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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

System efficiency of packed bed TES with radial flow vs. axial flow – Influence of aspect ratio

Here, this paper compares the net system efficiency, including thermal efficiency and pressure drop effects, of radial versus axial flow packed beds for thermal energy storage. The traditional packed bed system is a cylindrical geometry where fluid flows axially from one end to another. However, issues of thermal stratification and high-pressure drop have led to recent studies on radial flow systems. One potential benefit is the reduced pressure drop in a radial flow system. This paper compares the performance of radial flow and axial flow systems at a range of aspect ratios (AR = H/D bed ) from 0.21 to 1.92 using a numerical model where the storage volume is held constant in all cases. When the radial flow bed is at a low aspect ratio (short/wide), the thermal front is improved but the pressure drop is high. At a high aspect ratio, the velocity is reduced in radial flow, leading to decreased pressure drop but an increased spreads in the thermal front that lowers thermal efficiency. The opposite trends are noted in axial flow. Thermal efficiencies of 83–91 % were noted for radial flow, while they ranged from 85 to 94 % in axial flow. Net efficiencies including pressure drop ranged from 74 to 82 % for radial flow and 80–87 % for axial flow. In both systems, a peak net efficiency was noted between the highest and lowest aspect ratio. While some aspect ratios with radial flow outperform axial flow from a net efficiency perspective, the results show that the highest net efficiency from axial flow is higher than that from radial flow. Overall, this paper highlights the importance of innovative TES designs and their potential to improve energy efficiency.

25 ENERGY STORAGE↗
DOE OSTI2022

Potential energy savings benefits and limitations of radiative cooling coatings for U.S. residential buildings

We report radiative coatings are a promising strategy to implement passive cooling that provides an eco-friendly pathway to achieve energy efficiency in buildings. This study investigates the benefits and limitations of implementing radiative coatings on the building envelope, particularly the roof, of residential buildings in the United States. The analysis compares the performance of an ideal radiative coating with broadband surface properties to a realistic radiative coating with diffuse and semitransparent properties. To enhance the net energy efficiency of buildings, we carried out a comprehensive parametric analysis comprising key radiation surface properties to maximize cooling energy savings while minimizing heating energy penalties. A countrywide assessment for 68 locations across the United States showed that net energy savings are a strong function of the climatic and atmospheric conditions. Broadly, southern locations showed high overall energy savings whereas northern locations exhibited high overall energy penalties. Locations in International Energy Conservation Code (IECC) climate zones 1A, 2A, and 2B showed a net annual energy savings above 5%, whereas locations in IECC climate zones 5B, 6B, and 7 showed a net annual energy penalty greater than 3%. The hot and dry climate of Phoenix, Arizona, showed the highest overall energy savings of 426 kWh (6.2%). Additionally, the mathematical correlation reveals that the net energy savings of a location is positive only when its average cooling degree days is greater than 5.5 or its average heating degree days is less than 10.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Tetramethylpyrazine inhibits neutrophil extracellular traps formation and alleviates hepatic ischemia/reperfusion injury in rat liver transplantation

Hepatic ischemia/reperfusion injury (IRI) is an adverse effect for liver transplantation which is characterized by immune response mediated inflammation. Recent studies report that neutrophil extracellular traps (NETs) are implicated in hepatic IRI. The aim of this study was to explore the mechanism of action of tetramethylpyrazine (TMP), the main chemical composition of Ligusticum chuanxiong in treatment of ischemic related diseases. Data showed that hepatic IRI increases the leak of alanine aminotransferase (ALT) and aspartate transaminase (AST), and stimulates formation of NETs. Extracellular DNA/NETs assay, hematoxylin-eosin (HE) staining, immunofluorescence assay, terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) and Western blot assay, showed that TMP significantly reduces formation of NETs and alleviates hepatic IRI. Moreover, TMP and Diphenyleneiodonium (DPI) suppressed ROS production in neutrophils. In addition, analysis showed that activation of NADPH oxidase plays a role in formation of NETs triggered by hepatic IRI. Notably, TMP inhibited formation of NETs though inhibition of NADPH oxidase. Additionally, Combination treatment using TMP and DPI was more effective compared with monotherapy of either of the two drugs. These findings show that combination therapy using TMP and DPI is a promising method for treatment hepatic IRI.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Multi-scale predictions of massive conifer mortality due to chronic temperature rise

Global temperature rise and extremes accompanying drought threaten forests and their associated climatic feedbacks. Our ability to accurately simulate drought-induced forest impacts remains highly uncertain in part owing to our failure to integrate physiological measurements, regional-scale models, and dynamic global vegetation models (DGVMs). Here we show consistent predictions of widespread mortality of needleleaf evergreen trees (NET) within Southwest USA by 2100 using state-of-the-art models evaluated against empirical data sets. Experimentally, dominant Southwest USA NET species died when they fell below predawn water potential (Ψ pd ) thresholds (April–August mean) beyond which photosynthesis, hydraulic and stomatal conductance, and carbohydrate availability approached zero. The evaluated regional models accurately predicted NET Ψ pd , and 91% of predictions (10 out of 11) exceeded mortality thresholds within the twenty-first century due to temperature rise. The independent DGVMs predicted ≥50% loss of Northern Hemisphere NET by 2100, consistent with the NET findings for Southwest USA. Notably, the global models underestimated future mortality within Southwest USA, highlighting that predictions of future mortality within global models may be underestimates. Taken together, the validated regional predictions and the global simulations predict widespread conifer loss in coming decades under projected global warming.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Advances in understanding of high- Z material erosion and re-deposition in low- Z wall environment in DIII-D

Dedicated DIII-D experiments coupled with modeling reveal that the net erosion rate of high-Z materials, i.e. Mo and W, is strongly affected by carbon concentration in the plasma and the magnetic pre-sheath properties. We have investigated different methods such as electrical biasing and local gas injection to control high-Z material erosion. The net erosion rate of high-Z materials is significantly reduced due to the high local re-deposition ratio. The ERO modeling shows that the local re-deposition ratio is mainly controlled by the electric field and plasma density within the magnetic pre-sheath. The net erosion can be significantly suppressed by reducing the sheath potential drop. A high carbon impurity concentration in the background plasma is also found to reduce the net erosion rate of high-Z materials. Both DIII-D experiments and modeling show that local 13 CH 4 injection can create a carbon coating on the metal surface. The profile of 13 C deposition provides quantitative information on radial transport due to E × B drift and the cross-field diffusion. The deuterium gas injection upstream of the W sample can reduce W net erosion rate by plasma perturbation. The inter-ELM W erosion we measured in H-mode plasmas, rates at different radial locations are well reproduced by ERO modeling taking into account charge-state-resolved carbon ion flux in the background plasma calculated using the OEDGE code.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Climate indices strongly influence old-growth forest carbon exchange

