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NASA NTRS1989

An adaptive data-smoothing routine

An adaptive noise reduction algorithm that can be implemented on a microcomputer is developed. Smoothing polynomials are used where the polynomial coefficients are chosen such that the mean-square-error between the noisy and smoothed data is minimized. This approach is equivalent to the implementation of a low-pass finite impulse response filter. The noise reduction depends on the order of the smoothing polynomial. A whiteness test on the error sequence is incorporated to search for the optimal smoothing. Expansion coefficients may be computed via the fast Fourier transform, and the resulting smoothing process is the equivalent of the implementation of an adaptive ideal low-pass filter. Results are obtained for an analytical signal with added white Gaussian noise. The routine may be applied to any smooth signal with additive random noise.

Taylor, Clayborne D.↗

NASA NTRS1999

Probabilistic Structural Analysis Methods (PSAM) for Select Space Propulsion System Components

Probabilistic Structural Analysis Methods (PSAM) are described for the probabilistic structural analysis of engine components for current and future space propulsion systems. Components for these systems are subjected to stochastic thermomechanical launch loads. Uncertainties or randomness also occurs in material properties, structural geometry, and boundary conditions. Material property stochasticity, such as in modulus of elasticity or yield strength, exists in every structure and is a consequence of variations in material composition and manufacturing processes. Procedures are outlined for computing the probabilistic structural response or reliability of the structural components. The response variables include static or dynamic deflections, strains, and stresses at one or several locations, natural frequencies, fatigue or creep life, etc. Sample cases illustrates how the PSAM methods and codes simulate input uncertainties and compute probabilistic response or reliability using a finite element model with probabilistic methods.

Source record↗

NASA NTRS2001

Planetary Accretion, Oxygen Isotopes and the Central Limit Theorem

The accumulation of presolar dust into increasingly larger aggregates (CAIs and Chondrules, Asteroids, Planets) should result in a very drastic reduction in the numerical spread in oxygen isotopic composition between bodies of similar size, in accord with the Central Limit Theorem. Observed variations in oxygen isotopic composition are many orders of magnitude larger than would be predicted by a simple, random accumulation model that begins in a well-mixed nebula - no matter which size-scale objects are used as the beginning or end points of the calculation. This discrepancy implies either that some as yet unspecified process acted on the solids in the Solar Nebula to increase the spread in oxygen isotopic composition during each and every stage of accumulation or that the nebula was heterogeneous and maintained this heterogeneity throughout most of nebular history. Large-scale nebular heterogeneity would have significant consequences for many areas of cosmochemistry, including the application of some well-known isotopic systems to the dating of nebular events or the prediction of bulk compositions of planetary bodies on the basis of a uniform cosmic abundance.

Nuth, Joseph A., III↗

NASA NTRS1989

Acute hemodynamic responses to weightlessness in humans

As NASA designs space flights requiring prolonged periods of weightlessness for a broader segment of the population, it will be important to know the acute and sustained effects of weightlessness on the cardiovascular system since this information will contribute to understanding of the clinical pharmacology of drugs administered in space. Due to operational constraints on space flights, earliest effects of weightlessness have not been documented. We examined hemodynamic responses of humans to transitions from acceleration to weightlessness during parabolic flight on NASA's KC-135 aircraft. Impedance cardiography data were collected over four sets of 8-10 parabolas, with a brief rest period between sets. Each parabola included a period of 1.8 Gz, then approximately 20 seconds of weightlessness, and finally a period of 1.6 Gz; the cycle repeated almost immediately for the remainder of the set. Subjects were semi-supine (Shuttle launch posture) for the first set, then randomly supine, sitting and standing for each subsequent set. Transition to weightlessness while standing produced decreased heart rate, increased thoracic fluid content, and increased stroke index. Surprisingly, the onset of weightlessness in the semi-supine posture produced little evidence of a headward fluid shift. Heart rate, stroke index, and cardiac index are virtually unchanged after 20 seconds of weightlessness, and thoracic fluid content is slightly decreased. Semi-supine responses run counter to Shuttle crewmember reports of noticeable fluid shift after minutes to hours in orbit. Apparently, the headward fluid shift commences in the semi-supine posture before launch. is augmented by launch acceleration, but briefly interrupted immediately in orbit, then resumes and is completed over the next hours.

