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Carbon Storage and Utilization: Perspective on Water Usage and Opportunities
Disseminate technical and non-technical perspective on the opportunities for water recovery from produced water and CO2 storage in the I-WEST region. Concerned citizens and advocacy groups; farmers and ranchers; tribal, state, city, and county leaders; and project leaders working on water-related initiatives are all encouraged to participate in the workshop. This workshop is an opportunity for regional stakeholders to join the conversation about the challenges and opportunities water presents for energy transition in the Inter- Mountain West.
Diagnosing Failure Mechanisms in Capacitors within Pulsed Power Usage
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Lessons Learned from AskGDR: Usage and Impact Analysis of the Geothermal Data Repository's AI Research Assistant: Preprint
In October of 2024, the Department of Energy's (DOE) Geothermal Data Repository (GDR) team officially launched AskGDR, an AI research assistant resulting from the integration of a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets. AskGDR allows GDR users to ask deeper questions about the origin of datasets, the methods used to collect them, and the findings they help support. Using Retrieval Augmented Generation (RAG), AskGDR can be used to summarize findings spread across dozens of papers and technical reports or to extract relevant information describing a single data field. However, generative AI is experimental. The National Renewable Energy Laboratory (NREL) has been collecting metrics on AskGDR and documenting lessons learned during its deployment. This paper will outline the efficacy and impact of AskGDR through analysis of its use, operating costs, number and types of questions asked, and the quality of answers provided.
Residential electricity conservation in response to auto-generated, multi-featured, personalized eco-feedback designed for large scale applications with utilities
While past research has shown that providing residents with feedback about their electricity usage can reduce demand and its associated environmental burdens, some questions remain regarding what makes such feedback most effective. We followed the electricity usage of 36 residents who each received 14 feedback messages over 2 months. Using approaches borrowed from Natural-Language-Processing, feedbacks were generated automatically, using 10 features in random combinations. Unlike in previous studies, each resident received varying types of messages over time. In 504 observations, the average prompted reduction in electricity usage was 11 ± 3%, compared to a control group of 89 residents who received no messages. Feedback types prompting the largest reductions were self-comparisons with one’s own earlier usage (average reduction 14%) and messages of high variety from one feedback-cycle to the next (average reduction 16%). Comparisons with neighbors did not prompt higher reductions on average. Instead, they prompted reductions only when a resident’s recent usage happened to be higher than the average usage of neighbors, and increases when the reverse was true. Finally, this behavior was exhibited by all residents and is likely explained by a norm-conforming mean reversion of residents to their neighbors’ average usage, rather than an anti-conform “boomerang” behavior previously suggested in similar contexts.
On the memory attribution problem: A solution and case study using MPI
As parallel applications running on large–scale computing systems become increasingly memory constrained, the ability to attribute memory usage to the various components of the application is becoming increasingly important. We present the design and implementation of memnesia, a novel memory usage profiler for parallel and distributed message–passing applications. Our approach captures both application– and message–passing library–specific memory usage statistics from unmodified binaries dynamically linked to a message–passing communication library. Using microbenchmarks and proxy applications, we evaluated our profiler across three Message Passing Interface (MPI) implementations and two hardware platforms. Furthermore, the results show that our approach and the corresponding implementation can accurately quantify memory resource usage as a function of time, scale, communication workload, and software or hardware system architecture, clearly distinguishing between application and MPI library memory usage at a per–process level. With this new capability, we show that job size, communication workload, and hardware/software architecture influence peak runtime memory usage. In practice, this tool provides a potentially valuable source of information for application developers seeking to measure and optimize memory usage.
Self-reported health impacts of do-it-yourself air cleaner use in a smoke-impacted community
Smoke exposure from wildfires or residential wood burning for heat is a public health problem for many communities. Do-It-Yourself (DIY) portable air cleaners (PACs) are promoted as affordable alternatives to commercial PACs, but evidence of their effect on health outcomes is limited. Pilot test an evaluation of the effect of DIY PAC usage on self-reported symptoms, and investigate barriers and facilitators of PAC use, among members of a tribal community that routinely experiences elevated concentrations of fine particulate matter (PM 2.5 ) from smoke. We conducted studies in Fall 2021 (“wildfire study”; N = 10) and Winter 2022 (“wood stove study”; N = 17). Each study included four sequential one-to-two-week phases: 1) initial, 2) DIY PAC usage ≥8 h/day, 3) commercial PAC usage ≥8 h/day, and 4) air sensor with visual display and optional PAC use. We continuously monitored PAC usage and indoor/outdoor PM 2.5 concentrations in homes. Concluding each phase, we conducted phone surveys about participants’ symptoms, perceptions, and behaviors. We analyzed symptoms associated with PAC usage and conducted an analysis of indoor PM 2.5 concentrations as a mediating pathway using mixed effects multivariate linear regression. We categorized perceptions related to PACs into barriers and facilitators of use. No association was observed between PAC usage and symptoms, and the mediation analysis did not indicate that small observed trends were attributable to changes in indoor PM 2.5 concentrations. Small sample sizes hindered the ability to draw conclusions regarding the presence or absence of causal associations. DIY PAC usage was low; loud operating noise was a barrier to use. This research is novel in studying health effects of DIY PACs during wildfire and wood smoke exposures. Such research is needed to inform public health guidance. Recommendations for future studies on PAC use during smoke exposure include building flexibility of intervention timing into the study design.
Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach
This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.
Pumped Storage Hydro Model Evaluation Report Task 1: Tool Assessment
Pumped storage hydro (PSH) projects support various aspects of power system operations. However, determining the value of PSH projects and the many services and contributions to the system they provide can be a challenge. The PSH techno-economic studies being carried out by a 5-lab consortium are defining extensive modeling approaches for evaluating the economic benefits of PSH projects. To enhance the impact of the valuation approach and follow-on techno-economic studies, Pacific Northwest National Laboratory (PNNL) will be developing a set of online tools that industry could adopt and use to advance PSH projects in the US. The problem is that the research team is not entirely clear on the features of the tools that would offer the most significant impact to industry. Thus, year one of this effort is designed to first define the needs of industry before developing the PSH valuation tool. The first step in defining industry needs is to evaluate existing tool packages, including those created by DOE and those by industry groups, to both measure usage levels and define tool attributes that maximize industry impact. The second step in this process will be to hold a workshop designed to elicit direct industry input. Given the breadth and nature of the task, which is to define ideal model attributes, the research team did not constrain the assessment to storage valuation tools. Several tools were assessed, and their characteristics compared against a list of “ideal” attributes that included ease of use, generalizability of results, and analytical flexibility. Information regarding usage and impact were then compared to specific model attributes to define which attributes appear to drive success. The following key lessons and implications can be drawn from the analysis: Models with high ease of use were often the most adopted by industry, though they did not necessarily have high publication reference records, meaning a published article made direct reference to the tool. For example, REScheck TM and COMcheck TM , two tools which share a site, have seen a high amount of web traffic and usage. Similarly, Storage Value Estimate Tool (StorageVET®), an energy storage valuation model, has a large number of organizations using it despite a low publication reference record. A common feature between these models is that they are web-hosted, theoretically making them more easily accessible with graphic user interfaces (GUIs) that are more navigable; Models with higher usage statistics were typically free and/or open source. While it may be unsurprising that this attribute could be considered a draw for users, there is the potential for overestimating the tool's value based on popularity due to the fact that some users may have used the model simply because it was free and not because it is the best model; Models that offer more in-depth analysis and a wide coverage of applications (e.g. VOLTTRON™, GridLAB-D™, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVest)) have shown a high number of publication references as well as showing a high number of downloads in the case of GridLAB-D™. It should be noted that the figure below does not include the model with the highest number of publication references (EnergyPlus™) as it has been removed to show clearer distinction between the remaining tools; The most popular model within the evaluation list was EnergyPlus™. This tool has the highest number of publication references (2,034) of all models considered and contains both a high ease of use level as well as broad application flexibility. Its high usage may be correlated, however, with the presumed wider audience conducting energy consumption modeling compared to the audiences for some of the other tools in this list; Models that were low in both general usage and publication reference generally did not exhibit characteristics considered ideal by the research team. Some of these models offered a narrower range of analytical applications or lower generalizability of results. This would automatically restrict the pool of potential users to those who fall within a narrow range of specific technologies or applications. This report also explores two model structures for the PSH tool: decision tree-based, which would guide the user through the valuation methods defined in the PSH techno-economic studies currently being carried out by DOE, and an all-inclusive comprehensive model. The strengths of each were explored and a hybrid approach was defined that would embed a price taker model into the decision tree approach. Such an approach would provide a comprehensive approach for modeling small-scale PSH while recognizing the challenges associated with implementing a price-maker approach in an all-inclusive model. Insight gained in this analysis of other models can aid in narrowing down the list of effective and sought-after attributes, and in defining model structure. Next steps toward developing a model for storage valuation will be to gain direct industry input through workshops.
