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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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NASA NTRS1978

The central, compact source in the Cygnus A galaxy

Single, optically thin, component models as well as double, optically thick at low frequencies, component models have been examined in trying to explain the central source in the nucleus of Cygnus A. In the course of exploring those models, it is found that this compact radio source may be quite similar to other compact and variable sources in Seyfert galaxies and quasars. Double component models generally do better because they fit the X-rays, and there is a good chance that the X-rays arise in the central source; these models can naturally explain variability which may already have been detected in X-rays. The IR is most likely due to emission by cold dust in the nucleus of Cygnus A. Optical emission is, very likely, primarily thermal, except perhaps at UV wavelengths. A number of observations are suggested to check the predictions of this work.

Kafatos, M.↗
NASA NTRS2015

Check-Cases for Verification of 6-Degree-of-Freedom Flight Vehicle Simulations: Appendices - Volume 2

This NASA Engineering and Safety Center (NESC) assessment was established to develop a set of time histories for the flight behavior of increasingly complex example aerospacecraft that could be used to partially validate various simulation frameworks. The assessment was conducted by representatives from several NASA Centers and an open-source simulation project. This document contains details on models, implementation, and results.

Murri, Daniel G.↗
DOE OSTI2025

Anomaly Detection in Seismic Data with Deep Learning: Application for Instrument Failure Detection and Forecasting

Seismic data quality assessment (QA) is the first and one of the most important steps before conducting any further data analysis. Traditional methods involve checking various metrics, such as spike detection and power spectral density, by setting strict thresholds or comparing data against synthetic benchmarks. However, these approaches often rely on pre-existing knowledge and assumptions about data anomalies, leading to potential misclassification of unusual cases. Here, in this study, we propose a deep autoencoder model, an unsupervised learning approach that evaluates data quality without making assumptions about normal and anomalous data, which can be used to identify deviations in recorded data that may indicate nascent instrument failure. We test the model with the U.S. International Monitoring System (IMS) seismic stations and demonstrate the capability of detecting anomalies on a monthly scale. This could prompt station operators to examine potential problems early, allowing sufficient time for instrument maintenance to prevent data outages. In addition, we use a new manually selected testing dataset to compare our model performance against two supervised machine learning (ML) approaches and a standard QA package, as baseline models. When applied to the dataset containing known data anomalies, performance of the supervised and unsupervised ML approaches is similar, with an accuracy of 88.1% for our model compared to ∼90% for the supervised ML approach and 78.2% for the standard QA package. Our model outperforms the baseline models when applied to new stations, where new types of data anomalies can be station-specific and not included in the training dataset. Finally, we show model transferability by training the model with data from the Global Seismograph Network only and applying it to the IMS network data. The results suggest that our model is generalizable and can be applied to new stations with good accuracy.

Lin, Jiun-Ting [Lawrence Livermore National Labora↗
DOE OSTI2013

Functional Bounding Content Envelope for Gamma and Neutron Emitting Isotopes in the Model 9977 Packaging

