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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Finite-temperature density matrix embedding theory

We describe a formulation of the density matrix embedding theory at finite temperature. We present a generalization of the ground-state bath orbital construction that embeds a mean-field finite-temperature density matrix up to a given order in the Hamiltonian, or the Hamiltonian up to a given order in the density matrix. In this work, we assess the performance of the finite-temperature density matrix embedding on the one-dimensional Hubbard model both at half-filling and away from it, and the two-dimensional Hubbard model at half-filling, comparing to exact data where available, as well as results from finite-temperature density matrix renormalization group, dynamical mean-field theory, and dynamical cluster approximations. The accuracy of finite-temperature density matrix embedding appears comparable to that of the ground-state theory, with, at most, a modest increase in bath size, and competitive with that of cluster dynamical mean-field theory.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Embedding via the Exact Factorization Approach

We present a quantum electronic embedding method derived from the exact factorization approach to calculate static properties of a many-electron system. The method is exact in principle but the practical power lies in utilizing input from a low-level calculation on the entire system in a high-level method computed on a small fragment, as in other embedding methods. Here, the exact factorization approach defines an embedding Hamiltonian on the fragment. Further, various Hubbard models demonstrate that remarkably accurate ground-state energies are obtained over the full range of weak to strongly correlated systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Augmenting Graph Convolution with Distance Preserving Embedding for Improved Learning

Graph convolution incorporates topological information of a graph into learning. Message passing corresponds to traversal of a local neighborhood in classical graph algorithms. We show that incorporating additional global structures, such as shortest paths, through distance preserving embedding can improve performance. Our approach, Gavotte, significantly improves the performance of a range of popular graph neu-ral networks such as GCN, GA T,Graph SAGE, and GCNII for transductive learning. Gavotte also improves the performance of graph neural networks for full-supervised tasks, albeit to a smaller degree. As high-quality embeddings are generated by Gavotte as a by-product, we leverage clustering algorithms on these embed dings to augment the training set and introduce Gavotte+. Our results of Gavotte+ on datasets with very few labels demonstrate the advantage of augmenting graph convolution with distance preserving embedding.

Cong, Guojing↗
DOE OSTI2020

Low-cost embedded optical sensing systems for distribution transformer monitoring

There is a mounting need for low-cost monitoring with online sensing technologies to maintain grid reliability and uptime. Here we introduce an innovative low-cost, embedded optical sensing technology initially focused on transformers that was developed and demonstrated at a major electric utility, Con Edison. A version of it that can be retrofitted onto existing transformers in the field was also developed. Two new 500 kVA distribution network transformers were built with embedded fiber-optic (FO) sensors and qualified per industry standards. Vibration, temperature, and corrosion were key parameters monitored. The first transformer with embedded FO sensors was installed at a Con Edison facility and monitored at our team’s office. The second one was installed in an urban street-side underground location with online data processing/feature extraction algorithms and monitored through a wireless router. Additionally, an older transformer was also retrofitted. Data analysis was done on these transformers showing promising correlations with their corresponding loading cycles. Furthermore, key events such as the transformer primary-side energizing, and other events were detected. In general, the technology was demonstrated over 6 months across the 3 transformers instrumented with promising results. Thus, it has the potential to enable predictive maintenance for transformers and other grid assets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Efficient Simulation of Cascading Outages Using an Energy Function-Embedded Quasi-Steady-State Model

Here, this paper proposed an energy function-embedded quasi-steady-state model for efficient simulation of cascading outages on a power grid while addressing transient stability concerns. Compared to quasi-steady-state models, the proposed model incorporates short-term dynamic simulation and an energy function method to efficiently evaluate the transient stability of a power grid together with outage propagation without transient stability simulation. Cascading outage simulation using the proposed model conducts three steps for each disturbance such as a line outage. First, it performs time-domain simulation for a short term to obtain a post-disturbance trajectory. Second, along the trajectory, the system state with the local maximum potential energy is found and used as the initial point to search for a relevant unstable equilibrium by Newton's method. Third, the transient energy margin is estimated based on this unstable equilibrium to predict an out-of-step condition with generators. The proposed energy function-embedded quasi-steady-state model is tested in terms of its accuracy and time performance on an NPCC 140-bus power system and compared to a quasi-steady-state model embedding transient stability simulation.

