Lawrence Berkeley Research Articles

Using the United States Wind Turbine Database to Identify Increasing Turbine Size, Capacity and Other Development Trends

The purpose of this article was to analyze data associated with advances in wind energy across the United States. While governments, academia, and the private sector generally know patterns of wind turbine development (i.e. turbine size and capacity growing in recent years), there is no known independent, reliable, and/or updated summary of these variables. Using data collected by the Lawrence Berkeley National Laboratory and partners, this study used descriptive statistics to show turbine development and growth patterns from 1981-2019. The newly created United States Wind Turbine Database (USWTDB) represents the most comprehensive account of wind turbine information and was updated in January 2020. Variables I am interested in here are turbine manufacturer, state of project, turbine and project capacity, and turbine size. Findings provide empirical evidence to support the common, yet previously unrefined statements that wind turbines are growing larger in number, size and capacity. This growth is varied over spatial and temporal scales. I also provide evidence to show patterns of turbine manufacturing, with GE Wind dominating much of the US wind energy landscape today. I hope this work provides a timely resource for those interested in a variety of questions surrounding wind energy development in the United States. Perhaps more importantly, this analysis will hopefully inspire others to use what the USWTDB provides and answer larger questions surrounding wind energy futures.

Temperature response and brine availability to heated boreholes in bedded salt

AbstractThere is a growing need for disposal of high‐level nuclear waste. To reduce uncertainty associated with brine availability to repository excavations in salt formations, a collaboration between Sandia, Los Alamos, and Lawrence Berkeley National Laboratories is performing a series of borehole‐scale coupled process tests. Here, we report on the first round of the Brine Availability Test in Salt (BATS) project, a “shakedown” experiment called Phase 1s. Experimental testing included placing a resistive heater, a 260‐W radiative heater, and a 750‐W radiative heater within previously drilled horizontal boreholes at the Waste Isolation Pilot Plant (WIPP) while monitoring temperature and water inflow. The experiments successfully achieved the targeted temperature of 120 °C when using the 750‐W radiative heater. Simulations using FEHM (Finite Element Heat and Mass transfer code) and TOUGH‐FLAC (Transport Of Unsaturated Groundwater and Heat–Fast Lagrangian Analysis of Continua) were able to accurately predict the coupled thermo–hydro–mechanical–chemical response of salt, matching the observed temperature and brine production. Due to the extremely low permeability of salt, these systems take many years to reach steady state when perturbed by mining activities. Long‐term numerical simulations are used to develop the initial pressure and saturation conditions. The inclusion of a damaged rock zone with higher permeability around the borehole also affects the saturation and pressure distributions and plays an important role in dissipating the potential for thermal pressurization. Knowledge gained from this round of experimentation and simulation will be used to conduct the next BATS project experiment in new boreholes at WIPP.

Proposed design and optimization of a higher harmonic cavity for ALS-U.

Higher harmonic cavities (HHCs), also known as Landau cavities, have been proposed to increase the beam lifetime and Landau damping by lengthening the bunch and increasing the synchrotron tune spread. Here, we present an optimized 1.5 GHz normal conducting HHC design for the Advanced Light Source Upgrade project at Lawrence Berkeley National Lab with a superconducting-like geometry for lower R/Q. The optimization goal is to reach the required shunt impedance while maintaining a relatively high Q value of the cavities. A multi-objective genetic algorithm (MOGA)-based optimization process is applied to optimize the radio frequency (RF) design. This study serves as an example of how a genetic algorithm can be used to optimize RF cavities. Detailed exploration and characterization of the MOGA-based RF cavity optimization have been demonstrated from the aspects of minimizing the coupled bunch instabilities and analyzing the higher-order modes and the corresponding impedance of the HHC.

