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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dependence of Convective Cloud Microphysical Properties on Environmental Conditions during the TRACER and ESCAPE Field Campaigns: A Synergistic Approach of Observations, Machine Learning and Parcel Models

The sensitivity of convective clouds to aerosols and their interactions with environment, combined with limited observational constraints in parameterizations, introduces significant uncertainties in atmospheric models. Here, this study investigates the dependence of convective cloud microphysical properties on environmental conditions using a synergistic approach that combines unique observations from the TRACER and ESCAPE field campaigns, machine learning techniques, and parcel model simulations with a super-droplet microphysics scheme. A random forest algorithm identifies in-situ vertical velocity (w), temperature (T), and surface fine-mode aerosol mass concentration as the three most important environmental conditions influencing cloud properties including liquid water content (LWC), number concentration for particles with D max < 50 μm (N c ,<50), 50 μm ≤ D max ≤ 3000 μm (N c,50–3000 ), and droplet effective diameter (D e ). Results show that LWC, N c,<50 , and N c,50–3000 significantly increase with w in updrafts. Across w bins, as T decreases, LWC, D e , and N c,50–3000 increase, while N c,<50 decreases, which are closely linked to the distance above cloud bases. Warmer cloud bases yield higher LWC, greater N c,50–3000 , and smaller N c,<50 , while polluted environments produce greater N c,<50 . Parcel model simulations successfully replicate these observed dependencies. The simulation results indicate that warmer cloud bases enhance condensation generating larger droplets, and differences in droplet sizes are then amplified through collision-coalescence, resulting in a greater N c,50–3000 . Polluted conditions result in a greater N c,<50 primarily due to enhanced cloud condensation nuclei activation despite increased collision-coalescence rates compared to pristine conditions. This study provides observed quantitative patterns characterizing cloud microphysical properties as a function of key environmental parameters, offering valuable constraints for improving physics parameterizations and numerical models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Embedding condition describing shearing, dissipative collapse

Highlights: • Perturbative approach to the time-dependent Karmarkar condition in shearing collapse. • Analysis of the (in)stability of the collapsing fluid using the TOV equation. • Study of the temperature profiles using a causal heat transport equation. In this work we employ the time-dependent Karmarkar condition to model dissipative gravitational collapse in the presence of shear which leads to a final static configuration. We believe that this is a first attempt at using the time-dependent Karmarkar condition describing a radiating star with shear in which the collapsing core dissipates energy in the form of a radial heat flux. The final static configuration which is described by an embedding class I spacetime was obtained by Hansraj and Moodly who showed that this solution satisfies all the physical requirements for a static compact object. As the collapse proceeds and the heat flux tends to zero the dynamical core evolves into a static compact core with the exterior spacetime being described by the vacuum Schwarzschild solution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Extended FFT-based micromechanical formulation to consider general non-periodic boundary conditions

Here, this paper presents a new approach for applying non-periodic boundary conditions in the context of FFT-based methods to solve micromechanical problems in heterogeneous solids. The domain of the original problem is extended to satisfy the periodicity requirements at the boundary of the extended domain. The velocity constraint on the boundary of the original domain is replaced by a corresponding constraint on the velocity gradient in the extended volume, and a two-level augmented Lagrangian method is used to enforce the constraint. The proposed method is implemented as an extension of the large-strain elasto-viscoplastic FFT-based (LS-EVPFFT) model of Zecevic et al. (2022). The proposed method is verified in the cases of fully imposed velocity boundary conditions and mixed velocity/traction-free boundary conditions. The accuracy and convergence of the method are studied next, followed by applications to bending and indentation of polycrystals that illustrate the extended capabilities of the proposed formulation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Highly cascaded first-order fiber Bragg gratings in highly multimode optical fibers for distributed temperature sensing under harsh environment conditions

