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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Secured Reserve Scheduling of Pumped-Storage Hydropower Plants in ISO Day-ahead Market

Due to the fast response ability and high ramping rates, pumped-storage hydropower (PSH) plants are playing an increasingly critical role in mitigating power generation fluctuations induced by the growing proliferation of renewable energy resources. However, because they are energy-limited resources, actual reserve deployment from PSHs would cause the deviation of state-of-charge (SOC) from the scheduled value, which, once accumulated over time, could induce potential violations on SOC limits and cause non-dispatchability of PSHs. In recognizing this potential issue, it can become important to securing the reserve schedule of PSHs and other energy limited resources preventively in the day-ahead market clearing. To ensure reserve dispatchability of PSHs despite SOC deviations, this paper proposes the reserve secure constraints, which are cast as a distributionally robust chance-constrained formulation by judiciously introducing random parameters to describe deployment ratios of scheduled regulation reserve from PSHs. Furthermore, these constraints are converted into tractable second-order cone constraints, and further approximated as linear constraints to be compatible with the current ISO day-ahead market clearing model. A modified IEEE 118-bus system with multiple PSHs is adopted to verify the proposed approach.

13 HYDRO ENERGY↗
DOE OSTI2021

Night shift schedule causes circadian dysregulation of DNA repair genes and elevated DNA damage in humans

Circadian disruption has been identified as a risk factor for health disorders such as obesity, cardiovascular disease, and cancer. Although epidemiological studies suggest an increased risk of various cancers associated with circadian misalignment due to night shift work, the underlying mechanisms have yet to be elucidated. Here, we sought to investigate the potential mechanistic role that circadian disruption of cancer hallmark pathway genes may play in the increased cancer risk in shift workers. In a controlled laboratory study, we investigated the circadian transcriptome of cancer hallmark pathway genes and associated biological pathways in circulating leukocytes obtained from healthy young adults during a 24‐hour constant routine protocol following 3 days of simulated day shift or night shift. The simulated night shift schedule significantly altered the normal circadian rhythmicity of genes involved in cancer hallmark pathways. A DNA repair pathway showed significant enrichment of rhythmic genes following the simulated day shift schedule, but not following the simulated night shift schedule. In functional assessments, we demonstrated that there was an increased sensitivity to both endogenous and exogenous sources of DNA damage after exposure to simulated night shift. Our results suggest that circadian dysregulation of DNA repair may increase DNA damage and potentiate elevated cancer risk in night shift workers.

circadian misalignment↗
DOE OSTI2020

Study on Intelligent Compaction-Equipment Logistics Scheduling and Propagation Characteristics of Vibration Wave in Nonlinear Systems with Multistability Based on Field Test

The equipment scheduling and propagation characteristics of vibration wave from vibratory roller ⟶ filling material nonlinear systems with multistability are the core problems of subgrade intelligent construction technology, and the logistics scheduling of the equipment is directly related to the construction efficiency. Aiming at the shortages, one typical subgrade located at the Gu’an station of Beijing-Xiong’an city railway is selected to research and finish the field tests; some findings are shown as follows: first, some valuable suggestions about the logistics scheduling of intelligent equipment are proposed, which can break the barriers between the organizations and improve construction efficiency; second, when the vibration wave propagates from the vibratory roller ⟶ surface of filling material ⟶ different buried depths of filling material, the peak acceleration of vibration wave gradually decreases and is hyperbolic distribution approximately. At the same time, the sensitive of attenuation is shown as follows: Z R 2 >0.9), and the concrete functional relationship among different amplitudes of harmonics can be summarized as y = Ae −BX ; fourth, the vibration energy is mainly concentrated near 10–30 Hz in the vibratory roller, but when the vibration wave propagates from vibratory roller⟶filling material, the vibration energy gradually decreases with the increase in depth, and the marginal spectrum gradually changes from one peak to two peaks, that is, 30–50 Hz and 50–100 Hz; fifth, the vibration energy in the vibrational wheel is distributed averagely in the compaction process, and the effective compaction time is two seconds, which will be helpful for revealing the propagation characteristics of vibration wave, optimizing the compaction quality control models and providing some support for the development of intelligent compaction theory of railway subgrade.

