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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Targeted Energy Management System Toolset

TEMST integrates advanced sensor systems with advanced computational intelligence, e.g. neural networks and fuzzy arithmetic, to enable effective energy management while minimizing negative human comfort consequences and promoting improved occupant satisfaction. Preliminary work suggests that energy cost reductions above ten percent are easily achievable. In addition to optimizing existing Energy Management Systems (EMS), the proposed toolset will be both resilient and secure in order to proactively maintain a safe level of operational normalcy in response to anomalies and malicious actions.

Rieger, CraigG.↗
DOE OSTI2023

Design of an ultra-low mode volume piezo-optomechanical quantum transducer

Coherent transduction of quantum states from the microwave to the optical domain can play a key role in quantum networking and distributed quantum computing. We present the design of a piezo-optomechanical device formed in a hybrid lithium niobate on silicon platform, that is suitable for microwave-to-optical quantum transduction. Our design is based on acoustic hybridization of an ultra-low mode volume piezoacoustic cavity with an optomechanical crystal cavity. The strong piezoelectric nature of lithium niobate allows us to mediate transduction via an acoustic mode which only minimally interacts with the lithium niobate, and is predominantly silicon-like, with very low electrical and acoustic loss. We estimate that this transducer can realize an intrinsic conversion efficiency of up to 35% with <0.5 added noise quanta when resonantly coupled to a superconducting transmon qubit and operated in pulsed mode at 10 kHz repetition rate. The performance improvement gained in such hybrid lithium niobate-silicon transducers make them suitable for heralded entanglement of qubits between superconducting quantum processors connected by optical fiber links.

Chiappina, Piero↗
DOE OSTI2024

2D material platform for overcoming the amplitude–phase tradeoff in ring resonators

Compact and high-speed electro-optic phase modulators play a vital role in various large-scale applications including optical computing, quantum and neural networks, and optical communication links. Conventional electro-refractive phase modulators such as silicon (Si), III-V and graphene on Si suffer from a fundamental tradeoff between device length and optical loss that limits their scaling capabilities. High-finesse ring resonators have been traditionally used as compact intensity modulators, but their use for phase modulation has been limited due to the high insertion loss associated with the phase shift. Here, we show that high-finesse resonators can achieve a strong phase shift with low insertion loss by simultaneous modulation of the real and imaginary parts of the refractive index, to the same extent, i.e., Δ n Δ k ∼1. To implement this strategy, we demonstrate an active hybrid platform that combines a low-loss SiN ring resonator with 2D materials such as graphene and transition metal dichalcogenide [tungsten disulphide (WSe 2 )], which induces a strong change in the imaginary and real parts of the index. Our platform consisting of a 25 µm long Gr-Al 2 O 3 -WSe 2 capacitor embedded on a SiN ring of 50 µm radius (∼8% ring coverage) achieves a continuous phase shift of (0.46±0.05) π radians with an insertion loss (IL) of 3.18±0.20 dB and a transmission modulation (Δ T Ring ) of 1.72±0.15dB at a probe wavelength ( λ p ) of 1646.18 nm. We find that our Gr-Al 2 O 3 -WSe 2 capacitor exhibits a phase modulation efficiency ( V π 2 ⋅ L ) of 0.530±0.016V⋅cm and can support an electro-optic bandwidth of 14.9±0.1GHz. We further show that our platform can achieve a phase shift of π radians with an IL of 5 dB and a minimum Δ T of 0.046 dB. We demonstrate the broadband nature of the binary phase response, by measuring a phase shift of (1.00±0.10) π radians, with an IL of 5.20±0.31dB and a minimal Δ T Ring of 0.015±0.006dB for resonances spanning from 1564 to 1650 nm. This SiN–2D hybrid platform provides the design for compact and high-speed reconfigurable circuits with graphene and transition metal dichalcogenide (TMD) monolayers that can enable large-scale photonic systems.

