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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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In this software, we implement an adaptive, multivariate partitioning algorithm for solving mixed-integer nonlinear programs (MINLP) to global optimality. The algorithm combines ideas that exploit the structure of convex relaxations to MINLPs and bound tightening procedures
JuTrack is a Julia-based accelerator modeling and tracking package that utilizes compiler-level automatic differentiation (AD) to enable fast and accurate derivative calculations. While JuTrack provides a solid foundation for beam dynamics simulations, its capabilities must be extended to support the Facility for Rare Isotopes (FRIB) linac. This includes modeling heavy-ion linac accelerator components such as the liquid-lithium charge stripper, which facilitates efficient acceleration by remove electrons from heavy isotopes, and incorporating multi-charge state acceleration tracking, which allows for charge-dependent beam dynamics. These extensions address challenges such as the beam matching and optimization of multi charge state through various accelerating structures and beam-material interaction modeling while maintaining the auto differentiation capability. This work focuses on adapting JuTrack to incorporate these elements, enhancing its online modeling abilities. We present modifications to JuTrack’s framework and demonstrate their performance in FRIB simulations.
The NSLS-II injector consists of a 200 MeV linac and a 3 GeV full-energy booster synchrotron. The linac contains five traveling-wave S-band accelerating structures driven by two high-power klystrons. Optimization of the NSLS-II injector has continued since user operation started in October 2014. Beam losses have been minimized by the injector tuning and stabilization, and high level software has been developed to characterize the booster and injection parameters. However, due to the recently increased frequency of klystrons arcing which shortens the lifetime of the klystron, operation of the linac with lowered klystron output power would allow injection to be maintained with fewer trips and extended lifetime of klystrons. Apart from the energy difference, the low energy klystron operation is quite similar to the nominal 200 MeV operation. Injection trials were carried out with scaled magnet settings of the linac, linac-to- booster (LtB) transport line, and the booster. The beam was successfully injected into the booster on May 31, 2017 at the low energy mode of the 170 MeV LtB injection. Overall transfer efficiency from the linac to the storage ring is identical to the standard 200 MeV operation. The 170 MeV injection mode has been adopted in NSLS-II user operation ever since the beam study. In the most recent injector studies on June 28 and July 3, 2017, the booster injection energy was further decremented to the final target value of 100 MeV via applying the same method established in the 170 MeV operation. The ultimate value of 100 MeV is determined by the special requirement of operating the injector at single klystron while repairing the other klystron or with two klystrons with significantly reduced outputs for the purpose of maximizing klystron lifetimes.
Utility-scale systems that combine solar photovoltaic and battery (PV+battery) technologies are growing in popularity on the U.S. bulk power system. The business case for PV+battery systems depends on both their ability to reduce costs and their ability to generate value synergies associated with the provision of energy, capacity, and ancillary services. Capacity value can constitute a significant portion of the value PV+battery hybrids provide to the grid (e.g., through avoided or deferred capacity) and receive through revenues. Throughout this report, we define capacity value as the monetary value of a plant's contribution towards the planning reserve margin, which ultimately depends on market rules and structures. PV+battery hybrids do not always fit into current market structures because of the interactions between the PV and battery components. Unique considerations for the capacity value of PV+battery hybrids include the disparate nature of participation models for PV and battery technologies in existing market rules and the potential influence of a shared interconnection capacity; limitations imposed by a shared inverter; limited ability to charge the battery in advance of capacity events if charging must be sourced from the coupled PV; and challenges or uncertainties associated with co-optimizing the operations of the PV and battery components. Grid operators are currently considering how market structures can be modified to optimally determine the capacity value provided by PV+battery systems, and the rules of how they are integrated into markets are still being written. As with any resource, poorly designed rules could increase the cost of energy and reduce system reliability, while well-designed rules could allow markets to receive the full benefits hybrid systems can offer without overcompensating them for the services they provide. Well-designed rules for PV+battery systems must consider the unique aspects listed above, while leveraging the commonalities with existing resource types. In this report, we summarize the technical capability and market rules that influence the capacity value of PV+battery systems. We further discuss the potential tradeoffs between computational complexity and accuracy for the various ways in which grid operators can credit PV+battery systems for capacity. Finally, we describe markets for capacity, survey current wholesale market rules applying to PV+battery systems, and provide a snapshot of the current regulatory landscape for PV+battery systems.
