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At least 1,351 records · Page 75

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Optimizing Performance on Trinity Utilizing Machine Learning, Proxy Applications and Scheduling Priorities

The sheer number of nodes continues to increase in today’s supercomputers, the first half of Trinity alone contains more than 9400 compute nodes. Since the speed of today’s clusters are limited by the slowest nodes, it more important than ever to identify slow nodes, improve their performance if it can be done, and assure minimal usage of slower nodes during performance critical runs. This is an ongoing maintenance task that occurs on a regular basis and, therefore, it is important to minimize the impact upon its users by assessing and addressing slow performing nodes and mitigating their consequences while minimizing down time. These issues can be solved, in large part, through a systematic application of fast running hardware assessment tests, the application of Machine Learning, and making use of performance data to increase efficiency of large clusters. Proxy applications utilizing both MPI and OpenMP were developed to produce data as a substitute for long runtime applications to evaluate node performance. Machine learning is applied to identify underperforming nodes, and policies are being discussed to both minimize the impact of underperforming nodes and increase the efficiency of the system. In this paper, I will describe the process used to produce quickly performing proxy tests, consider various methods to isolate the outliers, and produce ordered lists for use in scheduling to accomplish this task.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Influence of alveolar mixing and multiple breaths of aerosol intake on particle deposition in the human lungs

Predictive dosimetry models play an important role in assessing health effect of inhaled particulate matter and in optimizing delivery of inhaled pharmaceutical aerosols. Here, in this study, the commonly used 1D Multiple-Path Particle Dosimetry model (MPPD) was improved by including a mechanistically based model component for alveolar mixing of particles and by extending the model capabilities to account for multiple breaths of aerosol intake. These modifications increased the retained fraction of particles and consequently particle deposition predictions in the deep lung during tidal breathing. Comparison with an existing dataset (J. Aerosol Sci., 99:27-39, 2016) obtained under two breathing conditions referred to as slow and fast breathing showed significant differences in 1 µm particle deposition between predictions (slow: 30±1%, fast: 21±1%) and measurements (slow: 43±9%, fast: 30±5%) when the prior version of MPPD (single breath and no mixing, J. Aerosol Sci., 151:105647, 2021) was used. Adding a mixing model and multiple breaths moved the predictions (slow: 34±2%, fast:25±2%) closer to the range of deposition measurements. For 2.9 µm particles, predictions from both the original (slow: 70±2%, fast: 57±2%) and the revised MPPD model (slow: 71±2%, fast: 59±3%) compared well with experiments (slow: 67±8%, fast: 58±10%). This was expected as suspended fraction of 2.9 µm particles was small and thus the addition of alveolar mixing and multi breath capability only slightly increased the retained fraction for particles of this size and greater. The revised 1D model improves dose predictions in the deep lung and support human risk assessment from exposure to airborne particles.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Quantitative and mechanistic insights into proton dynamics for fast energy storage

Proton conduction in hydrogen-bond-rich protic electrolytes enables fast mass and charge transport, crucial for electrochemical energy storage and power conversion. Such transport can give proton-based batteries exceptional rate capability and low-temperature operation beyond other working ions. Here we show that in phosphoric acid (H 3 PO 4 ) electrolytes, vehicular and structural proton transport coexist, and their contributions to conductivity can be quantitatively distinguished. We link structural diffusion directly to hydrogen-bond strength, enabling the precise tuning of proton migration. Guided by this, we reveal a double conductivity peak from regulated structural diffusion. The optimal electrolyte (5.8-M H 3 PO 4 ) achieves high overall (232.9 mS cm −1 ) and structural (164.9 mS cm−1) conductivity. A MoO 3 ‖CuFe-TBA battery with this electrolyte outperforms a deep-eutectic benchmark (8.3-M H 3 PO 4 ), delivering >17,474 W kg −1 at room temperature and retaining 15.1 Wh kg −1 at −75 °C. These findings provide a framework for designing advanced protic electrolytes across electrochemical systems.

Li, Ziyue [Fudan University, Shanghai (China, Peop↗

NASA NTRS1993

Fast parallel algorithms that compute transitive closure of a fuzzy relation

The notion of a transitive closure of a fuzzy relation is very useful for clustering in pattern recognition, for fuzzy databases, etc. The original algorithm proposed by L. Zadeh (1971) requires the computation time O(n(sup 4)), where n is the number of elements in the relation. In 1974, J. C. Dunn proposed a O(n(sup 2)) algorithm. Since we must compute n(n-1)/2 different values s(a, b) (a not equal to b) that represent the fuzzy relation, and we need at least one computational step to compute each of these values, we cannot compute all of them in less than O(n(sup 2)) steps. So, Dunn's algorithm is in this sense optimal. For small n, it is ok. However, for big n (e.g., for big databases), it is still a lot, so it would be desirable to decrease the computation time (this problem was formulated by J. Bezdek). Since this decrease cannot be done on a sequential computer, the only way to do it is to use a computer with several processors working in parallel. We show that on a parallel computer, transitive closure can be computed in time O((log(sub 2)(n))2).