This study presents a pioneering technique for fabricating highly cascaded first-order fiber Bragg gratings (FBGs) using a femtosecond laser-assisted point-by-point inscription method in highly multimode optical fibers, specifically Sapphire crystalline fiber, and pure silica coreless fiber. Notably, it marks the first successful demonstration of a distributed array comprising 10 FBGs within highly multimode fibers. This achievement is facilitated by a high-power laser technique that yields larger reflectors characterized by a Gaussian intensity profile. These first-order FBGs offer various advantages, including enhanced reflectivity, reduced fabrication time, and simplified spectral characteristics, enhancing their accessibility for interpretation when contrasted with higher-order FBGs. In addition to that it encompasses a comprehensive analysis of the robustness and efficacy of these FBGs, with particular emphasis on their ability to endure extreme temperatures. These FBGs demonstrate an advantageous capability for localized multi-point temperature monitoring, reaching temperatures up to 1500°C with sapphire crystalline fiber and 1100°C with pure silica coreless fiber. This resilience makes them suitable for deployment in harsh environmental conditions. This innovative approach substantially broadens the potential applications of highly multimode optical fibers, particularly in the arena of sensing and communication, where challenges related to thermal gradients and harsh environments prevail. Furthermore, these groundbreaking first-order FBGs signify a substantial advancement in the realm of distributed temperature sensing, offering supreme capabilities for temperature monitoring and signal stability. As such, our work holds the promise of a substantial impact on industries and applications that demand unwavering reliability under extreme conditions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

MULTI-FIDELITY MODELING AND UNCERTAINTY QUANTIFICATION OF INVERTER BASED RESOURCES IN INTEGRATED T&D SYSTEMS

Uncertainty quantification plays a pivotal role in improving the accuracy and reliability of inverter operation within modern power systems that are increasingly dominated by inverter-based resources (IBRs). IBRs, especially those operating under grid forming (GFM) control, rely heavily on a complex set of control parameters and system measurements to maintain voltage, frequency, and power balance. Traditional deterministic modeling approaches often fail to capture these parameter deviations, potentially resulting in suboptimal control actions, reduced system stability, or even instability under high penetration of IBRs. In this paper, we demonstrate the application of model calibration and uncertainty quantification (UQ) principles to an integrated transmission and distribution (T&D) model involving a GFM converter and provide a framework for prioritizing control improvements, guiding robust design, and informing adaptive strategies that can accommodate real-time variability in system conditions. The proposed approach could be valuable in enhancing the robustness of current and future power systems under increased IBR penetrations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Meta Biome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities

ABSTRACT Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we presentMetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach—encompassing microenvironmental conditions, agent information, and metabolic pathways—we simulated different communities to showcase the potential of the model. Using ourin-silicoplatform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases.MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities. IMPORTANCE Our study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes inE. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

Microbiology↗
DOE OSTI2021

Upscaling Reactive Transport and Clogging in Shale Microcracks by Deep Learning

Fracture networks in shales exhibit multiscale features. A rock system may contain a few main fractures and thousands of microcracks, whose length and aperture are orders of magnitude smaller than the former. It is computationally prohibitive to resolve all the fractures explicitly for such multiscale fracture networks. One traditional approach is to model the small-scale features (e.g., microcracks in shales) as an effective medium. Although this fracture-matrix conceptualization significantly reduces the problem complexity, there are classes of physical processes that cannot be accurately upscaled by effective medium approximations, e.g., microcrack clogging during mineral reactions. Here, we employ deep learning in place of effective medium theory to upscale physical processes in small-scale features. Specifically, we consider reactive transport in a fracture-microcrack network where microcracks can be clogged by precipitation. A deep learning multiscale algorithm is developed, in which the microcracks are upscaled as a wall boundary condition of the main fractures. The wall boundary condition is constructed by recurrent neural networks, which take concentration histories as input and predict the solute transport from main fractures to microcracks. The deep learning multiscale algorithm is firstly employed in specific scenarios, then a general model is developed which can work under various conditions. The new approach is validated against fully resolved simulations and an analytical solution, providing a reliable and efficient solution for problems that cannot be upscaled by effective medium models.

