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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development of a Discrepancy Checker for the Digital Twin in a Supervisory Control System for a Thermal Energy Delivery System

Defined as a virtual representation of a physical object, process, or service, and used to support real-world decision-making, a digital twin (DT) can be utilized to combine classical and novel frameworks in sensors, state predictions, and multi-input/multi-output systems, and to enable optimal autonomous operations. However, a DT’s usefulness largely depends on its ability to adequately mirror the state of its physical counterpart, and this adequacy should be reflected by the level of uncertainty in the underlying simulation models when estimating and predicting quantities of interest (QOIs). Moreover, simulation models in a DT may involve multiple fidelities of representations—ranging from physics-based models to data-driven ones—but classical uncertainty quantification (UQ) methods struggle to handle numerous uncertainty sources, nor are they designed for real-time applications. This work presents a UQ-based discrepancy checking and diagnosis tool for a DT-based supervisory control system applied to a thermal energy delivery system (TEDS) at Idaho National Laboratory. The discrepancy checker was developed using metadata from an automated DT development process, and these metadata included different combinations of physical model forms and model parameters, training data and hyperparameters for surrogate models, and design parameters for supervisory control systems. Next, correlations between the uncertainty results and the metadata were established and then applied to the DT operations. The discrepancy checker evaluates the discrepancies between model predictions from virtual and sensor measurements and backtraces them to the corresponding major sources of uncertainty. The discrepancy checker showed reasonable performance in detecting discrepancies and diagnosing sources of uncertainty in testing scenarios.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Spatially dependent cluster dynamics model of He plasma surface interaction in tungsten for fusion relevant conditions

In fusion reactors, plasma facing components (PFC) and, in particular, the divertor will be irradiated with high fluxes of low-energy (similar to 100 eV) helium and hydrogen ions. Tungsten is one of the leading candidate divertor materials for ITER and DEMO fusion reactors. However, the behaviour of tungsten under high dose, coupled helium/hydrogen exposure remains to be fully understood. The PFC response and performance changes are intimately related to microstructural changes, such as the formation of point defect clusters, helium and hydrogen bubbles or dislocation loops. Computational materials' modelling results are described here that investigate the mechanisms controlling microstructural evolution in tungsten. The aim of this study is to understand and predict sub-surface helium bubble growth under high flux helium ion implantation (similar to 10 22 m -2 s -1 ) at high temperatures (>1000 K). Here, we report results from a spatially dependent cluster dynamics model based on reaction-diffusion rate theory to describe the evolution of the microstructure under these conditions. The key input parameters to the model (diffusion coefficients, migration and binding energies, initial defect production) are determined from a combination of atomistic modelling and available experimental data. The results are in good agreement with results of an analytical model that is presented in a separate paper. In particular, it is found that the sub-surface evolution with respect to bubble size and concentration of the helium bubbles strongly depends on the flux and temperature.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

SISGR: Chemomechanics of Far-From-Equilibrium Interfaces (COFFEI)