Kreinovich, Vladik YA.↗

NASA NTRS1993

Uncertainty reasoning in expert systems

Intelligent control is a very successful way to transform the expert's knowledge of the type 'if the velocity is big and the distance from the object is small, hit the brakes and decelerate as fast as possible' into an actual control. To apply this transformation, one must choose appropriate methods for reasoning with uncertainty, i.e., one must: (1) choose the representation for words like 'small', 'big'; (2) choose operations corresponding to 'and' and 'or'; (3) choose a method that transforms the resulting uncertain control recommendations into a precise control strategy. The wrong choice can drastically affect the quality of the resulting control, so the problem of choosing the right procedure is very important. From a mathematical viewpoint these choice problems correspond to non-linear optimization and are therefore extremely difficult. In this project, a new mathematical formalism (based on group theory) is developed that allows us to solve the problem of optimal choice and thus: (1) explain why the existing choices are really the best (in some situations); (2) explain a rather mysterious fact that fuzzy control (i.e., control based on the experts' knowledge) is often better than the control by these same experts; and (3) give choice recommendations for the cases when traditional choices do not work.

Kreinovich, Vladik↗

NASA NTRS1998

Langley Aerothermodynamic Facilities Complex: Enhancements and Testing Capabilities

Description, capabilities, recent upgrades, and utilization of the NASA Langley Research Center (LaRC) Aerothermodynamic Facilities Complex (AFC) are presented. The AFC consists of five hypersonic, blow-down-to-vacuum wind tunnels that collectively provide a range of Mach number from 6 to 20, unit Reynolds number from 0.04 to 22 million per foot and, most importantly for blunt configurations, normal shock density ratio from 4 to 12. These wide ranges of hypersonic simulation parameters are due, in part, to the use of three different test gases (air, helium, and tetrafluoromethane), thereby making several of the facilities unique. The Complex represents nearly three-fourths of the conventional (as opposed to impulse)-type hypersonic wind tunnels operational in this country. AFC facilities are used to assess and optimize the hypersonic aerodynamic performance and aeroheating characteristics of aerospace vehicle concepts and to provide benchmark aerodynamic/aeroheating data fr generating the flight aerodynamic databook and final design of the thermal protection system (TPS) (e.g., establishment of flight limitations not to exceed TPS design limits). Modifications and enhancements of AFC hardware components and instrumentation have been pursued to increase capability, reliability, and productivity in support of programmatic goals. Examples illustrating facility utilization in recent years to generate essentially all of the experimental hypersonic aerodynamic and aeroheating information for high-priority, fast-paced Agency programs are presented. These programs include Phase I of the Reusable Launch Vehicle (RLV) Advanced Technology Demonstrator, X-33 program, PHase II of the X-33 program, X-34 program, the Hyper-X program ( a Mach 5,7, and 10 airbreathing propulsion flight experiment), and the X-38 program (Experimental Crew Return Vehicle, X-CRV). Current upgrades/enchancements and future plans for the AFC are discussed.

Micol, J. R.↗

NASA NTRS2005

Performance Dependences of Multiplication Layer Thickness for InP/InGaAs Avalanche Photodiodes Based on Time Domain Modeling

InP/InGaAs avalanche photodiodes (APDs) are being widely utilized in optical receivers for modern long haul and high bit-rate optical fiber communication systems. The separate absorption, grading, charge, and multiplication (SAGCM) structure is an important design consideration for APDs with high performance characteristics. Time domain modeling techniques have been previously developed to provide better understanding and optimize design issues by saving time and cost for the APD research and development. In this work, performance dependences on multiplication layer thickness have been investigated by time domain modeling. These performance characteristics include breakdown field and breakdown voltage, multiplication gain, excess noise factor, frequency response and bandwidth etc. The simulations are performed versus various multiplication layer thicknesses with certain fixed values for the areal charge sheet density whereas the values for the other structure and material parameters are kept unchanged. The frequency response is obtained from the impulse response by fast Fourier transformation. The modeling results are presented and discussed, and design considerations, especially for high speed operation at 10 Gbit/s, are further analyzed.

