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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 OSTI2017

Linear Power-Flow Models in Multiphase Distribution Networks: Preprint

This paper considers multiphase unbalanced distribution systems and develops approximate power-flow models where bus-voltages, line-currents, and powers at the point of common coupling are linearly related to the nodal net power injections. The linearization approach is grounded on a fixed-point interpretation of the AC power-flow equations, and it is applicable to distribution systems featuring (i) wye connections; (ii) ungrounded delta connections; (iii) a combination of wye-connected and delta-connected sources/loads; and, (iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. The proposed linear models can facilitate the development of computationally-affordable optimization and control applications -- from advanced distribution management systems settings to online and distributed optimization routines. Performance of the proposed models is evaluated on different test feeders.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Method for automatic correction of offset drift in online sensors

Abstract Successful operation and optimization of water treatment systems hinge on the availability of high-quality online sensor measurements. Ideally, the available measurements should be simultaneously accurate (i.e., unbiased and precise), representative, voluminous, and timely. This remains a pain-point in current water infrastructures, forming a barrier to a wider adoption of advanced and autonomous control systems. While short-lived symptoms, such as outliers and spikes, can be detected or corrected with state-of-the-art tools for fault detection and identification, it is much more difficult to detect, diagnose, and correct the symptoms of slow faults, such as changes in offset or sensitivity due to drift. The time scale of drift is often longer than the time scales of the system dynamics of interest. Moreover, sensor drift has been shown to occur at the same time and with similar rates when sensors are exposed to the same conditions. This challenges data quality management strategies based on redundancy. In this contribution, we develop a new method, including both a hands-off sensor calibration mechanism and an information-seeking control architecture that can handle the unique challenge of simultaneous and similar drift in online sensors.

Chowdhury, Dhrubajit↗
DOE OSTI2023

An Online Prototype Toolset for Predicting and Optimizing P&T Performance (FY23 Status Report)

A new web-based toolset is being developed to support ongoing remediation optimization efforts and implementation of an adaptive site management strategy for the 200 West Area Pump-and-Treat (P&T) system at the Hanford Site. This toolset, comprising the well performance index tool and the well optimization pre-screening tool, will offer a user-friendly interface to predict and optimize the P&T well network’s performance at a preliminary level. Efforts in fiscal year (FY) 2023 focused on three main components: updating the existing deep learning model for predicting P&T performance, designing and developing a prototype of a web-based performance index tool, and initiating the conceptual design of the well optimization pre-screening tool. The well performance index tool is based on a pre-trained deep learning model that allows users to select a target contaminant and well screen length, then visualize the predicted performance of potential new wells across the site. The well optimization pre-screening tool includes two separate modules: the pre-computed scenario viewer, which organizes and visualizes offline optimization simulation results, and the quick analysis module, which provides real-time model prediction using user-specified well locations. In FY24, the plan is to add web-based applications to SOCRATES for both the well performance prediction tool and the optimization prescreening tool, with accompanying user and theory guides. These tools are intended to enable an accessible, easily applied, and transparent approach to remedy planning and decision-making.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. Here, this paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Bayesian optimization algorithms for accelerator physics

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques toward solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP

With ever-increasing volumes of scientific data produced by high-performance computing applications, significantly reducing data size is critical because of limited capacity of storage space and potential bottlenecks on I/O or networks in writing/reading or transferring data. SZ and ZFP are two leading BSD licensed open source C/C++ libraries for compressed floating-point arrays that support high throughput read and write random access. However, their performance is not consistent across different data sets and across different fields of some data sets, which raises the need for an automatic online (during compression) selection between SZ and ZFP, with minimal overhead. In this paper, the automatic selection optimizes the rate-distortion, an important statistical quality metric based on the signal-to-noise ratio. To optimize for rate-distortion, we investigate the principles of SZ and ZFP. We then propose an efficient online, low-overhead selection algorithm that predicts the compression quality accurately for two compressors in early processing stages and selects the best-fit compressor for each data field. Here, we implement the selection algorithm into an open-source library, and we evaluate the effectiveness of our proposed solution against plain SZ and ZFP in a parallel environment with 1,024 cores. Evaluation results on three data sets representing about 100 fields show that our selection algorithm improves the compression ratio up to 70 percent with the same level of data distortion because of very accurate selection (around 99 percent) of the bestfit compressor, with little overhead (less than 7 percent in the experiments).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Formulation and Comparison of Two Real-Time Predictive Gear Shift Algorithms for Connected/Automated Heavy-Duty Vehicles