Datta, Ipshita (ORCID:0000000318842882)↗
DOE OSTI2024

Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation

Here, we present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks (GANs), normalizing flows, and variational auto-encoders (VAEs), are usually considered as unsupervised learning models, because labeled data are usually unavailable for training. Despite the success of the generative models, there are several issues with the unsupervised training, e.g., requirement of reversible architectures, vanishing gradients, and training instability. To enable supervised learning in generative models, we utilize the score-based diffusion model to generate labeled data. Unlike existing diffusion models that train neural networks to learn the score function, we develop a training-free score estimation method. This approach uses mini-batch-based Monte Carlo estimators to directly approximate the score function at any spatial-temporal location in solving an ordinary differential equation (ODE), corresponding to the reverse-time stochastic differential equation (SDE). This approach can offer both high accuracy and substantial time savings in neural network training. Once the labeled data are generated, we can train a simple, fully connected neural network to learn the generative model in the supervised manner. Compared with existing normalizing flow models, our method does not require the use of reversible neural networks and avoids the computation of the Jacobian matrix. Compared with existing diffusion models, our method does not need to solve the reverse-time SDE to generate new samples. As a result, the sampling efficiency is significantly improved. We demonstrate the performance of our method by applying it to a set of 2D datasets as well as real data from the University of California Irvine (UCI) repository.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Fiats: Functional inference and training for surrogates

Fiats provides a platform for research on the training and deployment of neural-network surrogate models for computational science. Fiats also supports exploring, advancing, and combining functional, object-oriented, and parallel programming patterns in Fortran 2023. As such, the Fiats name has dual expansions: “Functional Inference And Training for Surrogates” or “Fortran Inference And Training for Science.” Fiats inference and training procedures are pure and therefore satisfy a language constraint imposed on procedure invocations inside Fortran’s parallel loop construct: do concurrent. Furthermore, the Fiats training procedures are built around a do concurrent parallel reduction. Several compilers can automatically parallelize do concurrent on Central Processing Units (CPUs) or Graphics Processing Units (GPUs). Fiats thus aims to achieve performance portability through standard language mechanisms.

Rouson, Damian [Lawrence Berkeley National Laborat↗
DOE OSTI2019

Computational Bayesian Framework for Quantification and Reduction of Predictive Uncertainty in Subsurface Environmental Modeling

Subsurface environmental systems are open and complex, in which intricate hydrologic, microbiologic, and geochemical processes occur and interact at multiple scales. Understanding and predicting system responses to natural forces (e.g., climate changes) and human activities (e.g., contaminant remediation and CO 2 sequestration) is indispensable for managing water resources, cleaning subsurface contamination at many sites of the Department of Energy (DOE), and providing expert analysis to inform policy-making on long-term stewardship of nuclear waste disposal and CO 2 storage sites. Groundwater reactive transport modeling is necessary for understanding and predicting the complex subsurface environmental system. However, as complex systems are never completely predictable, model predictions of the subsurface system are inherently uncertain, and uncertainty is one of the greatest obstacles in simulating the subsurface environment. Quantifying predictive uncertainty of the complex system is critical to test model predictive power, support decision-making, and guide experimental design and data collection for uncertainty reduction. This project tackled two major obstacles to efficient and effective quantification and reduction of predictive uncertainty in simulating the complex subsurface environment. One obstacle is that existing methods of uncertainty quantification are too fragmented and incomplete for understanding and predicting the complex subsurface environment as a whole. The other obstacle is that existing methods of uncertainty quantification are computationally demanding and may not be applicable to groundwater reactive transport modeling that is always computationally expensive. The above two obstacles to uncertainty quantification directly affect the design of data collection schemes for uncertainty reduction. In this project, we developed a comprehensive Bayesian framework to remove the first obstacle. The second obstacle was tackled by using the sparse grid methods, which were originally developed and analyzed for numerical integration in the field of applied mathematics. We integrated the sparse grid methods into the Bayesian framework seamlessly, which led to a sparse-grid-based method of Bayesian uncertainty analysis. The developed computational Bayesian framework has been applied to a number of subsurface environmental problems through collaborating with scientists at the Pacific Northwest National Laboratory and Oak Ridge National Laboratory. The applications demonstrated that the computational Bayesian framework is general and compatible with any models and numerical codes of subsurface modeling. It is thus can be applied to gain insights for subsurface environmental modeling. This project helped the PI achieve his career goal to advance the state-of-the-art computational methods in uncertainty quantification for subsurface environmental modeling. This project supported two doctoral students, two master students, and one post-doc. The two doctoral students, after their graduation, worked as post-docs at the Pacific Northwest National Laboratory and the Oak Ridge National Laboratory. The project produced a total of 29 journal articles (four under review), one book chapter, and a number of conference proceedings and abstracts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Multi-scale Molecular Systems Biology: Reconstruction and Model Optimization (Final Report)