Editorial introductions

Current Opinion in Hematology was launched in 1994. It is part of a successful series of review journals whose unique format is designed to provide a systematic and critical assessment of the literature as presented in the many primary journals. The field of hematology is divided into nine sections that are reviewed once a year. Each section is assigned a Section Editor, a leading authority in the area, who identifies the most important topics at that time. Here we are pleased to introduce the Editor and the Section Editor for this issue. EDITOR IN CHIEF Narla MohandasNarla MohandasDr Narla Mohandas is Vice President for Research at New York Blood Center, New York, USA. He received his doctoral degree from Washington University, St. Louis, USA in Chemical Engineering. After completing post-doctoral training in hematology research with Dr Marcel Bessis, Institute of Cellular Pathology in Paris, France, he joined the Faculty of Laboratory Medicine at University of California, San Francisco where he spent 13 years. In 1989, he moved to Lawrence Berkeley National Laboratory, University of California to head the Hematopoiesis group. During his 12-year tenure at the Berkeley Laboratory he also served as Interim Director of Human Genome Project for three years. In 2001, he moved to the New York Blood Center. Dr Mohandas’ scientific interests during his 45-year research career have focused on red cell physiology and pathology. In particular, his efforts have contributed to improved understanding of the molecular and structural basis for red cell membrane disorders, developing detailed mechanistic insights into pathophysiology of thalassemias and sickle cell disease, characterizing structural and functional changes induced in red cells by the malarial parasite, plasmodium falciparum. His research efforts during last ten years are focused on molecular understanding of normal and disordered human erythropoiesis including Diamond-Blackfan anemia and Myelodysplasia. Dr Mohandas served as a member of the National Institutes of Health Hematology study section for 18 years and is currently member of the NHLBI Advisory Council. He has been a member of numerous committees including the Executive committee of American Society of Hematology. He served as Associate Editor of Blood from 2003–2012 and is currently Editor-in-Chief of Blood Cells, Molecules and Disease and of Current Opinion in Hematology. SECTION EDITOR David C. DaleDavid C. DaleDr David C. Dale is Professor of Medicine and former Dean of the School of Medicine at the University of Washington. He is a native of Knoxville, Tennessee, and a graduate of the Harvard Medical School. He received post-graduate training at Massachusetts General Hospital, the National Institutes of Health and the University of Washington. He has career-long interests in medical education, medical history and basic and clinical research focused on inflammation, neutrophils, and the clinical problem of neutropenia. Dr Dale has served as President of the American College of Physicians, the Society of General Internal Medicine, the Western Association of Physicians, the Medical Honor Society-Alpha Omega Alpha, and the American Clinical and Climatological Association. He is Editor-in-Chief Emeritus for the Scientific American Medicine the medicine text also known as ACP Medicine. He is the Founder and Co-Director of the Severe Chronic Neutropenia International Registry and Co-President (with Rose Marie Dale) of the Friends of the University of Washington School of Medicine. Dr Dale is actively involved in teaching, research and patient care at the University of Washington and a member of the Council for the Washington Chapter of ACP.

KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response

Integrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks - the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.Funding: This work was supported by grants from the Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy [to J.R., D.U., S.C., N.L.H., M.J., C.J.M], the Laboratory Directed Research and Development (LDRD) Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231, the NIH (Monarch R24 OD011883, Illuminating the Druggable Genome U01 CA239108-01), a Training Grant from the NLM, NIH to the University of Colorado Anschutz Medical Campus Computational Bioscience Training Program [T15LM009451 to T.J.C.], the National Virtual Biotechnology Laboratory (NVBL), and the Google Cloud COVID-19 Research Grants program. Conflict of Interest: The authors declare no competing interests.

