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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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A new electrostriction technique was developed to measure the isothermal compressibility along isochores and isotherms near the critical point. Initial measurements that validate this new technique will also be presented.
The status of an investigation of four numerical techniques for the time-dependent compressible Navier-Stokes equations is presented. Results for free shear layer calculations in the Reynolds number range from 1000 to 81000 indicate that a sequential alternating-direction implicit (ADI) finite-difference procedure requires longer computing times to reach steady state than a low-storage hopscotch finite-difference procedure. A finite-element method with cubic approximating functions was found to require excessive computer storage and computation times. A fourth method, an alternating-direction cubic spline technique which is still being tested, is also described.
Here, discrete wavelet methods, originally formulated in the setting of regularly sampled signals, can be adapted to data defined on a point cloud if some multiresolution structure is imposed on the cloud. A wide variety of hierarchical clustering algorithms can be used for this purpose, and the multiresolution structure obtained can be encoded by a hierarchical tree of subsets of the cloud. Prior work introduced the use of Haar-like bases defined with respect to such trees for approximation and learning tasks on unstructured data. This paper builds on that work in two directions. First, we present an algorithm for constructing Haar-like bases on general discrete hierarchical trees. Second, with an eye towards data compression, we present thresholding techniques for data defined on a point cloud with error controlled in the $L$ $\infty$ norm and in a Hölder-type norm. In a concluding trio of numerical examples, we apply our methods to compress a point cloud dataset, study the tightness of the $L$ $\infty$ error bound, and use thresholding to identify MNIST classifiers with good generalizability.
The main motivation of the project was to measure the magnetic field in pulsed power driven plasma compression using advanced non-intrusive spectroscopic techniques. The strength of the project is that it encompasses a combination of laboratory experiments with diagnostics for both the magnetic field and plasma properties and computational modeling. The following issues were addressed: (1) Plasma and magnetic field dynamics, (2) Field compression and diffusion, (3) Energy balance in the system, (4) Onset of instabilities, and (5) Correlation of the plasma and field evolutions with the initial conditions. The unique feature of the research was the use of advanced spectroscopy to measure both the magnetic field and various plasma properties in multiple locations using both side-on and end-on views, simultaneously. The experimental system allowed for imploding different gas mixtures (which facilitated local measurements) and for flexibility in modifying the initial conditions. Two current generators were used with significantly different current rise times (200 ns and 1.6 µs) but at the same current level of 400 kA. The slower pulsed power generator (at rise time 1.6 µs) was based at the Weizmann Institute of Science (WIS) and imploded plasma into which an axial magnetic field (up to 1 T) was initially embedded. The faster current rise time generator (at rise time 200 ns) based at University of California San Diego (UCSD) was used for plasma parameter studies initially, with collaborative efforts we successfully implemented the advanced spectroscopy techniques at UC San Diego. One graduate student (Nick Aybar) and a postdoctoral scholar (Maylis Dozeries) were trained. They both are based at the Lawrence Livermore National Laboratory now.
Welding repair of irradiated nickel-based alloys, such as Alloy 182, poses a significant challenge due to helium-induced cracking (HeIC) and grain boundary degradation (GBD) in the heat affected zone, driven by helium accumulation at grain boundaries and welding-induced tensile stresses. This study investigates the weldability of irradiated Alloy 182 up to 15 wppm doped boron using both conventional laser welding and the Auxiliary Beam Stress Improved (ABSI) laser welding technique. While HeIC was observed at the weld toe of the entry pass in both methods due to the higher effective heat input associated with the initial pass directly on the base metal, no additional cracking occurred elsewhere, even at elevated helium concentrations. Optical and scanning electron microscopy analysis revealed that the ABSI technique, which introduces additional compressive stresses to counteract solidification-induced tensile stresses, significantly reduced GBD formation, lowering its total count from 1,230 to 339 and decreasing both average and maximum GBD lengths. In conclusion, these results demonstrate that the ABSI technique is a promising approach to mitigate helium-induced damage and improve the weldability of irradiated Alloy 182, offering a viable solution for structural repairs for long-term operation of existing nuclear reactors.
Curved flows with shear and disturbance in ducts, using similarity techniques to calculate viscous and compressibility effects
Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous high-performance computing (HPC) architecture, GPU-accelerated error-bounded compressors (such as CUSZ and cuZFP) have been developed. However, they suffer from either low performance or low compression ratios. To this end, we propose CUSZ+ to target both high compression ratios and throughputs. We identify that data sparsity and data smoothness are key factors for high compression throughputs. Our key contributions in this work are fourfold: (1) We propose an efficient compression workflow to adaptively perform run-length encoding and/or variable-length encoding. (2) We derive Lorenzo reconstruction in decompression as multidimensional partial-sum computation and propose a fine-grained Lorenzo reconstruction algorithm for GPU architectures. (3) We carefully optimize each of CUSZ kernels by leveraging state-of-the-art CUDA parallel primitives. (4) We evaluate CUSZ+ using seven real-world HPC application datasets on V100 and A100 GPUs. Experiments show CUSZ+ improves the compression throughputs and ratios by up to 18.4x and 5.3x, respectively, over CUSZ on the tested datasets.
