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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modular DC circuit breaker with integrated energy storage for future DC networks

A T-breaker is an all-in-one solution for dc microgrid fault protection, power flow control, and power quality improvement. A T-breaker features a modular multilevel “T” structure with integrated energy storage devices. The two horizontal arms of the T-breaker realize fault current breaking, load voltage compensation, and power flow control; and the vertical arm of the T-breaker realizes shunt compensation. The configuration provides excellent voltage scalability and relaxes the requirements on the switching signal synchronization during fault transients. The local energy storage in sub-modules eases the fault energy dissipation requirement placed on the traditionally-adopted surge arrestors. The modular multilevel structure also offers immense control flexibility for all types of targeted functions of the provided T-breaker.

Wang, Jin↗
DOE OSTI2025

Ultra-Fast Non-Volatile Resistive Switching Devices with Over 512 Distinct and Stable Levels for Memory and Neuromorphic Computing

Low-current multilevel programmability with inherent non-volatility and high stability of resistance states is required for both multi-bit memory storage and deep learning accelerators but is difficult to achieve. Here, in a resistive switching system, this work realizes >512 (>9 bits) distinct non-volatile conductance levels with stable retention for each state with current levels down to the nanoampere range, highly promising for potential integration with small processing nodes with ultra-low power consumption requirements. This is achieved by demonstrating a new thin film design concept that encompasses three key features: an ultra-thin epitaxial oxygen ionic switching layer that provides a tunable energy barrier at the bottom electrode, an overcoat amorphous layer that acts as an ion migration barrier for stable state retention, and a partial conductive filament as a localized electronic transport channel to the epitaxial switching layer. A large dynamic resistance range of up to seven orders of magnitude is achieved with reset-free transitions among intermediate states, and programmability is demonstrated with ultra-fast (20 ns) pulses. Artificial neural network (ANN) simulations, based on the experimental performance and its non-idealities, demonstrate close-to-ideal inference accuracies for various Modified National Institute of Standards and Technology (MNIST) data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey

Uncertainty quantification (UQ) includes the characterization, integration, and propagation of uncertainties that result from stochastic variations and a lack of knowledge or data in the natural world. Monte Carlo (MC) method is a sampling-based approach that has widely used for quantification and propagation of uncertainties. However, the standard MC method is often time-consuming if the simulation-based model is computationally intensive. This article gives an overview of modern MC methods to address the existing challenges of the standard MC in the context of UQ. Specifically, multilevel Monte Carlo (MLMC) extending the concept of control variates achieves a significant reduction of the computational cost by performing most evaluations with low accuracy and corresponding low cost, and relatively few evaluations at high accuracy and corresponding high cost. Multifidelity Monte Carlo (MFMC) accelerates the convergence of standard Monte Carlo by generalizing the control variates with different models having varying fidelities and varying computational costs. Multimodel Monte Carlo method (MMMC), having a different setting of MLMC and MFMC, aims to address the issue of UQ and propagation when data for characterizing probability distributions are limited. Multimodel inference combined with importance sampling is proposed for quantifying and efficiently propagating the uncertainties resulting from small data sets. All of these three modern MC methods achieve a significant improvement of computational efficiency for probabilistic UQ, particularly uncertainty propagation. Finally, an algorithm summary and the corresponding code implementation are provided for each of the modern MC methods. The extension and application of these methods are discussed in detail.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Method of Green’s Potentials for Elliptic PDEs in Domains with Random Apertures

Problems with topological uncertainties appear in many fields ranging from nano-device engineering to the design of bridges. In many of such problems, a part of the domains boundaries is subjected to random perturbations making inefficient conventional schemes that rely on discretization of the whole domain. Here, we study elliptic PDEs in domains with boundaries comprised of a deterministic part and random apertures, and apply the method of modified potentials with Green’s kernels defined on the deterministic part of the domain. This approach allows to reduce the dimension of the original differential problem by reformulating it as a boundary integral equation posed on the random apertures only. The multilevel Monte Carlo method is then applied to this modified integral equation and its optimal ϵ -2 asymptotical complexity is shown. Finally, we provide the qualitative analysis of the proposed technique and support it with numerical results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

System risk quantification and decision making support using functional modeling and dynamic Bayesian network

