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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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ORCA is a modeling toolset to accelerate real-time control and optimization of digital twins, including virtual models of facilities, physical facilities, and interconnections to allow optimal control of physical facilities using virtual models. ORCA is enabled by INL's RAVEN and DeepLynx software codes.
This dataset contains data-intensive quantum chemical electronic structure calculations for 96,766 organic molecules of the GDB-9-Ex dataset. Calculations were performed using the Time Dependent Density Functional Theory (TDDFT) first principles method using the ORCA software. It provides UV-vis spectra calculations of molecules with a high level of accuracy. The optical spectra behavior was collected based on the optimized molecular geometries in the DFTB method with 3ob parameters. All calculations utilized the def2-TZVP basis sets with the auxiliary def2/J and def2-TZVP/C basis sets. The time-dependent density-functional theory (TDDFT) approach with the PBE0 exchange-correlation functional and ORCAs default integration grid was employed. For the excitation energy calculations, the lowest 50 excitation states were calculated.
This is the AmeriFlux version of the carbon flux data for the site US-MRf Mary's River (Fir) site. Site Description - The Marys River Fir site is part of the "Synthesis of Remote Sensing and Field Observations to Model and Understand Disturbance and Climate Effects on the Carbon Balance of Oregon and Northern California (ORCA)". Located in the western region of Oregon the Marys River site represents the western extent of the climate gradient that spans eastward into the semi-arid basin of central Oregon. The sites that make up the eastern extent of the ORCA climate gradient is the Metolius site network (US-Me1, US-ME2, US-ME4, US-Me5) all of which are part of the TERRA PNW project at Oregon State University.
The State of Hawai'i has set a target to achieve a 100% Renewable Portfolio Standard (RPS) by 2045, and is well suited to become the first state to achieve this goal due to its relatively small load, high electricity prices, heavy reliance on imported fossil fuels, and favorable conditions for wind and solar. The Bureau of Ocean Energy Management contracted NREL to conduct a cost and feasibility study to provide information to decision makers on Hawai'i about the viability of floating offshore wind to be a part of the 100% RPS. We used NREL's Offshore Regional Cost Analyzer (ORCA) spatial cost model to evaluate the Levelized Cost of Energy (LCOE) in the region surrounding O'ahu as this is the island with the highest energy demand. The ORCA results showed that LCOE could range from around $\$$83 MWh to $\$$194 MWh for commercial operation dates in 2019 but has the potential to decrease to $\$$48 MWh - $\$$109 MWh by 2032 due to maturing global supply chains, increasing turbine rating, and new technological innovations. These costs are expected to be competitive with global floating wind costs in the early 2030s. The strong wind resource, proximity to infrastructure on land, and benign metocean conditions can potentially compensate for the logistical complexities of installing projects in Hawai'i far from mainland supply chains if sufficient investments are made to develop ports, grid infrastructure, and workforce on O'ahu to support the construction and operation of offshore wind projects. In addition to the cost results, this report also discusses the likely technologies that would comprise floating wind projects near O'ahu, the existing infrastructure available to projects, unique conditions facing offshore wind in the region such as exposure to hurricanes and limits on allowable export cable capacity, newly developed wind resource data sets for the region, and local stakeholder perspectives on offshore wind.
The Orcas Power and Light Co-op (OPALCO) is a non-profit utility that provides energy services to approximately 11,700 customers across 20 islands in San Juan County, Washington. OPALCO is developing a diverse set of local renewable energy resources to reduce dependence on mainland Washington State for energy and to reduce the regional need for fossil-fueled power. OPALCO’s load doubles in winter but solar production is roughly 20% of levels reached in summer periods. Tidal energy is strong year-round, night and day, and is predictable, requiring much less storage to firm it. To assess the technical and economic feasibility of tidal power, working in combination with multiple other distributed energy resources (DERs), Argonne National Laboratory (Argonne) employed an optimization model to evaluate several economic benefits associated with varying scales of tidal power of between 2.4 and 9.6 megawatts (MW) and other DERs. In addition to existing photovoltaic (PV) and battery energy storage system (BESS) resources located on Decatur Island, Argonne also evaluated the addition of a BESS on southern Orcas Island with power and energy capacities ranging from 1-4 MW and 2-4 hours in storage duration.