short duration↗

NASA NTRS2007

Environmental Testing of the NEXT PM1R Ion Engine

The NEXT propulsion system is an advanced ion propulsion system presently under development that is oriented towards robotic exploration of the solar system using solar electric power. The subsystem includes an ion engine, power processing unit, feed system components, and thruster gimbal. The Prototype Model engine PM1 was subjected to qualification-level environmental testing in 2006 to demonstrate compatibility with environments representative of anticipated mission requirements. Although the testing was largely successful, several issues were identified including the fragmentation of potting cement on the discharge and neutralizer cathode heater terminations during vibration which led to abbreviated thermal testing, and generation of particulate contamination from manufacturing processes and engine materials. The engine was reworked to address most of these findings, renamed PM1R, and the environmental test sequence was repeated. Thruster functional testing was performed before and after the vibration and thermal-vacuum tests. Random vibration testing, conducted with the thruster mated to the breadboard gimbal, was executed at 10.0 Grms for 2 min in each of three axes. Thermal-vacuum testing included three thermal cycles from 120 to 215 C with hot engine re-starts. Thruster performance was nominal throughout the test program, with minor variations in a few engine operating parameters likely caused by facility effects. There were no significant changes in engine performance as characterized by engine operating parameters, ion optics performance measurements, and beam current density measurements, indicating no significant changes to the hardware as a result of the environmental testing. The NEXT PM1R engine and the breadboard gimbal were found to be well-designed against environmental requirements based on the results reported herein. The redesigned cathode heater terminations successfully survived the vibration environments. Based on the results of this test program and confidence in the engineering solutions available for the remaining findings of the first test program, specifically the particulate contamination, the hardware environmental qualification program can proceed with confidence

Snyder, John S.↗

DOE OSTI2021

Randomized Algorithms for Scientific Computing (RASC)

Randomized algorithms have propelled advances in artificial intelligence (AI) and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. Advances in data collection and numerical simulation have changed the dynamics of scientific research and motivate the need for randomized algorithms. For instance, advances in imaging technologies such as X-ray ptychography, electron microscopy, electron energy loss spectroscopy, or adaptive optics lattice light-sheet microscopy collect hyperspectral imaging and scattering data in terabytes, at breakneck speed enabled by state-of-the-art detectors. The data collection is exceptionally fast compared with its analysis. Likewise, advances in high-performance architectures have made exascale computing a reality and changed the economies of scientific computing in the process. Floating-point operations that create data are essentially free in comparison with data movement. Thus far, most approaches have focused on creating faster hardware. Ironically, this faster hardware has exacerbated the problem by making data still easier to create. Under such an onslaught, scientists often resort to heuristic deterministic sampling schemes (e.g., low-precision arithmetic, sampling every nth element) and sacrifice potentially valuable accuracy. Dramatically better results can be achieved via randomized algorithms, reducing the data size as much as or more than naive deterministic subsampling can achieve, while retaining the high accuracy of computing on the full data set. By randomized algorithms we mean those algorithms that employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability. Examples include matrix sketching for solving large-scale least-squares problems (see Figure 1) and stochastic gradient descent for training machine learning models. We are not recommending heuristic methods but rather randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality and near-optimality. Such approaches can be useful beyond acceleration, for example, in understanding how to avoid measure zero worst-case scenarios that plague methods such as QR matrix factorization.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Room-temperature fabrication of a delafossite CuCrO 2 hole transport layer for perovskite solar cells

