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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-F-E-15: Initial Experience Implementing a Case Method Teaching Approach to Radiation Oncology Physics Residents, Graduate Students and Doctorate of Medical Physics Students

Purpose: Case Method Teaching approach is a teaching tool used commonly in business school to challenge students with real-world situations—i.e. cases. The students are placed in the role of the decision maker and have to provide a solution based on the multitude of information provided. Specifically, students must develop an ability to quickly make sense of a complex problem, provide a solution incorporating all of the objectives (at time conflicting) and constraints, and communicate that solution in a succinct, professional and effective manner. The validity of the solution is highly dependent on the auxiliary information provided in the case and the basic didactic knowledge of the student. A Case Method Teaching approach was developed and implemented into an on-going course focused on AAPM Task Group reports at UTHSCSA. Methods: A current course at UTHSCSA reviews and discusses 15 AAPM Task Group reports per semester. The course is structured into three topic modules: Imaging QA, Stereotactic Radiotherapy, and Special Patient Measurements—i.e. pacemakers, fetal dose. After a topic module is complete, the students are divided into groups (2–3 people) and are asked to review a case study related to the module topic. Students then provide a solution presented in an executive summary and class presentation. Results: Case studies were created to address each module topic. Through team work and whole-class discussion, a collaborative learning environment was established. Students additionally learned concepts such vendor relations, financial negotiations, capital project management, and competitive strategy. Conclusion: Case Method Teaching approach is an effective teaching tool to further enhance the learning experience of radiation oncology physics students by presenting them with though-provoking dilemmas that require students to distinguish pertinent from peripheral information, formulate strategies and recommendations for action, and confront obstacles to implementation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Analyzing the subsurface structure using seismic refraction method: Case study STMKG campus

A geophysic survey is performed to detect subsurface structure under STMKG Campus in Pondok Betung, South Tangerang, Indonesia, using seismic refraction method. The survey used PASI 16S24-U24. The waveform data is acquired from 3 different tracks on the research location with a close range from each track. On each track we expanded 24 geofons with spacing between receiver 2 meters and the total length of each track about 48 meters. The waveform data analysed using 2 different ways. First, used a seismic refractionapplication WINSISIM 12 and second, used a Hagiwara Method. From both analysis, we known the velocity of P-wave in the first and second layer and the thickness of the first layer. From the velocity and the thickness informations we made 2-D vertical subsurface profiles. In this research, we only detect 2 layers in each tracks. The P-wave velocity of first layer is about 200-500 m/s with the thickness of this layer about 3-6 m/s. The P-wave velocity of second layer is about 400-900 m/s. From the P-wave velocity data we interpreted that both layer consisted by similar materials such as top soil, soil, sand, unsaturated gravel, alluvium and clay. But, the P-wave velocity difference between those 2 layers assumed happening because the first layer is soil embankment layer, having younger age than the layer below.

58 GEOSCIENCES↗
DOE OSTI2015

Estimation of missing values in solar radiation data using piecewise interpolation methods: Case study at Penang city

The solar radiation values have been composed by automatic weather station using the device that namely pyranometer. The device is functions to records all the radiation values that have been dispersed, and these data are very useful for it experimental works and solar device’s development. In addition, for modeling and designing on solar radiation system application is needed for complete data observation. Unfortunately, lack for obtained the complete solar radiation data frequently occur due to several technical problems, which mainly contributed by monitoring device. Into encountering this matter, estimation missing values in an effort to substitute absent values with imputed data. This paper aimed to evaluate several piecewise interpolation techniques likes linear, splines, cubic, and nearest neighbor into dealing missing values in hourly solar radiation data. Then, proposed an extendable work into investigating the potential used of cubic Bezier technique and cubic Said-ball method as estimator tools. As result, methods for cubic Bezier and Said-ball perform the best compare to another piecewise imputation technique.

