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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Energy Transduction in Nitrogenase

Nitrogenase is a complicated two-component enzyme system that uses ATP binding and hydrolysis energy to achieve one of the most difficult chemical reactions in nature, the reduction of N2 to NH3. One component of the Mo-based nitrogenase system, Fe protein, delivers electrons one at a time to the second component, the catalytic MoFe protein. This process occurs through a series of synchronized events collectively called the “Fe protein cycle”. Elucidating details of the events associated with this cycle has constituted an important challenge in understanding the nitrogenase mechanism. Electron delivery is a multistep process involving three metal clusters with intra- and interprotein events. It is proposed that the first electron transfer event is a gated intraprotein transfer of one electron from the MoFe protein P-cluster to the FeMo cofactor. Measurement of the effect of osmotic pressure on the rate of this electron transfer process revealed that it is gated by protein conformational changes. This first electron transfer is activated by binding of the Fe protein containing two bound ATP molecules. The mechanism of how this protein-protein association triggers electron transfer remains unknown. The second electron transfer event is proposed to be a rapid interprotein “backfill” with transfer of one electron from the reduced Fe protein 4Fe-4S cluster to the oxidized P-cluster. In this way, electron delivery can be viewed as a case of “deficit spending”. Such a deficit-spending electron transfer process can be envisioned as a way to achieve one-direction electron flow, limiting the potential for back electron flow. Hydrolysis of two ATP molecules associated with the Fe protein occurs after the electron transfer and therefore is not used to directly drive the electron transfer. Rather, ATP hydrolysis is proposed to contribute to relaxation of the “activated” conformational state associated with the ATP form of the complex, with the free energy from ATP hydrolysis being used to pay back energy associated with component protein association and electron transfer. Release of inorganic phosphate (Pi) and protein-protein dissociation follow electron transfer and ATP hydrolysis. The ratelimiting step for the Fe protein cycle is not dissociation of the two proteins, as previously believed, but rather is release of Pi after ATP hydrolysis, which is then followed by rapid protein-protein complex dissociation. Nitrogenase is composed of two catalytic halves that do not function independently but rather exhibit anticooperative nuclear motion in which electron transfer in one-half of the complex partially inhibits electron transfer and ATP hydrolysis in the other half. Calculations indicated the existence of anticooperative interactions across the entire nitrogenase complex, suggesting a mechanism for the control of events on opposite ends of this large complex. The mechanistic necessity for this anticooperative process remains unknown. This Account presents a working model for how all of these processes work together in the nitrogenase “machine” to transduce the energy from ATP binding and hydrolysis to drive N2 reduction.

Seefeldt, Lance C.↗
DOE OSTI2020

Gas Production from Hot Water Circulation through Hydraulic Fractures in Methane Hydrate-Bearing Sediments: THC-Coupled Simulation of Production Mechanisms

Methane hydrates, widely found in permafrost and deep marine sediments, have great potential as a future energy 6 source. Conventional production schemes perform poorly for challenging hydrate reservoirs with low permeability. We propose an 7 efficient production scheme by combining hydraulic fracturing from horizontal wells and hot water circulation through fractures. A 8 fully coupled thermo-hydro-chemical (THC) model is developed to simulate the key physical processes during gas production from 9 a hydrate reservoir representative of typical geological settings in Shenhu, South China Sea. We found that the gas production 10 process has two distinct stages divided by thermal breakthrough: a relatively short prebreakthrough stage and a postbreakthrough 11 stage yielding stable gas production. Heat advection along and near the hydraulic fracture dominates the prebreakthrough stage, 12 whereas conduction-driven thermal recovery in the volume around fractures dominates the postbreakthrough stage. We identified 13 that the steady-state injection temperature has a strong effect on the performance of the proposed scheme while the fluid mass 14 circulation rate has a moderate impact beyond a threshold. The proposed scheme proves to be efficient and robust over a range of 15 reservoir conditions with respect to initial hydrate saturation and intrinsic permeability, including their spatial heterogeneities, 16 thereby offering a promising solution for challenging reservoir conditions.

