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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Identification of Noise Sources in a Rocket Engine Test Stand using Microphone Phased Array

A new, phased array of microphones, suitable for the harsh environment of a rocket launch, was built and tested during a static firing of a RS-25 engine. It uses 70 piezo-resistive, dynamic pressure sensors, optimally distributed on a 10.5ft diameter open frame dome structure, and has a 200-ft long cable bundle to carry the signals to a weather-protected cabinet containing the data systems. The test stand was imaged using an infra-red camera and a visible wavelength camera, and the beamformed noise maps were superimposed on the photographs. The first-time data from a full-scale engine test stand showed that the plume deflector at the bottom of the engine to be the primary noise source. The openings of the test stand around the nozzle exit were also found to be noise sources particularly at higher frequencies. The final goal is to use the array during NASA’s Artemis-II launch at Kennedy Space Center.

Identification of Noise Source↗
DOE OSTI2022

Designing for Zero Energy and Zero Carbon on a Multi-Building Scale Using URBANopt: Preprint

Groundbreaking efforts are necessary to mitigate contributors increasing impacts of climate change. In parallel to inventing pioneering clean energy technologies it is even more fundamental to rethink designing energy systems within a singular facility and collectively to function as a district. Facilities should not be continuously passive by just consuming; there is a need to shift to perform more dynamically. Designing for zero energy and zero carbon on a multi-building scale can uncover opportunities for building energy efficiency, decarbonization, demand flexibility, and resiliency that are not accessible at an individual building scale. This approach can be challenging without innovative tools to evaluate the multitude of possibilities. As an investigated result, we highlight the use of a campus-scale energy modeling platform - URBANopt™ - for the expansion of the National Renewable Energy Laboratory's (NREL's) South Table Mountain campus in Golden, Colorado. Programmatic growth included the design of three new all-electric, zero-energy, and zero-carbon, mixed used buildings (a combination of research laboratories and office space). This investigation is critical to NREL reaching net-zero emissions for its operational footprint, which will occur in phases over the next decade. Leveraging URBANopt's capabilities, we evaluate 1) high-performance building energy efficiency and decarbonization measures, 2) 4th generation district heating and cooling (4th GDHC) systems, 3) optimized onsite generation and energy storage assets that meet zero-energy and zero-carbon targets at minimum life-cycle costs, and 4) cost-optimal distributed energy technology mixes, dispatch strategies, and associated capacities that increase resiliency to grid outages. This work demonstrates the use and capabilities of URBANopt through a real-world case study on a multi-building scale.

community energy model↗
NASA NTRS1985

Factorization and reduction methods for optimal control of distributed parameter systems

A Chandrasekhar-type factorization method is applied to the linear-quadratic optimal control problem for distributed parameter systems. An aeroelastic control problem is used as a model example to demonstrate that if computationally efficient algorithms, such as those of Chandrasekhar-type, are combined with the special structure often available to a particular problem, then an abstract approximation theory developed for distributed parameter control theory becomes a viable method of solution. A numerical scheme based on averaging approximations is applied to hereditary control problems. Numerical examples are given.

Burns, J. A.↗
NASA NTRS1991

One shot methods for optimal control of distributed parameter systems 1: Finite dimensional control

The efficient numerical treatment of optimal control problems governed by elliptic partial differential equations (PDEs) and systems of elliptic PDEs, where the control is finite dimensional is discussed. Distributed control as well as boundary control cases are discussed. The main characteristic of the new methods is that they are designed to solve the full optimization problem directly, rather than accelerating a descent method by an efficient multigrid solver for the equations involved. The methods use the adjoint state in order to achieve efficient smoother and a robust coarsening strategy. The main idea is the treatment of the control variables on appropriate scales, i.e., control variables that correspond to smooth functions are solved for on coarse grids depending on the smoothness of these functions. Solution of the control problems is achieved with the cost of solving the constraint equations about two to three times (by a multigrid solver). Numerical examples demonstrate the effectiveness of the method proposed in distributed control case, pointwise control and boundary control problems.

Taasan, Shlomo↗
NASA NTRS1993

An inverse method for computation of structural stiffness distributions of aeroelastically optimized wings

An inverse method has been developed to compute the structural stiffness properties of wings given a specified wing loading and aeroelastic twist distribution. The method directly solves for the bending and torsional stiffness distribution of the wing using a modal representation of these properties. An aeroelastic design problem involving the use of a computational aerodynamics method to optimize the aeroelastic twist distribution of a tighter wing operating at maneuver flight conditions is used to demonstrate the application of the method. This exercise verifies the ability of the inverse scheme to accurately compute the structural stiffness distribution required to generate a specific aeroelastic twist under a specified aeroelastic load.