Han, Yixuan↗
DOE OSTI2025

Flexible Resource Scheduler for FAST-DERMS (FRS-FASTDERMS) v0.9

The Flexible Resource Scheduler is a hierarchical controller that manages the distributed energy resources in a distribution substation or distribution feeder to provide a firm commitment of power flow at the substation or feeder head to be scheduled in transmission-level markets as an aggregated demand resource. It is the reference controller for the FAST-DERMS Architecture, developed in tandem with the architecture under the DOE FAST-DERMS project. It is comprised of a day-ahead stochastic optimization, which schedules substation power flow and reserves, a intra-hour MPC, which generates dispatch base points for DER, and a real-time PID controller maintaining that dispatches DER to maintain the substation power around the base points. The repository also includes a representative aggregator controller, and all of the necessary components to run a simulation using PNNL's GridAPPS-D software with the controller.

MacDonald, Jason [Lawrence Berkeley National Labor↗
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 OSTI2022

Milestone Report: Co-Scheduling Applications on Current and Future Architectures

This report evaluates the viability and performance of co-scheduling scientific applications and workloads between CPUs and GPUs on current and future hardware architectures. This study was performed by collecting hardware independent and memory performance characteristics of 6 ECP mini-applications through Byfl and NSight Systems, by measuring the applications’ co-scheduled performance on current node architectures, and by gathering memory bandwidth utilization data with Intel Advisor and nvprof to begin reasoning about the feasibility of co-scheduling applications on future APU architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

DeepSurveySim: Simulation Software and Benchmark Challenges for Astronomical Observation Scheduling

Modern astronomical surveys have multiple competing scientific goals. Optimizing the observation schedule for these goals presents significant computational and theoretical challenges, and state-of-the-art methods rely on expensive human inspection of simulated telescope schedules. Automated methods, such as Reinforcement Learning (RL), have recently been explored to accelerate scheduling. DeepSurveySim provides methods for tracking and approximating sky conditions for a set of observations from a user-supplied telescope configuration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Scheduler Modeling of Distributed Energy Resources for Providing Ancillary Services

Distribution energy resources (DERs) have been integral components of modern power systems, and their capability to provide grid services has been widely studied. To promote the deployment of these resources in providing grid services in real-world utility operations, this paper proposes a day-ahead scheduler model for a distribution system connected DER plant. A certain amount of generation capacity of this DER plant is reserved for frequency services, and some ancillary services for the distribution system-including peak load reduction, voltage regulation, and power factor control-are integrated into the model. The model is tested on a real-world distribution system. From the simulation results, the energy and reserve schedule of the solar photovoltaic (PV) unit and battery energy storage system (BESS) can be determined, and voltage and power factor are well maintained. Additionally, in order to demonstrate the specific characteristics of the co-located and hybrid operation modes for the PV and BESS, these two modes are analyzed both theoretically and through real-time simulation. Simulation results show that most of PV's variability is transferred to the net power in the co-located mode, whereas it is transferred to the BESS in the hybrid mode. This proposed scheduler model and the comparison of co-located and hybrid modes can provide practical guidance for the applications of DER plant in the real-world utility.

14 SOLAR ENERGY↗
DOE OSTI2024

Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation

Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided.

Nunes, Pedro (ORCID:0000000180012172)↗
DOE OSTI2021

A Decision Support Framework for Grid-Aware Electric Bus Charge Scheduling

While there are many advantages to electric public transit vehicles, they also pose new challenges for fleet operators. One key challenge is defining a charge scheduling policy that minimizes operating costs and power grid disruptions while maintaining schedule adherence. An uncoordinated policy could result in some buses running out of charge before completing their trip, while a grid agnostic policy might incur higher energy costs or cause an adverse impact on the grid’s distribution system. We present a grid aware decision-theoretic framework for electric bus charge scheduling that accounts for energy price, grid load, and bus status. The framework connects environmental models for traffic (SUMO) and the electric grid (GridLAB-D) to a planner, which evaluates bus charging decisions with regard to their long-term effect on grid reliability and cost. The framework has been evaluated on a simulation of Richland, WA’s bus and grid network, and compared to greedy methods.

