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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multiview Incomplete Knowledge Graph Integration with application to cross-institutional EHR data harmonization

Objective: The growing availability of electronic health records (EHR) data opens opportunities for integrative analysis of multi-institutional EHR to produce generalizable knowledge. A key barrier to such integrative analyses is the lack of semantic interoperability across different institutions due to coding differences. We propose a Multiview Incomplete Knowledge Graph Integration (MIKGI) algorithm to integrate information from multiple sources with partially overlapping EHR concept codes to enable translations between healthcare systems. Methods: The MIKGI algorithm combines knowledge graph information from (i) embeddings trained from the co-occurrence patterns of medical codes within each EHR system and (ii) semantic embeddings of the textual strings of all medical codes obtained from the Self-Aligning Pretrained BERT (SAPBERT) algorithm. Due to the heterogeneity in the coding across healthcare systems, each EHR source provides partial coverage of the available codes. MIKGI synthesizes the incomplete knowledge graphs derived from these multi-source embeddings by minimizing a spherical loss function that combines the pairwise directional similarities of embeddings computed from all available sources. MIKGI outputs harmonized semantic embedding vectors for all EHR codes, which improves the quality of the embeddings and enables direct assessment of both similarity and relatedness between any pair of codes from multiple healthcare systems. Results: With EHR co-occurrence data from Veteran Affairs (VA) healthcare and Mass General Brigham (MGB), MIKGI algorithm produces high quality embeddings for a variety of downstream tasks including detecting known similar or related entity pairs and mapping VA local codes to the relevant EHR codes used at MGB. Based on the cosine similarity of the MIKGI trained embeddings, the AUC was 0.918 for detecting similar entity pairs and 0.809 for detecting related pairs. For cross-institutional medical code mapping, the top 1 and top 5 accuracy were 91.0% and 97.5% when mapping medication codes at VA to RxNorm medication codes at MGB; 59.1% and 75.8% when mapping VA local laboratory codes to LOINC hierarchy. When trained with 500 labels, the lab code mapping attained top 1 and 5 accuracy at 77.7% and 87.9%. MIKGI also attained best performance in selecting VA local lab codes for desired laboratory tests and COVID-19 related features for COVID EHR studies. Compared to existing methods, MIKGI attained the most robust performance with accuracy the highest or near the highest across all tasks. Conclusions: The proposed MIKGI algorithm can effectively integrate incomplete summary data from biomedical text and EHR data to generate harmonized embeddings for EHR codes for knowledge graph modeling and cross-institutional translation of EHR codes.

Zhou, Doudou↗
DOE OSTI2017

Dependence of Strain Rate Sensitivity on the Slip System: A Molecular Dynamics Simulation

The strain rate of deformation might affect mechanical properties of metallic materials, especially at elevated temperatures. Due to the nature of dislocation slip, it is anticipated that strain rate sensitivity (SRS) depends on the slip system. While the dependency of SRS on the temperature and strain rate of the deformation is well recognized, its dependence on the slip system is not well understood. Accordingly, the molecular dynamics simulation was used to investigate the dependence of strain rate sensitivity of pure Al single crystals on the slip system. In this study, the embedded atom method (EAM) potential for Al was employed. SRS and shear strength of the material were studied in four different slip systems and at two temperatures of 300 and 500 K. It was found that SRS of the material depends on the slip system in addition to the temperature, and SRS was higher in less compact systems with more difficult slip. The dislocation theories were used to rationalize the simulation results.

