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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) for Transportation Hubs (Final Report)

This report summarizes the work performed under the award number EE0008524. The project develops the Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) platform as the next-generation transportation solution based on autonomous electric vehicles (AEV) serving passenger trips from and to urban transportation hubs, to substantially reduce transportation energy consumption. Extensive data collection and analyses were first conducted to understand the demand patterns and energy consumption of hub-based on-road trips. Then, a data-driven framework that consists of an analytical module and a simulation module was proposed. For the analytical module, five planning + operation tools were developed to support the planning and energy-efficient operations of urban AEV services: the charging station planning that robotically allocates charging supplies based on the stationary charging demand distribution; the transit planning and demand adaptive scheduling model that efficiently generates\ candidate transit routes from hubs to other places and dynamically adjusts the transit time table to fit the current demand; the online energy-efficient routing that learns the energy-optimal paths from observations of link-level energy consumption in real-time; the hub-based ridesharing that matches trip requests together with account for the uncertainty of future trip demand and vehicle supply; and finally, the integrated demand prediction and anomaly detection pipeline that leverages the flight/train time table and support other planning/operation tools. To demonstrate the performance of these tools, a scalable high-performance agent-based simulator was built. We divided the urban space into multiple service zones where each zone was considered as an agent for passenger generation and vehicle charging. Two types of AEV agents were coded to model two types of mobility services: AEV taxi and AEV transit. For the AEV taxi, the team implemented the functions of pickup/drop-off passengers, energy-efficient routing, ridesharing, fleet rebalancing, and recharging. For the AEV bus, the team implemented the functions of demand-adaptive route scheduling, passenger boarding, and recharging. A high-performance computing framework was introduced to receive various profiling information (such as link energy updates, vehicle speed) from the simulator instances and communicate the operational commands back to the instances. The numerical experiments show that each of the proposed operational algorithms can reduce energy consumption and improve system efficiency. Furthermore, there exists the need to collectively consider multiple planning + operational strategies as multiple strategies can influence each other in terms of performance impacts. Recommendations for future work related to AEV planning and simulation are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Occupancy schedule development and its effect on OpenStudio prototype college building model

College buildings have unique characteristics compared with school buildings. Therefore, defining the realistic occupancy schedule in a prototype college building has significant research opportunities. In this study, the actual operating schedules of each space type were collected and generated based on the class reservation schedule and compared with the previous reference schedule (primary/secondary school). Here, the schedules were analyzed for their effect on the OpenStudio prototype college building model. The findings highlight that the use of a typical school building schedule in a college building impairs the granularity of information. The analysis shows significant differences between the previous occupancy schedule and the updated occupancy schedule of the college building, leading to a considerable decrease in occupancy density. Furthermore, the effect of these occupancy pattern changes on the prototype building model is examined. The variations were observed in minimum ventilation requirements, the average mechanical ventilation rate, and energy consumption attributed to changes in occupancy density.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Astrometric microlensing probes of the isolated neutron star population with Roman

Notoriously hard to detect and study, isolated neutron stars (NSs) might provide valuable answers to fundamental questions about stellar evolution and explosion physics. With the upcoming Roman Space Telescope, scheduled for launch in 2026, a new and powerful channel for their detection will become available: astrometric microlensing. We set out to create a realistic sample of simulated gravitational microlensing events as observed by Roman with the Galactic Bulge Time Domain Survey. We focus in particular on the population of NS lenses, which has until now been largely understudied. We used dedicated Galactic models tailored for application to microlensing by compact objects. In addition to populations of stars, white dwarfs, and black holes, we simulated four different NS populations with Maxwellian natal kick distributions: v = (150, 250, 350, 450) km/s. For each simulation, we applied projected Roman precision, cadence, and detectability criteria. We found that the parameter space log10 t E –log 10 θ E , which will be accessible to Roman observations, is efficient for the classification of stellar remnants. We found a feature in this space that is characteristic of NSs; using this feature, optimal samples of NS candidates can be constructed from Roman -like datasets. We describe the dependence of the observable parameter distributions on the assumed mean kick velocities. As the effects of natal kicks are very complex and mutually counteracting, we suggest that more detailed studies focused on the dynamics of NSs are needed in anticipation of Roman and future surveys. We estimate that Roman will observe approximately 11 000 microlensing events, including ~100 with NS lenses, whose photometric and astrometric signals are detectable; the event yield decreases by 38% when gap-filling low-cadence observations are not included. We make all simulated microlensing event datasets publicly available in preparation for Roman data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

