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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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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Hypervelocity Impact Performance of 3D Printed Aluminum Panels

With the continued development of additive manufacturing methods, control over the shape of ligaments, cell regularity, and macroscopic shape can all be easily tuned. This capability allows for tailoring of component architecture and promotes potential mass savings in a space vehicle structure. Additionally, it allows one the flexibility of combining structural elements such as MMOD protection and vehicle stiffness for launch loads for an overall mass reduction. At NASA JSC this technology is being explored in many different ways with the goal being a multifunctional structural component. For this study, four different types of aluminum panels have been 3D printed for testing, three being of a body centric cubic (BCC) lattice structure core and one being kelvin cell structure core. All samples have a 5.33 cm (0.05”) nominally thick aluminum face sheet printed on the front and back side of each panel, with all core materials having a 5.08 cm (2.0”) nominal thickness (see Table 1 for test sample summary and Figures 1 – 2 for sample illustrations). These tests will evaluate the performance of 3D printed aluminum panels under hypervelocity impact (HVI) conditions. The hypervelocity impact tests are being conducted at the JSC White Sands Test Facility (WSTF) Remote Hypervelocity Test Laboratory (RHTL), located in Las Cruces, New Mexico. All tests will be conducted with a 3.4mm Al 2017-T4 sphere at 6.8 km/s impacting at 0° to surface normal (i.e., impacting with no obliquity). Each sample will be trapped between two metal frames, with gasket material residing between the sample and frame, which will be the shipping and testing configuration for all tests. There will be an Al 2017-T4 witness plate staged 5.08 cm (2.0”) from each sample to capture signature of debris, if the rear face sheet of the sample were to perforate from the HVI test event.

Davis, B. A.↗
DOE OSTI2023

Tiling Framework for Heterogeneous Computing of Matrix based Tiled Algorithms

Tiling matrix operations can improve the load balancing and performance of applications on heterogeneous computing resources. Writing a tile-based algorithm for each operation with a traditional, hand-tuned tiling approach that uses for loops in C/C++ is cumbersome and error prone. Moreover, it must enable and support the heterogeneous memory management of data objects and also explore architecture-supported, native, tiled-data transfer APIs instead of copying the tiled data to continuous memory before the data transfer. The tiling framework provides a tiled data structure for heterogeneous memory mapping and parameterization to a heterogeneous task specification API. We have integrated our tiled framework into MatRIS (Math kernels library using IRIS). IRIS is a heterogeneous run-time framework with a heterogeneous programming model, memory model, and task execution model. Experiments reveal that the tiled framework for BLAS operations has improved the programmability of tiled BLAS and improved performance by ~20% when compared against the traditional method that copies the data to continuous memory locations for heterogeneous computing.

Miniskar, Narasinga Rao↗
DOE OSTI2025

Characterizing the Impact of GPU Power Management on an Exascale System

As GPU-accelerated high-performance computing (HPC) systems approach exascale performance, controlling energy consumption without compromising throughput is essential. Architectures such as the AMD MI250X-based Frontier supercomputer provide runtime mechanisms like frequency and power capping, enabling energy tuning without modifying application code. Although both target energy reduction, they operate via distinct hardware control paths and influence workloads differently. We present a comprehensive evaluation of these strategies on a leadership-class system using diverse HPC proxy applications representative of production workloads. Our study analyzes performance–energy trade-offs across multiple capping levels, node counts (1 and 32), and application profiles. Results show that frequency capping generally achieves higher energy efficiency and scalability, with gains of up to 13.2% without performance loss, while power capping is more effective for single-node runs or bursty GPU utilization. We also provide practical guidelines to help system administrators and users balance energy efficiency and performance in large-scale scientific workloads.