Accelerate Scientific Deep Learning Models on Heterogeneous Computing Platform with FPGA

AI and deep learning are experiencing explosive growth in almost every domain involving analysis of big data. Deep learning using Deep Neural Networks (DNNs) has shown great promise for such scientific data analysis applications. However, traditional CPU-based sequential computing without special instructions can no longer meet the requirements of mission-critical applications, which are compute-intensive and require low latency and high throughput. Heterogeneous computing (HGC), with CPUs integrated with GPUs, FPGAs, and other science-targeted accelerators, offers unique capabilities to accelerate DNNs. Collaborating researchers at SHREC1at the University of Florida, CERN Openlab, NERSC2at Lawrence Berkeley National Lab, Dell EMC, and Intel are studying the application of heterogeneous computing (HGC) to scientific problems using DNN models. This paper focuses on the use of FPGAs to accelerate the inferencing stage of the HGC workflow. We present case studies and results in inferencing state-of-the-art DNN models for scientific data analysis, using Intel distribution of OpenVINO, running on an Intel Programmable Acceleration Card (PAC) equipped with an Arria 10 GX FPGA. Using the Intel Deep Learning Acceleration (DLA) development suite to optimize existing FPGA primitives and develop new ones, we were able accelerate the scientific DNN models under study with a speedup from 2.46x to 9.59x for a single Arria 10 FPGA against a single core (single thread) of a server-class Skylake CPU.

Direct time-resolved detection and quantification of key reactive intermediates in diethyl ether oxidation at T = 450-600 K.

High-pressure multiplexed photoionization mass spectrometry (MPIMS) with tunable vacuum ultraviolet (VUV) ionization radiation from the Lawrence Berkeley Labs Advanced Light Source is used to investigate the oxidation of diethyl ether (DEE). Kinetics and photoionization (PI) spectra are simultaneously measured for the species formed. Several stable products from DEE oxidation are identified and quantified using reference PI cross-sections. In addition, we directly detect and quantify three key chemical intermediates: peroxy (ROO˙), hydroperoxyalkyl peroxy (˙OOQOOH), and ketohydroperoxide (HOOP[double bond, length as m-dash]O, KHP). These intermediates undergo dissociative ionization (DI) into smaller fragments, making their identification by mass spectrometry challenging. With the aid of quantum chemical calculations, we identify the DI channels of these key chemical species and quantify their time-resolved concentrations from the overall carbon atom balance at T = 450 K and P = 7500 torr. This allows the determination of the absolute PI cross-sections of ROO˙, ˙OOQOOH, and KHP into each DI channel directly from experiment. The PI cross-sections in turn enable the quantification of ROO˙, ˙OOQOOH, and KHP from DEE oxidation over a range of experimental conditions that reveal the effects of pressure, O2 concentration, and temperature on the competition among radical decomposition and second O2 addition pathways.

Proton Light Yield of Fast Plastic Scintillators for Neutron Imaging

Plastic organic scintillators have been tailored in composition to achieve ultra-fast temporal response, thereby enabling the design and development of fast neutron detection systems with high timing resolution. Eljen Technology's plastic organic scintillators -- EJ-230, EJ-232, and EJ-232Q -- are prospective candidates for use in emerging neutron imaging systems, where fast timing is paramount. To support the neutron response characterization of these materials, the relative proton light yields of EJ-230, EJ-232, and EJ-232Q were measured at the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory. Using a broad-spectrum neutron source and a double time-of-flight technique, the proton light yield relations were obtained over a proton recoil energy range of approximately 300 keV to 4 MeV. The EJ-230, EJ-232, and EJ-232Q scintillators exhibited similar proton light yield relations to each other as well as to other plastic scintillators with the same polymer base material. A comparison of the relative proton light yield of different sized cylindrical EJ-232 and EJ-232Q scintillators also revealed consistent results. This work provides key input data for the realistic computational modeling of neutron detection technologies employing these materials, thereby supporting new capabilities in near-field radionuclide detection for national security applications.

