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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A multi-dimensional search for new heavy resonances decaying to boosted $\text{W}$ $\text{W}$, $\text{W}$ $\text{Z}$, or $\text{Z}$ $\text{Z}$ boson pairs in the dijet final state at 13 $\text {Te}\text{V}$

A search in an all-jet final state for new massive resonances decaying to $\text{ W }{}{}\text{ W }{}{}$, $\text{ W }{}{}\text{ Z }{}{}$, or $\text{ Z }{}{}\text{ Z }{}{}$ boson pairs using a novel analysis method is presented. The analysis is performed on data corresponding to an integrated luminosity of 77.3 $\,\text {fb}^{-1}$ recorded with the CMS experiment at the LHC at a centre-of-mass energy of 13 $\text {Te}\text {V}$. The search is focussed on potential narrow-width resonances with masses above 1.2 $\text {Te}\text {V}$, where the decay products of each $\text{ W }{}{}$ or $\text{ Z }{}{}$ boson are expected to be collimated into a single, large-radius jet. The signal is extracted using a three-dimensional maximum likelihood fit of the two jet masses and the dijet invariant mass, yielding an improvement in sensitivity of up to 30% relative to previous search methods. No excess is observed above the estimated standard model background. In a heavy vector triplet model, spin-1 ${\text {Z}}^{\prime }$ and ${\text {W}}^{\prime }$ resonances with masses below 3.5 and 3.8 $\text {Te}\text {V}$, respectively, are excluded at 95% confidence level. In a bulk graviton model, upper limits on cross sections are set between 27 and 0.2 $\,\text {fb}$ for resonance masses between 1.2 and 5.2 $\text {Te}\text {V}$, respectively. The limits presented in this paper are the best to date in the dijet final state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Boosting new physics searches in $$t{\bar{t}}Z$$ and tZj production with angular moments

Abstract The angular moments of the Z boson can be used as analyzers for the underlying production dynamics for the $$t{{\bar{t}}} Z$$ t t ¯ Z and tZj processes. In this manuscript, we derive these angular moments at leading and next-to-leading order in QCD at the LHC. We show that these observables work as efficient probes to beyond the Standard Model effects, considering the Standard Model Effective Field theory framework. Remarkably, we observe that these probes unveil blind directions to CP-odd operators, providing sizable new physics sensitivity at the 14 TeV LHC with 3 ab $$^{-1}$$ - 1 of data.

Physics↗
DOE OSTI2025

Quantum Transfer Learning to Boost Dementia Detection

Dementia is a devastating condition with profound implications for individuals, families, and healthcare systems. Early and accurate detection of dementia is critical for timely intervention and improved patient outcomes. While classical machine learning and deep learning approaches have been explored extensively for dementia prediction, these solutions often struggle with high-dimensional biomedical data and large-scale datasets, quickly reaching computational and performance limitations. To address this challenge, quantum machine learning (QML) has emerged as a promising paradigm, offering faster training and advanced pattern recognition capabilities. This work aims to demonstrate the potential of quantum transfer learning (QTL) to enhance the performance of a weak classical deep learning model applied to a binary classification task for dementia detection. Besides, we show the effect of noise on the QTL-based approach, investigating the reliability and robustness of this method. Using the OASIS 2 dataset, we show how quantum techniques can transform a suboptimal classical model into a more effective solution for biomedical image classification, highlighting their potential impact on advancing healthcare technology.

Bhowmik, Sounak [University of Tennessee, Knoxvill↗
DOE OSTI2021

Al Substitution for Mn during Co-Precipitation Boosts the Electrochemical Performance of LiNi 0.8 Mn 0.1 Co 0.1 O 2

We report nickel-rich layered oxides, such as LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC 811), are considered as one of the most promising candidates for the next-generation cathode because of their high energy densities and relatively low cost. However, the poor first Coulombic efficiency of NMC 811 leads to around a 15% capacity loss in the first cycle at a cut-off voltage of 4.4 V. Moreover, the structure degradation during cycling results in capacity fading and safety concerns, due to potential oxygen loss after charging. Here, with aluminum substitution for manganese through a developed continuous co-precipitation approach, the electrochemical performance of NMC 811 cathodes has been greatly enhanced. Among different Al% substituted samples, LiNi 0.8 Mn 0.06 Co 0.1 Al 0.04 O 2 cathodes reduced by 50% the first capacity loss of pristine NMC 811(18.0 vs 35.9 mAh g -1 ) and improved the capacity retention from 81.4 to 96.4% after 60 cycles at 0.5C in the voltage range of 2.8–4.4 V.