gas hydrate, fracture flow, gas production, numeri↗
DOE OSTI2021

Multiobjective Modular Biorefinery Configuration under Uncertainty

With increasing interest in using biomass as a raw material for fuel, the development of biorefineries is an active area of research. However, increasing market competition, uncertainty, and environmental concerns are a few of the challenges that need to be addressed. In this work, mathematical optimization formulations are proposed to address these challenges simultaneously. A multiobjective deterministic optimization framework is proposed to address economic and environmental objectives. A two-stage stochastic optimization framework is proposed to account for uncertainties in material flow and product yields. To model the environmental objective, an environmental risk metric is proposed. Finally, as modularization is gaining popularity in the process industry due to its ability to save capital cost because of standardization and reduce time to market, a formulation for simultaneously achieving modular process design and biorefinery configuration is proposed. Furthermore, the results demonstrate the efficacy of the proposed approach.

09 BIOMASS FUELS↗
DOE OSTI2017

Model Order Reduction Algorithm for Estimating the Absorption Spectrum

The ab initio description of the spectral interior of the absorption spectrum poses both a theoretical and computational challenge for modern electronic structure theory. Due to the often spectrally dense character of this domain in the quantum propagator’s eigenspectrum for medium-to-large sized systems, traditional approaches based on the partial diagonalization of the propagator often encounter oscillatory and stagnating convergence. Electronic structure methods which solve the molecular response problem through the solution of spectrally shifted linear systems, such as the complex polarization propagator, offer an alternative approach which is agnostic to the underlying spectral density or domain location. This generality comes at a seemingly high computational cost associated with solving a large linear system for each spectral shift in some discretization of the spectral domain of interest. In this work, we present a novel, adaptive solution to this high computational overhead based on model order reduction techniques via interpolation. Model order reduction reduces the computational complexity of mathematical models and is ubiquitous in the simulation of dynamical systems and control theory. The efficiency and effectiveness of the proposed algorithm in the ab initio prediction of X-ray absorption spectra is demonstrated using a test set of challenging water clusters which are spectrally dense in the neighborhood of the oxygen K-edge. On the basis of a single, user defined tolerance we automatically determine the order of the reduced models and approximate the absorption spectrum up to the given tolerance. We also illustrate that, for the systems studied, the automatically determined model order increases logarithmically with the problem dimension, compared to a linear increase of the number of eigenvalues within the energy window. Furthermore, we observed that the computational cost of the proposed algorithm only scales quadratically with respect to the problem dimension.

IDREAM, K-Edge, theoretical, computational, electr↗
DOE OSTI2018

Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. We report our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Novel Relative Permeability Model for Gas and Water Flow in Hydrate-Bearing Sediments With Laboratory and Field-Scale Application

In a producing gas hydrate reservoir the effective porosity available for fluid flow constantly changes with dissociation of gas hydrate. Therefore, accurate prediction of relative permeability using legacy models (e.g. Brooks-Corey (B-C), van Genuchten, etc.) that were developed for conventional oil and gas reservoirs would require empirical parameters to be calibrated at various S h over its range of variation, but such calibrations are precluded because of lack of experimental relative permeability data. This study proposes a new relative permeability model for gas hydrate-bearing media that is a function of maximum capillary pressure, capillary entry pressure, pore size distribution index, residual saturations, hydrate saturation, and four other constants. The three novel features of the proposed model are: (i) requires fitting its six empirical parameters only once using experimental data from any single S h , and the same set of empirical parameters predict relative permeability at all S h , (ii) includes the effect of capillarity, and (iii) includes the effect of pore-size distribution. From practical standpoint, the model can be used to simulate multiphase flow in gas hydrate-bearing sediments where the proposed relative permeability can account for the evolving hydrate saturation. The proposed model is implemented in a numerical simulator and the wall time required to perform simulations using the proposed model is shown to be similar to the time it takes to run same simulations with the B-C model. The proposed model is a step forward towards achieving the goal of physically accurate modeling of multiphase flow for gas hydrate-bearing sediments that accounts for the effect of gas hydrate saturation change on relative permeability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Hybrid approach for estimating dynamic states of synchronous generators

