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DOE OSTI2025

Post-composing ontology terms for efficient phenotyping in plant breeding

Abstract Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored to diverse use cases, post-composing user-defined terms reconciles the demands for standardization on the one hand and flexibility on the other. In many instances of Breedbase, a digital ecosystem for plant breeding designed for genomic selection, the goal is to capture phenotypic data using highly curated and rigorous crop ontologies, while adapting to the specific requirements of plant breeders to record data quickly and efficiently. For example, post-composing enables users to tailor ontology terms to suit specific and granular use cases such as repeated measurements on different plant parts and special sample preparation techniques. To achieve this, we have implemented a post-composing tool based on orthogonal ontologies providing users with the ability to introduce additional levels of phenotyping granularity tailored to unique experimental designs. Post-composed terms are designed to be reused by all breeding programs within a Breedbase instance but are not exported to the crop reference ontologies. Breedbase users can post-compose terms across various categories, such as plant anatomy, treatments, temporal events, and breeding cycles, and, as a result, generate highly specific terms for more accurate phenotyping.

Mathematical & Computational Biology↗

DOE OSTI2017

Social Media Account Resolution and Verification

In this SAND report, we discuss various authorship attribution techniques for social media platforms to identify separate accounts controlled by one user. We look at three main categories of techniques: stylometric, semantic, and temporal, and we test several algorithms in each category. We then combine these techniques to increase predictive power, and perform cross-platform account verification on Twitter and Stack Exchange datasets. Additionally, we briefly examine graphical techniques, and analyze the predictive power of unsupervised approaches. Finally, we apply our techniques to unbalanced data sets. Our experiments reveal that normalized compression distance shows great promise in analyzing social media.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Application of power time-projection on the operator-splitting coupling scheme of the TRACE/S3K coupled code

This paper reports refinement studies on the temporal coupling scheme and time-stepping management of TRACE/S3K, a dynamically coupled code version of the thermal-hydraulics system code TRACE and the 3D core simulator Simulate-3K. The studies were carried out for two test cases, namely a PWR rod ejection accident and the Peach Bottom 2 Turbine Trip Test 2. The solution of the coupled calculation, especially the power peak, proves to be very sensitive to the time-step size with the currently employed conventional operator-splitting. Furthermore, a very small time-step size is necessary to achieve decent accuracy. This degrades the trade-off between accuracy and performance. A simple and computationally cheap implementation of time-projection of power has been shown to be able to improve the convergence of the coupled calculation. This scheme is able to achieve a prescribed accuracy with a larger time-step size. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2019

A novel occupancy detection solution using low-power IR-FPA based wireless occupancy sensor

Significant energy savings can be achieved by operating heating, ventilation, and air conditioning control systems with indoor occupancy measurement information. This paper presents a novel plug-and-play occupancy sensing method which will enable temporal minimization of building energy consumption to meet building usage behavior without privacy concerns. The proposed wireless occupancy sensing platform is based on long-wave infra-red (LWIR) focal-plane arrays (FPAs), or thermal imagers, that detect thermal energy rather than visible light. We developed an advanced sensor package consisting of multiple thermal imagers with low-cost optical enhancements to increase field of view and increase sensitivity to occupant detection (filtering building clutter). These imagers can be coupled with radio frequency and ultrasonic-based radar to enhance data collection at key occupant zone boundaries to improve accuracy. Standard filtering and estimation techniques from the image processing and computer vision communities are introduced to overcome the accuracy issues suffered by traditional PIR based sensing, especially when occupants remain relatively still. Accurate low-level counting of individuals can be achieved with minimal impact on privacy. As a result, the proposed occupancy detection method can work as a retrofit to enable real-time understanding of the building operational state and usage behavior. Sensor data obtained from real test building zones was used to test the efficacy of the method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Spatio-temporal ion temperature and velocity measurements in a Z pinch using fast-framing spectroscopy