Guo, Zhenping [Univ. of Tennessee, Knoxville, TN (↗
DOE OSTI2017

Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures

In this study, quantifying simulation uncertainties is a critical component of rigorous predictive simulation. A key component of this is forward propagation of uncertainties in simulation input data to output quantities of interest. Typical approaches involve repeated sampling of the simulation over the uncertain input data, and can require numerous samples when accurately propagating uncertainties from large numbers of sources. Often simulation processes from sample to sample are similar and much of the data generated from each sample evaluation could be reused. We explore a new method for implementing sampling methods that simultaneously propagates groups of samples together in an embedded fashion, which we call embedded ensemble propagation. We show how this approach takes advantage of properties of modern computer architectures to improve performance by enabling reuse between samples, reducing memory bandwidth requirements, improving memory access patterns, improving opportunities for fine-grained parallelization, and reducing communication costs. We describe a software technique for implementing embedded ensemble propagation based on the use of C++ templates and describe its integration with various scientific computing libraries within Trilinos. We demonstrate improved performance, portability and scalability for the approach applied to the simulation of partial differential equations on a variety of CPU, GPU, and accelerator architectures, including up to 131,072 cores on a Cray XK7 (Titan).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

REGULATORY REQUIREMENTS, GUIDANCE, AND REVIEW OF EMBEDDED DIGITAL DEVICES IN SAFETY SYSTEMS

Interest in embedded digital devices (EDDs) for monitoring and providing control of components is increasing in the nuclear power market as more and more users both within and outside the nuclear sector are employing EDDs. EDDs in other industries or countries may be known by the related terms as smart devices, devices of limited functionality, or intelligent devices. EDDs can sometimes perform a safety role when embedded in components such as temperature transmitters, pressure transmitters, voltage regulators, gas analyzers, boiler controllers, relays, and radiation monitors. The introduction of EDDs can affect safety by creating new hazards, vulnerabilities, failure modes, triggering mechanisms, and other potential safety concerns at both the component and system levels. The U.S. Nuclear Regulatory Commission’s (NRC’s) existing regulations and guidance cover the use of EDDs in safety-related and important to safety systems in nuclear power plants (NPPs). In addition to potential future challenges, the nature of EDDs being implemented in similar or identical manners between different plants and industries opens up the potential of leveraging the reviews of other regulators or utilization of industry certifications.Key words: embedded digital device, firmware

Halverson, D. S.↗
DOE OSTI2021

A Bayesian Calibration Framework with Embedded Model Error for Model Diagnostics

We study the utility and performance of a Bayesian model error embedding construction in the context of molecular dynamics modeling of metallic alloys, where we embed model error terms in existing interatomic potential model parameters. To alleviate the computational burden of this approach, we propose a framework combining likelihood approximation and Gaussian process surrogates. Here we leverage sparse Gaussian process techniques to construct a hierarchy of increasingly accurate but more expensive surrogate models. This hierarchy is then exploited by multilevel Markov chain Monte Carlo methods to efficiently sample from the target posterior distribution. We illustrate the utility of this approach by calibrating an interatomic potential model for a family of gold-copper alloys. In particular, this case study highlights effective means for dealing with computational challenges with Bayesian model error embedding in large-scale physical models, and the utility of embedded model error for model diagnostics.

Bayesian inference↗
DOE OSTI2025

Toward particle accelerator machine state embeddings as a modality for large language models

Understanding and diagnosing the state of a particle accelerator requires navigating high-dimensional control system data, often involving hundreds of interdependent parameters. We propose a novel multimodal embedding framework that jointly learns representations of machine states from both numerical control system readouts and natural language descriptions. This enables the translation of complex machine conditions into human-readable summaries while maintaining fidelity to the underlying physical system. The obtained embeddings are subsequently adapted to an open-weights large language model via cross-attention conditioning. We demonstrate a first implementation trained on European XFEL machine state data. This work covers the embedding model architecture, training methodology, and presents initial examples demonstrating the model's capabilities in action. Due to the general concept of machine state, the model can be easily adapted to other facilities and control system environments.