58 GEOSCIENCES↗
DOE OSTI2022

Pit Stability Predictions of Additively Manufactured SS316 Surfaces Using Finite Element Analysis

Stainless steels are susceptible to localized forms of corrosion attack, such as pitting. The size and lifetime of a nucleated pit can vary, depending on a critical potential or current density criterion, which determines if the pit repassivates or continues growing. This work uses finite element method (FEM) modeling to compare the critical pit radii predicted by thermodynamic and kinetic repassivation criteria. Experimental electrochemical boundary conditions are used to capture the active pit kinetics. Geometric and environmental parameters, such as the pit shape and size (analogous to additively manufactured lack-of-fusion pores), solution concentration, and water layer thickness were considered to assess their impact on the pit repassivation criterion. The critical pit radius (the transition point from stable growth to repassivation) predicted for a hemispherical pit was larger when using the repassivation potential (E rp ) criteria, as opposed to the current density criteria (pit stability product). Including both the pit stability product and E rp into its calculations, the analytical maximum pit model predicted a critical radius two times more conservative than the FEA approach, under the conditions studied herein. The complex pits representing lack-of-fusion pores were shown to have minimal impact on the critical radius in atmospheric conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A model investigation of the impact of lower hybrid wave scattering angle on current drive profile in EAST and Alcator C-Mod

Lower hybrid current drive (LHCD) is beneficial for developing a steady-state operation scenario in a tokamak. This paper conducts a modeling investigation to identify an optimum rotation angle of the initial lower hybrid perpendicular (to the background magnetic field $\overrightarrow{B}$) wavevector for best matching the experimental RF current profile. It is hypothesized that central RF power deposition widely observed in the present-day LHCD experiments arises from wave scattering by turbulence. In a standard model without considering such interactions, the predicted power deposition profile is generally broad with off-axis peaking, not in agreement with experimental observations. A heuristic approach is adopted by introducing a spectral broadening mechanism by modifying the initial orientation of the perpendicular wavevector. The ray-tracing/Fokker–Planck solver GENRAY/CQL3D is utilized within the python-based π-scope framework. A focus is given to identify the perpendicular wavenumber orientation angle with respect to the magnetic surface normal vector at the initial ray location. Our modeling study shows that rotating the perpendicular wavevector in such a way as to increase the initial poloidal component is effective in reproducing the centrally peaked current profile observed in normal shear plasmas on both EAST and C-Mod. These waves can readily be absorbed to the central plasma, which reduces the sensitivity of the power deposition profile to a slight change of the plasma condition. The same approach is also found to help broaden the off-axis power deposition profile in a reverse-shear EAST plasma, leading to a better agreement with the experiment. The results presented here suggest that spectral modification arising from edge density fluctuations in a tokamak may need to be considered in understanding wave propagation and absorption. A further experimental and theoretical/modeling study is vital as a reverse approach is adopted in this study. Finally, our work suggests that mitigation or control measures are critical for parasitic effects occurring on the first pass in a reactor regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Energy Evaluation of Electron Beam Treatment of Perfluoroalkyl Substances in Water: A Critical Review

Due to their recalcitrant nature and ubiquitous use, per- and polyfluoroalkyl substances (PFAS) will continue to be major water treatment hurdles. Although effective water treatment technologies exist for physical removal of many PFAS from water (e.g., activated carbon and ion-exchange resin), a PFAS-concentrated waste stream is generated as an end product that can potentially reintroduce PFAS back into the environment. Thus, there is an increased interest in developing destructive technologies to decompose and mineralize PFAS directly in water or in these waste streams. High energy electron beam (e-beam) accelerators have been used for water treatment to degrade a wide range of recalcitrant contaminants, including PFAS, since the 1960s. However, large-scale applications of e-beam for water treatment are restricted due to its high energy consumption and inability to treat large flow rates. Considering there are very few available technologies for destructive removal of PFAS, this study provides a critical review on the treatment of PFAS by direct irradiation of contaminated water by e-beam from an energy consumption point of view. To date, very limited studies have been conducted to investigate the success of this technology to treat PFAS. Results from the limited studies were not directly comparable due to the variation in operating conditions and water quality parameters used in the studies. Here, for the first time, we develop and apply the concept of electrical energy per order (EE/O) to assess the performance of e-beam for PFAS treatment. Results show that EE/O is a better performance parameter than the G value of e-beam for interstudy comparisons and to evaluate the effects of water quality and operating parameters on e-beam performance. We additionally developed a kinetic scheme to predict the performance of e-beam to treat PFAS and revealed that the competition between species to react with aqueous electrons is the determinant factor influencing PFAS degradation efficiency. Comparison of EE/O values of e-beam (range: 31–176 kWh m –3 order –1 ) with other destructive technologies (range: 5–9595 kWh m –3 order –1 ) suggest that e-beam, for PFAS treatment, is a promising approach under favorable conditions. Furthermore, this review further elucidates the feasibility and limitations of e-beam technology that could be improved upon to potentially make e-beam viable for large-scale water treatment applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Multi-Segment Decentralized Control Strategies for Renewables-Rich Microgrids in Extreme Conditions

