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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Practical Comparison of Data-Driven Prognostics Methods for Energy Systems

This study explores data-driven prognostics for nuclear power plant (NPP) condensers, focusing on tube fouling. We utilized the Asherah nuclear power plant simulator (ANS) to compare four methods: Random Forest (RF), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory Neural Network (LSTM). By simulating various fouling scenarios in the ANS, we generated data with different degradation rates under transient operations. The models were trained and tested on these data, with performance evaluated visually and numerically including uncertainty assessment. The LSTM model excelled, exhibiting minimal prediction noise and the most accurate remaining useful life estimates across all degradation levels. Its ability to capture long-term dependencies and produce cleaner outputs makes it a strong candidate, although accurate training data across the entire component lifespan are crucial. The RF model emerged as a robust alternative, providing reliable predictions with high confidence. The FCNN and SVR models, while less effective overall, showed potential under specific conditions. FCNN offers a less complex alternative to LSTM and might benefit from larger datasets. SVR excels in precision when the quality of the training data is high. Furthermore, this study highlights the operational benefits of advanced prognostics in the energy sector and emphasizes the need for further research in NPP condenser health management through real-life experiments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Scaling up of High-Performance Single Crystalline Ni-rich Cathode Materials (CRADA 509)

This is a collaborative effort between Battelle Memorial Institute as manager and operator of Pacific Northwest National Laboratory (PNNL) and Albemarle Corporation (“Participant”) to develop and scale up an innovative and low-cost synthesis approach for preparing high-performance single crystalline Ni-rich cathode materials, i.e., LiNi0.8Mn0.1Co0.1O2 (NMC811) and LiNi0.9Mn0.05Co0.05O2 (NMC90) for next-generation LIBs. At the end of this project, the team will (1) develop a cost-effective synthesis approach for preparing single crystalline MNC811 (>200 mAh/g) and NMC90 (>210 mAh/g) by using advanced lithium salts, (2) address the performance issues of single crystals prepared from large-scale synthesis, and (3) demonstrate the processing/scaling up capabilities of up to 1 kg/batch of high-performance NMC811 and NMC90 single crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Heteroepitaxial control of thickness, strain, and domain architecture in few-layer ferroelectric tin monochalcogenides

Thin-film epitaxy and epitaxial strain have been widely exploited to tune domain configurations, switching behavior, and ferroic properties in conventional three-dimensional ferroelectric thin films; however, its application to controlling the properties of two-dimensional (2D) ferroelectrics has remained largely unexplored. Here, using SnX (X = Se, S) as a model system, we demonstrate heteroepitaxial control of thickness, strain state, and domain architecture in few-layer ferroelectric SnX via growth on monolayer MoS2 van der Waals (vdW) templates. Compared with conventional growth, MoS2-templated heteroepitaxy promotes epitaxial alignment, yielding ultrathin SnSe films with improved crystalline quality, full areal coverage, and enlarged lateral dimensions. Strong interfacial epitaxial coupling induces pronounced in-plane strain and stabilizes a hierarchical ferroelastic domain architecture, in which long-range 90° stripe domains are further subdivided into nanoscale rotational variants, as revealed by scanning transmission electron microscopy and synchrotron X-ray microscopy. Piezoresponse force microscopy and second-harmonic polarimetry confirm robust in-plane polarization, while ferroelectricity in SnSe is established through polarization-electric field hysteresis and nonvolatile ferroelectric resistive switching with on/off ratios approaching 1000. A nonvolatile, switchable ferroelectric diode effect further evidences direct coupling between polarization and charge transport. Notably, ferroelectric switchability exhibits a strong thickness dependence and is preserved only below ∼10 layers. This vdW heteroepitaxial strategy is further extended to ferroelectric SnS. The seamless heteroepitaxial integration of 2D ferroelectrics with CMOS-compatible, wafer-scale MoS2 templates provides a general and scalable route for strain-enabled structural and ferroelectric engineering in emerging memory and low-power optoelectronic applications.

