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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 1,063 records · Page 59

Electrostatic Interactions Control the Nanostructure of Conjugated Polyelectrolyte–Polymeric Ionic Liquid Blends

Polyelectrolyte complexation offers unique opportunities to compatibilize polymers with very different backbone chemistries and to control the morphology of the resulting blend via electrostatic manipulation. In this study, we demonstrate the ability to formulate homogeneous complexes of a conjugated polyelectrolyte with a polymeric ionic liquid, utilizing the electrostatic attraction among their oppositely charged side chains. Variation of electrostatic parameters, such as counterion concentration or polymer charge fraction, tunes the morphology of these polymer complexes from homogeneously disordered blend to weakly structured microemulsion where the local ordering arises from backbone-immiscibility-induced microphase segregation. Our experimental observations are in qualitative agreement with both field-theoretic simulation and random-phase approximation calculations. Simulated morphology snapshots suggest and experimental evidence also indicates that the microphase-segregated complex likely takes on a cocontinuous microemulsion structure. Our findings show that ionic interactions are an effective pathway to compatibilize polymers at macroscopic length scales while achieving controlled nanostructures in these ionic blends. Such systems have great potential for engineering the nanostructure of polymers to tailor applications such as nanofiltration, catalysis, and energy storage, where local ordering can enhance the physical properties of an otherwise macroscopically homogeneous structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Spatiotemporal Variations of Evapotranspiration in Amazonia Using the Wavelet Phase Difference Analysis

The relationships and seasonal-to-annual variations among evapotranspiration (ET), precipitation (P), terrestrial water storage anomalies (TWSA), radiation (downward shortwave radiation, DSR), and phenology (leaf area index, LAI) are complex across the Amazon basin. Here, to analyze how ET is controlled by these influencing factors, we used wavelet phase difference (WPD) to investigate the effects of P, TWSA, DSR, and LAI on ET at different spatiotemporal scales. The Amazon-scale averaged ET has strong correlations with these factors at the annual and multi-year periodicities. The patterns of WPDs have south-north and west-east divides due to the significant variation in climatic conditions. The results demonstrate that ET is mainly affected by water and energy availability while vegetation regulates both processes. The deep soil moisture/groundwater can provide strong subsidies to ET during the meteorological dry season in the water-limited area of Amazon. The WPD can well reflect the responses of ET to the variations of P, TWSA, DSR, and LAI, and the process of vegetation sustaining ET in the dry years in the water-limited area of the Amazon.

54 ENVIRONMENTAL SCIENCES↗

Incorporating Operational Uncertainties into the Dispatch of an Integrated Solar and Storage System

The economic assessment of hybrid energy systems (HES) pairing battery energy storage systems (BESSs) and photovoltaics (PV) is highly important for advancing their deployment in power systems. This paper presents an innovative assessment framework, including an optimal control policy for dispatch under uncertainty and procedures for exploring control parameters that maximize economic benefits. The proposed dispatch policy consists of two steps using system forecast information. The first step is to determine whether a BESS will be used within an operational scheduling time frame based on the probability of events and their thresholds. Once the dispatch of BESS is triggered, a model predictive control (MPC) is carried out in the second step for scheduling using the expected value of system information. By exercising this policy with different thresholds, one can explore the trade-offs between short-term benefits and battery lifetime, and identify an optimal threshold that maximizes the total economic benefits within the battery lifetime. An evaluation study in a real-world HES project is presented to illustrate the proposed framework. Compared with traditional optimal dispatch algorithms, the proposed method can significantly improve the economic benefits of an HES scheduled under forecast uncertainties.

Ma, Xu↗

What to Support When You’re Compressing

Over the last nearly 20 years, lossy compression has become an essential aspect of HPC applications’ data pipelines, allowing them to overcome limitations in storage capacity and bandwidth and, in some cases, increase computational throughput and capacity. However, with the adoption of lossy compression comes the requirement to assess and control the impact lossy compression has on scientific outcomes. In this work, we take a major step forward in describing the state of practice and by characterizing workloads. We examine applications’ needs and compressors’ capabilities across 9 different supercomputing application domains. We present 24 takeaways that provide best practices for applications, operational impacts for facilities achieving compressed data, and gaps in application needs not addressed by production compressors that point towards opportunities for future compression research.