Microgrids provide a promising approach to accommodating various distributed energy resources (DERs), while requiring significant communication infrastructures that may be affected by extreme conditions such as natural disasters and cyber-attacks. In this paper, a fully decentralized control strategy without the need for communication is proposed for islanded microgrids with high renewables penetration. First, special multi-segment power/frequency characteristic curves are designed, so that different DERs can be automatically coordinated in a prioritized manner such as renewables first to maintain power balance while DER frequencies are regulated at their reference values. Second, piecewise linear served load versus frequency models are designed to prioritize loads according to their significance, so that only noncritical loads will be curtailed as needed while critical loads are supplied without any interruptions during power deficiency. The proposed strategy can effectively deal with various normal and extreme system conditions including 100% renewables penetration, loads and renewables variations, power deficiencies requiring load curtailments, disconnection of existing DERs, connection of new DERs as well as network sectionalization and reconfiguration. As a result, the proposed control strategy is validated in the real-time digital simulator (RTDS) model of the IIT Campus Microgrid to demonstrate its effectiveness in enhancing the resiliency of renewables-rich microgrids in extreme conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Application of electron beam technology to decompose persistent emerging drinking water contaminants: poly- and perfluoroalkyl substances (PFAS) and 1,4-Dioxane

Poly- and perfluoroalkyl substances (PFAS) and 1,4-dioxane are persistent emerging contaminants that are currently under consideration for federal and state-specific regulations in drinking water. Both PFAS and 1,4-dioxane are highly resistant to degradation and are not effectively removed by conventional drinking water treatment systems. Results from the Unregulated Contaminant Monitoring Rule 3 survey showed that >540 sites across the nation are contaminated with both PFAS and 1,4-dioxane. Hence, there is a need to identify technologies that can effectively remove both these contaminants. Water treatment via electron beam (e-beam) has been proven effective at treating a wide range of contaminants, including perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), polychlorinated biphenyls, and trichloroethylene. While the e-beam process is often considered similar to advanced oxidation processes (AOPs), e-beam technology is unique in that it produces both highly oxidizing and reducing species at the same time. The specific objectives of this study were to: (i) determine the effectiveness of 9 MeV electrons provided by the Fermilab’s Accelerator Application Development and Demonstration (A2D2) tool to decompose PFAS and 1,4-dioxane; (ii) assess the formation of byproducts during water treatment; (iii) apply the optimized treatment to field groundwater samples contaminated with PFAS and 1,4-dioxane, and (iv) assess the energy demands for the treatment of these contaminants using e-beam. Results from this study showed that e-beam is effective in treating both 1,4-dioxane and PFAS. Complete degradation of 1,4-dioxane was observed at a dose of 5 kGy for an initial concentration of up to 1 ppm without the need for any sample modification. The electrical energy per order (EEo) for treatment of 1,4-dioxane ranged from 0.46 to 0.72 kWh/m 3 /order and was comparable and even lower, in some cases, than other AOP technologies. Alkaline conditions (pH 13) and low dissolved oxygen concentration (2 mg/L) highly favored the treatment of PFAS by e-beam. Greater than 90% removal of PFOA and PFOS from an initial concentration of 100 to 500 ppb was achieved at a dose of 250 kGy and 500 kGy, respectively, under optimized conditions. The degradation efficiency was not significantly changed when treating other PFAS of fluorinated carbon chain length of 5 to 7 individually at 250 kGy with a removal ranging from 85¬–99% for different compounds. Short chain PFAS (perfluorobutanoate: PFBA and perfluorobutane sulfonate: PFBS) did not degrade under the same conditions at 250 kGy, but 70 to 99% degradation was observed at a higher dose of 1000 kGy. Short chain PFAS (perfluorohexanoate: PFHxA (C5) and perfluoroheptanoate: PFHpA (C6)) were detected after treatment of PFOA, but not after PFOS treatment. Inability to close the mass balance through targeted analysis suggests the presence of other intermediates not detectable by available analytical methods. When treating PFAS mixture containing ten compounds at equimolar concentration of 0.05 µM each, preferential degradation of polyfluorinated compound (6:2 fluorotelomer sulfonate or 6:2 FTS) followed by C8 and C7 compounds was observed as a function of increasing e-beam dose. About 30% degradation of ΣPFAS was observed at 250 kGy and no further removal was observed up to a dose of 1000 kGy. C4 to C6 PFASs showed no degradation, while C3 PFAS (PFBS) showed an increase in concentration by 34% at 1000 kGy due to formation from the breakdown of other long chain PFAS. These results suggested that (a) the reaction kinetics is likely different for different PFAS based on chain length, functional group, and the degree of fluorination of the carbon chain, and (b) there may be intermediates generated from the degradation of 6:2 FTS and C7/C8 compounds that can potentially scavenge hydrated electrons needed for reaction with the untreated PFAS molecules. Treatment of three PFAS-contaminated groundwater samples from two US states showed similar trends as observed in the treatment of equimolar PFAS mixtures. Up to 71% removal of ΣPFAS was achieved in real groundwater samples at 750 kGy and data trend suggested that higher degradation is feasible if higher doses (>1MGy) are applied to treat field samples to overcome matrix effects and competing species. Calculated EEo for PFAS ranged from as low as ~48 to 1081 kWh/m 3 /order depending on the type of PFAS treated. These values are comparable and even lower, in some cases, than other destructive technologies employed for PFAS treatment such as ultrasound, plasma, and photochemical treatment. The results from this study indicate e-beam is a promising approach under favorable conditions and should be explored further as an end-of-train treatment option for PFAS destruction.

