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Open Access Publications from the University of California

LBL Publications

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.

Deep Generative Models for Fast Photon Shower Simulation in ATLAS

(2024)

Abstract: The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.

Artificial Intelligence for the Electron Ion Collider (AI4EIC)

(2024)

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.

Software Performance of the ATLAS Track Reconstruction for LHC Run 3

(2024)

Charged particle reconstruction in the presence of many simultaneous proton–proton (pp) collisions in the LHC is a challenging task for the ATLAS experiment’s reconstruction software due to the combinatorial complexity. This paper describes the major changes made to adapt the software to reconstruct high-activity collisions with an average of 50 or more simultaneous pp interactions per bunch crossing (pile-up) promptly using the available computing resources. The performance of the key components of the track reconstruction chain and its dependence on pile-up are evaluated, and the improvement achieved compared to the previous software version is quantified. For events with an average of 60pp collisions per bunch crossing, the updated track reconstruction is twice as fast as the previous version, without significant reduction in reconstruction efficiency and while reducing the rate of combinatorial fake tracks by more than a factor two.

Cover page of Estimating geographic variation of infection fatality ratios during epidemics.

Estimating geographic variation of infection fatality ratios during epidemics.

(2024)

OBJECTIVES: We aim to estimate geographic variability in total numbers of infections and infection fatality ratios (IFR; the number of deaths caused by an infection per 1,000 infected people) when the availability and quality of data on disease burden are limited during an epidemic. METHODS: We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing. We demonstrate the robustness, accuracy, and precision of this framework, and apply it to the United States (U.S.) COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs. RESULTS: The estimators for the numbers of infections and IFRs showed high accuracy and precision; for instance, when applied to simulated validation data sets, across counties, Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928, respectively, and they showed strong robustness to model misspecification. Applying the county-level estimators to the real, unsimulated COVID-19 data spanning April 1, 2020 to September 30, 2020 from across the U.S., we found that IFRs varied from 0 to 44.69, with a standard deviation of 3.55 and a median of 2.14. CONCLUSIONS: The proposed estimation framework can be used to identify geographic variation in IFRs across settings.

Cover page of Helping Faculty Teach Software Performance Engineering

Helping Faculty Teach Software Performance Engineering

(2024)

Over the academic year 2022–23, we discussed the teaching of software performance engineering with more than a dozen faculty across North America and beyond. Our outreach was centered on research-focused faculty with an existing interest in this course material. These discussions revealed an enthusiasm for making software performance engineering a more prominent part of a curriculum for computer scientists and engineers. Here, we discuss how MIT’s longstanding efforts in this area may serve as a launching point for community development of a software performance engineering curriculum, challenges in and solutions for providing the necessary infrastructure to universities, and future directions.

Cover page of Clean Industry in China: A Techno-Economic Comparison of Electrified Heat Technologies, Barriers, and Policy Options

Clean Industry in China: A Techno-Economic Comparison of Electrified Heat Technologies, Barriers, and Policy Options

(2024)