Numerical techniques for solving the compressible Euler and Navier-Stokes equations are discussed with an emphasis on characteristic-based schemes. Two popular approaches, flux difference splitting and flux vector splitting, are described in one-dimensional Cartesian coordinates and then extended to three-dimensional generalized coordinates. A technique for increasing the spatial accuracy is presented, followed by a discussion of numerical dissipation mechanisms. An introduction to the use of implicit time integration schemes for accelerating the convergence rate to steady-state solutions including Newton's method, relaxation strategies, and approximate factorization techniques and their implementation on a vector processor concludes the chapter.
Micro-pillar compression is a popular experimental technique used for characterizing the mechanical behavior of nano- and micro-laminates. The compressive stress–strain response of the column-shaped thin-film composite can be measured, and the deformation and damage features can be revealed by post-test cross-section microscopy. The development of plastic instability in the form of localized strain concentration (shear bands), leading to eventual failure, is frequently observed. In the present study, a computational approach is used to illustrate the commonality of shear band formation from a continuum standpoint. Systematic finite element analyses are conducted, showing that the strain field tends to become localized once plastic yielding commences. Distinct shear offsets of the layered structure can be revealed from the numerical model, which is similar to those observed in experiments. The actual appearance of shear bands depends on the materials’ constitutive behavior and precise geometries. Post-yield strain hardening reduces the propensity of shear band formation, while strain softening enhances it. Imperfections such as the undulated layer geometry, as well as the frictional characteristics between the specimen and test apparatus, can also influence the shear band morphology and overall stress–strain response.
The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.
The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.
This project developed a novel statistical understanding of compression analytics (CA), which has challenged and clarified some core assumptions about CA, and enabled the development of novel techniques that address vital challenges of national security. Specifically, this project has yielded the development of novel capabilities including 1. Principled metrics for model selection in CA, 2. Techniques for deriving/applying optimal classification rules and decision theory to supervised CA, including how to properly handle class imbalance and differing costs of misclassification, 3. Two techniques for handling nonlocal information in CA, 4. A novel technique for unsupervised CA that is agnostic with regard to the underlying compression algorithm, 5. A framework for semisupervised CA when a small number of labels are known in an otherwise large unlabeled dataset. 6. The academic alliance component of this project has focused on the development of a novel exemplar-based Bayesian technique for estimating variable length Markov models (closely related to PPM [prediction by partial matching] compression techniques). We have developed examples illustrating the application of our work to text, video, genetic sequences, and unstructured cybersecurity log files.
High pressure gas sealing involves not only balancing the viscous force with the pressure gradient force but also accounting for fluid inertia--especially for choked flow. The conventional finite element method which uses a Rayleigh-Ritz solution technique is not convenient for nonlinear problems. For these problems, a finite element method with a Galerkin solution technique (FEMGST) was formulated. One example, a three-dimensional axisymmetric flow formulation has nonlinearities due to compressibility, area expansion, and convective inertia. Solutions agree with classical results in the limiting cases. The development of the choked flow velocity profile is shown.
Current research in high-efficiency, high-performance traveling wave tubes (TWT's) has led to the development of novel thermal/ mechanical computer models for use with helical slow-wave structures. A three-dimensional, finite element computer model and analytical technique used to study the structural integrity and thermal operation of a high-efficiency, diamond-rod, K-band TWT designed for use in advanced space communications systems. This analysis focused on the slow-wave circuit in the radiofrequency section of the TWT, where an inherent localized heating problem existed and where failures were observed during earlier cold compression, or 'coining' fabrication technique that shows great potential for future TWT development efforts. For this analysis, a three-dimensional, finite element model was used along with MARC, a commercially available finite element code, to simulate the fabrication of a diamond-rod TWT. This analysis was conducted by using component and material specifications consistent with actual TWT fabrication and was verified against empirical data. The analysis is nonlinear owing to material plasticity introduced by the forming process and also to geometric nonlinearities presented by the component assembly configuration. The computer model was developed by using the high efficiency, K-band TWT design but is general enough to permit similar analyses to be performed on a wide variety of TWT designs and styles. The results of the TWT operating condition and structural failure mode analysis, as well as a comparison of analytical results to test data are presented.