An interactive map showing Populus trichocarpa GWAS population structure estimated by ADMIXTURE (k=3, selected as optimal from k=2-11). Sampling locations are colored by their predominant ancestry proportion among the three inferred populations and geographic origins are searchable by genotype or river system using the search bar.
We present a prescription for parametrizing the chemical profile in the core of white dwarfs in light of the recent discovery that pulsation modes may sometimes be deeply confined in some cool pulsating white dwarfs. Such modes may be used as unique probes of the complicated chemical stratification that results from several processes that occurred in previous evolutionary phases of intermediate-mass stars. This effort is part of our ongoing quest for more credible and realistic seismic models of white dwarfs using static, parametrized equilibrium structures. Inspired by successful techniques developed in design optimization fields (such as aerodynamics), we exploit Akima splines for the tracing of the chemical profile of oxygen (carbon) in the core of a white dwarf model. A series of tests are then presented to better seize the precision and significance of the results that can be obtained in an asteroseismological context. We also show that the new parametrization passes an essential basic test, as it successfully reproduces the chemical stratification of a full evolutionary model.
Factories, refineries, utilities (water/wastewater, electric) and related industrial sites are complex systems and structures with inspection and maintenance procedures required for optimal operation and regulatory compliance. As a specific example, consider just the bulk electric power system which is comprised of more than 200,000 miles of highvoltage transmission lines, thousands of generation plants and millions of digital controls. More than 1,800 entities own and operate portions of the grid system, with thousands more involved in the operation of distribution networks across North America. The interconnected and interdependent nature of the bulk power system requires a consistent and systematic application of risk mitigation across the entire grid system to be truly effective. Similar situations are found throughout automation where frequently an aging infrastructure is in place too. Consider, for example, the situation present in a refinery or chemical processing setting with the requirement for leak detection inspection of pipes, interconnects and systems stretching across the plant. The current practices and challenges relating just to this task - leak detection and repair (LDAR) – of detecting any fugitive emissions present and documenting all measurements thereby meeting air compliance regulations are typically “handled” by a small army of individuals with handheld or backpack-sized detectors who crawl through piping racks conducting measurements at each flange. Such work is performed in difficult conditions (temperature, humidity, physically challenging) with frequently a high level of employee turnover. Finaly, enter low cost sensors and mobile platforms – in other words unmanned aerial systems (UASs, or drones) with enhanced sensing capabilities.
An artificial intelligence system called MENNDL, which used 25,200 NVIDIA Volta GPUs on Oak Ridge National Laboratory's Summit machine, automatically designed an optimal deep learning network in order to extract structural information from raw atomic-resolution microscopy data. In a few hours, MENNDL creates and evaluates millions of networks using a scalable, parallel, asynchronous genetic algorithm augmented with a support vector machine to automatically find a superior deep learning network topology and hyper-parameter set than a human expert can find in months. For the application of electron microscopy, the system furthers the goal of improving our understanding of the electron-beam-matter interactions and real-time image-based feedback, which enables a huge step beyond human capacity towards nanofabricating materials automatically. MENNDL has been scaled to the 4,200 available nodes of Summit achieving a measured 152.5 PFlops, with an estimated sustained performance of 167 PFlops when the entire machine is available.