Portable, reliable, and deployable devices for energy storage and conversion require fundamental changes in design of solid-state composites comprising ceramics and metals. These materials comprise the electrodes and electrolytes of next-generation solid-oxide fuel cells and all solid-state batteries, forming solid-state functional composites. The advent of solid-state batteries – which replace liquid electrolytes with solid electrolytes capable of lithium ion transport for reliable energy storage in portable batteries – and the increased demand space for all solid-state fuel cells capable of oxygen reduction at intermediate temperatures remain important challenges for improved material stability and decreased system cost. However, little is understood about three fundamental facets of materials that enable such solid-state energy applications. First, how do such materials deform, fracture, or delaminate under operando conditions? Second, how does such mechanical deformation limit or facilitate electronic and ionic transport within and across such material interfaces? Third, how we can predictably design interface-rich composites to engineer both structural and electrochemical stability? This COFFEI Group comprised expertise from Materials Science & Engineering and Nuclear Science & Engineering to integrate unique in situ experiments, simulations, and fabricated interfaces that address these fundamental questions in solid-state interfaces of nanoscale composites that will guide solid-state electrochemistry, transport kinetics, and mechanical deformation for nonstoichiometric materials that enable such applications. In particular, we built on COFFEI’s understanding of chemomechanical coupling among defect concentrations, ionic transport, electron transport, and stored elastic energy that is particularly acute in the far-from-equilibrium conditions typical of energy device applications. By tailoring our focus to solid-state interfaces, we addressed these important issues by (a) developing and applying advanced in situ and ex situ characterization tools to characterize model materials and interfaces synthesized with molecular-level control, under both laboratory-controlled and extreme environments representative of energy device operation; and (b) employing computational modeling and simulation frameworks to predict transport mechanisms, reactivity and stability of these model materials and interfaces under significant chemical strains typical of energy device operation. Recent progress provided insights to additional materials systems and electrochemomechanical fatigue and fracture that were not fully envisioned when the program was initiated. Specifically, in the final three years of COFFEI we pursued two integrated thrusts, with complementary focus. Thrust I focused on failure-resistant electrochemomechanical composites, while Thrust II focused on strain-modulated conductivity and reactivity across interfaces. In contrast to our initial COFFEI focus, these thrusts concentrated wholly on solid-state material interfacial interactions and included greater integration of multiscale visualization including in situ electron microscopy of strained structures/interactions and mesoscale simulations. Successful development of functionally superior and long-lived battery and fuel cell systems and stress adaptable oxides requires a deeper, fundamental understanding of the coupling among the historically important subfields of solid-state electrochemistry, transport kinetics, and mechanical deformation for nonstoichiometric metal oxide electrodes. In this program, the understanding and the application of chemomechanical coupling of defect concentrations, ionic transport, electro-catalytic activity and stored elastic energy, particularly acute in the far-from-equilibrium conditions typical of energy device applications, are being refined and implications for device operation clarified, including for miniaturized solid-state batteries and fuel cells.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Quantify Plasma Response to Non-Axisymmetric (3D) Magnetic Fields in Tokamaks, Final Report for FES (Fusion Energy Sciences) FY2014 Joint Research Target

The goal of the 2014 Joint Research Target (JRT) has been to conduct experiments and analysis to investigate and quantify the response of tokamak plasmas to non-axisymmetric (3D) magnetic fields. Although tokamaks are conceptually axisymmetric devices, small asymmetries often result from inaccuracies in the manufacture and assembly of the magnet coils, or from nearby magnetized objects. In addition, non-axisymmetric fields may be deliberately applied for various purposes. Even at small amplitudes of order 10 -4 of the main axisymmetric field, such “3D” fields can have profound impacts on the plasma performance. The effects are often detrimental (reduction of stabilizing plasma rotation, degradation of energy confinement, localized heat flux to the divertor, or excitation of instabilities) but may in some case be beneficial (maintenance of rotation, or suppression of instabilities). In general, the magnetic response of the plasma alters the 3D field, so that the magnetic field configuration within the plasma is not simply the sum of the external 3D field and the original axisymmetric field. Typically the plasma response consists of a mixture of local screening of the external field by currents induced at resonant surfaces in the plasma, and amplification of the external field by stable kink modes. Thus, validated magnetohydrodynamic (MHD) models of the plasma response to 3D fields are crucial to the interpretation of existing experiments and the prediction of plasma performance in future devices. The non-axisymmetric coil sets available at each facility allow well-controlled studies of the response to external 3D fields. The work performed in support of the 2014 Joint Research Target has included joint modeling and analysis of existing experimental data, and collaboration on new experiments designed to address the goals of the JRT. A major focus of the work was validation of numerical models through quantitative comparison to experimental data, in order to gain confidence in their ability to predict the performance of ITER and other burning plasmas. An important element of this effort was also to verify experimentally the limits of validity of the models. The rest of this overview will summarize the key results of the work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2011