Delafossite oxides are promising hole transport layer (HTL) candidates for perovskite solar cells, due to their wide band gap, favorable energy band alignment relative to the perovskite absorber and simplicity of processing. In this paper, we investigate the properties of CuCrO2 (CCO) delafossite films using an integration of experimental and computational techniques. Phase-pure CCO films are deposited at room temperature by spin-casting suspensions of hydrothermally-synthesized nanoparticles, for use in a glass/ITO/CCO/CH3NH3PbI3/C60/BCP/Ag device structure. Although density functional theory (DFT) calculations predict an elevated hole effective mass along certain crystallographic directions, the nearly isotropic shape and small size of the CCO nanoparticles preserves transport properties within the films by randomizing particle orientation, preventing these unfavorable directions from dominating the film conductivity. Experimental measurements confirm that the CCO films display appropriate optical and electrical properties for use as an HTL, in good agreement with the DFT calculations. Solar cells made using these films exhibit stabilized power conversion efficiencies exceeding 14%, with only minor hysteresis in the current–voltage characteristics.

Dunlap-Shohl, Wiley A.↗

NASA NTRS1973

On board satellite processing study

The use of satellite-based, world-wide data collection and location systems for scientific purposes is discussed. The configuration of satellite borne data handling equipment to prevent premature saturation and to provide maximum capacity without degradation of performance is described. An analysis of a data collection and location system based on random time and frequency access to the satellite was conducted. The subjects presented are: (1) a general description of the concept of a random access system, (2) the quantitative characteristics of random access systems, and (3) the development of a model for comparing the various types of digital communications systems.

Reed, D. L.↗

NASA NTRS1976

Classical trajectory study of internal energy distributions in unimolecular processes

Energy flow in a molecular system such as CD3Cl or CD3H representing a chemical activation experiment is studied by the method of classical trajectories. A correlation function method is used to obtain energy distributions before and after the breakup of the activated molecule. The energy distribution in the final product is found to be randomly distributed for a surface with no exit channel barrier or strong intermode couplings. Nonrandom energy distributions result when these special forces are present. Product channel barriers result in an excess of translational energy and exit channel intermode couplings result in nonrandom vibrational distributions.

Mcdonald, J. D.↗

NASA NTRS1994

Correlation of finite element free vibration predictions using random vibration test data

Finite element analysis is regularly used during the engineering cycle of mechanical systems to predict the response to static, thermal, and dynamic loads. The finite element model (FEM) used to represent the system is often correlated with physical test results to determine the validity of analytical results provided. Results from dynamic testing provide one means for performing this correlation. One of the most common methods of measuring accuracy is by classical modal testing, whereby vibratory mode shapes are compared to mode shapes provided by finite element analysis. The degree of correlation between the test and analytical mode shapes can be shown mathematically using the cross orthogonality check. A great deal of time and effort can be exhausted in generating the set of test acquired mode shapes needed for the cross orthogonality check. In most situations response data from vibration tests are digitally processed to generate the mode shapes from a combination of modal parameters, forcing functions, and recorded response data. An alternate method is proposed in which the same correlation of analytical and test acquired mode shapes can be achieved without conducting the modal survey. Instead a procedure is detailed in which a minimum of test information, specifically the acceleration response data from a random vibration test, is used to generate a set of equivalent local accelerations to be applied to the reduced analytical model at discrete points corresponding to the test measurement locations. The static solution of the analytical model then produces a set of deformations that once normalized can be used to represent the test acquired mode shapes in the cross orthogonality relation. The method proposed has been shown to provide accurate results for both a simple analytical model as well as a complex space flight structure.