Schuster, David M.↗
DOE OSTI2022

A Deep Reinforcement Learning-based Reserve Optimization in Active Distribution Systems for Tertiary Frequency Regulation

Federal Energy Regulatory Commission (FERC)Orders 841 and 2222 have recommended that distributed energy resources (DERs) should participate in energy and reserve markets; therefore, a mechanism needs to be developed to facilitate DERs’ participation at the distribution level. Although the available reserve from a single distribution system may not be sufficient for tertiary frequency regulation, stacked and coordinated contributions from several distribution systems can enable them participate in tertiary frequency regulation at scale. This paper proposes a deep reinforcement learning (DRL)-based approach for optimization of requested aggregated reserves by system operators among the clusters of DERs. The co-optimization of cost of reserve, distribution network loss, and voltage regulation of the feeders are considered while optimizing the reserves among participating DERs. The proposed framework adopts deep deterministic policy gradient (DDPG), which is an algorithm based on an actor-critic method. The effectiveness of the proposed method for allocating reserves among DERs is demonstrated through case studies on a modified IEEE 34-node distribution system.

deep reinforcement learning, distributed energy re↗
DOE OSTI2022

Towards Optimal and Executable Distribution Grid Restoration Planning With a Fine-Grained Power-Communication Interdependency Model

Distribution service restoration (DSR) under natural disasters is always a critical and challenging problem for utility companies. An effective solution must not ignore the power-communication interdependency as various systems are getting increasingly connected in the Smart Grid era. In this paper, we propose a two-layer distribution system model with both power and communication components. Based on this model, we formulate the restoration process as a routing problem that schedules the path and action sequence of utility crews that involves repairing damaged components, closing power switches, and enabling communication paths between the control center and remote field devices. Further, we develop a simulation-based method to quantitatively evaluate the restoration process with public reference models of large-scale power systems. The experimental results show that our method improves the total restored energy up to 57.6% and reduces the recovery time up to 63% by considering the power-communication interdependency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems

Resource allocation in High Performance Computing (HPC) environments presents a complex and multifaceted challenge for job scheduling algorithms. Beyond the efficient allocation of system resources, schedulers must account for and optimize multiple performance metrics, including job wait time and system throughput. Traditional heuristic-based scheduling algorithms increasingly struggle and lack the efficiency needed to meet the demands and address the complexity and scale of modern HPC systems. Consequently, recent research efforts have focused on leveraging advancements in Artificial Intelligence (AI) and Deep Learning (DL), particularly Reinforcement Learning (RL), to develop more adaptable and intelligent scheduling strategies. Previous RL-based scheduling approaches have explored a range of algorithms, from Deep Q-Networks (DQN) to Proximal Policy Optimization (PPO), and more recently, hybrid methods that integrate Graph Neural Networks (GNNs) with RL techniques. However, a common limitation across these methods is their reliance on relatively small datasets, with few methods being evaluated using large-scale, multi-million-job trace datasets representative of real-world HPC workloads. Moreover, existing RL schedulers face scalability issues due to centralized policy updates, which hinder training efficiency and performance when applied to large datasets. This study introduces a novel RL-based scheduler utilizing Decentralized Distributed Proximal Policy Optimization (DD-PPO) algorithm, which supports large-scale distributed training across multiple workers without requiring parameter synchronization at every step. By eliminating reliance on centralized updates to a shared policy, the DD-PPO scheduler enhances scalability, training efficiency, and sample utilization. Experimental validation using a large real-world dataset containing over 11.5 million job traces collected from petascale HPC systems over six years assesses the influence of dataset scale on training effectiveness and compares DD-PPO performance to traditional and advanced scheduling approaches. The experimental results demonstrate improved scheduling performance in comparison to both heuristic-based schedulers and existing RL-based scheduling algorithms.

AI↗
DOE OSTI2025

Sensitivity-based voltage constraints for optimal power flow in low-voltage distribution feeders

The optimal power flow (OPF) problem for distribution systems can include network details down to the low-voltage (LV) points of interconnection of individual customers. This paper addresses the implementation of voltage magnitude constraints, and sets forth a practicable approach for capturing the effects on voltage from the switching behavior of loads (e.g., heat pumps, air conditioners, water heaters, or pool pumps) and from the variability of renewable generation (e.g., rooftop solar). The proposed method adjusts the OPF voltage constraints based on forecasts of load and generation upper and lower bounds, in conjunction with sensitivity factors derived from the power flow equations. An illustrative OPF formulation is also provided, which incorporates transformer models that include core loss. We demonstrate that accurate modeling of these LV network components is critical to avoid voltage violations at customer points of interconnection. Furthermore, the ideas are validated through numerical case studies on a realistic distribution feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Hierarchical, Grid-Aware, and Economically Optimal Coordination of Distributed Energy Resources in Realistic Distribution Systems