We present a decade and a half (1998–2013) of carbon dioxide fluxes from an old-growth stand in the American Pacific Northwest to identify ecosystem-level responses to Pacific teleconnection patterns, including the El Niño/Southern Oscillation (ENSO). This study provides the longest, continuous record of old-growth eddy flux data to date from one of the longest running Fluxnet stations in the world. From 1998 to 2013, average annual net ecosystem exchange (F NEE ) at Wind River AmeriFlux was –32 ± 84 g C m –2 yr –1 indicating that the late seral forest is on average a small net sink of atmospheric carbon. However, interannual variability is high (>300 g C m –2 yr –1 ) and shows that the stand switches from net carbon sink to source in response to climate drivers associated with ENSO. The old-growth forest is a much stronger sink during La Niña years (mean F NEE = –90 g C m –2 yr –1 ) than during El Niño when the stand turns carbon neutral or into a small net carbon source (mean F NEE = +17 g C m –2 yr –1 ). Forest inventory data dating back to the 1930s show a similar correlation with the lower frequency Pacific North American (PNA) and Pacific Decadal Oscillation (PDO) whereby higher aboveground net primary productivity (F ANPP ) is associated with cool phases of both the PNA and PDO. Furthermore, these measurements add evidence that carbon exchange in old-growth stands may be more sensitive to climate variability across shorter time scales than once thought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Benchmarking carbon fluxes of the ISIMIP2a biome models

The purpose of this study is to evaluate the eight ISIMIP2a biome models against independent estimates of long-term net carbon fluxes (i.e. Net Biome Productivity, NBP) over terrestrial ecosystems for the recent four decades (1971–2010). Here, we evaluate modeled global NBP against 1) the updated global residual land sink (RLS) plus land use emissions (E LUC ) from the Global Carbon Project (GCP), presented as R + L in this study by Le Quéré et al (2015), and 2) the land CO 2 fluxes from two atmospheric inversion systems: Jena CarboScope s81_v3.8 and CAMS v15r2, referred to as F Jena and F CAMS respectively. The model ensemble-mean NBP (that includes seven models with land-use change) is higher than but within the uncertainty of R + L, while the simulated positive NBP trend over the last 30 yr is lower than that from R + L and from the two inversion systems. ISIMIP2a biome models well capture the interannual variation of global net terrestrial ecosystem carbon fluxes. Tropical NBP represents 31 ± 17% of global total NBP during the past decades, and the year-to-year variation of tropical NBP contributes most of the interannual variation of global NBP. According to the models, increasing Net Primary Productivity (NPP) was the main cause for the generally increasing NBP. Significant global NBP anomalies from the long-term mean between the two phases of El Niño Southern Oscillation (ENSO) events are simulated by all models (p < 0.05), which is consistent with the R + L estimate (p = 0.06), also mainly attributed to NPP anomalies, rather than to changes in heterotrophic respiration (Rh). The global NPP and NBP anomalies during ENSO events are dominated by their anomalies in tropical regions impacted by tropical climate variability. Multiple regressions between R + L, F Jena and F CAMS interannual variations and tropical climate variations reveal a significant negative response of global net terrestrial ecosystem carbon fluxes to tropical mean annual temperature variation, and a non-significant response to tropical annual precipitation variation. According to the models, tropical precipitation is a more important driver, suggesting that some models do not capture the roles of precipitation and temperature changes adequately.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Background-Aware 3-D Point Cloud Segmentation With Dynamic Point Feature Aggregation

With the proliferation of LiDAR sensors and 3-D vision cameras, 3-D point cloud analysis has attracted significant attention in recent years. In this article, we propose a novel 3-D point cloud learning network, referred to as dynamic point feature aggregation network (DPFA-Net), by selectively performing the neighborhood feature aggregation (FA) with dynamic pooling and an attention mechanism. DPFA-Net has two variants for semantic segmentation and classification of 3-D point clouds. As the core module of the DPFA-Net, we propose an FA layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism. In contrast to other segmentation models, which aggregate features from fixed neighborhoods, our approach can aggregate features from different neighbors in different layers providing a more selective and broader view to the query points and focusing more on the relevant features in a local neighborhood. In addition, to further improve the performance of semantic segmentation, we exploit the background–foreground (BF) information and present two novel approaches, namely, two-stage BF-Net and BF regularization. Experimental results show that the proposed DPFA-Net achieves the state-of-the-art overall accuracy score of 89.22% for semantic segmentation on the Stanford large-scale 3-D Indoor Spaces (S3DIS) dataset and provides consistently satisfactory performance across different tasks of semantic segmentation, part segmentation, and 3-D object classification. Furthermore, our model achieves 93.1% accuracy on the ModelNet40 dataset and provides a mean shape intersection-over-union (IoU) value of 85.5% for part segmentation on the ShapeNet-Part dataset. It is a also computationally more efficient compared to other methods.

3-D↗
DOE OSTI2024

Impact of Sugarcane Cultivation on C Cycling in Southeastern United States Following Conversion From Grazed Pastures

ABSTRACT The expansion of sugarcane, a tropical high‐yielding feedstock, will likely reshape the Southeastern United States (SE US) bioenergy landscape. However, the sustainability of sugarcane, particularly as it displaces grazed pastures, is highly uncertain. Here, we investigated how pasture conversion to sugarcane in subtropical Florida impacts net ecosystem CO 2 exchange (NEE) and net ecosystem carbon (C) balance (NECB). Measurements were made over three full growth cycles (> 3 years) in sugarcane—plant cane, PC; first ratoon cane, FRC; second ratoon cane, SRC—and in improved (IM) and semi‐native (SN) pastures, which make up ca. 37% of agricultural land in the region. Immediately following conversion, PC was a stronger net source of CO 2 than pastures, indicating the importance of CO 2 losses related to land disturbance. Sugarcane, however, shifted to a strong net sink of CO 2 after first regrowth, and overall sugarcane was a stronger net CO 2 sink than pastures. Both stand age and low water availability during cane emergence and tillering substantially decreased its potential gross CO 2 uptake. Accounting for all C gains and removals (i.e., NECB), greater frequency of burn events and repeated harvest increased removals and overall made sugarcane a stronger C source relative to pastures despite substantial C inputs from the previous land use and a stronger CO 2 sink strength. Time since conversion substantially reduced C losses from sugarcane, and the NECB of SRC was similar to that of IM pasture but lower than that of SN pasture, indicating a rapid shift in the NECB of cane. We conclude that the C‐balance implications following conversion will depend on the proportion of IM versus SN pastures converted to sugarcane. Furthermore, our findings suggest that no‐burn harvest management strategies will be critical to the development of a sustainable bioenergy landscape in SE US.