Accelerator Physics↗
DOE OSTI2013

A Multi-scale Approach to Statistical and Model-based Structural Health Monitoring with Application to Embedded Sensing for Wind Energy [Dissertation]

This dissertation presents a systems-level approach to multi-scale structural health monitoring (SHM) with specific focus on wind turbine rotor blades, combining innovative sensing platforms for incipient damage detection with state estimation for structural performance assessment. The practical implementation of this approach rests in three areas: hardware development and deployment for embedded data acquisition; demonstration of incipient damage detection using embedded systems for active-sensing SHM, including an in depth assessment of sensor diagnostics; and development of a nonlinear observer for state and loads estimation applied to a geometrically nonlinear beam model. Structural Health Monitoring is generally defined as the development of an in-situ damage assessment capability, and when combined predictive loading and failure models, enables risk-informed models for decision-making. These decision models require contributions from a wide variety of technology areas. Sensing systems (in many cases, capable of providing multiple data types) must be developed specifically to provide the data necessary for structural damage detection and performance assessment. A means of sensor diagnostics is necessary to provide confidence in the recorded data. Statistical modeling and classification feed the development of optimal detectors necessary to ascertain the presence, location, and severity of damage. Methods of state estimation are needed to map kinematic measurements to physical performance metrics. A probabilistic representation of future loads applied to a structural model enables an assessment of the structure’s future performance. Finally, a cost model is combined with a probabilistic risk assessment, given the detectors’ output and the structure’s estimated future performance, to render the risk-minimizing decision. This dissertation presents key contributions among the underpinnings of this ultimate decision model: (1) embedded sensor development and deployment; (2) sensor diagnostics for active-sensing methods; (3) an assessment of incipient damage detection performance for large-scale composite structures; and (4) the development and application of a state observer, demonstrated in the specific case of a geometrically nonlinear beam model.

17 WIND ENERGY↗
DOE OSTI2015

Embedded Volttron Specification - Benchmarking Small Footprint Compute Devices for Volttron

An embedded system is a small footprint computing unit that typically serves a specific purpose closely associated with measurements and control of hardware devices. These units are designed for reasonable durability and operations in a wide range of operating conditions. Some embedded systems support real-time operations and can demonstrate high levels of reliability. Many have failsafe mechanisms built to handle graceful shutdown of the device in exception conditions. The available memory, processing power, and network connectivity of these devices are limited due to the nature of their specific-purpose design and intended application. Industry practice is to carefully design the software for the available hardware capability to suit desired deployment needs. Volttron is an open source agent development and deployment platform designed to enable researchers to interact with devices and appliances without having to write drivers themselves. Hosting Volttron on small footprint embeddable devices enables its demonstration for embedded use. This report details the steps required and the experience in setting up and running Volttron applications on three small footprint devices: the Intel Next Unit of Computing (NUC), the Raspberry Pi 2, and the BeagleBone Black. In addition, the report also details preliminary investigation of the execution performance of Volttron on these devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

ROPE: Recoverable Order-Preserving Embedding of Natural Language

We present a novel Recoverable Order-Preserving Embedding (ROPE) of natural language. ROPE maps natural language passages from sparse concatenated one-hot representations to distributed vector representations of predetermined fixed length. We use Euclidean distance to return search results that are both grammatically and semantically similar. ROPE is based on a series of random projections of distributed word embeddings. We show that our technique typically forms a dictionary with sufficient incoherence such that sparse recovery of the original text is possible. We then show how our embedding allows for efficient and meaningful natural search and retrieval on Microsoft’s COCO dataset and the IMDB Movie Review dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Embedded Sensors and Controls to Improve Component Performance and Reliability -- Bench-scale Testbed Design Report

Embedded instrumentation and control systems that can operate in extreme environments are challenging due to restrictions on sensors and materials. As a part of the Department of Energy's Nuclear Energy Enabling Technology cross-cutting technology development programs Advanced Sensors and Instrumentation topic, this report details the design of a bench-scale embedded instrumentation and control testbed. The design goal of the bench-scale testbed is to build a re-configurable system that can rapidly deploy and test advanced control algorithms in a hardware in the loop setup. The bench-scale testbed will be designed as a fluid pump analog that uses active magnetic bearings to support the shaft. The testbed represents an application that would improve the efficiency and performance of high temperature (700 C) pumps for liquid salt reactors that operate in an extreme environment and provide many engineering challenges that can be overcome with embedded instrumentation and control. This report will give details of the mechanical design, electromagnetic design, geometry optimization, power electronics design, and initial control system design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Investigation on Smart Parts with Embedded Piezoelectric Sensors via Additive Manufacturing