The overall goal of this effort is to acquire a large bio-optical database, encompassing most environmental variability in the Arctic, to develop algorithms for phytoplankton biomass and production and other optically active constituents. A large suite of bio-optical and biogeochemical observations have been collected in a variety of high latitude ecosystems at different seasons. The Ocean Research Consortium of the Arctic (ORCA) is a collaborative effort between G.F. Cota of Old Dominion University (ODU), W.G. Harrison and T. Platt of the Bedford Institute of Oceanography (BIO), S. Sathyendranath of Dalhousie University and S. Saitoh of Hokkaido University. ORCA has now conducted 12 cruises and collected over 500 in-water optical profiles plus a variety of ancillary data. Observational suites typically include apparent optical properties (AOPs), inherent optical property (IOPs), and a variety of ancillary observations including sun photometry, biogeochemical profiles, and productivity measurements. All quality-assured data have been submitted to NASA's SeaWIFS Bio-Optical Archive and Storage System (SeaBASS) data archive. Our algorithm development efforts address most of the potential bio-optical data products for the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and GLI, and provides validation for a specific areas of concern, i.e., high latitudes and coastal waters.
High pressure oxygen onboard the ISS provides support for Extra Vehicular Activities (EVA) and contingency metabolic support for the crew. This high pressure 02 is brought to the ISS by the Space Shuttle and is transferred using the Oxygen Recharge Compressor Assembly (ORCA). There are several drivers that must be considered in managing the available high pressure 02 on the ISS. The amount of O2 the Shuttle can fly up is driven by manifest mass limitations, launch slips, and on orbit Shuttle power requirements. The amount of 02 that is used from the ISS high pressure gas tanks (HPGT) is driven by the number of Shuttle docked and undocked EVAs, the type of EVA prebreath protocol that is used and contingency use of O2 for metabolic support. Also, the use of the ORCA must be managed to optimize its life on orbit and assure that it will be available to transfer the planned amount of O2 from the Shuttle. Management of this resource has required long range planning and coordination between Shuttle manifest on orbit plans. To further optimize the situation hardware options have been pursued.
This paper establishes the scientific motivation of the Orbiting Artificial Star for High-resolution Coronal Imaging from the Ground (ORCAS-Helio), and presents the Preliminary Science Traceability Matrix (STM), defining the scientific objectives, the expected significance, and the impact of the mission. Furthermore, the paper describes the payload, mission configuration, and spacecraft architecture and shows it could meet its scientific and engineering requirements. By doing so, a viable mission configuration for the ORCAS-Helio mission is established.
This paper presents an integration of Differential Dynamic Programming (DDP) with the Optimal Reciprocal Collision Avoidance (ORCA) algorithm as the basis for a new algorithm, titled Combined Bernstein Polynomial Optimal Reciprocal Collision Avoidance DDP (COBRA-DDP), for trajectory replanning and collision avoidance for Urban Air Mobility (UAM) vehicles. State-constrained variants of DDP provide the ability to plan trajectories while avoiding obstacles, but these methods require a large increase in computational time per iteration which hinders the overall speed of the algorithm. ORCA utilizes simplified dynamics to recognize potential collisions along a trajectory and provides an optimal velocity for the avoidance of multiple vehicles. These velocity commands, however, may not result in a dynamically feasible trajectory for DDP to plan around. As such, a Bernstein polynomial curve that considers the general dynamic constraints of the vehicle is generated to approximate a trajectory based on the velocity commands. COBRA-DDP optimizes this suggested trajectory via unconstrained DDP to provide a dynamically feasible trajectory that provides collision avoidance. This new trajectory can be applied to the vehicle or used to warm start the state constrained DDP algorithms to decrease computation time. Its benefits and effectiveness of the algorithm are demonstrated on a UAM Vertical Takeoff and Landing (VTOL) vehicle simulation with highly nonlinear dynamics.
This report aims to demonstrate workflow-generating algorithms for optimizing dispatch across a broad range of Integrated Energy System applications using the Framework for Optimization of Resources and Economics (FORCE) tool suite. The optimization is performed at two different time scales. In the coarse time scale, the optimization focuses on a class of energy sources and consumers and aims to find the optimal combinations and flows of energy based on real-time price data information. In the fine time scale, the optimization focuses on a specific thermal energy delivery system and aims to find the optimal setpoints of components in order to meet the energy demands from coarse-time-scale optimizations. In this demonstration, the coarse-scale optimization is implemented using the newly developed Dispatch Optimization Variable Engine (DOVE), while the fine-scale optimization used Optimization of Real-Time Capacity Allocation (ORCA).