Costa, Mariana [Universidade Federal do Rio Grande↗
NASA NTRS1985

Finite element engine blade structural optimization

The Structural Tailoring of Engine Blades (STAEBL) computer program was developed to perform engine fan blade numerical optimizations. These blade optimizations seek a minimum weight or cost design that satisfies realistic blade design constraints, by tuning one to twenty design variables. The STAEBL system has been generalized to include both fan and compressor blade numerical optimizations. The system analyses have been significantly improved through the inclusion of an efficient plate finite element analysis for blade stress and frequency determinations. Additionally, a finite element based approximate severe foreign object damage (FOD) analysis has been included. The new FOD analysis gives very accurate estimates of the full nonlinear bird ingestion solution. Optimizations of fan and compressor blades have been performed using the system, showing significant cost and weight reductions, while comparing very favorably with refined design validation procedures.

Brown, K. W.↗
DOE OSTI2022

Tuning discharge voltage by Schottky electron barrier in P2-Na 2/3 Mg 0.205 Ni 0.1 Fe 0.05 Mn 0.645 O 2

Recently, Mg doped Na metal oxide layered cathode compounds have attracted strong interest for Na-ion battery applications. We report a new type of asymmetric phase evolution between charge and discharge is found to show much-enhanced discharge voltage in P2-Na 2/3 Mg 0.205 Ni 0.1 Fe 0.05 Mn 0.645 O 2 over the parent cathode compound. P2 solid solution is found to show an abnormal coexistence with O2-P2 two-phase reaction during discharge with simultaneous reduction of O, Ni, and Fe redoxes, distinct from the conventional P2-O2 two-phase reaction in the charge. Our analysis suggests that the P2 and O2 two-phase boundary forms a novel unidirectional Schottky barrier to impede electron and Na diffusions in discharge only, thus making the kinetically preferred P2 solid solution phase abnormally coexist with the two-phase region for high discharge voltage and low polarization. Our work demonstrates tuning dynamic evolution of electronic Schottky barrier as a new dimension for advanced kinetic design of high-performance battery cathode materials.

25 ENERGY STORAGE↗
DOE OSTI2019

Nanoscale MnO 2 cathodes for Li-ion batteries: effect of thermal and mechanical processing

This study investigates the effect of thermal and mechanical processing of MnO 2 nanoparticle cathodes on the electrochemical performance, ion intercalation mechanism and structural changes upon cycling. Specifically, thermal processing at different temperatures is utilized to tune the sub-nanoparticle polymorph composition, while mechanical processing (high energy ball-milling) is used to change the particle size and create a better contact between active material and conductive graphite additive. The combination of thermal and mechanical processing is also investigated for possible synergistic effects. The structural changes due to processing suggest that thermal treatment of the MnO 2 nanoparticles at 400 °C forms a high concentration of pyrolusite domains (1 $\times$ 1 channels) leading to improved reversibility of lithium intercalation/de-intercalation and capacity retention. It is also found that ball-milling for a short time (20 min) leads to some amorphization and decrease of the crystallite sizes resulting in improved capacity. The structural changes and ion intercalation mechanism of the pristine and processed electrode materials during cycling are investigated by ex situ synchrotron x-ray diffraction (XRD) and ex situ and in situ x-ray absorption near edge structure (XANES) studies.

25 ENERGY STORAGE↗
DOE OSTI2025

Calibration and simulation of ionization signal and electronics noise in the ICARUS liquid argon time projection chamber

The ICARUS liquid argon time projection chamber (LArTPC) neutrino detector has been taking physics data since 2022 as part of the Short-Baseline Neutrino (SBN) Program. This paper details the equalization of the response to charge in the ICARUS time projection chamber (TPC), as well as data-driven tuning of the simulation of ionization charge signals and electronics noise. The equalization procedure removes non-uniformities in the ICARUS TPC response to charge in space and time. This work leverages the copious number of cosmic ray muons available to ICARUS at the surface. The ionization signal shape simulation applies a novel procedure that tunes the simulation to match what is measured in data. The end result of the equalization procedure and simulation tuning allows for a comparison of charge measurements in ICARUS between Monte Carlo simulation and data, showing good performance with minimal residual bias between the two.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Microscopic Imprints of Learned Solutions in Tunable Networks

