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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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MiniGhost is a miniapp in the Mantevo project. Using a finite difference method, it mimics the inter-process communication requirements commonly found in applications of this sort. Random values are inserts into the grid, and therefore no "real problem" is solved.
To validate and verify the python-based code (PySL), designed to replicate the programs used by STAYSL for Beam Correction Factor (BCF) and Self-Shielding Factor (SHIELD), a series of tests were performed. To test BCF a python script was written to generate a random flux history file and both versions of the code processed the data. The test verified matching values up to at least one decimal place, approximately 10,000 tests where run and each one passed. Isotopes began to fail the tests once neutron saturation was reached. To verify this the total time of exposure was varied the isotopes that failed were compared to a list of their half-lives. The test process for SHIELD was very similar but, in this case, the code began by producing an input file with varying thickness and device type/environment for the SHIELD input. The failure condition for this test was if any of the data points for an isotope had a difference above 3%. Approximately 40 of these tests were run and there were only 3 isotopes that had reoccurring failures but only 2% of their points were above the 3% difference. A visual comparison was conducted by plotting the results from both programs. Although the test failed, the differences between their values were minuscule, and the self-shielding factor’s shape was preserved when plotted. Next steps for this project will be validating and verifying the python-based SigPhi code and then reproducing and testing the least squares unfolding performed by STAYSL.
A magneto-optical trap of neutral atoms is essentially a dissipative quantum system. The fast thermal atoms continuously dissipate their energy to the environment via spontaneous emissions during the cooling. The atoms are, therefore, strongly coupled with the vacuum reservoir and the laser field. The vacuum fluctuations as well as the field fluctuations are imparted to the atoms as random photon recoils. Consequently, the external and internal dynamics of atoms becomes stochastic. In this paper, we have investigated the stochastic dynamics of the atoms in a magneto-optical trap during the loading process. The time series analysis of the fluorescence signal shows that the dynamics of the atoms evolves, like all dissipative systems, from deterministic to the chaotic regime. The subsequent disappearance and revival of chaos was attributed to chaos synchronization between spatially different atoms in the magneto-optical trap.
This paper examines filter tuning techniques for a sequential orbit determination (OD) covariance analysis. Recently, there has been a renewed interest in sequential OD, primarily due to the successful flight qualification of the Tracking and Data Relay Satellite System (TDRSS) Onboard Navigation System (TONS) using Doppler data extracted onboard the Extreme Ultraviolet Explorer (EUVE) spacecraft. TONS computes highly accurate orbit solutions onboard the spacecraft in realtime using a sequential filter. As the result of the successful TONS-EUVE flight qualification experiment, the Earth Observing System (EOS) AM-1 Project has selected TONS as the prime navigation system. In addition, sequential OD methods can be used successfully for ground OD. Whether data are processed onboard or on the ground, a sequential OD procedure is generally favored over a batch technique when a realtime automated OD system is desired. Recently, OD covariance analyses were performed for the TONS-EUVE and TONS-EOS missions using the sequential processing options of the Orbit Determination Error Analysis System (ODEAS). ODEAS is the primary covariance analysis system used by the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD). The results of these analyses revealed a high sensitivity of the OD solutions to the state process noise filter tuning parameters. The covariance analysis results show that the state estimate error contributions from measurement-related error sources, especially those due to the random noise and satellite-to-satellite ionospheric refraction correction errors, increase rapidly as the state process noise increases. These results prompted an in-depth investigation of the role of the filter tuning parameters in sequential OD covariance analysis. This paper analyzes how the spacecraft state estimate errors due to dynamic and measurement-related error sources are affected by the process noise level used. This information is then used to establish guidelines for determining optimal filter tuning parameters in a given sequential OD scenario for both covariance analysis and actual OD. Comparisons are also made with corresponding definitive OD results available from the TONS-EUVE analysis.
A laser scanner is discussed in which the scanning beam is random-access addressable and perpendicular to the image input plane and the irradiance of the scanned beam is controlled so that a constant average irradiance is maintained after passage through the image plane. The scanner's optical system and design are described, and its performance is evaluated. It is noted that with this scanner, data in the form of large-format transparencies can be processed without the expense, space, maintenance, and precautions attendant to the operation of a high-power laser with large-aperture collimating optics. It is shown that the scanned format as well as the diameter of the scanning beam may be increased by simple design modifications and that higher scan rates can be achieved at the expense of resolution by employing acousto-optic deflectors with different relay optics.