Atomic level engineering of nanoparticles (NPs) with ability to precisely tune its functional properties can help us realize many promising applications in catalytic science. Such a bottom-up approach would require fundamental level understanding of nucleation and subsequent chemical evolution of nanoparticles during synthesis process. In particular, transition metal based nanoparticles such as Cobalt (Co), Nickel (Ni), Iron (Fe), Paladium (Pd), and Platinum (Pt) can serve as catalysts for many important reactions such as syngas conversion, hydrogenation-dehydrogenation reactions and hydro-deoxygenation reactions. The porous SiO2 is widely used as support medium due to their chemical inertness under reducible and reactive chemical environments often encountered during catalytic process. Nevertheless, even chemically benign SiO2 can have interaction with catalyst particles under high temperatures and adversely affect catalytic activity due to the presence of reactive surface functional groups. Although the influence of the support on catalyst activity and the selectivity is widely studied with respect to the strong metal-support interaction (SMSI) and more recently on the electronic effect of metal-support interaction (EMSI), the choice of support and the catalyst preparation method are still mostly determined by the empirical optimization. For this study, we focus on local structural evolution of cobalt and nickel oxide nanoparticles and its interaction with SiO2 support using multi-modal spectroscopic approach involving X-ray absorption spectroscopy (XAS), nuclear magnetic resonance (NMR) and density functional theory (DFT) based computational methods.
Nonaqueous redox flow batteries (RFB) hold the potential for high energy density grid scale storage, though are often limited by the solubility of the redox-active species in their electrolytes. A systematic approach enables an increase the concentration of redox-active species in electrolytes for nonaqueous RFB, starting from a metal-coordination-cation-based ionic liquid. As an example, starting with an ionic liquid consisting of a metal coordination cation (MetIL), ferrocene-containing ligands and iodide anions can be substituted into the original MetIL structure, enabling a nearly 4× increase in capacity compared to original MetIL structure. Application of this strategy to other chemistries, optimizing electrolyte melting point and conductivity could yield >10 M redox-active electrons.
We are introducing machine learning methods to the play fairway analysis to generate geothermal potential maps to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Nevada Great Basin region. Our project aims to identify new ways to combine the play fairway data and empirically organize relationships between feature weights and labels in an improved workflow. As a means of doing this, we introduce machine learning methods to evaluate the influence of certain geological and geophysical features/feature sets in predicting geothermal favorability. This report highlights promising approaches based on supervised and unsupervised learning methods. First, we demonstrate a filter method applied to supervised classification modeling. The supervised filter method is based on permutation analysis to evaluate every possible feature combination/drop out scenario and rank feature influence based on the performance variance of supervised classification models. Additionally, we present an unsupervised factor analysis based on principal component analysis coupled with a semi-supervised kmeans clustering algorithm. This analysis allows us to identify the optimal number of groups/clusters for training sites and structural settings to identify feature patterns including correlation, variance, and latent and dominant feature relationships. The results from these methods offer a promising avenue for identifying favorable sources of predictive information to identify the locations of blind geothermal systems and furthering our understanding of complex geothermal feature and label relationships in the Great Basin region and beyond.
Abstract The fundamental challenge in electron‐transporting organic mixed ionic‐electronic conductors (OMIECs) is simultaneous optimization of electron and ion transport. Beginning from Y6‐type/U‐shaped non‐fullerene solar cell acceptors, we systematically synthesize and characterize molecular structures that address the aforementioned challenge, progressively introducing increasing numbers of oligoethyleneglycol (OEG; g) sidechains from 1 g to 3 g, affording OMIECs 1gY, 2gY, and 3gY, respectively. The crystal structure of 1gY preserves key structural features of the Y n series: a U‐shaped/planar core, close π–π molecular stacking, and interlocked acceptor groups. Versus inactive Y6 and Y11, all of the new glycolated compounds exhibit mixed ion‐electron transport in both conventional organic electrochemical transistor (cOECT) and vertical OECT (vOECT) architectures. Notably, 3gY with the highest OEG density achieves a high transconductance of 16.5 mS, an on/off current ratio of ~10 6 , and a turn‐on/off response time of 94.7/5.7 ms in vOECTs. Systematic optoelectronic, electrochemical, architectural, and crystallographic analysis explains the superior 3gY‐based OECT performance in terms of denser n gY OEG content, increased crystallite dimensions with decreased long‐range crystalline order, and enhanced film hydrophilicity which facilitates ion transport and efficient redox processes. Finally, we demonstrate an efficient small‐molecule‐based complementary inverter using 3gY vOECTs, showcasing the bioelectronic applicability of these new small‐molecule OMIECs.