In physical networks trained using supervised learning, physical parameters are adjusted to produce desired responses to inputs. An example is an electrical contrastive local learning network of nodes connected by edges that adjust their conductances during training. When an edge conductance changes, it upsets the current balance of every node. In response, physics adjusts the node voltages to minimize the dissipated power. Learning in these systems is therefore a coupled double-optimization process, in which the network descends both a cost landscape in the high-dimensional space of edge conductances and a physical landscape—the power dissipation—in the high-dimensional space of node voltages. Because of this coupling, the physical landscape of a trained network contains information about the learned task. Here, we derive a structure-function relation for trained tunable networks and demonstrate that all the physical information relevant to the trained input-output relation can be captured by a tuning susceptibility, an experimentally measurable quantity. We supplement our theoretical results with simulations to show that the tuning susceptibility is correlated with functional importance and that we can extract physical insight into how the system performs the task from the conductances of highly susceptible edges. Our analysis is general and can be applied directly to mechanical networks, such as networks trained for protein-inspired function such as allostery.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enhancing Distribution System Resilience: A First-Order Meta-RL Algorithm for Critical Load Restoration

The increasing frequency of extreme events and the integration of distributed energy resources (DERs) into modern grids have elevated the need for resilient and efficient critical load restoration strategies in distribution systems. However, the stochastic nature of renewable DERs, limited energy resource availability and the intricate nonlinearities inherent in complex grid control problem make the problem challenging. Although reinforcement learning (RL) and warm-start RL methods have shown promising results, their performance often falls short in rapidly adapting to new, unseen situations and typically requires exhaustive problem-specific tuning. To address these gaps, we propose a First-Order Meta-based RL (FOM-RL) algorithm within an online framework for adaptive and robust critical load restoration. By harnessing local DERs as the enabling technology, FOM-RL allows the RL agent to swiftly adapt to new unseen scenarios by leveraging previously acquired knowledge of different tasks. Experimental results provide evidence that proposed algorithm learns more efficiently and showcases generalization capabilities across diverse set of operational scenarios. Moreover, a rigorous theoretical analysis yields a tight sublinear regret bound, sensitive to temporal variability, with a task-averaged optimality gap bounded by O(VM+D*/(Tsquare root(M))). These results suggest that optimality improves with task similarity and an increased number of tasks M, reaffirming the efficacy and scalability of the proposed approach in addressing the complexities of critical load restoration in distribution systems.

complexity theory↗
DOE OSTI2022

Inverse Analysis with Variational Autoencoders: A Comparison of Shallow and Deep Networks

Inverse problems are applied to determine unknown properties by matching observational data with a physical model that often takes many parameters as input. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented involving regularization with a technique known as a variational autoencoder (VAE), which is trained to map a high-dimensional parameter space with a complex structure to a low-dimensional latent space with a simple structure. We apply this approach to unconditioned realizations of the parameters (heterogeneous hydraulic fields) for a hydrogeological inverse problem. Two types of hydraulic conductivity fields are used to evaluate the characterization for the different levels of heterogeneity complexity of the physical inputs. This approach keeps the computational cost of generating the training data low. The reason is unconditioned realizations neither rely on the observational data used to perform the inverse analysis nor require any groundwater flow model forward runs. In addition, this approach applies regularization on a low-dimensional latent space from the VAE and increases optimization efficiency through automatic differentiation. Furthermore, two different neural network (NN) structures are tested for their utility in using the VAE for inverse analysis. The performance of a deep, convolutional neural network strongly depends on the dimensionality of the latent space, which requires tuning. In contrast, a shallow, dense neural network provides consistently accurate characterization without tuning. Furthermore, our approach evaluates the advantages of the shallow, dense neural network over the deep, convolutional one and enables future application to a wide range of inverse problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Effect of changes of physical properties on CO2 capture solvents on its absorption rate

