Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “schedule”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

System and method for hardware scheduling of conditional barriers and impatient barriers

A method and a system are provided for hardware scheduling of barrier instructions. Execution of a plurality of threads to process instructions of a program that includes a barrier instruction is initiated, and when each thread reaches the barrier instruction during execution of program, it is determined whether the thread participates in the barrier instruction. The threads that participate in the barrier instruction are then serially executed to process one or more instructions of the program that follow the barrier instruction. A method and system are also provided for impatient scheduling of barrier instructions. When a portion of the threads that is greater than a minimum number of threads and less than all of the threads in the plurality of threads reaches the barrier instruction each of the threads in the portion is serially executed to process one or more instructions of the program that follow the barrier instruction.

Lindholm, John Erik↗
DOE OSTI2019

Devices, base stations, and methods for communicating scheduling requests via an underlay control channel in a wireless communication system

Wireless communication systems, base stations, and user equipment are disclosed that enable communication of scheduling requests via an underlay control channel that has an energy below a noise level of the spectrum. The scheduling requests may be sent and received at any time, including during downlink and uplink data communication periods of the base station.

42 ENGINEERING↗
DOE OSTI2019

Temperature-aware task scheduling and proactive power management

Systems, apparatuses, and methods for performing temperature-aware task scheduling and proactive power management. A SoC includes a plurality of processing units and a task queue storing pending tasks. The SoC calculates a thermal metric for each pending task to predict an amount of heat the pending task will generate. The SoC also determines a thermal gradient for each processing unit to predict a rate at which the processing unit's temperature will change when executing a task. The SoC also monitors a thermal margin of how far each processing unit is from reaching its thermal limit. The SoC minimizes non-uniform heat generation on the SoC by scheduling pending tasks from the task queue to the processing units based on the thermal metrics for the pending tasks, the thermal gradients of each processing unit, and the thermal margin available on each processing unit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM

Demand response (DR) serves an important role in improving the efficiency and stability of power systems. In recent years, with advances in communication and smart device technologies, many aggregators have emerged to facilitate end customer participation in DR programs. These aggregators, equipped with customized optimal control algorithms, are capable of providing various grid services. Among them is load scheduling during DR events, namely following a load signal provided by the utility company while minimizing overall customer discomfort. However, as the number of aggregators keeps increasing, it becomes challenging for utility companies to conduct load scheduling for multiple aggregators and generate reference signals for each of them. This paper proposes an optimization framework using hierarchical alternating direction method of multipliers (H-ADMM) to optimally generate load following signals for multiple aggregators. Under this framework, utility and multiple aggregators work in a cooperative manner, aiming at minimizing an overall system cost from different levels of the power system hierarchy, while protecting user privacy. A case study has been conducted in a system with multiple aggregators, based on control of HVAC loads. Experimental results validate the effectiveness of the proposed algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

System and method for optimal load and source scheduling in context aware homes

A controller for controlling energy consumption in a home includes a constraints engine to define variables for multiple appliances in the home corresponding to various home modes and persona of an occupant of the home. A modeling engine models multiple paths of energy utilization of the multiple appliances to place the home into a desired state from a current context. An optimal scheduler receives the multiple paths of energy utilization and generates a schedule as a function of the multiple paths and a selected persona to place the home in a desired state.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

On-the fly scheduling of execution of dynamic hardware behaviors

Methods for dynamically executing computer code across multiple disparate processing unit architectures are disclosed. During execution of a first portion of computer code on a first processing unit, it is determined that a first dynamic hardware behavior of a plurality of dynamic hardware behaviors will occur at a subsequent point in time, based on a second dynamic hardware behavior that is occurring. The methods include determining to execute code corresponding to the first dynamic hardware behavior on a second processing unit, rather than the first processing unit, and scheduling computer program code corresponding to the first dynamic hardware behavior to execute on the second processing unit rather than the first processing unit. Upon completion of execution of the computer code corresponding to the first dynamic hardware behavior, a remaining portion of the computer code is scheduled to execute on the first processing unit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Fuzzified PaCcET for Economic-Emission Scheduling of Microgrids