Gomez‐Casanovas, Nuria↗
DOE OSTI2015

TU-EF-BRA-00: MR Basics I

NMR, and Proton Density MRI of the 1D Patient - Anthony Wolbarst Net Voxel Magnetization, m(x,t). T1-MRI; The MRI Device - Lisa Lemen ‘Classical’ NMR; FID Imaging in 1D via k-Space - Nathan Yanasak Spin-Echo; S-E/Spin Warp in a 2D Slice - Ronald Price Magnetic resonance imaging not only reveals the structural, anatomic details of the body, as does CT, but also it can provide information on the physiological status and pathologies of its tissues, like nuclear medicine. It can display high-quality slice and 3D images of organs and vessels viewed from any perspective, with resolution better than 1 mm. MRI is perhaps most extraordinary and notable for the plethora of ways in which it can create unique forms of image contrast, reflective of fundamentally different biophysical phenomena. As with ultrasound, there is no risk from ionizing radiation to the patient or staff, since no X-rays or radioactive nuclei are involved. Instead, MRI harnesses magnetic fields and radio waves to probe the stable nuclei of the ordinary hydrogen atoms (isolated protons) occurring in water and lipid molecules within and around cells. MRI consists, in essence, of creating spatial maps of the electromagnetic environments around these hydrogen nuclei. Spatial variations in the proton milieus can be related to clinical differences in the biochemical and physiological properties and conditions of the associated tissues. Imaging of proton density (PD), and of the tissue proton spin relaxation times known as T1 and T2, all can reveal important clinical information, but they do so with approaches so dissimilar from one another that each is chosen for only certain clinical situations. T1 and T2 in a voxel are determined by different aspects of the rotations and other motions of the water and lipid molecules involved, as constrained by the local biophysical surroundings within and between its cells – and they, in turn, depend on the type of tissue and its state of health. Three other common applications of MRI exploit its capability to detect and image distinct movements of fluids: MR angiography (MRA), which rivals CT angiography but often requires no contrast medium, monitors the bulk flow of blood; functional MRI (f MRI), distinguishes the perfusion of oxygenated blood from that of de-oxygenated, and lights up parts of the brain that are activated by a stimulus, rather like PET; and diffusion tensor imaging (DTI) indicates the diffusion of free water along tracts of axons, thereby bringing nerve trunks into view. There are variants on all of these themes, and on others as well. Magnetic Resonance Spectroscopy (MRS), for example, can perform non-invasive ‘virtual biopsies’ that allow identification of certain cancers and other lesions. And an MRI-guided needle biopsy can sample brain tissue from a region only millimeters in dimensions. MRI, however, involves deeper and more complex aspects of physics, technology, and biology than do most other imaging modalities, and it is widely considered to be correspondingly more difficult to learn. We could probably cover all of this rather comprehensively if we had 50 hours available rather than 2 ̶ but, to paraphrase a former Secretary of Defense, you tell your story in the time you have allotted. The four presenters and another physicist, Kevin King from GE, have combined their efforts to co-author a single slide show that describes essentials of MRI as simply as possible. It is obviously far from thorough, but hopefully it will succeed in explaining some of the basics in a simplified but still valid fashion; in providing a taste of the numerous capabilities and complexities of the modality; and in whetting your appetite to learn more. Part I. NMR, and Proton Density MRI of the 1D Patient (Wolbarst), begins with an introductory case study that illustrates a half dozen ways in which MRI provides valuable clinical information. It then explores the nuclear magnetic resonance (NMR) phenomenon, which underlies MRI. NMR can be introduced with either of two approaches. In the first, one thinks (loosely) of the nuclei of hydrogen atoms as (rotating and charged and therefore) magnetic objects, whose spin-axes tend to align in a strong external magnetic field, much like a compass needle. As with the Bohr atom, this spin-up/spin-down picture is a highly abridged version of the full quantum mechanical treatment, but still it leads to some useful, legitimate pictures of the NMR process occurring within a voxel: When RF photons of the correct (Larmor) frequency elevate protons in a fixed magnetic field out of their lower-energy spin state into the upper, the NMR phenomenon is indicated by the detectable absorption of RF power. With the addition of a linear gradient field along a multi-voxel, one-dimensional patient/phantom, as well, we can determine the water content of each compartment – an example of a real MRI study, albeit in 1D. Part I concludes with a discussion of the net magnetization at position x, m0(x), under conditions of dynamic thermal equilibrium, which leads into: Part II. Net Voxel Magnetization, m(x,t); T1-MRI; The MRI Device (Lemen), investigates the biophysics of the form of proton spin relaxation process characterized by the time T1. It then moves on to the creation of an MR image that displays the spatial variation in the values of this clinically relevant parameter, again in 1D. Finally, the design and workings of a clinical MRI machine are sketched, in preparation for: Part III. ‘Classical’ NMR; FID Imaging in 1D via k-Space (Yanasak) presents the second standard approach to NMR and MRI, the classical model. It focuses on the time dependence of the net nuclear magnetization, m(x,t), the overall magnetic field generated by the cohort of protons in the voxel at position x. Quite remarkably, this nuclear net magnetization itself acts in a strong magnetic field like a gyroscope in a gravitational field. This tack is better for explaining Free Induction Decay (FID), which involves a brief introduction to the Fourier transform and k-space. This leads to conventional Spin-Echo (S-E) reconstruction techniques for creating clinical images from raw data, and sets the stage for: Part IV. Spin-Echo; S-E / Spin Warp in a 2D Slice (Price) discusses application of the S- E sequence of radiofrequency pulses and gradient magnetic fields to the 1D patient. T2 is introduced but not explained. This Part also considers how to manipulate the image acquisition parameters so as to generate clinical pictures with contrast dominated by spatial variations in PD, T1, or T2. We conclude by demonstrating the spin-warp approach to imaging in 2D with a simple 2×2, 4-voxel example. Much of this material is presented in more detail in the chapter “MRI of the One-dimensional Patient, Part I”, in Advances in Medical Physics, Vol 5 (2014). Copies are on display at the Medical Physics Publishing booth. Learning Objectives: The participant will learn about the processes of NMR and T1 spin relaxation in a tissue voxel in a uniform magnetic field. The participant will learn about combining spin-up/spin-down NMR and T1 processes with a linear gradient to effect frequency-encoding of voxel spatial position. This approach can be used to create proton density and T1 MRI maps, respectively, of the contents of multi-voxel 1D phantoms. The participant will learn about how the ‘classical’ model of NMR it can generate Free Induction Decay (FID) images of 1D phantoms, which involves the use of the Fourier transform in k-space. This can lead simply into standard Spin-Echo images. The participant will learn about extending Spin-Echo imaging into 2 and more dimensions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-EF-BRA-01: NMR and Proton Density MRI of the 1D Patient