25 ENERGY STORAGE↗
DOE OSTI2020

Decoy selection for protein structure prediction via extreme gradient boosting and ranking

Background: Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of balance in positive and negative samples (native and non-native decoys) in a decoy set makes the problem even more complicated. Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity. Recent investigations into energy landscape-based decoy selection approaches show promises. However, lack of generalization over varied test cases remains a bottleneck for these methods. Results: We propose a novel decoy selection method, ML-Select, a machine learning framework that exploits the energy landscape associated with the structure space probed through a template-free decoy generation. The proposed method outperforms both clustering and energy ranking-based methods, all the while consistently offering better performance on varied test-cases. Moreover, ML-Select shows promising results even for the decoy sets consisting of mostly low-quality decoys. Conclusions: ML-Select is a useful method for decoy selection. This work suggests further research in finding more effective ways to adopt machine learning frameworks in achieving robust performance for decoy selection in template-free protein structure prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Boosting SNR of cascaded FBGs in a sapphire fiber through a rapid heat treatment

This Letter reports the performance of femtosecond (fs) laser-written distributed fiber Bragg gratings (FBGs) under high-temperature conditions up to 1600°C and explores the impact of rapid heat treatment on signal-to-noise ratio (SNR) enhancement. FBGs are essential for reliable optical sensing in extreme temperature environments. Comprehensive tests demonstrate the remarkable performance and resilience of FBGs at temperatures up to 1600°C, confirming their suitability for deployment in such conditions. Here, the study also reveals significant fringe visibility improvements of up to ∼10 dB on a 1-m-long sapphire optical fiber through rapid heat treatment, representing a first-time achievement to the best of our knowledge. These enhancements are vital for improving the SNR and overall performance of optical fiber systems in extreme temperatures. Furthermore, the research attains long-term stability for the cascaded FBGs over a 24-hr period at 1600°C. This research expands our understanding of the FBG behavior in high-temperature environments and opens avenues for developing robust optical fiber systems for energy, aerospace, oil and gas, and high-temperature distributed sensing applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Gradient-Based Novelty Detection Boosted by Self-Supervised Binary Classification

Novelty detection aims to automatically identify out-of-distribution (OOD) data, without any prior knowledge of them. It is a critical step in data monitoring, behavior analysis and other applications, helping enable continual learning in the field. Conventional methods of OOD detection perform multi-variate analysis on an ensemble of data or features, and usually resort to the supervision with OOD data to improve the accuracy. In reality, such supervision is impractical as one cannot anticipate the anomalous data. In this paper, we propose a novel, self-supervised approach that does not rely on any pre-defined OOD data: (1) The new method evaluates the Mahalanobis distance of the gradients between the in-distribution and OOD data. (2) It is assisted by a self-supervised binary classifier to guide the label selection to generate the gradients, and maximize the Mahalanobis distance. In the evaluation with multiple datasets, such as CIFAR-10, CIFAR-100, SVHN and TinyImageNet, the proposed approach consistently outperforms state-of-the-art supervised and unsupervised methods in the area under the receiver operating characteristic (AUROC) and area under the precision-recall curve (AUPR) metrics. We further demonstrate that this detector is able to accurately learn one OOD class in continual learning.