The contents approved for shipment in a Type B radioactive material transportation package have historically been descriptions of discrete items, or groupings of well-defined similar items, in the package safety basis documentation. The need for a comprehensive functional content envelope of both gamma and neutron emitting nuclides compliant with regulatory limits has become necessary as the DOE complex requires shipments of unique mixtures of radionuclides and impurities. Recent publications have presented a calculational model and a corresponding content envelope intended to be compliant with federal regulatory external radiation limits as well as design decay heat limits based on the Model 9977 Packaging. The methodology used to develop this content envelope consisted of determining the external radiation dose rates based on one gram of a given isotope combined, in the case of actinides, with various levels of light element impurities and determining the allowable mass to the regulatory limits based on these dose rates. The method of ratioing from these calculations to the masses that meet the regulatory limits fails in the case of some combinations of actinides and light element impurities because of the effect of subcritical multiplication, resulting in non-conservative and non-compliant dose rates in some cases. This is particularly true for many actinides combined with beryllium, boron, fluorine, lithium, and sodium. In this study, the source was modeled as a sphere with the appropriate dimensions based on the actinide or gamma source density and placed at the bottom of the containment vessel for maximum conservatism. The results presented in this report rectify the above-noted deficiencies by adjusting the previously determined mass limits so that the resulting dose rates are compliant with regulatory limits. Some amount of iteration was required in some instances to adjust the masses for dose compliance. In some instances the masses were increased since they were too conservative. In addition, the method of ratioing the masses when applied to several gamma emitting isotopes also resulted in either non-conservative or overly conservative data. The mass imits presented here are for bare sources and are complaint with the design decay heat limit of 19W. The masses are based on package surface dose rates (the limiting dose rate) that fall in the range 185-195 mrem/h, thus giving an additional margin of between 7.5% and 2.5% to the regulatory limit of 200 mrem/h. This report presents a revised set of mass limits (i.e., content envelope) for several neutron emitting actinides with varying levels of light element impurities compliant with both external radiation and design decay heat limits. In addition, revised limits for gamma sources are also presented. The neutron emitter mass limits are in Tables 1 through 9 and the gamma emitter mass limits are in Table 10. The revised content envelope covering a wide range of materials present in the DOE complex, was developed for the Model 9977 Packaging, providing conservative limits for other commonly-used Type B Packagings such as the Models 9975 and 9978. It should be noted that for the Model 9977 package, shipment is also possible with three shielded containers – lead or tungsten for gamma sources and polyethylene for neutron emitters. The design decay heat limits for these are as follows: 19W for tungsten, 6W for lead and 3W for polyethylene. The limits presented in this report would need to be checked against the lower decay heat limits for the lead and polyethylene and adjusted as required for compliance. Criticality safety is not factored into these limits and should be evaluated on a case by case basis.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Physics-guided Deep Learning for Power System State Estimation

In the past decade, dramatic progress has been made in the field of machine learning. This paper explores the possibility of applying deep learning in power system state estimation. Traditionally, physics-based models are used including weighted least square (WLS) or weighted least absolute value (WLAV). These models typically consider a single snapshot of the system without capturing temporal correlations of system states. In this paper, a physics-guided deep learning (PGDL) method is proposed. Specifically, inspired by autoencoders, deep neural networks (DNNs) are used to learn the temporal correlations. The estimated system states from DNNs are then checked against physics laws by running through a set of power flow equations. Hence, the proposed PGDL is both data-driven and physics-guided. The accuracy and robustness of the proposed PGDL method are compared with traditional methods in standard IEEE cases. Simulations show promising results and the applicability is further discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1985

Physical concepts in the development of constitutive equations

Proposed viscoplastic material models include in their formulation observed material response but do not generally incorporate principles from thermodynamics, statistical mechanics, and quantum mechanics. Numerous hypotheses were made for material response based on first principles. Many of these hypotheses were tested experimentally. The proposed viscoplastic theories and the experimental basis of these hypotheses must be checked against the hypotheses. The physics of thermodynamics, statistical mechanics and quantum mechanics, and the effects of defects, are reviewed for their application to the development of constitutive laws.

Cassenti, B. N.↗
NASA NTRS2021

Validation Assessment for the Soil Moisture Active Passive (SMAP) Level 4 Carbon (L4_C) Data Product Version 5