Diagnostic Accuracy of Amyloid versus FDG PET in Patients with Cognitive Impairment and Autopsy

Background: β-Amyloid and Flurodeoxyglucose (FDG) PET are clinically available diagnostic tools in the assessment of neurodegenerative disorders. We compared the diagnostic accuracy of [11C]Pittsburgh Compound B (PIB) and [18F]FDG PET in a large sample of patients with cognitive decline and neuropathology. Methods: 101 individuals underwent PIB and FDG during life and post-mortem neuropathological assessment. PET scans were visually interpreted by three raters blinded to clinical information. PIB scans were rated as positive or negative for cortical retention while FDG scans were read as showing an Alzheimer’s disease (AD) or non-AD metabolic pattern. AD neuropathological changes (ADNC) were measured at autopsy. Majority visual reads were compared to intermediate-high ADNC as the gold standard. Findings: 60 males and 41 females were included (average age: 67·2Y, mean MMSE: 21·9, mean PET-to-autopsy interval: 4·4Y). At autopsy, 32 patients showed pure AD pathology, 13 mixed AD and non-AD and 56 non-AD neuropathology. Compared to FDG, PIB showed higher sensitivity for detecting intermediate-high ADNC (96% [95% confidence interval: 89-100%] vs 80% [68-92%] for FDG, p=0·02) and higher negative predictive value (96% [91-100%] vs 84% [74-93%], p=0·01), but equivalent specificity (86% [76-95%] vs 84% [74-93%], p=0·80) and positive predictive value (84% [74-94%] vs 80% [68-92%], p=0·53). In patients with congruent PIB and FDG reads (77/101), combined sensitivity was 97% [92-100%] and specificity 98% [93-100%]. Nine of 24 patients with incongruent reads were found to have co-occurrence of AD and non-AD pathologies. Interpretation: PIB had higher sensitivity to detect intermediate-high ADNC compared to FDG, particularly at early clinical stages, with similar specificity. When both modalities are congruent, sensitivity and specificity approach 100%, while mixed pathology should be considered when PIB and FDG are incongruent. Funding Statement: National Institute of Health (R01-AG045611, P01-AG1972403, P50-AG023501, P30-AG010129, R01-AG032306, K24-AG053435, R01-AG038791, K08-AG052648, P30-AG010129, K23-AG045289), the Alzheimer’s Association (AARF-16-443577), Bluefield Project to Cure FTD, Tau Consortium. Declaration of Interests: Orit H Lesman-Segev – none, Renaud La Joie – none, Iryna Lobach - none, Howard J Rosen - Dr Rosen has received research support from Biogen Pharmaceuticals, has consulting agreements with Wave Neuroscience and Ionis Pharmaceuticals, and receives research support from NIH. Sang Won Seo - none, Mustafa Janabi - none, Suzanne L Baker - consult for Genentech, Lauren Edwards - none, Julie Pham - none, John Olichney - has received grant funding from Roche/Genentech, Adam Boxer - Dr. Boxer receives research support from NIH, the Tau Research Consortium, the Association for Frontotemporal Degeneration, Bluefield Project to Cure Frontotemporal Dementia, Corticobasal Degeneration Solutions, the Alzheimer’s Drug Discovery Foundation and the Alzheimer's Association. He has served as a consultant for Aeton, Abbvie, Alector, Amgen, Arkuda, Arvinas, Asceneuron, Ionis, Lundbeck, Novartis, Passage BIO, Sangamo, Samumed, Third Rock, Toyama and UCB, and received research support from Avid, Biogen, BMS, C2N, Cortice, Eli Lilly, Forum, Genentech, Janssen, Novartis, Pfizer, Roche and TauRx. Eric Huang - none, Marilu Gorno-Tempini – none, Ji-Hye L. Hwang - none, Charles DeCarli - none, Mackenzie Hepker - none, Salvatore Spina - none, Bruce L Miller – Dr. Miller receives grants in support of the Memory and Aging Center from the NIH/NIA, the Quest Diagnostics Dementia Pathway Collaboration, Cornell University and The Bluefield Project to Cure Frontotemporal Dementia. He serves as Medical Director for the John Douglas French Foundation; Scientific Director for the Tau Consortium; Director/Medical Advisory Board of the Larry L. Hillblom Foundation; and Past President of the International Society of Frontotemporal Dementia (ISFTD). Lea T Grinberg - Dr. Grinberg receives research support from Avid Radiopharmaceuticals, Eli Lilly. She has received consulting fees from Simon Foundation and Cura Sen Inc. She serves as associate editor for Frontiers in Aging Neurosciences, Frontiers in Dementia and Journal of Alzheimer Disease William W. Seeley - Dr. Seeley has received consulting fees from Biogen Idec, Third Rock Ventures, and Guidepoint Global Advisors. He serves on the editorial board of Acta Neuropathologica and Neuroimage Clinical. William J Jagust - Dr. Jagust has received honoraria as a consultant to Novartis, Genentech, and Curasen Gil D Rabinovici – Dr. Rabinovici receives research support from Avid Radiopharmaceuticals, Eli Lilly, GE Healthcare and Life Molecular Imaging. He has received honoraria as a consultant for Axon Neurosciences, Eisai and Merck, and speaking honoraria from GE Healthcare. Dr. Rabinovici serves as Associate Editor for JAMA Neurology. Ethics Approval Statement: Informed consent was obtained from all subjects or their surrogate decision makers, and the UCSF, UCD, University of California Berkeley (UCB) and/or Lawrence Berkeley National Laboratory (LBNL) Institutional Review Boards for human research approved the study.