EV, machine leaning, EV-Grid, Transportation, Grid↗
DOE OSTI2021

Method and apparatus for asynchronous scheduling

A method and apparatus of asynchronous scheduling in a graphics device includes sending one or more instructions from an instruction scheduler to one or more instruction first-in/first-out (FIFO) devices. An instruction in the one or more FIFO devices is selected for execution by a single-instruction/multiple-data (SIMD) pipeline unit. It is determined whether all operands for the selected instruction are available for execution of the instruction, and if all the operands are available, the selected instruction is executed on the SIMD pipeline unit. The self-timed arithmetic pipeline unit (SIMD pipeline unit) is effectively encapsulated in a synchronous, (e.g., clocked by global clock), scheduler and register file environment.

Kalamatianos, John↗
DOE OSTI2022

Generating traffic-based building occupancy schedules in Chattanooga, Tennessee from a grid of traffic sensors

Building occupancy significantly impacts energy use, timing for demand impacts, and is a significant source of uncertainty in building energy models. There are relatively few sources that define building occupancy schedules and number of occupants per building or space type. More importantly, these sources define traditional schedules that are likely not to reflect the true occupancy of a given building. We construct traffic-based occupancy schedules which are more responsive to changes in mobility patterns, and which can realistically estimate occupant arrivals, departures, and counts in individual buildings.

Berres, Andy↗
DOE OSTI2024

Scheduling and Performance of Asynchronous Tasks in Fortran 2018 with FEATS

Most parallel scientific programs contain compiler directives (pragmas) such as those from OpenMP (Hermanns in Parallel programming in Fortran 95 using openMP, 2002. School of Aeronautical Engineering, Universidad Politécnica de Madrid, España, 2011), explicit calls to runtime library procedures such as those implementing the Message Passing Interface (MPI) (in A message-passing interface standard version 4.0, 2021. https://www.mpi-forum.org/docs/mpi-4.0/mpi40-report.pdf), or compiler-specific language extensions such as those provided by CUDA (Ruetsch and Fatica in CUDA Fortran for scientists and engineers: best practices for efficient CUDA Fortran programming, Elsevier, 2013). By contrast, the recent Fortran standards empower developers to express parallel algorithms without directly referencing lower-level parallel programming models (Numrich in Parallel programming with co-arrays, CRC Press, 2018, and Curcic in Modern Fortran: building efficient parallel applications, Manning Publications, 2020). Fortran’s parallel features place the language within the Partitioned Global Address Space (PGAS) class of programming models. When writing programs that exploit data parallelism, application developers often find it straightforward to develop custom parallel algorithms. Problems involving complex, heterogeneous, staged calculations, however, pose much greater challenges. Such applications require careful coordination of tasks in a manner that respects dependencies prescribed by a directed acyclic graph. When rolling one’s own solution proves difficult, extending a customizable framework becomes attractive. Further, the paper presents the design, implementation, and use of the Framework for Extensible Asynchronous Task Scheduling (FEATS), which we believe to be the first task scheduling tool written in modern Fortran. We describe the benefits and compromises associated with choosing Fortran as the implementation language, and we propose ways in which future Fortran standards can best support the use case in this paper.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Integrating tactical planning, operational planning and scheduling using data-driven feasibility analysis

Supply chain operations and scheduling are well-studied problems in the literature. Although these problems are related, they are often solved sequentially. This uncoordinated approach usually leads to sub-optimal solutions. Here this paper proposes a methodology for integrating scheduling operations into the supply chain network, motivated by the available enterprise data, and feasibility analysis. Existing literature on integrated models for addressing the supply chain is reviewed and classified. Then the mathematical model formulation for each level is discussed, after which the proposed data-driven integrated framework is described. The methodology is tested on two different case studies of varying dimensions. For each case study, the solution solves a multi-objective problem with the overall aim of an optimal solution that is robust towards achieving optimal cost while keeping customer satisfaction in mind.