Wang, Yueyin↗
DOE OSTI2021

A Memory Efficient Lock-Free Circular Queue

Hardware queues are import in many applications, such as data transfer, synchronization of concurrent modules with the need of mutual exclusion constructs. State of the art bounded (of a fixed size) lock free circular queues are implemented either by read/write atomic operations, or barrier conditions, or by separating dequeue and enqueue operations. However, these queues always require an unused element at all the times to safe-guard the front and rear pointers of the queue, so as to avoid data race conditions, which leads to the waste of memory. The waste of memory is especially disadvantageous in applications such as I/O data transfer, and image transfer between processing filters, when large element size is needed, We propose a lock-free solution of the bounded circular queue through read/write atomic operations, but without the need of an extra element in the queue. The proposed solution is implemented and verified in both Verilog and ’C’ languages. We also demonstrate its effectiveness by comparing its area and delay metrics with the implementations of other existing designs of queue.

Miniskar, Narasinga Rao↗
DOE OSTI2021

Experimental study on rheological and settling properties of shape memory polymer for fracture sealing in geothermal formations

This article studies the rheology and annular flow of a smart lost circulation material (LCM) at 160 °C, using a high-temperature flow loop. The smart LCM is a shape memory polymer (SMP) that activates at high temperatures, and its particle size increases to seal fractures in geothermal formations. SMP was mixed with the base fluid in two different concentrations, 1.0 and 3.0 wt%, to study rheology, wellbore hydraulics, activation process, and settling behavior under different testing conditions. The results of this study showed that the SMP could be activated at high temperatures. An increase of 80–100% in the particle size was observed at 160 °C. The mud samples showed a high shear-thinning behavior at the two concentrations with a Power-law flow index (n) ranging between 0.025 and 0.101. No additional frictional pressure losses were observed when SMP was added to the base fluid. SMP particles showed an excellent suspension at 1.0 wt% while, at 3.0 wt%, a bed was formed at a low flow rate and without pipe rotation. Increasing the drill pipe rotational speed or flow rate effectively removed the bed and homogeneously dispersed the SMP particles, ensuring a better sealing efficiency. SMP particle dispersion in inclined wells was better than in horizontal wells. Moreover, the findings of this study help optimize the lost circulation treatment by considering a wide range of operating parameters that can further be extended to different systems and geometries.

02 PETROLEUM↗
DOE OSTI2026

Enhancing mobility and interface state engineering via UV-ozone treatment in BEOL-compatible ultrathin TiO 2 transistors

It has emerged as a potential candidate to improve the performance of monolithic-three-dimensional (M3D) integration of fused logic and memories through low-temperature in situ synthesis of high-performance metal–oxide–semiconductor (MOS) transistors. Here, we report the demonstration of the BEOL-compatible low thermal budget (350 °C) fabrication process of ultrathin-TiO2 transistors by the combination of RTA and UV-ozone treatment (RTA-UVOz). UV–ozone (UVOz) treatment of TiO2 anatase films significantly enhances stability, boosting ION current and field effect mobility (μ FE ) by two times of magnitude in TiO2 TFTs. UV ozone treatment helps to eliminate pre-existing oxygen vacancies and carbon contamination on TiO2 channels even at low temperature (100 °C), resulting in high-quality channel/dielectric interfaces with low interface states (D it ). During UVOz treatment, oxygen species (O x ) passivate the oxygen vacancies ($V$$^{2+}_{o}$), and hence low concentration of $V$$^{2+}_{o}$ would be left for ionization/de-ionization under PBS/NBS, leading to improved bias stress stability. Furthermore, the TiO 2 TFTs with thin ZrO 2 gate dielectric exhibited excellent performance including a lower subthreshold swing (SS) of 98 mV/dec with high drive I ON current ∼ 4.5 μA/μm, a high I ON /I OFF > 10 9 , and mobility μFE of 7 cm 2 /V-s under a battery powered voltage of 1 V. UV–ozone treatment enables high-performance, CMOS-compatible TiO 2 transistors with a low thermal budget, ideal for next-generation flexible, energy-efficient electronics.

