Machine learning enabled reduced-order scenario generation for stochastic analysis of solar power forecasts
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Energy & Fuels↗
SEARCH · Engineering Papers
Results for “Generator”
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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Oxidative Deboronation of Boronic Acids by Hydrogen Peroxide in Planta Generates Borate for Cross-Linking of Rhamnogalacturonan II
Vascular plants require boron to cross-link the rhamnogalacturonan-II (RG-II) domain of pectin to form functional cell walls. Boronic acids, which form reversible esters with cis-diols like borate, have been proposed to influence RG-II cross-linking, though the mechanism remains unclear. We used suspension-cultured rose cells adapted to grow without boron to investigate the effect of boronic acids on RG-II dimerization. When grown with phenylboronic acid (PBA) as the sole boron source, nearly all RG-II was crosslinked, whereas methylboronic acid (MBA) only partially restored cross-linking. In contrast, in vitro assays showed that homogeneous RG-II monomers did not dimerize with alkyl or aryl boronic acids unless supplemented with hydrogen peroxide (H2O2), which oxidatively converts boronic acids to boric acid. Real-time NMR spectroscopy and density functional theory calculations provided insight into the reaction mechanism and energetics of oxidation respectively. Together, our data show that exogenous boronic acids are a source of boric acid for plants, and that the deboronation reaction generates aryl or alkyl alcohol byproducts that can undergo further chemical modification in planta. The fate and potential roles of these byproducts in planta remain to be determined.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A novel approach for adaptive skeleton toolpath generation
Industry 4.0 is revolutionizing manufacturing through the integration of automation and real-time data sharing in cyber-physical systems. At the forefront of this revolution is large-format additive manufacturing. In large-format printing, parts are often designed to be an even number of beads wide to produce a completely dense part. However, voids can still arise. This is often due to the part not being an even number of bead widths wide in some areas, or in geometry containing acute angles, as the process of generating closed contours cannot completely fill the space. Voids can be tolerated in smaller models, but in large-format additive manufacturing they may cause mechanical defects. To fill these voids, open loop paths called skeletons are often used, but they are typically limited by the physical constraints defined in the slicing software. To address this, researchers at Oak Ridge National Laboratory have extended skeleton toolpaths via an adaptive methodology. These adaptive skeletons were found to better fill void spaces through manipulation of physical parameters of the build process and were calculated as part of the slicing process.
42 ENGINEERING↗
Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation
The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.
ChemBERTa↗
Automated Symbolic Upscaling: 2. Model Generation for Extended Applicability Regimes
Abstract In this second part of the two paper series, we detail an algorithmic procedure for systematically implementing the generalized closure form strategy presented in Part 1. This strategy extends the applicability of homogenized models with respect to classical homogenization theory, as demonstrated in Part 1 where upscaled models are rigorously derived in moderately reactive physical regimes. After encoding the algorithm into Symbolica, an automated upscaling framework, we upscale two reactive mass transport problems and numerically validate the resulting nonlinear homogenized models by showing the absolute error estimates predicted by homogenization theory are satisfied. In both problems, nontrivial closure forms and closure problems are automatically formulated using the encoded strategy with no human interaction, nor prior knowledge regarding the closure required for the systems. We hope these demonstrations spark further interest in automated analytical frameworks for multiscale modeling, as such capabilities are invaluable for generating rigorous multiscale models of complex phenomena in porous media.
Pietrzyk, Kyle↗
A systematic approach to generating accurate neural network potentials: the case of carbon
Abstract Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for generating potentials are promising; however, neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive, and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite, and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.
Chemistry↗
Characterization of mechanical deformation in aluminum by optical second harmonic generation
Second harmonic generation (SHG), a laser-based surface technique, was used to characterize near-surface defect evolution associated with mechanical deformation in 2024-T3 aluminum due to tensile loads. Electron backscatter diffraction (EBSD) measurements were also performed to determine the geometrically necessary dislocation density before and after tensile deformation. Comparison of the EBSD and SHG results found that dislocation density and SHG signal are inversely proportional; tensile deformation in the plastic regime led to an increase in dislocation density and a decrease in SHG response. A similar trend was seen in the SHG results on as-received (unpolished) Al samples; SHG response decreases with extent of plastic deformation. Furthermore, this study suggests that SHG has potential as a nondestructive evaluation technique.