42 ENGINEERING↗
DOE OSTI2020

Optimal pumping schedule with high-viscosity gel for uniform distribution of proppant in unconventional reservoirs

Hydrocarbon recovery from a hydraulic-fractured unconventional reservoir can be enhanced by replacing slickwater with non-Newtonian high-viscosity gel as a fracturing fluid. However, in the literature, the role of gel rheology on hydrocarbon production from a fractured reservoir is not very well understood. This lack of understanding makes it difficult for the industry to choose appropriate viscosity parameters of fracturing gels. Motivated by these limitations, first, we have developed a high-fidelity model of non-Newtonian fluid flow to simulate fracture propagation, fluid leak-off, and gel flowback processes to predict the oil production from a hydraulic fractured reservoir. Simulation results of the high-fidelity model show how fracture geometry, formation damage, and gel cleanup process varies with the fracturing fluid rheology. Therefore, the developed model is used to understand the effect of gel rheology on hydrocarbon production via a sensitivity analysis, which provides a set of rheological parameters that maximize the cumulative amount of oil production. Finally, utilizing this optimal high-viscosity gel, a model predictive controller is designed to obtain an optimal pumping schedule necessary for producing a target fracture geometry and proppant concentration. The closed-loop simulation results demonstrate that the obtained pumping schedule successfully achieved the desired fracture geometry and proppant concentration at the end of pumping, leading to the maximum oil production from an unconventional reservoir.

13 HYDRO ENERGY↗
DOE OSTI2020

An empirical study of moving horizon closed-loop demand response scheduling

The potential of electricity-intensive chemical plants to engage in demand response (DR) initiatives in support of power grid operations has been the subject of many conceptual studies. In this work, using an industrially-relevant model of an air separation unit, we undertake an extensive simulation study of moving horizon (MH) rescheduling approaches, where we "close the scheduling loop" based on updated information regarding disturbances such as changes in electricity prices, ambient conditions and chemical product demand. Our study produces several unexpected findings regarding the non-periodic nature of rescheduling solutions, the impact of the accuracy of disturbance forecasts on the economics of DR scheduling, and on the interplay of simultaneously dealing with fluctuations on both the supply side (i.e., electricity prices) and the product demand side of the plant. Furthermore, we posit that the latter fluctuations pose significant limitations to the potential of a chemical plant to engage in DR.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Dynamic force and stability prediction for milling using feed rate scheduling software and time-domain simulation

This paper describes: 1) the use of feed rate scheduling software to predict the radial depth of cut variation for three-axis milling toolpaths and; 2) the use of the radial depth profile in a time-domain simulation to predict dynamic cutting forces. The time-domain simulation, which also includes the tool tip frequency response functions and force model (which relates the cutting force components to the chip geometry) as inputs, enables dynamic force profiles to be predicted and parameter combinations that cause chatter to be identified. A ramp geometry is selected that provides constantly varying radial depth and force predictions are completed at multiple axial depths for comparison to measured forces. Both stable and unstable (chatter) milling conditions were observed with good agreement between time-domain simulation and measurement results. The value of combining the feed rate scheduling software and time-domain simulation is demonstrated.

42 ENGINEERING↗
DOE OSTI2020

CoSim: A Simulator for Co-Scheduling of Batch and On-Demand Jobs in HPC Datacenters

The increasing scale and complexity of scientific applications are rapidly transforming the ecosystem of tools, methods, and workflows adopted by the high-performance computing (HPC) community. Big data analytics and deep learning are gaining traction as essential components in this ecosystem in a variety of scenarios, such as, steering of experimental instruments , acceleration of high-fidelity simulations through surrogate computations, and guided ensemble searches. In this context, the batch job model traditionally adopted by the supercomputing in-frastructures needs to be complemented with support to schedule opportunistic on-demand analytics jobs, leading to the problem of efficient preemption of batch jobs with minimum loss of progress. In this paper, we design and implement a simulator, CoSim, that enables on-the-fly analysis of the trade-offs arising between delaying the start of opportunistic on-demand jobs, which leads to longer analytics latency, and loss of progress due to preemption of batch jobs, which is necessary to make room for on-demand jobs. To this end, we propose an algorithm based on dynamic programming with predictable performance and scalability that enables supercomputing infrastructure schedulers to analyze the aforementioned trade-off and take decisions in near real-time. Compared with other state-of-art approaches using traces of the Theta pre-Exascale machine, our approach is capable of finding the optimal solution, while achieving high performance and scalability.

batch job preemption↗