Renewable portfolio standards are targeting high levels of variable solar photovoltaics (PV) in electric distribution systems, which makes reliability more challenging to maintain for distribution system operators (DSOs). Distributed energy resources (DERs), including smart, connected appliances and PV inverters, represent responsive grid resources that can provide flexibility to support the DSO in actively managing their networks to facilitate reliability under extreme levels of solar PV. This flexibility can also be used to optimize system operations with respect to economic signals from wholesale energy and ancillary service markets. Here, we present a novel hierarchical scheme that actively controls behind-the-meter DERs to reliably manage each unbalanced distribution feeder and exploits the available flexibility to ensure reliable operation and economically optimizes the entire distribution network. Each layer of the scheme employs advanced optimization methods at different timescales to ensure that the system operates within both grid and device limits. The hierarchy is validated in a large-scale realistic simulation based on data from the industry. Simulation results show that coordination of flexibility improves both system reliability and economics, and enables greater penetration of solar PV. Discussion is also provided on the practical viability of the required communications and controls to implement the presented scheme within a large DSO.

distribution network↗
DOE OSTI2023

An Online Joint Optimization–Estimation Architecture for Distribution Networks

Here in this article, we propose an optimal joint optimization-estimation architecture for distribution networks, which jointly solves the optimal power flow (OPF) problem and static state estimation (SE) problem through an online gradient-based feedback algorithm. The main objective is to enable a fast and timely interaction between the OPF decisions and state estimators with limited sensor measurements. First, convergence and optimality of the proposed algorithm are analytically established. Then, the proposed gradient-based algorithm is modified by introducing statistical information of the inherent estimation and linearization errors for an improved and robust performance of the online OPF decisions. Overall, the proposed method eliminates the traditional separation of operation and monitoring, where optimization and estimation usually operate at distinct layers and different time scales. Hence, it enables a computationally affordable, efficient, and robust online operational framework for distribution networks under time-varying settings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Multi-Level Optimal Power Flow Solver in Large Distribution Networks

Solving optimal power flow (OPF) problems for large distribution networks incurs high computational complexity. We consider a large multi-phase distribution network of tree topology with a deep penetration of active devices. We divide the network into collaborating areas featuring subtree topology and subareas featuring subsubtree topology. We design a multilevel implementation of the primal-dual gradient algorithm to solve the voltage regulation OPF problems while preserving nodal voltage information and topological information within areas and subareas. Numerical results on a 4,521-node system verify that the proposed algorithm can significantly improve the computational speed without compromising any optimality.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2026

Enhancing weak lensing redshift distribution characterization by optimizing the Dark Energy Survey Self-Organizing Map Photo-z method

Characterization of the redshift distribution of ensembles of galaxies is pivotal for large scale structure cosmological studies. In this work, we focus on improving the Self-Organizing Map (SOM) methodology for photometric redshift estimation (SOMPZ), specifically in anticipation of the Dark Energy Survey Year 6 (DES Y6) data. This data set, featuring deeper and fainter galaxies than DES Year 3 (DES Y3), demands adapted techniques to ensure accurate recovery of the underlying redshift distribution. We investigate three strategies for enhancing the existing SOM-based approach used in DES Y3: 1) Replacing the Y3 SOM algorithm with one tailored for redshift estimation challenges; 2) Incorporating $\textit{g}$-band flux information to refine redshift estimates (i.e. using $\textit{griz}$ fluxes as opposed to only $\textit{riz}$); 3) Augmenting redshift data for galaxies where available. These methods are applied to DES Y3 data, and results are compared to the Y3 fiducial ones. Our analysis indicates significant improvements with the first two strategies, notably reducing the overlap between redshift bins. By combining strategies 1 and 2, we have successfully managed to reduce redshift bin overlap in DES Y3 by up to 66$\%$. Conversely, the third strategy, involving the addition of redshift data for selected galaxies as an additional feature in the method, yields inferior results and is abandoned. Our findings contribute to the advancement of weak lensing redshift characterization and lay the groundwork for better redshift characterization in DES Year 6 and future stage IV surveys, like the Rubin Observatory.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Real Time - Optimal Power Flow Based Distributed Energy Resources Management System (DERMS) (CRADA Number CRD-20-16909 Final Report)