1,4-Dioxane↗
DOE OSTI2025

Technical Assessment of the Application of Digital Twin and Prognostic Tools for Condition Monitoring

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to present use cases of the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components (SSCs). The advanced technologies considered in this work, collectively referred to as digital twin (DT) technologies, are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), and physics-based models. The report presents two use cases of reactor coolant pumps (RCPs) and heat pipes in nuclear power plants (NPPs) with technical and regulatory considerations and opportunities in using advanced technologies for conditional monitoring. Key findings from the exploration of these considerations are as follows: - Uncertainties in sensor data and model predictions must be rigorously addressed through validation and verification processes - Regulatory compliance is paramount, necessitating data driven models to be developed in line with existing codes and standards, as well as considering potential future guidelines for advanced reactors - Explainability and transparency in ML/AI models are essential for developing operator trust and regulatory review, including methods that enhance the interpretability of complex data-driven predictions - Condition monitoring programs must be evaluated for their effectiveness in reducing maintenance-preventable function failures (MPFF) and aligning with plant performance criteria - The deployment of advanced technologies for condition monitoring could lead to a transition from periodic to continuous monitoring, thereby optimizing maintenance schedules - Collaborative efforts between industry stakeholders, regulatory bodies, and technology developers are crucial for the successful adoption of advanced technologies for condition monitoring systems in nuclear facilities In summary, the introduction of advanced technologies into condition monitoring programs represents a significant leap forward in the domain of NPP maintenance. By harnessing the capabilities of advanced sensors, data analytics, and ML/AI, NPP operators can transition from a time-based to a condition-based maintenance approach. This shift can potentially enhance the reliability and safety of critical plant components while optimizing maintenance efforts and minimizing unnecessary outages. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of inservice inspection and inservice testing (ISI and IST) programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Risk-informed Graded Approach for Reliability and Performance Assessment for Advanced Condition Monitoring Techniques

