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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Preliminary Statistical Maneuver Analysis for A Low-Thrust NRHO to DRO Transfer

To understand practical implications of implementing transfers that leverage multibody dynamics, a statistical analysis is performed on an Earth-Moon Near Rectilinear Halo Orbit (NRHO) to distant retrograde orbit (DRO) transfer using solar electric propulsion. The statistical analysis includes perturbations due to maneu- ver execution and orbit determination error to understand operational character- istics of the transfer such as statistical ∆V and statistical state targeting errors at the final DRO. We explore the need for low-thrust trajectory correction maneuver (TCM) opportunities as well as the delivery accuracy to the final target. Finally, we explore options for long-term-stable target DROs.

low thrust↗
DOE OSTI2021

Functional trait data for 11 canopy tree species in the control and drought treatments at AU-DRO (2018-2019)

This data package contains foliar abscisic acid levels, xylem anatomy, foliar d13C, leaf mass per area, leaf water potentials, non-structural carbohydrates, and pressure volume curve traits for 11 canopy tree species in the control and drought treatments at the Daintree Rainforest Observatory in Queensland, Australia (AU-DRO). Data were collected at the end of the dry season (Nov 2018) and end of the wet season (May 2019). The species included in the dataset are: Castanospermum australe, Cleistanthus myrianthus, Cryptocarya mackinnoniana, Dysoxylum papuanum, Elaeocarpus angustifolius (Elaeocarpus grandis), Endiandra microneura, Mallotus paniculatus, Myristica globosa, Rockinghamia angustifolia, Synima cordierorum, Syzygium spp. These data correspond to the previously archived dataset “Leaf gas exchange data, Nov 2018 and May 2019, Daintree Rainforest, Australia. NGEE-Tropics Data Archive” (DOI: 10.15486/ngt/1773854).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

Preliminary test data using the MOS DRO with Si:In detector material

The initial testing performed on the Hughes Metal Oxide Semiconductor Direct Readout (MOS DRO) with a Si:In extrinsic infrared array is described. The testing to date was of a screening nature and the results are primarily qualitative rather than quantitative. At a later date the performance optimization phase will be initiated. An encouraging result is that this response is strongly dependent on the detector temperature, to the extent that thermal transients introduced during the chip readout will affect the performance. A responsivity of 1 A/W at 2.2 microns with a bias of 15 volts, which is well below what is optimum bias, was obtained.

Fowler, A. M.↗
NASA NTRS1989

Initial astronomical results with a new 5-14 micron Si:Ga 58x62 DRO array camera

A new array camera system was developed using a 58 x 62 pixel Si:Ga (gallium doped silicon) DRO (direct readout) photoconductor array detector manufactured by Hughes/Santa Barbara Research Center (SBRC). The camera system is a broad band photometer designed for 5 to 14 micron imaging with large ground-based optical telescopes. In a typical application a 10 micron photon flux of about 10(exp 9) photons sec(exp -1) m(exp -2) microns(exp -1) arcsec(exp -2) is incident in the telescope focal plane, while the detector well capacity of these arrays is 10(exp 5) to 10 (exp 6) electrons. However, when the real efficiencies and operating conditions are accounted for, the 2-channel 3596 pixel array operates with about 1/2 full wells at 10 micron and 10% bandwidth with high duty cycle and no real experimental compromises.

Gezari, Dan↗
NASA NTRS2017

Efficient NRHO to DRO transfers in cislunar space

There has been recently a growing interest in cislunar missions, in particular for supporting human deep space exploration. Understanding the dynamical envi-ronment between various cislunar orbits is therefore useful. The current study is focused on finding efficient transfer trajectory options between a Near-Rectilinear Halo Orbit (NRHO) and a Distant Retrograde Orbit (DRO) in the Earth-Moon system. A general methodology is introduced to design these trans-fers in a systematic way, including the use of solar perturbations and lunar fly-bys. Representative solutions are presented and compared in terms of delta-v and flight time, including a transfer requiring 56 m/s only.