Error-Bounded Lossy Compression↗

Real-Time Optimization Workflow Status Update

Economically optimal and safe operation of integrated energy systems (IES) requires optimization at many different time scales. A real-time optimization (RTO) workflow will attempt to maximize revenue and minimize operational costs on a time scale of minutes to hours. Such a workflow requires the use of a digital twin (DT), which is a virtual representation of a physical system. The DT is updated using real-time data from the physical system, and serves as a model in an optimization framework. The optimization results are then sent back to the physical system to complete the loop. This report details the progress made in developing building blocks for a DT/RTO framework. The Risk Analysis Virtual Environment (RAVEN) platform within the Framework for Optimization of Resources and Economics (FORCE) tool suite can perform many of the tasks required for building a DT and performing RTO. The first item of this report details RAVEN enhancements that enable RAVEN workflows to be run in various environments. Data communication between the physical system and its DT is essential for successful RTO. This includes preprocessing real-time data, loading data into a data warehouse, and querying the stored data. The second section of this report describes the progress made in implementing an adapter in Python in order for Deep Lynx to handle the data communication. Typical dispatch optimization frameworks are built on linear programming (LP). The prototype RTO workflow developed in this report uses an LP problem as a part of a receding-horizon- or economic model predictive control (EMPC) based optimization. The third section of this report details the framework of an RTO workflow in which the system consists of a simple electrical storage device. A DT can be built from a reduced-order model (ROM). Integrating a ROM into a typical LP optimization framework has been challenging because most optimization packages require the user to write algebraic expressions for the system model. The final section of this report shows how an externally built RAVEN ROM can be integrated in an RTO framework by using the Python package Pyomo. This demonstrates the RTO workflow capability from a software-only perspective and is an important step in demonstrating the capability to implement an RTO workflow for a physical system.

97 MATHEMATICS AND COMPUTING↗

Deploying Automation Technology Supporting Packaging and Transportation Operations

Various United States Department of Energy (DOE) sites are investigating the deployment of robotics to provide safer, effective, and more efficient operations. By implementing robotics in packaging and transportation operations, improvements toward production efficiency, worker ergonomics, radiological dose reduction, and security assurance can be realized. Robotic Systems must be designed and implemented in concert with a facility’s safety basis, classification, and security constraints, while improving overall operational effectiveness. The desired robotic implementation should provide a path to potential long-term cost savings, reduce/eliminate hazardous conditions and radiological exposure, while improving quality and reliability of an operation process. The Savannah River Site (SRS), a DOE Site located in Aiken South Carolina, has initiated several large scale projects supporting future nuclear material disposition activities. Incorporation of automation and robotic equipment in system designs will support effective and efficient material handling operations, including container movements and packaging activities. Robotic systems will execute tasks that are precise, procedural and integrated with control systems to verify and record data for material inventory and historical archiving. An overview of the current robotic system development to perform receipt inspection, packaging, storage and shipment preparation activities of designated Type A and B shipping packages will be presented.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science [Slides]

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

36 MATERIALS SCIENCE↗

The Rise of Intelligent Materials Science: Unleashing the Power of Machine Intelligence in Characterization

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

autonomous↗

Beyond Human Vision: Exploring Materials with Machine Intelligence

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

artificial intelligence↗

PV-BESS DC-Series Integration for Regulated DC Systems

DC-series integration introduces a novel approach to seamlessly integrate a solar photovoltaic (PV) array and a battery energy storage (BES) in series. This system, referred to as the PV-integrated battery energy storage system—dc series (PVBESS-DCS), simplifies integration and enhances power density by leveraging the inherent voltage-source characteristics of batteries and adopting the concept of partial power processing. However, addressing voltage variations of a PV array and a BES under different operational conditions remains a challenge to apply the PVBESS-DCS to a regulated dc system, where the dc-bus voltage is fixed. This article proposes a PVBESS-DCS solution tailored for regulated dc systems. Within this framework, we present a controllable series voltage source compensating for the voltage differences between the PV and BES sources and the dc bus. Thus, the system can perform PV maximum power point tracking and curtailment control while charging and discharging BES and connected to a regulated dc bus. Additionally, we present a single-stage multiport partial power processing dc–dc converter designed for the proposed PVBESS-DCS. It utilizes a triple active bridge dc–dc converter topology. Finally, this article presents operating principles, control strategies, simulation results, and experimental findings of the proposed PVBESS-DCS for regulated dc systems.

25 ENERGY STORAGE↗

Using energy storage systems to extend the life of hydropower plants

Despite their advantages, distributed energy resources (DERs) bring inherent uncertainty and variability into the landscape of modern power systems. As DER penetration grows, conventional generators like hydropower plants have to respond more often to arrest the imbalance in the net load. Hydropower turbines provide their best operational performance with minimal wear and tear when operating at regions of maximum efficiency. However, the current needs for hydropower plants require them to operate under varying load conditions and thus sub-optimal operating points leading to additional stress. To relieve the hydropower plants, this paper proposes a hybridization strategy where a hydropower unit is paired with an energy storage system (ESS) to increase operational flexibility and mitigate damage to the hydro plant. Models are developed to represent the operation of the hybrid system, quantify degradation, and assess economic benefits. Moreover, an innovative controller disaggregates the market dispatch signal into separate control setpoints for the ESS and hydropower unit. In case studies performed on a real-world hydropower facility, it was found that the ESS-based hybridization can extend the life of the hydropower plant by 5% on average. Notably, the economic benefits from reduced maintenance and deferred investment are estimated to be around $3.6 million.