NMR, and Proton Density MRI of the 1D Patient - Anthony Wolbarst Net Voxel Magnetization, m(x,t). T1-MRI; The MRI Device - Lisa Lemen ‘Classical’ NMR; FID Imaging in 1D via k-Space - Nathan Yanasak Spin-Echo; S-E/Spin Warp in a 2D Slice - Ronald Price Magnetic resonance imaging not only reveals the structural, anatomic details of the body, as does CT, but also it can provide information on the physiological status and pathologies of its tissues, like nuclear medicine. It can display high-quality slice and 3D images of organs and vessels viewed from any perspective, with resolution better than 1 mm. MRI is perhaps most extraordinary and notable for the plethora of ways in which it can create unique forms of image contrast, reflective of fundamentally different biophysical phenomena. As with ultrasound, there is no risk from ionizing radiation to the patient or staff, since no X-rays or radioactive nuclei are involved. Instead, MRI harnesses magnetic fields and radio waves to probe the stable nuclei of the ordinary hydrogen atoms (isolated protons) occurring in water and lipid molecules within and around cells. MRI consists, in essence, of creating spatial maps of the electromagnetic environments around these hydrogen nuclei. Spatial variations in the proton milieus can be related to clinical differences in the biochemical and physiological properties and conditions of the associated tissues. Imaging of proton density (PD), and of the tissue proton spin relaxation times known as T1 and T2, all can reveal important clinical information, but they do so with approaches so dissimilar from one another that each is chosen for only certain clinical situations. T1 and T2 in a voxel are determined by different aspects of the rotations and other motions of the water and lipid molecules involved, as constrained by the local biophysical surroundings within and between its cells – and they, in turn, depend on the type of tissue and its state of health. Three other common applications of MRI exploit its capability to detect and image distinct movements of fluids: MR angiography (MRA), which rivals CT angiography but often requires no contrast medium, monitors the bulk flow of blood; functional MRI ( f MRI), distinguishes the perfusion of oxygenated blood from that of de-oxygenated, and lights up parts of the brain that are activated by a stimulus, rather like PET; and diffusion tensor imaging (DTI) indicates the diffusion of free water along tracts of axons, thereby bringing nerve trunks into view. There are variants on all of these themes, and on others as well. Magnetic Resonance Spectroscopy (MRS), for example, can perform non-invasive ‘virtual biopsies’ that allow identification of certain cancers and other lesions. And an MRI-guided needle biopsy can sample brain tissue from a region only millimeters in dimensions. MRI, however, involves deeper and more complex aspects of physics, technology, and biology than do most other imaging modalities, and it is widely considered to be correspondingly more difficult to learn. We could probably cover all of this rather comprehensively if we had 50 hours available rather than 2 ̶ but, to paraphrase a former Secretary of Defense, you tell your story in the time you have allotted. The four presenters and another physicist, Kevin King from GE, have combined their efforts to co-author a single slide show that describes essentials of MRI as simply as possible. It is obviously far from thorough, but hopefully it will succeed in explaining some of the basics in a simplified but still valid fashion; in providing a taste of the numerous capabilities and complexities of the modality; and in whetting your appetite to learn more. Part I. NMR, and Proton Density MRI of the 1D Patient (Wolbarst), begins with an introductory case study that illustrates a half dozen ways in which MRI provides valuable clinical information. It then explores the nuclear magnetic resonance (NMR) phenomenon, which underlies MRI. NMR can be introduced with either of two approaches. In the first, one thinks (loosely) of the nuclei of hydrogen atoms as (rotating and charged and therefore) magnetic objects, whose spin-axes tend to align in a strong external magnetic field, much like a compass needle. As with the Bohr atom, this spin-up/spin-down picture is a highly abridged version of the full quantum mechanical treatment, but still it leads to some useful, legitimate pictures of the NMR process occurring within a voxel: When RF photons of the correct (Larmor) frequency elevate protons in a fixed magnetic field out of their lower-energy spin state into the upper, the NMR phenomenon is indicated by the detectable absorption of RF power. With the addition of a linear gradient field along a multi-voxel, one-dimensional patient/phantom, as well, we can determine the water content of each compartment – an example of a real MRI study, albeit in 1D. Part I concludes with a discussion of the net magnetization at position x, m0(x), under conditions of dynamic thermal equilibrium, which leads into: Part II. Net Voxel Magnetization, m(x,t); T1-MRI; The MRI Device (Lemen), investigates the biophysics of the form of proton spin relaxation process characterized by the time T1. It then moves on to the creation of an MR image that displays the spatial variation in the values of this clinically relevant parameter, again in 1D. Finally, the design and workings of a clinical MRI machine are sketched, in preparation for: Part III. ‘Classical’ NMR; FID Imaging in 1D via k-Space (Yanasak) presents the second standard approach to NMR and MRI, the classical model. It focuses on the time dependence of the net nuclear magnetization, m(x,t), the overall magnetic field generated by the cohort of protons in the voxel at position x. Quite remarkably, this nuclear net magnetization itself acts in a strong magnetic field like a gyroscope in a gravitational field. This tack is better for explaining Free Induction Decay (FID), which involves a brief introduction to the Fourier transform and k-space. This leads to conventional Spin-Echo (S-E) reconstruction techniques for creating clinical images from raw data, and sets the stage for: Part IV. Spin-Echo; S-E / Spin Warp in a 2D Slice (Price) discusses application of the S- E sequence of radiofrequency pulses and gradient magnetic fields to the 1D patient. T2 is introduced but not explained. This Part also considers how to manipulate the image acquisition parameters so as to generate clinical pictures with contrast dominated by spatial variations in PD, T1, or T2. We conclude by demonstrating the spin-warp approach to imaging in 2D with a simple 2×2, 4-voxel example. Much of this material is presented in more detail in the chapter “MRI of the One-dimensional Patient, Part I”, in Advances in Medical Physics, Vol 5 (2014). Copies are on display at the Medical Physics Publishing booth. Learning Objectives: The participant will learn about the processes of NMR and T1 spin relaxation in a tissue voxel in a uniform magnetic field. The participant will learn about combining spin-up/spin-down NMR and T1 processes with a linear gradient to effect frequency-encoding of voxel spatial position. This approach can be used to create proton density and T1 MRI maps, respectively, of the contents of multi-voxel 1D phantoms. The participant will learn about how the ‘classical’ model of NMR it can generate Free Induction Decay (FID) images of 1D phantoms, which involves the use of the Fourier transform in k-space. This can lead simply into standard Spin-Echo images. The participant will learn about extending Spin-Echo imaging into 2 and more dimensions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-EF-BRA-02: Longitudinal Proton Spin Relaxation and T1-Imaging

