SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
An analytic solution to the heavy ion transport in terms of Green's function is used to generate a highly efficient computer code for space applications. The efficiency of the computer code is accomplished by a nonperturbative technique extending Green's function over the solution domain. The computer code can also be applied to accelerator boundary conditions to allow code validation in laboratory experiments.
This Progress Report covering the period of December 1, 1992 to June 1, 1993 presents the development of an analytical solution to the heavy ion transport equation in terms of Green's function formalism. The mathematical development results are recasted into a highly efficient computer code for space applications. The efficiency of this algorithm is accomplished by a nonperturbative technique of extending the Green's function over the solution domain. The code may also be applied to accelerator boundary conditions to allow code validation in laboratory experiments. Results from the isotopic version of the code with 59 isotopes present for a single layer target material, for the case of an iron beam projectile at 600 MeV/nucleon in water is presented. A listing of the single layer isotopic version of the code is included.
The expansion of the Universe is understood to have accelerated during two epochs: in its very first moments during a period of Inflation and much more recently, at $z < 1$, when Dark Energy is hypothesized to drive cosmic acceleration. The undiscovered mechanisms behind these two epochs represent some of the most important open problems in fundamental physics. The large cosmological volume at $2 < z < 5$, together with the ability to efficiently target high-$z$ galaxies with known techniques, enables large gains in the study of Inflation and Dark Energy. A future spectroscopic survey can test the Gaussianity of the initial conditions up to a factor of ~50 better than our current bounds, crossing the crucial theoretical threshold of $\sigma(f_{NL}^{\rm local})$ of order unity that separates single field and multi-field models. Simultaneously, it can measure the fraction of Dark Energy at the percent level up to $z = 5$, thus serving as an unprecedented test of the standard model and opening up a tremendous discovery space.
We describe an electron bunch compression scheme suitable for use in a light source driven by a superconducting radio frequency (SRF) linac. The key feature is the use of a recirculating linac to perform the initial bunch compression. Phasing of the second pass beam through the linac is chosen to de-chirp the electron bunch prior to acceleration to the final energy in an SRF linac ('afterburner'). The final bunch compression is then done at maximum energy. This scheme has the potential to circumvent some of the most technically challenging aspects of current longitudinal matches; namely transporting a fully compressed, high peak current electron bunch through an extended SRF environment, the need for a RF harmonic linearizer and the need for a laser heater. Additional benefits include a substantial savings in capital and operational costs by efficiently using the available SRF gradient.
LCLS is presently operating with a third copper photocathode in the original rf gun, with a quantum efficiency (QE) of {approx}1 x 10{sup -4} and projected emittance {gamma}{var_epsilon}{sub x,y} = 0.45 {micro}m at 250 pC bunch charge. The spare LCLS gun is installed in the SLAC Accelerator Structure Test Area (ASTA), fully processed to high rf power. As part of a wider photocathode R and D program, a UV laser system and additional gun diagnostics are being installed at ASTA to measure QE, QE lifetime, and electron beam emittance under a variety of operating conditions. The near-term goals are to test and verify the spare photocathode production/installation sequence, including transfer from the final holding chamber to the rf gun. Mid- and longer-term goals include development of a rigorous understanding of plasma and laser-assisted surface conditioning and investigation of new, high-QE photocathode materials. In parallel, an x-ray photoemission spectroscopy station is nearing completion, to analyze Cu photocathode surface chemistry. In this paper we review the status and anticipated operating parameters of ASTA and the spectroscopy test chamber.
Currently two gravity field satellite missions, CHAMP and GRACE, are equipped with high sensitivity electrostatic accelerometers, measuring the non-conservative forces acting on the spacecraft in three orthogonal directions. During the gravity field recovery these measurements help to separate gravitational and non-gravitational contributions in the observed orbit perturbations. For precise orbit determination purposes all these missions have a dual-frequency GPS receiver on board. The reduced dynamic technique combines the dense and accurate GPS observations with physical models of the forces acting on the spacecraft, complemented by empirical accelerations, which are stochastic parameters adjusted in the orbit determination process. When the spacecraft carries an accelerometer, these measured accelerations can be used to replace the models of the non-conservative forces, such as air drag and solar radiation pressure. This approach is implemented in a batch least-squares estimator of the GPS High Precision Orbit Determination Software Tools (GHOST), developed at DLR/GSOC and DEOS. It is extensively tested with data of the CHAMP and GRACE satellites. As accelerometer observations typically can be affected by an unknown scale factor and bias in each measurement direction, they require calibration during processing. Therefore the estimated state vector is augmented with six parameters: a scale and bias factor for the three axes. In order to converge efficiently to a good solution, reasonable a priori values for the bias factor are necessary. These are calculated by combining the mean value of the accelerometer observations with the mean value of the non-conservative force models and empirical accelerations, estimated when using these models. When replacing the non-conservative force models with accelerometer observations and still estimating empirical accelerations, a good orbit precision is achieved. 100 days of GRACE B data processing results in a mean orbit fit of a few centimeters with respect to high-quality JPL reference orbits. This shows a slightly better consistency compared to the case when using force models. A purely dynamic orbit, without estimating empirical accelerations thus only adjusting six state parameters and the bias and scale factors, gives an orbit fit for the GRACE B test case below the decimeter level. The in orbit calibrated accelerometer observations can be used to validate the modelled accelerations and estimated empirical accelerations computed with the GHOST tools. In along track direction they show the best resemblance, with a mean correlation coefficient of 93% for the same period. In radial and normal direction the correlation is smaller. During days of high solar activity the benefit of using accelerometer observations is clearly visible. The observations during these days show fluctuations which the modelled and empirical accelerations can not follow.