DUNE Physics Program and Status

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino experiment with a 70-kt liquid argon detector at the Sanford Underground Research Facility (SURF) 1300 km from Fermilab. This programme includes studies of neutrino oscillations with a high-intensity muon-neutrino beam from Fermilab; as well as, proton decay and supernova neutrino burst searches. DUNE will resolve the neutrino mass hierarchy to a precision of 5σ, for all δ$_{CP}$ values, after 2 years of running with the nominal detector design and beam configuration. It has the potential to observe charge-parity violation in the neutrino sector to a precision of 3σ (5σ) after an exposure of 5 (10) years, for 50% of all δ$_{CP}$ values. The status and schedule of the project is also presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Policy Considerations When Federating Facilities for Experimental and Observational Data Analysis

Today’s computational, experimental, and observational facilities afford us tremendous opportunities to couple theory and experiment at increasingly large scales. Empirical sensing capabilities are growing dramatically with beam line and detector improvements, and with advances in our ability to deploy large-scale data gathering observations of the natural world. The coupling of computational simulations and analysis to process the data from experimental and observational facilities is giving rise to cross-facility workflows. Such federations of facilities are in fact becoming an explicit requirement for large-scale scientific discovery. As we scale up these pipelines of scientific discovery, each participating facility needs to establish and align policies so that the federation can work seamlessly in an end-to-end manner. This chapter outlines specific policy considerations in enabling the federation of facilities for data analysis. Design choices and vital policy decisions cover the areas of data acquisition and storage, data transfer, computational resource allocation and co-scheduling, seamless federated user access, and cross-cutting governance. By highlighting the explicit and implicit interdependencies between facilities, we aim to provide facility designers and policymakers the information on policy issues to address early in a facility’s operations, thus enabling successful cross-facility federation and improved experimental and observational data analysis outcomes.

Shankar, Mallikarjun (Arjun)↗
DOE OSTI2026

LSST Scheduler & Progress Monitoring

The NSF-DOE Vera C. Rubin Observatory scheduling team is providing a set of tools for monitoring the scheduler and progress on the Legacy Survey of Space and Time (LSST). These slides provide an overview of available pages and assorted figures on them: the night summary provides an overview of a completed night of observing, including maps of exposures and plots of data quality metrics; the pre-night briefing, an analysis of a simulation of an upcoming night; and a handful of figures that provide information on long-term progress toward LSST survey goals.

H.Neilsen, Eric, Jr. [Fermilab] (ORCID:00000002735↗
DOE OSTI2020

Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation

Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.

bayesian structural time series↗
DOE OSTI2022

Impact of coolant temperature on the combustion characteristics and emissions of a stratified-charge direct-injection spark-ignition engine fueled with E30

The direct injection spark ignition (DISI) engine has received considerable attention due to its potential to increase the power density of traditional spark ignition engines while significantly improving fuel economy through lean, unthrottled combustion. However, the market introduction of DISI engines operated in a lean combustion mode is inhibited by their unsatisfactory emissions, especially during cold start conditions that make proper mixture formation more challenging. Ethanol-blended gasoline, now a widely used fuel, makes the cold start of a DISI engine more difficult, leading to higher HC and soot emissions because of the high latent heat of vaporization of ethanol relative to gasoline. This work investigated the impact of coolant temperature on the characteristics of combustion and emissions in a stratified-charge DISI engine fueled with an E30 fuel (i.e. 30% ethanol in gasoline), while the coolant temperature was alternated between four levels (45, 60, 75, and 90 °C) to simulate different conditions throughout the warm-up process. The experiments showed that the coolant temperature affected the post-spark inflammation time, as well as the speed, intensity, and stability of the combustion process in the engine. When the coolant temperature rose, the engine produced more NOX and less CO, PM and HC. In addition, high-speed direct photography was used to obtain crank-angle resolved images of fuel sprays and flames in the cylinder. As the coolant temperature rose, the liquid spray lengths became shorter, reducing the possibility of wall wetting, and reduced irradiance from soot particles also indicated less nonpremixed combustion. The in-cylinder imaging results are consistent with the observed combustion and emission characteristics and shed light on the underlying processes. Finally, some potential solutions to the emissions challenges faced here could be either raising in-cylinder temperatures by using trapped residuals or modifying the injection schedule, for example by increasing the number of injections or to inject later in the cycle into a higher-density environment.

