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Open Access Publications from the University of California

Recent Work

Lawrence Berkeley National Laboratory (Berkeley Lab) has been a leader in science and engineering research for more than 70 years. Located on a 200 acre site in the hills above the Berkeley campus of the University of California, overlooking the San Francisco Bay, Berkeley Lab is a U.S. Department of Energy (DOE) National Laboratory managed by the University of California. It has an annual budget of nearly $480 million (FY2002) and employs a staff of about 4,300, including more than a thousand students.

Berkeley Lab conducts unclassified research across a wide range of scientific disciplines with key efforts in fundamental studies of the universe; quantitative biology; nanoscience; new energy systems and environmental solutions; and the use of integrated computing as a tool for discovery. It is organized into 17 scientific divisions and hosts four DOE national user facilities. Details on Berkeley Lab's divisions and user facilities can be viewed here.

Solar conversion of CO2 to formate

(2019)

The Statement of Work for this CRADA was initially focused on optimization of a photoelectrochemical device, to be constructed using expertise in the Joint Center for Artificial Photosynthesis (JCAP) at LBNL, for transformation of CO2 into formate using a new catalyst developed by TCRDL. The work was to be jointly performed by LBNL and TCRDL at LBNL, and to involve an 18 month stay by a TCRDL researcher, Dr. Takeo Arai, at LBNL. Due to a family emergency this plan had to be cancelled after 6 months, and was replaced by a new work plan involving synchrotron studies that could be performed without requiring exchange of personnel.

Cover page of Single-molecule 3D imaging of human plasma intermediate-density lipoproteins reveals a polyhedral structure

Single-molecule 3D imaging of human plasma intermediate-density lipoproteins reveals a polyhedral structure

(2019)

© 2018 Intermediate-density lipoproteins (IDLs), the remnants of very-low-density lipoproteins via lipolysis, are rich in cholesteryl ester and are associated with cardiovascular disease. Despite pharmacological interest in IDLs, their three-dimensional (3D) structure is still undetermined due to their variation in size, composition, and dynamic structure. To explore the 3D structure of IDLs, we reconstructed 3D density maps from individual IDL particles using cryo-electron microscopy (cryo-EM) and individual-particle electron tomography (IPET, without averaging from different molecules). 3D reconstructions of IDLs revealed an unexpected polyhedral structure that deviates from the generally assumed spherical shape model (Frias et al., 2007; Olson, 1998; Shen et al., 1977). The polyhedral-shaped IDL contains a high-density shell formed by flat surfaces that are similar to those of very-low-density lipoproteins but have sharper dihedral angles between nearby surfaces. These flat surfaces would be less hydrophobic than the curved surface of mature spherical high-density lipoprotein (HDL), leading to a lower binding affinity of IDL to hydrophobic proteins (such as cholesteryl ester transfer protein) than HDL. This is the first visualization of the IDL 3D structure, which could provide fundamental clues for delineating the role of IDL in lipid metabolism and cardiovascular disease.

Cover page of Pressure-induced phase transition in the AlCoCrFeNi high-entropy alloy

Pressure-induced phase transition in the AlCoCrFeNi high-entropy alloy

(2019)

© 2018 Elsevier Ltd The recently discovered pressure-induced polymorphic transitions (PIPT) in high-entropy alloys (HEAs) have opened an avenue towards understanding the phase stability and achieving atomic structural tuning of HEAs. So far, whether there is any PIPT in the body-centered cubic (bcc) HEAs remains unclear. Here, we studied an ordered bcc-structured (B2 phase) AlCoCrFeNi HEA using in situ synchrotron radiation X-ray diffraction (XRD) up to 42 GPa and ex situ transmission electron microscopy, a PIPT to a likely-distorted phase was observed. These results highlight the effect of the lattice distortion on the stability of HEAs and extend the polymorphism into ordered bcc-structured HEAs.

Cover page of Long-term simulation of space-charge effects

Long-term simulation of space-charge effects

(2019)

© 2018 Elsevier B.V. The long-term macroparticle tracking simulation is computationally challenging but needed in order to study space-charge effects in high intensity circular accelerators. To address the challenge, in this paper, we proposed using a fully symplectic particle-in-cell model for the long-term space-charge simulation. We analyzed the artificial numerical emittance growth in the simulation and suggested using threshold numerical filtering in frequency domain to mitigate the emittance growth in the simulation. We also explored alternative frozen space-charge simulations and observed qualitative agreement with the self-consistent simulations.

Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems

(2019)

© 2018 Elsevier Ltd Model predictive control (MPC) for buildings is attracting significant attention in research and industry due to its potential to address a number of challenges facing the building industry, including energy cost reduction, grid integration, and occupant connectivity. However, the strategy has not yet been implemented at any scale, largely due to the significant effort required to configure and calibrate the model used in the MPC controller. While many studies have focused on methods to expedite model configuration and improve model accuracy, few have studied the impact a wide range of factors have on the accuracy of the resulting model. In addition, few have continued on to analyze these factors’ impact on MPC controller performance in terms of final operating costs. Therefore, this study first identifies the practical factors affecting model setup, specifically focusing on the thermal envelope. The seven that are identified are building design, model structure, model order, data set, data quality, identification algorithm and initial guesses, and software tool-chain. Then, through a large number of trials, it analyzes each factor's influence on model accuracy, focusing on grey-box models for a single zone building envelope. Finally, this study implements a subset of the models identified with these factor variations in heating, ventilating, and air conditioning MPC controllers, and tests them in simulation of a representative case that aims to optimally cool a single-zone building with time-varying electricity prices. It is found that a difference of up to 20%25 in cooling cost for the cases studied can occur between the best performing model and the worst performing model. The primary factors attributing to this were model structure and initial parameter guesses during parameter estimation of the model.

Cover page of Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification

Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification

(2019)

© 2018 Elsevier Ltd With the rapid advances in sensing and digital technologies, cyber-physical systems are regarded as the most viable platforms for improving building design and management. Researchers investigated the possibility of integrating energy management systems with cyber-physical systems to form energy-cyber-physical systems in order to promote building energy management. However, minimizing energy consumption while fulfilling building functions for energy-cyber-physical systems is challenging due to the dynamics of building occupants. As occupant behavior is a major source of uncertainty for energy management, ignoring it often results in both energy waste caused by overheating and overcooling as well as discomfort due to insufficient thermal and ventilation services. To mitigate such uncertainties, this study proposes an occupancy-linked energy-cyber-physical system that incorporates WiFi probe-based occupancy detection. The proposed framework utilizes ensemble classification algorithms to extract three forms of occupancy information. It creates a data interface to link energy management systems and cyber-physical systems and allows for automated occupancy detection and interpretation by assembling multiple weak classifiers for WiFi signals. A validation experiment in a large office room was conducted to examine the performance of the proposed occupancy-linked energy-cyber-physical systems. The experiment and simulation results suggest that, with a proper classifier and occupancy data type, the proposed model can potentially save about 26.4% of energy consumption in cooling and ventilation demands.