The rapid growth in scientific data generation is outpacing advancements in computing systems necessary for efficient storage, transfer, and analysis, particularly in the context of exascale computing. With the deployment of first-generation exascale computing systems and next-generation experimental facilities, this gap is widening and necessitates effective data reduction techniques to manage enormous data volumes. Over the past decade, various data reduction methods, including lossless compression, error-controlled lossy compression, and data refactoring, have been developed to accelerate I/O in scientific workflows. Despite significant reductions in data volume, these methods introduce considerable computational overhead, which can become the new bottleneck in data processing. To mitigate this, GPU-accelerated data reduction algorithms have been introduced. However, challenges remain in their integration into exascale workflows, including limited portability across different GPU architectures, substantial memory transfer overhead, and reduced scalability on dense multi-GPU systems. To address these challenges, we propose HPDR, a high-performance and portable data reduction framework. HPDR is designed to enable the execution of state-of-the-art reduction algorithms across diverse processor architectures while reducing memory transfer overhead to 2.3 % of the original, resulting in up to 3.5× faster throughput compared to existing solutions. It also achieves up to 96% of the theoretical speedup in multi-GPU settings. In addition, evaluations on accelerating I/O operations at scale up to 1,024 nodes of the Frontier supercomputer demonstrate that HPDR can achieve up to 103 TB/s reduction throughput, providing up to 4× acceleration in parallel I/O performance compared to existing data reduction routines. This work highlights the potential of HPDR to significantly enhance data reduction efficiency in exascale computing environments.
Tycho's supernova remnant (SNR) is well-established as a source of particle acceleration to very high energies. Constraints from numerous studies indicate that the observed γ-ray emission results primarily from hadronic processes, providing direct evidence of highly relativistic ions that have been accelerated by the SNR. Here we present an investigation of the dynamical and spectral evolution of Tycho's SNR by carrying out hydrodynamical simulations that include diffusive shock acceleration of particles in the amplified magnetic field at the forward shock of the SNR. Our simulations provide a consistent view of the shock positions, the nonthermal emission, the thermal X-ray emission from the forward shock, and the brightness profiles of the radio and X-ray emission. We compare these with the observed properties of Tycho to determine the density of the ambient material, the particle acceleration efficiency and maximum energy, the accelerated electron-to-proton ratio, and the properties of the shocked gas downstream of the expanding SNR shell. We find that evolution of a typical Type Ia supernova in a low ambient density (n {sub 0} ∼ 0.3 cm{sup –3}), with an upstream magnetic field of ∼5 μG, and with ∼16% of the SNR kinetic energy being converted into relativistic electrons and ions through diffusive shock acceleration, reproduces the observed properties of Tycho. Under such a scenario, the bulk of observed γ-ray emission at high energies is produced by π{sup 0}-decay resulting from the collisions of energetic hadrons, while inverse-Compton emission is significant at lower energies, comprising roughly half of the flux between 1 and 10 GeV.
Ionization profile monitors (IPMs) are widely used in accelerators for non-destructive and fast diagnostics of high energy particle beams. At high beam intensities, the space-charge forces make the measured IPM profiles significantly different from those of the beams. We analyze dynamics of the secondaries in IPMs and develop an effective algorithm to reconstruct the beam sizes from the measured IPM profiles. Efficiency of the developed theory is illustrated in application to the Fermilab 8 GeV proton Booster IPMs.