Risk-informed decision-making requires a probabilistic assessment of the likelihood of success of control action, given the system status. This paper presents a systematic state transition modeling approach integrating dynamic probabilistic risk assessment with a decision-making process using a dynamic Bayesian network (DBN) coupled with functional modeling. A functional model designed with multilevel flow modeling (MFM) technique was used to build a system state structure inferred by energy, mass, and information flow so that one can verify the developed model with respect to system functionality. The MFM model represents the causal relationship among the nodes, which captures the structure of process parameters and control units. Each node may have multiple possible states, and the DBN structured by the MFM model represents the time-domain transitions among the defined states. Furthermore, the MFM-DBN integrated state transition modeling is a white-box approach that allows one to draw the system's risk profile by updating the system states and supports the decisions probabilistically with physical inference. An example of a simple heating system has been used to illustrate this process, including decision-making support based on quantitative risk profile. For demonstrating its applicability to a complex system operational decision making, a case study of station blackout accident scenario leading to the seal loss of coolant accident in a nuclear power plant is presented. The proposed approach effectively provided the risk profile along time for each option so that the operators can make the best decision, which minimizes the plant risk.

42 ENGINEERING↗
DOE OSTI2020

Modeling and Harmonic Stability of MMC-HVDC With Passive Circulating Current Filters

A modular multilevel converter (MMC) is an emerging converter technology widely used in wind power applications using high-voltage direct current (HVDC) transmission, and it has been largely investigated in medium-voltage solar harvesting as well as electric motor drives. Different from studies dealing with active circulating current control loops, this paper focuses on impedance modeling and harmonic stability studies for MMCs with passive circulating current filters (PCCFs). A power stage circuit including a PCCF is transformed and remodeled to obtain small-signal formulations. This transformation leads to additional high-order matrix computing complexity due to the added impedance subnetwork matrix from the PCCF as well as relevant crossed-frequency impacts. To simplify the computational burden, the proposed model aims to solve one arm equation instead of all six MMC arm equations. To achieve this result, additional challenges occur when the MMC is connected to a renewable energy source instead of the grid voltage, where low-level zero-sequence components might exist in the neutral point of three phases, especially in the case of no integrated advanced modular voltage balancing control, which prevents computational simplification. To overcome this challenge, further impedance matrix adjustments are conducted in this paper to theoretically suppress the impact of zero-sequence harmonics when obtaining small-signal impedance, taking into account the frequency coupling effect. Finally, simulations are carried out to validate the developed impedance model under different operating scenarios. Harmonic stability case studies of MMCs with PCCFs connected to renewable energy current sources are also presented, where frequency-domain stability analyses based on Bode diagrams are compared to time-domain simulation waveforms and FFTs, which validate the effectiveness of the proposed impedance model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Radiation-Induced Error Mitigation by Read-Retry Technique for MLC 3-D NAND Flash Memory

Here, we have evaluated the Read-Retry (RR) functionality of the 3-D NAND chip of multilevel-cell (MLC) configuration after total ionization dose (TID) exposure. The RR function is typically offered in the high-density state-of-the-art NAND memory chips to recover data once the default memory read method fails to correct data with error correction codes (ECCs). In this work, we have applied the RR method on the irradiated 3-D NAND chip that was exposed with a Co-60 gamma-ray source for TID up to 50 krad (Si). Based on our experimental evaluation results, we have proposed an algorithm to efficiently implement the RR method to extend the radiation tolerance of the NAND memory chip. Our experimental evaluation shows that the RR method coupled with ECC can ensure data integrity of MLC 3-D NAND for TID up to 50 krad (Si).

3-D NAND↗
DOE OSTI2024

Decision-making based on Markov decision process in integrated artificial reasoning framework—Part I: Theory

This paper presents a decision-making framework based on an integrated artificial reasoning framework and Markov decision process (MDP). The integrated artificial reasoning framework provides a physics-based approach that converts system information into state transition models, and the analysis result will be represented by the transition probabilities that can be used with an MDP to find a traceable and explainable optimal pathway. A dynamic Bayesian network (DBN) is well suited for representing the structure of an MDP. The causality information among process variables (or among subsystems) is mathematically represented in a DBN by the conditional probabilities of the node’s states provided different probabilities of the parent node’s states. To define node states in a physically understandable manner, we used multilevel flow modeling (MFM). An MFM follows the fundamental energy and mass conservation laws and supports the selection of process variables that represent the system of interest so that causal relations among process variables are properly captured. An MFM-based DBN supports developing state transition models in an MDP to capture the effect of process variables of system having physical relations. The operators of the target system can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from component degradation or random failures. We analyzed a simplified exemplary system to illustrate an optimal operational policy using the suggested approach.