Lantoine, Gregory↗
NASA NTRS1986

InSb arrays: Astronomy with a 32x32 CCD/development of a 58x62 DRO

Experience gained in operating infrared detector arrays for high sensitivity astronomical applications at the University of Rochester are summarized. Progress made in operating the 32 x 32 InSb array with bump-bonded Silicon CCD readout is described. Astronomical work done with the 32 x 32 camera is also described. Plans for the future, including improvements for the 32 x 32 camera system as well as implementing a new generation of 58 x 62 InSb array using switched-MOSFET direct readout multiplexing system in the place of the older CCD technology is discussed.

Forrest, W. J.↗
NASA NTRS1989

InSb DRO array characteristics

Researchers tested 58 x 62 low-doped InSb diode arrays bonded to MOSFET readouts for their performance potential in a low background space environment. Of primary concern were the quantum efficiency, dark current and read noise. The quantum efficiency (45 percent at 3.3 microns) and dark current (less than 2.4e(-)/s) were found to be adequate for the Space Infrared Telescope Facility (SIRTF) experiments, while the read noise (200 e(-) RMS) was found to be wanting. More subtle concerns, such as image quality, linearity/calibratibility and flat fielding were also investigated. In these respects the arrays appear to be well suited for the high sensitivity, photometric accuracy, and image clarity demanded by the SIRTF experiments.

Forrest, W. J.↗
DOE OSTI2024

Revealing Decision Conservativeness Through Inverse Distributionally Robust Optimization

This paper introduces Inverse Distributionally Robust Optimization (I-DRO) as a method to infer the conservativeness level of a decision-maker, represented by the size of a Wasserstein metric-based ambiguity set, from the optimal decisions made using Forward Distributionally Robust Optimization (F-DRO). By leveraging the Karush-Kuhn-Tucker (KKT) conditions of the convex F-DRO model, we formulate I-DRO as a bi-linear program, which can be solved using off-the-shelf optimization solvers. Additionally, this formulation exhibits several advantageous properties. We demonstrate that I-DRO not only guarantees the existence and uniqueness of an optimal solution but also establishes the necessary and sufficient conditions for this optimal solution to accurately match the actual conservativeness level in F-DRO. Furthermore, we identify three extreme scenarios that may impact I-DRO effectiveness. Our case study applies F-DRO for power system scheduling under uncertainty and employs I-DRO to recover the conservativeness level of system operators. Numerical experiments based on an IEEE 5-bus system and a realistic NYISO 11-zone system demonstrate I-DRO performance in both normal and extreme scenarios. An extended version of this paper with additional analyses is available at li2024revealing.

distributionally robust optimization↗
NASA NTRS2014

Asteroid Redirect Crewed Mission Nominal Design and Performance

In 2010, the President announced that, in 2025, the U.S. intended to launch a human mission to an asteroid [1]. This announcement was followed by the idea of a Capability Driven Framework (CDF) [2], which is based on the idea of evolving capabilities from less demanding to more demanding missions to multiple possible destinations and with increased flexibility, cost effectiveness and sustainability. Focused missions, such as a NASA inter-Center study that examined the viability and implications of sending a crew to a Near Earth Asteroid (NEA) [3], provided a way to better understand and evaluate the utility of these CDF capabilities when applied to an actual mission. The long duration of the NEA missions were contrasted with a concept described in a study prepared for the Keck Institute of Space Studies (KISS) [4] where a robotic spacecraft would redirect an asteroid to the Earth-Moon vicinity, where a relatively short duration crewed mission could be conducted to the captured asteroid. This mission concept was included in the National Aeronautics and Space Administration (NASA) fiscal year 2014 budget request, as submitted by the NASA Administrator [5]. NASA studies continued to examine the idea of a crewed mission to a captured asteroid in the Earth-Moon vicinity. During this time was an announcement of NASA's Asteroid Grand Challenge [6]. Key goals for the Asteroid Grand Challenge are to locate, redirect, and explore an asteroid, as well as find and plan for asteroid threats. An Asteroid Redirect Mission (ARM) study was being conducted, which supports this Grand Challenge by providing understanding in how to execute an asteroid rendezvous, capture it, and redirect it to Earth-Moon space, and, in particular, to a distant retrograde orbit (DRO). Subsequent to the returning of the asteroid to a DRO, would be the launch of a crewed mission to rendezvous with the redirected asteroid. This report examines that crewed mission by assessing the Asteroid Redirect Crewed Mission (ARCM) nominal design and performance costs associated with an Orion based crewed rendezvous mission to a captured asteroid in an Earth-Moon DRO. The ARM study includes two fundamental mission phases: 1) The Asteroid Redirect Robotic Mission (ARRM) and 2) the ARCM. The ARRM includes a solar electric propulsion based robotic asteroid return vehicle (ARV) sent to rendezvous with a selected near Earth asteroid, capture it, and return it to a DRO in the Earth-Moon vicinity. The DRO is selected over other possible asteroid parking orbits due to its achievability (by both the robotic and crewed vehicles) and by its stability (e.g., no orbit maintenance is required). After the return of the asteroid to the Earth-Moon vicinity, the ARCM is executed and carries a crew of two astronauts to a DRO to rendezvous with the awaiting ARV with the asteroid. The outbound and inbound transfers employ lunar gravity assist (LGA) flybys to reduce the Orion propellant requirement for the overall nominal mission, which provides a nominal mission with some reserve propellant for possible abort situations. The nominal mission described in this report provides a better understanding of the mission considerations as well as the feasibility of such a crewed mission, particularly with regard to spacecraft currently undergoing development, such as the Orion vehicle and the Space Launch System (SLS).