Concern over greenhouse gas emissions, such as CO2 from fossil fuel combustion, is leading to new regulations on fossil-fuel fired power generation facilities. Using amine-based solutions for CO2 capture in power plants is the most common method utilized industrially and studied in the laboratory environment. However, a limited number of amines are available for carbon capture research after balancing the capital cost and energy penalty. In the carbon capture process, physical properties such as viscosity, surface tension, wettability, etc. are found to play important roles in solvent performance. In this context, we are exploring an approach to enhance the CO2 capture of amine solvents by tuning the physical properties of the amine solvents by the addition of additives. These additives in the amine solution utilize controlled bubble generation to increase liquid-gas contact surface area. This method can increase mass transfer in the absorber column, specifically at the bottom where the mass transfer is limited. These additives can enhance the CO2 capture efficiency by changing the physical property of the solvent which in turn can lower the capital cost associated with the system. This work can be applied to all common aqueous amine solvent and would be of great interest to those in the CCS community.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1989

Voyager 2 approaches Neptune

What is to be expected when Voyager 2 visits Neptune is discussed. Equipment tuning which has been done in preparation for the event is reviewed. The agenda of measurements to be performed on Neptune's atmosphere, magnetic and radiation fields, ring material, and satellites Nereid and Triton is described.

Miner, Ellis D.↗
NASA NTRS1994

Applications Performance Under MPL and MPI on NAS IBM SP2

On July 5, 1994, an IBM Scalable POWER parallel System (IBM SP2) with 64 nodes, was installed at the Numerical Aerodynamic Simulation (NAS) Facility Each node of NAS IBM SP2 is a "wide node" consisting of a RISC 6000/590 workstation module with a clock of 66.5 MHz which can perform four floating point operations per clock with a peak performance of 266 Mflop/s. By the end of 1994, 64 nodes of IBM SP2 will be upgraded to 160 nodes with a peak performance of 42.5 Gflop/s. An overview of the IBM SP2 hardware is presented. The basic understanding of architectural details of RS 6000/590 will help application scientists the porting, optimizing, and tuning of codes from other machines such as the CRAY C90 and the Paragon to the NAS SP2. Optimization techniques such as quad-word loading, effective utilization of two floating point units, and data cache optimization of RS 6000/590 is illustrated, with examples giving performance gains at each optimization step. The conversion of codes using Intel's message passing library NX to codes using native Message Passing Library (MPL) and the Message Passing Interface (NMI) library available on the IBM SP2 is illustrated. In particular, we will present the performance of Fast Fourier Transform (FFT) kernel from NAS Parallel Benchmarks (NPB) under MPL and MPI. We have also optimized some of Fortran BLAS 2 and BLAS 3 routines, e.g., the optimized Fortran DAXPY runs at 175 Mflop/s and optimized Fortran DGEMM runs at 230 Mflop/s per node. The performance of the NPB (Class B) on the IBM SP2 is compared with the CRAY C90, Intel Paragon, TMC CM-5E, and the CRAY T3D.

Saini, Subhash↗
NASA NTRS2002

Estimator Based Controller for High Speed Flywheel Magnetic Bearing System

A flywheel system and its operator interface are described. Measurements of magnetic bearing negative stiffness are performed. Two digital magnetic bearing control algorithms (PD and estimator based) are defined and their implementations are described. Tuning of each controller is discussed. Comparison of the two controllers' stability, damping noise, and operating current are described. Results describing the superiority of the estimator-based controller are presented and discussed.

Dever, Timothy P.↗
NASA NTRS2004

Mars Exploration Rover Mission

Two rovers with a sophisticated geological payload have been operating on the surface of Mars since January of 2004. Future missions and their related technology developments will benefit from the lessons learned during these surface operations. The planning cycle was dictated by the communications opportunities and the times of day that the rovers could operate, and the team and tools were tuned to optimize the mission return for that cycle time. The ability to traverse and to approach and perform in situ investigations on targets was limited in speed by the same cycle time, due to required human involvement in the related planning and risk decisions. In addition traverse was limited by the speed of the on-board terrain and hazard assessment, and in situ operations were limited by a lack of autonomy. Different planning cycles and levels of autonomy should be considered for future surface missions, which will result in different approaches to science decision making.