BEOL↗
DOE OSTI2022

Optimized Quantum Phase Estimation for Simulating Electronic States in Various Energy Regimes

While quantum algorithms for simulation exhibit better asymptotic scaling than their classical counterparts, they currently cannot be implemented on real-world devices. Instead, chemists and computer scientists rely on costly classical simulations of these quantum algorithms. In particular, the quantum phase estimation (QPE) algorithm is among several approaches that have attracted much attention in recent years for its genuine quantum character. However, it is memory-intensive to simulate and intractable for moderate system sizes. Herein, this paper discusses the performance and applicability of QPESIM, a new simulation of the QPE algorithm designed to take advantage of modest computational resources. In particular, we demonstrate the versatility of QPESIM in simulating various electronic states by examining the ground and core-level states of H 2 O. For these states, we also discuss the effect of the active-space size on the quality of the calculated energies. For the high-energy core-level states, we demonstrate that new QPE simulations for active spaces defined by 15 active orbitals significantly reduce the errors in core-level excitation energies compared to earlier QPE simulations using smaller active spaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

The traffic volume of each movement at signalized intersections can provide valuable information on real-time traffic conditions that enable traffic control systems to dynamically respond to the fluctuated traffic demands. Real-time movement-based traffic volume prediction is challenging due to various nonlinear spatial relationships at different locations/approaches and the complicated underlying temporal dependencies. In this study, a novel deep intersection spatial-temporal network (DISTN) is developed for real-time movement-based traffic volume prediction at signalized intersections, which considers both spatial and temporal features by the convolutional neural network (CNN) and long short-term memory (LSTM), respectively. In addition, the within-day, daily, and weekly periodic trends of traffic volume are also considered in the proposed model. This is the first time that a deep-learning method has been applied for movement-based traffic volume prediction at signalized intersections. In the numerical experiment, the proposed model is evaluated using real-world data and simulation data to demonstrate its effectiveness. The impacts of various structures of traffic networks on the proposed model are also discussed. Results show that the proposed model outperforms some of the state-of-the-art volume prediction methods currently in the literature.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Spin–orbit torques in structures with asymmetric dusting layers

Current-induced spin–orbit torques (SOTs) in heavy metal/ferromagnet heterostructures have emerged as an efficient method for magnetization switching with applications in nonvolatile magnetic memory and logic devices. However, experimental realization of SOT switching of perpendicular magnetization requires an additional inversion symmetry breaking, calling for modifications of the conventional SOT heterostructures. In this work, we study SOTs and deterministic switching of perpendicular magnetization by inserting different asymmetric dusting layers at the heavy metal/ferromagnet interface. Similar to the previous works with lateral structural asymmetry, we study the emergence of current-induced perpendicular effective magnetic fields (H z eff ). By examining three different material combinations of heavy metal/dusting layers (W/IrMn, Pt/IrMn, and W/Ta), we shed light on the origins of H z eff ; we show that H z eff is generically created in all the studied asymmetric structures, has a close correlation with the interfacial magnetic anisotropy, and is independent of the signs of spin Hall angles of the materials. Furthermore, we show that the induction of H z eff enables field-free deterministic SOT switching of perpendicular magnetization. Our results can be used in designing SOT heterostructures for practical applications in nonvolatile technologies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Explaining System-Level Prognostics with Established Machine Learning Methods

System-level prognostics is crucial for ensuring reliability and enabling predictive maintenance in complex systems with interconnected components. This study presents a framework that integrates data-driven methods to predict the remaining useful life (RUL) of a subsystem under multiple and concurrent faults within a nuclear power plant system with explainable artificial intelligence (XAI). A nuclear power plant (NPP) operation was simulated to model the degradation behavior of NPP components, and four machine learning models—Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory (LSTM)—were evaluated for prognostics with a novel system RUL parameter. The LSTM model demonstrated potential superior repeatability, while SHAP (SHapley Additive exPlanations) for explainability provided consistent and trustworthy global explanations. In contrast, LIME (Local Interpretable Model-agnostic Explanations) offered localized interpretability but showed reduced stability for sequential data. Key findings include the interplay between component-level degradation and system-wide performance, with LSTM effectively capturing these dynamics through sequence-level predictions. The XAI techniques enhanced transparency by identifying critical features influencing model predictions and aligning with domain knowledge. Furthermore, this framework has significant implications for improving trust and understanding in predictive maintenance, particularly in safety-critical industries like nuclear energy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