Condon, Gerald↗
DOE OSTI2023

Residuals-based distributionally robust optimization with covariate information

We consider data-driven approaches that integrate a machine learning prediction model within distributionally robust optimization (DRO) given limited joint observations of uncertain parameters and covariates. Our framework is flexible in the sense that it can accommodate a variety of regression setups and DRO ambiguity sets. We investigate asymptotic and finite sample properties of solutions obtained using Wasserstein, sample robust optimization, and phi-divergence-based ambiguity sets within our DRO formulations, and explore cross-validation approaches for sizing these ambiguity sets. Through numerical experiments, we validate our theoretical results, study the effectiveness of our approaches for sizing ambiguity sets, and illustrate the benefits of our DRO formulations in the limited data regime even when the prediction model is misspecified.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches

We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the “demand-driven” properties of the SP and DRO solutions. Furthermore, our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictions without significant compromises in demand coverage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Distributionally Robust Decision Making Leveraging Conditional Distributions

Distributionally robust optimization (DRO) is a powerful tool for decision making under uncertainty. It is particularly appealing because of its ability to leverage existing data. However, many practical problems call for decision-making with some auxiliary information, and DRO in the context of conditional distributions is not straightforward. We propose a conditional kernel distributionally robust optimization (CKDRO) method that enables robust decision making under conditional distributions through kernel DRO and the conditional mean operator in the reproducing kernel Hilbert space (RKHS). In particular, we consider problems where there is a correlation between the unknown variable y and an auxiliary observable variable x. Given past data of the two variables and a queried auxiliary variable, CKDRO represents the conditional distribution P(y|x) as the conditional mean operator in the RKHS space and quantifies the ambiguity set in the RKHS as well, which depends on the size of the dataset as well as the query point. To justify the use of RKHS, we demonstrate that the ambiguity set defined in RKHS can be viewed as a ball under a metric that is similar to the Wasserstein metric. The DRO is then dualized and solved via a finite dimensional convex program. The proposed CKDRO approach is applied to a generation scheduling problem and shows that the result of CKDRO is superior to common benchmarks in terms of quality and robustness.

Chen, Yuxiao↗
DOE OSTI2022

Residuals-based distributionally robust optimization with covariate information

We consider data-driven approaches that integrate a machine learning prediction model within distributionally robust optimization (DRO) given limited joint observations of uncertain parameters and covariates. Our framework is flexible in the sense that it can accommodate a variety of regression setups and DRO ambiguity sets. We investigate asymptotic and finite sample properties of solutions obtained using Wasserstein, sample robust optimization, and phi-divergence-based ambiguity sets within our DRO formulations, and explore cross-validation approaches for sizing these ambiguity sets. Through numerical experiments, we validate our theoretical results, study the effectiveness of our approaches for sizing ambiguity sets, and illustrate the benefits of our DRO formulations in the limited data regime even when the prediction model is misspecified.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Coherence properties of a doubly resonant monolithic optical parametric oscillator

A doubly resonant optical parametric oscillator (DRO) pumped with the second harmonic of a narrow-linewidth Nd:YAG laser is described. The linewidth of the DRO signal was less than 13 kHz, the DRO was shown to generate a phase-locked subharmonic of the pump at degeneracy, and the signal and the idler were shown to be mutually coherent with the pump and to be phase-anticorrelated with each other away from degeneracy. The signal-idler heterodyne linewidth was 500 Hz, and pump phase modulation was shown to transfer to the DRO phase at degeneracy.