The post-launch Cal/Val phase of the SMAP mission is guided by two primary objectives for each science product team: 1) to calibrate, verify, and improve the performance of the science algorithms, and 2) validate accuracies of the science data products as specified in the SMAP Level-1 mission science requirements. Algorithm science and product maintenance activities during the SMAP extended mission phase have also involved periodic algorithm calibration and product refinements to maintain or enhance product consistency and performance as well as science utility. This report provides an assessment of the latest (Version 5) SMAP Level 4 Carbon (L4_C) product. The L4_C Version 5 (v5) global record now spans more than six years (March 2015 – present) of SMAP operations and has benefited from five major reprocessing updates to the operational product. These reprocessing events and L4_C product release updates have incorporated various algorithm refinements and calibration adjustments to account for similar refinements to the upstream GEOS land model assimilation system, SMAP brightness temperatures, and MODIS vegetation inputs used for L4_C processing. The SMAP L4_C algorithms utilize a terrestrial carbon flux model informed by daily surface and root zone soil moisture information contributed from the SMAP Level 4 Soil Moisture (L4_SM) product along with optical remote sensing-based (e.g. MODIS-based) land cover and canopy fractional photosynthetic active radiation (fPAR), and other ancillary biophysical data. The carbon flux model estimates global daily net ecosystem CO2 exchange (NEE) and the component carbon fluxes, namely, vegetation gross primary production (GPP) and soil heterotrophic respiration (Rh). Other L4_C product elements include surface (~0-5 cm depth) soil organic carbon (SOC) stocks and associated environmental constraints to these processes, including soil moisture-related controls on GPP and ecosystem respiration (Kimball et al. 2014, Jones et al. 2017). The L4_C product addresses SMAP carbon cycle science objectives by: 1) providing a direct link between terrestrial carbon fluxes and underlying freeze/thaw and soil moisture-related constraints to these processes, 2) documenting primary connections between terrestrial water, energy and carbon cycles, and 3) improving understanding of terrestrial carbon sink activity. The SMAP L4_C algorithms and operational product are mature and at a CEOS Validation Stage 4 level (Jackson et al. 2012) based on extensive validation of the multi-year record against a diverse array of independent benchmarks, well characterized global performance, and systematic refinements gained from five major reprocessing events. There are no Level-1 mission science requirements for the L4_C product; however, self-imposed requirements have been established focusing on NEE as the primary product field for validation, and on demonstrating L4_C accuracy and success in meeting product science requirements (Jackson et al. 2012). The other L4_C product fields also have strong utility for carbon science applications (e.g., Liu et al. 2019, Endsley et al. 2020); however, analysis of these other fields is considered secondary relative to primary validation activities focusing on NEE. The L4_C targeted accuracy requirements are to meet or exceed a mean unbiased root-mean-square error (ubRMSE, or standard deviation of the error) for NEE of 1.6 g C m-2 d-1 and 30 g C m-2 yr-1, emphasizing northern (≥45°N) boreal and arctic ecosystems; this accuracy is similar to that of tower eddy covariance measurement-based observations (Baldocchi 2008). Methods used for the latest v5 L4_C product performance and validation assessment have been established from the SMAP Cal/Val plan and previous studies (Jackson et al. 2012, Jones et al. 2017) and include: 1) consistency evaluations of the product fields against earlier product releases (version 4 or earlier); 2) comparisons of daily carbon flux estimates with independent tower eddy 4covariance measurement-based daily carbon (CO2) flux observations from core tower validation sites (CVS); and 3) consistency checks against other global carbon products, including soil carbon inventory records, global GPP records derived from tower observation upscaling methods, and satellite-based observations of canopy solar induced chlorophyll fluorescence (SIF) as a surrogate for GPP. Metrics used to evaluate relative agreement between L4_C product fields and observational benchmarks include correlation (r-value), RMSE differences, bias and model sensitivity diagnostics. Following these validation criteria, the present report provides a validation assessment of the latest L4_C product release (v5). Detailed descriptions of the L4_C algorithm and additional global product accuracy and performance results are given elsewhere (Jones et al. 2017, Endsley et al. 2020). The v5 L4_C product replaces earlier product versions and continues to show: (i) accuracy and performance levels meeting or exceeding SMAP L4_C science requirements; (ii) improvement over the previous product version (version 4); and (iii) suitability for a diversity of science applications. Example L4_C applications from the recent literature include clarifying environmental trends and controls on the northern terrestrial carbon sink (Liu et al. 2019), diagnosing drought-related impacts on ecosystem productivity (Li et al. 2020), and regional monitoring of cropland conditions for projecting annual yields (Wurster et al. 2020). 2EXPECTED L4_C ALGORITHM AND PRODUCTPERFORMANCE The L4_C algorithm performance, including variance and uncertainty estimates of model outputs, was determined during the mission pre-launch phase through spatially explicit model sensitivity studies using available model inputs similar to those currently being used for operational production and evaluating the resulting model simulations over the observed range of northern (≥45 °N) and global conditions (Kimball et al. 2012, Entekhabi et al. 2014). The L4_C algorithm options were also evaluated during the mission prelaunch phase, including deriving canopy fPAR from lower order NDVI (Normalized Difference Vegetation Index) inputs in lieu of using MODIS (MOD15) fPAR; and including an explicit model representation of boreal fire disturbance recovery impacts. These results indicated that the L4_C accuracy requirements (i.e., NEE ubRMSE ≤ 30 g C m-2 yr-1or ≤ 1.6 g C m-2 d-1) could be met from the baseline algorithms over more than 82% and 89% of global and northern vegetated land areas, respectively (Yi et al. 2013, Kimball et al. 2014). The global L4_C algorithm error budget for NEE derived during the mission prelaunch phase indicated that the estimated NEE ubRMSE uncertainty is proportional to GPP and is therefore larger in higher biomass productivity areas, including forests and croplands (Kimball et al. 2014). Likewise, NEE ubRMSE uncertainty is expected to be lower in less-productive areas, including grasslands and shrublands. Expected model NEE ubRMSE levels were also generally within targeted accuracy levels for characteristically less-productive boreal and Arctic biomes, even though relative model error as a proportion of total productivity (NEE RMSE / GPP) may be large in these areas. The estimated NEE uncertainty was lower than expected in some warmer tropical high biomass productivity areas (e.g. Amazon rainforest) because of reduced low temperature and moisture constraints to the L4_C respiration calculations so that the bulk of model uncertainty is contributed by GPP in these areas. Model NEE uncertainty in the African Congo was estimated to