Lyapunov exponents and phase diagrams reveal multi-factorial control over TRAIL-induced apoptosis

Receptor-mediated apoptosis proceeds via two pathways: one requiring only a cascade of initiator and effector caspases (type I behavior) and the second requiring an initiator–effector caspase cascade and mitochondrial outer membrane permeabilization (type II behavior). Here, we investigate factors controlling type I versus II phenotypes by performing Lyapunov exponent analysis of an ODE-based model of cell death. The resulting phase diagrams predict that the ratio of XIAP to pro-caspase-3 concentrations plays a key regulatory role: type I behavior predominates when the ratio is low and type II behavior when the ratio is high. Cell-to-cell variability in phenotype is observed when the ratio is close to the type I versus II boundary. By positioning multiple tumor cell lines on the phase diagram we confirm these predictions. We also extend phase space analysis to mutations affecting the rate of caspase-3 ubiquitylation by XIAP, predicting and showing that such mutations abolish all-or-none control over activation of effector caspases. Thus, phase diagrams derived from Lyapunov exponent analysis represent a means to study multi-factorial control over a complex biochemical pathway.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Adaptive Machine Learning for Robust Diagnostics and Control of Time-Varying Particle Accelerator Components and Beams

Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Assessing Impacts of Plant Stoichiometric Traits on Terrestrial Ecosystem Carbon Accumulation Using the E3SM Land Model

Carbon (C) enters into the terrestrial ecosystems via photosynthesis and cycles through the system together with other essential nutrients (i.e., nitrogen [N] and phosphorus [P]). Such a strong coupling of C, N, and P leads to the theoretical prediction that limited nutrient availability will limit photosynthesis rate, plant growth, and future terrestrial C dynamics. However, the lack of reliable information about plant tissue stoichiometric constraints remains a challenge for quantifying nutrient limitations on projected global C cycling. In this study, we harmonized observed plant tissue C:N:P stoichiometry from more than 6,000 plant species with the commonly used plant functional type framework in global land models. Using observed C:N:P stoichiometry and the flexibility of these ratios as emergent plant traits, we show that observationally constrained fixed plant stoichiometry does not improve model estimates of present-day C dynamics compared with unconstrained stoichiometry. However, adopting stoichiometric flexibility significantly improves model predictions of C fluxes and stocks. The 21st century simulations with RCP8.5 CO 2 concentrations show that stoichiometric flexibility, rather than baseline stoichiometric ratios, is the dominant controller of plant productivity and ecosystem C accumulation in modeled responses to CO 2 fertilization. The enhanced nutrient limitations and plant P use efficiency mainly explain this result. This study is consistent with the previous consensus that nutrient availability will limit xfuture land carbon sequestration but challenges the idea that imbalances between C and nutrient supplies and fixed stoichiometry limit future land C sinks. We show here that it is necessary to represent nutrient stoichiometric flexibility in models to accurately project future terrestrial ecosystem carbon sequestration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Multi-physics modeling of grain growth during solidification in electron beam additive manufacturing of Inconel 718

While experimental work has shown promising results regarding control of additive manufacturing metal grain structure, the effects of processing parameters on the grain structure is difficult to understand and predict from experiment alone. To this end, a modeling framework is developed which sequentially couples a macro-scale, semi-analytic thermal model, and a meso-scale, cellular automata-based microstructure model. This framework is applied to electron beam additive manufacturing of Inconel 718 using a complex spot scan pattern. The model shows that, with the same scan pattern, variations in the spot time and electron-beam current produce thermal histories with significant spatial and temporal differences, which then produce complex solidification conditions from the interplay between molten pools in the same layer and subsequent layers, resulting in vastly different grain structures. Additionally, it is noted that the framework can significantly reduce the computational expenses for coupled thermal-metallurgical problems, and has the potential to be used for component level problems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Digital twin framework for PIP-II linac: AI-driven multi-scale modeling from ion source to 800 MeV

