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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2023

A Task Based Approach for Co-Scheduling Ensemble Workloads on Heterogeneous Nodes

Scientific workflows consist of multiple, connected applications, with data and results flowing from one to another in a pipeline. Traditionally, such workflows are executed in sequential order, storing intermediate data in storage disks. Co-scheduling application workflows concurrently on the same compute nodes would greatly reduce the cost of moving data to/from storage and allow real-time analysis of intermediate results. Nevertheless, most parallel programming runtimes do not allow seamless integration of various applications in a scientific workflow, in part due to the complexity of managing data and resources. The situation is even more complicated for heterogeneous systems. In this work we extend the Minos Computing Library (MCL) runtime to accelerate pipe-lined and parallel workloads where multiple applications are running in the same system. MCL’s asynchronous task library and runtime dynamically manages resources to allow co-scheduling of multiple processes sharing heterogeneous resources. In addition, we design a custom ex- tension of the Open Compute Language (OpenCL) to enable multiple processes to share device memory. We enable MCL to coordinate these shared buffers to allow for easy, fast data sharing between applications. Using malleable micro-benchmarks and two application workflows that combine scientific simulation and AI-based analysis, we show that our method outperforms traditional approaches.

Index Terms—Parallel systems, Scheduling and Task ↗
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.

ADMM↗
DOE OSTI2018

Heuristic approach to Satellite Range Scheduling with Bounds using Lagrangian Relaxation

Here, this paper focuses on scheduling antennas to track satellites using a novel heuristic method. The objectives pursued in developing a schedule are two-fold: (1) minimize the priority weighted number of time periods that satellites are not tracked; and (2) equalize the percent of time each satellite is uncovered. The heuristic method is a population-based local search tailored to the unique characteristics of this problem. In order to validate the performance of the heuristic, bounds are developed using Lagrangian relaxation. The heuristic method and the bounds are applied to several test problems. In all cases, the heuristic identifies a solution that is better than the upper bound and is generally quite close (but obviously larger) than the lower bound with about an order of magnitude reduction in computation time. Lastly, a comparison with CPLEX 12.7 is provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Design and Scheduling of Microgrids using Benders Decomposition

The distribution feeder laterals in a distribution feeder with relatively high PV generation as compared to the load can be operated as microgrids to achieve reliability, power quality and economic benefits. However, renewable resources are intermittent and stochastic in nature. A novel approach for sizing and scheduling an energy storage system and microturbine for reliable operation of microgrids is proposed. The size and schedule of an energy storage system and microturbine are determined using Benders' decomposition, considering PV generation as a stochastic resource.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Optimal Energy Storage Schedules for Load Leveling and Ramp Rate Control in Distribution Systems

Continued integration of distributed energy resources (DERs) into the grid, such as solar PVs, at a large-scale, contributes into the famous Duck Curve. New DER management algorithms are therefore deemed necessary to alleviate rapid variations within net load profiles of distribution systems. Herein this paper proposes a process to determine the optimal energy storage schedules for leveling the distribution circuit feederhead net load. A series of sensitivity analyses shows how the proposed method can be used to determine the optimal energy storage schedules with different capacities, state of charge requirements, and net load ramp rate limitations.

25 ENERGY STORAGE↗
DOE OSTI2018

Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Furthermore, our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

WE-H-BRA-03: Development of a Model to Include the Evolution of Resistant Tumor Subpopulations Into the Treatment Optimization Process for Schedules Involving Targeted Agents in Chemoradiation Therapy

