Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Boosting”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Series-Connected Buck Boost Regulators

A series-connected buck boost regulator (SCBBR) is an electronic circuit that bucks a power-supply voltage to a lower regulated value or boosts it to a higher regulated value. The concept of the SCBBR is a generalization of the concept of the SCBR, which was reported in "Series-Connected Boost Regulators" (LEW-15918), NASA Tech Briefs, Vol. 23, No. 7 (July 1997), page 42. Relative to prior DC-voltage-regulator concepts, the SCBBR concept can yield significant reductions in weight and increases in power-conversion efficiency in many applications in which input/output voltage ratios are relatively small and isolation is not required, as solar-array regulation or battery charging with DC-bus regulation. Usually, a DC voltage regulator is designed to include a DC-to-DC converter to reduce its power loss, size, and weight. Advances in components, increases in operating frequencies, and improved circuit topologies have led to continual increases in efficiency and/or decreases in the sizes and weights of DC voltage regulators. The primary source of inefficiency in the DC-to-DC converter portion of a voltage regulator is the conduction loss and, especially at high frequencies, the switching loss. Although improved components and topology can reduce the switching loss, the reduction is limited by the fact that the converter generally switches all the power being regulated. Like the SCBR concept, the SCBBR concept involves a circuit configuration in which only a fraction of the power is switched, so that the switching loss is reduced by an amount that is largely independent of the specific components and circuit topology used. In an SCBBR, the amount of power switched by the DC-to-DC converter is only the amount needed to make up the difference between the input and output bus voltage. The remaining majority of the power passes through the converter without being switched. The weight and power loss of a DC-to-DC converter are determined primarily by the amount of power processed. In the SCBBR, the unswitched majority of the power is passed through with very little power loss, and little if any increase in the sizes of the converter components is needed to enable the components to handle the unswitched power. As a result, the power-conversion efficiency of the regulator can be very high, as shown in the example of Figure 1. A basic SCBBR includes a DC-to-DC converter (see Figure 2). The switches and primary winding of a transformer in the converter is connected across the input bus, while the secondary winding and switches are connected in series with the output bus, so that the output voltage is the sum of the input voltage and the secondary voltage of the converter. In the breadboard SCBBR, the input voltage applied to the primary winding is switched by use of metal oxide/semiconductor field-effect transistors (MOSFETs) in a full bridge circuit; the secondary winding is center-tapped, with two MOSFET switches and diode rectifiers connected in opposed series in each leg. The sets of opposed switches and rectifiers are what enable operation in either a boost or a buck mode. In the boost mode, input voltage and current, and the output voltage and current are all positive; that is, the secondary voltage is added to the input voltage and the net output voltage can be regulated at a value equal or greater than the input voltage. In the buck mode, input voltage is still positive and the current still flows in the same direction in the secondary, but the switches are controlled such that some power flows from the secondary to the primary. The voltage across the secondary and the current into the primary are reversed. The result is that the output voltage is lower than the input voltage, and some power is recirculated from the converter secondary back to the input. Quantitatively, the advantage of an SCBBR is a direct function of the regulation range required. If, for example, a regulation range of 20 percent is required for a 500-W supply, th it suffices to design the DC-to-DC converter in the SCBBR for a power rating of only 100 W. The switching loss and size are much smaller than those of a conventional regulator that must be rated for switching of all 500 W. The reduction in size and the increase in efficiency are not directly proportional to switched-power ratio of 5:1 because the additional switches contribute some conduction loss and the input and output filters must be larger than those typically required for a 100-W converter. Nevertheless, the power loss and the size can be much smaller than those of a 500-W converter.