Markov decision process↗
NASA NTRS1996

New Bandwidth Efficient Parallel Concatenated Coding Schemes

We propose a new solution to parallel concatenation of trellis codes with multilevel amplitude/phase modulations and a suitable iterative decoding structure. Examples are given for throughputs 2 bits/sec/Hz with 8PSK and 16QAM signal constellations.

trellis codes multilevel amplitude modulations dec↗
NASA NTRSDate not supplied

The effect of modeling dose uncertainty on low-boom community noise dose-response curves

In logistic dose-response modeling, failing to account for uncertainty in estimated doses can cause an artificial flattening or attenuation of the slope of the summary curve. In Lee et al. [J. Acoust. Soc. Am. 147(4), pp. 2222-2234 (2020)], data from two NASA low-amplitude sonic boom community noise survey tests were modeled using a Bayesian multilevel logistic regression (MLR) statistical model that assumed there was no uncertainty in the noise dose estimates. However, in these community tests, the noise dose uncertainty was estimated by Page et al. [NASA/CR-2014-218180 and NASA/CR-2020-220589/Volume I] using a leave-one-out method. In the current work, a term was added to extend the Bayesian MLR model to account for the estimated noise dose uncertainty quantified in the Page et al. analyses. This uncertainty term was included in two ways, either as classical or as Berkson uncertainty, and yield similar results. When the uncertainty is accounted for in the Bayesian MLR model, the dose-response curves become 5-10% steeper, but the difference in the noise dose that elicits a 5% highly annoyed response is small (less than 1 dB). This result is encouraging for future X-59 community tests whose survey area will be sparsely populated with noise monitors.

X-59↗
DOE OSTI2025

Artificial Intelligence and Multiscale Modeling for Sustainable Biopolymers and Bioinspired Materials

Abstract Biopolymers and bioinspired materials contribute to the construction of intricate hierarchical structures that exhibit advanced properties. The remarkable toughness and damage tolerance of such multilevel materials are conferred through the hierarchical assembly of their multiscale (i.e., atomistic to macroscale) components and architectures. Here, the functionality and mechanisms of biopolymers and bio‐inspired materials at multilength scales are explored and summarized, focusing on biopolymer nanofibril configurations, biocompatible synthetic biopolymers, and bio‐inspired composites. Their modeling methods with theoretical basis at multiple lengths and time scales are reviewed for biopolymer applications. Additionally, the exploration of artificial intelligence‐powered methodologies is emphasized to realize improvements in these biopolymers from functionality, biodegradability, and sustainability to their characterization, fabrication process, and superior designs. Ultimately, a promising future for these versatile materials in the manufacturing of advanced materials across wider applications and greater lifecycle impacts is foreseen.

Wang, Xing Quan [Department of Mechanical Engineer↗
DOE OSTI2023

Suppressing Structural Relaxation in Nanoscale Antimony to Enable Ultralow‐Drift Phase‐Change Memory Applications

Abstract Phase‐change random‐access memory (PCRAM) devices suffer from pronounced resistance drift originating from considerable structural relaxation of phase‐change materials (PCMs), which hinders current developments of high‐capacity memory and high‐parallelism computing that both need reliable multibit programming. This work realizes that compositional simplification and geometrical miniaturization of traditional GeSbTe‐like PCMs are feasible routes to suppress relaxation. While to date, the aging mechanisms of the simplest PCM, Sb, at nanoscale, have not yet been unveiled. Here, this work demonstrates that in an optimal thickness of only 4 nm, the thin Sb film can enable a precise multilevel programming with ultralow resistance drift coefficients, in a regime of ≈10 −4 –10 −3 . This advancement is mainly owed to the slightly changed Peierls distortion in Sb and the less‐distorted octahedral‐like atomic configurations across the Sb/SiO 2 interfaces. This work highlights a new indispensable approach, interfacial regulation of nanoscale PCMs, for pursuing ultimately reliable resistance control in aggressively‐miniaturized PCRAM devices, to boost the storage and computing efficiencies substantially.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A multilayer network analysis of Alzheimer's disease pathogenesis: Roles for p‐tau, synaptic peptides, and physical activity