Ion Doppler spectroscopy (IDS) is a diagnostic technique that measures plasma ion temperature and velocity without perturbing the plasma with a physical probe. The ZaP-HD Flow Z-Pinch Experiment at the University of Washington uses this technique to resolve radial temperature and velocity profiles of a Z-pinch plasma. The pinch lifetime is approximately 100 ms, therefore diagnostics capable of sub-microsecond resolution are required to measure the evolution of temperature and velocity profiles. The previous IDS diagnostic system was only capable of collecting a single measurement during a plasma pulse. Here, an improved system has been developed to measure radially-resolved ion temperature and velocity for the entire Z-pinch lifetime. A Kirana 05M ultra-fast framing camera and SIL ultraviolet intensifier are used to record up to 100 spectra per plasma pulse. The temperature is computed from Doppler broadening of the carbon-III (229.687 nm) impurity ion radiation and the velocity from the Doppler shift of carbon-III. Measurements are able to resolve the evolution of the ion temperature and velocity over the course of a plasma pulse. The diagnostic has significantly reduced the number of pulses required and provides a more coherent measurement of plasma dynamics than the previous system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Application of a Distributed Element Roughness Model to Additively Manufactured Internal Cooling Channels

Abstract Design for cooling effectiveness in turbine blades relies on accurate models for dynamic losses and heat transfer of internal cooling passages. Metal additive manufacturing (AM) has expanded the design space for these configurations, but can give rise to large-scale roughness features. The range of roughness length scales in these systems makes morphology resolved computational fluid dynamics (CFD) impractical. However, volumetric roughness models can be leveraged, as they have computational costs orders of magnitude lower. In this work, a discrete element roughness model (DERM), based on the double-averaged Navier–Stokes equations, is presented and applied to additively manufactured rough channels, representative of gas turbine blade cooling passages. Unique to this formulation of DERM is a generalized sheltering-based treatment of drag, a two-layer model for spatially averaged Reynolds stresses, and explicit treatment of dispersion. Six different AM rough surface channel configurations are studied, with roughness trough to peak sizes ranging from 15% to 60% nominal channel passage half-width, and the roughness Reynolds number ranges from Rek = 60 to 300. DERM predictions for spatially and temporally averaged mean flow quantities are compared to previously reported direct numerical simulation results. Good agreement in the mean velocity profiles, stress balances, and drag partitions are observed. While DERM models are typically calibrated to specific deterministic roughness morphologies at comparatively small roughness Reynolds numbers, the present more generalized DERM formulation has wider applicability. Here, it is demonstrated that the model can accommodate random roughness of large scale, typical of AM.

Engineering↗

DOE OSTI2019

Tomographic Reconstruction of the Neutron Time-Energy Spectrum from a Dense Plasma Focus

A dense plasma focus (DPF) nuclear fusion generator is an attractive pulsed neutron source in many applications, due to its relatively large neutron yield, produced in a short time duration. In order to design a DPF that generates neutrons within a specified time profile, or generates a neutron energy spectrum with specific properties, it is necessary to understand and be able to fully characterize the results of the fusion process releasing the neutrons. The time-energy spectrum of fusion neutrons is an ideal quantity to use to validate multiphysics codes that simulate the pinch and fusion processes, because it is a quantity that requires the physics of each of the stages leading up to and ending in the fusion reaction to be simulated correctly. In particular, since DPF fusion neutrons are not monoenergetic – and there can often be several fusion pinches creating neutrons – a computer simulation matching high quality neutron spectrum measurements provides great confidence in the fidelity of the simulation. In order to make such a comparison, it is first necessary to have quality measurements from which to infer the spectrum. In this work we pose neutron spectroscopy as the classical tomographic inverse problem from neutron time of flight data at multiple distances, but enhanced by using detector pairs designed to reduce the scatter background and with an additional measurement of the time profile of the fusion pinch near its source. The detector pairs enhance the quality of the time of flight measurements and the additional constraint posed by the measured time profile allows for temporal resolution in the spectrum reconstruction that is equal to the measurement resolution, which can be subnanosecond, and energy resolution in the 10’s of keV, without the problem being underconstrained. We present results from a Deuterium-fueled DPF at the U.S. Department of Energy’s Nevada National Security Site and show that we can infer the time-energy spectrum from our measurements for both single and multi-pinch fusion reactions with equal fidelity.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2017

DG-IMEX Stochastic Galerkin Schemes for Linear Transport Equation with Random Inputs and Diffusive Scalings