SMAP↗
NASA NTRS2012

NASA Integrated Network Monitor and Control Software Architecture

The National Aeronautics and Space Administration (NASA) Space Communications and Navigation office (SCaN) has commissioned a series of trade studies to define a new architecture intended to integrate the three existing networks that it operates, the Deep Space Network (DSN), Space Network (SN), and Near Earth Network (NEN), into one integrated network that offers users a set of common, standardized, services and interfaces. The integrated monitor and control architecture utilizes common software and common operator interfaces that can be deployed at all three network elements. This software uses state-of-the-art concepts such as a pool of re-programmable equipment that acts like a configurable software radio, distributed hierarchical control, and centralized management of the whole SCaN integrated network. For this trade space study a model-based approach using SysML was adopted to describe and analyze several possible options for the integrated network monitor and control architecture. This model was used to refine the design and to drive the costing of the four different software options. This trade study modeled the three existing self standing network elements at point of departure, and then described how to integrate them using variations of new and existing monitor and control system components for the different proposed deployments under consideration. This paper will describe the trade space explored, the selected system architecture, the modeling and trade study methods, and some observations on useful approaches to implementing such model based trade space representation and analysis.

Next Generation Uplink↗
DOE OSTI2015

Three-family particle physics models from global F-theory compactifications

We construct four-dimensional, globally consistent F-theory models with three chiral generations, whose gauge group and matter representations coincide with those of the Minimal Supersymmetric Standard Model, the Pati-Salam Model and the Trinification Model. These models result from compactification on toric hypersurface fibrations X with the choice of base $\mathbb P^{3}$. We observe that the F-theory conditions on the G 4 -flux restrict the number of families to be at least three. We comment on the phenomenology of the models, and for Pati-Salam and Trinification models discuss the Higgsing to the Standard Model. A central point of this work is the construction of globally consistent G 4 -flux. For this purpose we compute the vertical cohomology H V (2,2) (X) in each case and solve the conditions imposed by matching the M- and F-theoretical 3D Chern-Simons terms. Finally, we explicitly check that the expressions found for the G 4 -flux allow for a cancelation of D3-brane tadpoles. We also use the integrality of 3D Chern-Simons terms to ensure that our G 4 -flux solutions are adequately quantized.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Multicolor photometry of the merging galaxy cluster A2319: Dynamics and star formation properties