INTRODUCTION: In the aging brain, cognitive abilities emerge from the coordination of complex pathways arising from a balance between protective lifestyle and environmental factors and accumulation of neuropathologies. METHODS: As part of the Rush Memory and Aging Project (n = 440), we measured accelerometer-based actigraphy, cognitive performance, and after brain autopsy, selected reaction monitoring mass spectrometry. Multilevel network analysis was used to examine the relationships among the molecular machinery of vesicular neurotransmission, Alzheimer's disease (AD) neuropathology, cognition, and late-life physical activity. RESULTS: Synaptic peptides involved in neuronal secretory function were the most influential contributors to the multilayer network, reflecting the complex interdependencies among AD pathology, synaptic processes, and late-life cognition. Older adults with lower physical activity evidenced stronger adverse relationships among phosphorylated tau peptides, markers of synaptic integrity, and tangle pathology. DISCUSSION: Network-based approaches simultaneously model interdependent biological processes and advance understanding of the role of physical activity in age-associated cognitive impairment. Highlights: Network-based approaches simultaneously model interdependent biological processes. Secretory synaptic peptides were influential contributors to the multilayer network. Older adults with lower physical activity had adverse relationships among pathology. There was interdependence among phosphorylated tau, synaptic integrity, and tangles. Network methods elucidate the role of physical activity in cognitive impairment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Performance and power modeling and prediction using MuMMI and 10 machine learning methods

Energy-efficient scientific applications require insight into how high performance computing system features impact the applications' power and performance. This insight can result from the development of performance and power models. Here, in this article, we use the modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and 10 machine learning methods to model and predict performance and power consumption and compare their prediction error rates. We use an algorithm-based fault-tolerant linear algebra code and a multilevel checkpointing fault-tolerant heat distribution code to conduct our modeling and prediction study on the Cray XC40 Theta and IBM BG/Q Mira at Argonne National Laboratory and the Intel Haswell cluster Shepard at Sandia National Laboratories. Our experimental results show that the prediction error rates in performance and power using MuMMI are less than 10% for most cases. By utilizing the models for runtime, node power, CPU power, and memory power, we identify the most significant performance counters for potential application optimizations, and we predict theoretical outcomes of the optimizations. Based on two collected datasets, we analyze and compare the prediction accuracy in performance and power consumption using MuMMI and 10 machine learning methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Achieving Multimodal and Multicolor Luminescence in LaAlO 3 :Pr 3+ , Gd 3+ via Trap Engineering and Energy Transfer

Achieving multimodal luminescence within a single phosphor is vital for multifunctional applications but remains challenging due to complex color tuning and trap engineering. In this study, we report Pr 3+ and Gd 3+ co‐doped LaAlO 3 (LAO:PG) phosphors, designed through careful modulation of multilevel traps and Pr 3+ → Gd 3+ energy transfer dynamics. These materials exhibit diverse luminescence modes, including down‐conversion luminescence (DCL), up‐conversion luminescence (UCL), persistent luminescence (PersL), optically stimulated luminescence (OSL), and thermally stimulated luminescence (TSL) across a wide spectral range. Unlike previously studied Pr 3+ ‐doped LAO, the co‐doped LAO:PG shows DCL in both UV‐visible and NIR regions and displays ultraviolet‐C UCL under visible excitation. Notably, we observe, for the first time, PersL lasting several minutes in these phosphors—an improvement over the non‐PersL behavior of Pr 3+ ‐only doped LAO. Additionally, the LAO:PG phosphors exhibit strong OSL response. TSL analysis reveals five distinct trap levels linked to these properties. Density functional theory calculations further correlate intrinsic defects to these traps, supporting a proposed mechanism for the observed multimodal luminescence. These findings highlight LAO:PG as a promising platform for developing advanced phosphors with integrated luminescence modes, paving the way for future applications in data storage, phototherapy, and anti‐counterfeiting technologies.