42 ENGINEERING↗
DOE OSTI2020

Controlled workplace charging of electric vehicles: The impact of rate schedules on transformer aging

To accelerate adoption of non-residential charging for electric vehicles, sites must maximize utilization of existing electrical infrastructure. In this study we model electric vehicle charging at a workplace using real charging data and evaluate the lifetime of the site’s transformer as the number of charging stations is incrementally increased. We implement and compare a range of control schemes for workplace charging including minimizing the peak load, capping the total load, minimizing bills under different rate structures with time-of-use energy costs and demand charges, and directly minimizing the transformer’s aging. These are compared by the number of vehicles they allow the transformer to support, the transformer’s health, and the operator’s electricity bill. We draw a connection between minimizing the peak load and improving the transformer’s health. We observe that minimizing the electricity bill is the best scheme by both criteria when the bill includes a demand charge; in our experiment it allowed the infrastructure to support over 67% more cars than under uncontrolled charging. To protect the transformer we recommend that demand charges or capacity management be applied to parking lots of charging electric vehicles with high infrastructure utilization, and operators schedule charging to minimize their electricity bills.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Maritime Fuel Cell Generator Project: 2018 – 2023

This report summarizes activity in the Maritime Fuel Cell (MarFC) Generator Project from 2018 – 2023. FY 2018 saw the implementation of upgrades and repairs, making the unit more reliable and operator friendly. In FY2019 the team engaged the Scripps Institution of Oceanography (SIO) to use the MarFC to provide zero-emission shore power to the research vessel R/V Robert Gordon Sproul while in port at the Nimitz Marine Facility in San Diego, CA. In FY2020, the MarFC unit was shipped to San Diego, CA. A fueling contract with IGX was established to support MarFC operations at SIO, with renewable hydrogen provided by the California State University Los Angeles (CSULA) hydrogen station. The project team (Sandia, Cummins/Hydrogenics) provided training to SIO staff on the technical details of the MarFC, the safe use of hydrogen in general and the MarFC in particular. The first fueling of the unit at the SIO pier was successfully completed by IGX. The first powering of a vessel with fuel-cell shore power was conducted with the R/V Robert Gordon Sproul. While the mechanical systems (lights, AC, ventilation, hydraulics, pumps and cranes) were powered without incident, problems arose when powering the computer systems. Inspections carried out in FY2021 revealed the MarFC needed routine maintenance. Maintenance was performed and the unit was upgraded. The MarFC was turned on after the year pause, and initial test data on power levels and stability were collected. FY2022 was a year spent repairing, upgrading and testing the MarFC unit. Spikes in power and voltage were observed above 60 kW that could potentially extend below 60 kW with time. Such spikes could cause problems with the Sproul electrical systems. These age-related problems, the extended time for the Sproul spent in dry dock for scheduled upgrades, and the associated need to reschedule the vessel’s high-priority science missions made it no longer possible to deploy the unit at SIO. After due consideration, the decision was made by DOE, MARAD and the project team to cease the deployment, remove the MarFC from SIO, and not pursue further deployment activities. On December 2, 2022, the MarFC unit was removed from the Scripps Nimitz Marine Facility and shipped to Fridley, Michigan. The Cummins/Hydrogenics plan for the unit is to assess the condition of the MarFC subcomponents, and then use it as a training/learning system for technical employees new to hydrogen fuel-cell technology. After summarizing project activity from 2018 – 2023, this report provides a review of lessons learned. This report provides next steps in contemplating a follow-on project that would further advance the use of fuel-cell-based shore power in a marine setting. A comparison is made of the project results to the original objectives. This report ends with an accounting of presentations stemming from the project, and a list of references.