36 MATERIALS SCIENCE↗
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 OSTI2021

Open Radiation Monitoring: Conceptual System Design

The Open Radiation Monitoring (ORM) Project seeks to develop and demonstrate a modular radiation detection architecture designed specifically for use in arms control treaty verification (ACTV) applications that will facilitate rapid development of trusted systems to meet the needs of potential future treaties. Development of trusted systems to support potential future treaties is a complex and costly endeavor that typically results in a purpose-built system designed to perform one specific task. The majority of prior trusted system development efforts have relied on the use of commercial embedded computers or microprocessors to control the system and process the acquired data. These processors are complex, making authentication and certification of measurement systems and collected data challenging and time consuming. We believe that a modular architecture can be used to reduce more complex systems to a series of single-purpose building blocks that could be used to implement a variety of detection modalities with shared functionalities. With proper design, the functionality of individual modules can be confirmed through simple input/output testing, thereby facilitating equipment inspection and in turn building trust in the equipment by all treaty parties. Furthermore, a modular architecture can be used to control data flow within the measurement system, reducing the risk of "hidden switches" and constraining the amount of sensitive information that could potentially be inadvertently leaked. This report documents a conceptual modular system architecture that is designed to facilitate inspection in an effort to reduce overall authentication and certification burden. As of publication, this architecture remains in a conceptual phase and additional funding is required to prove out the utility of a modular architecture and test the assumptions used to rationalize the design.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Multicomponent density functional theory embedding formulation

Multicomponent density functional theory (DFT) methods have been developed to treat two types of particles, such as electrons and nuclei, quantum mechanically at the same level. In the nuclear-electronic orbital (NEO) approach, all electrons and select nuclei, typically key protons, are treated quantum mechanically. For multicomponent DFT methods developed within the NEO framework, electron-proton correlation functionals based on explicitly correlated wavefunctions have been designed and used in conjunction with well-established electronic exchange-correlation functionals. Herein a general theory for multicomponent embedded DFT is developed to enable the accurate treatment of larger systems. In the general theory, the total electronic density is separated into two subsystem densities, denoted as regular and special, and different electron-proton correlation functionals are used for these two electronic densities. In the specific implementation, the special electron density is defined in terms of spatially localized Kohn-Sham electronic orbitals, and electron-proton correlation is included only for the special electron density. The electron-proton correlation functional depends on only the special electron density and the proton density, whereas the electronic exchange-correlation functional depends on the total electronic density. This scheme includes the essential electron-proton correlation, which is a relatively local effect, as well as the electronic exchange-correlation for the entire system. This multicomponent DFT-in-DFT embedding theory is applied to the HCN and FHF{sup −} molecules in conjunction with two different electron-proton correlation functionals and three different electronic exchange-correlation functionals. The results illustrate that this approach provides qualitatively accurate nuclear densities in a computationally tractable manner. The general theory is also easily extended to other types of partitioning schemes for multicomponent systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Overview of the Langmuir probe system on the Mega Ampere Spherical Tokamak (MAST) Upgrade

A detailed description of the Langmuir probe system on Mega Ampere Spherical Tokamak Upgrade is presented. The system features 850 tile-embedded probes and 40 bespoke electronic modules that each have the capability to drive and acquire data from up to 16 probes in a time-multiplexed manner. The system provides spatiotemporal-resolved measurements (1 cm and ~1 ms, respectively) in the divertor region of ion saturation current, electron temperature, and floating potential. The standard interpretation of current-voltage (IV) characteristics is to apply a four-parameter fit, based on unmagnetized probe theory, which includes a linear model for the ion saturation region. In conclusion, to mitigate the effect of the magnetic field, analysis is restricted to the region of the IV characteristic, which is sensitive to only the tail of the electron energy distribution function.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution

Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models. However, in the absence of complete prior knowledge of a dynamical system's physical characteristics, determining the optimal structure and optimization strategy for these models can be difficult. In this work, we explore methods for discovering neural state space dynamics models for system identification. Starting with a design space of block-oriented state space models and structured linear maps with strong physical priors, we encode these components into a model genome alongside network structure, penalty constraints, and optimization hyperparameters. Demonstrating the overall utility of the design space, we employ an asynchronous genetic search algorithm that alternates between model selection and optimization and obtains accurate physically consistent models of three physical systems: an aerodynamics body, a continuous stirred tank reactor, and a two tank interacting system.