The integration of behind-the-meter distributed energy resources (DERs) into distribution systems brings transformative changes to power systems. This requires operators and planners to find solutions to modernize electric grids and to effectively manage DERs for grid services. NREL developed novel DER management algorithms (referred to as Real-Time Optimal Power Flow, RT-OPF) through U.S. Department of Energy (DOE)-funded efforts, including Advanced Research Projects Agency–Energy (ARPA-E) Network Optimized Distributed Energy Systems (NODES) funding. This cutting-edge control technology aims to modernize distribution systems with large amounts of DER integration, which will help utilities solve issues brought by renewable integration and build resilient and renewable-based electric grids nationwide. Utilidata worked with NREL to investigate the commercialization opportunity of this RT-OPF-based distributed energy resource management system (DERMS). In this project, NREL performs the technology transfer of the RT-OPF to Utilidata to help them fully understand the RT-OPF solution, to identify potential engineering hurdles, and to assess the expected commercial value of various RT-OPF use cases and deployment. The technology transfer work includes two major tasks. First, NREL performs an in-depth knowledge transfer of the entire RT-OPF solution to Utilidata to help them gain an extensive and detailed understanding of the complete RT-OPF solution. In this task, NREL provides exhaustive information (e.g., documentation, code packages, laboratory and field trial data, performance results) while conducting in-depth training sessions to provide a thorough explanation of the entire solution. NREL hosts meetings to present different topics related to the RT-OPF solution, and question-and-answer sessions are included in each meeting to better explain the RT-OPF-related work. Second, the RT-OPF simulations are performed in a laboratory environment. The main objectives of this task are to walk through with Utilidata engineers how to set up a simulation of RT-OPF, identifying each RT-OPF code block/component in operation, learning how these components interact with each other, and eventually running RT-OPF simulations under various system conditions. This task helps Utilidata engineers understand performance limitations and constraints while also quantifying the commercial value of the RT-OPF for different use cases. Based on these two tasks, Utilidata engineers should be able to define and prioritize the next steps of RT-OPF implementation with an eye toward commercial success and scalability of the solution. The next steps are expected to be part of a new project following the conclusion of this project.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Target Tracking with Distributed Sensing and Optimal Data Migration

The paper presents an Extended Kalman Filter based framework for airborne target tracking using adaptive information fusion from multi-modal multi-rate distributed sensors network. First, the tracking algorithm execution location is determined using an optimal data migration strategy, which also computes the associated delays for each sensor data to arrive at the computing location. Next, the fast (zero-delay) sensors information is dynamically fused in the filter correction procedure at the arrival instance of each valid sensor reading. Finally, the target estimation is updated based on the valid slow (delayed) data, which are grouped according to the delay-time steps before application of the Larsen's method. This approach is applied to the synthetic sensor data generated by means of the ground based radar and camera models for the simulated target flight in Reflection simulation environment.

Distributed sensing↗
NASA NTRSDate not supplied

Target Tracking with Distributed Sensing and Optimal Data Migration

The paper presents an Extended Kalman Filter based framework for airborne target tracking using adaptive information fusion from multi-modal multi-rate distributed sensors network. First, the tracking algorithm execution location is determined using an optimal data migration strategy, which also computes the associated delays for each sensor data to arrive at the computing location. Next, the fast (zero-delay) sensors information is dynamically fused in the filter correction procedure at the arrival instance of each valid sensor reading. Finally, the target estimation is updated based on the valid slow (delayed) data, which are grouped according to the delay-time steps before application of the Larsen's method. This approach is applied to the synthetic sensor data generated by means of the ground based radar and camera models for the simulated target flight in Reflection simulation environment.

Distributed sensing↗
DOE OSTI2021

Optimized Dispatch of Distributed Energy Resources for Resiliency and Power Quality Improvements at the Grid-Edge

Distributed Energy Resources (DERs) installed on low-voltage distribution systems, both utility-owned and nonutility owned may be employed to make improvements to system resiliency and power quality, in addition to simply serving load demand. By selectively dispatching active and reactive power from these existing DERs, improvements in voltage profiles and service availability during adverse events may be achieved. Further benefits can be gained by optimally choosing which DERs to dispatch power from, and the distribution of power from those DERs. For a given operating condition, using Linear Programming methods, optimal power dispatch values for individual DERs can be determined, while considering variables such as voltage gains, losses in the lines and the DERs, and DER reserve capacities. One such optimization technique is developed in this paper, and results from a real-time simulation study on a modified IEEE 13-bus distribution system with three DERs, are being presented. The operation of the optimized DER selection and dispatch algorithm is shown during normal operation of the transmission system when the DERs operate in grid-connected mode. Similar analyses can drive decision making during transmission system failure when the DERs operate in grid-forming or islanded mode.

Chowdhury, Prithwiraj Roy↗