Accurate and robust estimation of dynamic states is essential for monitoring and controlling a power grid. To achieve higher accuracy and robustness, this paper proposes a hybrid estimation approach to estimate the dynamic states of synchronous generators using multiple models, classified into a set of local models and a wide-area model. The proposed estimation approach, first, estimates dynamic states using the extended Kalman filter (EKF) based on a local model and a wide-area model. Then, at each time step, probability indexes, which quantify the likelihood of both local and wide-area models, are determined using hypothesis testing based on measurements innovations. Finally, using the weighted fix approach, the states estimated from the local and wide-area models are combined based on their probability indexes. Simulation studies using the two-area four-machine system shows that the proposed approach can improve estimation accuracy and increase robustness against model errors.

Akhlaghi, Shahrokh↗
DOE OSTI2020

Transient pulse‐based impedance characterisation of DC‐link capacitor for EV systems

In this study, a method for impedance characterisation of DC‐link capacitors based on transient pulse analysis is proposed. The fundamentals of the concept are presented and design considerations are discussed. The functionality of the proposed approach is supported by SPICE simulation with two different commercial capacitors from different manufacturers and validated with experimental results. Experimental results for equivalent series resistance, equivalent series inductance (ESL) and capacitance estimation are presented for commercial DC‐link capacitors and compared with component analyser results. The simulation and experimental results show that the proposed method is a promising candidate for capacitance and ESL estimation of capacitors in EV traction systems.

Gurpinar, Emre↗
DOE OSTI2017

Approach to fitting parameters and clustering for characterising measured voltage dips based on two-dimensional polarisation ellipses

An alternative approach to characterise real voltage dips is proposed and evaluated in this study. The proposed methodology is based on voltage-space vector solutions, identifying parameters for ellipses trajectories by using the least-squares algorithm applied on a sliding window along the disturbance. The most likely patterns are then estimated through a clustering process based on the k-means algorithm. The objective is to offer an efficient and easily implemented alternative to characterise faults and visualise the most likely instantaneous phase-voltage evolution during events through their corresponding voltage-space vector trajectories. This novel solution minimises the data to be stored but maintains extensive information about the dips including starting and ending transients. The proposed methodology has been applied satisfactorily to real voltage dips obtained from intensive field-measurement campaigns carried out in a Spanish wind power plant up to a time period of several years. A comparison to traditional minimum root mean square-voltage and time-duration classifications is also included in this study.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Burning plasmas with ultrashort soft-x-ray flashing

Fast ignition with narrow-band coherent x-ray pulses has been revisited for cryogenic deuterium-tritium (DT) plasma conditions achieved on the OMEGA Laser System. In contrast to using hard-x-rays (hv = 3–6 keV) proposed in the original x-ray fast-ignition proposal, we find that soft-x-ray sources with hv ≈ 500 eV photons can be suitable for igniting the dense DT-plasmas achieved on OMEGA. Two-dimensional radiation–hydrodynamics simulations have identified the break-even conditions for realizing such a “hybrid” ignition scheme (direct-drive compression with soft-x-ray heating) with 50-μm-offset targets: ∼10 ps soft-x-ray pulse (hv ≈ 500 eV) with a total energy of 500–1000 J to be focused into a 10 μm spot-size. A variety of x-ray pulse parameters have also been investigated for optimization. It is noted that an order of magnitude increase in neutron yield has been predicted even with x-ray energy as low as ∼50 J. Scaling this idea to a 1 MJ large-scale target, a gain above ∼30 can be reached with the same soft-x-ray pulse at 1.65 kJ energy. Even though such energetic x-ray sources do not currently exist, we hope that the proposed ignition scheme may stimulate efforts on generating powerful soft-x-ray sources in the near future.