13 HYDRO ENERGY↗

Low-power anisotropic molecular electronic memristors

A molecular electronic memristor, programmable resistive memory device, promises to revolutionize next-generation flexible data storage units, offering fast, dense and ultralow power solutions. Here we report anisotropic resistive switching in molecular κ-(BEDT-TTF) 2 Cu[N(CN) 2 ]Cl memristors, consisting of alternatively segregated bis(ethylenedithio)tetrathiafulvalene (BEDT-TTF) and Cu[N(CN) 2 ]Cl layers. Electron resistance switching behavior controlled by charge tunneling in molecular memristors show a low set voltage of 0.5 V (10 V/cm) with the ON/OFF ratio of 2.3×10 3 along a-axis and a high-level endurance of 1.25×10 4 cycles along all axes. Finally, the findings of such molecular electronic crystals promise for low-power data storage memristors.

36 MATERIALS SCIENCE↗

Fast Grid Frequency Support from Distributed Energy Resources

This report describes research related to electric power system frequency support from inverter-coupled distributed energy resources (DERs). This research was initiated under the U.S. Department of Energy’s Grid Modernization Laboratory Consortium (GMLC) and also contains work funded through a Cooperative Research and Development Agreement with Hawaiian Electric. The purpose of this report is to summarize the findings of simulations, hardware testing, power hardware-in-the-loop (PHIL) simulation, and analysis examining the effects of DER frequency support on the Oahu power system. This report also includes recommendations for consideration in the use of DER grid frequency support functionality, and related topics. Note that a previous GMLC Technical Report from this project also provided specific recommendations for the activation of one specific grid frequency support function, frequency-watt control, for overfrequency events in Hawaii. Those recommendations included recommended frequency-watt control settings, which have since been approved by the Hawaii Public Utilities Commission on October 20, 2017, for system-wide activation. This report builds on the previous report by considering not only overfrequency events but also underfrequency events, and by considering distributed energy storage systems in addition to photovoltaic (PV) systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Accelerated CO2 Storage Optimization Using Multi-Resolution Fourier Neural Operator at the Illinois Basin Decatur Project (IBDP)

This paper presents a deep learning-based approach for optimizing CO2 injection in carbon capture and storage (CCS) operations. We developed a multi-resolution machine learning model to significantly reduce data generation costs. Utilizing this proxy model, we implemented a multi-objective genetic algorithm to optimize well control during the CO2 injection process. The proposed approach was applied to the Illinois Basin Decatur Project (IBDP), successfully optimizing the CO2 injection schedule based on three key objectives: maximizing the amount of CO2 stored, maximizing sweep efficiency, and minimizing pressure increase. The use of the proxy model accelerated the optimization workflow by two orders of magnitude, while the cost of data generation for the proxy model was reduced by 90% by utilizing a coarse-scale model.

accelerated CO2 storage optimization↗

Interface‐Controlled Redox Chemistry in Aqueous Mn 2 ⁺/MnO₂ Batteries

Manganese dioxide (MnO 2 ) deposition/dissolution (Mn 2+ /MnO 2 ) chemistry, involving a two-electron-transfer process, holds promise for safe and eco-friendly large-scale energy storage. However, challenges like electrode/electrolyte interface environment fluctuations (H + and H 2 O activity), irreversible Mn degradation, and limited understanding of degradation mechanisms hinder the reversibility of the Mn 2+ /MnO 2 conversion. This study demonstrates a vanadyl/pervanadyl (VO 2+ /VO 2 + ) redox-mediated interface designed for high-energy Mn 2+ /MnO 2 batteries. Unlike flow systems, this work uncovers, for the first time, the mechanism of a static redox-mediated interface in regulating interfacial H + and H 2 O activities. Significantly, the VO 2+ /VO 2 + chemical redox mediation targets Mn 3+ intermediates, suppressing their hydrolysis and enabling 100% Mn 2+ /MnO 2 conversion. The redox-mediated interface enhances the Mn redox electron transfer process, achieving a stable ≈95% coulombic efficiency and ultrahigh capacity of 100 mAh cm −2 with an areal energy density of 111 mWh cm −2 , outperforming flow systems. The electrode also exhibits an average specific capacity of 593 mAh g −1 , approaching the theoretical limit of 616 mAh g −1 , and a specific energy density of 721 Wh kg −1 at high MnO 2 loadings (50–150 mg cm −2 ). Furthermore, the findings highlight the critical role of interfacial redox mediation in regulating H + and H 2 O activities and underscore the significance of interface dynamics.