Abstract The fundamental challenge in electron‐transporting organic mixed ionic‐electronic conductors (OMIECs) is simultaneous optimization of electron and ion transport. Beginning from Y6‐type/U‐shaped non‐fullerene solar cell acceptors, we systematically synthesize and characterize molecular structures that address the aforementioned challenge, progressively introducing increasing numbers of oligoethyleneglycol (OEG; g) sidechains from 1 g to 3 g, affording OMIECs 1gY, 2gY, and 3gY, respectively. The crystal structure of 1gY preserves key structural features of the Y n series: a U‐shaped/planar core, close π–π molecular stacking, and interlocked acceptor groups. Versus inactive Y6 and Y11, all of the new glycolated compounds exhibit mixed ion‐electron transport in both conventional organic electrochemical transistor (cOECT) and vertical OECT (vOECT) architectures. Notably, 3gY with the highest OEG density achieves a high transconductance of 16.5 mS, an on/off current ratio of ~10 6 , and a turn‐on/off response time of 94.7/5.7 ms in vOECTs. Systematic optoelectronic, electrochemical, architectural, and crystallographic analysis explains the superior 3gY‐based OECT performance in terms of denser n gY OEG content, increased crystallite dimensions with decreased long‐range crystalline order, and enhanced film hydrophilicity which facilitates ion transport and efficient redox processes. Finally, we demonstrate an efficient small‐molecule‐based complementary inverter using 3gY vOECTs, showcasing the bioelectronic applicability of these new small‐molecule OMIECs.
This paper presents a level-set topology optimization approach that uses conformal meshes for the analysis of the displacement field. The structure’s boundary is represented by the iso-contour of a level-set field discretized on a fixed background design mesh. The conformal mesh is updated for each design iteration via a PDE based mesh morphing process that identifies the set of facets in the background mesh that are homeomorphic to the boundary and relaxes the homeomorphic mesh to conform to the structure’s boundary and ensure high element quality. The conformal mesh allows for a more accurate computation of the response versus density and some level-set based methods which interpolate material properties using the volume fraction. Numerical examples illustrate the proposed approach by optimizing linear-elastic two- and three-dimensional structures, wherein insight into the performance of the mesh morphing process is provided. The examples also highlight the scalability of the approach.
Multi-principal-element alloys (MPEAs) based on 3d-transition metals show remarkable mechanical properties. In this study, the stacking fault energy (SFE) in face-centered cubic (fcc) alloys is a critical property that controls underlying deformation mechanisms and mechanical response. Here, we present an exhaustive density-functional theory study on refractory- and copper-reinforced Cantor-based systems to ascertain the effects of refractory metal chemistry on SFE. We find that even a small percent change in refractory metal composition significantly changes SFEs, which correlates favorably with features like electronegativity variance, size effect, and heat of fusion. For fcc MPEAs, we also detail the changes in mechanical properties, such as bulk, Young's, and shear moduli, as well as yield strength. A Labusch-type solute-solution-strengthening model was used to evaluate the temperature-dependent yield strength, which, combined with SFE, provides a design guide for high-performance alloys. We also analyzed the electronic structures of two down-selected alloys to reveal the underlying origin of optimal SFE and strength range in refractory-reinforced fcc MPEAs. These new insights on tuning SFEs and modifying composition-structure-property correlation in refractory- and copper-reinforced MPEAs by chemical disorder, provide a chemical route to tune twinning- and transformation-induced plasticity behavior in fcc MPEAs.