The goal of this proposed research is to design, fabricate, and evaluate “smart parts” with embedded sensors for energy systems. The “smart parts” will be fabricated using Electron Beam Melting (EBM) 3D printing technique with built-in piezoceramic sensors. The objectives of the proposed project are: 1) Fabricate energy system related components with embedded sensors, 2) Evaluate the mechanical properties and sensing functionalities of the “smart parts” with embedded piezoceramic sensors, and 3) Assess in-situ sensing capability of energy system parts. The second year’s research of the research is centered on fabrication of the “smart parts” with considerations of overall material property as well as demonstration of sensing functionalities. The results for the final report are presented here, including all research accomplishment, project management. Details are included such as: how the design and fabrication of sensor packaging could improve the sensor performance, demonstration of “smart parts” sensing capabilities, analysis on the elements that constitute the “smart sensors”, advanced “stop and go” fabrication process, smart injector fabrication using SLM technology, smart injector testing in combustion environments etc. Research results to date have generated several posters and papers.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Optimal Control for Fast Frequency Response and Black-Start using Embedded Storages with Grid-Forming Control

The grid-forming inverter is regarded as the solution for integrating high levels of renewable resources into future power systems. Ensuring the stable operation of grids necessitates that grid-forming inverters offer fast frequency response. This report introduces an optimal control method that coordinates the embedded storage within the grid-forming control model with conventional synchronous generators. The optimized active power reference for the embedded storage is generated using receding horizon optimization control, aiming to keep the center of inertia frequency within acceptable limits. The effectiveness of the proposed control is verified through testing in the IEEE 39-bus system. In addition, with grid-forming capability, we will also investigate the application of using mobile embedded storages as black-start units to provide cranking power to energize non-blackstart generators in a black-start process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Understanding Generative AI Content with Embedding Models

The construction of high-quality numerical features is critical to any quantitative data analysis. Feature engineering has been historically addressed by carefully hand-crafting data representations based on domain expertise. This work views the internal representations of modern deep neural networks (DNNs), called embeddings, as an implicit form of traditional feature engineering. For trained DNNs, we show that these embeddings can reveal interpretable, high-level concepts in unstructured sample data. We use these embeddings in natural language and computer vision tasks to uncover both inherent heterogeneity in the underlying data and human-understandable explanations for it. In particular, we find empirical evidence that there is inherent separability between real data and those generated from AI models.

Vargas, Max↗
DOE OSTI2025

Data and Code for Understanding Generative AI Content with Embedding Models

This repository contains code for the experiments in the paper "Understanding Generative AI Content with Embedding Models". Constructing high-quality features is critical to any quantitative data analysis. While feature engineering was historically addressed by carefully hand-crafting data representations based on domain expertise, deep neural networks (DNNs) now offer a radically different approach. DNNs implicitly engineer features by transforming their input data into hidden feature vectors called embeddings. For embedding vectors produced by foundation models -- which are trained to be useful across many contexts -- we demonstrate that simple and well-studied dimensionality-reduction techniques such as Principal Component Analysis uncover inherent heterogeneity in input data concordant with human-understandable explanations. Of the many applications for this framework, we find empirical evidence that there is intrinsic separability between real samples and those generated by artificial intelligence (AI).

Vargas, Max [Pacific Northwest National Laboratory↗
DOE OSTI2023

Direct Ink-Write Printing of Ceramic Clay with an Embedded Wireless Temperature and Relative Humidity Sensor

This research presents a simple method to additively manufacture Cone 5 porcelain clay ceramics by using the direct ink-write (DIW) printing technique. DIW has allowed the application of extruding highly viscous ceramic materials with relatively high-quality and good mechanical properties, which additionally allows a freedom of design and the capability of manufacturing complex geometrical shapes. Clay particles were mixed with deionized (DI) water at different ratios, where the most suitable composition for 3D printing was observed at a 1:5 w/c ratio (16.2 wt.%. of DI water). Differential geometrical designs were printed to demonstrate the printing capabilities of the paste. In addition, a clay structure was fabricated with an embedded wireless temperature and relative humidity (RH) sensor during the 3D printing process. The embedded sensor read up to 65% RH and temperatures of up to 85 °F from a maximum distance of 141.7 m. The structural integrity of the selected 3D printed geometries was confirmed through the compressive strength of fired and non-fired clay samples, with strengths of 70 MPa and 90 MPa, respectively. This research demonstrates the feasibility of using the DIW printing of porcelain clay with embedded sensors, with fully functional temperature- and humidity-sensing capabilities.

42 ENGINEERING↗