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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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The interpretation of the experimental data in superhigh energy cosmic rays requires the calculations using various models of elementary hadron interaction. One should prefer the models justified by accelerator data and giving definite predictions for superhigh energies. The model of quark-gluon pomeron strings (the QGPS models) satisfies this requirement.
The use of the External propulsion Accelerator (EPA) for launching models of hypersonic aerodynamic configurations into an instrumented ballistic range is discussed. The aerodynamic model is encased inside an axisymmetric projectile designed to be accelerated to high speed in the EPA. Accelerator lengths required to achieve hypersonic speeds are estimated to vary from 10 meters for Mach 7, 40 meters for Mach 10, 150 meters for Mach 15, and 700 meters for Mach 30, assuming a limit of 50,000 g's acceleration. For a model span of 10 cm to 25 cm, the launch tube diameters are 40 cm and 100 cm, respectively. Using this EPA launcher will enable exact simulation of hypersonic flight in ground facilities where both the gas composition and pressure can be controlled in the ballistic range.
The Atmospheric Radiation Measurement (ARM) user facility published its first decadal vision document in 2014 (U.S. DOE 2014a). The overarching vision described in that document was to develop an integrated observation-modeling framework to accelerate the application of ARM observations for process studies and model applications.
PV technologies based on hybrid perovskites offer the potential for reducing solar cell costs, but they are particularly vulnerable to degradation by environmental factors such as moisture, oxygen, and illumination. Commercialization will require not only stable materials and device architectures but also accelerated testing protocols and models that can predict degradation from the accelerated testing data. Here, we report results from in situ photoluminescence (PL) and photoconductivity (PC) measurements during perovskite degradation with simultaneous optical transmittance (Tr) measurements or reflected dark field (DF) imaging. From PL, PC, Tr, and DF, we determine (respectively) the steady-state quasi-Fermi level splitting, the mean effective carrier diffusion length, the extent of conversion of perovskite to higher bandgap degradation products, and the extent of scattering from domains with different orientation or composition, all as a function of time during degradation. Simultaneous measurement of PL-PC-Tr or PL-PC-DF on perovskite absorbers in an environmental chamber over a wide range of humidity, oxygen, temperature, and illumination levels yields a rich data set. We use machine learning to develop a model that accurately predicts (within 10%) the time for the diffusion length to decrease to 85% of its initial value. The model takes the environmental conditions and the first few measurements from the PL-PC-Tr or PL-PC-DF experiment as input. Thus, the model provides a framework to interpret the results of accelerated testing of absorber materials. One of the dominant features in the model of degradation for CH 3 NH 3 PbI 3 is the initial rate at which transmittance increase. For devices with opaque contacts, the pixel-averaged rate of change of the intensity in dark-field images can be used in place of transmittance. Results are also presented on full PV devices with in-situ current-voltage (JV) measurements. Here, the data from simultaneous PL-DF-JV under environmental stresses reveal intimate connection between degradation and shunts and provide a framework to extend accelerated testing to devices. Further, the presentation reveals underlying universal behavior in degradation pathways over a broad range of environmental stresses and perovskite compositions.
Unprecedented climate change and anthropogenic activities have induced increasing ecohydrological problems, which have motivated the development of large-scale modeling for solutions. Water age/quality is as important as water quantity for understanding the water cycle. However, current scientific progress in tracking water parcels at large-scale with high spatiotemporal resolutions is far behind that in water balance/quantity owing to the lack of powerful tools. EcoSLIM is a particle tracking model that works with the hydrologic model ParFlow-CLM, which couples surface-subsurface hydrology with land surface processes. Here, we demonstrate a parallel framework to accelerate EcoSLIM to continental-scale on a distributed, multi-GPU platform with CUDA-Aware MPI. In tests from catchment-, to regional-, and then to continental-scale using 25-million to 1.6-billion particles, EcoSLIM shows significant speedup and excellent parallel performance. The parallel framework is portable to atmospheric and oceanic particle tracking models, where parallelization is inadequate and a standard parallel framework is absent. Parallelized EcoSLIM is a promising tool to accelerate our understanding of the terrestrial water cycle and the upscaling of subsurface hydrology to Earth system models.
The study presents preliminary results of a two-fluid hydrodynamical simulation of cosmic-ray-modified shock structure by extending the steady-state calculations of Zank et al. (1991) to the time-dependent case. The present simulations model diffusive shock acceleration of the energetic particle component at planar shocks. A new model for the injection of cosmic ray particles is presented. The quadratic artificial viscosity algorithm is found to be numerically unstable and therefore unusable for strong cosmic ray shocks. There is visible ringing and overshoot in the various profiles. A large enhancement in the fluid density appears downstream of the subshock. This 'overcompression' results from the upstream fluid being compressed in the precursor, then traversing a strong shock with compression ratio near the adiabatic limit of four.
Neural network relationships between the full-scale, experimental hub accelerations and the corresponding pilot floor vertical vibration are studied. The present physics-based, quantitative effort represents an initial systematic study on the UH-60A Black Hawk hub accelerations. The NASA/Army UH-60A Airloads Program flight test database was used. A 'maneuver-effect-factor (MEF)', derived using the roll-angle and the pitch-rate, was used. Three neural network based representation-cases were considered. The pilot floor vertical vibration was considered in the first case and the hub accelerations were separately considered in the second case. The third case considered both the hub accelerations and the pilot floor vertical vibration. Neither the advance ratio nor the gross weight alone could be used to predict the pilot floor vertical vibration. However, the advance ratio and the gross weight together could be used to predict the pilot floor vertical vibration over the entire flight envelope. The hub accelerations data were modeled and found to be of very acceptable quality. The hub accelerations alone could not be used to predict the pilot floor vertical vibration. Thus, the hub accelerations alone do not drive the pilot floor vertical vibration. However, the hub accelerations, along with either the advance ratio or the gross weight or both, could be used to satisfactorily predict the pilot floor vertical vibration. The hub accelerations are clearly a factor in determining the pilot floor vertical vibration.