In order to improve their MS/MS sequencing, structure of sequence-controlled synthetic polymers can be optimized based on considerations regarding their fragmentation behavior in collision-induced dissociation conditions, as demonstrated here for two digitally encoded polymer families. In poly(triazole amide)s, the main dissociation route proceeded via cleavage of the amide bond in each monomer, hence allowing the chains to be safely sequenced. However, a competitive cleavage of an ether bond in a tri(ethylene glycol) spacer placed between each coding moiety complicated MS/MS spectra while not bringing new structural information. Changing the tri(ethylene glycol) spacer to an alkyl group of the same size allowed this unwanted fragmentation pathway to be avoided, hence greatly simplifying the MS/MS reading step for such undecyl-based poly(triazole amide)s. In poly(alkoxyamine phosphodiester)s, a single dissociation pathway was achieved with repeating units containing an alkoxyamine linkage, which, by very low dissociation energy, made any other chemical bonds MS/MS-silent. Structure of these polymers was further tailored to enhance the stability of those precursor ions with a negatively charged phosphate group per monomer in order to improve their MS/MS readability. Increasing the size of both the alkyl coding moiety and the nitroxide spacer allowed sufficient distance between phosphate groups for all of them to be deprotonated simultaneously. Because the charge state of product ions increased with their polymerization degree, MS/MS spectra typically exhibited groups of fragments at one or the other side of the precursor ion depending on the original α or ω end-group they contain, allowing sequence reconstruction in a straightforward manner. .
The Hubbard model provides a simple framework in which one can study how certain aspects of the electronic structure of strongly interacting systems can be tuned to optimize the superconducting pairing correlations and how these changes affect the mechanisms giving rise to them. Here we use a weak-coupling random phase approximation to study a two-dimensional Hubbard model with a unidirectional modulation of the hopping amplitudes as the system evolves from the uniform square lattice to an array of weakly coupled two-leg ladders. We find that the pairing correlations retain their dominant d x 2 - y 2- wavelike structure and that they are significantly enhanced for a slightly modulated lattice. This enhancement is traced backed to an increase in the strength of the spin-fluctuation pairing interaction due to favorable Fermi surface nesting in the modulated system. We then use a random-phase approximation BCS framework to examine the evolution of the neutron resonance in the superconducting state. We find that it changes only weakly for moderate modulations, but breaks up into two distinct resonances at incommensurate wave vectors in the limit of weakly coupled ladders.
Low initial Coulombic efficiency and severe capacity/voltage fading during cycling caused by serious irreversible oxygen release, especially in the initial cycle, and resultantly induced unstable electrode/electrolyte interfacial chemistry, largely prohibit the commercial application of high-capacity Li-rich layered oxide cathodes (LLOs). Here, in this work, a dual reductive gas interface cotreatment strategy is applied to regulate the lattice oxygen redox activity and reversibility with a multiple defective structure design including Li/O/TM (TM = transition metal) vacancies and the intrinsic TM doping as well as a full-surface protective layer, which can suppress the irreversible TM migration and then undesirable phase transformation, resisting the corrosion of electrolyte during cycling effectively. Importantly, the introduced reversible SO 3 2- /SO 4 2- redox couple that provides extra capacity compensation could alleviate the distortion of oxygen-central octahedral structure and structural collapse caused by immoderate oxygen oxidation. Thus, the lattice oxygen redox chemistry is optimized, with negligible oxygen loss during the initial cycle. And the designed AS-LLO cathode with greatly enhanced structure stability shows high-capacity retentions of 99.2% at 0.3C after 100 cycles and 82.4% even after 1000 cycles at 5C. This work provides a guideline for manipulating the oxygen redox chemistry to achieve long-lifespan Li-rich layered oxide cathodes for high-energy-density lithium batteries.