Our work investigates the problem of predictive gear scheduling for fuel consumption minimization in connected/automated heavy trucks. The literature highlights the fuel economy benefits of such predictive scheduling, but there is a need to optimize such scheduling online, in real time. To address this need, we begin by using dynamic programming (DP) to schedule gear shifting offline, in a manner that achieves a globally optimal Pareto tradeoff between the conflicting objectives of minimizing fuel consumption and shift frequency. The computational cost of DP is unfavorable for online implementation, but we present two algorithms addressing this issue. Both algorithms rely on the fact that in the Pareto limit where fuel consumption minimization is the sole objective, DP furnishes a simple static shift map . Our first algorithm trains a recurrent neural network to prune the shift schedule generated by this map. The second algorithm performs this pruning in a direct manner tailored to reduce the schedule's rain flow count. We simulate these algorithms for different drive cycles. Both algorithms achieve a reasonable tradeoff between fuel consumption and gear shift frequency. Yet, the rain flow count algorithm is both more effective in approaching the DP-based Pareto front and more computationally efficient.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Multi-time Scale Coordination of Distributed Energy Resources in Isolated Power Systems

In isolated power systems, including microgrids, distributed assets, such as renewable energy resources (e.g. wind, solar) and energy storage, can be actively coordinated to reduce dependency on fossil fuel generation. The key challenge of such coordination arises from significant uncertainty and variability occurring at small time scales associated with increased penetration of renewables. Specifically, the problem is with ensuring economic and efficient utilization of DERs, while also meeting operational objectives such as adequate frequency performance. One possible solution is to reduce the time step at which tertiary controls are implemented and to ensure feedback and look-ahead capability are incorporated to handle variability and uncertainty. However, reducing the time step of tertiary controls necessitates investigating time-scale coupling with primary controls so as not to exacerbate system stability issues. In this paper, an optimal coordination (OC) strategy, which considers multiple time-scales, is proposed for isolated microgrid systems with a mix of DERs. This coordination strategy is based on an online moving horizon optimization approach. The effectiveness of the strategy was evaluated in terms of economics, technical performance, and computation time by varying key parameters that significantly impact performance. The illustrative example with realistic scenarios on a simulated isolated microgrid test system suggests that the proposed approach is generalizable towards designing multi-time scale optimal coordination strategies for isolated power systems.

Mayhorn, Ebony↗
NASA NTRSDate not supplied

Progress of 2.05 μm Fiber Laser Development for CO2 DIAL Measurements of Martian CO2 and Pressure

The Decadal Survey by the US National Academy of Sciences in the United States and NASA’s Science Mission Directorate (SMD) Science Plan both require global CO2 observations for the Martian atmospheric pressure, dynamics and CO2 variations. However, there are significant observational gaps in polar regions and during nighttime for global air pressure and CO2 on Mars. Therefore, recently we proposed a new concept of Martian differential absorption lidar (DIAL) operating in the 2.05 μm CO2 absorption band for global, including poles, atmospheric CO2 and pressure observations day and night. Based on the concept, we are awarded to develop 2.05 μm fiber lasers by the NASA Planetary Instrument Concepts for the Advancement of Solar System Observations (PICASSO) Program. The laser is designed to be an all-fiber master oscillator and power amplifier (MOPA) system with laser out put of ~3 mJ at a repetition frequency of 2 kHz. The primary master oscillator (PMO) is locked to the center (2.0504280 μm) of the selected CO2 absorption line. The frequency of a second MO (SMO) is locked to that of PMO. This SMO frequency is adjustable and switched between the online (2.05044156 μm) and offline (2.05050812 μm) wavelengths. The online wavelength is optimally selected so that the CO2 absorption optical depth (AOD) is ~1.1 at 3 km and the measurement in the low Martian atmosphere has largest signal-to-noise ratio. The online wavelength can also be adjusted to a line slope location where AOD is larger to observe atmospheric pressure at higher altitudes. We will present more details about this project and instrument development.