A new quench detection method for HTS magnets: stray-capacitance change monitoring

Fast quench detection is a key requirement for the successful implementation of superconducting magnet technology. In high temperature superconductor magnets, this issue is especially challenging due to the low quench propagation velocity, and presently represents one of the main factors limiting their application. A new detection technique based on stray-capacitance monitoring is proposed. The capacitance between electrically-insulated magnet elements, such as magnet structure and end parts, is utilized as an indication of local heat deposition in the conductor. In fact, the relative permittivity of helium drops when it changes from the liquid to the gaseous phase. Thus, when heating occurs, part of the helium impregnating the insulation layers boils off, and the monitored stray-capacitance decreases. The proposed technique is successfully demonstrated on three small-scale Bi-2212 magnets manufactured at the Lawrence Berkeley National Laboratory. Results from the detection of thermal runaways and spot-heater induced quenches are reported and discussed. Advantages and limitations of the stray-capacitance method with respect to conventional quench detection methods are assessed.

Platinum atoms liberate hydrogen from methanol

Fuel-cell automobiles powered with clean-burning hydrogen are slowly being commercialized for private use. The pace could accelerate, benefiting the environment, if hydrogen was supplied not as a high-pressure gas but rather as an inexpensive liquid that was shipped and pumped using today’s vast liquid-fuel infrastructure. A study describing a single-atom catalyst that efficiently strips hydrogen from methanol, a multimillion-metric-ton commodity, nudges that car-fuel scenario a step closer to reality (J. Am. Chem. Soc. 2019, DOI: 10.1021/jacs.9b09431). Ji Su and Gabor A. Somorjai of Lawrence Berkeley National Laboratory and the University of California, Berkeley, and coworkers have developed a procedure for using ascorbic acid and a platinum precursor to anchor isolated platinum atoms on a porous ceria support. The dispersion of the atoms, which maximizes the use of expensive metals, was confirmed via microscopy and spectroscopy; it shows that the atoms remain stable at elevated temperatures for more than 120 hours.

Actinium Biokinetics and Dosimetry: What is the Impact of Ac-227 in Accelerator-Produced Ac-225?

