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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

MetaText: Compositional Generalization in Deep Language Models

Compositional generalization, the ability to infer and reason using novel combinations of previously encountered entities and structures, is a trait of great utility across a variety of deep learning tasks; it implies a certain ability to reason using consistent and therefore interpretable rules, to understand the constituent parts of its input at multiple levels of granularity, to be robust to spurious differences between semantically equivalent inputs, and more. We seek to understand the degree to which existing natural language models achieve or fall short of compositional generalization across a variety of tasks, whether there are distinct types of compositional generalization in practice, and to identify avenues of intervention through which we can improve models’ compositional generalization ability. After exploring these questions using an array of models, tasks, and potential interventions we see that large, pretrained language models have encountered sufficient training data to account for a variety of fine-grained compositional behavior but still struggle to reason at the level of phrases or larger language structures. Non-intrusive, data-based interventions in the form of augmenting individual sequences with compressed versions of themselves or deriving new examples from induced grammars prove insufficient to encourage greater levels of compositional reasoning, indicating that future work might benefit most from focusing on changes to a model’s inductive bias at the architectural or loss level, or by integrating compositionality-boosting data interventions into the large-scale pretraining process itself.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Complementarity between neutrinoless double beta decay and collider searches for heavy neutrinos in composite-fermion models

Composite-fermion models predict excited quarks and leptons with mass scales which can potentially be observed at high-energy colliders like the LHC; the most recent exclusion limits from the CMS and ATLAS Collaborations corner excited-fermion masses and the compositeness scale to the multi-TeV range. At the same time, hypothetical composite Majorana neutrinos would lead to observable effects in neutrinoless double beta decay ($0\nu \beta \beta$) experiments. In this work, we show that the current composite-neutrino exclusion limit $M_N>4.6$ TeV, as extracted from direct searches at the LHC, can indeed be further improved to $M_N>8.8$ TeV by including the bound on the nuclear transition $^{136}$Xe $\to ^{136}$Ba + $2e^-$. Looking ahead, the forthcoming HL-LHC will allow probing a larger portion of the parameter-space, nevertheless, it will still benefit from the complementary limit provided by $0 \nu \beta \beta$ future detectors to explore composite-neutrino masses up to $12.6$ TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A Nonlinear Regression Method for Composite Protection Modeling of Induction Motor Loads

Protection equipment are used to prevent damages to induction motor loads by isolating those from the power network in the event of severe faults. Modeling the response of induction motor loads and their protection is vital for power system planning and operation, especially in understanding system's response moments after a fault has occurred. This article proposes an optimization based framework to generate composite protection models for commercial building motor loads. Introducing a mathematical abstraction, the task of finding a suitable (simplified) model of the composite protection scheme is formulated as a nonlinear regression problem. Numerical examples are provided to illustrate the application of the framework.

Kundu, Soumya↗
DOE OSTI2021

Parameter Reduction of Composite Load Model Using Active Subspace Method

Over the past decades, the increasing penetration of distributed energy resources (DERs) has dramatically changed the power load composition in the distribution networks. The traditional static and dynamic load models can hardly capture the dynamic behavior of modern loads especially for fault-induced delayed voltage recovery (FIDVR) events. Thus, a more comprehensive composite load model with combination of static load, different types of induction motors, single-phase A/C motor, electronic load and DERs has been proposed by Western Electricity Coordinating Council (WECC). However, due to the large number of parameters and model complexity, the WECC composite load model (WECC CMLD) raises new challenges to power system studies. To overcome these challenges, in this paper, a cutting-edge parameter reduction (PR) approach for WECC CMLD based on active subspace method (ASM) is proposed. Firstly, the WECC CMLD is parameterized in a discrete-time manner for the application of the proposed method. Then, parameter sensitivities are calculated by discovering the active subspace, which is a lower-dimensional linear subspace of the parameter space of WECC CMLD in which the dynamic response is most sensitive. The interdependency among parameters can be taken into consideration by our approach. Finally, the numerical experiments validate the effectiveness and advantages of the proposed approach for WECC CMLD model.