CO2↗
NASA NTRSDate not supplied

Development of 2.05 µm Fiber Lasers for CO2 DIAL Lidar Measuring Martian CO2 and Pressure

The Decadal Survey by the National Academy of Sciences in the United States and NASA’s Science Mission Directorate (SMD) Science Plan both require global CO 2 observations for the Martian atmospheric pressure, dynamics and chemistry. However, there are significant observational gaps in polar regions and during nighttime for global air pressure and CO 2 on Mars. Therefore, recently we proposed a new concept of Martian differential absorption lidar (DIAL) operating in the 2.05 µm CO 2 absorption band for global, including poles, atmospheric CO 2 and pressure observations day and night [1]. Based on the concept, we are awarded to develop 2.05 µm fiber lasers by the NASA Planetary Instrument Concepts for the Advancement of Solar System Observations (PICASSO) Program. The laser is designed to be an all-fiber master oscillator and power amplifier (MOPA) system with laser output of ~3 mJ at a repetition frequency of 2 kHz. The primary master oscillator (PMO) is locked to the center (2.0504280 µm) of the selected CO 2 absorption line. The frequency of a second MO (SMO) is locked to that of PMO. This SMO frequency is adjustable and switched between the online (2.05044156 µm) and offline (2.05050812 µm) wavelengths. The online wavelength is optimally selected so that the CO 2 absorption optical depth (AOD) is ~1.1 at 3 km and the measurement in the low Martian atmosphere has largest signal-to-noise ratio. The online wavelength can also be adjusted to a line slope location where AOD is larger to observe atmospheric pressure at higher altitudes. We will present more detail about this project and instrument development at the conference.

Zhaoyan Liu↗
DOE OSTI2016

SU-F-J-11: Radiobiologically Optimized Patient Localization During Prostate External Beam Localization

Purpose: To present a novel positioning strategy which optimizes radiation delivery with radiobiological response knowledge, and to evaluate its application during prostate external beam radiotherapy. Methods: Ten patients with low or intermediate risk prostate cancer were evaluated retrospectively in this IRB-approved study. For each patient, a VMAT plan was generated on the planning CT (PCT) to deliver 78 Gy in 39 fractions with PTV = prostate + 7 mm margin, except for 5mm in the posterior direction. Five representative pretreatment CBCT images were selected for each patient, and prostate, rectum, and bladder were delineated on all CBCT images. Each CBCT was auto-registered to the corresponding PCT. Starting from this auto-matched position (AM-position), a search for optimal treatment position was performed utilizing a score function based on radiobiological and dosimetric indices (D98-DTV, NTCP-rectum, and NTCP-bladder) for the daily target volume (DTV), rectum, and bladder. DTV was defined as prostate + 4 mm margin to account for intra-fraction motion as well as contouring variability on CBCT. We termed the optimal treatment position the radiobiologically optimized couch shift position (ROCS-position). Results: The indices, averaged over the 10 patients’ treatment plans, were (mean±SD): 77.7±0.2 Gy (D98-PTV), 12.3±2.7% (NTCP-rectum), and 53.2±11.2% (NTCP-bladder). The corresponding values calculated on all 50 CBCT images at the AM-positions were 72.9±11.3 Gy (D98-DTV), 15.8±6.4% (NTCP-rectum), and 53.0±21.1% (NTCP-bladder), respectively. In comparison, calculated on CBCT at the ROCS-positions, the indices were 77.0±2.1 Gy (D98-DTV), 12.1±5.7% (NTCP-rectum), and 60.7±16.4% (NTCP-bladder). Compared to autoregistration, ROCS-optimization recovered dose coverage to target volume and lowered the risk to rectum. Moreover, NTCPrectum for one patient remained high after ROCS-optimization and therefore could potentially benefit from adaptive planning. Conclusion: These encouraging results illustrate the potential utility of applying radiobiologically optimized correction for online image-guided radiotherapy of prostate patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