Chambers, Jeffrey A.↗

DOE OSTI2019

Informative Path Planning for Mapping Radiation

This project involves making measurements of contaminated areas using portable radiation detectors and small robots. Tasks involve operating detectors and small robots, combining data acquisition for the two systems, and automating the systems. The end result is a radiation map of contamination areas. Localization of radioactive sources and mapping of contamination distribution is essential when responding to emergencies, or when dealing with cleanup efforts. Radiation maps are used for situational awareness and for establishing a safe perimeter. However, it may be hazardous or impossible for humans to safely enter the site and survey the area. Informative Path Planning (IPP) is used to design a path for a robotic sensor platform to gather the most information about the radiation distribution while operating under the set of constraints given by the dynamics of the robot and other requirements such as minimizing the overall mission time. The key challenge for IPP is the tight coupling in multiple layers of decisions: Selection of locations to visit; Allocation of sensors to those locations; Processing of information gathered. Given a robot equipped with a basic gamma detector and Lidar sensor, how can we efficiently explore an unknown environment in such a way that we can satisfy the robot's constraints while simultaneously maximizing the accuracy of the map and minimizing the total runtime? This work presents a systematic procedure for IPP and environmental mapping that: Selects the best locations at which to sample; Finds the best path for robots; Estimates the measurement value at unknown locations using a Gaussian Process regression framework. Given a set of points where radiation measurements were taken, we are utilizing a Gaussian Process Regression with a Matern kernel to reconstruct the shape of the underlying ground truth. Mapping is done via the Robot Operating System's OpenSlam Gmapping package, which creates a 2D occupancy grid map. Simulated trials were run with a radiation source placed at random locations, and allowing the robot to select and move towards 20 samples at separate locations, as well as sampling along the path while moving. The simulated sensor model assumed an efficiency of 0.3, collection time of 1 second. Various weighting schemes were tested and compared. Quantitative evaluations often favored exploitation-heavy weighting schemes where μ was emphasized, with the Root Mean Squared Error (RMSE) quickly tending towards 2, whereas qualitative comparisons would favor exploration-biased schemes with greater weight on σ. The difference comes from operators often favoring knowing the general location of a source, rather than the exact properties of a source. Weighting of the path-length π often reduced the quality of the regression as less exploration occurred, however this may be deemed a worthwhile trade-off under certain circumstances, such as with sensitive robots. Successfully developed a methodology for informative path-planning for a robot to efficiently gather information and create a map of radiation. In the future, we would like to focus on: Hardware trials with integration of the robot platform and the IPP software; More refined sensor and environmental modeling; Multiple robot platforms.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Statistical analysis of spatial distribution of pores in metal additive manufacturing

We report mechanical properties of additively manufactured parts are sensitive to the presence of pores form during the manufacturing process. The impact of pores on the mechanical performance has been investigated extensively with respect to different parameters, such as pore volume fraction, shape, and size. However, statistical investigations focusing on the relationships between the spatial distribution of pores and process parameters; and consequently, the performance of the manufactured parts are scattered and limited. Also, these sparse investigations usually suffer from an ambiguous definition of terminologies. For instance, the required criteria to consider a point pattern as complete spatial randomness (CSR) are generally not clarified. Moreover, no numerical formalism is yet developed to show how much the observed spatial results are statistically significant. To address these shortcomings, the statistical definition of CSR in a point pattern and the procedure to quantitatively determine the deviation of a pattern from CSR were discussed. The explained statistical approach was used to investigate the effect of scanning speed parameter on the spatial distribution of gas pores in laser powder bed fusion manufactured stainless steel parts. Furthermore, and to highlight the impact of the spatial distribution of pores on mechanical properties, fatigue performances of parts with clustered and randomly distributed pores were simulated by finite element analysis. It is shown that by reducing the scanning speed, the spatial distribution of gas pores deviated more from CSR, and correspondingly fatigue performance deteriorated.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Analysis of an optical imaging system prototype for autonomously monitoring zooplankton in an aquaculture facility