14 SOLAR ENERGY↗
DOE OSTI2014

Correcting for telluric absorption: Methods, case studies, and release of the TelFit code

Ground-based astronomical spectra are contaminated by the Earth's atmosphere to varying degrees in all spectral regions. We present a Python code that can accurately fit a model to the telluric absorption spectrum present in astronomical data, with residuals of ∼3%-5% of the continuum for moderately strong lines. We demonstrate the quality of the correction by fitting the telluric spectrum in a nearly featureless A0V star, HIP 20264, as well as to a series of dwarf M star spectra near the 819 nm sodium doublet. We directly compare the results to an empirical telluric correction of HIP 20264 and find that our model-fitting procedure is at least as good and sometimes more accurate. The telluric correction code, which we make freely available to the astronomical community, can be used as a replacement for telluric standard star observations for many purposes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

A critical evaluation of worst case optimization methods for robust intensity-modulated proton therapy planning

Purpose: To critically evaluate and compare three worst case optimization methods that have been previously employed to generate intensity-modulated proton therapy treatment plans that are robust against systematic errors. The goal of the evaluation is to identify circumstances when the methods behave differently and to describe the mechanism behind the differences when they occur. Methods: The worst case methods optimize plans to perform as well as possible under the worst case scenario that can physically occur (composite worst case), the combination of the worst case scenarios for each objective constituent considered independently (objectivewise worst case), and the combination of the worst case scenarios for each voxel considered independently (voxelwise worst case). These three methods were assessed with respect to treatment planning for prostate under systematic setup uncertainty. An equivalence with probabilistic optimization was used to identify the scenarios that determine the outcome of the optimization. Results: If the conflict between target coverage and normal tissue sparing is small and no dose-volume histogram (DVH) constraints are present, then all three methods yield robust plans. Otherwise, they all have their shortcomings: Composite worst case led to unnecessarily low plan quality in boundary scenarios that were less difficult than the worst case ones. Objectivewise worst case generally led to nonrobust plans. Voxelwise worst case led to overly conservative plans with respect to DVH constraints, which resulted in excessive dose to normal tissue, and less sharp dose fall-off than the other two methods. Conclusions: The three worst case methods have clearly different behaviors. These behaviors can be understood from which scenarios that are active in the optimization. No particular method is superior to the others under all circumstances: composite worst case is suitable if the conflicts are not very severe or there are DVH constraints whereas voxelwise worst case is advantageous if there are severe conflicts but no DVH constraints. The advantages of composite and voxelwise worst case outweigh those of objectivewise worst case.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

On multichannel film dosimetry with channel-independent perturbations

Purpose: Different multichannel methods for film dosimetry have been proposed in the literature. Two of them are the weighted mean method and the method put forth byMicke et al. [“Multichannel film dosimetry with nonuniformity correction,” Med. Phys. 38, 2523–2534 (2011)] and Mayer et al. [“Enhanced dosimetry procedures and assessment for EBT2 radiochromic film,” Med. Phys. 39, 2147–2155 (2012)]. The purpose of this work was to compare their results and to develop a generalized channel-independent perturbations framework in which both methods enter as special cases. Methods: Four models of channel-independent perturbations were compared: weighted mean, Micke–Mayer method, uniform distribution, and truncated normal distribution. A closed-form formula to calculate film doses and the associated type B uncertainty for all four models was deduced. To evaluate the models, film dose distributions were compared with planned and measured dose distributions. At the same time, several elements of the dosimetry process were compared: film type EBT2 versus EBT3, different waiting-time windows, reflection mode versus transmission mode scanning, and planned versus measured dose distribution for film calibration and for γ-index analysis. The methods and the models described in this study are publicly accessible through IRISEU. Alpha 1.1 ( http://www.iriseu.com ). IRISEU. is a cloud computing web application for calibration and dosimetry of radiochromic films. Results: The truncated normal distribution model provided the best agreement between film and reference doses, both for calibration and γ-index verification, and proved itself superior to both the weighted mean model, which neglects correlations between the channels, and the Micke–Mayer model, whose accuracy depends on the properties of the sensitometric curves. With respect to the selection of dosimetry protocol, no significant differences were found between transmission and reflection mode scanning, between 75 ± 5 min and 20 ± 1 h waiting-time windows or between employing EBT2 or EBT3 films. Significantly better results were obtained when a measured dose distribution was used instead of a planned one as reference for the calibration, and when a planned dose distribution was used instead of a measured one as evaluation for the γ-analysis. Conclusions: The truncated normal distribution model of channel-independent perturbations was found superior to the other three models under comparison and the authors propose its use for multichannel dosimetry.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2014

SU-D-19A-07: Dosimetric Comparison of HDR Plesiotherapy and Electron Beam Therapy for Superficial Lesions