DERMS Online: A New Voltage Sensitivity-Enabled Feedback Optimization Framework

This paper proposes a distributed energy resource management system (DERMS) solution by developing a new voltage sensitivity-enabled feedback optimization framework. The key idea is to adopt a measurement feedback scheme to reformulate the original nonlinear optimization into a linear programming (LP) problem via perturb-and-observe-based voltage sensitivity analysis. The proposed solution eliminates the dependence on load knowledge and can be implemented online thanks to an efficient open-source solver for LP problems. Comparison results with other control methods on a real distribution feeder in Southern California highlight the feasibility as well as benefits for the proposed framework.

distributed energy resource management↗
DOE OSTI2022

DERMS Online: A New Voltage Sensitivity-Enabled Feedback Optimization Framework: Preprint

This paper proposes a distributed energy resource management system (DERMS) solution by developing a new voltage sensitivity enabled feedback optimization framework. The key idea is to adopt a measurement feedback scheme to reformulate the original nonlinear optimization into a linear programming (LP) problem via perturb and observe-based voltage sensitivity analysis. The proposed solution eliminates the dependence on load knowledge and can be implemented online thanks to an efficient open-source solver for LP problems. The proposed DERMS online platform is generalizable to deal with various types of distributed energy resources (DERs), including distributed photovoltaics (PVs), energy storage, electric vehicles, demand response, etc. Comparison results with other control methods on a realistic distribution feeder in Southern California highlight the feasibility as well as benefits for the proposed framework.

distributed energy resources management↗
DOE OSTI2025

Risk-Aware Reinforcement Learning Framework for User-Centric O-RAN

The evolution of Open Radio Access Networks (O-RAN) presents an opportunity to enhance network performance by enabling dynamic orchestration of configuration and optimization parameters (COPs) through online learning methods. However, leveraging this potential requires overcoming the limitations of traditional cell-centric RAN architectures, which lack the necessary flexibility. On the other hand, despite their recent popularity, the practical deployment of online learning frameworks, such as Deep Reinforcement Learning (DRL)-based COP optimization solutions, remains limited due to their risk of deteriorating network performance during the exploration phase. In this article, we propose and analyze a novel risk-aware DRL framework for user-centric RAN (UC-RAN), which offers both the architectural flexibility and COP optimization to exploit this flexibility. We investigate and identify UC-RAN COPs that can be optimized via a soft actor-critic algorithm implementable as an O-RAN application (rApp) to jointly maximize latency satisfaction, reliability satisfaction, area spectral efficiency, and energy efficiency. We use the offline learning on UC-RAN to reliably accelerate DRL training, thus minimizing the risk of DRL deteriorating cellular network performance. Results show that our proposed solution approaches near-optimal performance in just a few hundred iterations with a decrease in risk score by a factor of ten.

6G and beyond↗
DOE OSTI2018

Sparse-Sensor Placement for Wind Farm Control

The objective of this paper is to incorporate sparse sensor data to improve flow-field estimates in a wind farm, which can then be used to perform better online wind farm optimization and control. A sparse-sensing algorithm is used to determine the optimal locations of sensors to improve the overall estimation precision of the flow field within the wind farm. This algorithm takes advantage of the dominant atmospheric structures in a wind farm to reconstruct the flow field from point measurements in the field. These measurements, in their optimal locations, have the ability to improve the observability of a wind farm and thus provide faster, more accurate, state estimation.