Birchenough, Arthur G.↗
DOE OSTI2024

Subsampling of Parametric Models with Bifidelity Boosting

Least squares regression is a ubiquitous tool for building emulators (a.k.a. surrogate models) of problems across science and engineering for purposes such as design space exploration and uncertainty quantification. When the regression data are generated using an experimental design process (e.g., a quadrature grid) involving computationally expensive models, or when the data size is large, sketching techniques have shown promise at reducing the cost of the construction of the regression model while ensuring accuracy comparable to that of the full data. However, random sketching strategies, such as those based on leverage scores, lead to regression errors that are random and may exhibit large variability. To mitigate this issue, we present a novel boosting approach that leverages cheaper, lower-fidelity data of the problem at hand to identify the best sketch among a set of candidate sketches. This in turn specifies the sketch of the intended high-fidelity model and the associated data. We provide theoretical analyses of this bifidelity boosting (BFB) approach and discuss the conditions the low- and high-fidelity data must satisfy for a successful boosting. In doing so, we derive a bound on the residual norm of the BFB sketched solution relating it to its ideal, but computationally expensive, high-fidelity boosted counterpart. Finally, empirical results on both manufactured and PDE data corroborate the theoretical analyses and illustrate the efficacy of the BFB solution in reducing the regression error, as compared to the nonboosted solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Comprehensive Loss Model and Comparison of AC and DC Boost Converters

DC microgrids have become a prevalent topic in research in part due to the expected superior efficiency of DC/DC converters compared to their AC/DC counterparts. Although numerous side-by-side analyses have quantified the efficiency benefits of DC power distribution, these studies all modeled converter loss based on product data that varied in component quality and operating voltage. To establish a fair efficiency comparison, this work derives a formulaic loss model of a DC/DC and an AC/DC PFC boost converter. These converters are modeled with identical components and an equivalent input and output voltage. Simulated designs with real components show AC/DC boost converters between 100 W to 500 W having up to 2.5 times more loss than DC/DC boost converters. Although boost converters represent a fraction of electronics in buildings, these loss models can eventually work toward establishing a comprehensive model-based full-building analysis.

42 ENGINEERING↗
DOE OSTI2025

Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks

The production of multiple Higgs bosons at the CERN LHC provides a direct way to measure the trilinear and quartic Higgs self-interaction strengths as well as potential access to beyond the standard model effects that can enhance production at large transverse momentum p T . The largest event fraction arises from the fully hadronic final state in which every Higgs boson decays to a bottom quark-antiquark pair ($b\bar{b}$). This introduces a combinatorial challenge known as the jet assignment problem: assigning jets to sets representing Higgs boson candidates. Symmetry-preserving attention networks (SPA-Nets) have been developed to address this challenge. However, the complexity of jet assignment increases when simultaneously considering both H → $b\bar{b}$ reconstruction possibilities, i.e., two “resolved” small-radius jets each containing a shower initiated by a b quark or one “boosted” large-radius jet containing a merged shower initiated by a $b\bar{b}$ pair. The latter improves the reconstruction efficiency at high p T . In this work, we introduce a generalization to the SPA-Net approach to simultaneously consider both boosted and resolved reconstruction possibilities and unambiguously interpret an event as “fully resolved”, “fully boosted”, or in between. We report the performance of baseline methods, the original SPA-Net approach, and our generalized version on nonresonant HH and HHH production at the LHC. Considering both boosted and resolved topologies, our SPA-Net approach increases the Higgs boson reconstruction purity by 56–80% and the efficiency by 37–38% compared to the baseline method depending on the final state.

Higgs Production↗
DOE OSTI2025

Search for inelastic boosted dark matter with the ICARUS detector at the Gran Sasso Underground National Laboratory

We present the result of a search for inelastic boosted dark matter using the data corresponding to an exposure of 0.13 kton · year , collected by the ICARUS T-600 detector during its 2012–2013 operational period at the INFN Gran Sasso Underground National Laboratory. The benchmark boosted dark matter model features a multiparticle dark sector with a U ( 1 ) ′ gauge boson, the dark photon. The kinetic mixing of the dark photon with the Standard Model photon allows for a portal between the dark sector and the visible sector. The inelastic boosted dark matter interaction occurs when a dark matter particle inelastically scatters with an electron in the ICARUS detector, producing an outgoing, heavier dark sector state which subsequently decays back down to the dark matter particle, emitting a dark photon. The dark photon subsequently couples to a Standard Model photon through kinetic mixing. The Standard Model photon then converts to an electron-positron pair in the detector. This interaction process provides a distinct experimental signature that consists of a recoil electron from the primary interaction and an associated electron-positron pair from the secondary vertex. After analyzing 4,134 triggered events, the search results in zero observed events. Exclusion limits are set in the dark photon mass and coupling ( m X , ε ) parameter space for several selected optimal boosted dark matter mass sets and cover previously unexplored parameter space. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Addressing bias in bagging and boosting regression models