An Entropy-Based Test and Development Framework for Uncertainty Modeling in Level-Set Visualizations

We present a simple comparative framework for testing and developing uncertainty modeling in uncertain marching cubes implementations. The selection of a model to represent the probability distribution of uncertain values directly influences the memory use, run time, and accuracy of an uncertainty visualization algorithm. We use an entropy calculation directly on ensemble data to establish an expected result and then compare the entropy from various probability models, including uniform, Gaussian, histogram, and quantile models. Our results verify that models matching the distribution of the ensemble indeed match the entropy. We further show that fewer bins in nonparametric histogram models are more effective whereas large numbers of bins in quantile models approach data accuracy.

Sisneros, Robert↗
DOE OSTI2026

Optical control of integer and fractional Chern insulators

Optical control of topology, particularly in the presence of electron correlations, is an interesting topic with broad scientific and technological impact. Twisted MoTe 2 bilayer (tMoTe 2 ) is a zero-field fractional Chern insulator (FCI), exhibiting the fractionally quantized anomalous Hall effect. As the chirality of the edge states and sign of the Chern number are determined by the underlying ferromagnetic polarization, manipulation of ferromagnetism would realize control of the Chern insulator (CI)/FCI states. Here, in this work, we demonstrate control of ferromagnetic polarization, and thus the CI and FCI states, by circularly polarized optical pumping in tMoTe 2 . At low excitation power, we achieve on-demand preparation of ferromagnetic polarization by optical training, that is, electrically tuning the system from non-ferromagnetic to desirable ferromagnetic states under helicity-selective optical pumping. With increased excitation power, we further realize direct optical switching of ferromagnetic polarization at a temperature far below the Curie temperature. Both optical training and direct switching are most effective near CI and FCI states, which we attribute to a gap-enhanced valley polarization of optically pumped holes. The magnetization can be dynamically switched by modulating the helicity of optical excitation. Spatially resolved measurements further demonstrate optical writing of ferromagnetic, and thus CI (or FCI) domains. Our work realizes precise optical control of a topological quantum many-body system with potential applications in topological spintronics, quantum memories and creation of exotic edge states by programmable patterning of integer and fractionally quantized anomalous Hall domains.

ferromagnetism↗
DOE OSTI2024

Fail-Safe Logic Design Strategies Within Modern FPGA Architectures

Fail-safe computing refers to computing systems that revert to a non-operational safe state when a fault occurs. In this paper, we investigate a circuit level technique as mitigation for single event upsets (SEUs) and fault injection attacks on field programmable gate arrays (FPGAs), and analyze the effectiveness of the technique as a fail-safe monitor for an encryption algorithm. The propagation of fault effects through FPGA primitives including lookup tables (LUTs) and programmable interconnect points (PIPs) is assessed within an FPGA architecture created using an open source tool, and validated using fault injection experiments on an FPGA. The analysis reveals additional vulnerabilities exist within reconfigurable architectures over those in equivalent fail-safe application specific integrated circuit (ASIC), thus requiring a more elaborate network of redundant circuits and checking logic. The configuration memory bits (CMBs), which configure routing and designate logic functions within the LUTs of the FPGA, add complexity to fail-safe design strategies by introducing additional fault conditions and fault propagation paths. A resource-efficient fail-safe circuit design technique called DEsign for Fail-safe in reCONfigurable systems (DEFCON) is proposed. The benefits and limitations associated with DEFCON are described in the context of fault injection experiments carried out as simulations and in FPGA hardware.