Within the Biological and Environmental Research program, the goal of the Genomic Science program is to achieve a predictive, systems-level understanding of plants, microbes, and biological communities to enable biobased solutions to DOE mission challenges in energy and environment. To achieve this goal, an ultimate objective is to develop the knowledgebase, computational infrastructure, and modeling capabilities to advance predictive understanding and manipulation of biological systems. Our project has increased the modeling capabilities and computational infrastructure required to advance predictive understanding and manipulation of biological systems. In particular, we have developed novel algorithms, for modelling biochemical reaction kinetics at genome-scale. These new algorithms have been publicly disseminated as open source software with extensive documentation and tutorials that enable widespread application of this advanced software to practical modelling scenarios. The technical effectiveness of all novel software has been conclusively demonstrated and the quality of this software is maintained by a dedicated continuous integration system. Ultimately, the results of this project will benefit the public because it will enable biologists to more accurately computationally model biological systems that need to be understood to address DOE challenges in energy and environment.

09 BIOMASS FUELS↗
DOE OSTI2024

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA): Molecular and Computational Technologies for Environmental Microbiology (Final Scientific/Technical Report)

The ENIGMA science focus area (SFA) is a multi-disciplinary, multi-institutional research effort focused on addressing foundational knowledge gaps in environmental microbial communities by studying groundwater and sediment microbiomes in the shallow subsurface at the contaminated Oak Ridge Reservation (ORR). We seek to discover and characterize the reciprocal interactions between the microbial communities and the geochemical and geophysical parameters of the shallow subsurface within the contamination plume. The primary goal of this subcontract was to develop experimental and computational tools to advance our understanding of microbial adaptation and community assembly in contaminated environments, with specific efforts in high-throughput genomic methods, microbial ecology tools, and studies of heavy metal contamination impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Algebraic Bethe Circuits

The Algebraic Bethe Ansatz (ABA) is a highly successful analytical method used to exactly solve several physical models in both statistical mechanics and condensed-matter physics. Here we bring the ABA into unitary form, for its direct implementation on a quantum computer. This is achieved by distilling the non-unitary R matrices that make up the ABA into unitaries using the QR decomposition. Our algorithm is deterministic and works for both real and complex roots of the Bethe equations. We illustrate our method on the spin-$\frac{1}{2}$ XX and XXZ models. We show that using this approach one can efficiently prepare eigenstates of the XX model on a quantum computer with quantum resources that match previous state-of-the-art approaches. We run small-scale error mitigated implementations on the IBM quantum computers, including the preparation of the ground state for the XX and XXZ models on 4 sites. Finally, we derive a new form of the Yang-Baxter equation using unitary matrices, and also verify it on a quantum computer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The Astronomy Commons Platform: A Deployable Cloud-based Analysis Platform for Astronomy

Abstract We present a scalable, cloud-based science platform solution designed to enable next-to-the-data analyses of terabyte-scale astronomical tabular data sets. The presented platform is built on Amazon Web Services (over Kubernetes and S3 abstraction layers), utilizes Apache Spark and the Astronomy eXtensions for Spark for parallel data analysis and manipulation, and provides the familiar JupyterHub web-accessible front end for user access. We outline the architecture of the analysis platform, provide implementation details and rationale for (and against) technology choices, verify scalability through strong and weak scaling tests, and demonstrate usability through an example science analysis of data from the Zwicky Transient Facility’s 1Bn+ light-curve catalog. Furthermore, we show how this system enables an end user to iteratively build analyses (in Python) that transparently scale processing with no need for end-user interaction. The system is designed to be deployable by astronomers with moderate cloud engineering knowledge, or (ideally) IT groups. Over the past 3 yr, it has been utilized to build science platforms for the DiRAC Institute, the ZTF partnership, the LSST Solar System Science Collaboration, and the LSST Interdisciplinary Network for Collaboration and Computing, as well as for numerous short-term events (with over 100 simultaneous users). In a live demo instance, the deployment scripts, source code, and cost calculators are accessible. 4 4 http://hub.astronomycommons.org/