As artificial intelligence (AI) becomes widespread, there is increasing attention on investigating bias in machine learning (ML) models. Previous research concentrated on classification problems, with little emphasis on regression models. This paper presents an easy-to-apply and effective methodology for mitigating bias in bagging and boosting regression models, that is also applicable to any model trained through minimizing a differentiable loss function. Our methodology measures bias rigorously and extends the ML model's loss function with a regularization term to penalize high correlations between model errors and protected attributes. We applied our approach to three popular tree-based ensemble models: a random forest model (RF), a gradient-boosted model (GBT), and an extreme gradient boosting model (XGBoost). We implemented our methodology on a case study for predicting road-level traffic volume, where RF, GBT, and XGBoost models were shown to have high accuracy. Despite high accuracy, the ML models were shown to perform poorly on roads in minority-populated areas. Our bias mitigation approach reduced minority-related bias by over 50%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Overcoming timestep limitations in boosted-frame particle-in-cell simulations of plasma-based acceleration

Explicit electromagnetic Particle-In-Cell (PIC) codes are typically limited by the Courant- Friedrichs-Lewy (CFL) condition, which implies that the timestep multiplied by the speed of light must be smaller than the smallest cell size. In the case of boosted-frame PIC simulations of plasma-based acceleration, this limitation can be a major hinderance as the cells are often very elongated along the longitudinal direction and the timestep is thus limited by the small, transverse cell size. This entails many small-timestep PIC iterations, and can limit the potential speed-up of the boosted-frame technique. Here, by using a CFL-free analytical spectral solver, and by mitigating additional numerical instabilities that arise at large timestep, we show that it is possible to overcome traditional limitations on the timestep and thereby realize the full potential of the boosted-frame technique over a much wider range of parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Comprehensive Efficiency Analysis of Seven-Switch Current-Source Inverter Based on Voltage-Boost Function and Genetic Algorithm

This paper investigates the voltage-boost features of the emerging seven-switch current-source inverter (H7-CSI) to improve the efficiency of the CSI-based motor drive system. The switching transient and power loss model of the H7-CSI topology during voltage-boost operation are analyzed in detail. The effects of the boost function on the switching and conduction loss of the H7-CSI topology are investigated. A genetic algorithm has been introduced that makes it possible to optimize the H7-CSI’s modulation index to minimize its power loss. Experimental results for the H7-CSI unit show that this optimization can reduce the power loss by 10.6%.

42 ENGINEERING↗
DOE OSTI2020

Four-Switch Buck-Boost Converter Based on Model Predictive Control with Smooth Mode Transition Capability

Four-switch buck-boost converter supports both voltage step-up and step-down functionalities, but it suffers from mode transfer challenge that would need reliable mode detection when designed to operate in multi different modes. In this paper, a novel control method based on model predictive current control is proposed for such converter with inherent smooth mode transfer capability without extra design on mode detection and transfer scheme. Modulator and mode detection are replaced by an optimization process through cost function. This largely simplifies the design and makes it easily implemented. Therefore, seamless transfer between buck and boost modes is achieved with many other system level benefits. Simulation and experimental results are provided to verify the effectiveness of proposed method for the four-switch buck-boost converter.

42 ENGINEERING↗
NASA NTRSDate not supplied

Differential Equation Approximation Using Gradient-Boosted Quantile Regression

The operation of cyber-physical-human (CPH) systems is subject to various epistemic and aleatory uncertainties. Overall trustworthiness of CPH systems relies on the trustworthiness of its components and their interactions. It is important that computational models comprising the cyber component of CPH provide predictions accompanied by a measure of confidence in model outcomes. Uncertainty quantification (UQ) and propagation are especially important in safety critical CPH systems. Gradient-boosted trees is a modeling approach capable both of learning the dynamics of a system and performing UQ. In this paper, we devise a method for using gradient boosting to learn the dynamics of a second order differential equation and estimate uncertainty at the same time. We do this by creating a custom loss function that trains the model to approximate the second derivative of a noisy time series, and to penalize based on a parameter that corresponds to the desired quantile. The resulting gradient boosting model can simulate stochastic trajectories of the system given a single starting point, that is, it can estimate both the expected trajectory and its uncertainty. We show that the uncertainty estimation is well calibrated and that the model can learn the dynamics even in the presence of noise. We demonstrate the approach on a simple cartpole system.