Traditional approaches to biomonitoring in aquatic systems, such as sample collection, sorting, and identification, require significant time and effort, thereby limiting the spatiotemporal resolution of sample collection. Additionally, collection and preservation of samples for subsequent taxonomic identification and enumeration leads to mortality of organisms. Recent advances in technologies that utilize optical imaging and machine learning have provided new opportunities to expedite biomonitoring and lead to significant cost savings. These technologies can be advantageous to scientists or managers that conduct routine biomonitoring to inform operations, as in the case of aquaculture facilities. The Small Aquatic Organism optical imaging system (SAO) is a high-throughput optical imaging and classification prototype system that relies on computer vision and machine learning (Support Vector Machines, or SVMs) to autonomously identify and enumerate aquatic organisms. The SAO provides a more sustainable method of collecting large volumes of data and has the benefit of being used in situ. In this study, we tested the performance of the SAO in providing comparable results to manual zooplankton community monitoring in ten ponds at an aquaculture facility. We performed a side-by-side study comparing the sampling methods of plankton tow nets, where major zooplankton taxonomic classes were manually identified and enumerated, to sampling with the SAO. Vouchered samples were used to develop a training library for the SAO, where classes consisted of water boatman and zooplankton groups: cladocerans, copepod adults, copepod nauplii, and rotifers. SAO imagery was manually classified and compared with predicted results for validation. Accuracy for the SVM classifier of the SAO was 37.4 %. Convolutional Neural Networks (CNN) and Random Forest classifiers were also applied to SAO imagery and image features for comparison. The best CNN model and our Random Forest model had accuracies of 80.4 % and 46.6 % respectively. Challenges faced included the small size of copepod nauplii and rotifers and the limited resolution of the imaging camera, although there are tradeoffs between imaging resolution and the sample processing rate. Furthermore, our comparison shows that advancement in both optical imaging and ML are needed in order for the SAO prototype to yield comparable results to manual community monitoring in an aquaculture facility.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Cost Analysis of Complex Radiation Therapy for Patients With Head and Neck Cancer

Purpose: This cost analysis aimed to prospectively assess differences in costs between TomoTherapy and volumetric modulated arc therapy (VMAT) in patients with head and neck cancer. Methods and Materials: Economic data were gathered from a multicenter study. However, randomization was not possible due to the availability of equipment. Costs were calculated using the microcosting technique from the hospital's perspective (in 2013 euros), and the time horizon was radiation therapy. Only resources that entered the hospital production process and which were likely to vary between the strategies being compared were considered. Acute adverse events observed within the time horizon were also assessed. Results: The cost analysis was based on a total of 173 patient treatments given between 2010 and 2012 in 14 French cancer centers: 73 patients were treated with TomoTherapy, 92 with VMAT RapidArc, and 8 with VMAT SmartArc. Estimated costs of SmartArc were removed from the comparison due to the small sample size. The mean ± SD cost per patient of the treatment planning phase was €314 (±€214) for TomoTherapy and €511 (±€590) for RapidArc. Mean costs ± SD per patient of irradiation reached €3144 (±€565) for TomoTherapy and €1350 (±€299) for RapidArc. The most sensitive parameter of irradiation was the annual operating time of accelerators. Ninety-five percent confidence intervals for the mean costs of irradiation were €3016 to €3272 for TomoTherapy and €1281 to €1408 for RapidArc. The number of acute adverse events during radiation therapy was not significantly different between strategies. Conclusions: TomoTherapy appeared to be more expensive than RapidArc mainly due to the higher price of the accelerator, the higher costs of maintenance, and the longer duration of treatment sessions. Because strategies were not significantly different in clinical effect, RapidArc appeared to be the strategy to be recommended at this stage of knowledge.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2023

Meta-analysis of biogas upgrading to renewable natural gas through biological CO 2 conversion

Biogas upgrading through CO 2 conversion by hydrogenotrophic methanogenesis is receiving an increasing attention worldwide because of the demand for renewable natural gas. Herein, a holistic and statistical study of the operation conditions, driving forces, performances, and potential implementation of biogas upgrading via biological CO 2 conversion was conducted. Based on a systematic review and meta-analysis of 46 existing publications that were selected from 1475 papers, we have compiled a global dataset of CO 2 bioconversion biogas upgrading, encompassing 308 study cases. Subsequently, we employed a rigorous analytical framework incorporating data processing and mixed effects linear regression analysis to examine the dataset. This analysis revealed a significant positive relationship between the H 2 :CO 2 ratio and the methane percentage in the upgraded biogas when using the study as a random effect. Furthermore, we performed meta-analysis on observations taken when the ratio was close to 4:1 and found that ex situ reactors (91.93% [88.11%, 95.75%]) can perform better than in situ reactors (84.74% [80.69%, 88.80%]). No evidence of differential performance was found based on the present dataset between different temperature regimes or operation modes. Furthermore, those findings establish a database that will contribute to a deeper understanding of the biogas upgrading via biological CO 2 conversion.