Chemistry↗
DOE OSTI2022

Weighted relaxation for multigrid reduction in time

Current trends in computer architectures now mean that faster computation speed must come primarily from increased concurrency, not faster clock speeds, which are stagnating. Thus, this situation creates bottlenecks for serial algorithms, including the well-known bottleneck for sequential time-integration, where each individual time-value (i.e., time-step) is computed sequentially. One approach to alleviate this and achieve parallelism in time is with multigrid. Here, in this work, we consider multigrid-reduction-in-time (MGRIT), a multilevel method applied to the time dimension that computes multiple time-steps in parallel. Like all multigrid methods, MGRIT relies on the complementary relationship between relaxation on a fine-grid and a correction from the coarse grid to solve the problem. All current MGRIT implementations are based on unweighted-Jacobi relaxation; here we introduce the concept of weighted relaxation to MGRIT. We derive new convergence bounds for weighted relaxation, and use this analysis to guide the selection of relaxation weights. Numerical results then demonstrate that by choosing appropriate non-unitary relaxation weights, one can achieve faster convergence rates and lower iteration counts for MGRIT when compared with unweighted relaxation. In most cases, weighted relaxation yields a 10%–20% saving in iterations, which is significant when using large high-performance computers. For A-stable integration schemes, results also illustrate that under-relaxation can restore convergence in some cases where unweighted relaxation is not convergent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Maintaining Trust in Reduction: Preserving the Accuracy of Quantities of Interest for Lossy Compression

As the growth of data sizes continues to outpace computational resources, there is a pressing need for data reduction techniques that can significantly reduce the amount of data and quantify the error incurred in compression. Compressing scientific data presents many challenges for reduction techniques since it is often on non-uniform or unstructured meshes, is from a high-dimensional space, and has many Quantities of Interests (QoIs) that need to be preserved. To illustrate these challenges, we focus on data from a large scale fusion code, XGC. XGC uses a Particle-In-Cell (PIC) technique which generates hundreds of PetaBytes (PBs) of data a day, from thousands of timesteps. XGC uses an unstructured mesh, and needs to compute many QoIs from the raw data, f.One critical aspect of the reduction is that we need to ensure that QoIs derived from the data (density, temperature, flux surface averaged momentums, etc.) maintain a relative high accuracy. We show that by compressing XGC data on the high-dimensional, nonuniform grid on which the data is defined, and adaptively quantizing the decomposed coefficients based on the characteristics of the QoIs, the compression ratios at various error tolerances obtained using a multilevel compressor (MGARD) increases more than ten times. We then present how to mathematically guarantee that the accuracy of the QoIs computed from the reduced f is preserved during the compression. We show that the error in the XGC density can be kept under a user-specified tolerance over 1000 timesteps of simulation using the mathematical QoI error control theory of MGARD, whereas traditional error control on the data to be reduced does not guarantee the accuracy of the QoIs.

Gong, Qian↗
DOE OSTI2024

MatRIS: Addressing the Challenges for Portability and Heterogeneity Using Tasking for Matrix Decomposition (Cholesky)

The ubiquitous in-node heterogeneity of HPC and cloud computing platforms makes software portability and performance optimization extremely challenging. Described here, the MatRIS multilevel math library abstraction framework employs tasking to alleviate these difficulties. MatRIS includes the IRIS task-based runtime on the bottom level and exposes different layers of abstraction to render algorithms architecturally agnostic. MatRIS ensures the decomposition and creation of tasks that represent the necessary encapsulation of the optimized kernels from both vendor and open-source math libraries. Once built, MatRIS can select different combinations of accelerators at runtime, making it portable even on diverse heterogeneous architectures. By leveraging the IRIS runtime’s features for managing heterogeneity, MatRIS deploys algorithms that remove the need to specify orchestration and data transfer. This study describes how the serial task abstraction of a tiled Cholesky factorization is made portable and scalable in the case of multi-device and multi-vendor heterogeneity on a node with NVIDIA and AMD GPUs by using MatRIS. First, we demonstrate that Cholesky in MatRIS provides multi-GPU scalability that offers competitive performance versus cuSolverMG. Then, we present the challenges and opportunities for heterogeneous execution.

Monil, M. A. H.↗