Sun, Jingbo↗
DOE OSTI2017

Development and Implementation of an Automatic Continuous Online Monitoring and Control Platform for Polymerization Reactions to Sharply Boost Energy and Resource Efficiency in Polymer Manufacturing

The project goal was to create an energy saving paradigm shift in how polymers are manufactured in the 21st century. It used Automatic Continuous Online Monitoring of Polymerization reactions (ACOMP) integrated for the first time with automatic active control to create the innovative ‘ACOMP/Control Interface’, or ‘ACOMP/CI’. ACOMP/CI will begin the transformation from old, inefficient processes into highly evolved, energy and resource efficient ones. The ACOMP platform is broadly applicable to many types of reactions and processes throughout the vast polymer industry. The industry provides materials for sectors such as automotive, aerospace, oil recovery, agriculture, paints, resins, adhesives, pharmaceuticals and therapeutic proteins, optics, electronics, lightweight building materials, and many more. The U.S. chemical industry is one of the last major sectors in which the U.S. has top global stature. It consumes 24% of all U.S. manufacturing energy, produces over $800B of product annually, supports 25% of the U.S. GDP and employs over 6 million people. It is also a major source of GHG emissions. Polymers make up approximately 30% of this sector. It is estimated that annually 60 TBtu of energy could be saved and 3 million tons less of GHG emissions produced by optimizing production in the polyolefin manufacturing sector alone. The project scope included first time design and prototyping of an ACOMP/CI, creation of active reaction controllers, and demonstration of control capabilities on ideal, low concentration polymerization reactions. All these elements of the scope were met, including advances and findings not originally anticipated. Extensions to more complex reactions, beyond the reactor capabilities of the current project ACOMP/CI, such as polyolefins and other high pressure/high temperature reactions, are being proposed in Fall 2017 to CESMII, a DoE based NNMI. The initial proposal was for a three year funded project, but this was reduced to a two year project and budget due to funding constraints. Hence, some of the original plans, such as adaptation of the ACOMP/CI to more relevant industrial processes, such as emulsion and dispersion technologies, could not be carried out. A third year of funding was requested at the end of the project, but DoE did not have resources to grant this. The sub-contractor Fluence Analytics (previously Advanced Polymer Monitoring Technologies, Inc) designed, prototyped, and commissioned a working ACOMP/CI by June 2015. The reaction characteristics to be automatically controlled were i) conversion kinetics, ii) molecular weight, iii) copolymer composition, and iv) simultaneous molecular weight and composition. A two pronged control strategy was used. The Tulane/Fluence group took a basic principles approach that did not rely on kinetic models. The LSU group took a more complex, non-linear model-oriented approach involving complete kinetic descriptions of the reaction system Each of these approaches proved successful in their own way. By April 2016 fully automatic control of conversion and weight average molecular weight, Mw, trajectories was achieved using the Tulane/Fluence (TF) basic principles controller. Similar results were obtained by the LSU non-linear model controller by August 2016. The demonstration system was aqueous free radical polymerization of acrylamide, Am. The control variables were temperature and semi-batch feed to the reactor of Am monomer and initiator. A demonstration of active manual conversion control in an industrial process using high solids in inverse emulsion polymerization of Am was achieved. During Summer 2016 the TF controller was used in conjunction with a chain transfer agent, another control variable, to automatically produce multi-modal molecular weight distributions, MWD, in a single reactor. Industrially, multi-modal MWD are produced by mixing products made in separate reactors, requiring significant extra time, energy, and reactor resources. Recognizing the industrial potential a patent on automatic production of multi-modal polymers was filed, and DoE acknowledged. In Fall 2016 the TF team developed a basic principles controller for copolymer composition and demonstrated it on aqueous free radical copolymerization of comonomers Am and styrene sulfonate, SS. TF then fused the Mw and composition controllers to achieve simultaneous control of both Mw and copolymer composition trajectories. Numerous simultaneous trajectories were demonstrated, including a trimodal composition distribution with constant Mw. Meanwhile, the LSU group developed a Kalman filter to improve the results of their automatic Mw and conversion controller and successful tests were made. During the project the TF team developed a means of computing full MWD during polymer synthesis without need for any chromatographic separation, based on model distributions. This means the polymer product is ‘born characterized’ and this can eliminate post-manufacture analytical laboratory quality control. TF filed a joint patent application on this new approach to chromatography-free determination of MWD with acknowledgment to DoE. The Tulane group obtained a 60MHz NMR during the project and recently completed the first work on separating three comonomers, Am, SS, and Na-acrylate, with a first demonstration of terpolymer composition control with the TF basic principles controller. Widespread dissemination of ACOMP/CI in the polymer manufacturing sector will bolster DoE goals of energy efficiency and reduced GHG emissions: The ability to monitor and actively control polymerization reactions will lead to more efficient use of energy and non-renewable resources, plant and labor time, increase the safety of manufacturing personnel, and will enhance product quality and lead to feasibility of manufacturing of polymers currently too complex for industrial scale production, while leading to less GHG emissions per kilo of product, and allowing for increased U.S. competitiveness in this enormous manufacturing sector. When ACOMP/CI is expanded to the polyolefin industry it is estimated that 60 TeraBTU/year of energy can be saved. Much of this saving is anticipated to come from optimized control of grade changeovers in steady state reactors and maintenance of steady states. Conclusions: ACOMP’s ability to provide continuous realtime data streams of measured polymer and reaction characteristics made it possible, for the first time, to directly and automatically control free radical polymerization reactions. An industrial client of Fluence Analytics has requisitioned the first ACOMP/CI which uses the TF basic principles controller. This sets the stage for FA to add control features to the ACOMP systems it has begun to install on the industrial scale beginning in 2014. Recommendations: This successful project was mainly limited to ideal polymerizations not of an industrial sort. The most energy intensive portion of polymer manufacturing is polyolefins. Adoption of ACOMP/CI to this enormous industrial sector faces the enormous challenges of high temperature, high pressure continuous sampling and high temperature sensor operation to obtain the continuous data needed for direct reaction control. The project team has a strategy for achieving this ambitious goal and will present it in Fall 2017 as a proposal to CESMII/DoE. It is recommended that this upcoming proposal be funded in order to make full use of the achievements of this just ended DoE project as the next step towards making polyolefin ACOMP/CI an energy saving reality. It is projected that ACOMP/CI can have its first polyolefin testbed demonstrations within two years of beginning the proposed project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Boosting Energy Efficiency and Economic Development through Chambers of Commerce (Final Technical Report)

