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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Timing reconstruction utilising multiple detection subsystems at the Short-Baseline Near Detector experiment

The Short-Baseline Near Detector (SBND) is the near detector of the Short-Baseline Neutrino Program, located only 110 m away from the Booster Neutrino Beam (BNB) target at Fermilab (Illinois, USA). SBND employs key detection technologies: a 112-ton Liquid Argon Time Projection Chamber for ionisation electrons, an advanced Photon Detection System (PDS) made up of photomultiplier tubes and X-ARAPUCAs for scintillation light, and a 4π coverage of Cosmic Ray Taggers (CRTs) for cosmic muons. This setup offers an exceptional energy, spatial and timing reconstruction of neutrino interactions. In this poster, we present ongoing timing reconstruction efforts of the PDS and CRTs, as well as combining complementary information across multiple detection subsystems to improve the timing resolution. This results in nanosecond level timing information that can be leveraged as an analysis tool, including the reconstruction of the BNB substructure to accept or reject signals detected in-between neutrino bunches as well as a timing-based discrimination for cosmic rejection.

Corallo, Annalea [INFN, Ferrara]↗
DOE OSTI2026

Towards searches for heavy neutral leptons with the Short-Baseline Near Detector experiment

Heavy Neutral Leptons (HNLs) are hypothetical long-lived particles that extend the Standard Model and provide a natural explanation for the origin of neutrino masses, the generation of the baryon asymmetry through leptogenesis, and the nature of dark matter. The Short-Baseline Near Detector (SBND) is a 112-ton liquid argon time projection chamber located 110 m away from the Booster Neutrino Beam (BNB) target at Fermilab (Illinois, USA). The close location to the BNB origin makes the experiment sensitive to HNLs produced from meson decays in the beam. These heavy particles arrive at the detector later than neutrinos and decay into observable final states such as 𝜈e+e- or 𝜈π0, generating isolated electromagnetic showers. Thanks to SBND’s advanced scintillation light detection system, a timing resolution at the nanosecond level further boosts the experiment capabilities. In this poster, we present ongoing reconstruction efforts towards HNLs as well as expected sensitivities factoring in the reconstruction improvements.

Romeo Araujo, Jorge [Madrid, CIEMAT]↗
DOE OSTI2026

Tachyon: Intelligent Multi-Scale Modeling of Distributed Resilient Infrastructure and Workflows for Data Intensive HEP Analyses

The DOE High Energy Physics (HEP) program in Neutrino and Collider science drives data-intensive science and simulation on extreme-scale platforms. Modeling and optimizing the complex distributed components from experimental to leadership computing facilities are essential for HEP workflows to achieve required response times and resilience under various conditions. Tachyon proposes a framework for scalable modeling, simulation, and validation of key performance characteristics for the distributed infrastructure between FNAL and ALCF, along with associated HEP workflows.

Carothers, Chris [Rensselaer Poly.]↗
DOE OSTI2026

Tachyon: Intelligent Multi-Scale Modeling of Distributed Resilient Infrastructure and Workflows for Data Intensive HEP Analyses

The DOE High Energy Physics (HEP) program in Neutrino and Collider science drives data-intensive science and simulation on extreme-scale platforms. Modeling and optimizing the complex distributed components from experimental to leadership computing facilities are essential for HEP workflows to achieve required response times and resilience under various conditions. Tachyon proposes a framework for scalable modeling, simulation, and validation of key performance characteristics for the distributed infrastructure between FNAL and ALCF, along with associated HEP workflows.

Carothers, Chris [Rensselaer Poly.]↗
DOE OSTI2020

Basis light-front quantization approach to nucleon

We obtain the light-front wavefunctions for the nucleon in the valence quark Fock space from an effective Hamiltonian, which includes the transverse and longitudinal confinement and the one gluon exchange interaction with fixed coupling. The wavefunctions are generated by solving the eigenvalue equation in a basis light front quantization. Fitting the model parameters, the wavefunctions lead to good simultaneous description of electromagnetic form factors, radii, and parton distribution functions for the proton.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gate-model quantum computing systems, we employ the quantum variational classifier method and the quantum kernel estimator method in two recent LHC flagship physics analyses: $t\bar{t}H$ (Higgs boson production in association with a top quark pair) and $H\rightarrow\mu\mu$ (Higgs boson decays to two muons). We have obtained early results with 10 qubits on the IBM quantum simulator and the IBM quantum hardware. On the quantum simulator, the quantum machine learning methods perform similarly to classical algorithms such as SVM (support vector machine) and BDT (boosted decision tree), which are often employed in LHC physics analyses. On the quantum hardware, the quantum machine learning methods have shown promising discrimination power, comparable to that on the quantum simulator. This study demonstrates that quantum machine learning has the ability to differentiate between signal and background in realistic physics datasets.