Autonomous systems↗
DOE OSTI2024

Generalized boost transformations in finite volumes and application to Hamiltonian methods

The investigation of hadron interactions within lattice QCD has been facilitated by the well-known quantisation condition, linking scattering phase shifts to finite-volume energies. Additionally, the ability to utilise systems at finite total boosts has been pivotal in smoothly charting the energy-dependent behaviour of these phase shifts. The existing implementations of the quantization condition at finite boosts rely on momentum transformations between rest and moving frames, defined directly in terms of the energy eigenvalues. This energy dependence is unsuitable in the formulation of a Hamiltonian. In this work, we introduce a novel approach to generalise the three-momentum boost prescription, enabling the incorporation of energy-independent finite-volume Hamiltonians within moving frames. We demonstrate the application of our method through numerical comparisons, employing a phenomenological ππ scattering example.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Reconstruction and identification of boosted di-τ systems in a search for Higgs boson pairs using 13 TeV proton-proton collision data in ATLAS

In this paper, a new technique for reconstructing and identifying hadronically decaying τ+τ- pairs with a large Lorentz boost, referred to as the di-τ tagger, is developed and used for the first time in the ATLAS experiment at the Large Hadron Collider. A benchmark di-τ tagging selection is employed in the search for resonant Higgs boson pair production, where one Higgs boson decays into a boosted $b\overline b$ pair and the other into a boosted τ+τ- pair, with two hadronically decaying τ-leptons in the final state. Using 139 fb -1 of proton-proton collision data recorded at a centre-of-mass energy of 13 TeV, the efficiency of the di-τ tagger is determined and the background with quark- or gluon-initiated jets misidentified as di-τ objects is estimated. The search for a heavy, narrow, scalar resonance produced via gluon-gluon fusion and decaying into two Higgs bosons is carried out in the mass range 1–3 TeV using the same dataset. No deviations from the Standard Model predictions are observed, and 95% confidence-level exclusion limits are set on this model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Search for high-mass resonances decaying to a jet and a Lorentz-boosted resonance in proton-proton collisions at s = 13 TeV

A search is reported for high-mass hadronic resonances that decay to a parton and a Lorentz-boosted resonance, which in turn decays into a pair of partons. The search is based on data collected with the CMS detector at the LHC in proton-proton collisions at $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. The boosted resonance is reconstructed as a single wide jet with substructure consistent with a two-body decay. The high-mass resonance is thus considered as a dijet system. The jet substructure information and the kinematic properties of cascade resonance decays are exploited to disentangle the signal from the large quantum chromodynamics multijet background. The dijet mass spectrum is analyzed for the presence of new high-mass resonances, and is found to be consistent with the standard model background predictions. Results are interpreted in a warped extra dimension model where the high-mass resonance is a Kaluza-Klein gluon, the boosted resonance is a radion, and the final state partons are all gluons. Limits on the production cross section are set as a function of the Kaluza-Klein gluon and radion masses. These limits exclude at 95% confidence level models with Kaluza-Klein gluon masses in the range from 2.0 to 4.3 TeV and radion masses in the range from 0.20 to 0.74 TeV. By exploring a novel experimental signature, the observed limits on the Kaluza-Klein gluon mass are extended by up to about 1 TeV compared to previous searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Cosmic-ray boosted inelastic dark matter from neutrino-emitting active galactic nuclei

Cosmic rays may scatter off dark matter particles in active galactic nuclei, where both the densities of cosmic rays and dark matter are expected to be very large. These scatterings could yield a flux of boosted dark matter particles directly detectable on Earth, which enhances the sensitivity of dark matter direct detection and neutrino experiments to light and inelastic dark matter models. Here we calculate the cosmic-ray boosted dark matter flux from the neutrino-emitting active galactic nuclei, NGC 1068 and TXS 0506+056, by considering realistic cosmic-ray distributions, deep inelastic scatterings, and mass splittings in the dark sector. From this we derive novel bounds from these sources on light and/or inelastic dark matter models with Super-K and XENONnT. We find that cosmic-ray boosted dark matter from neutrino-emitting active galactic nuclei can test regions of parameter space favored to reproduce the observed relic abundance of dark matter in the Universe, and that are otherwise experimentally inaccessible.