In this paper, we consider the linear transport equation under diffusive scaling and with random inputs. The method is based on the generalized polynomial chaos approach in the stochastic Galerkin framework. Several theoretical aspects will be addressed. Additionally, a uniform numerical stability with respect to the Knudsen number ϵ, and a uniform in ϵ error estimate is given. For temporal and spatial discretizations, we apply the implicit–explicit scheme under the micro–macro decomposition framework and the discontinuous Galerkin method, as proposed in Jang et al. (SIAM J Numer Anal 52:2048–2072, 2014) for deterministic problem. Lastly, we provide a rigorous proof of the stochastic asymptotic-preserving (sAP) property. Extensive numerical experiments that validate the accuracy and sAP of the method are conducted.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Synchronization of mobile chaotic oscillator networks

We study synchronization of systems in which agents holding chaotic oscillators move in a two-dimensional plane and interact with nearby ones forming a time dependent network. Due to the uncertainty in observing other agents' states, we assume that the interaction contains a certain amount of noise that turns out to be relevant for chaotic dynamics. We find that a synchronization transition takes place by changing a control parameter. But this transition depends on the relative dynamic scale of motion and interaction. When the topology change is slow, we observe an intermittent switching between laminar and burst states close to the transition due to small noise. This novel type of synchronization transition and intermittency can happen even when complete synchronization is linearly stable in the absence of noise. We show that the linear stability of the synchronized state is not a sufficient condition for its stability due to strong fluctuations of the transverse Lyapunov exponent associated with a slow network topology change. Since this effect can be observed within the linearized dynamics, we can expect such an effect in the temporal networks with noisy chaotic oscillators, irrespective of the details of the oscillator dynamics. When the topology change is fast, a linearized approximation describes well the dynamics towards synchrony. These results imply that the fluctuations of the finite-time transverse Lyapunov exponent should also be taken into account to estimate synchronization of the mobile contact networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Cross-correlation analysis of X-ray photon correlation spectroscopy to extract rotational diffusion coefficients

Coefficients for translational and rotational diffusion characterize the Brownian motion of particles. Emerging X-ray photon correlation spectroscopy (XPCS) experiments probe a broad range of length scales and time scales and are well-suited for investigation of Brownian motion. While methods for estimating the translational diffusion coefficients from XPCS are well-developed, there are no algorithms for measuring the rotational diffusion coefficients based on XPCS, even though the required raw data are accessible from such experiments. In this paper, we propose angular-temporal cross-correlation analysis of XPCS data and show that this information can be used to design a numerical algorithm (Multi-Tiered Estimation for Correlation Spectroscopy [MTECS]) for predicting the rotational diffusion coefficient utilizing the cross-correlation: This approach is applicable to other wavelengths beyond this regime. We verify the accuracy of this algorithmic approach across a range of simulated data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Pushing the Boundaries of Coherence in Superconducting Quantum Systems at the SQMS Center for Computing, Sensing, and Metrology

This talk will highlight recent advances at the SQMS Center in understanding and mitigating decoherence in superconducting quantum systems, focusing on both transmon qubits and 3D superconducting cavities. I will present results from systematic studies of materials and devices, aimed at identifying key sources of loss: including two-level systems (TLS), quasiparticles, and other noise mechanisms. These studies span microwave loss characterization in materials such as niobium, tantalum, aluminum, and their native oxides, as well as substrate losses in silicon and sapphire. Using both qubits and cavities, we disentangle subsystem contributions to loss and develop a hierarchy of mitigation strategies. Through these efforts, we have achieved transmon coherence times exceeding one millisecond. I will also present investigations into quasiparticle dynamics, including bursts observed in qubits located above ground and in the Gran Sasso underground laboratory. Additional studies explore temperature dependence to distinguish between TLS and quasiparticle losses, and reveal that applying a magnetic field can reduce temporal T1 fluctuations in transmons. Building on these results, we demonstrate a record-coherence two-cell cavity-qudit system with coherence times exceeding 20 milliseconds. By leveraging sideband interactions and error-resilient protocols including measurement-based correction and post-selection, we achieve high-fidelity quantum state control. This includes preparation of Fock states up to N = 20 with fidelities over 95%, and two-mode entanglement with coherence-limited fidelities reaching 99.9% after post-selection. These achievements position the SQMS platform as a powerful foundation for scalable quantum information processing and high-dimensional qudit encodings. Finally, I will discuss how these ultra-coherent systems are being deployed in emerging quantum sensing applications, including dark matter searches and gravitational wave detection.