Xiao, Yegao↗

NASA NTRS2014

Annualized TASAR Benefits for Virgin America Operations

The Traffic Aware Strategic Aircrew Request (TASAR) concept offers onboard automation for the purpose of advising the pilot of traffic compatible trajectory changes that would be beneficial to the flight. A fast-time simulation study was conducted to assess the benefits of TASAR to Virgin America. The simulation compares historical trajectories without TASAR to trajectories developed with TASAR and evaluated by controllers against their objectives. It was estimated that about 25,000 gallons of fuel and about 2,500 minutes could be saved annually per aircraft. These savings were applied fleet-wide to produce an estimated annual cost savings to Virgin America in excess of $5 million due to fuel, maintenance, and depreciation cost savings. Switching to a more wind-optimal trajectory was found to be the use case that generated the highest benefits out of the three TASAR use cases analyzed. Virgin America TASAR requests peaked at two to four requests per hour per sector in high-altitude Oakland and Salt Lake City center sectors east of San Francisco.

Source record↗

NASA NTRS2015

Annualized TASAR Benefit Estimate for Virgin America Operations

Henderson, Jeffrey↗

NASA NTRSDate not supplied

SimUAM: A Comprehensive Microsimulation Toolchain to Evaluate the Impact of Urban Air Mobility in Metropolitan Areas

Over the past several years, Urban Air Mobility (UAM) has galvanized enthusiasm from investors and researchers, marrying expertise in aircraft design, transportation, logistics, artificial intelligence, battery chemistry, and broader policymaking. However, two significant questions remain unexplored: (1) What is the value of UAM in a region’s transportation network? and (2) How can UAM be effectively deployed to realize and maximize this value to all stakeholders, including riders and local economies? To adequately understand the value proposition of UAM for metropolitan areas, the authors develop a holistic multi-modal toolchain, SimUAM, to model and simulate UAM and its impacts on travel behavior. This toolchain has several components: (1) Microsimulation Analysis for Network Traffic Assignment (MANTA): A fast, high-fidelity regional-scale traffic microsimulator, (2) VertiSim: Agranular, discrete-event vertiport and pedestrian simulator, (3) Flexible Engine for Fast-time Evaluation of Flight Environments (Fe3): A high-fidelity, trajectory-based aerial microsimulation. SimUAM, rooted in granular, GPU-based microsimulation, models millions of trips and their movements in the street network and in the air, producing interpretable and actionable performance metrics for UAM designs and deployments. Once the ground-air interface is modeled, the authors find that the market for UAM decreases across all network designs relative to models with static assumptions about transfer times. However, significant improvements can be made to balance the demand and optimize the networks for transfer time, likely increasing the number of benefited trips. The modularity, extensibility, and speed of the platform will allow for rapid scenario planning and sensitivity analysis, effectively acting as a detailed performance assessment tool.

urban air mobility↗

DOE OSTI2024

Droning on to Delivery: Examining the Energy Impacts of Using Drones for Moving Goods

The demand for fast, localized delivery has grown significantly in recent years. Whether for cheesy snacks, prepared food, medical solutions, or business deliveries, fast and efficient delivery is increasingly a demand and differentiator. Drone delivery in the freight sector offers to revolutionize last-mile logistics and improve services. This research analyzes the impacts of drone energy for delivery operations, aiming to compare various types of drones (large and small, rotary and VTOL) and various types of business methods. The project executes novel open-air and laboratory-based testing to look at the impacts of weights, operations, temperatures and weather conditions. The study combines this real-world experimental data with fleet optimization mathematical models to assess energy consumption of different scenarios as well as examine the minimum fleet size and additional battery requirements. These fleet optimization models compare energy between types of deployments with existing delivery methods. The model was also extended to look at mixed fleets of aerial drones and ground vehicles to accommodate different restrictions on drones or when weather prohibits their use. The analysis provided insights into factors such as drone design, payload weight, flight distance, weather conditions and operational parameters and impacts of each. It showed how to combine different types of vehicles to reduce energy and improve services. And it showed that drone speed, routing restrictions, and unfavorable weather significantly influence energy consumption. Our open experiment data and optimization models can assist stakeholders and industry in understanding drone package delivery and offers keys to improving deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1987

Differential sampling for fast frequency acquisition via adaptive extended least squares algorithm

This paper presents a differential signal model along with appropriate sampling techinques for least squares estimation of the frequency and frequency derivatives and possibly the phase and amplitude of a sinusoid received in the presence of noise. The proposed algorithm is recursive in mesurements and thus the computational requirement increases only linearly with the number of measurements. The dimension of the state vector in the proposed algorithm does not depend upon the number of measurements and is quite small, typically around four. This is an advantage when compared to previous algorithms wherein the dimension of the state vector increases monotonically with the product of the frequency uncertainty and the observation period. Such a computational simplification may possibly result in some loss of optimality. However, by applying the sampling techniques of the paper such a possible loss in optimality can made small.