genetic algorithms, neural architecture search, ne↗
DOE OSTI2019

Stochastic embedding DFT: Theory and application to p -nitroaniline in water

Over this past decade, we combined the idea of stochastic resolution of identity with a variety of electronic structure methods. In our stochastic Kohn-Sham density functional theory (DFT) method, the density is an average over multiple stochastic samples, with stochastic errors that decrease as the inverse square root of the number of sampling orbitals. Here in this paper, we develop a stochastic embedding density functional theory method (se-DFT) that selectively reduces the stochastic error (specifically on the forces) for a selected subsystem(s). The motivation, similar to that of other quantum embedding methods, is that for many systems of practical interest, the properties are often determined by only a small subsystem. In stochastic embedding DFT, two sets of orbitals are used: a deterministic one associated with the embedded subspace and the rest, which is described by a stochastic set. The method agrees exactly with deterministic calculations in the limit of a large number of stochastic samples. We apply se-DFT to study a p-nitroaniline molecule in water, where the statistical errors in the forces on the system (the p-nitroaniline molecule) are reduced by an order of magnitude compared with nonembedding stochastic DFT.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Resilient Operating Constraints for Power Distribution Systems under Setpoint Attacks

Integration and operation of distributed generation (DG) and energy storage (ES) in power distribution systems are enabled by communication networks and embedded sensor and control devices that increase the vulnerability of the systems to cyber-threats, broadening the attack surface and making adversary actions more unpredictable. This paper proposes a methodology that uses ellipsoidal approximations to quantify the potential damage caused by successful attacks that affect, directly or indirectly, the desired operation setpoints and may drive the power distribution operation to unsafe states by violating the limits of voltage or line flows. More specifically, a new methodology is introduced to find the optimal non-symmetric operating constraints that can be imposed to each DG and ES in order to guarantee that the power distribution system is resilient to any malicious setpoints. The proposed method takes as inputs the system topology, DG and ES capabilities, and load limits to solve a convex optimization problem formulated using linear matrix inequalities (LMIs) and the power flow equations. The proposed solution is agnostic to the attacker's action or load profile and it does not require any assumption about the location or means of the attack. The numerical results on a test distribution feeder with several DG and ES illustrate how the proposed resilient operating constraints guarantee the security of the power distribution system under setpoint attacks.

Giraldo, Jairo↗
DOE OSTI2017

Aspen-based performance and energy modeling frameworks

Now with the anticipation of exascale architectures, energy consumption is becoming one of the critical design parameters, especially in light of the energy budget of 20-30 Megawatts set by the U.S. Department of Energy. Understanding an application’s execution pattern and its energy footprint is critical to improving application operation on a diverse heterogeneous architecture. Applying application-specific performance optimization can consequently improve energy consumption. However, this approach is only applicable to current systems. As we enter a new era of exascale architectures that is projected to contain more complex memory hierarchies, increased levels of parallelism, heterogeneity in hardware, and complex programming models and techniques, energy and performance management is getting more cumbersome. We therefore propose techniques that predict the energy consumption beforehand or at runtime to enable proactive tuning. Such energy prediction approaches must be generic and adapt themselves at runtime to changing application and hardware configurations. Most existing energy estimation and prediction approaches are empirical in nature and thus tied to current systems. To overcome this limitation, we propose two energy estimation techniques: ACEE (Algorithmic and Categorical Energy Estimation), which uses a combination of analytical and empirical modeling techniques; and AEEM (Aspen’s Embedded Energy Estimation), a system-level analytical energy estimation technique. Both of these models incorporate the Aspen domain specific language for performance modeling. We present the methodologies of these two models and test their accuracy using five proxy applications. We also describe three use cases.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Phase dependence of optical bistability and multistability in a four-level quantum system near a plasmonic nanostructure