Roy, Tanay [Fermilab]↗

DOE OSTI2014

A generalized multi-dimensional mathematical model for charging and discharging processes in a supercapacitor

A generalized three dimensional computational model based on unied formulation of electrode- electrolyte-electrode system of a electric double layer supercapacitor has been developed. The model accounts for charge transport across the solid-liquid system. This formulation based on volume averaging process is a widely used concept for the multiphase ow equations ([28] [36]) and is analogous to porous media theory typically employed for electrochemical systems [22] [39] [12]. This formulation is extended to the electrochemical equations for a supercapacitor in a consistent fashion, which allows for a single-domain approach with no need for explicit interfacial boundary conditions as previously employed ([38]). In this model it is easy to introduce the spatio-temporal variations, anisotropies of physical properties and it is also conducive for introducing any upscaled parameters from lower length{scale simulations and experiments. Due to the irregular geometric congurations including porous electrode, the charge transport and subsequent performance characteristics of the super-capacitor can be easily captured in higher dimensions. A generalized model of this nature also provides insight into the applicability of 1D models ([38]) and where multidimensional eects need to be considered. In addition, simple sensitivity analysis on key input parameters is performed in order to ascertain the dependence of the charge and discharge processes on these parameters. Finally, we demonstarted how this new formulation can be applied to non-planar supercapacitors

Single Domain↗

DOE OSTI2025

Identifying High Ionic Conductivity Compositions of Ionic Liquid Electrolytes Using Features of the Solvation Environment

Binary mixtures of ionic liquids with molecular solvents are gaining interest in electrochemical applications due to the improvement in their performance over neat ionic liquids. Dilution with suitable molecular solvents can reduce the viscosity and facilitate faster diffusion of ions, thereby yielding substantially higher ionic conductivity than that for a pure ionic liquid. Although viscosity and diffusion coefficients typically behave as monotonic functions of concentration, ionic conductivity often passes through a peak value at an optimum molar ratio of the molecular solvent to the ionic liquid. The ionic conductivity maximum is generally explained in terms of a balance between the ease of charge transport and the concentration of the charge carriers. In this work, fluctuation in the local environment surrounding an ion is invoked as a plausible explanation for the ionic conductivity mechanism with a binary mixture of 1-ethyl-3-methylimidazolium tetrafluoroborate and ethylene glycol as an example. The magnitude of the dynamism in the local environment is captured by measuring the spatial and temporal features of the solvation environment. Standard deviation in the number of ions in the solvation environment serves as a spatial feature, while the cage correlation lifetimes for oppositely charged ions within the first solvation shell serve as a temporal feature. Large standard deviations in the cluster ion population and short cage correlation lifetimes are indicators of highly dynamic ionic environment at the molecular level and consequently yield high ionic conductivity. Such compositions were found to be in good agreement with the optimum ionic liquid mole fractions obtained through experimental measurement. Short cage correlation lifetimes enable the identification of optimum mixture compositions using simulation trajectories significantly shorter than those required to implement the Nernst–Einstein or Einstein formalisms for calculating ionic conductivity. We validated the applicability of this approach across force fields and in six ionic liquid-molecular solvent electrolytes formed with combination of cations, anions, and solvents. We offer a computationally efficient approach of screening ionic liquid-molecular solvent binary mixture electrolytes to identify molar ratios that yield high ionic conductivity.

25 ENERGY STORAGE↗

DOE OSTI2022

Effect of injected flux and current temporal phasing on self-organization in the HIT-SI3 experiment

The HIT-SI3 device at the University of Washington uses three oscillating inductive helicity injectors to form and sustain spheromak plasma equilibria. By adjusting the temporal phase of the injector waveforms with respect to each other, the toroidal spectrum of the imposed perturbations can be controlled. Using a recently implemented GPU-based control system, the available mode spectra were explored experimentally by scanning the space of relative injector phasing. In this space, significant variation in the toroidal mode spectrum ($n$ = 1, 2, 3) of the perturbations was observed. Additionally, variation in characteristics of driven equilibria was also observed, including a ≈ $30$% range in toroidal current gain ($I$ $Φ$ / $I$ $Inj$ ). Experimental results are compared with both a composite-equilibria and nonlinear dynamic model, including extended MHD simulations using the NIMROD code and composite Taylor state equilibria computed using the PSI-Tet code. In conclusion, qualitative agreement is seen with the nonlinear models, but not with composite-equilibria models, suggesting the use of nonlinear models to better capture observed plasma dynamics and provide predictive use for future experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2013