Hu, S. X.↗
DOE OSTI2014

Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Parametric study of natural circulation flow in molten salt fuel in molten salt reactor

The Molten Salt Reactor (MSR) is one of the most promising system proposed by Generation IV Forum (GIF) for future nuclear reactor systems. Advantages of the MSR are significantly larger compared to other reactor system, and is mainly achieved from its liquid nature of fuel and coolant. Further improvement to this system, which is a natural circulating molten fuel salt inside its tube in the reactor core is proposed, to achieve advantages of reducing and simplifying the MSR design proposed by GIF. Thermal hydraulic analysis on the proposed system was completed using a commercial computation fluid dynamics (CFD) software called FLUENT by ANSYS Inc. An understanding on theory behind this unique natural circulation flow inside the tube caused by fission heat generated in molten fuel salt and tube cooling was briefly introduced. Currently, no commercial CFD software could perfectly simulate natural circulation flow, hence, modeling this flow problem in FLUENT is introduced and analyzed to obtain best simulation results. Results obtained demonstrate the existence of periodical transient nature of flow problem, hence improvements in tube design is proposed based on the analysis on temperature and velocity profile. Results show that the proposed system could operate at up to 750MW core power, given that turbulence are enhanced throughout flow region, and precise molten fuel salt physical properties could be defined. At the request of the authors and the Proceedings Editor the name of the co-author Andrea Cioncolini was corrected from Andrea Coincolini. The same name correction was made in the Acknowledgement section on page 030004-10 and in reference number 4. The updated article was published on 11 May 2015.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

On the feasibility of future colliders: report of the Snowmass'21 Implementation Task Force

Colliders are essential research tools for particle physics. Numerous future collider proposal were discussed in the course of the US high energy physics community strategic planning exercise Snowmass'21. The Implementation Task Force (ITF) has been established to evaluate the proposed future accelerator projects for performance, technology readiness, schedule, cost, and environmental impact. Corresponding metrics has been developed for uniform comparison of the proposals ranging from Higgs/EW factories to multi-TeV lepton, hadron and ep collider facilities, based on traditional and advanced acceleration technologies. Here, this article describes the metrics and approaches, and presents evaluations of future colliders performed by the ITF.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

An inexact regularized Newton framework with a worst-case iteration complexity of $ {\mathscr O}(\varepsilon^{-3/2}) $ for nonconvex optimization

Abstract An algorithm for solving smooth nonconvex optimization problems is proposed that, in the worst-case, takes ${\mathscr O}(\varepsilon ^{-3/2})$ iterations to drive the norm of the gradient of the objective function below a prescribed positive real number $\varepsilon $ and can take ${\mathscr O}(\varepsilon ^{-3})$ iterations to drive the leftmost eigenvalue of the Hessian of the objective above $-\varepsilon $. The proposed algorithm is a general framework that covers a wide range of techniques including quadratically and cubically regularized Newton methods, such as the Adaptive Regularization using Cubics (arc) method and the recently proposed Trust-Region Algorithm with Contractions and Expansions (trace). The generality of our method is achieved through the introduction of generic conditions that each trial step is required to satisfy, which in particular allows for inexact regularized Newton steps to be used. These conditions center around a new subproblem that can be approximately solved to obtain trial steps that satisfy the conditions. A new instance of the framework, distinct from arc and trace, is described that may be viewed as a hybrid between quadratically and cubically regularized Newton methods. Numerical results demonstrate that our hybrid algorithm outperforms a cubically regularized Newton method.