Current research in high-efficiency, high-performance traveling wave tubes (TWT's) has led to the development of novel thermal/mechanical computer models for use with helical slow-wave structures. A three-dimensional, finite element computer model and analytical technique used to study the structural integrity and thermal operation of a high-efficiency, diamond-rod, K-band TWT designed for use in advanced space communications systems. This analysis focused on the slow-wave circuit in the radiofrequency section of the TWT, where an inherent localized heating problem existed and where failures were observed during earlier cold compression, or 'coining' fabrication technique that shows great potential for future TWT development efforts. For this analysis, a three-dimensional, finite element model was used along with MARC, a commercially available finite element code, to simulate the fabrication of a diamond-rod TWT. This analysis was conducted by using component and material specifications consistent with actual TWT fabrication and was verified against empirical data. The analysis is nonlinear owing to material plasticity introduced by the forming process and also to geometric nonlinearities presented by the component assembly configuration. The computer model was developed by using the high efficiency, K-band TWT design but is general enough to permit similar analyses to be performed on a wide variety of TWT designs and styles. The results of the TWT operating condition and structural failure mode analysis, as well as a comparison of analytical results to test data are presented.
Recurrent Neural Networks (RNN) is widely applied to temporal sequence analysis, where real-time performance is usually in demand. However, RNN suffers a heavy computational workload as the model comes with a large weight matrix. To alleviate the pain, model compression (pruning) schemes have been proposed for RNN that pruning the redundant (near-zero) weight-values. On the one hand, the non-structured pruning methods achieve a considerable pruning rate while bringing the computational irregularity, which is un-friendly to parallel-hardware. On the other hand, the existing structured pruning methods consider the hardware parallelism; However, they suffer a poor pruning rate due to the restrict constraints on pruning structure. This paper presents CSB-RNN, an optimized full-stack RNN framework with the novel compressed structured block (CSB) technique. The CSB-pruned RNN model comes with both fine-granularity that benefits the pruning rate and regular structure that facilitates the hardware-parallelism. Further, we propose a novel hardware architecture for inferencing the CSB-pruned model. Different from conventional parallel hardware, this architecture solves the block-workload imbalance issue and achieves an over 95% hardware utilization. With the experiments on 10 RNN models in 5 application domains, the CSB-RNN realizes 7×-20× lossless compression and up to 50× acceptable lossy-compression, which is 2×-7× to the prior art. With the addition of the novel hardware, the compressed-RNN inference reaches a super real-time latency of 10-400µs with FPGA implementation.
It is demonstrated that even using the best current techniques for generating a dynamic shock compression Hugoniot curve, the effects from small changes in alloying elements or compressibility are difficult to discriminate from each other.
The Galileo mission operations concept is undergoing substantial redesign, necessitated by the deployment failure of the High Gain Antenna, while the spacecraft is on its way to Jupiter. The new design applies state-of-the-art technology and processes to increase the telemetry rate available through the Low Gain Antenna and to increase the information density of the telemetry. This paper describes the mission planning process being developed as part of this redesign. Principal topics include a brief description of the new mission concept and anticipated science return (these have been covered more extensively in earlier papers), identification of key drivers on the mission planning process, a description of the process and its implementation schedule, a discussion of the application of automated mission planning tool to the process, and a status report on mission planning work to date. Galileo enhancements include extensive reprogramming of on-board computers and substantial hard ware and software upgrades for the Deep Space Network (DSN). The principal mode of operation will be onboard recording of science data followed by extended playback periods. A variety of techniques will be used to compress and edit the data both before recording and during playback. A highly-compressed real-time science data stream will also be important. The telemetry rate will be increased using advanced coding techniques and advanced receivers. Galileo mission planning for orbital operations now involves partitioning of several scarce resources. Particularly difficult are division of the telemetry among the many users (eleven instruments, radio science, engineering monitoring, and navigation) and allocation of space on the tape recorder at each of the ten satellite encounters. The planning process is complicated by uncertainty in forecast performance of the DSN modifications and the non-deterministic nature of the new data compression schemes. Key mission planning steps include quantifying resource or capabilities to be allocated, prioritizing science observations and estimating resource needs for each, working inter-and intra-orbit trades of these resources among the Project elements, and planning real-time science activity. The first major mission planning activity, a high level, orbit-by-orbit allocation of resources among science objectives, has already been completed; and results are illustrated in the paper. To make efficient use of limited resources, Galileo mission planning will rely on automated mission planning tools capable of dealing with interactions among time-varying downlink capability, real-time science and engineering data transmission, and playback of recorded data. A new generic mission planning tool is being adapted for this purpose.