We propose a term structure of forward rates driven by a kernel-correlated Levy random field under the HJM framework. The kernel-correlated Levy random field is composed of a kernel-correlated Gaussian random field and a centered Poisson random measure. We shall give a criterion to preclude arbitrage under the risk-neutral pricing measure. As applications, an interest rate derivative with general payoff functional is priced under this pricing measure.
This letter shows how incoherent dynamics can lead to metrological advantages in quantum sensing. The results rely on the fact that incoherent dynamics lead to an additive contribution to the quantum Fisher information about time. Such an additive contribution can reduce the error of optimal estimation protocols, as implied by the quantum Cramér–Rao bound. I characterize regimes in which the estimation of a time interval or a frequency is enhanced by decoherence, thereby identifying cases in which incoherent dynamics serve as a metrological resource. The decoherence processes that yield enhanced precision of quantum sensors can be engineered by randomized Hamiltonian dynamics. I illustrate the results with protocols that display improved sensing of time intervals or global fields by qubit and photonic sensors. Enhanced precision of time intervals is achieved with Hamiltonians that include randomized global parameters. Enhanced precision in field estimation is obtained by randomized sensing times.
The Effects of elevated free-stream turbulence (FST) on the natural and periodically excited separation bubbles were studied experimentally, due to the relevance of this flow to low-pressure turbine blades at low Reynolds numbers. A bubble was formed at the leading edge of a flat plate and the FST level was altered by placing a grid across the flow at different locations upstream of the plate. The mixing across the separated shear-layer, forming the free boundary of the bubble, increased due to the elevated FST and due to nominally two-dimensional periodic excitation, both flattening and shortening the bubble. Periodic excitation at frequencies that were at least an order of magnitude lower than those associated with the initial shear-layer instability, were very effective at low FST, because the amplitudes of the excitation frequency and its harmonic were amplified over the bubble. High frequency excitation (F+ 3, based on the length of the baseline low FST bubble) had a major effect close to the separation location, while farther downstream the excited fluctuations rapidly decayed in the reattachment region. Low frequency excitation, that generated waves comparable to the length of the unperturbed bubble (F+ 1) were less effective and their magnitude decayed at a slower rate downstream of reattachment. An increase in the level of the FST reduced the net effect of the periodic excitation on the mixing enhancement and subsequent reattachment process, probably due to a destructive interference between the nominally 2D excitation and the random (in space and time) FST, reducing the spanwise coherence and therefore the effectiveness of the current control strategy. However, even at the reduced effectiveness of 2D periodic excitation at elevated FST, it accelerated the reattachment process and the recovery rate of the reattached boundary layer, enhancing the boundary layer resistance to repeat separation and reducing its momentum loss further downstream.
Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.
Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.
A methodology is presented for objective and automated determination of the uncertainty in lidar aerosol measurements. This methodology is based on standard error-propagation procedures, a large data base on atmospheric behavior, and long experience in lidar data processing. Algebraic expressions for probable error are derived as a function of the relevant parameters. The validity of these expressions is then tested by making simulated measurements and analyses in which random errors of appropriate size are injected at proper steps of the measurement and analysis process. An illustrative example is given where the methodology is applied to a new lidar system now being used for airborne measurements of the stratospheric aerosol.
The source of long-period comets was investigated using a Monte Carlo simulation of comet evolution under the influence of a combination of physical and dynamical processes. The perturbation of cometary orbits by major planets and by non-gravitational forces was modeled, as was physical loss of comets due to random disruption (splitting) and planetary collision; a model was also derived for loss of all volatiles. The importance of each of these processes was examined. The primary end states found for long-period comets were: ejection on hyperbolic orbit, 65.2%; random disruption, 27.6%; and formation of silicate crusts, 7.1%. The basic correctness of the Ort hypothesis was confirmed.