NMR, and Proton Density MRI of the 1D Patient - Anthony Wolbarst Net Voxel Magnetization, m(x,t). T1-MRI; The MRI Device - Lisa Lemen ‘Classical’ NMR; FID Imaging in 1D via k-Space - Nathan Yanasak Spin-Echo; S-E/Spin Warp in a 2D Slice - Ronald Price Magnetic resonance imaging not only reveals the structural, anatomic details of the body, as does CT, but also it can provide information on the physiological status and pathologies of its tissues, like nuclear medicine. It can display high-quality slice and 3D images of organs and vessels viewed from any perspective, with resolution better than 1 mm. MRI is perhaps most extraordinary and notable for the plethora of ways in which it can create unique forms of image contrast, reflective of fundamentally different biophysical phenomena. As with ultrasound, there is no risk from ionizing radiation to the patient or staff, since no X-rays or radioactive nuclei are involved. Instead, MRI harnesses magnetic fields and radio waves to probe the stable nuclei of the ordinary hydrogen atoms (isolated protons) occurring in water and lipid molecules within and around cells. MRI consists, in essence, of creating spatial maps of the electromagnetic environments around these hydrogen nuclei. Spatial variations in the proton milieus can be related to clinical differences in the biochemical and physiological properties and conditions of the associated tissues. Imaging of proton density (PD), and of the tissue proton spin relaxation times known as T1 and T2, all can reveal important clinical information, but they do so with approaches so dissimilar from one another that each is chosen for only certain clinical situations. T1 and T2 in a voxel are determined by different aspects of the rotations and other motions of the water and lipid molecules involved, as constrained by the local biophysical surroundings within and between its cells – and they, in turn, depend on the type of tissue and its state of health. Three other common applications of MRI exploit its capability to detect and image distinct movements of fluids: MR angiography (MRA), which rivals CT angiography but often requires no contrast medium, monitors the bulk flow of blood; functional MRI ( f MRI), distinguishes the perfusion of oxygenated blood from that of de-oxygenated, and lights up parts of the brain that are activated by a stimulus, rather like PET; and diffusion tensor imaging (DTI) indicates the diffusion of free water along tracts of axons, thereby bringing nerve trunks into view. There are variants on all of these themes, and on others as well. Magnetic Resonance Spectroscopy (MRS), for example, can perform non-invasive ‘virtual biopsies’ that allow identification of certain cancers and other lesions. And an MRI-guided needle biopsy can sample brain tissue from a region only millimeters in dimensions. MRI, however, involves deeper and more complex aspects of physics, technology, and biology than do most other imaging modalities, and it is widely considered to be correspondingly more difficult to learn. We could probably cover all of this rather comprehensively if we had 50 hours available rather than 2 ̶ but, to paraphrase a former Secretary of Defense, you tell your story in the time you have allotted. The four presenters and another physicist, Kevin King from GE, have combined their efforts to co-author a single slide show that describes essentials of MRI as simply as possible. It is obviously far from thorough, but hopefully it will succeed in explaining some of the basics in a simplified but still valid fashion; in providing a taste of the numerous capabilities and complexities of the modality; and in whetting your appetite to learn more. Part I. NMR, and Proton Density MRI of the 1D Patient (Wolbarst), begins with an introductory case study that illustrates a half dozen ways in which MRI provides valuable clinical information. It then explores the nuclear magnetic resonance (NMR) phenomenon, which underlies MRI. NMR can be introduced with either of two approaches. In the first, one thinks (loosely) of the nuclei of hydrogen atoms as (rotating and charged and therefore) magnetic objects, whose spin-axes tend to align in a strong external magnetic field, much like a compass needle. As with the Bohr atom, this spin-up/spin-down picture is a highly abridged version of the full quantum mechanical treatment, but still it leads to some useful, legitimate pictures of the NMR process occurring within a voxel: When RF photons of the correct (Larmor) frequency elevate protons in a fixed magnetic field out of their lower-energy spin state into the upper, the NMR phenomenon is indicated by the detectable absorption of RF power. With the addition of a linear gradient field along a multi-voxel, one-dimensional patient/phantom, as well, we can determine the water content of each compartment – an example of a real MRI study, albeit in 1D. Part I concludes with a discussion of the net magnetization at position x, m0(x), under conditions of dynamic thermal equilibrium, which leads into: Part II. Net Voxel Magnetization, m(x,t); T1-MRI; The MRI Device (Lemen), investigates the biophysics of the form of proton spin relaxation process characterized by the time T1. It then moves on to the creation of an MR image that displays the spatial variation in the values of this clinically relevant parameter, again in 1D. Finally, the design and workings of a clinical MRI machine are sketched, in preparation for: Part III. ‘Classical’ NMR; FID Imaging in 1D via k-Space (Yanasak) presents the second standard approach to NMR and MRI, the classical model. It focuses on the time dependence of the net nuclear magnetization, m(x,t), the overall magnetic field generated by the cohort of protons in the voxel at position x. Quite remarkably, this nuclear net magnetization itself acts in a strong magnetic field like a gyroscope in a gravitational field. This tack is better for explaining Free Induction Decay (FID), which involves a brief introduction to the Fourier transform and k-space. This leads to conventional Spin-Echo (S-E) reconstruction techniques for creating clinical images from raw data, and sets the stage for: Part IV. Spin-Echo; S-E / Spin Warp in a 2D Slice (Price) discusses application of the S- E sequence of radiofrequency pulses and gradient magnetic fields to the 1D patient. T2 is introduced but not explained. This Part also considers how to manipulate the image acquisition parameters so as to generate clinical pictures with contrast dominated by spatial variations in PD, T1, or T2. We conclude by demonstrating the spin-warp approach to imaging in 2D with a simple 2×2, 4-voxel example. Much of this material is presented in more detail in the chapter “MRI of the One-dimensional Patient, Part I”, in Advances in Medical Physics, Vol 5 (2014). Copies are on display at the Medical Physics Publishing booth. Learning Objectives: The participant will learn about the processes of NMR and T1 spin relaxation in a tissue voxel in a uniform magnetic field. The participant will learn about combining spin-up/spin-down NMR and T1 processes with a linear gradient to effect frequency-encoding of voxel spatial position. This approach can be used to create proton density and T1 MRI maps, respectively, of the contents of multi-voxel 1D phantoms. The participant will learn about how the ‘classical’ model of NMR it can generate Free Induction Decay (FID) images of 1D phantoms, which involves the use of the Fourier transform in k-space. This can lead simply into standard Spin-Echo images. The participant will learn about extending Spin-Echo imaging into 2 and more dimensions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-EF-BRA-03: Free Induction Decay (without the Decay) and Spin-Echo Imaging