Chan, Jay↗
DOE OSTI2022

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Structure and conformational dynamics of Clostridioides difficile toxin A

Clostridioides difficile toxin A and B (TcdA and TcdB) are two major virulence factors responsible for diseases associated with C. difficile infection (CDI). Here, we report the 3.18-Å resolution crystal structure of a TcdA fragment (residues L843–T2481), which advances our understanding of the complete structure of TcdA holotoxin. Our structural analysis, together with complementary single molecule FRET and limited proteolysis studies, reveal that TcdA adopts a dynamic structure and its CROPs domain can sample a spectrum of open and closed conformations in a pH-dependent manner. Furthermore, a small globular subdomain (SGS) and the CROPs protect the pore-forming region of TcdA in the closed state at neutral pH, which could contribute to modulating the pH-dependent pore formation of TcdA. A rationally designed TcdA mutation that trapped the CROPs in the closed conformation showed drastically reduced cytotoxicity. Taken together, these studies shed new lights into the conformational dynamics of TcdA and its roles in TcdA intoxication.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Efficient Synthesis of Pinoresinol, an Important Lignin Dimeric Model Compound

Pinoresinol is a high-value monolignol-derived lignan used in plant defense and with human health-supporting effects. The synthetic yield and isolation efficiency of racemic pinoresinol from coniferyl alcohol by conventional radical coupling methods is sub-optimal. In this work, a facile and efficient synthetic approach was developed to synthesize pinoresinol with much higher yield. By using 5-bromoconiferyl alcohol, which was synthesized in high yield from 5-bromovanillin, to make 5,5′-bromopinoresinol via a peroxidase-mediated radical coupling reaction takes advantage of the smaller variety of radical coupling products from the 5-substituted monolignol, producing simpler product mixtures from which 5,5′-bromopinoresinol may be readily crystalized with good yield (total yield of 44.1% by NMR; isolated crystalline yield of 24.6%). Hydro-debromination of the crystalline 5,5′-bromopinoresinol to pinoresinol was essentially quantitative. Gram quantities of pinoresinol were conveniently synthesized by using this approach. This simple alternative pathway to make pinoresinol will impact pinoresinol-related research including structural characterization and modification of lignins, as well as clinical applications of pinoresinol and its derivatives.

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

Investigation of Cutter–Woodchip Contact Pressure in a New Biomass Comminution System

A Crumbler ® rotary shear system was recently developed to process fuel-grade woodchips into feedstocks having more uniform particle sizes for improved flowability and higher thermochemical output yield than a traditional hammer mill. It represents a significant innovation in the state of the art of biomass comminution equipment. However, the milling unit has experienced significant abrasive wear of the feeding teeth and cutters when processing hard and dusty feedstocks, such as logging residue and corn stover. Here, we present initial simulation results from a finite-element stress analysis of the rotary shear, which is used to investigate the dynamics of interaction between the cutters and woodchips. This is the first step for optimizing the cutter design to improve the system lifetime and processing efficiency, thus reducing downtime and improving overall productivity.

42 ENGINEERING↗
DOE OSTI2022

Flowability of Crumbler Rotary Shear Size-Reduced Granular Biomass: An Experiment-Informed Modeling Study on the Angle of Repose