In this paper, a new approach is proposed to solve a multi-objective economic-emission scheduling problem in microgrids (MGs) by simultaneously minimizing the energy and emission costs of the MG with various distributed energy resources (DERs). The proposed approach is an extension of a computationally effective multiobjective optimization technique, Pareto concavity elimination transformation (PaCcET). The proposed approach, referred to as Fuzzified-PaCcET, employs a fuzzy logic controller to dynamically revise crossover and mutation rates in the original PaCcET leading to the faster convergence of the solution. The proposed approach finds the best Pareto front, also referred to as a Non-dominated set (NDS) of solutions, instead of finding a single optimal solution. In order to find the solutions on concave areas of the Pareto front, an iterative objective space transformation is performed in the PaCcET algorithm to allow a linear combination of objective functions (in the transformed objective space). The proposed Fuzzified-PaCcET-based scheduling is implemented on a MG with various dispatchable and non-dispatchable DERs to find the set of optimal solutions according to the total fuel cost of DERs, as well as the most optimum environmental cost. In order to extract the best compromise solution (BCS) among NDS of solutions, a fuzzy-based method is implemented. The comparison of the simulation results of the Fuzzified-PaCcET with that of PaCcET shows that Fuzzified-PaCcET can generate better solution with less computational burden.

42 ENGINEERING↗
DOE OSTI2020

Scheduling and Control of Flexible Building Loads for Grid Services Based on a Virtual Battery Model

This paper presents a framework for modeling, scheduling, and controlling residential thermostatically controlled loads (TCLs) to provide multiple grid services, such as energy shifting, peak load reduction, and ancillary services. A modeling method is proposed to characterize the aggregate flexibility from heterogeneous TCLs using a battery-equivalent model. Based on the flexibility model, a multi-period optimal scheduling formulation is developed to best utilize the flexibility from building loads and maximize total benefits from stacked value streams. An algorithm is proposed to control individual TCLs to follow the desired power consumption in real-time. The proposed methods are illustrated and validated through simulations.

Wu, Di↗
DOE OSTI2022

Systems and methods for tensor scheduling

A technique for efficient scheduling of operations in a program for parallelized execution thereof using a multi-processor runtime environment having two or more processors includes constraining the type or number of loop optimization transforms that may be explored such that memory and processing capacity available for the scheduling task are not exceeded, while facilitating a tradeoff between memory locality, parallelization, and/or data communication between memory modules of the multi-processor runtime environment.

Meister, Benoit J.↗
DOE OSTI2022

Cooperative workgroup scheduling and context prefetching based on predicted modification of signal values

A first workgroup is preempted in response to threads in the first workgroup executing a first wait instruction including a first value of a signal and a first hint indicating a type of modification for the signal. The first workgroup is scheduled for execution on a processor core based on a first context after preemption in response to the signal having the first value. A second workgroup is scheduled for execution on the processor core based on a second context in response to preempting the first workgroup and in response to the signal having a second value. A third context it is prefetched into registers of the processor core based on the first hint and the second value. The first context is stored in a first portion of the registers and the second context is prefetched into a second portion of the registers prior to preempting the first workgroup.

Dutu, Alexandru↗
DOE OSTI2022

Use Probabilistic Forecasts in Reliable and Economic Electricity Market Scheduling and Operations

The value of probabilistic forecasts in electricity market operations is being increasingly recognized lately, however, the use is still limited. This study demonstrates two cases of using probabilistic forecasts in scheduling and operations of the California electricity market. We first develop a data-driven method to give weather-informed estimates of ramping reserves based on short-term probabilistic solar irradiance forecasts. By using forecasts across multiple sites, our proposed method presents advantages over the real-world baseline in terms of system reliability and economics. Our second case uses probabilistic hydro power forecasts in the scheduling of a hydro power plant under economic and environmental constraints. By optimizing the bids into both the day-ahead and real-time markets, our results demonstrate considerable economic benefits.