36 MATERIALS SCIENCE↗
Non-target RNA depletion strategy to improve sensitivity of next-generation sequencing for the detection of RNA viruses in poultry
PCR-based assays have become the benchmark for detecting pathogens of poultry and other livestock; however, these techniques are limited in their ability to detect multiple infecting agents, provide limited genetic information on the pathogen, and, for RNA viruses, must be reviewed frequently to assure high sensitivity and specificity. In contrast, untargeted, high-throughput sequencing can rapidly detect all infecting agents in a sample while providing genomic sequence information to allow more in-depth characterization of viruses. Although next-generation sequencing (NGS) offers many advantages, one of its primary limitations is low sensitivity to pathogens given the abundance of host and other non-target sequences in sequencing libraries. We explored methods for improving the sensitivity of NGS to detect respiratory and enteric viruses in poultry from RNA extracts of swab samples. We employed commercial and custom-designed negative enrichment strategies to selectively deplete the most abundant rRNA reads from the host and non-target bacteria; host RNA was diminished from up to 40% of total reads to as low as 3%, and the total number of reads assigned to abundant bacterial classes were reduced greatly. Our treatment resulted in up to a 700-fold increase in the number of viral reads, detection of a greater number of viral agents, and higher average genome coverage for pathogens. Depletion assays added only 2 h to the NGS library preparation workflow. Custom depletion probe design offered significant cost savings (US$7–12 per sample) compared to commercial kits (US$30–50 per sample).
Parris, D. Joshua↗
Optimizing the fuel efficiency of an opposed piston engine for electric power generation
This paper investigates the optimal crankshaft motion for an opposed piston (OP) engine in a novel hybrid architecture to maximize fuel efficiency. The OP engine was selected for this work due to its inherent thermodynamic benefits and the balanced nature of the engine which can achieve downsizing through reducing the number of cylinders rather than the individual cylinder volume. The typical geartrain required on an OP engine was exchanged for two electric motors, reducing friction loss and decoupling the crankshafts. Using the motors to control the crankshaft motion profiles, this architecture introduces capabilities to dynamically vary compression ratio, combustion volume, and scavenging dynamics. To leverage these opportunities, an optimization scheme was developed utilizing nonlinear optimization of a 0-D model to compute the crankshaft motion profile that maximizes the work generated by the system. This optimization was then iteratively coupled with a high fidelity model which supplies the cylinder flow boundary conditions. This iterative approach reduces the model complexity used in the optimal control problem (OCP) while capturing the gas exchange dynamics critical to the 2-stroke cycle of the OP engine. By using the rate of change of motor torque as the input to the OCP, the torque fluctuation in a single cycle can be limited to ensure tracking feasibility. The results show crankshaft velocity slows during the compression stroke and conversely accelerates during the expansion stroke, reducing the peak motor torque required for control and thus reducing the motor losses. The extended residence time at top dead center, however, leads to an increase in heat transfer, illustrating the trade-off between the work extraction efficiency and the indicated engine efficiency.
Engineering↗
High-Fidelity Numerical Wave Tank Verification & Validation Study: Wave Generation Through Paddle Motion: Preprint
This paper presents a numerical benchmark study of wave propagation due to a paddle motion using different high-fidelity numerical models, which are capable of replicating the nearly actual physical wave tank testing. A full time series of the measured wave generation paddle motion which was used to generate wave propagation in the physical wave tank will be utilized in each of the models contributed by IEA OES Task 10's participants, which includes both computational fluid dynamics (CFD) and smooth hydrodynamic particle (SPH). The high-fidelity simulations of the physical wave testcase will allow for the evaluation of the initial transient effects from wave ramp-up and its evolution in the wave tank over time for two representative regular waves with varying levels of nonlinearity. A couple of interesting metrics like the predicted wave surface elevation at select wave probes, wave period, and phase-shift in time will be assessed to evaluate the relative accuracy of numerical models versus experimental data within specified time intervals. These models will serve as a guide for modelers in the wave energy community and provide a base case to allow further and more detailed numerical modeling of the fixed Kramer Sphere Cases under wave excitation force wave tank testing.
HYDRO ENERGY,TIDAL AND WAVE POWER↗
Low-threshold supercontinuum generation in polycrystalline media
Until recently, the generation of super-octave continua in bulk materials at full repetition rates of femtosecond (fs) oscillators has been limited to a few special cases of Kerr lens mode-locked Ti:sapphire lasers. In 2019, we described a 3.4-octave fs source with 50 nJ pulse energy at the repetition rate of 78 MHz based on polycrystalline Cr:ZnS [Vasilyev et al. , Optica 6 , 126 ( 2019 ) OPTIC8 2334-2536 10.1364/OPTICA.6.000126 ]. Here we explain the mechanism of fs supercontinuum generation in transition-metal doped polycrystalline II-VI semiconductors at relatively low (nJ-level) pulse energy. We demonstrate that this new supercontinuum regime is enabled by a complex, yet well-reproducible, interplay between the effects arising from the third-order nonlinearity, the quadratic nonlinearities, and thermal optical effects in the medium. We also demonstrate the control of fs pulse propagation in disordered χ <#comment/> ( 2 ) media via the control of the material microstructure.