The optical bistability and multistability properties of a four-level quantum system near a plasmonic nanostructure embedded in a unidirectional ring cavity are studied theoretically. Two orthogonal circularly polarized laser fields with the same frequency, different phases and electric fields amplitude are interacted by four-level quantum system. It is found that in the presence of the plasmonic nanostructure, the bistable behaviors related to one of the laser fields propagating through the unidirectional ring cavity can be modified by relative phase and amplitude control of another laser fields. Our obtained results show that the optical bistability can be converted into the optical multistability by varying the value of distance between the quantum system and the surface of the plasmonic nanostructure. Moreover, it is shown that under specific condition related to the distance, the lasing without population inversion can be obtained.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Overlapped embedded fragment stochastic density functional theory for covalently-bonded materials

The stochastic density functional theory (DFT) is a valuable linear-scaling approach to Kohn-Sham DFT that does not rely on the sparsity of the density matrix. Linear (and often sub-linear) scaling is achieved by introducing a controlled statistical error in the density, energy, and forces. The statistical error (noise) is proportional to the inverse square root of the number of stochastic orbitals and thus decreases slowly; however, by dividing the system into fragments that are embedded stochastically, the statistical error can be reduced significantly. This has been shown to provide remarkable results for non-covalently-bonded systems; however, the application to covalently bonded systems had limited success, particularly for delocalized electrons. Here, we show that the statistical error in the density correlates with both the density and the density matrix of the system and propose a new fragmentation scheme that elegantly interpolates between overlapped fragments. In conclusion, we assess the performance of the approach for bulk silicon of varying supercell sizes (up to N e = 16 384 electrons) and show that overlapped fragments reduce significantly the statistical noise even for systems with a delocalized density matrix.

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

Root Cause Correlation Analysis of Software Failures via Orthogonal Defect Classification and Natural Language Processing

Systems theoretic process analysis (STPA) is becoming an increasingly popular technique to assess how complex digital software systems can fail. Rather than defining failures by their observable failure events, which may be sparse especially for safety rated nuclear digital instrumentation and control systems (DI&C), failures are defined as postulated unsafe actions under specific contextual conditions. This permits a top-down analysis of system hazards and identifies whether imposed constraints and requirements can sufficiently address undesirable hazards. However, STPA is a qualitative approach at identifying inadequacies in the development process and cannot currently be used to quantify unsafe action likelihoods for probabilistic risk assessment. Therefore, in this work, we examine the root causes of software failure and explore whether a consistent correlation can be linked to specific unsafe action classes. We implement Lbl2Vec, an unsupervised document classification and retrieval algorithm, on a database of 4,096 software defect reports acquired from various open-source software systems. By analyzing sentence structure, embedded labels, and word vectors, we show that certain defect types positively correlate to specific unsafe action classes over others. The correlations developed can be used to estimate the failure probability of safety intended DI&C systems which provides a licensing basis for nuclear plant modernization efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Root Cause Correlation Analysis of Software Failures via Orthogonal Defect Classification and Natural Language Processing

Systems theoretic process analysis (STPA) is becoming an increasingly popular technique to assess how complex digital software systems can fail. Rather than defining failures by their observable failure events, which may be sparse especially for safety rated nuclear digital instrumentation and control systems (DI&C), failures are defined as postulated unsafe actions under specific contextual conditions. This permits a top-down analysis of system hazards and identifies whether imposed constraints and requirements can sufficiently address undesirable hazards. However, STPA is a qualitative approach at identifying inadequacies in the development process and cannot currently be used to quantify unsafe action likelihoods for probabilistic risk assessment. Therefore, in this work, we examine the root causes of software failure and explore whether a consistent correlation can be linked to specific unsafe action classes. We implement Lbl2Vec, an unsupervised document classification and retrieval algorithm, on a database of 4,096 software defect reports acquired from various open-source software systems. By analyzing sentence structure, embedded labels, and word vectors, we show that certain defect types positively correlate to specific unsafe action classes over others. The correlations developed can be used to estimate the failure probability of safety intended DI&C systems which provides a licensing basis for nuclear plant modernization efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Electron-electron interactions in low-dimensional Si:P delta layers