13 HYDRO ENERGY↗
DOE OSTI2023

Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning

Modern astronomical experiments are designed to achieve multiple scientific goals, from studies of galaxy evolution to cosmic acceleration. These goals require data of many different classes of night-sky objects, each of which has a particular set of observational needs. These observational needs are typically in strong competition with one another. This poses a challenging multi-objective optimization problem that remains unsolved. The effectiveness of Reinforcement Learning (RL) as a valuable paradigm for training autonomous systems has been well-demonstrated, and it may provide the basis for self-driving telescopes capable of optimizing the scheduling for astronomy campaigns. Simulated datasets containing examples of interactions between a telescope and a discrete set of sky locations on the celestial sphere can be used to train an RL model to sequentially gather data from these several locations to maximize a cumulative reward as a measure of the quality of the data gathered. We use simulated data to test and compare multiple implementations of a Deep Q-Network (DQN) for the task of optimizing the schedule of observations from the Stone Edge Observatory (SEO). We combine multiple improvements on the DQN and adjustments to the dataset, showing that DQNs can achieve an average reward of 87%+-6% of the maximum achievable reward in each state on the test set. This is the first comparison of offline RL algorithms for a particular astronomical challenge and the first open-source framework for performing such a comparison and assessment task.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

A dynamic control approach for energy-efficient production scheduling on a single machine under time-varying electricity pricing

This paper proposes a dynamic control algorithm to enable an energy-aware single machine scheduling under the time-varying electricity pricing policy, in which price rates remain fixed day-to-day over the season. The key issue is to assign a set of jobs to available time periods where different electricity prices are assigned, while considering requested due dates of jobs so as to minimize total penalty costs for earliness and tardiness of jobs and total energy consumption costs, simultaneously. As the first contribution of this study, we develop a new mixed integer nonlinear programming (MINLP) model that aims at determining job arrival times and resulting earliness and tardiness of jobs and energy consumption costs for machine idle and normal processing. Second, an efficient heuristic approach based on continuous-time variable control models and algorithm is developed. The proposed heuristic adaptively changes job arrival times and due dates, which finally determine production sequence over the time periods of different electricity prices, machine turn-off, and machine idle with minimum energy consumption costs and just-in-time (JIT) penalty. Energy and JIT performance of the proposed approach is examined using real energy and machining parameters of a HAAS machine and compared to those of the metaheuristic approach. For relatively large size data groups, the proposed approach incurs about higher energy consumption costs on average, which are offset by up to lower JIT costs, resulting in lower total costs on average compared to the metaheuristic approach. Further, the proposed time-scaled heuristic algorithm yields extremely short computational time, which enables production managers to flexibly select proper production strategies and to implement them for different production environments.

42 ENGINEERING↗
NASA NTRS2012

Spitzer Operations: Scheduling the Out Years

Spitzer Warm Mission operations have remained robust and exceptionally efficient since the cryogenic mission ended in mid-2009. The distance to the now exceeds 1 AU, making telecommunications increasingly difficult; however, analysis has shown that two-way communication could be maintained through at least 2017 with minimal loss in observing efficiency. The science program continues to emphasize the characterization of exoplanets, time domain studies, and deep surveys, all of which can impose interesting scheduling constraints. Recent changes have significantly improved on-board data compression, which both enables certain high volume observations and reduces Spitzer's demand for competitive Deep Space Network resources.

Spitzer↗
DOE OSTI2023

Multistage robust optimization for the day-ahead scheduling of hybrid thermal-hydro-wind-solar systems

The integration of large-scale uncertain and uncontrollable wind and solar power generation has brought new challenges to the operations of modern power systems. In a power system with abundant water resources, hydroelectric generation with high operational flexibility is a powerful tool to promote a higher penetration of wind and solar power generation. In this paper, we study the day-ahead scheduling of a thermal-hydro-wind-solar power system. The uncertainties of renewable energy generation, including uncertain natural water inflow and wind/solar power output, are taken into consideration. We explore how the operational flexibility of hydroelectric generation and the coordination of thermal-hydro power can be utilized to hedge against uncertain wind/solar power under a multistage robust optimization (MRO) framework. To address the computational issue, mixed decision rules are employed to reformulate the original MRO model with a multi-level structure into a bi-level one. Column-and-constraint generation (C &CG) algorithm is extended into the MRO case to solve the bi-level model. The proposed optimization approach is tested in three real-world cases. Furthermore, the computational results demonstrate the capability of hydroelectric generation to promote the accommodation of uncertain wind and solar power.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Alternative mixed integer linear programming optimization for joint job scheduling and data allocation in grid computing

This paper presents a novel approach to the joint optimization of job scheduling and data allocation in grid computing environments. We formulate this joint optimization problem as a mixed integer quadratically constrained program. To tackle the nonlinearity in the constraint, we alternatively fix a subset of decision variables and optimize the remaining ones via Mixed Integer Linear Programming (MILP). We solve the MILP problem at each iteration via an off-the-shelf MILP solver. Our experimental results show that our method significantly outperforms existing heuristic methods, employing either independent optimization or joint optimization strategies. We have also verified the generalization ability of our method over grid environments with various sizes and its high robustness to the algorithm setting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Microgrid energy scheduling under uncertain extreme weather: Adaptation from parallelized reinforcement learning agents