Kumar, Rajendra↗

DOE OSTI2022

Twisted A-D-A Type Acceptors with Thermally-Activated Delayed Crystallization Behavior for Efficient Nonfullerene Organic Solar Cells

We report that molecular aggregation and crystallization during film coating play a crucial role in the realization of high-performing organic photovoltaics. Strong intermolecular interactions and high solid-state crystallinity are beneficial for charge transport. However, fast crystallization during thin-film drying often limits the formation of the finely phase-separated morphology required for efficient charge generation. Herein, the authors show that twisted acceptor-donor-acceptor (A-D-A) type compounds, containing an indacenodithiophene (IDT) electron-rich core and two naphthalenediimide (NDI) electron-poor units, leads to formation of mostly amorphous phases in the as-cast film, which can be readily converted into more crystalline domains by means of thermal annealing. This design strategy solves the aforementioned conundrum, leading to an optimal morphology in terms of reduced donor/acceptor domain-separation sizes (ca. 13 nm) and increased packing order. Solar cells based on these acceptors with a PBDB-T polymer donor show a power conversion efficiency over 10% and stable morphology, which results from the combined properties of desirable excited-state dynamics, high charge mobility, and optimal aggregation/crystallization characteristics. These results demonstrate that the twisted A-D-A motif featuring thermally-induced crystallization behavior is indeed a promising alternative design approach toward more morphologically robust materials for efficient organic photovoltaics.

14 SOLAR ENERGY↗

DOE OSTI2022

Atikokan Digital Twin: Machine learning in a biomass energy system

The Atikokan Generating Station, operated by Ontario Power Generation, has a 200 MW, biomass-fired tower boiler that operates on a dispatch schedule with a five-minute cycle. The boiler is generally operated in the range of 40–100 MW using two of five burner levels. In order to optimize boiler performance, we propose the implementation of a unique digital twin. Our digital twin abstraction couples Bayesian inference from science-based models and from observations (machine learning) with decision theory to predict operating-variable set points that optimize the physical asset (the boiler) in the presence of uncertainty (artificial intelligence). We focus this paper on the continuous Bayesian machine learning part of the Atikokan Digital Twin; we discuss decision theory in a companion paper. We identify and learn about 12 operational, model, and measured-output parameters and their uncertainties from high-fidelity, science-based simulations of the Atikokan boiler and from the observed measurements at the power plant. Since the goal of the Atikokan Digital Twin is to implement it online in real time, we require fast function evaluations for the quantities of interest extracted from the simulations in the Bayesian analysis. We use Gaussian process regression/interpolation to create accurate, robust surrogate models. We define the Bayesian priors and likelihood function and solve for the posterior distributions of the 12 parameters. Here we then propagate these distributions (i.e., parameters with uncertainty) into the predicted distributions of 790 quantities of interest to learn about the relative importance of various sources of error including experimental, model, and operating-parameter errors.

09 BIOMASS FUELS↗

NASA NTRS1996

Construction of a Piezoresistive Neural Sensor Array

The construction of a piezoresistive - piezoelectric sensor (or actuator) array is proposed using 'neural' connectivity for signal recognition and possible actuation functions. A closer integration of the sensor and decision functions is necessary in order to achieve intrinsic identification within the sensor. A neural sensor is the next logical step in development of truly 'intelligent' arrays. This proposal will integrate 1-3 polymer piezoresistors and MLC electroceramic devices for applications involving acoustic identification. The 'intelligent' piezoresistor -piezoelectric system incorporates printed resistors, composite resistors, and a feedback for the resetting of resistances. A model of a design is proposed in order to simulate electromechanical resistor interactions. The goal of optimizing a sensor geometry for improving device reliability, training, & signal identification capabilities is the goal of this work. At present, studies predict performance of a 'smart' device with a significant control of 'effective' compliance over a narrow pressure range due to a piezoresistor percolation threshold. An interesting possibility may be to use an array of control elements to shift the threshold function in order to change the level of resistance in a neural sensor array for identification, or, actuation applications. The proposed design employs elements of: (1) conductor loaded polymers for a 'fast' RC time constant response; and (2) multilayer ceramics for actuation or sensing and shifting of resistance in the polymer. Other material possibilities also exist using magnetoresistive layered systems for shifting the resistance. It is proposed to use a neural net configuration to test and to help study the possible changes required in the materials design of these devices. Numerical design models utilize electromechanical elements, in conjunction with structural elements in order to simulate piezoresistively controlled actuators and changes in resistance of sensors. The construction of these devices may show significant improvement in ability to interrogate signals and in the control of effective compliance. This work focuses on the development a variety of series/parallel interconnected piezoresistive control elements for the neural sensing function.