The open-source statistical mechanics software described here, Arkane–Automated Reaction Kinetics and Network Exploration–facilitates computations of thermodynamic properties of chemical species, high-pressure limit reaction rate coefficients, and pressure-dependent rate coefficient over multi-well molecular potential energy surfaces (PES) including the effects of collisional energy transfer on phenomenological kinetics. Arkane can use estimates to fill in information for molecules or reactions where quantum chemistry information is missing. The software solves the internal energy master equation for complex unimolecular reaction systems. Inputs to the software include converged electronic structure computations performed by the user using a variety of supported software packages (Gaussian, Molpro, Orca, TeraChem, Q-Chem, Psi4). The software outputs high-pressure limit rate coefficients and pressure-dependent phenomenological rate coefficients, as well as computed thermodynamic properties (enthalpy, entropy, and constant pressure heat capacity) with added energy corrections. Some of the key features of Arkane include treatment of 1D, 2D or ND hindered internal rotation modes, treatment of free internal rotation modes, quantum tunneling effect consideration, transition state theory (TST) and Rice-Ramsperger-Kassel-Marcus (RRKM) rate coefficient computations, master equation solution with four implemented methods, inverse-Laplace transform of high-pressure limit rate coefficients into the energy domain, energy corrections based on bond-additivity or isodesmic reactions, automated and efficient PES exploration, and PES sensitivity analysis. The present work describes the design of Arkane, how it should be used, and refers to the theory that it employs. Arkane is distributed via the RMG-Py software suite (https://github.com/ReactionMechanismGenerator/RMG-Py).
Fragment-based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many-body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose-built to handle the combinatorial complexity of fragment-based calculations. To fill this void we introduce “Fragme∩t”, an open-source software application that provides a tool for community validation of fragment-based methods, a platform for developing new approximations, and a framework for analyzing many-body interactions. Fragme∩t includes algorithms for automatic fragment generation and structure modification, and for distance- and energy-based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q-Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large-scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non-experts. Fragme∩t conforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license.
Combined quantum mechanical and molecular mechanical (QM/MM) methods play an important role in multiscale modeling and simulations. QMMM 2023 is a general-purpose program for single-point calculations, geometry optimizations, transition-state optimizations, and molecular dynamics (MD) at the QM/MM level. It calls a QM package and an MM package to perform the required single-level calculations and combines them into a QM/MM energy by a variety of schemes. QMMM 2023 supports GAMESS-US, Gaussian, and ORCA as QM packages and Tinker as the MM package. Four types of treatments are available for embedding the QM subsystem in the MM environment: mechanical embedding with gas-phase calculations of the QM region, electronic embedding that allows polarization of the QM region by the MM environment, polarizable embedding for mutual polarization of the QM and MM regions, and flexible embedding for both mutual polarization and partial charge transfer between the QM and MM regions. Boundaries between QM and MM regions that pass through covalent bonds can be treated by several methods, including the redistributed charge (RC) scheme, redistributed charge and dipole (RCD) scheme, balanced-RC scheme, balanced-RCD scheme, screened charge scheme that takes account of charge penetration effects, and smeared charge scheme that delocalizes the MM charges near the QM–MM boundary. Geometry optimization can be done using the optimizer implemented in QMMM 2023 or the Berny optimizer in Gaussian through external calls to Gaussian. Molecular dynamics simulations can be performed at the pure-MM level, pure-QM level, fixed-partitioning QM/MM level, and adaptive-partitioning QM/MM level. As a result, the adaptive-partitioning treatments permit on-the-fly relocation of the QM–MM boundary by dynamically reclassifying atoms or groups into the QM or MM subsystems.
Bottom-simulating reflections (BSRs) identified in seismic data are well documented; and are commonly interpreted to indicate the presence of gas hydrates along continental margins, as well as to estimate regional volumes of gas hydrate. A BSR is defined as a reflection that sub-parallels the seafloor but is opposite in polarity and cross-cuts dipping sedimentary strata. BSRs form as a result of a strong negative acoustic impedance contrast. BSRs, however, are a diverse seismic phenomena that manifest in strikingly contrasting ways in different geological settings, and in different seismic data types. We investigate the characteristics of BSRs, using conventional and high resolution, 2D and 3D seismic data sets in three locations: the Terrebonne and Orca Basins in the Gulf of Mexico, and Blake Ridge on the US Atlantic Margin. The acquisition geometry and frequency content of the seismic data significantly impact the resultant character of BSRs, as observed with depth and amplitude maps of the BSRs. Furthermore, our amplitude maps reinforce the concept that the BSR represents a zone, over which the transition from hydrate to free gas occurs, as opposed to the conventional model of the BSR occurring at a single interface. Our results show that a BSR can be mapped in three dimensions but it is not spatially continuous, at least not at the basin scale. Rather, a BSR manifests itself as a discontinuous, or patchy, reflection and only at local scales is it continuous. We suggest the discontinuous nature of BSRs is the result of variable saturation and distribution of free gas and hydrate, acquisition geometry and frequency content of the recorded seismic data. Lastly, the commonly accepted definition of a BSR should be broadened with careful consideration of these factors, to represent the uppermost extent of enhanced amplitude at the shallowest occurrence of free gas trapped by overlying hydrate-bearing sediments.