Biogas upgrading↗

DOE OSTI2022

Hard-to-soft transition-enhanced piezoelectricity in poly(vinylidene fluoride) via relaxor-like secondary crystals activated by high-power ultrasonication

We report although high piezoelectric coefficients have recently been observed in poly(vinylidene fluoride-co-trifluoroethylene) [P(VDF-TrFE)] random copolymers, they have low Curie temperatures, which makes their piezoelectricity thermally unstable. It has been challenging to achieve high piezoelectric performance from the more thermally stable PVDF homopolymer. In this report, we describe how high-power ultrasonic processing was used to induce a hard-to-soft piezoelectric transition and improve the piezoelectric coefficient d 31 in neat PVDF. After high-power ultrasonication for 20 min, a uniaxially stretched and poled PVDF film exhibited a high d 31 of 50.2 ± 1.7 pm V -1 at room temperature. Upon heating to 65 °C, the d 31 increased to a maximum value of 76.2 ± 1.2 pm V -1 , and the high piezoelectric performance persisted up to 110 °C. The enhanced piezoelectricity was attributed to the relaxor-like secondary crystals in the oriented amorphous fraction, broken off from the primary crystals by ultrasonication, as suggested by differential scanning calorimetry and broadband dielectric spectroscopy studies.

relaxor-like secondary crystals↗

DOE OSTI2021

Removal of spurious data in Bragg coherent diffraction imaging: an algorithm for automated data preprocessing

Bragg coherent diffraction imaging (BCDI) provides a powerful tool for obtaining high-resolution structural information from nanocrystalline materials. Here a BCDI sample consisting of a large number of randomly oriented nanoscale crystals is considered. Ideally, only one crystal is oriented to produce a Bragg peak on the detector. However, diffraction from other crystals often produces additional signals on the detector. Before the measured diffraction patterns can be processed into structural images, scientists routinely need to manually identify and remove the `alien' intensities from sources other than the intended crystal. With the development of modern high-coherence storage rings, such as the upgraded Advanced Photon Source (APS), the already slow process of manual preprocessing will be untenable for the large volumes of data that will be produced. An automated method of identifying and deleting alien intensities is proposed. This method exploits the fact that BCDI of a perfect crystal produces diffraction data with inversion symmetry around the Bragg peak. This approach uses the machine learning clustering method DBSCAN to distinguish between diffraction from multiple sources, and then calculates cluster size and inversion symmetry to assess whether clusters of intensity belong to desired data or alien signals. This approach can dramatically reduce the amount of time spent manually processing data, allowing BCDI data processing capabilities to keep pace with the technological advances of fourth-generation synchrotron light sources.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Optimizing transmit field inhomogeneity of parallel RF transmit design in 7T MRI using deep learning

Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) provides a higher signal-to-noise ratio and, thereby, higher spatial resolution. However, UHF MRI introduces challenges such as transmit radiofrequency (RF) field (B+1) inhomogeneities, leading to uneven flip angles and image intensity anomalies. These issues can significantly degrade imaging quality and its medical applications. This study addresses B+1 field homogeneity through a novel deep learning-based strategy. Traditional methods like Magnitude Least Squares (MLS) optimization have been effective but are time-consuming and dependent on the patient’s presence. Recent machine learning approaches, such as RF Shim Prediction by Iteratively Projected Ridge Regression and deep learning frameworks, have shown promise but face limitations like extensive training times and oversimplified architectures. We propose a two-step deep learning strategy. First, we obtain the desired reference RF shimming weights from multi-channel B+1 fields using random-initialized Adaptive Moment Estimation. Then, we employ Residual Networks (ResNets) to train a model that maps B+1 fields to target RF shimming outputs. Our approach does not rely on pre-calculated reference optimizations for the testing process and efficiently learns residual functions. Comparative studies with traditional MLS optimization demonstrate our method’s advantages in terms of speed and accuracy. The proposed strategy achieves a faster and more efficient RF shimming design, significantly improving imaging quality at UHF. This advancement holds potential for broader applications in medical imaging and diagnostics.

Lu, Zhengyi [Vanderbilt University]↗