NMR, and Proton Density MRI of the 1D Patient - Anthony Wolbarst Net Voxel Magnetization, m(x,t). T1-MRI; The MRI Device - Lisa Lemen ‘Classical’ NMR; FID Imaging in 1D via k-Space - Nathan Yanasak Spin-Echo; S-E/Spin Warp in a 2D Slice - Ronald Price Magnetic resonance imaging not only reveals the structural, anatomic details of the body, as does CT, but also it can provide information on the physiological status and pathologies of its tissues, like nuclear medicine. It can display high-quality slice and 3D images of organs and vessels viewed from any perspective, with resolution better than 1 mm. MRI is perhaps most extraordinary and notable for the plethora of ways in which it can create unique forms of image contrast, reflective of fundamentally different biophysical phenomena. As with ultrasound, there is no risk from ionizing radiation to the patient or staff, since no X-rays or radioactive nuclei are involved. Instead, MRI harnesses magnetic fields and radio waves to probe the stable nuclei of the ordinary hydrogen atoms (isolated protons) occurring in water and lipid molecules within and around cells. MRI consists, in essence, of creating spatial maps of the electromagnetic environments around these hydrogen nuclei. Spatial variations in the proton milieus can be related to clinical differences in the biochemical and physiological properties and conditions of the associated tissues. Imaging of proton density (PD), and of the tissue proton spin relaxation times known as T1 and T2, all can reveal important clinical information, but they do so with approaches so dissimilar from one another that each is chosen for only certain clinical situations. T1 and T2 in a voxel are determined by different aspects of the rotations and other motions of the water and lipid molecules involved, as constrained by the local biophysical surroundings within and between its cells – and they, in turn, depend on the type of tissue and its state of health. Three other common applications of MRI exploit its capability to detect and image distinct movements of fluids: MR angiography (MRA), which rivals CT angiography but often requires no contrast medium, monitors the bulk flow of blood; functional MRI ( f MRI), distinguishes the perfusion of oxygenated blood from that of de-oxygenated, and lights up parts of the brain that are activated by a stimulus, rather like PET; and diffusion tensor imaging (DTI) indicates the diffusion of free water along tracts of axons, thereby bringing nerve trunks into view. There are variants on all of these themes, and on others as well. Magnetic Resonance Spectroscopy (MRS), for example, can perform non-invasive ‘virtual biopsies’ that allow identification of certain cancers and other lesions. And an MRI-guided needle biopsy can sample brain tissue from a region only millimeters in dimensions. MRI, however, involves deeper and more complex aspects of physics, technology, and biology than do most other imaging modalities, and it is widely considered to be correspondingly more difficult to learn. We could probably cover all of this rather comprehensively if we had 50 hours available rather than 2 ̶ but, to paraphrase a former Secretary of Defense, you tell your story in the time you have allotted. The four presenters and another physicist, Kevin King from GE, have combined their efforts to co-author a single slide show that describes essentials of MRI as simply as possible. It is obviously far from thorough, but hopefully it will succeed in explaining some of the basics in a simplified but still valid fashion; in providing a taste of the numerous capabilities and complexities of the modality; and in whetting your appetite to learn more. Part I. NMR, and Proton Density MRI of the 1D Patient (Wolbarst), begins with an introductory case study that illustrates a half dozen ways in which MRI provides valuable clinical information. It then explores the nuclear magnetic resonance (NMR) phenomenon, which underlies MRI. NMR can be introduced with either of two approaches. In the first, one thinks (loosely) of the nuclei of hydrogen atoms as (rotating and charged and therefore) magnetic objects, whose spin-axes tend to align in a strong external magnetic field, much like a compass needle. As with the Bohr atom, this spin-up/spin-down picture is a highly abridged version of the full quantum mechanical treatment, but still it leads to some useful, legitimate pictures of the NMR process occurring within a voxel: When RF photons of the correct (Larmor) frequency elevate protons in a fixed magnetic field out of their lower-energy spin state into the upper, the NMR phenomenon is indicated by the detectable absorption of RF power. With the addition of a linear gradient field along a multi-voxel, one-dimensional patient/phantom, as well, we can determine the water content of each compartment – an example of a real MRI study, albeit in 1D. Part I concludes with a discussion of the net magnetization at position x, m0(x), under conditions of dynamic thermal equilibrium, which leads into: Part II. Net Voxel Magnetization, m(x,t); T1-MRI; The MRI Device (Lemen), investigates the biophysics of the form of proton spin relaxation process characterized by the time T1. It then moves on to the creation of an MR image that displays the spatial variation in the values of this clinically relevant parameter, again in 1D. Finally, the design and workings of a clinical MRI machine are sketched, in preparation for: Part III. ‘Classical’ NMR; FID Imaging in 1D via k-Space (Yanasak) presents the second standard approach to NMR and MRI, the classical model. It focuses on the time dependence of the net nuclear magnetization, m(x,t), the overall magnetic field generated by the cohort of protons in the voxel at position x. Quite remarkably, this nuclear net magnetization itself acts in a strong magnetic field like a gyroscope in a gravitational field. This tack is better for explaining Free Induction Decay (FID), which involves a brief introduction to the Fourier transform and k-space. This leads to conventional Spin-Echo (S-E) reconstruction techniques for creating clinical images from raw data, and sets the stage for: Part IV. Spin-Echo; S-E / Spin Warp in a 2D Slice (Price) discusses application of the S- E sequence of radiofrequency pulses and gradient magnetic fields to the 1D patient. T2 is introduced but not explained. This Part also considers how to manipulate the image acquisition parameters so as to generate clinical pictures with contrast dominated by spatial variations in PD, T1, or T2. We conclude by demonstrating the spin-warp approach to imaging in 2D with a simple 2×2, 4-voxel example. Much of this material is presented in more detail in the chapter “MRI of the One-dimensional Patient, Part I”, in Advances in Medical Physics, Vol 5 (2014). Copies are on display at the Medical Physics Publishing booth. Learning Objectives: The participant will learn about the processes of NMR and T1 spin relaxation in a tissue voxel in a uniform magnetic field. The participant will learn about combining spin-up/spin-down NMR and T1 processes with a linear gradient to effect frequency-encoding of voxel spatial position. This approach can be used to create proton density and T1 MRI maps, respectively, of the contents of multi-voxel 1D phantoms. The participant will learn about how the ‘classical’ model of NMR it can generate Free Induction Decay (FID) images of 1D phantoms, which involves the use of the Fourier transform in k-space. This can lead simply into standard Spin-Echo images. The participant will learn about extending Spin-Echo imaging into 2 and more dimensions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-EF-BRA-04: Into 2, 3, and 4 Dimensions