Microgrids are useful solutions for integrating renewable energy resources and providing seamless green electricity to minimize carbon footprint. In recent years, extreme weather events happened often worldwide and caused significant economic and societal losses. Such events bring uncertainties to the microgrid energy scheduling problems and increase the challenges of microgrid operation. Traditional optimization approaches suffer from the inaccuracy of the uncertain microgrid model and the unseen events. Existing reinforcement learning (RL) - based approaches are also hampered by the limited generalization and the increasing computational burden when stochastic formulations are required to accommodate the uncertainties. This paper proposes a new parallelized reinforcement learning (PRL) method based on the probabilistic events to handle the microgrid energy uncertainties. Specifically, several local learning agents are employed to interact with pertinent microgrid environments in a distributed manner and report outcomes to the global agent, which will optimize microgrid energy resources online during extreme events. The stochastic microgrid energy optimization problem is reformulated to include all possible scenarios with probabilities. The advantage estimate functions of learning agents are designed with a backward sweep to transfer the outcomes to the value function updating process. Two simulation studies, stochastic optimization and online testing, are performed to compare with several existing RL approaches. Results substantiate that the proposed PRL method can achieve up to 20% optimization performance improvement with 4 and 28 times less computation cost than Q-learning with experience replay and multi-agent Q-learning approaches, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Outcomes Associated With 3 Treatment Schedules of High-Dose-Rate Brachytherapy Monotherapy for Favorable-Risk Prostate Cancer

Purpose: We report the outcomes associated with 3 high-dose-rate (HDR) brachytherapy regimens used as monotherapy for favorable-risk prostate cancer. Methods and Materials: Four hundred ninety-four patients with stage ≤T2b prostate cancer, Gleason score ≤7, and prostate-specific antigen levels ≤15 ng/mL underwent HDR brachytherapy as monotherapy. Of those, 319 received 38 Gy in 4 fractions, 79 received 24 Gy in 2 fractions, and 96 received 27 Gy in 2 fractions. Acute and chronic genitourinary (GU) and gastrointestinal (GI) toxicities were defined as side effects occurring ≤6 and >6 months, respectively, after radiation therapy (RT) and were graded according to the Common Terminology Criteria for Adverse Events version 3.0. The time to toxicity was calculated from the date of RT completion. Variables were analyzed with χ{sup 2} test. P values <.05 were considered significant. Results: The median overall follow-up time was 4 years (range, 5.5, 3.5, and 2.5 years for 38 Gy, 24 Gy, and 27 Gy, respectively, P<.001). Acute and chronic grade ≥2 GU and GI toxicity profiles were similar among groups. Acceptable rates of grade 2 GU toxicities were seen with overall acute/chronic frequency/urgency, dysuria, retention, incontinence, and hematuria rates of 14%/20%, 6%/7%, 7%/4%, 1.5%/2%, and 1.5%/7%, respectively. Minimal grade 3 and no grade 4 or 5 toxicities were seen. Grade 1, 2, and 3 chronic urethral stricture rates were 0.3%, 2%, and 1%, respectively. All GI toxicities were similar between groups, with overall rates of acute/chronic grade 2 diarrhea, rectal pain/tenesmus, rectal bleeding, and proctitis of 1%/1%, <1%/0.5%, 0%/2%, and <1%/1%, respectively. No grade 3, 4, or 5 toxicities were seen. All comparisons were similar for hormone-naïve patients. The median time to maximal GU/GI toxicity was similar between groups, ranging from 1 to 1.6 to 0.9 to 1.2 years, respectively. There were no differences in clinical outcomes between the 3 groups at 5 years. Conclusions: The acute and chronic toxicity profiles associated with these 3 HDR brachytherapy schedules were similar and were well tolerated. Acceptable grade 2, minimal grade 3, and no grade 4 or 5 toxicities were seen. This, combined with the fact that the clinical outcomes were similar, leads to the conclusion that all 3 regimens may be acceptable options for the management of low-risk to intermediate-risk prostate cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