Asymmetric X-ray emission and a powerful cluster-scale radio halo indicate that A2319 is a merging cluster of galaxies. This paper presents our multicolor photometry for A2319 with 15 optical intermediate filters in the Beijing-Arizona-Taiwan-Connecticut (BATC) system. There are 142 galaxies with known spectroscopic redshifts within the viewing field of 58' × 58' centered on this rich cluster, including 128 member galaxies (called sample I). A large velocity dispersion in the rest frame, 1622{sub −70}{sup +91} km s{sup –1}, suggests merger dynamics in A2319. The contour map of projected density and localized velocity structure confirm the so-called A2319B substructure, at ∼10' northwest to the main concentration A2319A. The spectral energy distributions (SEDs) of more than 30,000 sources are obtained in our BATC photometry down to V ∼ 20 mag. A u-band (∼3551 Å) image with better seeing and spatial resolution, obtained with the Bok 2.3 m telescope at Kitt Peak, is taken to make star-galaxy separation and distinguish the overlapping contamination in the BATC aperture photometry. With color-color diagrams and photometric redshift technique, 233 galaxies brighter than h {sub BATC} = 19.0 are newly selected as member candidates after an exclusion of false candidates with contaminated BATC SEDs by eyeball-checking the u-band Bok image. The early-type galaxies are found to follow a tight color-magnitude correlation. Based on sample I and the enlarged sample of member galaxies (called sample II), subcluster A2319B is confirmed. The star formation properties of cluster galaxies are derived with the evolutionary synthesis model, PEGASE, assuming a Salpeter initial mass function and an exponentially decreasing star formation rate (SFR). A strong environmental effect on star formation histories is found in the manner that galaxies in the sparse regions have various star formation histories, while galaxies in the dense regions are found to have shorter SFR time scales, older stellar ages, and higher interstellar medium metallicities. For the merging cluster A2319, local surface density is a better environmental indicator rather than the cluster-centric distance. Compared with the well-relaxed cluster A2589, a higher fraction of star-forming galaxies is found in A2319, indicating that the galaxy-scale turbulence stimulated by the subcluster merger might have played a role in triggering the star formation activity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SU-F-T-139: Meeting the Challenges of Quality Control in the TOPAS Monte Carlo Simulation Toolkit for Proton Therapy

Purpose: Monte Carlo particle transport simulation (MC) codes have become important tools in proton therapy and biology, both for research and practice. TOPAS is an MC toolkit serving users worldwide (213 licensed users at 95 institutions in 21 countries). It provides unprecedented ease in 4D placement of geometry components, beam sources and scoring through its user-friendly and reproducible parameter file interface. Quality control (QC) of stochastic simulation software is inherently difficult, and the versatility of TOPAS introduces additional challenges. But QC is vital as the TOPAS development team implements new features, addresses user feedback and reacts to upgrades of underlying software (i.e. Geant4). Methods: Whenever code is committed to our repository, over 50 separate module tests are automatically triggered via a continuous integration service. They check that the various module options execute successfully and that their results are statistically consistent with previous reference values. Prior to each software release, longer end-to-end tests automatically validate TOPAS against experimental data and a TOPAS benchmark. These include simulating multiple properties of spread-out Bragg peaks, validating nuclear models, and searching for differences in patient simulations. Results: Continuous integration has proven effective in catching regressions at the time they are introduced, particularly when implementing new features that involve refactoring code (e.g. multithreading and ntuple output). Code coverage statistics highlight untested portions of code and guide development of new tests. The various end-to-end tests demonstrate that TOPAS accurately describes the physics of proton therapy within clinical tolerances. Conclusion: The TOPAS QC strategy of frequent short tests and pre-release long tests has led to a more reliable tool. However, the versatility of TOPAS makes it difficult to predict how users might combine different modules, and so QC ultimately remains a partnership between the developer and the user. This work was funded by National Cancer Institute grant R01 CA 140735.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-494: A Multi-Institutional Study of Independent Dose Verification Using Golden Beam Data

