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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
NASA NTRS2017

Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

space archtiecture↗
NASA NTRS2017

Multi-Objective Reinforcement Learning-based Deep Neural Networks for Cognitive Space Communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

space archtiecture↗
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↗
NASA NTRSDate not supplied

Cryogenically-Operable Electronics for Surviving the Lunar Night

Current solar-powered robotic lunar landers and rovers have not demonstrated survivability of the extreme cold temperatures seen during the 354-hour lunar night. To address this capability gap, we have been investigating the feasibility of a passive hibernation approach to enable low-cost solar- and battery-powered missions to operate over multiple lunar cycles. This strategy relies on isolating the batteries from the main bus during the lunar night and allowing cells to freeze as temperatures descend as low as 50K. The entire power architecture remains in a hibernation state with no electrical activity until lunar dawn. When the sun returns over the horizon, the now-illuminated solar arrays begin generating power. In order to manage the array power, architecture consisting of cryogenically-operable COTS electronics must be utilized. This architecture will distribute and control power to subsystems - importantly controlling heaters to bring the frozen lithium-ion battery cells to normal operating conditions. This alternative approach to lunar operations will enable low-cost commercial lunar landers and payloads to passively survive the night and continue to operate over multiple lunar cycles without a significant impact to cost or payload mass. Research last year focused on creating a power supply that was capable of operation at cryogenic temperatures. At the end of FY23, our team successfully designed and manufactured a Sequential Switching Shunt Regulator (S3R) that is operational in a cryogenic vacuum chamber. It is capable of full operation down to the coldest lunar night temperatures (~50K). Our presentation will provide an overview of the Lunar Hibernation strategy but will focus mainly on the technical details of this cryogenically-operable shunt regulator and test results.

Cryogenic↗
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↗
NASA NTRS2024

Hybrid Thermally Efficient Core (HyTEC) HyTEC Phase 1 – Combustor Final Report

The objective of the HyTEC – Combustor Technology project is to develop technology for a compact, low emissions, rich-burn combustor that maintains a high-level of durability. To accomplish this, the combustor will incorporate CMC liners and a CMC Dome to enable improved air utilization for mixing and NOx reduction. In particular, three technology areas were matured: metallic swirler attachment to the CMC dome, Nickel diffusion in CMC, and EBC advancements with improved durability. The maturation of these technology areas is critical to successfully designing a combustor for an engine intended to demonstrate increased thermal efficiency with integrated high-power density-core engine technologies. As part of the project, multiple swirler attachment architectures were matured through the design process, manufactured, and tested in simulated engine conditions. These architectures utilized two different approaches to attachment: a pressure loaded approach and a clamped approach. Each approach required testing at simulated engine conditions to mature the technology. These tests were developed to ensure that the probable failure mechanisms for each architecture were assessed relative to its long-term durability. After successfully testing both approaches, the pressure loaded approach was chosen based on its simplicity and its similarity to the attachment approach used on lean burn combustors. Finally, this successful testing resulted in the swirler attachment technology achieving TRL 4 status. Another aspect of concern with the metallic swirler attachment is the potential for nickel diffusion into the CMC to cause durability shortfalls. A series of tests were completed to understand both the amount of nickel that is expected to diffuse into the CMC and the effect this diffusion will have on mechanical properties of the CMC. After successfully completing this testing, the reduction in local mechanical properties was found to not have an impact on the overall durability of the CMC Dome. To further improve the overall durability, three different EBC coating architectures were also studied. One of these coatings being the current state of the art for CMC liners with the other two being proposed improvements. Coupons were produced using each coating and subjected to a series of laboratory testing. One coating was found to have an overall superior durability in laboratory testing and survived rig testing without indication of degradation. This successful testing resulted in the EBC technology achieving TRL 5 status. Overall, a design approach was successfully down-selected that achieves both the TPM requirements and the need for TRL 4+ status. This design incorporates a pressure loaded swirler attachment with an improved EBC coating (Architecture 1). This combination allows the continued use of a CMC dome and results in an improved durability for the proposed compact core combustor.