Biomass has potential as a carbon-neutral alternative to petroleum for chemical and energy products. However, complete replacement of fossil fuel is contingent upon efficient processes to eliminate undesirable characteristics of biomass, e.g., low bulk density, variability, and storage-induced quality problems. Mechanical size reduction via comminution is a processing operation to engineer favorable biomass flowability in handling. Crumbler rotary shear mill has been empirically demonstrated to produce more uniformly shaped particles with higher flowability than hammermilled biomass. This study combines modeling and experimentation to unveil fundamental understandings of the relation between granular particle characteristics and biomass flow behavior, which elucidate underlying mechanisms and guide selection of critical processing parameters. For this purpose, the impact of critical material attributes, including particle size (2–6 mm), particle shape (briquette, chip, clumped-sphere, cube, etc.), and surface roughness, on the angle of repose (AOR) of milled pine chips were investigated using discrete element method (DEM) simulations. Forest Concepts Crumbler rotary shear system is used to produce milled pine particles within the same size range considered in DEM simulations. AOR of different sets of these particles were measured experimentally to benchmark DEM results against experimental data. Specific energy consumption for the comminution of biomass with different particle size and moisture content are measured for technoeconomic analysis. Our results show that the smaller size (2 mm) of pine particle achieves better followability (i.e., smaller AOR) while the energy cost of comminution is significantly higher and bulk density is almost the same as the 6-mm pine particles. For the 2-mm particle size, Crumbles from veneer have better flow properties than Crumbles from chips. Contrarily, no significant difference was observed between the AOR of the two materials for the 6-mm particle size. Furthermore, from DEM simulations, mechanical interlocking between particles was found as a dominant factor in determining AOR of complex-shaped particles such as milled pine, which cannot be accurately captured by using simple particle shapes (e.g., mono-sphere) with a rolling resistance model. Conversely, clumped-sphere model alleviates this limitation without increasing computational cost significantly and can be used for accurate representation of biomass granular particles when simulating free-flow behavior.

09 BIOMASS FUELS↗
DOE OSTI2022

Standard Operating Protocol for Ion-Exchange Capacity of Anion Exchange Membranes

Ion-exchange capacity (IEC) is the measure of a material’s capability to displace ions formerly incorporated within its structure. IEC is a key feature of anion-exchange membranes (AEM), as it determines the AEM’s ability to conduct the ions required to sustain the electrochemical reactions where they are utilized. As an intrinsic property, measuring the IEC accurately is essential to study AEMs and understand their performance within devices. In this method article, a facile and accurate standard operating procedure (SOP) to measure the IEC of AEMs is proposed. When compared to conventional acid-base back-titration or Mohr titration, the proposed method combines the fast reaction between silver and halide ions and the accuracy of the potentiometric titration, providing a convenient and precise protocol for researchers in the field.

42 ENGINEERING↗
DOE OSTI2023

Finite element modeling of biomass hopper flow

Hoppers are widely used biomass handling devices that channel bulk biomass from storage to subsequent handling equipment. Jenike’s longstanding approach, based on the Mohr-Coulomb model, has been successfully used to design hoppers handling cohesionless granular materials such as grains and other agricultural produces. However, designing a hopper to ensure reliable biomass flow is found to be challenging due to cohesion, irregular particle shape, and bulk material elastoplasticity. This study aims to address the biomass handling engineering challenge with alternative constitutive material models concerning the flow behavior of bulk solids. Finite element modeling is an approach that allows for implementation of different material models, whose underlying constitutive theories assist in investigating the origin and manifestation of bulk mechanical behavior of granular materials. This study focuses on the incipient gravity hopper flow of two types of biomass feedstocks, i.e., ground corn stover and Douglas fir wood. Three widely used constitutive material models, i.e., Mohr-Coulomb model, modified Cam-Clay model, and Drucker-Prager/Cap model, are implemented. Using the flow pattern represented by the volume of biomass exhibiting more than 7% of axial strain (Kamath and Puri, 1999), the finite element model predicts that the bulk corn stover particulate material forms an arch, which represents a hampered transition from the static state to the dynamic flow-state out of the hopper, whereas bulk Douglas fir wood particulate material develops a reliable mass flow pattern. A laboratory scale hopper was used to experimentally determine the biomass flow conditions, which were subsequently compared with the predicted onset of flow by a finite element model (FEM). The developed FEM was found to correctly predict the initiation of mass flow for the milled Douglas fir wood, whereas corn stover was predicted to establish a strong core flow suggesting an unreliable handling characteristic. This observation aligns with the reported poor handling of milled corn stover.