active subspace↗
DOE OSTI2019

A Generic Modeling and Development Approach for WECC Composite Load Model

The composite load model (CLM) proposed by Western Electricity Coordinating Council (WECC) gains increasing traction in the industry, particularly in North America. At the same time, it is recognized that further improvements in the structure, initialization and aggregation methods are needed. However, lack of an open implementation of WECC CLM becomes a roadblock for many researchers to work on improving it. This paper presents the first open reference implementation of WECC CLM. Individual load components and the CLM are first developed and tested in Matlab, then translated to the high performance computing (HPC), parallel simulation platform GridPACK. Main contributions of the paper include: 1) presenting some important yet undocumented details of modeling and initializing the CLM, particularly for a parallel simulation platform like GridPACK; 2) implementation details of the load components such as the single-phase air conditioner motor; 3) implementing the CLM in a modular and extensible manner. The implementation has been tested at both the component as well as system levels and bench-marked against commercial simulation programs.

Huang, Qiuhua↗
DOE OSTI2020

Natural top-bottom mass hierarchy in composite Higgs models

We consider composite two-Higgs doublet models based on gauge-Yukawa theories with strongly interacting fermions generating the top-bottom mass hierarchy. The model features a single "universal" Higgs-Yukawa coupling, $g$, which is identified with the top quark $g\equiv g_t \sim \mathcal{O}(1)$. The top-bottom mass hierarchy arises by soft breaking of a $\mathbb{Z}_2$ symmetry by a condensate of strongly interacting fermions. A mass splitting between vector-like masses of the confined techni-fermions controls this top-bottom mass hierarchy. This mechanism can be present in a variety of models based on vacuum misalignment. For concreteness, we demonstrate it in a composite two-Higgs scheme.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Investigation of precipitate refinement in Mg alloys by an analytical composite failure model

An analytical model is developed to simulate precipitate refinement in second phase strengthened magnesium alloys. The model is developed based on determination of the stress fields inside elliptical precipitates embedded in a rate dependent inelastic matrix. The stress fields are utilized to determine the failure mode that governs the refinement behavior. Using an AZ31 Mg alloy as an example, the effects the applied load, aspect ratio and orientation of the particle is studied on the macroscopic failure of a single α-Mg17Al12 precipitate. Additionally, a temperature dependent version of the corresponding constitutive law is used to incorporate the effects of temperature. In plane strain compression, an extensional failure mode always fragments the precipitates. The critical strain rate at which the precipitates start to fail strongly depends on the orientation of the precipitate with respect to loading direction. The results show that the higher the aspect ratio is, the easier the precipitate fractures. Precipitate shape is another factor influencing the failure response. In contrast to elliptical precipitates with high aspect ratio, spherical precipitates are strongly resistant to sectioning. In pure shear loading, in addition to the extensional mode of precipitate failure, a shearing mode may get activated depending on orientation and aspect ratio of the precipitate. The effect of temperature in relation to strain rate was also verified for plane strain compression and pure shear loading cases.

Tabei, Ali↗
DOE OSTI2016

Salt Composition Derived from Veazey Composition by Thermodynamic Modeling and Predicted Composition of Drum Contents

This report describes the derivation of the salt composition from the Veazey salt stream analysis. It also provides an estimate of the proportions of the kitty litter, nitrate salt and neutralizer that was contained in drum 68660. While the actinide content of waste streams was judiciously followed in the 1980s in TA-55, no record of the salt composition could be found. Consequently, a salt waste stream produced from 1992 to 1994 and reported by Gerry Veazey provided the basis for this study. While chemical analysis of the waste stream was highly variable, an average analysis provided input to the Stream Analyzer software to calculate a composition for a concentrated solid nitrate salt and liquid waste stream. The calculation predicted the gas / condensed phase compositions as well as solid salt / saturated liquid compositions. The derived composition provides an estimate of the nitrate feedstream to WIPP for which kinetic measurements can be made. The ratio of salt to Swheat in drum 68660 contents was estimated through an overall mass balance on the parent and sibling drums. The RTR video provided independent confirmation concerning the volume of the mixture. The solid salt layer contains the majority of the salt at a ratio with Swheat that potentially could become exothermic.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

The Building ENergy Demand (BEND) Model: A Comprehensive Composite Building Energy Modeling Framework