The PIP-II superconducting linac at Fermilab is designed to deliver multi-megawatt proton beams for neutrino physics and other high-intensity applications. To expedite commissioning and enhance operational reliability, we have developed an EPICS-based data flow framework that seamlessly integrates digital twins (DT) with physical twins (PT). These digital twins comprise high-fidelity beam dynamics models or data-driven surrogate models connected to their physical counterparts through real-time diagnostics and advanced machine-learning algorithms.Central to this framework is Linac_Gen, an accelerated simulation tool that incorporates convolutional neural networks, random forests, and genetic algorithms to provide up to a tenfold speedup in optimizing the accelerator geometry model. An EPICS translator layer ensures interoperability by efficiently mapping lattice parameters across diverse simulation platforms.Our EPICS-based framework supports multiple operational modes—monitoring, passive learning, closed-loop control, and online learning—covering the entire machine lifecycle. By leveraging HPC resources and multi-objective optimization techniques, the digital twin enables adaptive trajectory correction, real-time fault detection, and predictive modeling of beam stability. This comprehensive approach paves the way for robust, high-intensity operation and data-driven accelerator R&D at Fermilab.

Pathak, Abhishek [Fermilab]↗
DOE OSTI2022

Evaluation of PV Module Packaging Strategies of Monofacial and Bifacial PERC Using Degradation Pathway Network Modeling

As the PV industry is rapidly expanding, it is important to thoroughly investigate the long-term impact of packaging strategies on the performance of PV modules. In this study, the variants in sets differ on the basis of manufacturer (A/B), encapsulant (EVA/POE), rear encapsulant (UV-cutoff/opaque/transparent), module architecture (GB/DG) and cell type (monofacial/bifacial). The minimodules were exposed for 2520 hours in modified damp heat, with or without full spectrum light. Every 504 hours, stepwise electrical characterization techniques were employed to track changes in minimodules. Degradation pathway modeling using network structural equation modeling was employed to study pairwise relationships between variables and service lifetime prediction in minimodules. Through this study, differences in quality control are identified in minimodules made by different manufacturers. Minimodules with UV-cutoff rear encapsulant show relatively better stability, whereas the ones with opaque rear encapsulant show greater power loss. In addition, GB having UV-cutoff rear encapsulation and GB with POE having opaque rear encapsulation were identified to be stable as they lack a best model fit. Here, the primary power loss mechanism in degrading variants is interconnect corrosion.

14 SOLAR ENERGY↗
DOE OSTI2020

Improved Predictability of the Indian Ocean Dipole Using a Stochastic Dynamical Model Compared to the North American Multimodel Ensemble Forecast

This study assesses the predictive skill of eight North American Multimodel Ensemble (NMME) models in predicting the Indian Ocean dipole (IOD). We find that the forecasted ensemble-mean IOD–El Niño–Southern Oscillation (ENSO) relationship deteriorates away from the observed relationship with increasing lead time, which might be one reason that limits the IOD predictive skill in coupled models. We are able to improve the IOD predictive skill using a recently developed stochastic dynamical model (SDM) forced by forecasted ENSO conditions. The results are consistent with the previous result that operational IOD predictability beyond persistence at lead times beyond one season is mostly controlled by ENSO predictability and the signal-to-noise ratio of the Indo-Pacific climate system. The multimodel ensemble (MME) investigated here is found to be of superior skill compared to each individual model at most lead times. Importantly, the skill of the SDM IOD predictions forced with forecasted ENSO conditions were either similar or better than those of the MME IOD forecasts. Moreover, the SDM forced with observed ENSO conditions exhibits significantly higher IOD prediction skill than the MME at longer lead times, suggesting the large potential skill increase that could be achieved by improving operational ENSO forecasts. We find that both cold and warm biases of the predicted Niño-3.4 index may cause false alarms of negative and positive IOD events, respectively, in NMME models. Many false alarms for IOD forecasts at lead times longer than one season in the original forecasts disappear or are significantly reduced in the SDM forced by forecasted ENSO conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