Purpose: In general, beam data of individual linac is measured for independent dose verification software program and the verification is performed as a secondary check. In this study, independent dose verification using golden beam data was compared to that using individual linac’s beam data. Methods: Six institutions were participated and three different beam data were prepared. The one was individual measured data (Original Beam Data, OBD) .The others were generated by all measurements from same linac model (Model-GBD) and all linac models (All-GBD). The three different beam data were registered to the independent verification software program for each institute. Subsequently, patient’s plans in eight sites (brain, head and neck, lung, esophagus, breast, abdomen, pelvis and bone) were analyzed using the verification program to compare doses calculated using the three different beam data. Results: 1116 plans were collected from six institutes. Compared to using the OBD, the results shows the variation using the Model-GBD based calculation and the All-GBD was 0.0 ± 0.3% and 0.0 ± 0.6%, respectively. The maximum variations were 1.2% and 2.3%, respectively. The plans with the variation over 1% shows the reference points were located away from the central axis with/without physical wedge. Conclusion: The confidence limit (2SD) using the Model-GBD and the All-GBD was within 0.6% and 1.2%, respectively. Thus, the use of golden beam data may be feasible for independent verification. In addition to it, the verification using golden beam data provide quality assurance of planning from the view of audit. This research is partially supported by Japan Agency for Medical Research and Development(AMED)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Gas Puff Imaging Diagnostics of Edge Plasma Turbulence in Magnetic Fusion Devices

Gas puff imaging (GPI) is a diagnostic of plasma turbulence which usesa puff of neutral gas at the plasma edge to increase the local visiblelight emission for improved space-time resolution of plasmafluctuations. This paper reviews gas puff imaging diagnostics of edgeplasma turbulence in magnetic fusion research, with a focus on theinstrumentation, diagnostic cross-checks, and interpretationissues. The gas puff imaging hardware, optics, and detectors aredescribed for about 10 GPI systems implemented over the past ~15years. Comparison of GPI results with other edge turbulence diagnosticresults are described and many common features are observed. Severalissues in the interpretation of GPI measurements are discussed, andpotential improvements in hardware and modeling are suggested.

Gas puff imaging↗
DOE OSTI2018

Fission TPC FY2018 Report

The primary motivation for the fission TPC experiment is to provide highly precise, accurate data for evaluation of the 239 Pu(n,f) cross section. As with any precision measurement careful validation of the results and rigorous quantification of the uncertainties is required. Our approach has been to make multiple experimental configuration changes and to use several analysis techniques to test both data correction models and detector performance. This report will focus one efforts during FY18 to validate the preliminary 239 Pu/ 235 U results that were released in FY17 and to make a preliminary measurement of the 238 U/ 239 Pu ratio. These validation efforts have led to the conclusion that further investigation and cross-checking is required before final results can be published

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1981

Shaded computer graphic techniques for visualizing and interpreting analytic fluid flow models

Mathematical models which predict the behavior of fluid flow in different experiments are simulated using digital computers. The simulations predict values of parameters of the fluid flow (pressure, temperature and velocity vector) at many points in the fluid. Visualization of the spatial variation in the value of these parameters is important to comprehend and check the data generated, to identify the regions of interest in the flow, and for effectively communicating information about the flow to others. The state of the art imaging techniques developed in the field of three dimensional shaded computer graphics is applied to visualization of fluid flow. Use of an imaging technique known as 'SCAN' for visualizing fluid flow, is studied and the results are presented.