Nabors, C. D.↗
DOE OSTI2019

Controlling risk and demand ambiguity in newsvendor models

We use distributionally robust optimization (DRO) to model a general class of newsvendor problems with unknown demand distribution. The goal is to find an order quantity that minimizes the worst-case expected cost among an ambiguity set of distributions. The ambiguity set consists of those distributions that are not far—in the sense of the total variation distance—from a nominal distribution. The maximum distance allowed in the ambiguity set (called level of robustness) places the DRO between the risk-neutral stochastic programming and robust optimization models. An important problem a decision maker faces is how to determine the level of robustness—or, equivalently, how to find an appropriate level of risk-aversion. We answer this question in two ways. Our first approach relates the level of robustness and risk to the regions of demand that are critical (in a precise sense we introduce) to the optimal cost. Our second approach establishes new quantitative relationships between the DRO model and the corresponding risk-neutral and classical robust optimization models. To achieve these goals, we first focus on a single-product setting and derive explicit formulas and properties of the optimal solution as a function of the level of robustness. Then, we demonstrate the practical and managerial relevance of our results by applying our findings to a healthcare problem to reserve operating room time for cardiovascular surgeries. Finally, we extend some of our results to the multi-product setting and illustrate them numerically.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

A data-driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems

The increasing integration of distributed energy resources, including demand-side resources and distributed photovoltaics (PVs), into distribution systems has resulted in more complicated power system operation. A data-driven network optimisation approach is proposed to coordinate the control of distributed PVs and smart buildings in distribution networks considering the uncertainties of solar power, outdoor temperature and heat gain associated with building thermal dynamics. These uncertain parameters have a significant impact on the operation and control of distributed PVs and smart buildings, bringing challenges to the distribution system operation. In the proposed data-driven distributionally robust optimisation (DRO) approach, the Wasserstein ball is used to construct an ambiguity set for the uncertain parameters, which does not require the probability distributions to be known. Furthermore, a conditional value-at-risk is incorporated into the Wasserstein-based DRO model and converted into a computationally tractable mixed-integer convex optimisation problem. Benchmarked with robust optimisation and chance-constrained programming, the proposed data-driven model can give a less conservative robust solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Enhancing the Survivability of Power Systems With Grid-Edge DERs Against DoS Attacks

Power system survivability, defined as the ability of a system to maintain steady-state functionality under varying operational conditions, reflects its resilience against disturbances. While existing research primarily focuses on physical-layer disturbances, the increasing prevalence of grid-edge DERs, which are primarily used for integrating renewable energy, has significantly expanded the cyber attack surface. As a result, operational disruptions caused by cyber threats are posing significant challenges to system survivability and cannot be overlooked. To fill this gap, we redefine system survivability to incorporate the cyber layer’s status and propose a Distributionally Robust Optimization (DRO) approach to enhance power system survivability against potential cyber-physical threats. In this paper, we first analyze the operational guidelines of systems with a high penetration of DERs under various cyber network conditions and redefine survivability in this context. Next, we focus on the most common cyber threat, Denial-of-Service (DoS) attacks, and develop a corresponding attack model. This model allows for the creation of a kernel-based ambiguity set that captures attack uncertainties using historical data. Finally, we transform the proposed DRO model as a tractable optimization problem, with its solution providing an optimal cyber redundancy plan to enhance system survivability in DoS attack scenarios. Simulation results on the IEEE 13-node and 123-node test feeders demonstrate the effectiveness of our proposed model in improving system survivability. This model can also be expanded to include other types of common attacks and serve as a comprehensive planning tool to improve overall cyber physical survival of the system.

cybersecurity↗