08 HYDROGEN↗
DOE OSTI2022

Assessment of the Electrical Substation-Grid Testbed with Inside/Outside Devices and Distributed Ledger Technology

The electrical substation-grid testbed was created to integrate the GOOSE and/or DNP (Distributed Network Protocol) messages with time synchronized sources and Distributed Ledger Technology (DLT). The objective was to study the impact of faults and cyber-events at an electrical substation with inside (protective relays) and outside (power meters) substation devices. The electrical substation-grid testbed was based on the design of a 34.5/ 12.47 kV electrical substation (sectionalized bus configuration) with two power transformers, connected to radial power lines and load feeders. The electrical substation-grid testbed was installed at 252 lab space (Advanced Power System Protection), Grid Research Integration and Deployment Center (GRID-C), Oak Ridge National Laboratory. This testbed was created for Task 5, DarkNet project. The electrical substation-grid testbed was created to simulate fault and/or cyber events that could potentially result in damage to the electrical infrastructure. In addition, tests were run that are usually not allowed to be performed in an operational electrical power grid, because these test scenarios could trip breakers and/or generate fault situations that could potentially damage equipment. The number of tests performed in the electrical substation-grid testbed were executed in a better way than in a real electrical substation and/or power grid, because multiple tests could be run in a short period of time, and complex permits, and safety/ schedule restrictions like in a real electrical substation environment were not needed. The electrical substation-grid testbed was created using real measurement, communication, and protection devices that are used by electrical utilities, to have same conditions that we could observe in a real power grid or electrical substation. The electrical substation-grid testbed was based on using a real time simulator and expansion box with amplifiers that were wired to electrical substation-grid devices. This hardware-in-the-loop (HIL) was provided by protective relays, power meters, ethernet switches, remote terminal units, synchronized timing network clock, DLT devices, workstations, and servers. This report includes the design, installation, and assessment of the electrical substation-grid testbed that was similar to an operational electrical substation, integrating the power system protection, communication, and control systems. The results for the electrical substation-grid testbed were based on:• verifying the analog signals for protective relays and power meters, • observing the synchronized time source frame at devices, • authenticating the GOOSE (IEC 61850) and DNP messages from power meters and protective relays, and • verifying the trip conditions of protective relays at fault tests with the power system fault event detection, using DLT devices. For future work, the electrical substation-grid testbed with protective relays and power meters, using DLT and synchronized time source from DarkNet, will be used to study the impact of cyber-events at inside and outside substation devices. Advanced algorithms for detecting cyber-events produced by non-desired protective relay settings will be studied, to improve the detection and reliability of protection, control, and communication systems at power grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

High-dimensional Data-driven Energy optimization for Multi-Modal Transit Agencies (HD-EMMA) (Final Technical Report)

Public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO 2 emission annually. Electric vehicles (EVs) can have a much lower environmental impact than comparable internal combustion engine vehicles (ICEVs), especially in urban areas. Unfortunately, EVs are also much more expensive than ICEVs. As a result, many public transit agencies can afford only mixed fleets of transit vehicles, consisting of EVs, hybrids (HEVs), and ICEVs. Transit agencies that operate such mixed fleets of vehicles face a challenging optimization problem: these agencies need to decide which vehicles are assigned to serving which transit trips. Since the advantage of EVs over ICEVs varies depending on the route and time of day (e.g., the benefit of EVs is higher in slower traffic with frequent stops and lower on highways), the assignment can have a significant effect on energy use and, hence, environmental impact. Through this project, we have developed reference data about energy collections and constructed a set of machine learning models that can accurately predict the energy consumption for the whole fleet at the level of each trip. We have used these models to develop a scheduling and assignment strategy that can rotate the different vehicle types across the transit agencies’ routes. The optimization algorithm ensures that the vehicles are matched to trips considering weather patterns, expected congestion, and road gradients to minimize the overall energy usage. We list the key observations from our project for other practitioners below. Details are available in the report, and the list of source code and our publications are included in the appendix. 1. We have demonstrated the feasibility of collecting, merging and analyzing large volumes of high-resolution real-world telemetry data from a mixed vehicle fleet. To mitigate the inherent noise of the recorded GPS points, the team developed an algorithm that filters data and maps the points onto a street. The algorithm considers previous and subsequent location measurements and different characteristics of nearby streets to determine how likely the vehicle travels on them. Then, the team segmented the time series into disjoint contiguous samples based on adjacent road segments and repeated the outlier detection and removal. For each data point, the team added features corresponding to elevation changes within the samples, weather features, such as temperature, and traffic data, such as speed ratio between actual speed and free-flow speed. 2. We have developed two forms of machine learning models that be used to understand and analyze the energy operations of a mixed vehicle transit fleet. The micro prediction model provides estimates of instantaneous energy prediction for all types of buses (diesel, hybrid, and electric). Such a model is important in evaluating the energy impacts of real-time bus operation strategies, but it is challenging due to diversified driving cycles of transit buses. The model can help the drivers understand the impact of their driving behaviors and short-term congestions. The macro prediction models estimate average energy consumption across the whole trip considering the features: distance traveled, various road-type features, elevation change, day of the week, time of day, various weather features (temperature, humidity, etc.), and traffic features (speed ratio and jam factor). 3. We have demonstrated that it is possible to transfer the machine learning models we have developed in this project to other teams and cities by using inductive transfer learning. We also showed that the performance of the macro energy prediction models can be improved using a multi-task learning approach where the learning parameters are shared between the models being developed for different vehicle types. The advantage of this approach is improved learning performance as the models can exploit common spatio-temporal and environmental characteristics. 4. Finally, we have developed trip and vehicle assignment and scheduling algorithms that use the energy prediction models and develop a trip to vehicle type (diesel, electric, hybrid) assignment for the whole operation to reduce overall emissions and cost. We have shown through simulations that the proposed algorithms can save $\$$ 48,910 in energy costs and 175 metric tons of CO 2 emission annually for CARTA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2024