An Overview of the EGS Collab Project: Field Validation of Coupled Process Modeling of Fracturing and Fluid Flow at the Sanford Underground Research Facility, Lead, SD

Enhanced Geothermal Systems (EGS) development will require an ability to accurately predict the flow rates and temperatures of the production wells. While simple in concept, complex heterogeneous fracture pathways can lead to channeling, short-circuiting, and premature thermal breakthrough complicating EGS. The EGS Collab project will establish a suite of intermediate-scale (~10-20 m) field test beds coupled with stimulation and interwell flow tests to provide a basis to better understand the fracture geometries and processes that control heat transfer between rock and stimulated fractures. These experiments will provide a means of testing tools, codes, and concepts that could later be employed under geothermal reservoir conditions at FORGE and in EGS. We will perform well-controlled, in situ experiments focused on rock fracture behavior and permeability enhancement. Pre- and post-test modeling of each test will allow for model prediction and validation. Comprehensive instrumentation will be used to collect high-quality, high-resolution geophysical and fracture characterization and fluid flow data. ,These data will be analyzed and compared with models and field observations to further elucidate the basic relationships between stress, induced seismicity, and permeability enhancement. To the maximum extent achievable, we will observe and quantify other key governing parameters that impact permeability, and attempt to understand how these parameters might change throughout the development and operation of an EGS project with the goal of enabling commercial viability of EGS. The Sanford Underground Research Facility (SURF) in South Dakota was selected as the EGS Collab project experimental site based on the evaluation of information from several sites. Our team has designed and constructed the first field experiment planned for this project, which is supported by the US Department of Energy’s Geothermal Technologies Office.

EGS, FORGE, field fracture stimulation and flow ex↗
DOE OSTI2016

WE-AB-202-11: Radiobiological Modeling of Tumor Response During Radiotherapy Based On Pre-Treatment Dynamic PET Imaging Data

Purpose: To evaluate the ability of a multiscale radiobiological model of tumor response to predict mid-treatment hypoxia images, based on pretreatment imaging of perfusion and hypoxia with [18-F]FMISO dynamic PET and glucose metabolism with [18-F]FDG PET. Methods: A mechanistic tumor control probability (TCP) radiobiological model describing the interplay between tumor cell proliferation and hypoxia (Jeong et al., PMB 2013) was extended to account for intra-tumor nutrient heterogeneity, dynamic cell migration due to nutrient gradients, and stromal cells. This extended model was tested on 10 head and neck cancer patients treated with chemoradiotherapy, randomly drawn from a larger MSKCC protocol involving baseline and mid-therapy dynamic PET scans. For each voxel, initial fractions of proliferative and hypoxic tumor cells were obtained by finding an approximate solution to a system of linear equations relating cell fractions to voxel-level FDG uptake, perfusion (FMISO K{sub 1}) and hypoxia (FMISO k{sub 3}). The TCP model then predicted their evolution over time up until the mid treatment scan. Finally, the linear model was reapplied to predict each lesion’s median hypoxia level (k{sub 3}[med,sim]) which in turn was compared to the FMISO k{sub 3}[med] measured at mid-therapy. Results: The average k3[med] of the tumors in pre-treatment scans was 0.0035 min{sup −1}, with an inter-tumor standard deviation of σ[pre]=0.0034 min{sup −1}. The initial simulated k{sub 3}[med,sim] of each tumor agreed with the corresponding measurements within 0.1σ[pre]. In 7 out of 10 lesions, the mid-treatment k{sub 3}[med,sim] prediction agreed with the data within 0.3σ[pre]. The remaining cases corresponded to the most extreme relative changes in k{sub 3}[med]. Conclusion: This work presents a method to personalize the prediction of a TCP model using pre-treatment kinetic imaging data, and validates the modeling of radiotherapy response by predicting changes in median hypoxia values during treatment. Variations from predicted response may be a useful biomarker, which should be further explored. Partially supported by NIH grant #1 R01 CA157770-01A1 and a grant from Varian Corporation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Recent Approaches to Modeling Transport of Mercury in Surface Water and Groundwater - Case Study in Upper East Fork Poplar Creek, Oak Ridge, TN - 13349