Development of a Short-Duration Drive Cycle to Represent Long-Term Measured Drive Cycle Data: Evaluation of Truck Efficiency Technologies in Class 8 Tractor Trailers
Quantifying the fuel savings and emissions reductions that can be achieved from truck fuel efficiency technologies for a fleet's specific usage allows the fleet to select a combination of technologies that will yield the greatest operational efficiency and profitability. An accurate characterization of usage for the fleet is critical for such an evaluation; however, short-term measured drive cycle data do not generally reflect overall usage very effectively. This study presents a detailed analysis of vehicle usage in a commercial vehicle fleet and demonstrates the development of a short-duration synthetic drive cycle with measured drive cycle data collected over an extended period of time. The approach matched statistical measures of the vehicle speed with acceleration history and integrated measured grade data to develop a compressed drive cycle that accurately represents total usage. Drive cycle measurements obtained during a full year from six tractor trailers in normal operations in a less-than-truckload carrier were analyzed to develop a synthetic drive cycle. The vehicle mass was also estimated to account for the variation of loads that the fleet experienced. These drive cycle and mass data were analyzed with a tractive energy analysis to quantify the benefits in terms of fuel efficiency and reduced carbon dioxide emissions that can be achieved on Class 8 tractor trailers by using advanced efficiency technologies, either individually or in combination. Although differences exist between Class 8 tractor trailer fleets, this study provides valuable insight into the energy and emissions reduction potential that various technologies can bring in this important trucking application. Finally, the methodology employed for generating the synthetic drive cycle serves as a rigorous approach to develop an accurate usage characterization that can be used to effectively compress large quantities of drive cycle data.
Method of recommending items to a user based on user interest
Although recording of usage data is common in scholarly information services, its exploitation for the creation of value-added services remains limited due to concerns regarding, among others, user privacy, data validity, and the lack of accepted standards for the representation, sharing and aggregation of usage data. A technical, standards-based architecture for sharing usage information is presented. In this architecture, OpenURL-compliant linking servers aggregate usage information of a specific user community as it navigates the distributed information environment that it has access to. This usage information is made OAI-PMH harvestable so that usage information exposed by many linking servers can be aggregated to facilitate the creation of value-added services with a reach beyond that of a single community or a single information service.
Impacts of COVID-19 related stay-at-home restrictions on residential electricity use and implications for future grid stability
“Stay-at-home” orders and other health precautions enacted during the COVID-19 pandemic have led to substantial changes in residential electricity usage. Here, we conduct a case study to analyze data from 390 apartments in New York City (NYC) to examine the impacts of two key drivers of residential electricity usage: COVID-19 case-loads and the outdoor temperature. We develop a series of regression models to predict two characteristics of residential electricity usage on weekdays: The average occupied apartment’s consumption (kWh) over a 9am-5pm window and the hourly peak demand (Watt) over a 12pm-5pm window. Via a Monte Carlo simulation, we forecast the two usage characteristics under a possible scenario in which stay-at-home orders in NYC, or a similar metropolitan region, coincide with warm summer weather. Under the scenario, the 9am-5pm residential electricity usage on weekdays is predicted to be 15% – 24% higher than under prior, pre-pandemic conditions. This could lead to substantially higher utility costs for residents. Additionally, we predict that the residential hourly peak demand between 12pm and 5pm on weekdays could be 35% – 53% higher than that under pre-pandemic conditions. We conclude that the projected increase in peak demand - which might arise if stay-at-home guidelines coincided with hot weather conditions - could pose grid management challenges, especially for residential feeders. We also note that, if there is a longer lasting shift towards work and study-from-home, utilities will have to rethink load profile considerations. The applications of our predictive models to managing future smart-grid technology are also highlighted.
Analysis of impact metrics for the Protein Data Bank
Since 1971, the Protein Data Bank (PDB) archive has served as the single, global repository for open access to atomic-level data for biological macromolecules. The archive currently holds >140,000 structures (>1 billion atoms). These structures are the molecules of life found in all organisms. Knowing the 3D structure of a biological macromolecule is essential for understanding the molecule’s function, providing insights in health and disease, food and energy production, and other topics of concern to prosperity and sustainability. PDB data are freely and publicly available, without restrictions on usage. Through bibliometric and usage studies, we sought to determine the impact of the PDB across disciplines and demographics. Our analysis shows that even though research areas such as molecular biology and biochemistry account for the most usage, other fields are increasingly using PDB resources. PDB usage is seen across 150 disciplines in applied sciences, humanities, and social sciences. Data are also re-used and integrated with >400 resources. Our study identifies trends in PDB usage and documents its utility across research disciplines.