China’s manufacturing sector generates 61% of the country’s CO2 emissions, nearly three-quarters of which is related to industrial process heating. To meet China’s climate targets and attain a zero-carbon industrial sector, decarbonizing these industrial heating processes is a necessity. If China’s electricity grid is similarly decarbonized, direct electrification is the most practical means of supplying this heat efficiently at the required scale. In addition to reducing greenhouse gas emissions, industrial electrification would help reduce conventional pollution that was responsible for 1.85 million premature deaths in China in 2019, and it would improve China’s energy security, as the country imported 85% of its petroleum products and crude oil as well as 46% of its natural gas in 2021. Direct electrification would also help Chinese firms avoid volatile fossil fuel prices and future carbon pricing costs, and ensure competitiveness when selling products to environmentally-conscious buyers and governments that may use carbon border adjustment mechanisms or similar efforts to encourage the procurement of cleaner materials. Two electrified technologies stand out as means for China to decarbonize its industrial process heating: industrial heat pumps and thermal batteries. Heat pumps can be the most efficient and cost-effective method to supply clean, low-temperature heat for industrial processes. They can achieve efficiencies several times higher than other electrical technologies because they do not convert their input electricity into heat. Instead, heat pumps move heat from a low-temperature to a high-temperature area, operating much like a refrigerator or air conditioner. Industrial heat pumps can extract heat from a source (such as the air, ground, or waste heat from another industrial process) and output heat at temperatures up to 165 °C. Heat pumps that raise temperature by 40 to 60 °C typically have efficiencies of 300-400%. Notably, no other heating technology can generate heat at an efficiency beyond 100%; this exceptional efficiency makes heat pumps a particularly cost-effective electrification route. For higher temperature processes, thermal batteries can provide up to 1,700 °C, making them a viable option for supporting over two-thirds of China’s manufacturing sector’s process heating needs. Thermal batteries contain thermal storage material with a high specific heat capacity that resists chemical breakdown at high temperatures. The storage material is enclosed in a highly insulated shell to minimize heat loss, losing as little as 1% a day in some systems. Electrical resistance heaters inside the battery convert their electricity to heat that is absorbed by the storage material and can then be extracted when the industrial facility is ready to use the heat. The storage capability of thermal batteries means that they can provide steady-state heat in both on- and off-grid configurations. Off-grid batteries would be able to procure electricity at wholesale prices from dedicated renewables projects, smoothing over the variability of day-night cycles or lulls due to weather conditions. Similarly, for grid-connected batteries, energy can be purchased during the cheapest times of day and banked for future use. While many Chinese manufacturing firms are located in the eastern provinces where there may be limited land for creating new off-grid renewables projects, grid-connected thermal batteries offer firms and utilities the benefits of price-hunting and optimization. Additionally, by reducing industrial electricity demand when electricity is in short supply, direct electrification with thermal batteries could aid in grid regulation, help the grid integrate variable renewables, and cut peak demand, lowering the required grid-related capital costs of transitioning to clean industry. Performing a techno-economic comparison of these two electrified heating technologies and their alternatives in China, we found that for temperatures under 100 °C, industrial heat pumps were the second-cheapest heating option with a levelized cost of $38/MWhth (¥260/MWhth), remaining competitive with combined heat and power (CHP) variants and considerably cheaper than natural gas or electric boilers (Figure ES-1). While coal-fired boilers currently offer the lowest levelized cost of heat production, when incorporating a 2030 estimated carbon cost, industrial heat pumps become the lowest-cost option for low-temperature heat. For temperature ranges of 100-165 °C, industrial heat pumps cost about $58/MWhth (¥391/MWhth), but are broadly competitive with natural gas, and may improve in terms of costs and efficiency with additional research and development. Industrial thermal batteries are costed in-between the two heat pump variants at $46/MWhth (¥314/MWhth) and can support far higher temperatures. Relative to coal-fired technologies, heat pumps were found to achieve significant reductions in five pollutants (CO2, NOx, SOx, PM10, and PM2.5) and thermal batteries in three pollutants (SOx, PM10, and PM2.5), accounting for the pollutant emissions associated with the electricity they use. As China’s grid increasingly shifts to zero-emissions electricity sources, electrified technologies’ pollutant emissions will decline, ultimately reaching zero if China’s grid becomes fully decarbonized. Smart policy is necessary to overcome the barriers to industrial electrification in China. Fossil fuel prices are considerably lower in China than the cost of electricity for industrial energy buyers. Limited availability of electrified equipment, especially high-temperature industrial heat pumps and industrial thermal batteries, also presents a current hurdle. Additionally, upgrading and electrifying existing industrial equipment can be technically challenging, and doing so outside of the equipment’s natural replacement cycle can incur additional costs. Policymakers can incentivize the transition using equipment rebates, retooling grants, and access to low-interest financing mechanisms to offset the capital expenditures related to adopting these technologies. Enhancing existing energy-efficiency standards, emissions standards, and green public procurement programs can likewise encourage the transition to direct electrification. China’s research laboratories, such as those operated by the Chinese Academy of Sciences, can collaborate with private industry on research and development (R&D) programs to move these early-stage technologies forward. Grant funding is not limited to supporting laboratory-scale R&D but can also fund pilot or demonstration plants that provide proof-of-concept and encourage industrial players to transition. Creating a competitive landscape between coal and electricity is also important and can be achieved by carbon pricing or by subsidizing the cost of clean electricity and the cost of upgrades to support electrification. Inter-provincial electricity trading and optimization of China’s Green Electricity Certificate system can help facilitate access to clean electricity. Direct electrification of industrial process heating in China has the potential to reduce greenhouse gas emissions immensely and would yield massive benefits to the country and the globe. While existing technologies offer a path forward, China must incentivize their adoption by creating a supportive environment for industrial decarbonization through the right policy approaches. Given the country’s large industrial capacity, China has the potential to lead in clean industrial technology while achieving its climate targets.

Cover page of Managing changes in peak demand from building and transportation electrification with energy efficiency

Managing changes in peak demand from building and transportation electrification with energy efficiency

(2024)

The Department of Energy funded Berkeley Lab to provide technical assistance to two municipal utilities on how energy efficiency and demand flexibility can mitigate the peak demand impacts of building and transportation electrification. Berkeley Lab worked with these utilities, Sacramento Municipal Utility District (SMUD) and Fort Collins Utilities, to identify research questions that supported their planning needs. For both utilities, Berkeley Lab developed scenario-based load forecasts that considered baseline and high-efficiency building electrification. For SMUD, the forecast also explored the sensitivity of peak demand to extreme weather (a winter cold snap) at the system-evel. For Fort Collins Utilities, the forecast addressed the impacts of low, medium, and high levels of building and transportation technology adoption on select distribution feeders. Berkeley Lab is also developing a guidance document for utilities that will draw on lessons learned from the technical assistance and provide a framework for conducting similar analyses.