The U.S. Department of Energy’s (DOE) Building Technology Office (BTO), in its mission to improve energy efficiency in commercial and residential buildings, recognized the unique challenges posed by small- and medium-sized commercial buildings. This project targeted these buildings by using a business-to-business model originally pioneered in Cleveland, Ohio, by the Greater Cleveland Partnership’s Council of Smaller Enterprises (COSE). Rather than attempting to take programs and materials designed for large commercial buildings and simply disseminate them to owners of small- and medium-sized buildings, COSE targeted the tenants and owners of these buildings directly through existing business partnership channels and built additional business partnerships with banks willing to finance cost-effective retrofits, and leveraging utility efficiency programs that enabled them to provide free audits via an established network of auditors. By delivering a customized message to their members and leveraging their connections in the local business community, Chambers of Commerce and other business-oriented organizations use their existing relationship and trust to deliver clear and concise messages about the opportunities, impacts, and value of energy efficiency investments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

An August boost for key roles in Weapons Production

In August, the Associate Laboratory Directorate of Weapons Production (ALDWP) hosted two virtual hiring events and welcomed the first cohort of a new college pipeline program. With hiring steadily on the increase in support of the national security mission, creative methods like these have been key to finding successful candidates.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Co-Optimization of Boost Converter Reliability and Volumetric Power Density [Brief]

Optimized designs were achieved using a genetic algorithm to evaluate multi-objective trade space, including Mean-Time-Between-Failure (MTBF) and volumetric power density. This work provides a foundational platform that can be used to optimize additional power converters, such as an inverter for the EV traction drive system as well as trade-offs in thermal management due to the use of different device substrate materials.

33 ADVANCED PROPULSION SYSTEMS↗