Scientists have long studied the physics of highly disordered conducting systems, seeking to understand the multitude of quantum phenomena that govern how electrons move through material systems. Recently, research into silicon-based quantum computing has made disordered conducting systems, such as Si:P monolayers embedded in isotopically pure Si, technically relevant. Consequently, applying and advancing the theoretical frameworks developed to describe electron behavior in disordered systems is a necessary objective in this field of research. This study investigates key components of dopant-based Si quantum computing devices: embedded regions of highly doped delta layers (δ layers). We examine the transport behavior and the electron-electron interaction (EEI) physics in embedded Si:P δ layers by means of self-consistent magnetotransport measurements. Parameters associated with the electronic transport offer a meaningful quantitative characterization of δ-layer quality and dopant diffusion. In addition, by examining EEI behaviors in a set of samples with embedded Si:P δ layers produced with different PH3 exposure procedures prior to Si encapsulation, we show how details of material synthesis affect the dimensionality of charge carrier interactions in embedded Si:P δ layers. Furthermore, the relationship between δ-layer confinement and EEI screening lengths is established here. This understanding will help validate important models used for device simulation and design and lead to improvements in the control of electrostatic gating of and tunneling transport through Si:P single atom transistors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

SU-E-T-142: Automatic Linac Log File: Analysis and Reporting

Purpose: End to end QA for IMRT/VMAT is time consuming. Automated linac log file analysis and recalculation of daily recorded fluence, and hence dose, distribution bring this closer. Methods: Matlab (R2014b, Mathworks) software was written to read in and analyse IMRT/VMAT trajectory log files (TrueBeam 1.5, Varian Medical Systems) overnight, and are archived on a backed-up network drive (figure). A summary report (PDF) is sent by email to the duty linac physicist. A structured summary report (PDF) for each patient is automatically updated for embedding into the R&V system (Mosaiq 2.5, Elekta AG). The report contains cross-referenced hyperlinks to ease navigation between treatment fractions. Gamma analysis can be performed on planned (DICOM RTPlan) and treated (trajectory log) fluence distributions. Trajectory log files can be converted into RTPlan files for dose distribution calculation (Eclipse, AAA10.0.28, VMS). Results: All leaf positions are within +/−0.10mm: 57% within +/−0.01mm; 89% within 0.05mm. Mean leaf position deviation is 0.02mm. Gantry angle variations lie in the range −0.1 to 0.3 degrees, mean 0.04 degrees. Fluence verification shows excellent agreement between planned and treated fluence. Agreement between planned and treated dose distribution, the derived from log files, is very good. Conclusion: Automated log file analysis is a valuable tool for the busy physicist, enabling potential treated fluence distribution errors to be quickly identified. In the near future we will correlate trajectory log analysis with routine IMRT/VMAT QA analysis. This has the potential to reduce, but not eliminate, the QA workload.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Coupled cluster valence bond theory for open-shell systems with application to very long range strong correlation in a polycarbene dimer

Here, the Coupled Cluster Valence Bond (CCVB) method, previously presented for closed-shell (CS) systems, is extended to open-shell (OS) systems. The theoretical development is based on embedding the basic OS CCVB wavefunction in a fictitious singlet super-system. This approach reveals that the OS CCVB amplitude equations are quite similar to those of CS CCVB, and thus that OS CCVB requires the same level of computational effort as CS CCVB, which is an inexpensive method. We present qualitatively correct CCVB potential energy curves for all low-lying spin states of P 2 and Mn 2 + . CCVB is successfully applied to the low-lying spin states of some model linear polycarbenes, systems that appear to be a hindrance to standard density functionals. We examine an octa-carbene dimer in a side-by-side orientation, which, in the monomer dissociation limit, exhibits maximal strong correlation over the length of the polycarbene.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