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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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DOE OSTI2020

Optimal PV Inverter Control in Distribution Systems via Data-Driven Distributionally Robust Optimization

Distribution systems with high penetration of uncertain solar generation call for advanced control strategies of photovoltaics (PVs) inverters. This paper proposes a data-driven distributionally robust optimization (DDDRO) approach to optimally controlling the PV inverters to improve the system operation performance under solar power uncertainties. In the proposed DDDRO approach, a Wasserstein ball-based method is proposed to construct the distributional ambiguity set to model the uncertainties of PV generation through partial observations of historical data without knowing exact probability distributions. We further reformulate the computationally intractable DDDRO model to a mixed integer second order cone programming (MISOCP) problem. The effectiveness and out-of-sample performance of the proposed approach have been demonstrated on a modified IEEE 33-node system. We conduct a comparative study to compare the proposed method with traditional chance constrained programming (CCP). It shows that the proposed DDDRO approach can provide a less conservative yet robust solution to minimize the worse-case expectation of the total network loss while maintaining nodal voltages in a secure range.

Xue, Yaosuo↗
DOE OSTI2021

A Computationally Improved Heuristic Algorithm for Transmission Switching Using Line Flow Thresholds for Load Shed Reduction

We present a computationally improved heuristic algorithm for transmission switching (TS) to recover load shed. Research from the past showed that changing power system topology may control power flows and remove line congestion. Hence, TS may reduce the required load shed. One of the main challenges is to find a potential TS candidate in a suitable time. Here, we propose a novel heuristic method that is capable of finding the potential TS candidate faster than existing algorithms in literature. The proposed method is compatible with both the AC and DC optimal power flows (OPF). Three metrics are used to compare the proposed algorithm with the state-of-the-art from literature to show the speedup and accuracy achieved. The proposed method is implemented on the IEEE 30-bus system, PEGASE 89-bus system, IEEE 118-bus system, and Polish 2383- bus system. The results on the large-scale Polish 2383-bus system shows that the proposed algorithm is scalable to large real-world systems. Parallel computing is implemented to further improve the computational performance of the proposed algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Data-Centric Approach to Capture Non-Polynomial Nonlinear Dynamics

We propose an analytical construction of observable functions in the extended dynamic mode decomposition (EDMD) algorithm. EDMD is a numerical method for approximating the spectral properties of the Koopman operator. The choice of observable functions is fundamental for applying EDMD to nonlinear problems arising in systems and control. Existing methods either start from a set of dictionary functions and look for the subset that best fits the underlying nonlinear dynamics or rely on machine learning algorithms to “learn” observable functions. Conversely, in this paper, we start from the dynamical system model and lift it through the Lie derivatives, rendering it into a polynomial form. This proposed transformation into a polynomial form is exact and provides an adequate set of observable functions. The strength of the proposed approach is its applicability to a broader class of nonlinear dynamical systems, particularly those with nonpolynomial functions and compositions thereof. Moreover, it retains the physical interpretability of the underlying dynamical system and can be readily integrated into existing numerical libraries. We demonstrate the proposed approach with an application to electric power systems. The modeled system consists of a single generator connected to an infinite bus, where nonlinear terms include sine and cosine functions. The results demonstrate the effectiveness of the proposed procedure in off-attractor nonlinear dynamics for estimation and prediction; the observable functions obtained from the proposed construction outperform methods that use dictionary functions comprising monomials or radial basis functions.

extended dynamic mode decomposition↗
DOE OSTI2012

Innovative Process for Comprehensive Treatment of Liquid Radioactive Waste - 12551