Among the growing list of alpha-emitting isotopes now available for pharmaceutical development, Ac-225 can act as an in vivo alpha-generator radionuclide and is of great interest for new targeted alpha-therapy applications. To seek further development of Ac-225 bioconjugate therapeutics, ongoing efforts aim at addressing current limitations, including lacking supply of the radioisotope, insufficient understanding of its biodistribution and biodosimetry, poor retention of alpha-emitting daughter products at the target site, as well as inadequate chelation, one of the major drawbacks. The U.S. Department of Energy’s Isotope Program has been exploring new pathways for the production of the radioisotope Ac-225 at accelerator facilities to address a potential increased need for medical applications. However, the presence of co-produced, long-lived Ac-227 (21.8 y half-life) is observed in about 0.15-0.3 activity percent in the accelerator derived product at the end of target bombardment. Up to 0.5 activity percent values due to Ac-227 could be anticipated in a research/clinical setting. One goal of our work is to delineate the biodistribution of Ac-225, its short-lived daughter products, and potential trace contaminants such as Ac-227 that may be co-produced in new larger-scale accelerator derived processes. The biokinetics of unchelated, chelated (with classic macrocyclic structures, amino-polycarboxylic acids, or new hydroxypyridinone ligands under development), and bio-conjugated Ac-225 and Ac-227 were determined in several mouse models. The resulting biodistribution and dosimetry profiles highlight significant differences among isotope and ligand combinations that must be considered and addressed in order to ensure bioconjugated Ac-225 is safe for use in the clinic. Part of the discussed work was supported by the U.S. Department of Energy’s Isotope Program in the Office of Nuclear Physics at the Lawrence Berkeley National Laboratory under Contract DE-AC02-05CH11231.

Development of a new high temperature oven for the production of intense metal ion beams with ECR ion sources.

High Temperature Ovens (HTOs) have widely been used to evaporate refractory materials in electron cyclotron resonance ion sources to produce hundreds of microamperes of multiply and highly charged metal ion beams. To meet the demands of milliamperes of multiply charged uranium and other heavy metal ion beams for future accelerators, a new and low-cost HTO is under development at Lawrence Berkeley National Laboratory for better long-term stability at high evaporation rates. ANSYS simulations have been carried out to optimize the new HTO with low heating current to reduce the electromagnetic forces as an HTO is immersed in the strong ECRIS magnetic fields. A larger loading volume is employed to deal with higher material consumption. Off-line tests have shown that the unloaded new HTO operates stably up to 1800-1900 °C with low temperature gradients and good repeatability. This paper presents and discusses the conceptual design features of the new HTO and off-line tests.

Papers we should be reading

This issue's recommended paper comes from Axel Hübl (Lawrence Berkeley National Laboratory). Among other distinctions, Axel received the ACM/IEEE-CS George Michael Memorial High Performance Computing Fellowship in 2016.

Dipole Magnets above 20 Tesla: Research Needs for a Path via High-Temperature Superconducting REBCO Conductors

To enable the physics research that continues to deepen our understanding of the Universe, future circular colliders will require a critical and unique instrument—magnets that can generate a dipole field of 20 T and above. However, today’s maturing magnet technology for low-temperature superconductors (Nb-Ti and Nb3Sn) can lead to a maximum dipole field of around 16 T. High-temperature superconductors such as REBCO can, in principle, generate higher dipole fields but significant challenges exist for both conductor and magnet technology. To address these challenges, several critical research needs, including direct needs on instrumentation and measurements, are identified to push for the maximum dipole fields a REBCO accelerator magnet can generate. We discuss the research needs by reviewing the current results and outlining the perspectives for future technology development, followed by a brief update on the status of the technology development at Lawrence Berkeley National Laboratory. We present a roadmap for the next decade to develop 20 T-class REBCO accelerator magnets as an enabling instrument for future energy-frontier accelerator complex.

Chemical landmark rededicated

The American Chemical Society has rededicated Lawrence Berkeley National Laboratory, known as Berkeley Lab, as a National Historic Chemical Landmark. The ceremony took place on Aug. 11 at the Lawre...

Can an Off-Nominal Landing by an MMRTG-Powered Spacecraft Induce a Special Region on Mars When No Ice Is Present?