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Measurements of file transfer rates over dedicated long-haul connections

Wide-area file transfers are an integral part of several High-Performance Computing (HPC) scenarios. Dedicated network connections with high capacity, low loss rate and low competing traffic, are increasingly being provisioned over current HPC infrastructures to support such transfers. To gain insights into these file transfers, we collected transfer rate measurements for Lustre and xfs file systems between dedicated multi-core servers over emulated 10 Gbps connections with round trip times (rtt) in 0-366 ms range. Memory transfer throughput over these connections is measured using iperf, and file IO throughput on host systems is measured using xddprof. We consider two file system configurations: Lustre over IB network and xfs over SSD connected to PCI bus. Files are transferred using xdd across these connections, and the transfer rates are measured, which indicate the need to jointly optimize the connection and host file IO parameters to achieve peak transfer rates. In particular, these measurements indicate that (i) peak file transfer rate is lower than peak connection and host IO throughput, in some cases by as much as 50% or lower, (ii) xdd request sizes that achieve peak throughput for host file IO do not necessarily lead to peak file transfer rates, and (iii) parallelism in host IO and TCP transport does not always improve the file transfer rates.

log-haul connections↗
DOE OSTI2016

Measurements of file transfer rates over dedicated long-haul connections

Wide-area file transfers are an integral part of several High-Performance Computing (HPC) scenarios. Dedicated network connections with high capacity, low loss rate and low competing traffic, are increasingly being provisioned over current HPC infrastructures to support such transfers. To gain insights into these file transfers, we collected transfer rate measurements for Lustre and xfs file systems between dedicated multi-core servers over emulated 10 Gbps connections with round trip times (rtt) in 0-366 ms range. Memory transfer throughput over these connections is measured using iperf, and file IO throughput on host systems is measured using xddprof. We consider two file system configurations: Lustre over IB network and xfs over SSD connected to PCI bus. Files are transferred using xdd across these connections, and the transfer rates are measured, which indicate the need to jointly optimize the connection and host file IO parameters to achieve peak transfer rates. In particular, these measurements indicate that (i) peak file transfer rate is lower than peak connection and host IO throughput, in some cases by as much as 50% or lower, (ii) xdd request sizes that achieve peak throughput for host file IO do not necessarily lead to peak file transfer rates, and (iii) parallelism in host IO and TCP transport does not always improve the file transfer rates.

Rao, Nageswara S↗
DOE OSTI2016

Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification

Learning the representation of shape cues in 2D & 3D objects for recognition is a fundamental task in computer vision. Deep neural networks (DNNs) have shown promising performance on this task. Due to the large variability of shapes, accurate recognition relies on good estimates of model uncertainty, ignored in traditional training of DNNs, typically learned via stochastic optimization. This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (SG-MCMC) to learn weight uncertainty in DNNs. It yields principled Bayesian interpretations for the commonly used Dropout/DropConnect techniques and incorporates them into the SG-MCMC framework. Extensive experiments on 2D & 3D shape datasets and various DNN models demonstrate the superiority of the proposed approach over stochastic optimization. Our approach yields higher recognition accuracy when used in conjunction with Dropout and Batch-Normalization.

convolutional neural network↗
DOE OSTI2021

Proof-of-work for securing IoT and autonomous systems

A first device in a network issues a proof-of-work challenge to a second device in the network. The second device computes a cryptographic function result to satisfy a criterion indicated in the challenge. The second device returns a challenge response comprising inputs to the cryptographic function that the second device used to compute the cryptographic function result satisfying the criterion. The first device, upon receiving the challenge response, verifies that the inputs to the cryptographic function yield a result satisfying the criterion. Responsive to verifying the challenge response, the first device updates a blockchain based upon the challenge response.

Chan, Calvin↗