Purpose: Large superficial (skin, soft tissue sarcoma) lesions located on curved areas are hard to treat with electrons. The Freiburg Flap (Nucletron, Netherlands) is a flexible mesh style surface which can be easily shaped to fit curved surfaces for reproducible HDR fraction delivery. To understand the fundamental dosimetric differences, a dosimetric comparison was made between HDR plesiotherapy (Freiburg applicator for lesions over 4cm) and external electron beam radiotherapy over cases with varying target curvature (both stylized and clinical cases). Methods: Four stylized cases with variable complexity were created using artificial DICOM axial CT slices and RT structures (a square and three curved structures on a 4.5cm radius cylinder). They were planned using Oncentra v4.3 and exported to Pinnacle v9.6 for electrons planning. The HDR source dwell positions were optimized for the best coverage of the targets using graphical optimization. Electron treatment plans were created in Pinnacle using the same CT and RT structures of three HDR cases with surface lesions previously treated with the Freiburg flap. The En face electron plans used 6-12 MeV electrons and 0.5–1 cm bolus was added to increase surface dose. The electron plans were prescribed to an isodose line to conform to the target. Results: For all lesions, the average target dose coverage was similar (D90ave of 100% for HDR vs 101% for electrons). For lesions with high curvature, the HDR coverage was better (D90 102% vs D90 97% for electron). For all cases, adjacent structures high dose region was lower for HDR than electrons (D1cc 100% for HDR vs D1cc 111% for electrons). Conclusion: HDR plesiotherapy offers excellent target conformity for superficial targets similar to electrons. However, for lesions with complex curved surfaces, HDR has the advantage to achieve better dose distributions using graphical optimization to spare adjacent normal tissue while maximizing target coverage.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Analytical method of waste allocation in waste management systems: Concept, method and case study

Waste is not a rejected item to dispose anymore but increasingly a secondary resource to exploit, influencing waste allocation among treatment operations in a waste management (WM) system. The aim of this methodological paper is to present a new method for the assessment of the WM system, the “analytical method of the waste allocation process” (AMWAP), based on the concept of the “waste allocation process” defined as the aggregation of all processes of apportioning waste among alternative waste treatment operations inside or outside the spatial borders of a WM system. AMWAP contains a conceptual framework and an analytical approach. The conceptual framework includes, firstly, a descriptive model that focuses on the description and classification of the WM system. It includes, secondly, an explanatory model that serves to explain and to predict the operation of the WM system. The analytical approach consists of a step-by-step analysis for the empirical implementation of the conceptual framework. With its multiple purposes, AMWAP provides an innovative and objective modular method to analyse a WM system which may be integrated in the framework of impact assessment methods and environmental systems analysis tools. Its originality comes from the interdisciplinary analysis of the WAP and to develop the conceptual framework. AMWAP is applied in the framework of an illustrative case study on the household WM system of Geneva (Switzerland). It demonstrates that this method provides an in-depth and contextual knowledge of WM. - Highlights: • The study presents a new analytical method based on the waste allocation process. • The method provides an in-depth and contextual knowledge of the waste management system. • The paper provides a reproducible procedure for professionals, experts and academics. • It may be integrated into impact assessment or environmental system analysis tools. • An illustrative case study is provided based on household waste management in Geneva.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Cross-Market Price Difference Forecast Using Deep Learning for Electricity Markets

Price forecasting is in the center of decision making in electricity markets. Many researches have been done in forecasting energy prices while little research has been reported on forecasting price difference between day-ahead and realtime markets due to its high volatility, which however plays a critical role in virtual trading. To this end, this paper takes the first attempt to employ novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units to forecast the price difference between day-ahead and real-time markets for the same node. The raw data is collected from PJM market, processed and fed into the proposed network. The Root Mean Squared Error (RMSE) and customized performance metric are used to evaluate the performance of the proposed method. Case studies show that it outperforms the traditional statistical models like ARIMA, and machine learning models like XGBoost and SVR methods in both RMSE and the capability of forecasting the sign of price difference. Additionally, to cross-market price difference forecast, the proposed approach has the potential to be applied to solve other forecasting problems such as price spread forecast in DA market for Financial Transmission Right (FTR) trading purpose.