Bhakta, Priya A. [Univ. of New Mexico, Albuquerque↗
DOE OSTI2026

Proximity Ferroelectricity in Compositionally Graded Structures

Proximity ferroelectricity is a novel paradigm for inducing ferroelectricity in a non-ferroelectric polar material, such as AlN or ZnO that are typically unswitchable with an external field below their dielectric breakdown field. When placed in direct contact with a thin switchable ferroelectric layer (such as Al 1-x Sc x N or Zn 1-x Mg x O), they become a practically switchable ferroelectric. Using the thermodynamic Landau-Ginzburg-Devonshire theory, in this work, we perform the finite element modeling of the polarization switching in the compositionally graded AlN-Al 1-x Sc x N, ZnO-Zn 1-x Mg x O, and MgO-Zn 1-x Mg x O structures sandwiched in both a parallel-plate capacitor geometry as well as in a sharp probe-planar electrode geometry. We reveal that the compositionally graded structure allows the simultaneous switching of spontaneous polarization in the whole system by a coercive field significantly lower than the electric breakdown field of unswitchable polar materials. The physical mechanism is the depolarization electric field determined by the gradient of chemical composition “x”. The field lowers the steepness of the switching barrier in the otherwise unswitchable parts of the compositionally graded AlN-Al 1-x Sc x N and ZnO-Zn 1-x Mg x O structures. In the MgO-like regions of the compositionally graded MgO-Zn 1-x Mg x O structure, a shallow double-well free energy potential emerges. Proximity ferroelectric switching of the compositionally graded structures placed in the probe-electrode geometry occurs due to nanodomain formation under the tip. We predict that a gradient of chemical composition “x” significantly lowers effective coercive fields of the compositionally graded AlN-Al 1-x Sc x N and ZnO-Zn 1-x Mg x O structures compared to the coercive fields of the corresponding multilayers with a uniform chemical composition in each layer. A tip-induced switching further lowers the coercive field, enabling control of ferroelectric domains in otherwise unswitchable compositionally graded structures, which can provide nanoscale domain control for memory, actuation, sensing, and optical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Homotypic aggregates contribute to heterogeneity in B cell fates due to an intrinsic gradient of stimulant exposure

Highlights: • CD40-signaled B cells in cultures form three-dimensional homotypic aggregates. • These tight aggregates hinder the free diffusion of large molecule stimulants. • The resulting concentration gradient of stimulants leads to heterogeneous cell fates. • We describe a flow cytometric approach to quantify this positional information. Monocultures of several cell types result in the formation of robust clusters called homotypic aggregates (HAs). How this physical aggregation affects cell fates in immune cell cultures, is poorly understood. We studied anti-CD40-stimulated primary B cell cultures, where cells assembled into large three-dimensional LFA1-driven HAs by 72 h. The dense packing in these aggregates restricts the infiltration of stimulants, such as antibodies, to cells inside the clusters. This creates a concentration gradient of stimulant availability across the cross-section of HAs. We describe a method to retain this positional information even after the disruption of HAs, for analysis by flow cytometry. Comparison of stage-specific cell-surface markers showed that the extent of stimulant-binding affected multiple fates non-uniformly. While germinal center and lineage markers were moderately upregulated, immunoglobulins and markers associated with memory were more than doubled in the peripheral cells binding more anti-CD40. These cells also experienced a strong repression of the plasma cell regulator Prdm1 and an upregulation of the oncogene Myc. Thus, cells at different locations in HAs are subjected to unequal doses of stimulants, leading to a hitherto unreported source of heterogeneity in cell fates. These findings can be extrapolated to understand the dose-dependent effects of stimulants in other three-dimensional cell clusters.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Design Considerations for GPU-based Mixed Integer Programming on Parallel Computing Platforms

Mixed Integer Programming (MIP) is a powerful abstraction in combinatorial optimization that finds real-life application across many significant sectors. The recent proliferation of graphical processing unit (GPU)-based accelerated computing architectures in large-scale parallel computing or supercomputing presents new opportunities as well as challenges in the advancement of MIP solver technology to effectively use the new accelerated computing platforms and scale to large parallel systems. Here, we recount the conventional processor-based strategies and focus on configurations where the most promising intersection lies between parallel MIP solver approaches and the specific strengths of accelerated parallel platforms. We note that the best potential lies in solving problems whose individual matrix sizes (of the linear program relaxation) fit entirely within one accelerator's memory and whose branch-and-bound (or branch-and-cut) trees cannot be fully contained within a small number of computational nodes. Additionally, we identify ideal features of computational linear algebra support on GPU accelerators that would help advance this direction of scalable parallel solution of MIP problems on GPU-based accelerated computing architectures.

Perumalla, Kalyan↗