Cloud Properties and Radiative Heating Rates for TWP

A cloud properties and radiative heating rates dataset is presented where cloud properties retrieved using lidar and radar observations are input into a radiative transfer model to compute radiative fluxes and heating rates at three ARM sites located in the Tropical Western Pacific (TWP) region. The cloud properties retrieval is a conditional retrieval that applies various retrieval techniques depending on the available data, that is if lidar, radar or both instruments detect cloud. This Combined Remote Sensor Retrieval Algorithm (CombRet) produces vertical profiles of liquid or ice water content (LWC or IWC), droplet effective radius (re), ice crystal generalized effective size (Dge), cloud phase, and cloud boundaries. The algorithm was compared with 3 other independent algorithms to help estimate the uncertainty in the cloud properties, fluxes, and heating rates (Comstock et al. 2013). The dataset is provided at 2 min temporal and 90 m vertical resolution. The current dataset is applied to time periods when the MMCR (Millimeter Cloud Radar) version of the ARSCL (Active Remotely-Sensed Cloud Locations) Value Added Product (VAP) is available. The MERGESONDE VAP is utilized where temperature and humidity profiles are required. Future additions to this dataset will utilize the new KAZR instrument and its associated VAPs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

A cross-sectional study of the temporal evolution of electricity consumption of six commercial buildings

Current approaches to building efficiency diagnoses include conventional energy audit techniques that can be expensive and time consuming. In contrast, virtual energy audits of readily available 15-minute-interval building electricity consumption are being explored to provide quick, inexpensive, and useful insights into building operation characteristics. A cross sectional analysis of six buildings in two different climate zones provides methods for data cleaning, population-based building comparisons, and relationships (correlations) of weather and electricity consumption. Data cleaning methods have been developed to categorize and appropriately filter or correct anomalous data including outliers, missing data, and erroneous values (resulting in < 0.5% anomalies). The utility of a cross-sectional analysis of a sample set of building's electricity consumption is found through comparisons of baseload, daily consumption variance, and energy use intensity. Correlations of weather and electricity consumption 15-minute interval datasets show important relationships for the heating and cooling seasons using computed correlations of a Time-Specific-Averaged- Ordered Variable (exterior temperature) and corresponding averaged variables (electricity consumption)(TSAOV method). The TSAOV method is unique as it introduces time of day as a third variable while also minimizing randomness in both correlated variables through averaging. This study found that many of the pair-wise linear correlation analyses lacked strong relationships, prompting the development of the new TSAOV method to uncover the causal relationship between electricity and weather. We conclude that a combination of varied HVAC system operations, building thermal mass, plug load use, and building set point temperatures are likely responsible for the poor correlations in the prior studies, while the correlation of time-specific-averaged-ordered temperature and corresponding averaged variables method developed herein adequately accounts for these issues and enables discovery of strong linear pair-wise correlation R values. TSAOV correlations lay the foundation for a new approach to building studies, that mitigates plug load interferences and identifies more accurate insights into weather-energy relationship for all building types. Over all six buildings analyzed the TSAOV method reported very significant average correlations per building of 0.94 to 0.82 in magnitude. Our rigorous statistics-based methods applied to 15- minute-interval electricity data further enables virtual energy audits of buildings to quickly and inexpensively inform energy savings measures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. Overall, we focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Question-answering system extracts information on injection drug use from clinical notes

Background. Injection drug use (IDU) can increase mortality and morbidity. Therefore, identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients’ electronic health records (EHR) is difficult because there is no other structured data available, such as International Classification of Disease (ICD) codes, and IDU is most often documented in unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods. Here, to address this gap in clinical information, we design a question-answering (QA) framework to extract information on IDU from clinical notes for use in clinical operations. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We use 2323 clinical notes of 1145 patients curated from the US Department of Veterans Affairs (VA) Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model’s ability to extract IDU-related information from temporally out-of-distribution data. Results. Here, we show that for a strict match between gold-standard and predicted answers, the QA model achieves a 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains a 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions. Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care.

60 APPLIED LIFE SCIENCES↗