Large-scale simulation-based parametric analysis of an optimal precooling strategy for demand flexibility in a commercial office building

Achieving success with grid-interactive efficient buildings (GEBs) is closely tied to the utilization of flexible loads. A valuable strategy involves the implementation of precooling techniques before high-demand events, such as peak hours, by adjusting zone air temperature setpoints. This leads to a reduction in thermal loads and peak electricity demand during these times, as the building’s thermal mass stores and subsequently releases thermal energy. However, the effectiveness of the pre-cooling optimization is highly contingent on specific conditions such as building thermal properties, weather conditions, utility rate structure, HVAC equipment sizing, etc. Therefore, investigating the impacts of these condition-specific factors is crucial, especially when considering precooling strategies that utilize thermal mass in commercial buildings. In this paper, we first devised a novel heuristic control approach that incorporates parameterized optimal precooling thermostat schedules to enhance demand flexibility in a commercial office building. Subsequently, we conducted a thorough performance evaluation of this control strategy. Here, the optimal thermostat schedule was parameterized using three optimization variables: the precooling start time, the precooling end time, and the precooling temperature setpoint. Utilizing the DOE medium-sized office building as the virtual testbed, we showed that the parameterized schedule effectively approximates model predictive control and requires drastically reduced computational overhead. In addition, we investigated the impact of different influencing factors on the optimal precooling strategy. These factors include building thermal mass, outdoor air conditions, and energy price profiles. Using high-performance computing, we simulated a total of 225 scenarios, consisting of three levels of thermal mass, five typical outdoor air temperature profiles, and fifteen time-of-use price plans. The results demonstrate that optimal thermostat scheduling could save substantial energy cost in medium-sized office buildings with heavy thermal mass but with some energy penalty. Although the potential for cost savings is lower in buildings with low and medium thermal mass, the energy penalty remains consistent in all three thermal mass scenarios. The study also highlights the need to account for zone diversity and recognize that a one-size-fits-all-zone setpoint schedule may not be suitable for all zones and can lead to unnecessary energy wastage. Furthermore, the results highlight that while outdoor air conditions play a role in cost and energy performance, the cooling load exerts a more immediate and substantial influence on cost savings in precooling strategies. Although cost savings are comparable under certain conditions with the same cooling load, observed deviations in energy penalty indicate potential disparities in the efficiency of the HVAC system during the load-shifting process. In addition, the duration of peak pricing and the ratio between peak and off-peak times exhibit clear correlations with cost savings and energy consumption, aligning with intuitive expectations. These findings offer valuable insights for optimizing precooling strategies in office buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Air compressor load forecasting using artificial neural network

Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network inair compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems’ electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load managementand look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Networkand Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. Here, the forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient ofdetermination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and meanabsolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield goodresults for compressors using variable speed drive (average R 2 = 0.8 and no naïve forecasting), only the longshort-term memory model gives acceptable results for compressors using on/off control (average R 2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting active forecasting).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Optimizing workplace charging facility deployment and smart charging strategies