In the framework of the project “Novel high-energy physics studies using intense lasers and plasmas” we conducted the study of ion acceleration and “flying mirrors” with high intensity lasers in order to develop sources of ion beams and high frequency radiation for different applications. Since some schemes of laser ion acceleration are also considered a good source of “flying mirrors”, we proposed to investigate the mechanisms of “mirror” formation. As a result we were able to study the laser ion acceleration from thin foils and near critical density targets. We identified several fundamental factors limiting the acceleration in the RPA regime and proposed the target design to compensate these limitations. In the case of near critical density targets, we developed a concept for the laser driven ion source for the hadron therapy. Also we studied the mechanism of “flying mirror” generation during the intense laser interaction with thin solid density targets. As for the laser-based positron creation and capture we initially proposed to study different regimes of positron beam generation and positron beam cooling. Since the for some of these schemes a good quality electron beam is required, we studied the generation of ultra-low emittance electron beams. In order to understand the fundamental physics of high energy electron beam interaction with high intensity laser pulses, which may affect the efficient generation of positron beams, we studied the radiation reaction effects.
This report documents the analysis of the dose rate in the experiment area outside the Spallation Neutron Source (SNS) target station shielding monolith with proton beam energy of 1.3 GeV. The analysis implemented a coupled three dimensional (3D)/two dimensional (2D) approach that used both the Monte Carlo N-Particle Extended (MCNPX) 3D Monte Carlo code and the Discrete Ordinates Transport (DORT) two dimensional deterministic code. The analysis with proton beam energy of 1.3 GeV showed that the dose rate in continuously occupied areas on the lateral surface outside the SNS target station shielding monolith is less than 0.25 mrem/h, which complies with the SNS facility design objective. However, the methods and codes used in this analysis are out of date and unsupported, and the 2D approximation of the target shielding monolith does not accurately represent the geometry. We recommend that this analysis is updated with modern codes and libraries such as ADVANTG or SHIFT. These codes have demonstrated very high efficiency in performing full 3D radiation shielding analyses of similar and even more difficult problems.
Parker Precision Cooling Business Unit was awarded a Department of Energy grant (DE-EE0000412) to support the DOE-ITP goal of reducing industrial energy intensity and GHG emissions. The project proposed by Precision Cooling was to accelerate the development of a cooling technology for high heat generating electronics components. These components are specifically related to power electronics found in power drives focused on the inverter, converter and transformer modules. The proposed cooling system was expected to simultaneously remove heat from all three of the major modules listed above, while remaining dielectric under all operating conditions. Development of the cooling system to meet specific customer's requirements and constraints not only required a robust system design, but also new components to support long system functionality. Components requiring further development and testing during this project included pumps, fluid couplings, cold plates and condensers. All four of these major categories of components are required in every Precision Cooling system. Not only was design a key area of focus, but the process for manufacturing these components had to be determined and proven through the system development.
In recent years, a new kind of accelerated hardware has gained popularity in the artificial intelligence (AI) community which enables extremely high-performance tensor contractions in reduced precision for deep neural network calculations. In this article, we exploit Nvidia Tensor cores, a prototypical example of such AI-hardware, to develop a mixed precision approach for computing a dense matrix factorization of the inverse overlap matrix in electronic structure theory, S –1 . This factorization of S –1 , written as ZZT = S –1 , is used to transform the general matrix eigenvalue problem into a standard matrix eigenvalue problem. Here we present a mixed precision iterative refinement algorithm where Z is given recursively using matrix–matrix multiplications and can be computed with high performance on Tensor cores. To understand the performance and accuracy of Tensor cores, comparisons are made to GPU-only implementations in single and double precision. Additionally, we propose a nonparametric stopping criteria which is robust in the face of lower precision floating point operations. The algorithm is particularly useful when we have a good initial guess to Z, for example, from previous time steps in quantum-mechanical molecular dynamics simulations or from a previous iteration in a geometry optimization.
By using an ultra-fast scalable open-air plasma deposition technique to deposit the perovskite layer and combining that with monolithic scribing, we are able to achieve high efficiency and large area perovskite solar modules.
