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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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Fluid controlled solid rocket motors design for Mars mission with acceleration level as parameter, discussing mission specifications, system design and component considerations
The relaxation of the accelerating-gas boundary layer to the test-gas boundary layer over a flat plate in an expansion tube is analyzed. Several combinations of test gas and acceleration gas are considered. The problem is treated in two conically similar limits: (1) when the time lag between the arrival of the shock and the interface at the leading edge of the plate is very large, and (2) when this lag is negligible. The time-dependent laminar-boundary-layer equations of a binary mixture of perfect gases are taken as the flow-governing equations. This coupled set of differential equations, written in terms of the Lam-Crocco variables, has been solved by a line-relaxation finite-difference techniques. The results presented include the Stanton number and the local skin-friction coefficient as functions of shock Mach number and the nondimensional distance-time variable. The results indicate that more than 95 percent of the test-gas boundary layer exists over a length, measured from the leading edge of the plate, equal to about three-tenths of the distance traversed by the interface in the free stream.
High current SRF technology is being explored in present day accelerator science. The bERLinPro project is presently being built at HZB to address the challenges involved in high current SRF machines with the goal of generating and accelerating a 100 mA electron beam to 50 MeV in continuous wave (cw) mode at 1.3 GHz. One of the main challenges in this project is that of handling the high input RF power required for the photo-injector as well as booster cavities where there is no energy recovery process. A high power co-axial input power coupler is being developed to be used for the photo-injector and booster cavities at the nominal beam current. The coupler is based on the KEK–cERL design and has been modified to minimise the penetration of the coupler tip in the beam pipe without compromising on beam-power coupling (Qext ~105). Herein we report on the RF design of the high power (115 kW per coupler, dual couplers per cavity) bERLinPro (BP) coupler along with initial results on thermal calculations. We summarise the RF conditioning of the TTF-III couplers (modified for cw operation) performed in the past at BESSY/HZB. A similar conditioning is envisaged in the near future for the low current SRF photo-injector and the bERLinPro main linac cryomodule.
Many modern and most future accelerators rely on precise configuration of lattice and trajectory. The Integrable Optics Test Accelerator (IOTA) at Fermilab that is coming to final stages of construction will be used to test advanced approaches of control over particles dynamics. Various experiments planned at IOTA require high flexibility of lattice configuration as well as high precision of lattice and closed orbit control. Dense element placement does not allow to have ideal configuration of diagnostics and correctors for all planned experiments. To overcome this limitations advanced method of lattice an beneficial for other machines. Developed algorithm is based on LOCO approach, extended with various sets of other experimental data, such as dispersion, BPM BPM phase advances, beam shape information from synchrotron light monitors, responses of closed orbit bumps to variations of focusing elements and other. Extensive modeling of corrections for a big number of random seed errors is used to illustrate benefits from developed approach.
The overall goal of this work was to accelerate simulations supporting the nuclear deterrence (ND) mission through improved performance of key algorithms in the ASC IC Sierra multi-physics application suite. This work focused on porting and optimizing algorithms for the graphics processing units (GPU) on the second ASC advanced technology system (ATS-2), while maintaining or improving performance on commodity technology systems (CTS) and ATS-1. Furthermore, these algorithmic developments used the ASC developed Kokkos performance portability abstraction library to maintain high performance across platforms using identical code, and enable sustainable reduced-cost migration and performance optimization to emerging hardware.
Laser-driven proton acceleration, as produced during the interaction of a high-intensity (I > 1 × 10 18 W/cm 2 ), short pulse (<1 ps) laser with a solid target, is a prosperous field of endeavor for manifold applications in different domains, including astrophysics, biomedicine and materials science. These emerging applications benefit from the unique features of the laser-accelerated particles such as short duration, intense flux and energy versatility, which allow obtaining unprecedented temperature and pressure conditions. In this paper, we show that laser-driven protons are perfectly suited for producing, in a single sub-ns laser pulse, metallic nanocrystals with tunable diameter ranging from tens to hundreds of nm and very high precision. Our method relies on the intense and very quick proton energy deposition, which induces in a bulk material an explosive boiling and produces nanocrystals that aggregate in a plasma plume composed by atoms detached from the proton-irradiated surface. The properties of the obtained particles depend on the deposited proton energy and on the duration of the thermodynamical process. Suitably controlling the irradiated dose allows fabricating nanocrystals of a specific size with low polydispersity that can easily be isolated in order to obtain a monodisperse nanocrystal solution. Molecular Dynamics simulations confirm our experimental results.