Carlson, W. B.↗

NASA NTRS2011

Hybrid CFD/CAA Modeling for Liftoff Acoustic Predictions

This paper presents development efforts at the NASA Marshall Space flight Center to establish a hybrid Computational Fluid Dynamics and Computational Aero-Acoustics (CFD/CAA) simulation system for launch vehicle liftoff acoustics environment analysis. Acoustic prediction engineering tools based on empirical jet acoustic strength and directivity models or scaled historical measurements are of limited value in efforts to proactively design and optimize launch vehicles and launch facility configurations for liftoff acoustics. CFD based modeling approaches are now able to capture the important details of vehicle specific plume flow environment, identifY the noise generation sources, and allow assessment of the influence of launch pad geometric details and sound mitigation measures such as water injection. However, CFD methodologies are numerically too dissipative to accurately capture the propagation of the acoustic waves in the large CFD models. The hybrid CFD/CAA approach combines the high-fidelity CFD analysis capable of identifYing the acoustic sources with a fast and efficient Boundary Element Method (BEM) that accurately propagates the acoustic field from the source locations. The BEM approach was chosen for its ability to properly account for reflections and scattering of acoustic waves from launch pad structures. The paper will present an overview of the technology components of the CFD/CAA framework and discuss plans for demonstration and validation against test data.

Strutzenberg, Louise L.↗

DOE OSTI2021

Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols

Advancing lithium-ion battery technology requires the optimization of cycling protocols. A new data-driven methodology is demonstrated for rapid, accurate prediction of the cycle life obtained by new cycling protocols using a single test lasting only 3 cycles, enabling rapid exploration of cycling protocol design spaces with orders of magnitude reduction in testing time. We achieve this by combining lifetime early prediction with a hierarchical Bayesian model (HBM) to rapidly predict performance distributions without the need for extensive repetitive testing. The methodology is applied to a comprehensive dataset of lithium-iron-phosphate/graphite comprising 29 different fast-charging protocols. HBM alone provides high protocol-lifetime prediction performance, with 6.5% of overall test average percent error, after cycling only one battery to failure. Here, by combining HBM with a battery lifetime prediction model, we achieve a test error of 8.8% using a single 3-cycle test. In addition, the generalizability of the HBM approach is demonstrated for lithium-manganese-cobalt-oxide/graphite cells.

25 ENERGY STORAGE↗

DOE OSTI2024

Maximizing Marine Carbon Removal by Coupling Electrochemical and Biological Methods

Integrated development of emerging marine decarbonization strategies offers the possibility of lowering CO2 removal costs and enabling their widespread deployment. In this study we examine the feasibility and benefits of coupling electrochemical and biological marine carbon removal strategies. Bipolar membrane electrodialysis (BPMED) is used to generate acid and alkalinity from seawater and electricity, and the alkalinity is returned to the ocean for indirect CO2 removal from the atmosphere, but the acid stream is a waste product. Considering the large-scale of CO2 removal necessary, the acid storage, neutralization, and disposal have prohibitive costs and carbon footprint. Here we investigate the feasibility to valorize the acid stream to enhance the growth and CO2 uptake through photosynthesis in the fast-growing marine phytoplankter Picochlorum celeri. When added to active algae cultures, the BPMED-generated acidified seawater alters the carbonate-bicarbonate equilibrium thereby increasing the bioavailability of CO2 and the observed growth rates. Additions of up to 2 mM H+ from BPMED effluent streams increased algal productivity up to 3-fold. A high-level analysis conducted based on experimental data to estimate the potential of sequestered CO2 emissions when compared to conventional commercial means of acid utilization or disposal, is estimated to be ~30 kgCO2 / kgacid. Through further development and optimization in terms of choice of algal species, growth conditions, acid addition rates, etc. the combined electrochemical-biological approach has the potential to achieve higher net CO2 removal.

carbon dioxide, marine, marine algae↗