DA/RT price difference↗
DOE OSTI2014

A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model

Sensitivity analysis (SA) is a commonly used approach for identifying important parameters that dominate model behaviors. We use a newly developed software package, a Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), to evaluate the effectiveness and efficiency of ten widely used SA methods, including seven qualitative and three quantitative ones. All SA methods are tested using a variety of sampling techniques to screen out the most sensitive (i.e., important) parameters from the insensitive ones. The Sacramento Soil Moisture Accounting (SAC-SMA) model, which has thirteen tunable parameters, is used for illustration. The South Branch Potomac River basin near Springfield, West Virginia in the U.S. is chosen as the study area. The key findings from this study are: (1) For qualitative SA methods, Correlation Analysis (CA), Regression Analysis (RA), and Gaussian Process (GP) screening methods are shown to be not effective in this example. Morris One-At-a-Time (MOAT) screening is the most efficient, needing only 280 samples to identify the most important parameters, but it is the least robust method. Multivariate Adaptive Regression Splines (MARS), Delta Test (DT) and Sum-Of-Trees (SOT) screening methods need about 400–600 samples for the same purpose. Monte Carlo (MC), Orthogonal Array (OA) and Orthogonal Array based Latin Hypercube (OALH) are appropriate sampling techniques for them; (2) For quantitative SA methods, at least 2777 samples are needed for Fourier Amplitude Sensitivity Test (FAST) to identity parameter main effect. McKay method needs about 360 samples to evaluate the main effect, more than 1000 samples to assess the two-way interaction effect. OALH and LPτ (LPTAU) sampling techniques are more appropriate for McKay method. For the Sobol' method, the minimum samples needed are 1050 to compute the first-order and total sensitivity indices correctly. These comparisons show that qualitative SA methods are more efficient but less accurate and robust than quantitative ones.

58 GEOSCIENCES↗
DOE OSTI2021

Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track

Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine. We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing. We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Comparative and Cost Analysis of a Novel Predictive Power Ramp Rate Control Method: A Case Study in a PV Power Plant in Puerto Rico

One of the most important aspects that need to be addressed to increase solar energy penetration is the power ramp-rate control. In weak grids such as the one found in Puerto Rico, it is important to smooth power fluctuations caused by the intermittence of passing clouds. In this work, a novel power ramp-rate control strategy is proposed. Additionally, a comparison with some of the most common power ramp-rate control methods is performed using a proposed model and real solar radiation data from the Coto Laurel photovoltaic power plant located in Ponce, Puerto Rico. The proposed model was validated using one-year real data from Coto Laurel. The power ramp-rate control methods were compared in real-time simulations using the OP5700 from Opal-RT Technologies considering power ramp rate fluctuations, power ramp-rate violations, fluctuations in the state-of-charge, among other indicators. Moreover, the proposed power ramp-rate control strategy, called predictive dynamic smoothing was explained and compared. Results indicate that the predictive dynamic smoothing produced a considerably reduced Levelized Cost of Storage compared to other power ramp-rate control methods and provided a higher lifetime expectancy for lithium batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗
DOE OSTI2018

Evaluating Wind Direction Consensus Methods: A Case Study

Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving these cooperative wind farms is to develop algorithms that provide necessary information to ensure reliable and efficient operation of turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This presentation demonstrates a framework for implementing a cooperative wind. Farm that incorporates information from local sensors in real time to better align turbines in a wind farm. Oftentimes, measurements made at an individual turbine are noisy and unreliable. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction and could decrease dynamic yaw misalignment, decrease the amount of time a turbines spends yawing due to a more reliable input to the yaw controller, and increase resiliency to faulty wind-vane measurements. The concept was evaluated on data provided by WindESCo and was evaluated across several other averaging techniques to demonstrate the resiliency of the proposed consensus method.

17 WIND ENERGY↗
DOE OSTI2015

Structural and elastic anisotropy of crystals at high pressures and temperatures from quantum mechanical methods: The case of Mg{sub 2}SiO{sub 4} forsterite

We report accurate ab initio theoretical predictions of the elastic, seismic, and structural anisotropy of the orthorhombic Mg{sub 2}SiO{sub 4} forsterite crystal at high pressures (up to 20 GPa) and temperatures (up to its melting point, 2163 K), which constitute earth’s upper mantle conditions. Single-crystal elastic stiffness constants are evaluated up to 20 GPa and their first- and second-order pressure derivatives reported. Christoffel’s equation is solved at several pressures: directional seismic wave velocities and related properties (azimuthal and polarization seismic anisotropies) discussed. Thermal structural and average elastic properties, as computed within the quasi-harmonic approximation of the lattice potential, are predicted at high pressures and temperatures: directional thermal expansion coefficients, first- and second-order pressure derivatives of the isothermal bulk modulus, and P-V-T equation-of-state. The effect on computed properties of five different functionals, belonging to three different classes of approximations, of the density functional theory is explicitly investigated.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