This paper presents the results of research activities aimed at creation of a principally new LRW distilling treatment method. The new process is based on the instantaneous evaporation method widely used in distillation units. The main difference of the proposed process is that the vapor condensation is conducted without using heat exchangers in practically ideal mode by way of direct contacting in a vapor-liquid system. This process is conducted in a specially designed ejector unit in supersonic mode. Further recuperation of excess heat of vaporization is carried out in a standard heat exchanger. Such an arrangement of the process, together with use of the barometric height principle, allows to carry out LRW evaporation under low temperatures, which enables to use excess heat from NPS for heating initial LRW. Thermal calculations and model experiments have revealed that, in this case, the expenditure of energy for LRW treatment by distilling will not exceed 3 kilowatt-hour/m{sup 3}, which is comparable with the reverse-osmosis desalination method. Besides, the proposed devices are 4 to 5 times less metal-intensive than standard evaporation units. These devices are also characterized by versatility. Experiments have revealed that the new method can be used for evaporation of practically any types of LRW, including those containing a considerable amount of oil products. Owing to arrangement of the evaporation process at low temperatures, the new devices are not sensitive to 'scale formation'. This is why, they can be used for concentrating brines of up to 500-600 g/l. New types of such evaporating devices can be required both for LRW treatment processes at nuclear-power plants under design and for treating 'non-standard' LRW with complex physicochemical and radionuclide composition resulting from the disaster at the Fukushima I Nuclear Power Plant.) As a result of accidents at nuclear energy objects, as it has recently happened at NPP 'Fukushima-1', personnel faces the necessity to take emergency measures and to use marine water for cooling of reactor zone in contravention of the technological regulations. In these cases significant amount of liquid radioactive wastes of complex physicochemical composition is being generated, the purification of which by traditional methods is close to impossible. According to the practice of elimination of the accident after-effects at NPP 'Fukushima' there are still no technical means for the efficient purification of liquid radioactive wastes of complex composition like marine water from radionuclides. Therefore development of state-of-the-art highly efficient facilities capable of fast and safe purification of big amounts of liquid radioactive wastes of complex physicochemical composition from radionuclides turns to be utterly topical problem. Cesium radionuclides, being extremely dangerous for the environment, present over 90% of total radioactivity contained in liquid radioactive wastes left as a result of accidents at nuclear power objects. For the purpose of radiation accidents aftereffects liquidation VNIIHT proposes to create a plant for LRW reprocessing, consisting of 4 major technological modules: Module of LRW pretreatment to remove mechanical and organic impurities including oil products; Module of sorption purification of LWR by means of selective inorganic sorbents; Module of reverse osmotic purification and desalination; Module of deep evaporation of LRW concentrates. The first free modules are based on completed technological and designing concepts implemented by VNIIHT in the framework of LLRW Project in the period of 2000-2001 in Russia for comprehensive treatment of LWR of atomic fleet. These industrial plants proved to be highly efficient and secure during their long operation life. Module of deep evaporation is a new technological development. It will ensure conduction of evaporation and purification of LRW of different physicochemical composition, including those containing hardness salts, resulted in generation of LRW concentrate 300-600 g/l. The method is based on utilization of supersonic ejector for intensification of thermal physic processes and performance of evaporation in brine recycling mode. All proposed technological solutions are totally based on patented Russian developments. Proposed work will allow to construct modular plants, which will be totally prepared for efficient purification of any types of liquid radioactive wastes from radionuclides in case of force majeure. According to proposed scheme concentration level of cesium radionuclides in safe-for-storage form will make up not less than 5000. With respect to purification from cesium radionuclides of liquid radioactive wastes stored at NPP 'Fukushima' about 10 t of inorganic sorbents, loaded in 160 protective filter-containers, will be required for solving this problem. The amount of secondary wastes will be reduced approximately in 5 times in comparison with traditional schemes, applied in purification of secondary LRW of Fukushima-1 by Areva (France) and Kurion (USA) companies. All units of modular plants will be constructed and manufactured as totally automated, providing their twenty-four-hour safe operation. Modular design will ensure efficiency and let optimize the costs of secondary LRW treatment. In order to ensure off-line operation in emergency conditions the plant should be equipped with auxiliary modules: energy and ventilation ones. Under normal conditions these modules can be stored in 'mothballed' condition at special warehouses under the authority of federal bodies. It will be reasonable to choose required transport facilities, the most suitable for transportation of modules to target destination beforehand, using vessel classification list.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Healthcare Building Sustainability Assessment tool - Sustainable Effective Design criteria in the Portuguese context