Purpose: To develop a model that includes the process of resistance development into the treatment optimization process for schedules that include targeted therapies. Further, to validate the approach using clinical data and to apply the model to assess the optimal induction period with targeted agents before curative treatment with chemo-radiation in stage III lung cancer. Methods: Growth of the tumor and its subpopulations is modeled by Gompertzian growth dynamics, resistance induction as a stochastic process. Chemotherapy induced cell kill is modeled by log-cell kill dynamics, targeted agents similarly but restricted to the sensitive population. Radiation induced cell kill is assumed to follow the linear-quadratic model. The validation patient data consist of a cohort of lung cancer patients treated with tyrosine kinase inhibitors that had longitudinal imaging data available. Results: The resistance induction model was successfully validated using clinical trial data from 49 patients treated with targeted agents. The observed recurrence kinetics, with tumors progressing from 1.4–63 months, result in tumor growth equaling a median volume doubling time of 92 days [34–248] and a median fraction of pre-existing resistance of 0.035 [0–0.22], in agreement with previous clinical studies. The model revealed widely varying optimal time points for the use of curative therapy, reaching from ∼1m to >6m depending on the patient’s growth rate and amount of pre-existing resistance. This demonstrates the importance of patient-specific treatment schedules when targeted agents are incorporated into the treatment. Conclusion: We developed a model including evolutionary dynamics of resistant sub-populations with traditional chemotherapy and radiation cell kill models. Fitting to clinical data yielded patient specific growth rates and resistant fraction in agreement with previous studies. Further application of the model demonstrated how proper timing of chemo-radiation could minimize the probability of resistance, increasing tumor control significantly.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

A Cooperative Multi-Agent Deep Reinforcement Learning Framework for Real-Time Residential Load Scheduling

Internet-of-Things (IoT) enabled monitoring and control capabilities are enabling increasing numbers of household users with controllable loads to actively participate in smart grid energy management. Realizing an efficient real-time energy management system that takes advantage of these developments requires novel techniques for managing the increased complexity of the control action space in resolving multiple challenges such as the uncertainty in energy prices and renewable energy output along with the need to satisy physical grid constraints such as transformer capacity. Addressing these challenges, we develop a multi-household energy management framework for residential units connected to the same transformer and containing DERs such as PV, ESS and controllable loads. The goal of our framework is to schedule controllable household appliances and ESS such that the cost of procuring electricity from the utility over a horizon is minimized while physical grid constraints are satisfied at each scheduling step. Traditional energy management frameworks either perform global optimization to satisfy grid constraints but suffer from high computational complexity (for example Integer Program, Mixed Integer Programming frameworks and centralized reinforcement learning) or perform decentralized real-time energy management without satisfying global grid constraints (for example multi-agent reinforcement learning with no cooperation). In contrast, we propose a cooperative multiagent reinforcement learning (MARL) framework that i) operates in real-time, and ii) performs explicit collaboration to satisfy global grid constraints. The novelty in our framework is two fold. Firstly, our framework trains multiple independent learners (IL) for each household in parallel using historical data and performs real-time inferencing of control actions using the most recent system state. Secondly, our framework contains a low complexity knapsack based cooperation agent which combines the outputs of ILs to minimize cost while satisfying grid constraints. Simulation results show that our cooperative MARL approach achieves significant cost improvement over centralized reinforcement learning and day-ahead planning baselines. Moreover, our approach strictly satisfies physical constraints with no apriori knowledge of system dynamics while the baseline approaches have occasional violations. We also measure the training and inference time by ranging the number of households from 1 to 25. Results show that our cooperative MARL approach scales best among various approaches.

14 SOLAR ENERGY↗
DOE OSTI2016

Analysis of Application Power and Schedule Composition in a High Performance Computing Environment

As the capacity of high performance computing (HPC) systems continues to grow, small changes in energy management have the potential to produce significant energy savings. In this paper, we employ an extensive informatics system for aggregating and analyzing real-time performance and power use data to evaluate energy footprints of jobs running in an HPC data center. We look at the effects of algorithmic choices for a given job on the resulting energy footprints, and analyze application-specific power consumption, and summarize average power use in the aggregate. All of these views reveal meaningful power variance between classes of applications as well as chosen methods for a given job. Using these data, we discuss energy-aware cost-saving strategies based on reordering the HPC job schedule. Using historical job and power data, we present a hypothetical job schedule reordering that: (1) reduces the facility's peak power draw and (2) manages power in conjunction with a large-scale photovoltaic array. Lastly, we leverage this data to understand the practical limits on predicting key power use metrics at the time of submission.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A novel large-scale EV charging scheduling algorithm considering V2G and reactive power management based on ADMM