Parke, F. I.↗
NASA NTRS1997

Integrity Constraint Monitoring in Software Development: Proposed Architectures

In the development of complex software systems, designers are required to obtain from many sources and manage vast amounts of knowledge of the system being built and communicate this information to personnel with a variety of backgrounds. Knowledge concerning the properties of the system, including the structure of, relationships between and limitations of the data objects in the system, becomes increasingly more vital as the complexity of the system and the number of knowledge sources increases. Ensuring that violations of these properties do not occur becomes steadily more challenging. One approach toward managing the enforcement or system properties, called context monitoring, uses a centralized repository of integrity constraints and a constraint satisfiability mechanism for dynamic verification of property enforcement during program execution. The focus of this paper is to describe possible software architectures that define a mechanism for dynamically checking the satisfiability of a set of constraints on a program. The next section describes the context monitoring approach in general. Section 3 gives an overview of the work currently being done toward the addition of an integrity constraint satisfiability mechanism to a high-level program language, SequenceL, and demonstrates how this model is being examined to develop a general software architecture. Section 4 describes possible architectures for a general constraint satisfiability mechanism, as well as an alternative approach that, uses embedded database queries in lieu of an external monitor. The paper concludes with a brief summary outlining the, current state of the research and future work.

Fernandez, Francisco G.↗
DOE OSTI2017

Environmental impact of electricity relocation: A quasi-natural experiment from interregional electricity transmission

Pollutants such as sulfur would concentrate in the source regions and thus the localized impacts are more obvious. Local balance of electricity by transporting coal has resulted in dense concentration of coal-fired power plants in load centers and caused severe environmental problems. Electricity relocation through interregional transmission is another choice for energy transportation to achieve electricity balance across regions and pollution mitigation. Using interregional electricity transmission (IRET) lines in China as a quasi-natural experiment, this paper assesses the environmental impact of electricity relocation. In the assessment, the grid organization of “province as executor” in China is considered because it affects the sphere of IRET's influence on pollution mitigation. Here we show the environmental benefits of electricity relocation. We find that, electricity relocation through interregional transmission leads to the growth rate of sulfur dioxide (SO{sub 2}) emission decreasing 7% in landing areas and Sichuan province benefits most from electricity relocation. It is interesting that there is no significant increase of SO{sub 2} emission growth rate in sending areas compared to counterfactuals if there had no IRET due to more integration of clean energy and improved emission efficiency in sending areas. Placebo study and robustness check show that the results are quite convincing. Therefore, IRET provides an appealing choice for China's environmental control in eastern region, and it is not necessarily at the cost of pollution in western region. The methodology can be applied to assess the environmental impacts of other program or policy elsewhere. - Highlights: •Proposing a model to assess the environmental impact of electricity relocation •Interregional electricity transmission in China provides a quasi-natural experiment. •Grid organization of “province as executor” is considered that affects sphere of IRET. •Providing insight about environmental necessity of IRET using China as a case study •The model can be used to assess environmental impacts of other program or policy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Use of machine learning for a helium line intensity ratio method in Magnum-PSI

Optical emission spectroscopy (OES) of helium (He) line intensities has been used to measure the electron density, ne, and temperature, Te, in various plasma devices. In this study, a neural network with five hidden layers is introduced to model the relation between the OES data and ne/Te from laser Thomson scattering in the linear plasma device Magnum-PSI and compared to multiple regression analysis. It is shown that the neural network reduces the residual errors of prediction values (ne and Te) less than half those of the multiple regression analysis. We checked two different data splitting methods for training and validation data, i.e., with and without considering the unit of discharge. A comparison of the splitting methods suggests that the residual error will decrease to ~10% even for a new discharge data when accumulating a sufficient data set.

Kajita, Shin↗