This study introduces a workplace charging (WPC) optimization model that maximizes the total satisfied electric miles of employees’ plug-in electric vehicles, subject to a given annual budget. The model optimizes both planning decisions of charger number and power levels and operation decisions of charging spot assignment and charging schedule for the given temporal distribution of charging demands and varied electricity prices. Results of experiments based on national average travel data indicate that the actual WPC strategy varies by budget level. Through optimization, the strategy could reduce impacts of the varied electricity price by shifting charging schedules to periods when electricity prices are low. Also, the model is expanded to study the trade-off between providing WPC and addressing consequence of degraded charging service by including the per-mile shadow cost of unsatisfied charging demand. Finally, we observe that their relative competitiveness mainly depends on the actual shadow cost of WPC.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

`SkyPy`: A package for modelling the Universe

SkyPy is an open-source Python package for simulating the astrophysical sky. It comprises a library of physical and empirical models across a range of observables and a command-line script to run end-to-end simulations. The library provides functions that sample realisations of sources and their associated properties from probability distributions. Simulation pipelines are constructed from these models using a YAML-based configuration syntax, while task scheduling and data dependencies are handled internally and the modular design allows users to interface with external software. SkyPy is developed and maintained by a diverse community of domain experts with a focus on software sustainability and interoperability. By fostering development, it provides a framework for correlated simulations of a range of cosmological probes including galaxy populations, large scale structure, the cosmic microwave background, supernovae and gravitational waves. Version 0.4 implements functions that model various properties of galaxies including luminosity functions, redshift distributions and optical photometry from spectral energy distribution templates. Future releases will provide additional modules, for example, to simulate populations of dark matter halos and model the galaxy-halo connection, making use of existing software packages from the astrophysics community where appropriate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Data for EMSL Project 60929 from August 2023: PI Goemann MONet Request

Just as humans rely on a healthy gut microbiome for resilience to illness, plants rely on a healthy root microbiome for resilience to environmental abiotic stress (heat, drought). To achieve a healthy root microbiome, plants release carbon (C)-rich compounds as root exudates to stimulate microbial activity and increase local nutrient mineralization. However, the enhanced performance comes at a cost: up to 44% of a plant’s C can be lost to root exudates, diverting C from plant growth and respiration. Critical knowledge gaps include how the ‘C cost’ is managed and how root exudates alter the microbiome under different environmental conditions. In addition, historical climate conditions, particularly mean annual precipitation, is known to shape local soil microbiomes and alter their sensitivity to drought. Therefore, studies that better characterize the plant-microbe responses to environmental stress will aid in efforts to harness the microbiome to improve crop resilience. However, current knowledge gaps make it challenging to engineer beneficial plant-microbe interactions to improve plant productivity in agricultural systems and to predict how increased climate variability will alter terrestrial C fluxes and climate feedbacks. To fill this knowledge gap our research group at Montana State University – Bozeman is currently studying blue grama (Bouteloua gracilis), a prairie grass native across the Northern Great Plains, as a model for drought tolerance. Our goal is to investigate the above- and belowground responses of blue grama to drought and heat stress to improve our understanding of stress-induced carbon allocation and plant-microbe interactions. Most recently, we investigated the influence of climate history on the blue grama drought response. We collected soil from three blue grama-dominated sites (those proposed to sample here) across a 150 mm mean annual precipitation gradient in SW Montana, USA, to use as inoculum for a greenhouse drought experiment. Preliminary results indicate that soil climate history has a strong influence on the blue grama physiological response to drought as well as on the chemical composition of root exudates and rhizosphere microbiome composition. Metabarcoding data from this experiment is scheduled to be submitted to public databases within the next year. Having in-depth analyses of the soil biogeochemistry and metagenomic composition through the MONet project at each of the field sites associated with this experiment will allow us to link underlying ecological processes with observed patterns of plant growth and productivity at each site. In addition, we plan to utilize the MONet database for future meta-analyses to compare the genomic and biogeochemical signatures of our field sites to others across a wider precipitation gradient throughout the native range of blue grama. This will further provide critical insights into the mechanisms that drive ecosystem functioning and resilience to drought stress.

Peyton, Brent↗