In this case study, groundwater/surface water modeling was used to determine efficacy of stabilization in place with hydrologic isolation for remediation of mercury contaminated areas in the Upper East Fork Poplar Creek (UEFPC) Watershed in Oak Ridge, TN. The modeling simulates the potential for mercury in soil to contaminate groundwater above industrial use risk standards and to contribute to surface water contamination. The modeling approach is unique in that it couples watershed hydrology with the total mercury transport and provides a tool for analysis of changes in mercury load related to daily precipitation, evaporation, and runoff from storms. The model also allows for simulation of colloidal transport of total mercury in surface water. Previous models for the watershed only simulated average yearly conditions and dissolved concentrations that are not sufficient for predicting mercury flux under variable flow conditions that control colloidal transport of mercury in the watershed. The transport of mercury from groundwater to surface water from mercury sources identified from information in the Oak Ridge Environmental Information System was simulated using a watershed scale model calibrated to match observed daily creek flow, total suspended solids and mercury fluxes. Mercury sources at the former Building 81-10 area, where mercury was previously retorted, were modeled using a telescopic refined mesh with boundary conditions extracted from the watershed model. Modeling on a watershed scale indicated that only source excavation for soils/sediment in the vicinity of UEFPC had any effect on mercury flux in surface water. The simulations showed that colloidal transport contributed 85 percent of the total mercury flux leaving the UEFPC watershed under high flow conditions. Simulation of dissolved mercury transport from liquid elemental mercury and adsorbed sources in soil at former Building 81-10 indicated that dissolved concentrations are orders of magnitude below a target industrial groundwater concentration beneath the source and would not influence concentrations in surface water at Station 17. This analysis addressed only shallow concentrations in soil and the shallow groundwater flow path in soil and unconsolidated sediments to UEFPC. Other mercury sources may occur in bedrock and transport though bedrock to UEFPC may contribute to the mercury flux at Station 17. Generally mercury in the source areas adjacent to the stream and in sediment that is eroding can contribute to the flux of mercury in surface water. Because colloidally adsorbed mercury can be transported in surface water, actions that trap colloids and or hydrologically isolate surface water runoff from source areas would reduce the flux of mercury in surface water. Mercury in soil is highly adsorbed and transport in the groundwater system is very limited under porous media conditions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

N-flation with hierarchically light axions in string compactifications

We propose a possible embedding of axionic N-flation in type IIB string compactifications where most of the Kähler moduli are stabilised by perturbative effects, and so are hierarchically heavier than the corresponding N>> 1 axions whose collective dynamics drives inflation. This is achieved in the framework of the LARGE Volume Scenario for moduli stabilisation. Our set-up can be used to realise a model of either large field inflation or quintessence, just by varying the volume of the internal space which controls the scale of the axionic potential. Both cases predict a very high scale of supersymmetry breaking. A fully explicit stringy embedding of N-flation would require control over dangerous back-reaction effects due to a large number of species. A viable reheating of the Standard Model degrees of freedom can be achieved after the end of inflation due to the perturbative decay of the N light axions which drive inflation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Resolving Salt-Induced Agglomeration of Laponite Suspensions Using X-ray Photon Correlation Spectroscopy and Molecular Dynamics Simulations