The Building ENergy Demand (BEND) model was developed at Pacific Northwest National Laboratory (PNNL) to simulate climate-dependent hourly building energy demand with the ability to disaggregate down to or aggregate up to any geographic area of interest, including counties, states, electric utility service territories, and census regions. The BEND modeling framework is designed to study the effects of climate change, economics, technology advancement, and various mitigation policies on aggregate building energy demand. To make the process computationally feasible, BEND reduces granularity in building characteristics and weather forcing while maintaining enough detail to minimize systematic differences (biases) between BEND results and data from the Energy Information Administration (EIA). Based on the Commercial Building Energy Consumption Survey (CBECS) and Residential Energy Consumption Survey (RECS) databases, BEND establishes a set of representative buildings with predefined sets of building types, sizes, and vintages. These representative buildings are defined for each of the four U.S. census regions and the five climate zones used by the EIA for building surveys (hereafter “EIA climate zones”). BEND’s representative buildings are based on a statistical analysis of the CBECS and RECS databases that infers detailed parameters such as construction type, thermal properties, internal loads, equipment and mechanical systems, and operation schedules from the high-level descriptors in those databases (building type, vintage, size, and location). The representative buildings are simplified and consolidated into a set of prototype buildings for the purpose of simulating building energy demand. Combining the prototype buildings with weather files from selected locations and years, BEND creates a set of simulation-ready building models to be run using the EnergyPlus simulation engine (EnergyPlus). The base spatial unit of each BEND simulation is a subregion that is the intersection of an International Energy Conservation Code climate zone, a census region, and an EIA climate zone; analysis for each spatial unit is influenced by the weather file BEND selects to represent the climate-similar area. The major simulation outputs of interest to BEND are hourly energy consumption values for each simulated year, possibly for multiple fuel types. Based on building-stock weights derived from the CBECS/RECS data, the hourly output streams are aggregated to a single, 8760-hour load profile for each of the major building categories—residential and commercial—with the possibility of segregating by fuel types. To correct for biases inherent in the use of a limited suite of representative buildings, BEND is calibrated against observational targets of total annual building energy consumption and hourly load profiles (including peak demand) obtained for the decision-relevant geographic scales of interest (e.g., total electric load data for individual balancing authorities available from EIA). Based on available data such as population distribution, BEND is able to aggregate both up and down scales, allowing the study of building energy demand over a range of spatial scales (e.g., states or counties within the Western Electricity Coordinating Council, Eastern Interconnection, or the entire U.S.) and/or subsets of building types (e.g., office buildings with particular size ranges).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Design-time performance modeling of compositional parallel programs

Performance models are powerful instruments for understanding the performance of parallel systems and uncovering their bottlenecks. Already during system design, performance models can help ponder alternative development options. However, creating a performance model – whether theoretically or empirically – for an entire application that does not exist yet is challenging. In this paper, we propose to generate performance models of full programs from performance models of their components using formal composition operators derived from parallel design patterns. As long as the design of the overall system follows such a pattern, its performance model can be predicted with reasonable accuracy without an actual implementation. In conclusion, we demonstrate our approach with design patterns of varying complexity, including pipeline, task pool, and eventually MapReduce, which is representative of a broad class of data-analytics applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

High-Fidelity Large-Signal Order Reduction Approach for Composite Load Model

With the increasing penetration of electronic loads and distributed energy resources, conventional load models cannot capture their dynamics. Therefore, a new comprehensive composite load model is developed by Western Electricity Coordinating Council (WECC). However, this model is a complex high-order non-linear system with multi-time-scale property, which poses challenges on power system studies with heavy computational burden. In order to reduce the model complexity, the authors firstly develop a large-signal order reduction (LSOR) method using singular perturbation theory. In this method, the fast dynamics are integrated into the slow ones to preserve transient characteristics of the former. Then, accuracy assessment conditions are proposed and embedded into the LSOR to improve and guarantee the accuracy of reduced-order model. Finally, the reduced-order WECC composite load model is derived by using the proposed algorithm. Overall, simulation results show that the reduced-order large-signal model significantly alleviates the computational burden while maintaining similar dynamic responses as the original composite load model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Minimal Composite Higgs Models at the LHC