NMR, and Proton Density MRI of the 1D Patient - Anthony Wolbarst Net Voxel Magnetization, m(x,t). T1-MRI; The MRI Device - Lisa Lemen ‘Classical’ NMR; FID Imaging in 1D via k-Space - Nathan Yanasak Spin-Echo; S-E/Spin Warp in a 2D Slice - Ronald Price Magnetic resonance imaging not only reveals the structural, anatomic details of the body, as does CT, but also it can provide information on the physiological status and pathologies of its tissues, like nuclear medicine. It can display high-quality slice and 3D images of organs and vessels viewed from any perspective, with resolution better than 1 mm. MRI is perhaps most extraordinary and notable for the plethora of ways in which it can create unique forms of image contrast, reflective of fundamentally different biophysical phenomena. As with ultrasound, there is no risk from ionizing radiation to the patient or staff, since no X-rays or radioactive nuclei are involved. Instead, MRI harnesses magnetic fields and radio waves to probe the stable nuclei of the ordinary hydrogen atoms (isolated protons) occurring in water and lipid molecules within and around cells. MRI consists, in essence, of creating spatial maps of the electromagnetic environments around these hydrogen nuclei. Spatial variations in the proton milieus can be related to clinical differences in the biochemical and physiological properties and conditions of the associated tissues. Imaging of proton density (PD), and of the tissue proton spin relaxation times known as T1 and T2, all can reveal important clinical information, but they do so with approaches so dissimilar from one another that each is chosen for only certain clinical situations. T1 and T2 in a voxel are determined by different aspects of the rotations and other motions of the water and lipid molecules involved, as constrained by the local biophysical surroundings within and between its cells – and they, in turn, depend on the type of tissue and its state of health. Three other common applications of MRI exploit its capability to detect and image distinct movements of fluids: MR angiography (MRA), which rivals CT angiography but often requires no contrast medium, monitors the bulk flow of blood; functional MRI ( f MRI), distinguishes the perfusion of oxygenated blood from that of de-oxygenated, and lights up parts of the brain that are activated by a stimulus, rather like PET; and diffusion tensor imaging (DTI) indicates the diffusion of free water along tracts of axons, thereby bringing nerve trunks into view. There are variants on all of these themes, and on others as well. Magnetic Resonance Spectroscopy (MRS), for example, can perform non-invasive ‘virtual biopsies’ that allow identification of certain cancers and other lesions. And an MRI-guided needle biopsy can sample brain tissue from a region only millimeters in dimensions. MRI, however, involves deeper and more complex aspects of physics, technology, and biology than do most other imaging modalities, and it is widely considered to be correspondingly more difficult to learn. We could probably cover all of this rather comprehensively if we had 50 hours available rather than 2 ̶ but, to paraphrase a former Secretary of Defense, you tell your story in the time you have allotted. The four presenters and another physicist, Kevin King from GE, have combined their efforts to co-author a single slide show that describes essentials of MRI as simply as possible. It is obviously far from thorough, but hopefully it will succeed in explaining some of the basics in a simplified but still valid fashion; in providing a taste of the numerous capabilities and complexities of the modality; and in whetting your appetite to learn more. Part I. NMR, and Proton Density MRI of the 1D Patient (Wolbarst), begins with an introductory case study that illustrates a half dozen ways in which MRI provides valuable clinical information. It then explores the nuclear magnetic resonance (NMR) phenomenon, which underlies MRI. NMR can be introduced with either of two approaches. In the first, one thinks (loosely) of the nuclei of hydrogen atoms as (rotating and charged and therefore) magnetic objects, whose spin-axes tend to align in a strong external magnetic field, much like a compass needle. As with the Bohr atom, this spin-up/spin-down picture is a highly abridged version of the full quantum mechanical treatment, but still it leads to some useful, legitimate pictures of the NMR process occurring within a voxel: When RF photons of the correct (Larmor) frequency elevate protons in a fixed magnetic field out of their lower-energy spin state into the upper, the NMR phenomenon is indicated by the detectable absorption of RF power. With the addition of a linear gradient field along a multi-voxel, one-dimensional patient/phantom, as well, we can determine the water content of each compartment – an example of a real MRI study, albeit in 1D. Part I concludes with a discussion of the net magnetization at position x, m0(x), under conditions of dynamic thermal equilibrium, which leads into: Part II. Net Voxel Magnetization, m(x,t); T1-MRI; The MRI Device (Lemen), investigates the biophysics of the form of proton spin relaxation process characterized by the time T1. It then moves on to the creation of an MR image that displays the spatial variation in the values of this clinically relevant parameter, again in 1D. Finally, the design and workings of a clinical MRI machine are sketched, in preparation for: Part III. ‘Classical’ NMR; FID Imaging in 1D via k-Space (Yanasak) presents the second standard approach to NMR and MRI, the classical model. It focuses on the time dependence of the net nuclear magnetization, m(x,t), the overall magnetic field generated by the cohort of protons in the voxel at position x. Quite remarkably, this nuclear net magnetization itself acts in a strong magnetic field like a gyroscope in a gravitational field. This tack is better for explaining Free Induction Decay (FID), which involves a brief introduction to the Fourier transform and k-space. This leads to conventional Spin-Echo (S-E) reconstruction techniques for creating clinical images from raw data, and sets the stage for: Part IV. Spin-Echo; S-E / Spin Warp in a 2D Slice (Price) discusses application of the S- E sequence of radiofrequency pulses and gradient magnetic fields to the 1D patient. T2 is introduced but not explained. This Part also considers how to manipulate the image acquisition parameters so as to generate clinical pictures with contrast dominated by spatial variations in PD, T1, or T2. We conclude by demonstrating the spin-warp approach to imaging in 2D with a simple 2×2, 4-voxel example. Much of this material is presented in more detail in the chapter “MRI of the One-dimensional Patient, Part I”, in Advances in Medical Physics, Vol 5 (2014). Copies are on display at the Medical Physics Publishing booth. Learning Objectives: The participant will learn about the processes of NMR and T1 spin relaxation in a tissue voxel in a uniform magnetic field. The participant will learn about combining spin-up/spin-down NMR and T1 processes with a linear gradient to effect frequency-encoding of voxel spatial position. This approach can be used to create proton density and T1 MRI maps, respectively, of the contents of multi-voxel 1D phantoms. The participant will learn about how the ‘classical’ model of NMR it can generate Free Induction Decay (FID) images of 1D phantoms, which involves the use of the Fourier transform in k-space. This can lead simply into standard Spin-Echo images. The participant will learn about extending Spin-Echo imaging into 2 and more dimensions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Symmetry and Topology Code of the Cluster Self-Assembly of Intermetallic Compounds A{sub 2}{sup [16]}B{sub 4}{sup [12]} of the Friauf Families Mg{sub 2}Cu{sub 4} and Mg{sub 2}Zn{sub 4}