Cover page of One Year In: Tracking the Impacts of NEM 3.0 on California’s Residential Solar Market

One Year In: Tracking the Impacts of NEM 3.0 on California’s Residential Solar Market

(2024)

On December 15, 2022, the California Public Utilities Commission passed an overhaul of the net metering program for the state’s investor-owned utilities. The changes replaced the long-standing net energy metering (NEM) tariffs with a net billing tariff (NBT) structure—colloquially known as “NEM 3.0”—which significantly reduces the compensation for behind-the-meter solar photovoltaic (PV) systems. The NEM tariffs remained open for new interconnection applications until April 15, 2023, but after that date, all new interconnection applications were submitted under NBT. Now, one year later, we have an opportunity to evaluate how the California solar market has evolved under this new compensation regime. As a precursor to its annual Tracking the Sun report, Berkeley Lab has released a short technical brief describing key trends in the California residential solar market since the roll-out of the new NBT structure. The purpose of this analysis is to provide empirical insights into how the market has evolved over the past year, confirming some expectations while also revealing several striking surprises.

Cover page of Commercial, industrial, and institutional discount rate estimation for efficiency standards analysis Sector-level data 1998–2023

Commercial, industrial, and institutional discount rate estimation for efficiency standards analysis Sector-level data 1998–2023

(2024)

Underlying each of the U.S. Department of Energy’s (DOE’s) federal appliance and equipment energy conservation standards are a set of complex analyses of the projected costs and benefits of regulation. Any new or amended standard must be designed to achieve significant additional energy conservation, provided that it is technologically feasible and economically justified (42 U.S.C. 6295(o)(2)(A)). DOE determines economic justification based on whether the benefits exceed the burdens, considering a variety of factors, including the economic impact of the standard on consumers of the product and the savings in lifetime operating cost compared to any increase in price or maintenance expenses (42 U.S.C. 6295(o)(2)(B)). As part of this determination, DOE conducts a life-cycle cost (LCC) analysis, which models the combined impact of appliance first cost and operating cost changes on a representative commercial building sample to identify the fraction of customers achieving LCC savings or incurring net cost at the considered efficiency levels. Thus, the commercial discount rate value(s) used to calculate the present value of energy cost savings within the LCC model implicitly plays a role in estimating the economic impact of potential standard levels. This report provides an in-depth discussion of the commercial discount rate estimation process relying on the Capital Asset Pricing Model (CAPM) to estimate a business’ cost of equity, and by adding a risk adjustment factor to the risk-free rate associated with long-term U.S. Treasury bonds to estimate their cost of debt. It is an update to previous reports on estimating commercial discount rates from firm-level and sector-level financial data (e.g., Fujita, 2021, 2016). Major topics covered in this report include the following: • Discount rate estimation methods and rationale • Data sources used and data limitations • Discount rate distributions for use in standards analysis • Discount rate estimation methods and distributions specific to the small business subgroup analysis

Cover page of EPR Spin-Trapping for Monitoring Temporal Dynamics of Singlet Oxygen during Photoprotection in Photosynthesis.

EPR Spin-Trapping for Monitoring Temporal Dynamics of Singlet Oxygen during Photoprotection in Photosynthesis.

(2024)

A central goal of photoprotective energy dissipation processes is the regulation of singlet oxygen (1O2*) and reactive oxygen species in the photosynthetic apparatus. Despite the involvement of 1O2* in photodamage and cell signaling, few studies directly correlate 1O2* formation to nonphotochemical quenching (NPQ) or lack thereof. Here, we combine spin-trapping electron paramagnetic resonance (EPR) and time-resolved fluorescence spectroscopies to track in real time the involvement of 1O2* during photoprotection in plant thylakoid membranes. The EPR spin-trapping method for detection of 1O2* was first optimized for photosensitization in dye-based chemical systems and then used to establish methods for monitoring the temporal dynamics of 1O2* in chlorophyll-containing photosynthetic membranes. We find that the apparent 1O2* concentration in membranes changes throughout a 1 h period of continuous illumination. During an initial response to high light intensity, the concentration of 1O2* decreased in parallel with a decrease in the chlorophyll fluorescence lifetime via NPQ. Treatment of membranes with nigericin, an uncoupler of the transmembrane proton gradient, delayed the activation of NPQ and the associated quenching of 1O2* during high light. Upon saturation of NPQ, the concentration of 1O2* increased in both untreated and nigericin-treated membranes, reflecting the utility of excess energy dissipation in mitigating photooxidative stress in the short term (i.e., the initial ∼10 min of high light).