We consider composite Higgs models where the Higgs is a pseudo-Nambu Goldstone boson arising from the spontaneous breaking of an approximate global symmetry by some underlying strong dynamics. We focus on the SO(5) → SO(4) symmetry breaking pattern, assuming the “partial compositeness” paradigm. We study the consequences on Higgs physics of the fermionic representations produced by the strong dynamics, that mix with the Standard Model (SM) degrees of freedom. We consider models based on the lowest-dimensional representations of SO(5) that allow for the custodial protection of the Z b ¯ b coupling, i.e. the 5, 10 and 14. We find a generic suppression of the gluon fusion process, while the Higgs branching fractions can be enhanced or suppressed compared to the SM. Interestingly, a precise measurement of the Higgs boson couplings can distinguish between different realizations in the fermionic sector, thus providing crucial information about the nature of the UV dynamics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Distribution-Level Phasor Measurement Units Application to Composite Load Model Validation

The paper presents the results of composite load model validation based on the distribution level micro- phasor measurement units (PMU) measurements. The actual micro-PMU measurements from the distribution feeders are collected by Lawrence Berkley National Laboratory (LBNL). The composite load model parameters are initially generated using the load model data tool developed by Pacific Northwest National Laboratory (PNNL). The generated load models are calibrated to match the performances with the micro-PMU measurements using disturbance records “play-in” capabilities available in a GE PSLF simulation package. A trial-and-error approach based on engineering judgement is proposed in this paper to benchmark the generated load model parameters and is implemented in the prototype of load model validation tool, which would be a supplement to the load model data tool.

Liu, Yuan↗
DOE OSTI2012

Electroweak baryogenesis in non-minimal composite Higgs models

We address electroweak baryogenesis in the context of composite Higgs models, pointing out that modifications to the Higgs and top quark sectors can play an important rôle in generating the baryon asymmetry. Our main observation is that composite Higgs models that include a light, gauge singlet scalar in the spectrum [as in the model based on the symmetry breaking pattern SO(6) → SO(5)], provide all necessary ingredients for viable baryogenesis. In particular, the singlet leads to a strongly first-order electroweak phase transition and introduces new sources of CP violation in dimension-five operators involving the top quark. We discuss the amount of baryon asymmetry produced and the experimental constraints on the model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

Load Model Data Tool

The LMDT software automates the process of the load composite model data preparation in the format supported by the major power system software vendors (GE and Siemens). Proper representation of the load composite model in power system dynamic analysis is very important. Software tools for power system simulation like GE PSLF and Siemens PSSE already include algorithms for the load composite modeling. However, these tools require that the input information on composite load to be provided in custom formats. Preparation of this data is time consuming and requires multiple manual operations. The LMDT software enables to automate this process. Software is designed to generate composite load model data. It uses the default load composition data, motor information, and bus information as an input. Software processes the input information and produces load composition model. Generated model can be stored in .dyd format supported by GE PSLF package or .dyr format supported by Siemens PSSE package.

Etingov, Pavel↗
DOE OSTI2019

Universal relations in composite Higgs models

We initiate a phenomenological study of “universal relations” in composite Higgs models, which are dictated by nonlinear shift symmetries acting on the 125 GeV Higgs boson. These are relations among one Higgs couplings with two electroweak gauge bosons (HVV), two Higgses couplings with two electroweak gauge bosons (HHVV), one Higgs couplings with three electroweak gauge bosons (HVVV), as well as triple gauge boson couplings (TGC), which are all controlled by a single input parameter: the decay constant f of the pseudo-Nambu-Goldstone Higgs boson. Assuming custodial invariance in strong sector, the relation is independent of the symmetry breaking pattern in the UV, for an arbitrary symmetric coset G/H . The complete list of corrections to HVV, HHVV, HVVV and TGC couplings in composite Higgs models is presented to all orders in 1/ f , and up to four-derivative level, without referring to a particular G/H . We then present several examples of universal relations in ratios of coefficients which could be extracted experimentally. Measuring the universal relation requires a precision sensitive to effects of dimension-8 operators in the effective Lagrangian and highlights the importance of verifying the tensor structure of HHVV interactions in the standard model, which remains untested to date.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A critical examination of compound stability predictions from machine-learned formation energies

Machine learning has emerged as a novel tool for the efficient prediction of material properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests.

36 MATERIALS SCIENCE↗