09 BIOMASS FUELS↗
DOE OSTI2024

Applying colloidal silica suspensions injection and sequential gelation to block vertical water flow in well annulus: laboratory testing on rheology, gelation, and injection

We evaluated the application of silica suspension injection and sequential gelation to block vertical water flow in the annuli of long-screened wells. First, we studied the viscosity, rheological behavior, and gelation performance of colloidal silica suspensions in batch tests. Then, we tested the injection of silica suspensions and the water flow blocking efficiency of the later formed silica gel in column and bench-scale sandbox experiments. Micron-sized fumed powder silica suspensions and nanosized silica suspensions recovered from geothermal fluids were tested in this work. Fumed silica suspensions showed shear thinning, while nanosized silica suspensions exhibited Newtonian flow behavior. During the gelation process, the nanosized silica suspension changed from a Newtonian fluid to a shear thinning fluid while increasing its overall viscosity. At comparable concentrations, the nanosized silica suspensions have much lower viscosity than that of the fumed silica suspensions. Increases in the Na + concentration and silica particle concentration in these suspensions shortened the gelation time. Silica suspension gelation in sand columns completely blocked the water flow and sustained the injection pressure up to 50 psig (344.7 kPa). A silica suspension was successfully injected into the target zone in the annulus of a bench-scale sandbox mimicking long-screened wells in the field. The silica gel formed in the annulus effectively blocked chemical transport through the gelled zone. Our research reveals that a process using silica suspension injection and sequential gelation technology is promising for blocking the vertical water flow and chemical transport through the filter pack in targeted zones within the annulus of long-screened well systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Nek5000/RS performance on advanced GPU architectures

The authors explore performance scalability of the open-source thermal-fluids code, NekRS, on the U.S. Department of Energy's leadership computers, Crusher, Frontier, Summit, Perlmutter, and Polaris. Particular attention is given to analyzing performance and time-to-solution at the strong-scale limit for a target efficiency of 80%, which is typical for production runs on the DOE's high-performance computing systems. Several examples of anomalous behavior are also discussed and analyzed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Inhibition of tick-borne encephalitis virus in cell cultures by ribavirin

Tick-borne encephalitis virus (TBEV) belonging to arboviruses is a major member of zoonotic pathogens. TBEV infection causes severe human encephalitis without specific antiviral drugs. Due to its use of antiviral drug against a wide range of viruses, we investigated antiviral effect of ribavirin against TBEV in susceptible human cell lines A549 and SH-SY5Y. Ribavirin displayed minor cytotoxicity on multiple cell lines. Ribavirin obviously impaired TBEV replication and protected the infected cells from cytopathic effect. Importantly, ribavirin markedly inhibited TBEV propagation, as evidenced by impairment of TBEV production and viral RNA replication. Treatment with ribavirin (co-treatment and post-treatment) led to a dose-dependent reduction in TBEV titers as well as the viral RNA levels. Antiviral protein myxovirus resistance A mRNA expression was significantly up-regulated and signal transducer and activator of transcription 3 was activated in TBEV-infected A549 cells upon the ribavirin treatment. Induction of inflammatory cytokine tumor necrosis factor alpha by TBEV was decreased in A549 cells with the treatment of ribavirin, whereas interleukin 1 beta release appeared to be unaffected. These results suggest that ribavirin might represent a promising safe and effective antiviral drug against TBEV.

Tang, Wan-Da↗
DOE OSTI2024

Study on the effect of ascorbic acid on the biosynthesis of pigment and citrinin in red yeast rice based on comparative transcriptomics

Pigment is one of the most important metabolites in red yeast rice. However, citrinin may accumulate and cause quality security issues. In the present study, the effect of ascorbic acid (EAA) on the pigment and citrinin was studied, and the metabolic mechanism was discussed using comparative transcriptomics. The introduction of EAA increased the pigment by 58.2% and decreased citrinin by 65.4%. The acid protease activity, DPPH scavenging rate, and total reducing ability also increased by 18.7, 9.0, and 26.7%, respectively. Additionally, a total of 791 differentially expressed genes were identified, and 79 metabolic pathways were annotated, among which carbon metabolism, amino acid metabolism, and fatty acid metabolism were closely related to the biosynthesis of pigment and citrinin. Ethanol dehydrogenase ( M pigC ), oxidoreductase ( M pigE ), reductase ( M pigH ), and monooxygenase ( M pigN ) may be related to the increase of pigment. ctnC and pksCT contributed to the decline of citrinin.

Jiang, Zhilin↗