State estimation is a fundamental part of monitoring, control, and real-time optimization in continuous pharmaceutical manufacturing. For nonlinear dynamic systems with hard constraints, moving horizon estimation (MHE) can estimate the current state by solving a well-defined optimization problem where process complexities are explicitly considered as constraints. Traditional MHE techniques assume random measurement noise governed by some normal distributions. However, state estimates can be unreliable if noise is not normally distributed or measurements are contaminated with gross or systematic errors. In this paper, to improve the accuracy and robustness of state estimation, we incorporate robust estimators within the standard MHE skeleton, leading to an extended MHE framework. The proposed MHE approach is implemented on two pharmaceutical continuous feeding–blending system (FBS) configurations which include loss-in-weight (LIW) feeders and continuous blenders. Numerical results show that our MHE approach is robust to gross errors and can provide reliable state estimates when measurements are contaminated with outliers and drifts. Finally and moreover, the efficient solution of the MHE realized in this work, suggests feasible application of on-line state estimation on more complex continuous pharmaceutical processes.
This paper is concerned with some aspects of non-linear behavior of unsteady motions in combustion chambers. The emphasis is on conditions under which organized oscillations having discrete frequencies may exist in the presence of random motions. In order to treat the two types of motions together, and particularly to investigate coupling between noise and combustion instabilities, the unsteady field is represented as a synthesis of acoustic modes having time-varying amplitudes. Each of the amplitudes are written as the sum of two parts, one associated with the random field and the remainder representing the organized oscillations. After spatial averaging, the general problem is reduced to solution of a set of second-order ordinary differential equations whose structure depends on the sorts of nonlinear processes accounted for. This formulation accommodates any physical process; in particular, terms are included to represent noise sources, although only limited modeling is discussed. Our results suggest that random sources of noise have only small effects on combustion instabilities and seem not to be a cause of unstable motions. However, the coupling between the two sorts of unsteady motions may be important as an essential process in a proposed scheme for noise control. It is now a familiar observation that many nonlinear deterministic systems are capable of exhibiting apparently random motions called 'chaos.' This is a particularly interesting possibility for systems which also executed non-deterministic random motions. In combustion chambers, a nonlinear deterministic system (acoustical motions) exists in the presence of noise produced by flow separation, turbulent motions, and energy released by combustion processes. The last part of the paper is directed to the matter of discovering whether or not chaotic motions exist in combustion systems. Analysis has not progressed sufficiently far to answer the question. We report here recent results of processing data taken in one combustor to determine the dimensions of any attractors in the motions. No evidence has been found for chaos in the strict sense, but the method seems to be an important means of investigating the nonlinear behavior of combustion systems.
The inpainting of deliberately and randomly sub-sampled images offers a potential means to image specimens at a high resolution and under extremely low-dose conditions (≤1 e - /Å 2 ) using a scanning transmission electron microscope. We show that deliberate sub-sampling acquires images at least an order of magnitude faster than conventional low-dose methods for an equivalent electron dose. More importantly, when adaptive sub-sampling is implemented to acquire the images, there is a significant increase in the resolution and sensitivity which accompanies the increase in imaging speed. Lastly, we demonstrate the potential of this method for beam sensitive materials and in-situ observations by experimentally imaging the node distribution in a metal-organic framework.
The field of nuclear instrumentation covers a wide range of applications, including counting, spectrometry, pulse shape discrimination and multi-channel coincidence. These applications are the topic of many researches, new algorithms and implementations are constantly proposed thanks to advances in digital signal processing. However, these improvements are not yet implemented in instrumentation devices. This is especially true for neutron-gamma discrimination applications which traditionally use charge comparison method while literature proposes other algorithms based on frequency domain or wavelet theory which show better performances. Another example is pileups which are generally rejected while pileup correction algorithms also exist. These processes are traditionally performed offline due to two issues. The first is the Poissonian characteristic of the signal, composed of random arrival pulses which requires to current architectures to work in data flow. The second is the real-time requirement, which implies losing pulses when the pulse rate is too high. Despite the possibility of treating the pulses independently from each other, current architectures paralyze the acquisition of the signal during the processing of a pulse. This loss is called dead-time. These two issues have led current architectures to use dedicated solutions based on re-configurable components like Field Programmable Gate Arrays (FPGAs) to overcome the need of performance necessary to deal with dead-time. However, dedicated hardware algorithm implementations on re-configurable technologies are complex and time-consuming. For all these reasons, a programmable Digital pulse Processing (DPP) architecture in a high level language such as Cor C++ which