With the enlarging computation capacity of general Graphics Processing Units (GPUs), leveraging GPUs to accelerate parallel applications has become a critical topic in academia and industry. However, a wide range of irregular applications with the computation-/memory-intensive nature cannot easily achieve high GPU utilization. The challenges mainly involve the following aspects: first, data dependence leads to coarse-grained kernel and inefficient parallelism; second, heavy GPU memory usage may cause frequent memory evictions and extra overhead of I/O; third, specific computation patterns produce memory redundancies; last, workload balance and data reusability conjunctly benefit the overall performance, but there may exist a dynamic trade-off between them. Targeting these challenges, this dissertation proposes multiple optimizations to accelerate two real-world applications: many-body correlation functions to simulate nuclear physics in a large-scale scientific system; the other is the eALS-based matrix factorization recommendation system. To accelerate the calculations of many-body correlation functions, this dissertation presents three frameworks in GPU memory management and multi-GPU scheduling. Firstly, an optimized systematic GPU memory management framework, MemHC, utilizes a series of new memory reduction designs in GPU memory allocation, CPU/GPU communications, and GPU memory oversubscription. Secondly, an enhanced multi-GPU scheduling framework, MICCO, particularly by taking both data dimension (e.g., data reuse and data eviction) and computation dimension into account. MICCO designs a heuristic scheduling algorithm and a machine learning-based regression model to generate the optimal settings of a proposed new concept to manage the trade-off. Thirdly, a locality-aware multi-GPU scheduling framework. This scheduler leverages pipeline batch generation with a looking-ahead strategy by building local dependency graphs for memory transfer reduction and better data reuse, achieving up to 79.92% memory cost reduction and 1.67x speedup. To parallelize the eALS-based recommendation system, this dissertation proposes an efficient CPU/GPU heterogeneous recommendation system, HEALS. HEALS employs newly designed architecture-adaptive data formats to achieve load balance and good data locality on CPU and GPU. To mitigate the data dependence, HEALS presents a CPU/GPU collaboration model for both task parallelism and data parallelism with multiple kernel computation optimizations. In summary, this dissertation efficiently accelerates two typical irregular applications on GPUs by building four frameworks, including CPU/GPU collaboration, GPU memory management, and multi-GPU scheduling.
TiO 2 nanorod (NR) array for perovskite solar cells (PSCs) has attached great importance due to superb power conversion efficiency (PCE) compared to traditional mesoporous TiO 2 film. A TiO 2 compact layer for the growth of TiO 2 NR array via spin-coating cannot meet the requirements for efficient NR based PSCs. Herein we have developed and demonstrated inserting an bifunctional extra-thin TiO 2 interlayer (5 nm) by atomic layer deposition (ALD) at the interface of FTO/TiO 2 compact layer to achieve an alleviated electron exchange and a reduced energetic barrier. Thus, an accelerated extraction of electrons from TiO 2 NR arrays via the compact layer to FTO substrate is ready for improving the PSC efficiency. The thickness of the spin-coated TiO 2 compact layer on the ALD-deposited TiO 2 layer is spontaneously optimized. Finally, an outstanding efficiency of 20.28% has been achieved from a champion PSC with negligible hysteresis and high reliability. To the best of our knowledge, this is the first time to demonstrate the superiority of TiO 2 -NR based PSCs withstanding in dry heat and thermal cycling tests. The results are of great importance for the preparation of efficient and durable PSCs for our real-world applications.
Exploring low-cost and highly efficient electrocatalysts toward the oxygen evolution reaction (OER) is of significant importance, although facing great challenges for sustainable energy systems. In this study, amorphous NiFe-based porous nanocubes (Ni–Fe–O–P, Ni–Fe–O–B, and Ni–Fe–O–S) are successfully synthesized via simple and cost-effective one-step calcination of Ni–Fe based metal–organic frameworks (MOFs) and heteroatom containing molecules. The resulting three materials maintain a well-defined porous nanocube morphology with heteroatoms uniformly distributed in the structure. The unique porous structure can effectively provide more active sites and shorten the mass transport distance. Additionally, the introduction of P, B or S can tune the electronic structure, which is favorable for accelerating the charge transfer, and may lead to the formation of the higher average oxidative valence of Ni species during the OER process. Benefiting from the above desirable properties, all three materials exhibit excellent OER electrocatalytic activities and outstanding long-term stability in a home-made zinc air battery. Lastly, this work not only provides a general approach for the synthesis of highly efficient electrocatalysts based on earth-abundant elements but also highlights the potential prospects of MOFs in energy conversion and storage devices.
In the design of organic solar cells, there has been a need for materials with high power conversion efficiencies. Scharber’s model is commonly used to predict efficiency; however, it exhibits poor performance with new non-fullerene acceptor (NFA) devices, since it was designed for fullerene-based devices. In this work, an empirical model is proposed that can be a more accurate alternative for NFA organic solar cells. Additionally, many screening studies use computationally expensive methods. A model based on using semiempirical simplified time-dependent density functional theory (sTD-DFT) as an alternative method can accelerate the calculations and yield a similar accuracy. The models presented in this paper, termed organic photovoltaic efficiency predictor (OPEP) models, have shown significantly lower errors than previous models, with OPEP/B3LYP yielding errors of 1.53% and OPEP/sTD- DFT of 1.55%. As a result, the proposed computational models can be used for the fast and accurate screening of new high-efficiency NFAs/donor pairs.