulics - Thermomechanics modeling of Molten Chloride Reactor

Molten Salt Reactors (MSRs) are innovative Generation-IV nuclear reactors known for their enhanced safety features, fuel economy, and fuel cycle advancements. However, designing and studying these reactors present unique safety and modeling challenges. The Department of Energy (DOE)'s Nuclear Energy Advanced Modeling and Simulation program addresses these challenges through the development of flexible multi-fidelity, multiphysics simulation tools. This study exemplifies NEAMS tools' capabilities by modeling a chloride-based micro-MSR: Griffin for neutronics simulations, Pronghorn for thermal-hydraulics simulations, and BISON for thermomechanics simulations. These tools are integrated into a comprehensive 3D multiphysics model, successfully capturing steady-state and transient reactor operations. A loss of flow accident is examined as an illustrative example of transient behavior. Additionally, this paper contributes to demonstrating the theoretical feasibility of micro-MSR concepts, which have received less attention compared to other designs. The subsequent sections provide a brief description of the MSR concept studied, an overview of the computational tools used, and present results for both steady-state operation and a loss of flow accident.

42 - ENGINEERING↗
DOE OSTI2023

Slow dynamics in a single bead with mechanical conditioning and transient heating

The contact stiffness of an aluminum bead confined between two slabs diminishes upon mechanical conditioning, and then recovers like log(t) after the conditioning ceases. Here that structure is evaluated for its response to transient heating and cooling, with and without accompanying conditioning vibrations. It is found that, under heating or cooling alone, stiffness changes are mostly consistent with temperature dependent material moduli; there is little or no slow dynamics. Hybrid tests in which vibration conditioning is followed by heating or cooling lead to recoveries that begin like log(t) and then become more complex. On subtracting the known response to heating or cooling alone we discern the influence of higher or lower temperatures on slow dynamic recovery from vibrations. It is found that heating accelerates the initial log(t) recovery, but by an amount more than predicted by an Arrhenius model of thermally activated barrier penetrations. Furthermore, transient cooling has no discernable effect, in contrast to the Arrhenius prediction that it slows recovery.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Increased bundle‐sheath leakiness of CO 2 during photosynthetic induction shows a lack of coordination between the C 4 and C 3 cycles

Summary Use of a complete dynamic model of NADP‐malic enzyme C 4 photosynthesis indicated that, during transitions from dark or shade to high light, induction of the C 4 pathway was more rapid than that of C 3 , resulting in a predicted transient increase in bundle‐sheath CO 2 leakiness ( ϕ ). Previously, ϕ has been measured at steady state; here we developed a new method, coupling a tunable diode laser absorption spectroscope with a gas‐exchange system to track ϕ in sorghum and maize through the nonsteady‐state condition of photosynthetic induction. In both species, ϕ showed a transient increase to > 0.35 before declining to a steady state of 0.2 by 1500 s after illumination. Average ϕ was 60% higher than at steady state over the first 600 s of induction and 30% higher over the first 1500 s. The transient increase in ϕ , which was consistent with model prediction, indicated that capacity to assimilate CO 2 into the C 3 cycle in the bundle sheath failed to keep pace with the rate of dicarboxylate delivery by the C 4 cycle. Because nonsteady‐state light conditions are the norm in field canopies, the results suggest that ϕ in these major crops in the field is significantly higher and energy conversion efficiency lower than previous measured values under steady‐state conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics

We address a three-tier numerical framework based on nonlinear manifold learning for the forecasting of high-dimensional time series, relaxing the “curse of dimensionality” related to the training phase of surrogate/machine learning models. At the first step, we embed the high-dimensional time series into a reduced low-dimensional space using nonlinear manifold learning (local linear embedding and parsimonious diffusion maps). Then, we construct reduced-order surrogate models on the manifold (here, for our illustrations, we used multivariate autoregressive and Gaussian process regression models) to forecast the embedded dynamics. Finally, we solve the pre-image problem, thus lifting the embedded time series back to the original high-dimensional space using radial basis function interpolation and geometric harmonics. The proposed numerical data-driven scheme can also be applied as a reduced-order model procedure for the numerical solution/propagation of the (transient) dynamics of partial differential equations (PDEs). In conclusion, we assess the performance of the proposed scheme via three different families of problems: (a) the forecasting of synthetic time series generated by three simplistic linear and weakly nonlinear stochastic models resembling electroencephalography signals, (b) the prediction/propagation of the solution profiles of a linear parabolic PDE and the Brusselator model (a set of two nonlinear parabolic PDEs), and (c) the forecasting of a real-world data set containing daily time series of ten key foreign exchange rates spanning the time period 3 September 2001–29 October 2020.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Transient Optimization of the Cryogenic Moderator System Controller at the Spallation Neutron Source for Improved Performance

The high-energy neutron beam generated at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory is moderated to use cold (slow) neutrons for scientific discoveries. The Cryogenic Moderator System (CMS) removes heat from the neutron beam using cryogenic hydrogen (H 2 ) moderators connected via heat exchangers to a helium (He) refrigeration loop that dissipates heat using a compressor-brake system. However, the CMS is affected by sporadic losses in beam power, referred to as "beam trips," as these events generate significant disturbances in cooling requirements. To accommodate the heat load transients during beam trips, the CMS uses a decentralized control strategy consisting of four flow valves and one electric heater adjusted by independent proportional-integral (PI) controllers. During the CMS’s initial commissioning, the PI gains were calibrated based only on tracking performance, overlooking their effectiveness in disturbance rejection. A data-driven, control-oriented closed-loop model was developed to recalibrate the PI gains and minimize the transient disturbances caused by beam trips. The model consists of three main components: (1) a physics-based model of the He refrigeration loop, (2) a machine-learning model of the cryogenic H 2 cooling trains, and (3) the control logic used for feedback set-point tracking. Experimental results showed that the recalibrated gains obtained in this study improved the CMS’s transient response during beam trips.

Maldonado Puente, Bryan↗
DOE OSTI2024

Automated Calibration for Rapid Optical Spectroscopy Sensor Development for Online Monitoring

An automated platform has been developed to assist researchers in the rapid development of optical spectroscopy sensors to quantify species from spectral data. This platform performs calibration and validation measurements simultaneously. Real-time, in situ monitoring of complex systems through optical spectroscopy has been shown to be a useful tool; however, building calibration models requires development time, which can be a limiting factor in the case of radiological or otherwise hazardous systems. While calibration time can be reduced through optimized design of experiments, this study approached the challenge differently through automation. The ATLAS (Automated Transient Learning for Applied Sensors) platform used pneumatic control of stock solutions to cycle flow profiles through desired calibration concentrations for multivariate model construction. Additionally, the transients between desired concentrations based on flow calculations were used as validation measurements to understand model predictive capabilities. This automated approach yielded an incredible 76% reduction in model development time and a 60% reduction in sample volume versus estimated manual sample preparation and static measurements. The ATLAS system was demonstrated on two systems: a three-lanthanide system with Pr/Nd/Ho representing a use case with significant overlap or interference between analyte signatures and an alternate system containing Pr/Nd/Ni to demonstrate a use case in which broad-band corrosion species signatures interfered with more distinct lanthanide absorbance profiles. Both systems resulted in strong model prediction performance (RMSEP < 9%). Lastly, ATLAS was demonstrated as a tool to simulate process monitoring scenarios (e.g., column separation) in which models can be further optimized to account for day-to-day changes as necessary (e.g., baseline correction). Ultimately, ATLAS offers a vital tool to rapidly screen monitoring methods, investigate sensor fusion, and explore more complex systems (i.e., larger numbers of species).

47 OTHER INSTRUMENTATION↗