Xue, Xinzhe [University of California, Santa Cruz,↗

Nanoscale Control of Polar Surface Phases in Layered van der Waals CuInP 2 S 6

Antiferroelectric (AFE) materials, in which alternating dipole moments cancel out to a zero net macroscopic polarization, can be used for high-density energy storage and memory applications. The AFE phase can exist in bulk CuInP 2 Se 6 , CuBiP 2 S 6 , and a few other transition-metal thiophosphates below 200 K. The required low temperature poses challenges for practical applications. In this work, we report the coexistence of ferrielectric (FE) states and a stable surface phase that does not show piezoelectric response (“zero-response phase”) in bulk CuInP 2 S 6 at room temperature. Using piezoresponse force microscopy (PFM) tomographic imaging together with density functional theory, we find that direct and alternating voltages can locally and stably convert FE to zero-response phases and vice versa. While PFM loops show pinched hystereses reminiscent of antiferroelectricity, PFM tomography reveals that the zero-response areas form only on top of the FE phase in which the polarization vector is pointing up. Theoretical calculations suggest that the zero-response phase may correspond to AFE ordering where stacked CuInP 2 S 6 layers have alternating polarization orientations thereby leading to a net-zero polarization. Consistent with experimental findings, theory predicts that the FE polarization pointing down is robust up to the top surface, whereas FE polarization pointing up energetically favors the formation of an AFE surface layer, whose thickness is likely to be sensitive to local strains. AFE order is likely to be more robust against detrimental size effects than polar order, therefore providing additional opportunities to create multifunctional heterostructures with 2D electronic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

PHASE CHANGE MATERIAL FOR THERMAL ENERGY STORAGE IN BUILDINGS BASED ON SODIUM SULFATE DECAHYDRATE AND DISODIUM HYDROGEN PHOSPHATE DODECAHYDRATE

The worldwide increasing energy demand and 2050 net zero carbon target urge the globe to solve the energy challenge. Thermal Energy Storage (TES) has received significant attention in recent years as TES can be integrated into heating, ventilation, and air-conditioning systems where the energy would be stored during low-demand times and dispatched during high-demand times, resulting in controlling the peak load and improving energy savings. Material development is an integral part of TES. Salt hydrates are appealing due to cost-effectiveness, low- to no toxicity, and their high melting enthalpy, where energy is stored as latent heat. However, most salt hydrates are prone to incongruent melting (i.e., phase separation upon melting), which results in poor stability and large supercooling. In this study, we produced a highly stable novel energy storage material at a composition of 32 wt% sodium sulfate decahydrate, 52 wt% sodium phosphate dibasic dodecahydrate, 12 wt% milled expanded graphite, and 4 wt% borax. The material has a melting temperature of 28°C and an energy storage capacity of 167 kJ/kg with a supercooling of less than 3°C. The system showed no loss in energy storage performance after 150 cycles. The findings suggest that the novel energy storage material developed in this might be utilized in building heating and cooling applications.

Turnaoglu, Tugba↗

Skyshine Calculations for a Large Spent Nuclear Fuel Storage Facility with SCALE 6.2.3

The SCALE code system developed at Oak Ridge National Laboratory includes state-of-the-art capabilities for radiation source term and radiation transport simulations that can be used in numerous applications, including dose rate analyses of complex consolidated interim storage facilities (CISFs). A licensed CISF could be used to store tens of thousands of tonnes of spent nuclear fuel discharged from commercial power reactors using various cask and storage pad designs. A CISF design must comply with the regulatory requirements provided in 10 CFR Part 72, including requirements related to annual dose limits applicable to real individuals located beyond the area controlled by the licensee. Therefore, calculating a dose to the public is a necessary part of the licensing process for the construction of a CISF. These calculations are very challenging because of the complexity of the CISF design and the low magnitude of dose rate at large distances from the facility. This paper describes detailed far-field dose rate calculations performed for a proposed CISF using MAVRIC, the Monte Carlo radiation shielding sequence in SCALE 6.2.3, with automated variance reduction based on discrete ordinates calculations. The method presented in this paper uses a detailed Monte Carlo radiation transport simulation in one step from source to dose rate. A series of independent simulations was made using the complete site geometry (all casks present), but with only one cask containing radiation sources to obtain the dose rate maps produced by each storage cask. The CISF dose rate map was obtained by adding the dose rate maps produced by the independent individual cask simulations. Ample volumes of air and soil extending beyond the location of interest for dose rate calculation were included in the calculation model to properly simulate important radiation attenuation and scattering events that affect far-field dose rates. Furthermore, a comprehensive sensitivity study is included in this paper to illustrate the importance of selecting appropriate air volume, mass density, and composition for CISF skyshine dose rate calculations. Dry soil and soil containing water were analyzed to determine their effects on groundshine radiation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