An estimate is made of the extent to which cosmic rays would be accelerated as a result of their scattering off material entering into the galaxy. A model of the flow is presented, and the assumptions on which the applicability of the model depends are examined. As examples, the maximum acceleration efficiencies are calculated for two ratios of disk gas to total pressure. With these results, the upper limit for power supplied by this process is found to be essentially identical to the cosmic ray power requirement. This circumstance suggests the possibility that the infall might contribute appreciably to the cosmic ray acceleration.
Single-ion conducting polymer electrolytes (SICPEs) have many advantages for solid-state battery applications, while low conductivities remain the bottleneck for their practical applications. In this paper, a strategy that can significantly improve the ionic conductivity of SICPEs based on the concept of accelerated segmental dynamics was demonstrated by the evaluation of the thermal property, rheological behavior, morphology analysis, molecular dynamics simulation, and conductivity performance. With both “soft” poly(dimethylsiloxane) (PDMS) backbone and poly(ethylene glycol) (PEG) side chains, the obtained SICPE possesses faster segmental relaxation and higher lithium-ion conductivity. With lithium “transference” number close to unity, the obtained SICPE also shows excellent electrochemical stability against lithium metal electrodes. The clear relationship established between the segmental dynamics of polymer electrolyte and its ionic conductivity should contribute to achieve a solid electrolyte with improved ionic conductivity toward the next-generation solid-state battery.
Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.15–385.25 K). Three ML models, including support vector regression (SVR), feed forward neural networks (FFNNs), and categorical boosting (CatBoost), were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two-factor polynomial regression baselines. Further, quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features were used as inputs for the ML models. The CatBoost model is excellent at externally predicting DES viscosity, as indicated by high R 2 (0.99) and low root-mean-square-error (RMSE) and average absolute relative deviations (AARD) (5.22%) values for the testing data sets, and 98% of the data points lie within the 15% of AARD deviations. Furthermore, SHapley additive explanation (SHAP) analysis was employed to interpret the ML results and rationalize the viscosity predictions. The result is an ML approach that accurately predicts viscosity and will aid in accelerating the design of appropriate DESs for industrial applications.
Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.
Truncated Power Series Algebra (TPSA), or Differential Algebra (DA), is a well-established tool in accelerator physics, commonly used for generating high-order maps of dynamic systems, as well as in symplectic tracking, normal form analysis, verified integration, optimization, and fast multipole methods. This package is the first to perform symbolic DA computations, enabling traceability of initial condition contributions and runtime reduction for repeated DA calculations, potentially expanding DA’s applications.
Euclid Techlabs LLC, in collaboration with JLAB (R. Rimmer, C. Hovater) has been awarded the Phase I 2021 DOE SBIR DE-SC0021510 project to develop the first Fast Ferroelectric reactive tuner (FRT) for a SRF accelerator that is currently in operation – CEBAF at JLAB, and is studying the possibility of developing a tuner designs for various new applications: fast frequency switching, transient detuning, and microphonics compensation for the SRF accelerators.
A mesoscale model to predict helium bubble evolution is needed for tritium applications. Such a model requires that the conventional kinetic Monte Carlo (kMC) simulations be significantly accelerated. The objective of this report is to (a) highlight the concepts and mathematical expressions of the accelerated method for defect implementation that have not been published, (b) show an example input file to run the kMC code, and (c) provide suggestions on future improvement following my retirement.
The behavior of sloshing-dynamics-modulated fluid systems driven by orbital accelerations including gravity gradient and jitter accelerations has been studied. Partly liquid-filled rotating Dewars applicable to a full-scale Gravity Probe-B Spacecraft container with and without baffle are considered. Results show that a slosh wave excited along the liquid-vapor interface induced by jitter-acceleration-dominated orbital accelerations provides a torsional moment with an up-and-down movement of bubble oscillations in the rotating Dewar. The fluctuations of fluid forces and fluid moment (torque) exerted on the Dewar wall of the container caused by slosh-wave excitation driven by orbital accelerations are also investigated. Since the viscous force across a liquid-solid interface and the surface-tension force across a liquid-vapor or liquid-solid interface can greatly contribute to the damping effect of slosh-wave excitation, a rotating Dewar with a baffle provides more areas of liquid-solid, and liquid-vapor, and solid-vapor interfaces than does a rotating Dewar without a baffle. Results show that the damping effect provided by the baffle reduces the amplitudes of force and moment feedback from the fluids to the container-in particular, the components of fluctuations transverse to the direction of the baffle boards.