Electric vehicle aggregators (EVAs) that utilize vehicle-to-grid (V2G) technologies can function as both controllable loads and virtual power plants, providing key energy management services to the distribution system operator (DSO). EVAs can also balance the grid’s reactive power as a virtual static VAR compensator (SVC) and provide voltage stability by utilizing advanced electric vehicle (EV) chargers that are capable of four-quadrant operations to provide reactive power management. Finally, managed charging can benefit EVAs themselves by minimizing power factor penalties in their electricity bills. In this paper, we propose a novel EV charging scheduling algorithm based on a hierarchical distributed optimization framework that minimizes peak load and provides reactive power compensation for the DSO by collaboration with EVAs that manage both the active and the reactive charging and discharging power of participating EVs. Utilizing the alternative direction method of multipliers (ADMM), the proposed distributed optimization approach scales well with increased EV charging infrastructure by balancing active and reactive power while decreasing computational burden. In our proposed hierarchical approach, each EVA schedules the active and reactive EV charging and discharging power for 1) reactive power compensation in order to minimize power factor penalty and electricity cost accrued by the EVA, 2) satisfaction of each EV’s energy demand at minimal charging cost, and 3) peak shaving and load management for the DSO. When compared with an uncoordinated charging model, the efficacy of this proposed model is successfully demonstrated through a 300% decreased peak EV load for the DSO, 28% lower electricity costs for EV users, and 98.55% smaller power factor penalty, along with 17.58% lower overall electricity costs, for EVAs. The performance of our approach is validated in a case study with 50 EVs at multiple EVAs in an IEEE 13-bus test case and compared the results with uncoordinated EV charging.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that the impact of power flexibility is more prominent than the weighting factor to the resource allocation process. Allocation based purely on occupancy status could lead to an increase of PV curtailment. Further, it is necessary for the building agent to have multi-objective optimization to minimize unserved load ratio and maximize comfort simultaneously.

model predictive control↗
DOE OSTI2020

Multi-Size Proppant Pumping Schedule of Hydraulic Fracturing: Application to a MP-PIC Model of Unconventional Reservoir for Enhanced Gas Production

Slickwater hydraulic fracturing is becoming a prevalent approach to economically recovering shale hydrocarbon. It is very important to understand the proppant’s transport behavior during slickwater hydraulic fracturing treatment for effective creation of a desired propped fracture geometry. The currently available models are either oversimplified or have been performed at limited length scales to avoid high computational requirements. Another limitation is that the currently available hydraulic fracturing simulators are developed using only single-sized proppant particles. Motivated by this, in this work, a computationally efficient, three-dimensional, multiphase particle-in-cell (MP-PIC) model was employed to simulate the multi-size proppant transport in a field-scale geometry using the Eulerian–Lagrangian framework. Instead of tracking each particle, groups of particles (called parcels) are tracked, which allows one to simulate the proppant transport in field-scale geometries at an affordable computational cost. Then, we found from our sensitivity study that pumping schedules significantly affect propped fracture surface area and average fracture conductivity, thereby influencing shale gas production. Motivated by these results, we propose an optimization framework using the MP-PIC model to design the multi-size proppant pumping schedule that maximizes shale gas production from unconventional reservoirs for given fracturing resources.

42 ENGINEERING↗
NASA NTRS2005

Automating Deep Space Network scheduling and conflict resolution

The Deep Space Network (DSN) is a central part of NASA's infrastructure for communicating with active space missions, from earth orbit to beyond the solar system. We describe our recent work in modeling the complexities of user requirements, and then scheduling and resolving conflicts on that basis. We emphasize our innovative use of background 'intelligent' assistants' that carry out search asynchrnously while the user is focusing on various aspects of the schedule.

conflict resolution↗
NASA NTRS2002

The JPL Resource Allocation Planning and Scheduling Office (RAPSO) process

The Jet Propulsion Laboratory's Resource Allocation Planning and Scheduling Office is chartered to divide the limited amount of tracking hours of the Deep Space Network amongst the various missions in as equitable allotment as can be achieved. To best deal with this division of assets and time, an interactive process has evolved that promotes discussion with agreement by consensus between all of the customers that use the Deep Space Network (DSN). Aided by a suite of tools, the task of division of asset time is then performed in three stages of granularity. Using this approach, DSN loads are either forecasted or scheduled throughout a moving 10-year window.

RAPSO Resource Allocation Planning and Scheduling ↗