Linking the physics of the relaxation behavior of viscoelastic fluids as they form arrested gel states to the underlying chemical changes is essential for developing predictive controls on the properties of the suspensions. In this study, 3 wt.% laponite suspensions are studied as model systems to probe the influence of salt-induced relaxation behavior arising from the assembly of laponite nanodisks. X-ray Photon Correlation Spectroscopy (XPCS) measurements show that laponite suspensions prepared in the presence of 5 mM concentrations of CaCl2, MgCl2 and CsCl salts accelerate the formation of arrested gel states, with CaCl2 having a significant impact followed by CsCl and MgCl2 salts. The competing effects of ion size and charge on relaxation behavior are noted. For example, the relaxation times of laponite suspensions in the presence of Mg2+ ions are slower compared to Cs+ ions despite the higher charge, suggesting that cation size dominates in this scenario. The faster relaxation behavior of laponite suspensions in the presence of Ca2+ ions compared to Cs+ ions shows that a higher charge dominates the size of the ion. The trends in relaxation behavior are consistent with the cluster formation behavior of laponite suspensions and the electrostatic interactions predicted from MD simulations. Charge balance is achieved by the intercalation of the cations at the negatively charged surfaces of laponite suspensions. These studies show that the arrested gel state of laponite suspensions is accelerated in the presence of salts, with ion sizes and charges having a competing effect on relaxation behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Synchronous Machine Governor Upgrade

Conventional generation sources play a critical role in the stability and reliability of the electrical grid, particularly as we transition towards more renewable energy sources. To understand and accurately emulate their behavior for optimizing grid operations and ensuring seamless integration with renewable technologies, it is essential to better emulate the grid- and plant-level impacts of conventional generation sources, such as natural gas (NG) driven heat recovery steam generators (HRSGs) and combustion turbines (CTs). Therefore, a governor model is developed in a programmable logic controller (PLC) to investigate the performance of the conventional generator under various dynamic operating conditions and to identify the impact on grid stability in a controlled environment. The governor model aims to enable the hardware-in-the-loop (HIL) based emulation of these conventional generation sources using the existing 2 MVA synchronous machine/generator that is driven by a flexible 2.5 MW variable speed drive. This setup will allow us to replicate the dynamic characteristics and response behaviors of NG-driven HRSGs and CTs. The controls for the emulated conventional plants follow the industry standard and are adjustable, ensuring they accurately reflect the operational capabilities and limitations of real-world systems. These controls include load-following capabilities, ramp rates, startup and shutdown sequences, and emissions characteristics. By incorporating these adjustable controls, we aim to capture the nuanced impacts of conventional generation, such as their ability to provide ancillary services like frequency regulation, voltage support, and spinning reserve. In this report, we simulate two types of dynamic operations: grid-connected and islanding. For each dynamic operation, representative starting sequences are tested, including turbine purge, ignition, speed ramping up, generator excitation and synchronizing, and breaker close. The HIL based tests provides insights for field deployment, specifically the high-fidelity governor model provides results to predict the potential stability and reliability risk and suggest possible integration measures (e.g., generation and load balancing, tuning of governor control parameters). Ultimately, this enhanced emulation capability will be integrated into our Advanced Research on Integrated Energy Systems (ARIES), enabling us to conduct comprehensive studies on the interactions between conventional and renewable energy sources. By better understanding these interactions, we can develop strategies to optimize the overall performance and reliability of the grid. This will support the deployment of advanced grid management techniques, such as demand response, grid-forming inverters, and energy storage systems. The main contributions are summarized as follows: (1) This report introduces a PLC-based governor model for gas turbines. This model accurately simulates the dynamic behavior of conventional generation sources under various operational scenarios; (2) The model is integrated with an HIL testbed that includes a 2.5 MW variable speed drive and a 2 MVA synchronous machine. This setup enables realistic, real-time emulation of conventional power plants, particularly NG driven HRSGs and CTs; (3) The developed model is adaptable to various gas turbine configurations and allows for precise control over parameters such as MW ramp rates. This flexibility makes it a valuable tool for future research and industry collaboration; and (4) By incorporating the model into the National Renewable Energy Laboratory's Advanced Research on Integrated Energy Systems, the report lays the groundwork for future studies on interactions between conventional and renewable energy sources, enhancing the ability to develop advanced grid management strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18–72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18–73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I 2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen’s d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.

59 BASIC BIOLOGICAL SCIENCES↗