Vasilyev, Sergey (ORCID:0000000225203547)↗
High power picosecond green and deep ultraviolet generations with an all-fiberized MOPA
We demonstrate high power picosecond green and deep ultraviolet (DUV) lasers based on an all-fiberized master oscillator power amplifier (MOPA). The main power amplifier is fabricated with a highly Yb-doped large mode area (LMA) silicate glass fiber. It delivers 75.2-W laser output at 1029 nm with a pulse repetition rate of 10 MHz and a pulse duration of 70 ps. With a lithium triborate (LBO) crystal, a 43.0-W green output at 514.5 nm has been achieved with a pulse duration of 55 ps. With a caesium lithium borate (CLBO) crystal, a 14.5-W picosecond DUV output at 257 nm has been generated, which is the highest power for the all-fiber based DUV laser, to the best of our knowledge.
Pan, Lei (ORCID:0000000288896117)↗
Computationally-driven discovery of second harmonic generation in EuBa 3 (B 3 O 6 ) 3 through inversion symmetry breaking
Nonlinear optical (NLO) crystals with superior properties are significant for advancing laser technologies and applications. Introducing rare earth metals to borates is a promising and effective way to modify the electronic structure of a crystal to improve its optical properties in the visible and ultraviolet range. In this work, we computationally discover inversion symmetry breaking in EuBa 3 (B 3 O 6 ) 3 , which was previously identified as centric, and demonstrate noncentrosymmetry via synthesizing single crystals for the first time by the floating zone method. We determine the correct space group to be P 6¯. The material has a large direct bandgap of 5.56 eV and is transparent down to 250 nm. The complete anisotropic linear and nonlinear optical properties were also investigated with a d 11 of ∼0.52 pm/V for optical second harmonic generation. Further, it is Type I and Type II phase matchable. This work suggests that rare earth metal borates are an excellent crystal family for exploring future deep ultraviolet (DUV) NLO crystals. It also highlights how first principles computations combined with experiments can be used to identify noncentrosymmetric materials that have been wrongly assigned to be centrosymmetric.
He, Jingyang (ORCID:0000000238863659)↗
Literature Review on the Impact of Thermolytic Hydrogen Generation Rate from the Next Generation Solvent
A literature review was undertaken to assess the thermolytic hydrogen generation potential of the Next Generation Solvent (NGS) system, which is planned to replace the Original Caustic-Side Solvent Extraction (CSSX) Solvent at the Salt Waste Processing Facility. NGS uses a new extractant and suppressor and the same modifier and organic carrier solvent as the Original CSSX Solvent at different concentrations.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Dynamic Approach to Dependency Analysis in Human Reliability Analysis: Application in a Stream Generator Tube Rupture Scenario
Dependency analysis in human reliability analysis (HRA) is a method of adjusting the failure probability of a given action by considering the impact of the action preceding it. It plays a role in reasonably accounting for human actions in the context of probabilistic safety assessments (PSAs), preventing PSA results from being estimated too optimistically based on the HRA results. Nevertheless, the existing dependency methods present a couple of challenges in that the quantification approaches rarely explain the adjustment of human error probabilities (HEPs). For this reason, the authors’ previous research has pointed out challenges of the existing dependency approaches and conceptually, theoretically proposed a performance shaping factor (PSF)-based dynamic dependency analysis method for HRA in order to complement the existing dependency methods. The current paper explores the latest version of the method and guidance for applying it to a steam generator tube rupture (SGTR) scenario.
99 GENERAL AND MISCELLANEOUS↗
Development of the generation III (ATR generation I) Plutonium-238 production target design
A domestic supply chain to produce 238 Pu fuel for radioisotope thermoelectric generators is critical for enabling future space exploration. A multi-laboratory effort has worked to establish a common target design to efficiently produce 238 Pu in two research reactors. This approach ensures that the annual production goals set forth by NASA are met, while also establishing redundant production capabilities. Here, this paper describes the effort to develop the common target design as well as considerations for future applications for the irradiation platform.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Coupled transmission and distribution simulations to assess distributed generation response to power system faults
Not Available
Kenyon, Rick Wallace (ORCID:0000000344599708)↗