CMC↗
NASA NTRS2004

Adjoint Algorithm for CAD-Based Shape Optimization Using a Cartesian Method

Adjoint solutions of the governing flow equations are becoming increasingly important for the development of efficient analysis and optimization algorithms. A well-known use of the adjoint method is gradient-based shape optimization. Given an objective function that defines some measure of performance, such as the lift and drag functionals, its gradient is computed at a cost that is essentially independent of the number of design variables (geometric parameters that control the shape). More recently, emerging adjoint applications focus on the analysis problem, where the adjoint solution is used to drive mesh adaptation, as well as to provide estimates of functional error bounds and corrections. The attractive feature of this approach is that the mesh-adaptation procedure targets a specific functional, thereby localizing the mesh refinement and reducing computational cost. Our focus is on the development of adjoint-based optimization techniques for a Cartesian method with embedded boundaries.12 In contrast t o implementations on structured and unstructured grids, Cartesian methods decouple the surface discretization from the volume mesh. This feature makes Cartesian methods well suited for the automated analysis of complex geometry problems, and consequently a promising approach to aerodynamic optimization. Melvin et developed an adjoint formulation for the TRANAIR code, which is based on the full-potential equation with viscous corrections. More recently, Dadone and Grossman presented an adjoint formulation for the Euler equations. In both approaches, a boundary condition is introduced to approximate the effects of the evolving surface shape that results in accurate gradient computation. Central to automated shape optimization algorithms is the issue of geometry modeling and control. The need to optimize complex, "real-life" geometry provides a strong incentive for the use of parametric-CAD systems within the optimization procedure. In previous work, we presented an effective optimization framework that incorporates a direct-CAD interface. In this work, we enhance the capabilities of this framework with efficient gradient computations using the discrete adjoint method. We present details of the adjoint numerical implementation, which reuses the domain decomposition, multigrid, and time-marching schemes of the flow solver. Furthermore, we explain and demonstrate the use of CAD in conjunction with the Cartesian adjoint approach. The final paper will contain a number of complex geometry, industrially relevant examples with many design variables to demonstrate the effectiveness of the adjoint method on Cartesian meshes.

Nemec, Marian↗
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↗
DOE OSTI2021

Characterization of commercial vehicles’ start-up operations from in-use data

Diesel engines produce disproportionate levels of emissions when the engine and after-treatment systems are operating at low temperatures. This situation arises most commonly when the vehicle is first started after overnight. To quantify emissions attributable to vehicle starts, a sizable collection of on-road commercial vehicle operating data is analyzed to identify start-up events and inspect associated emissions. Data was obtained from the National Renewable Energy Laboratory’s (NREL’s) Fleet DNA and from the Center for Environmental Research & Technology (CE-CERT). Included are 500 + diesel vehicles with more than 42,000 recorded days, drawn from 25 vocational categories across the United States. Analysis shows that vehicle behavior, in terms of engine cold- and warm-operation, starts per day, soak time, and warm-up duration, differs significantly between vehicle vocations. Also, weighting factors for cold- and hot-starts currently used in the U.S. Environmental Protection Agency’s Federal Test Procedure (FTP) for heavy-duty emissions certification accurately represent real-world operations. Although the FTP includes a comparable fraction of cold operation, the hot fraction is much shorter than real-world operation due to limited test duration. The investigation also revealed that real-world engines operate for a significant amount of time when the engine coolant is in the “hot-stabilized” region, but the selective catalytic reduction (SCR) temperature is below its effective operating temperature of 200 °C. Of the vehicles under investigation, almost 20% of their operational time is within this condition. Therefore, novel approaches to raise and maintain SCR temperature are highly required to further reduce engine emissions.

33 ADVANCED PROPULSION SYSTEMS↗