A geometrical and topological analysis of intermetallic compounds A{sub 2}{sup [16]}B{sub 4}{sup [12]} of the Friauf families Mg{sub 2}Cu{sub 4} (formed in 273 binary and 775 ternary systems) and Mg{sub 2}Zn{sub 4} (formed in 67 binary and 121 ternary systems) has been performed. Basic 3D nets are determined in the form of a graph, whose sites correspond to the centers of tetrahedral cluster precursors В{sub 4}: Cu-type net with a coordination number (CN) of 12 for Mg{sub 2}Cu{sub 4} and a BN-type net with a CN of 8 for Mg{sub 2}Zn{sub 4}. The symmetry and topology code is determined for the crystal structure self-assembly in the following form: primary chain S{sub 3}{sup 1} → microlayer S{sub 3}{sup 2} → microframework S{sub 3}{sup 3}. An identical mechanism of primary chain S{sub 3}{sup 1} self-assembly from B{sub 4} clusters with participation of large template atoms A{sup [16]} for both families and a mechanism of S{sub 3}{sup 2} microlayer self-assembly from parallel S{sub 3}{sup 1} chains in Mg{sub 2}Cu{sub 4} (basic 2D net 4{sup 4}) and shifted chains in Mg{sub 2}Zn{sub 4} (basic 2D net 3{sup 6}) have been determined for the first time. The constant chemical composition of intermetallic compounds A{sub 2}{sup [16]}B{sub 4}{sup [12]} is related to the functional difference between the B atoms forming 3D frameworks and template atoms А. The numbers of chemically different В{sub 4} clusters for the Mg{sub 2}Cu{sub 4} and Mg{sub 2}Zn{sub 4} families are found to be 25 and 19, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5*

When using five climate model simulations of the response to an abrupt quadrupling of CO 2 , the authors perform the first simultaneous model intercomparison of cloud feedbacks and rapid radiative adjustments with cloud masking effects removed, partitioned among changes in cloud types and gross cloud properties. After CO 2 quadrupling, clouds exhibit a rapid reduction in fractional coverage, cloud-top pressure, and optical depth, with each contributing equally to a 1.1 W m -2 net cloud radiative adjustment, primarily from shortwave radiation. Rapid reductions in midlevel clouds and optically thick clouds are important in reducing planetary albedo in every model. As the planet warms, clouds become fewer, higher, and thicker, and global mean net cloud feedback is positive in all but one model and results primarily from increased trapping of longwave radiation. As was true for earlier models, high cloud changes are the largest contributor to intermodel spread in longwave and shortwave cloud feedbacks, but low cloud changes are the largest contributor to the mean and spread in net cloud feedback. The importance of the negative optical depth feedback relative to the amount feedback at high latitudes is even more marked than in earlier models. Furthermore, the authors show that the negative longwave cloud adjustment inferred in previous studies is primarily caused by a 1.3 W m -2 cloud masking of CO 2 forcing. Properly accounting for cloud masking increases net cloud feedback by 0.3 W m -2 K -1 , whereas accounting for rapid adjustments reduces by 0.14 W m -2 K -1 the ensemble mean net cloud feedback through a combination of smaller positive cloud amount and altitude feedbacks and larger negative optical depth feedbacks.

58 GEOSCIENCES↗
DOE OSTI2014

Technical Analysis of Installed Micro-Combined Heat and Power Fuel-Cell System

Combined heat and power fuel cell systems (CHP-FCSs) provide consistent electrical power and hot water with greater efficiency and lower emissions than alternative sources. These systems can be used either as baseload, grid-connected, or as off-the-grid power sources. This report presents a technical analysis of 5 kWe CHP-FCSs installed in different locations in the U.S. At some sites as many as five 5 kWe system is used to provide up to 25kWe of power. Systems in this power range are considered “micro”-CHP-FCS. To better assess performance of micro-CHP-FCS and understand their benefits, the U.S. Department of Energy worked with ClearEdge Power to install fifteen 5-kWe PBI high temperature PEM fuel cells (CE5 models) in the commercial markets of California and Oregon. Pacific Northwest National Laboratory evaluated these systems in terms of their economics, operations, and technical performance. These units were monitored from September 2011 until June 2013. During this time, about 190,000 hours of data were collected and more than 17 billion data points were analyzed. Beginning in July 2013, ten of these systems were gradually replaced with ungraded systems (M5 models) containing phosphoric acid fuel cell technology. The new units were monitored until June 2014 until they went offline because ClearEdge was bought by Doosan at the time and the new manufacturer did not continue to support data collection and maintenance of these units. During these two phases, data was collected at once per second and data analysis techniques were applied to understand behavior of these systems. The results of this analysis indicate that systems installed in the second phase of this demonstration performed much better in terms of availability, consistency in generation, and reliability. The average net electrical power output increased from 4.1 to 4.9 kWe, net heat recovery from 4.7 to 5.4 kWth, and system availability improved from 94% to 95%. The average net system electric efficiency, average net heat recovery efficiency, and overall net efficiency of the system increased respectively from 33% to 36%, from 38% to 41%, and from 71% to 76%. The temperature of water sent to sit however reduced by about 16% from 51⁰C to 43 ⁰C. This was a control strategy and the temperature can be controlled depending on building heat demands. More importantly, the number of shutdowns and maintenance events required to keep the systems running at the manufacturer’s rated performance specifications were substantially reduced by about 76% (for 8 to 10 units running over a one-year period). From July 2012 to June 2013, there were eight CE5 units in operation and a total of 134 scheduled and unscheduled shutdowns took place. From July 2013 to June 2014, between two to ten units were in operation and only 32 shutdowns were reported (all unscheduled). In summary, the number of shutdowns reduced from 10 shutdowns per month on average for eight CE5units to an average of 2.7 shutdowns per month for M5 units (between two to ten units).

combined heat and power↗
DOE OSTI2020

Distributed Solar Utility Tariff and Revenue Impact Analysis: A Guidebook for International Practitioners

Depending on a range of technical, economic, policy, regulatory, and market-related factors, the adoption of DPV may have a net positive or negative financial impact on utilities and ratepayers. This guidebook provides a methodological approach for quantifying the net financial impact of DPV on utilities (in the form of “net revenue losses”) and ratepayers (in the form of “net tariff impacts”). Written for technical staff at utilities, regulatory bodies, energy ministries, research institutes, and civil society organizations who wish to better understand the financial impacts of DPV in their jurisdiction, this guidebook can be used to inform important decisions regarding DPV compensation, tariff design, and regulatory cost allocations, among other aspects. It is focused exclusively on customer-sited DPV systems and adopts a primarily cost-based (as opposed to an equally valid value-based) perspective to understanding DPV financial impacts. The approach for calculating net revenue losses can be applied in nearly any utility jurisdiction, regardless of institutional arrangements, business models, or regulatory paradigms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