KM3NeT is a large research infrastructure, that will consist of a network of deep-sea neutrino telescopes in the Mediterranean Sea. The main objective of KM3NeT is the discovery and subsequent observation of high-energy neutrino sources in the Universe. A further physics perspective is the measurement of the mass hierarchy of neutrinos. A corresponding study, ORCA, is ongoing within KM3NeT. A cost effective technology for (very) large water Cherenkov detectors has been developed based on a new generation of low price 3-inch photo-multiplier tubes. Following the successful deployment and operation of two prototypes, the construction of the KM3NeT research infrastructure has started. The prospects of the different phases of the implementation of KM3NeT are summarised.
We present an efficient, open-source formulation for coupled-cluster theory through perturbative triples with domain-based local pair natural orbitals [DLPNO-CCSD(T)]. Similar to the implementation of the DLPNO-CCSD(T) method found in the ORCA package, the most expensive integral generation and contraction steps associated with the CCSD(T) method are linear-scaling. In this work, we show that the t1-transformed Hamiltonian allows for a less complex algorithm when evaluating the local CCSD(T) energy without compromising efficiency or accuracy. Our algorithm yields sub-kJ mol−1 deviations for relative energies when compared with canonical CCSD(T), with typical errors being on the order of 0.1 kcal mol−1, using our TightPNO parameters. We extensively tested and optimized our algorithm and parameters for non-covalent interactions, which have been the most difficult interaction to model for orbital (PNO)-based methods historically. To highlight the capabilities of our code, we tested it on large water clusters, as well as insulin (787 atoms).
Taking advantage of ∼4700 deg 2 optical coverage of the Southern sky offered by the VST ATLAS survey, we construct a new catalogue of photometrically selected galaxy groups and clusters using the orca cluster detection algorithm. The catalogue contains ∼22 000 detections with N 200 > 10 and ∼9000 with N 200 > 20. We estimate the photometric redshifts of the clusters using machine learning and find the redshift distribution of the sample to extend to z ∼ 0.7, peaking at z ∼ 0.25. We calibrate the ATLAS cluster mass-richness scaling relation using masses from the MCXC, Planck, ACT DR5, and SDSS redMaPPer cluster samples. We estimate the ATLAS sample to be > 95 per cent complete and > 85 per cent pure at z < 0.35 and in the M 200m >1 x 10 14 h -1 M ⊙ mass range. At z < 0.35, we also find the ATLAS sample to be more complete than redMaPPer, recovering a ~ 40 per cent higher fraction of Abell clusters. This higher sample completeness places the amplitude of the z < 0.35 ATLAS cluster mass function closer to the predictions of a ΛCDM model with parameters based on the Planck CMB analyses, compared to the mass functions of the other cluster samples. However, strong tensions between the observed ATLAS mass functions and models remain. We shall present a detailed cosmological analysis of the ATLAS cluster mass functions in paper II. In the future, optical counterparts to X-ray-detected eROSITA clusters can be identified using the ATLAS sample. The catalogue is also well suited for auxiliary spectroscopic target selection in 4MOST. The ATLAS cluster catalogue is publicly available at http://astro.dur.ac.uk/cosmology/vstatlas/cluster_catalogue/.
We propose the first method for water Cherenkov detectors to constrain GeV-scale dark matter (DM) below the solar evaporation mass. While previous efforts have highlighted the Sun and Earth as DM capture targets, we demonstrate that Jupiter is a viable target. Jupiter’s unique characteristics, such as its lower core temperature and significant gravitational potential, allow it to capture and retain light DM more effectively than the Sun, particularly in the mass range below 4 GeV where direct detection sensitivity diminishes. Our calculations provide the first sensitivity estimates to GeV-scale annihilating DM within Jupiter, predicting Hyper-K can reach spin dependent cross sections as low as $𝜎^{SD}_{𝑝𝜒}$ = 2×10 −35 cm 2 for DM masses below 2 GeV. This surpasses current solar limits and direct detection results. We additionally provide estimates for Super-K ORCA, and the IceCube-Upgrade, showing that these experiments could provide complimentary bounds to direct detection experiments.