With the shift away from time-based maintenance and toward condition-based maintenance, and to reduce overall maintenance costs, there has been an upsurge in the usage and development of advanced condition monitoring (ACM) techniques for real-time monitoring of nuclear power plant (NPP) components. ACM is particularly useful in the development of digital twins, which are designed to predict the failure or degradation of plant components. Successful implementation of ACM requires an assessment to inform the development of a risk-informed approach to evaluate the use of ACM to meet Nuclear Regulatory Committee (NRC) regulations for in-service testing (IST) programs. This includes the monitoring and diagnostics of reactor components and systems in current, new, and advanced reactors. A key component in ACM is the usage of machine learning (ML) and artificial intelligence (AI) algorithms that can employ real-time data from instrumentation and sensors to detect and predict reactor component degradations. Such predictive capabilities enable early detection of component degradation so as to help plant personnel plan and execute necessary maintenance. For successful implementation of ML/AI in ACM such that regulatory requirements are met, a risk-informed graded approach is needed to assess the reliability and performance of ML/AI for ACM. The American Society for Mechanical Engineers (ASME) developed their Operations and Maintenance (O&M) Code to provide guidance on safe, reliable O&M of NPPs. The IST section of the O&M Code specifically establishes requirements for IST and examination to gauge operational readiness of components in water-cooled NPPs. This paper presents a state-of-the-art review of how reliability and risk assessment can be integrated with ACM to assess component performance by non-nuclear industries. This is followed by different methodologies and approaches for conducting performance and reliability assessments so as to meet IST requirements for NPP components.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

The influence of cooling rates on strain phase diagrams and domain structures of ferroelectric thin films: A case study of PbTiO 3

Strain engineering has been established as an effective approach to control phase equilibria, domain configurations, and functional properties of ferroelectric thin films. Temperature-strain phase diagrams have been used as powerful tools for providing insights into strain engineering. However, almost all existing phase diagrams established using the phase-field approach assume quenching conditions without considering actual cooling rates during the post-deposition annealing process of ferroelectric thin films. Within this work, we systematically investigate the influence of cooling rates on domain structures and the strain-phase diagram of ferroelectric thin films using phase-field simulations, taking PbTiO 3 thin films as a model system. We found that both the position of phase boundaries in the strain phase diagrams and the domain morphology are significantly influenced by the cooling rates. It is revealed that while the paraelectric-ferroelectric phase boundary remains invariant, the phase boundaries between single-phase and multi-phase regions tend to shift toward the corresponding multi-phase region as the cool rate reduces. Slow cooling generally leads to more ordered domain structures with increased domain size. Using the obtained equilibrium domain structures, we calculated effective thermal conductivities and found significant variations that can be tuned by the cooling rates. In conclusion, this work reveals an underexplored yet critical impact of cooling rates on phase equilibria and domain structures in ferroelectric thin films, which may inspire further fine-tuning of domains and domain walls in low-dimensional ferroelectrics for multifunctional applications.

Domain structures↗
DOE OSTI2025

Machine learning modeling and model predictive control of a closed-circuit reverse osmosis system

Closed-circuit reverse osmosis (CCRO) offers a flexible and energy-efficient alternative to conventional reverse osmosis by operating in a semi-batch mode that recycles brine, enabling higher recovery rates and reduced specific energy consumption (SEC). However, developing accurate, system-level dynamic models for CCRO remains challenging due to its nonlinear, multi-phase operation and sensitivity to variable feed water conditions. Traditional modeling approaches, such as NARMAX (nonlinear autoregressive moving average with exogenous inputs), often struggle to generalize across varying inlet feed concentrations, necessitating frequent parameter re-estimation and limiting their utility for real-time control applications. To address these limitations, we developed a long short-term memory (LSTM) neural network model trained on an extensive experimental data set from a CCRO pilot plant. The model accepts three inputs, feed flow rate, recirculation flow rate, and initial feed conductivity, and predicts three key outputs: reject conductivity, feed pump power draw, and recirculation pump power draw. We validated the LSTM model against experimental data, demonstrating its ability to distinguish between different feed conductivities and adapt to variable flow rates. Subsequently, we incorporated the LSTM model within a nonlinear model predictive control (MPC) scheme and conducted closed-loop simulations to optimize the integrated SEC (iSEC). In conclusion, the results project up to a 6% reduction in iSEC by using MPC to optimize performance over the entire experiment duration, without requiring any random excitation for data collection or parameter re-estimation.

Desalination↗
DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