The growth in size of networked high performance computers along with novel accelerator-based node architectures has further emphasized the importance of communication efficiency in high performance computing. The world's largest high performance computers are usually operated as shared user facilities due to the costs of acquisition and operation. Applications are scheduled for execution in a shared environment and are placed on nodes that are not necessarily contiguous on the interconnect. Furthermore, the placement of tasks on the nodes allocated by the scheduler is sub-optimal, leading to performance loss and variability. Here, we investigate the impact of task placement on the performance of two massively parallel application codes on the Titan supercomputer, a turbulent combustion flow solver (S3D) and a molecular dynamics code (LAMMPS). Benchmark studies show a significant deviation from ideal weak scaling and variability in performance. The inter-task communication distance was determined to be one of the significant contributors to the performance degradation and variability. A genetic algorithm-based parallel optimization technique was used to optimize the task ordering. This technique provides an improved placement of the tasks on the nodes, taking into account the application's communication topology and the system interconnect topology. As a result, application benchmarks after task reordering through genetic algorithm show a significant improvement in performance and reduction in variability, therefore enabling the applications to achieve better time to solution and scalability on Titan during production.
Basic nuclear physics research requires continuing advances in detector technology. Often, new detection technology can have applications outside basic nuclear physics research. For over 80 years various sciences have been using radioactive isotopes as tracers for increasing our understanding of biological processes in living systems. New nuclear physics detection technologies using scintillators and photo multipliers have been used to develop imaging tools for radioisotope detection and imaging in bio-medical and plant biology applications. Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) benefit greatly from advances in nuclear physics detector technology. Bio-medical research utilizing animals as in pre-clinical research and in clinical applications such as cancer detection and treatment have benefited much from nuclear physics detector advances. New imaging tools are being developed for plant biology research to enhance our understanding of metabolic processes necessary for efficient biofuel production in a changing environment and of best bioassays for environmental remediation. The Radiation Detector and Imaging Group at Thomas Jefferson National Accelerator Facility has been applying detector technology to these applications. The examples of detector systems for medical and plant biology applications as well as scientific motivations driving use of radioisotope tracers in these applications will be presented.
Tantalum nitride (Ta 3 N 5 ) has gained significant attention as a potential photoanode material, yet it has been challenged by material quality issues. Defect-induced trap states are detrimental to the performance of any semiconductor material. Beyond influencing the performance of Ta 3 N 5 films, defects can also accelerate the degradation in water during desired electrochemical applications. Defect passivation has provided an enormous boost to the development of many semiconductor materials but is currently in its infancy for Ta 3 N 5 . This is in part due to a lack of experimental understanding regarding the spatial and energetic distribution of trap states throughout Ta 3 N 5 thin films. Here, we employ drive-level capacitance profiling (DLCP) to experimentally resolve the spatial and energetic distribution of trap states throughout Ta 3 N 5 thin films. The density of deeper energetic traps is found to reach ∼2.5 to 6 × 10 22 cm –3 at the interfaces of neat Ta 3 N 5 thin films, over an order of magnitude greater than the bulk. In addition to the spatial profile of deep trap states, we report neat Ta 3 N 5 thin films to be highly n-type in nature, owning a free carrier density of ∼9.74 × 10 17 cm –3 . This information, coupled with the present understanding of native oxide layers on Ta 3 N 5 , has facilitated the rational design of a targeted passivation strategy that simultaneously provides a means for catalyst immobilization. Loading catalyst via silatrane moieties suppresses the density of defects at the surface of Ta 3 N 5 thin films by two orders of magnitude, while also reducing the free carrier density of films by over one order of magnitude, effectively dedoping the films to ∼2.40 × 10 16 cm –3 . The surface passivation of Ta 3 N 5 films translates to suppressed defect-induced trapping and recombination of photoexcited carriers, as determined through absorption, photoluminescence, and transient photovoltage. Here, this illustrates how developing a deeper understanding of the distribution and influence of defects in Ta 3 N 5 thin films has the potential to guide future works and ultimately accelerate the integration and development of high-performance Ta 3 N 5 thin film devices.
The origin of the extended X-ray emission in the large-scale jets of active galactic nuclei (AGNs) poses challenges to conventional models of acceleration and emission. Although electron synchrotron radiation is considered the most feasible radiation mechanism, the formation of the continuous large-scale X-ray structure remains an open issue. As astrophysical jets are expected to exhibit some turbulence and shearing motion, we here investigate the potential of shearing flows to facilitate an extended acceleration of particles and evaluate its impact on the resultant particle distribution. Our treatment incorporates systematic shear and stochastic second-order Fermi effects. We show that for typical parameters applicable to large-scale AGN jets, stochastic second-order Fermi acceleration, which always accompanies shear particle acceleration, can play an important role in facilitating the whole process of particle energization. We study the time-dependent evolution of the resultant particle distribution in the presence of second-order Fermi acceleration, shear acceleration, and synchrotron losses using a simple Fokker–Planck approach and provide illustrations for the possible emergence of a complex (multicomponent) particle energy distribution with different spectral branches. We present examples for typical parameters applicable to large-scale AGN jets, indicating the relevance of the underlying processes for understanding the extended X-ray emission and the origin of ultrahigh-energy cosmic rays.