can reduce dead-time would be worthwhile for nuclear instrumentation. This would reduce prototyping and test duration by reducing the level of hardware expertise to implement new algorithms. However, today's programmable solutions do not meet the need of performance to operate online and not allow scaling with the increase in the number of measurement channel. That is why an innovative DPP architecture is proposed in this paper. This architecture is able to overcome dead-time while being programmable and is flexible with the number of measurement channel. Proposed architecture is based on an innovative execution model for pulse processing applications which can be summarized as follow. The signal is not composed of pulses only, consequently, pulses processing does not have to operate on the entire signal. Therefore, the first step of our proposal is pulse extraction by the use of dedicated components named pulse extractors. The triggering step can be achieved after the analog-to-digital conversion without any signal shaping or filtering stages. Pileup detection and accurate pulse time stamping are done at this stage. Any application downstream this step can work on adaptive variable-sized array of samples simplifying pulse processing methods. Then, once the data flow is broken, it is possible to distribute pulses on Functional Units (FUs) which perform processing. As the date of each pulse is known, they can be processed individually out-of-order to provide the results. To manage the pulses distribution, a scheduler and an interconnection network are used. pulses are distributed on the first FU which is not busy without congesting the interconnection network. For this reason, the process duration does not result anymore in dead-time if there are enough FUs. FUs are designed to be standalone and to comprises at least a programmable general purpose processor (ARM, Microblaze) allowing the implementation of complex algorithms without any modification of the hardware. An acquisition chain is composed of a succession of algorithms which lead to organize our FUs as a software macro-pipeline, A simple approach consists in assigning one algorithm per FU. Consequently, the global latency becomes the worst latency of algorithms execution on FU. Moreover, as algorithms are executed locally - i.e. on a FU - this approach limits shared memory requirement. To handle multichannel, we propose FUs sharing, this approach maximize the chance to find a non-busy FU to process an incoming pulse. This is possible since each channel receive random event independently, the pulse extractors associated to them do not necessarily need to access simultaneously to all Computing resources at the same time to distribute their pulses. The major contribution of this paper is the proposition of an execution model and its associated hardware programmable architecture for digital pulse processing that can handle multiple acquisition channels while maintaining the scalability thanks to the use of shared resources. This execution model and associated architecture are validated by simulation of a cycle accurate architecture SystemC model. Proposed architecture shows promising results in terms of scalability while maintaining zero dead-time. This work also permit the sizing of hardware resources requirement required for a predefined set of applications. Future work will focus on the interconnection network and a scheduling policy that can exploit the variable-length of pulses. Then, the hardware implementation of this architecture will be performed and tested for a representative set of application.
We have introduced a new explicit numerical method, based on a discrete stochastic process, for solving a class of fractional partial differential equations that model reaction subdiffusion. The scheme is derived from the master equations for the evolution of the probability density of a sum of discrete time random walks. We show that the diffusion limit of the master equations recovers the fractional partial differential equation of interest. This limiting procedure guarantees the consistency of the numerical scheme. The positivity of the solution and stability results are simply obtained, provided that the underlying process is well posed. We also show that the method can be applied to standard reaction–diffusion equations. This work highlights the broader applicability of using discrete stochastic processes to provide numerical schemes for partial differential equations, including fractional partial differential equations.
Deterministic optics fabrication using sub-aperture tools has been vital for manufacturing precision optical surfaces. The fabrication process requires the tool influence function and the tool path to calculate the dwell time that guides the tool to bring surface quality within tight design tolerances. Widely used spiral and raster paths may leave excess waviness from the tool path, and the unavoidable constant removal layer is added to obtain positive dwell time. This waviness can be removed by either using smaller tools sequentially or randomizing the tool path. However, the existing tool-path solutions can hardly adapt to different surface aperture shapes and localized surface errors. Process efficiency and accuracy are also not well considered in tool-path planning. We propose an innovative zonal Random Adaptive Path (RAP) to solve these problems in this study. Firstly, RAP can be flexibly adapted to different surface aperture shapes by introducing part boundary. Secondly, an average threshold strategy is used in the RAP planning to improve efficiency, enabling the surface errors to be selectively corrected. Finally, the threshold is performed in several passes within one processing cycle, each with its RAP, until the desired residual is achieved. The performance of the proposed RAP is studied by comparing it with the conventional tool paths. The results demonstrated that RAP takes the least processing time and achieves the best surface quality, which verifies the effectiveness of RAP in deterministic optics fabrication.