Mathematics↗
DOE OSTI2022

A Two-Step Control Approach for Torque Ripple and Vibration Reduction in Switched Reluctance Motor Drives

The objective of this work is to reduce the torque ripple and vibration in Switched Reluctance Motor (SRM) drives. To achieve such a goal, a two-step approach is proposed. In the first step, the reference current is profiled using an optimization method, aimed to reduce the torque pulsation. In the second, the optimum current profile is applied to the SRM through an adaptive hysteresis band controller to actively cancel the radial vibration. The proposed technique uses an optimization procedure based on the Field Reconstruction Method (FRM) and Non-Derivative Optimization Method to find the optimal current profile that mitigates the torque pulsation, however, the investigation has shown that the proposed excitation leads to a higher radial vibration, which has been mitigated by using an adaptive hysteresis band controller. The theoretical basis for the proposed approach is initially presented, followed by system modeling and simulation. In addition, experimental verification has been conducted using an 8/6 SRM to show the feasibility of the proposed approach. The proposed method alleviates the acoustic noise and torque pulsation in SRM and offers an effective step towards the prevalent use of SRM in a wide range of applications.

42 ENGINEERING↗
DOE OSTI2015

Fast and Accurate Support Vector Machines on Large Scale Systems

Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary --- also known as hyperplane --- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute to the definition of the boundary. However, existing parallel algorithms use the entire dataset for finding the boundary, which is sub-optimal for performance reasons. In this paper, we propose a novel distributed memory algorithm to eliminate the samples which do not contribute to the boundary definition in SVM. We propose several heuristics, which range from early (aggressive) to late (conservative) elimination of the samples, such that the overall time for generating the boundary is reduced considerably. In a few cases, a sample may be eliminated (shrunk) pre-emptively --- potentially resulting in an incorrect boundary. We propose a scalable approach to synchronize the necessary data structures such that the proposed algorithm maintains its accuracy. We consider the necessary trade-offs of single/multiple synchronization using in-depth time-space complexity analysis. We implement the proposed algorithm using MPI and compare it with libsvm--- de facto sequential SVM software --- which we enhance with OpenMP for multi-core/many-core parallelism. Our proposed approach shows excellent efficiency using up to 4096 processes on several large datasets such as UCI HIGGS Boson dataset and Offending URL dataset.

Vishnu, Abhinav↗
DOE OSTI2023

Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing

Performance anomalies can manifest as irregular execution times or abnormal execution events for many reasons, including network congestion and resource contention. Detecting such anomalies in real-time by analyzing the details of performance traces at scale is impractical due to the sheer volume of data High-Performance Computing (HPC) applications produce. In this paper, we propose formulating HPC performance anomaly detection as a signal-processing problem where anomalies can be treated as noise. We evaluate our proposed method in comparison with two other commonly used anomaly detection techniques of varying complexity based on their detection accuracy and scalability. Since real-time in-situ anomaly detection at a large scale requires lightweight methods that can handle a large volume of streaming data, we find that our proposed method provides the best trade-off. We then implement the proposed method in Chimbuko, the first online, distributed, and scalable workflow-level performance trace analysis framework. We compare our proposed signal-based anomaly detection algorithm with two other methods using a function of their accuracy, F1 score, and detection overhead. Our experiments demonstrate that our proposed approach achieves a 99% improvement for the benchmark datasets and a 93% improvement with Chimbuko traces.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Assessment of System Frequency Support Effect of PMSG-WTG Using Torque-Limit-Based Inertial Control

To release the 'hidden inertia' of variable-speed wind turbines for temporary frequency support, a method of torque-limit-based inertial control is proposed in this paper. This method aims to improve the frequency support capability considering the maximum torque restriction of a permanent magnet synchronous generator. The advantages of the proposed method are improved frequency nadir (FN) in the event of an under-frequency disturbance; and avoidance of over-deceleration and a second frequency dip during the inertial response. The system frequency response is different, with different slope values in the power-speed plane when the inertial response is performed. The proposed method is evaluated in a modified three-machine, nine-bus system. The simulation results show that there is a trade-off between the recovery time and FN, such that a gradual slope tends to improve the FN and restrict the rate of change of frequency aggressively while causing an extension of the recovery time. These results provide insight into how to properly design such kinds of inertial control strategies for practical applications.

17 WIND ENERGY↗