Gustafson, R. Andrew [Virginia Tech.; KEK, Tsukuba↗
DOE OSTI2025

Performance of heavy-flavour jet identification in Lorentz-boosted topologies in proton-proton collisions at √(s) = 13 TeV

Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to bb̅ or cc̅, have been developed and deployed across a range of physics analyses. This paper highlights their performance on simulated events, and summarizes novel calibration techniques using proton-proton collision data collected at √(s) = 13 TeV during the 2016–2018 LHC data-taking period. Three dedicated methods are used for the calibration in multijet events, leveraging either machine learning techniques, the presence of muons within energetic boosted jets, or the reconstruction of hadronically decaying high-energy Z bosons. The calibration results, obtained through a combination of these approaches, are presented and discussed.

Pattern recognition↗
DOE OSTI2025

Diffuse boosted cosmic neutrino background

Energetic cosmic rays scatter off the cosmic neutrino background throughout the history of the Universe, yielding a diffuse flux of cosmic relic neutrinos boosted to high energies. We calculate this flux under different assumptions of the cosmic-ray flux spectral slope and redshift evolution. The nonobservation of the diffuse flux of boosted relic neutrinos with current high-energy neutrino experiments already excludes an average cosmic neutrino background overdensity larger than ∼10 4 over cosmological distances. We discuss the future detectability of the diffuse flux of boosted relic neutrinos in light of neutrino overdensity estimates and cosmogenic neutrino backgrounds.

Astronomy & Astrophysics↗
DOE OSTI2022

Quancorde: Boosting fidelity with Quantum Canary Ordered Diverse Ensembles

On today's noisy imperfect quantum devices, execution fidelity tends to collapse dramatically for most applications beyond a handful of qubits. It is therefore imperative to employ novel techniques that can boost quantum fidelity in new ways. Here, this paper aims to boost quantum fidelity with Clifford canary circuits by proposing Quancorde: Quantum Canary Ordered Diverse Ensembles, a fundamentally newapproach to identifying the correct outcomes of extremely low-fidelity quantum applications. It is based on the key idea of diversity in quantum devices - variations in noise sources, make each (portion of a) device unique, and therefore, their impact on an application's fidelity, also unique. Quancorde utilizes Clifford canary circuits (which are classically simulable, but also resemble the target application structure and thus suffer similar structural noise impact) to order a diverse ensemble of devices or qubits/mappings approximately along the direction of increasing fidelity of the target application. Quancorde then estimates the correlation of the ensemble-wide probabilities of each output string of the application, with the canary ensemble ordering, and uses this correlation to weight the application's noisy probability distribution. The correct application outcomes are expected to have higher correlation with the canary ensemble order, and thus their probabilities are boosted in this process. Doing so, Quancorde improves the fidelity of evaluated quantum applications by a mean of 8.9x/4.2x (wrt. different baselines) and up to a maximum of 34x.

clifford↗
DOE OSTI2023

A Predictive Prescription Framework for Stochastic Unit Commitment Using Boosting Ensemble Learning Algorithms

To take unit commitment (UC) decisions under uncertain load, most existing stochastic optimization (SO) frameworks adopt a generic representation of uncertainty. While load levels that materialize on a particular day are influenced by various covariates (such as the day of the week or temperature), SO frameworks typically disregard such side observations, wasting actionable information that could significantly enhance decision quality. Here, this article proposes a contextual SO (CSO) framework for UC under uncertain load, which can effectively exploit covariate observations in conjunction with a class of machine learning (ML) algorithms to improve the out-of-sample performance of UC decisions. It shows how three ML algorithms, adaptive boosting, gradient boosted trees, and extreme gradient boosting, can be used to this end, constituting the first application of these algorithms in any CSO framework. Using real-world data harvested from the New York ISO grid, we measure the out-of-sample performance of the framework in terms of total operation cost, shed load values, locational marginal prices, and total payments by the loads, against several benchmark methods proposed in the literature. The article has an online companion (Yurdakul et al.), wherein we present additional results and lay out further mathematical formulations used in this work.

42 ENGINEERING↗