We report the start-to-end modeling of our accelerator lattice design employing a laser-assisted bunch compression (LABC) scheme in an X-ray free electron laser (XFEL), using the proposed Matter-Radiation Interactions in Extremes (MaRIE) XFEL parameters. The accelerator lattice utilized a two-stage bunch compression scheme, with the first bunch compressor performing a conventional bulk compression enhancing the beam current from 20 A to 500 A, at 750 MeV. The second bunch compression was achieved by modulating the beam immediately downstream of the first bunch compressor by a laser with 1-μm wavelength in a laser modulator, accelerating the beam to the final energy of 12 GeV, and compressing the individual 1-μm periods of the modulated beam into a sequence of microbunches with 3-kA current spikes by the second bunch compressor. The LABC architecture presented had been developed based on the scheme of enhanced self-amplified spontaneous emission (ESASE), but operated in a disparate regime of parameters. Enabled by the novel technology of the cryogenic normal conducting radiofrequency photoinjector, we investigated an electron beam with ultra-low emittance at the starting point of the lattice design. Our work aimed at mitigating the well-known beam instabilities to preserve the beam emittance and suppress the energy spread growth.
We highlight the virtues of 2D elliptic-multipole field expansions in modeling the magnetic fields of narrow-aperture, straight-axis bending magnets with parallel faces, addressing the limitations of the conventional circular multipole series when the beam-orbit sagitta exceeds the magnet's vertical half-gap. The elliptic multipoles provide a convenient way to represent the field in all aspects of the magnet development (design, particle-tracking simulations, measurements). We propose a numerically robust method of data analysis to determine the elliptic (or circular) multipoles from stretched-wire measurements with the wire moving on an arbitrary path.
Emerging supercomputing systems utilize a combination of central processing units (CPUs) and graphics processing units (GPUs) in an effort to reach exascale capabilities while minimizing the energy footprint of operating such systems. Such heterogeneous machines introduce new challenges for fluids solvers because the hardware architecture and operation of a GPU are fundamentally different from conventional CPUs. In this work, a general approach for efficient implementation of finite-volume based reacting flow solvers on such heterogeneous systems is presented. Three main challenges, namely, data access pattern, thread divergence, and thread safety, are addressed. Since compressible reacting flows require special methods to deal with chemical reactions, hyperbolic and nonlinear convection terms, and the presence of turbulence, specific algorithms that ensure GPU-based efficiency are developed. The approach is demonstrated on the widely available OpenFOAM open source software by modifying core algorithms for GPU accessibility. The scalability of the resulting solver, is demonstrated using practical test cases, including flow through a scramjet engine and the dynamics of a rotating detonation engine. Here, the solver provides near-ideal scaleup on a large number of GPUs (>3000), and extremely efficient use of the GPUs, with throughput nearly a constant even when processing a large number of control volumes.
Understanding and diagnosing the state of a particle accelerator requires navigating high-dimensional control system data, often involving hundreds of interdependent parameters. We propose a novel multimodal embedding framework that jointly learns representations of machine states from both numerical control system readouts and natural language descriptions. This enables the translation of complex machine conditions into human-readable summaries while maintaining fidelity to the underlying physical system. The obtained embeddings are subsequently adapted to an open-weights large language model via cross-attention conditioning. We demonstrate a first implementation trained on European XFEL machine state data. This work covers the embedding model architecture, training methodology, and presents initial examples demonstrating the model's capabilities in action. Due to the general concept of machine state, the model can be easily adapted to other facilities and control system environments.
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This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.
Significant particle-in-cell code development has enabled simulations of power flow in multi-MA accelerators to include the desorption of surface contaminants, their ionization into surface plasmas, and the impact of these plasmas on efficiency. The simulations base desorption on an Arrhenius equation, whose most significant unknown is the surface contaminant inventory. The sensitivity of power-flow simulations to this inventory is studied here using Sandia National Laboratories' Z accelerator with a 7-nH MagLIF load [Phys. Plasmas 17, 056303 (2010)]. Simulations are conducted in 3D cylindrical coordinates for the current-adder, or “convolute,” region of Z and in 2D for the final feed only. Simulated contaminant inventories are varied from 1 to 32 monolayers (MLs) in 2D, and 2 to 4 ML in 3D. The results reveal sensitivities to the local ratio of E/B. The high B-field, low E-field region near the short-circuit load is insensitive to the contaminant inventory, where assumed values of 4–32 ML change the load current by ≤ 2%, and agree with experiment to within 2% at peak current. A 1-ML value is the outlier, increasing the load current by 5%, but still within measurement uncertainty. In contrast, the relatively higher E-field, lower B-field convolute region has slower contaminant desorption and higher-magnitude E-field penetration of the surface plasmas. The current loss in the convolute region does increase with contaminant inventory. The loss assuming 4 ML is 12% larger than for 2 ML, with 4 ML being the better match to experiment.
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.
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Apollo spacecraft landing simulation to determine impact water pressures, accelerations, and landing dynamics