Evaluating the effectiveness of program data features for guiding memory management
Recent trends have led to the adoption of larger and more complex memory systems, often with multiple tiers of memory performance within the same platform. To utilize complex memory systems efficiently, current data management strategies must be altered to map usage demands to the underlying hardware. Applications, as the generators of memory accesses, are well-suited to guide this process, but building and maintaining separate source code versions for different memory systems is not feasible in most cases. One potential solution is to employ automated program profiling and analysis to facilitate the production of application-based guidance. By attaching memory usage information to static or lightweight program features, compilers and runtime systems can generate fine-grained guidance without additional efforts from users or developers. Recent works have employed this approach with some success, but it is not clear which program features are most useful for guiding data management.This work evaluates the effectiveness of using different program data features to predict memory usage and guide memory management. It employs a custom set of simulation tools, based in the Intel® Pin framework, to collect and analyze the usage characteristics of application data associated with common program data features, such as allocation sites, types, and context. The results show that even relatively simple features, such as object size, are useful for selecting data with similar usage properties, but finer-grained features, such as the instructions that access a particular object, are often much more effective. Additionally, this work evaluates the performance of using different data features and different program inputs to guide data placement on a heterogeneous memory platform with a limited amount of high performance memory.
Memory Optimization for Phase-field Simulations
Phase-field simulations are computationally and memory intensive applications. Many of the phase-field simulations being conducted in support of NEAMS were not capable of running on “normal clusters” with 2-4GB of RAM per core, and instead required specialized “big-memory” clusters with 64GB per core. To address this issue, the MOOSE team developed a new Python-based utility called MemoryLogger, and applied it to locate, diagnose, and eradicate memory bottlenecks within the MOOSE framework. MemoryLogger allows for a better understanding of the memory usage of an application being run in parallel across a cluster. Memory usage information is captured for every individual process in a parallel job, and communicated to the head node of the cluster. Console text output from the application itself is automatically matched with this memory usage information to produce a detailed picture of memory usage over time, making it straightforward to identify the subroutines which contribute most to the application’s peak memory usage. The information produced by the MemoryLogger quickly and effectively narrows the search for memory optimizations to the most data-intensive parts of the simulation.
Survey of Hearth Products in U.S. Homes
There are over 7 million gas-fired fireplaces currently installed in US households. On an annual basis, these use substantial energy. However, the details of the fireplace energy use and the factors that influence it are currently not well understood. Fireplaces are a type of hearth product, which is a product category that primarily consists of fireplaces, stoves, and gas log sets. For the purpose of this study, the fuels used in hearth products are primarily natural gas, propane, or electricity. They may be vented or unvented. This study reports the results of a web survey of 2,100 respondents in the United States performed in February 2016. The responses were cleaned and weighted using the raking method to form a nationally representative population. The reported data include hearth product characteristics, usage information, and repair and maintenance practices. The hearth product characteristics include the hearth product type, fuel type, ignition system type, features, venting, and installation details. The usage information includes seasonal usage of the main burner and standing pilot (if present), daily usage, and the primary utility (whether decorative or for heating). These raw data are further processed and combined with values from the literature to estimate the annual operating hours and energy use and to assess how these are impacted by the hearth product type, features, age, and the main heating appliance, if present. Based on the survey responses, the estimated average annual hours of usage was 234 for the main burner, and 4,593 for the standing pilot. The results presented provide the most comprehensive data regarding hearth products in the United States published to date. These new data allow for an improved understanding of hearth products’ energy use, which in turn may facilitate the development of more informed analyses, and ultimately more efficient hearth products and reduced energy use. These new data also provide insight into topics not previously studied, such as the effect of hearth product features on energy use.
Materials Control for Aerospace Applications
The distant future of mankind and the ultimate survivability of the human race, as it is known today, will depend on mans' ability to break earthly bonds and establish new territorial positions throughout the universe. Man must therefore be positioned to not only travel to, but also, to readily adapt to numerous and varying environments. For this mass migration across the galaxies nothing is as import to the human race as is NASA's future missions into Low Earth Orbit (LEO), to the moon, and/or Mars. These missions will form the building blocks to eternity for mankind. From these missions, NASA will develop the foundations for these building blocks based on sound engineering and scientific principles, both known and yet to be discovered. The integrity of the program will lead to development, tracking and control of the most basic elements of hardware production: That being development and control of applications of space flight materials. Choosing the right material for design purposes involves many considerations, such as governmental regulations associated with manufacturing operations, both safety of usage and of manufacturing, general material usage requirements, material longevity and performance requirements, material interfacing compatibility and material usage environments. Material performance is subject to environmental considerations in as much as a given material may perform exceptionally well at standard temperatures and pressures while performing poorly under non-standard conditions. These concerns may be found true for materials relative to the extreme temperatures and vacuum gradients of high altitude usage. The only way to assure that flight worthy materials are used in design is through testing. However, as with all testing, it requires both time on schedule and cost to the operation. One alternative to this high cost testing approach is to rely on a materials control system established by NASA. The NASA community relies on the MAPTIS materials control system founded at MSFC and supported by the other NASA Centers. This system is a data bank of all materials used in space flight operations. These materials are rated for several characteristics that are common concerns in high altitude or deep space usage: Odor, off gassing, material fluid compatibility, toxicity, corrosion susceptibility, stress corrosion susceptibility, etc.