Adler, M.↗
NASA NTRSDate not supplied

Pterodactyl: 6-DOF Integration of Guidance and Control Algorithms in Genesis

The NASA-funded Pterodactyl project seeks to advance the current state-of-the-art for entry vehicles by developing novel guidance and control technologies for Deployable Entry Vehicles. This paper builds upon the Pterodactyl architecture that employed eight individually articulating flaps with two options for guidance, bank angle modulation with the Fully Numerical Predictor Corrector Entry Guidance (FNPEG) technique and angle of attack and sideslip modulation with FNPEG uncoupled range control. These, with a linear quadratic regulator controller, have previously been presented in separate 3-DOF trajectory simulations, one for translational motion and another for rotational dynamics. This work will show results for integrated 6-DOF simulations, leveraging recent advancements in trajectory simulation tools, namely the Julia-based Genesis package. Results show similar performance for both schemes when compared to the previously presented 3-DOF results once the controllers were adequately tuned. Angle of attack and sideslip modulation necessitated a controller designed at high dynamic pressure conditions, and bank angle modulation necessitated a controller designed at lower dynamic pressure conditions. Notably, angle of attack and sideslip modulation could not achieve the parachute deploy point target Mach number of 2, when only tuning the controller gains, thus future work will be needed to tune guidance specific parameters to achieve acceptable range targeting and guidance command tracking.

DEV↗
NASA NTRSDate not supplied

Pterodactyl: 6-DOF Integration of Guidance and Control Algorithms in Genesis

The NASA-funded Pterodactyl project seeks to advance the current state-of-the-art for entry vehicles by developing novel guidance and control technologies for Deployable Entry Vehicles. This paper builds upon the Pterodactyl architecture that employed eight individually articulating flaps with two options for guidance, bank angle modulation with the Fully Numerical Predictor Corrector Entry Guidance (FNPEG) technique and angle of attack and sideslip modulation with FNPEG uncoupled range control. These, with a linear quadratic regulator controller, have previously been presented in separate 3-DOF trajectory simulations, one for translational motion and another for rotational dynamics. This work will show results for integrated 6-DOF simulations, leveraging recent advancements in trajectory simulation tools, namely the Julia-based Genesis flight simulation tool. Results show similar performance for both schemes when compared to the previously presented 3-DOF results once the controllers were adequately tuned. Angle of attack and sideslip angle modulation necessitated a controller designed at high dynamic pressure conditions, and bank angle modulation necessitated a controller designed at lower dynamic pressure conditions. Notably, the angle of attack and sideslip modulation approach could not achieve the parachute deploy point target Mach number of 2, when only tuning the controller gains, so future work will be needed to improve guidance and control tracking.

EDL↗
NASA NTRS2018

Optimization of Second Fault Detection Thresholds to Maximize Mission Probability of Success

In order to support manned spaceflight safety requirements, the Space Launch System (SLS) has defined program-level requirements for key systems to ensure successful operation under single fault conditions. The SLS program has also levied requirements relating to the capability of the Inertial Navigation System to detect a second fault. This detection functionality is required in order to feed abort analysis and ensure crew safety. Increases in navigation state error due to sensor faults in a purely inertial system can drive the vehicle outside of its operational as-designed environmental and performance envelope. As this performance outside of first fault detections is defined and controlled at the vehicle level, it allows for the use of system level margins to increase probability of mission success on the operational edges of the design. A top-down approach is utilized to assess vehicle sensitivity to second sensor faults. A wide range of failure scenarios in terms of both fault magnitude and time is used for assessment. The approach also utilizes a schedule to change fault detection thresholds autonomously. These individual values are optimized along a nominal trajectory in order to maximize probability of mission success in terms of system-level insertion requirements while minimizing the probability of false positives. This paper will describe an approach integrating Genetic Algorithms and Monte Carlo analysis to tune the threshold parameters to maximize vehicle resilience to second fault events over an ascent mission profile. The analysis approach and performance assessment and verification will be presented to demonstrate the applicability of this approach to second fault detection optimization to maximize mission probability of success through taking advantage of existing margin.

Anzalone, Evan J.↗