This work aims at addressing whether a catastrophic failure of an entry, descent, and landing event of a Multimission Radioisotope Thermoelectric Generator-based lander could embed the heat sources into the martian subsurface and create a local environment that (1) would temporarily satisfy the conditions for a martian Special Region and (2) could establish a transport mechanism through which introduced terrestrial organisms could be mobilized to naturally occurring Special Regions elsewhere on Mars. Two models were run, a primary model by researchers at the Lawrence Berkeley National Laboratory and a secondary model by researchers at the Jet Propulsion Laboratory, both of which were based on selected starting conditions for various surface composition cases that establish the worst-case scenario, including geological data collected by the Mars Science Laboratory at Gale Crater. The summary outputs of both modeling efforts showed similar results: that the introduction of the modeled heat source could temporarily create the conditions established for a Special Region, but that there would be no transport mechanism by which an introduced terrestrial microbe, even if it was active during the temporarily induced Special Region conditions, could be transported to a naturally occurring Special Region of Mars.

Python-based calculation tool of wind-pressure coefficients on building envelopes

Wind pressure distribution is an essential factor for calculation of airflow rates in controlled natural ventilation systems for both indoor air quality and cooling purposes. A pressure coefficient calculation model, CpCalc, with a modular parametric approach based on analysis of wind tunnel tests was developed by Mario Grosso within the COMIS workshop, held at Lawrence Berkeley Laboratory, UCB, CA, USA, in 1988-1992, and upgraded for the European Project PASCOOL in 1994-2001. This software allows for calculating Cp at any point of a building façade and roof slope as a function of various climate, environmental, and building geometry parameters. The present paper describes a further upgrading of this software using a Pyton script, which allows for applying CpCalc to hourly-based energy dynamic simulation codes as well as being connectable in future with parametric design software.

Impact of solar insulation film on the cooling load of an office building in Singapore - A simulation study

This study investigates the properties of a pre-existing high-performance Low-E Double Glazed Unit (DGU) commonly used for fenestration in buildings in Singapore through simulation. Lawrence Berkeley National Laboratory window series software Optics 6 and Window 7 were used to determine the thermal properties of the glazing systems. The window series software based calculations revealed that upon application of the film, Solar Heat Gain Coefficient (SHGC) and the Thermal Transmittance (U-Value) were reduced by 13% and 1%, respectively. Trimble SketchUp Pro was used to develop the geometry of the medium-rise office building, and the OpenStudio interface for EnergyPlus was used to simulate each scenario. Five studies were undertaken to holistically understand the impact of solar insulation film including the reduction in the cooling load and energy usage. The energy study consists of simulating two scenarios so that ACMV component capacities and flow rates are automatically calculated by EnergyPlus. These simulations quantified that after applying the window film, the cooling load reduction on a design day was 8%, and the annual cooling energy savings was approximately 9%. Higher cooling load reduction and cooling energy savings occurred for the shallow floor plan. The numerical calculations of Envelope Thermal Transfer Value (ETTV) suggest a comparative improvement of 14% with the application of a window film.

Outlier Detection in Wireless Sensor Networks Data by Entropy Based K-NN Predictor

Anomaly (outlier) detection is plays very significant role in ESN based monitoring application using on large data used for biomedical and defence. Wireless Sensor network monitor environmental parameters (temperature, humidity, pressure, vibration etc). Group of sensor nodes forms a (WSN) and observations collected from these sensor produces low data quality and reliability due to the limited energy, memory, computation capability and bandwidth. The dynamic environment of network and roughness of the working condition are also responsible to generate inaccuracy in measurements. In this paper, an approach for outliers detection based entropy value of received sensor voltages is applied using KNN prediction model .The algorithm development and analysis involves a real time database generated on 14 sets of MICA2 wireless sensor kit with anomaly inserted by real time motion based intrusion in the lab by volunteers from Intel Berkeley lab. On each sensor data pair segmentation is applied by fixed window size in order get large outliers’ measurements training dataset. The analysis demonstrates the measurement accuracy in detection of number of outliers that its 86%. Moreover, the algorithm also provides an analysis in terms of impact of variation in distance types and number of nearest neighbours in the KNN prediction model. This work is helpful in the application in the situations where high amount of noise or distortions are present. The outlier part from distorted data can be figured out and recollected to enhance application accuracy.