17 WIND ENERGY↗
DOE OSTI2022

Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration

This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to optimize the restoration. Due to the complexities stemming from the large policy search space, renewable uncertainty, and nonlinearity in a complex grid control problem, directly applying RL algorithms to train a satisfactory policy requires extensive tuning to be successful. To address this challenge, this paper leverages the curriculum learning (CL) technique to design a training curriculum involving a simpler steppingstone problem that guides the RL agent to learn to solve the original hard problem in a progressive and more effective manner. We demonstrate that compared with direct learning, CL facilitates controller training to achieve better performance. To study realistic scenarios where renewable forecasts used for decision-making are in general imperfect, the experiments compare the trained RL controllers against two model predictive controllers (MPCs) using renewable forecasts with different error levels and observe how these controllers can hedge against the uncertainty. Results show that RL controllers are less susceptible to forecast errors than the baseline MPCs and can provide a more reliable restoration process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Radiobiologically optimized couch shift: A new localization paradigm using cone-beam CT for prostate radiotherapy

Purpose: To present a novel positioning strategy which optimizes radiation delivery by utilizing radiobiological response knowledge and evaluate its use during prostate external beam radiotherapy. Methods: Five patients with low or intermediate risk prostate cancer were evaluated retrospectively in this IRB-approved study. For each patient, a VMAT plan with one 358° arc was generated on the planning CT (PCT) to deliver 78 Gy in 39 fractions. Five representative pretreatment cone beam CTs (CBCT) were selected for each patient. The CBCT images were registered to PCT by a human observer, which consisted of an initial automated registration with three degrees-of-freedom, followed by manual adjustment for agreement at the prostate/rectal wall interface. To determine the optimal treatment position for each CBCT, a search was performed centering on the observer-matched position (OM-position) utilizing a score function based on radiobiological and dosimetric indices (EUD{sub prostate}, D99{sub prostate}, NTCP{sub rectum}, and NTCP{sub bladder}) for the prostate, rectum, and bladder. We termed the optimal treatment position the radiobiologically optimized couch shift position (ROCS-position). Results: The dosimetric indices, averaged over the five patients’ treatment plans, were (mean ± SD) 79.5 ± 0.3 Gy (EUD{sub prostate}), 78.2 ± 0.4 Gy (D99{sub prostate}), 11.1% ± 2.7% (NTCP{sub rectum}), and 46.9% ± 7.6% (NTCP{sub bladder}). The corresponding values from CBCT at the OM-positions were 79.5 ± 0.6 Gy (EUD{sub prostate}), 77.8 ± 0.7 Gy (D99{sub prostate}), 12.1% ± 5.6% (NTCP{sub rectum}), and 51.6% ± 15.2% (NTCP{sub bladder}), respectively. In comparison, from CBCT at the ROCS-positions, the dosimetric indices were 79.5 ± 0.6 Gy (EUD{sub prostate}), 77.3 ± 0.6 Gy (D99{sub prostate}), 8.0% ± 3.3% (NTCP{sub rectum}), and 46.9% ± 15.7% (NTCP{sub bladder}). Excessive NTCP{sub rectum} was observed on Patient 5 (19.5% ± 6.6%) corresponding to localization at OM-position, compared to the planned value of 11.7%. This was mitigated with radiobiologically optimized localization, resulting in a reduced NTCP{sub rectum} value of 11.3% ± 3.5%. Overall, the treatment position optimization resulted in similar target dose coverage with reduced risk to rectum. Conclusions: These encouraging results illustrate the potential advantage of applying radiobiologically optimized correction for online image-guided radiotherapy of prostate patients.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2019

Flight Control Research at NASA Ames

An overview of the flight control work in the Intelligent Systems Division at NASA Ames is presented. The highlight focuses on efforts surrounding performance-adaptive aeroelastic wing shaping for aircraft with flexible wings. Topics covered include aeroservoelastic modeling capabilities, online drag-optimizing control designs, gust and maneuver load alleviation techniques, and related wind tunnel demonstrations.

Hashemi, Kelley↗