Tools and methods to improve current practices and quality in the healthcare building sector are necessary to support decision-making at different building life cycle phases. Furthermore, Healthcare Building Sustainability Assessment (HBSA) Methods are based on criteria organised into different levels, such as categories and indicators. These criteria highlight aspects of significant importance when designing and operating a sustainable healthcare building. To bring more objectivity to the sustainability assessments, the standardisation bodies (CEN and ISO) proposed core indicators that should be used in the evaluation of the environmental, societal and economic performances of buildings. Nevertheless, relying on state of the art analysis, it is possible to conclude that there are aspects of major importance for the operation of healthcare buildings that are not considered in the HBSA methods. Thus, the aim of this paper is to discuss the context of sustainability assessment methods in the field of healthcare buildings and to present a proposal for the incorporation of Sustainable-Effective Design (SED) criteria in a new HBSA method. The used research method is innovative since in the development of the list of sustainability criteria it considers the opinion of main healthcare buildings' stakeholders, the existing healthcare assessment methods and the ISO and CEN standardisation works in the field of the methods to assess the sustainability of construction works. As a result, the proposed method is composed of fifty-two sustainability indicators that cover the different dimensions of the sustainability concept to support decision making during the design of a new or retrofitted healthcare building in urban areas. - Highlights: •A new system to assess the sustainability of healthcare buildings is presented. •We propose a method to develop the list of sustainability indicators for hospitals. •We propose a new concept – Sustainable-Effective Design (SED). •The system is adapted to the Portuguese building sector sustainability contexts. •The approach can be used to develop similar systems in other countries.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Analysis of the decision-support function of policy assessment in real-world policy making in the field of poverty and social inequalities. Case study on migrant integration policies in the Brussels-Capital Region

Despite its high potential to support decision-making, the role of policy assessment in real-world policy making in the field of poverty and social inequalities remains largely questioned. In this study, we analyse policy assessment's role in a context of real-world policymaking, by means of a case study on a legislative proposal on integration policy for immigrant newcomers in the Brussels-Capital Region, for which we evaluate the potential effects on poverty and social inequalities. We first analyse the policy process surrounding the policy proposal – a process that is often treated as a black box within policy assessment research. Understanding the factors that influence and determine the decision-making process, enables us to gain insight into the potential decision-support function(s). Second, we develop an approach to policy assessment that aims to fully exploit its potential to contribute to the functions of both instrumental and conceptual learning. For this purpose, we propose to introduce the approach of realist evaluation and to focus on evaluating the underlying policy intervention theory from the perspective of poverty and social inequalities. Finally, we illustrate this new approach and its added value by applying it to the legislative proposal on integration policy and analyse its contribution to policy-oriented learning. - Highlights: •The field of policy assessment should draw on insights from policy studies. •We unpacked the policymaking black-box to identify the mechanisms of policy change. •The policy process is driven by an interaction of ideas, interests and institutions. •Policy assessment's potential lies in both instrumental and conceptual learning. •We propose to integrate realist evaluation's logic of inquiry within policy assessment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Rare Events via Cross-Entropy Population Monte Carlo

Rare events are events that happen with very low frequency. Estimating rare event probabilities using Monte Carlo techniques is computationally expensive, often to the point of intractability, and special methods are required. Importance sampling (IS) is a well known technique that uses a proposal distribution in place of a target distribution to lower the variance of the estimator. Key to the success of IS methods is the choice of a proposal distribution, or the parameters governing the distribution. Adaptive importance sampling improves the parameters of a family or population of proposal distributions iteratively through trials. We present a novel cross-entropy population Monte Carlo algorithm, which adapts the parameters of proposals through the cross-entropy method. The proposed method stands apart from previous work in that we are not optimizing a mixture distribution. Instead, we leverage deterministic mixture weights and optimize the distributions individually through a reinterpretation of the typical derivation of the cross-entropy method. Demonstrations on rare event examples show that the algorithm can outperform existing resampling based population Monte Carlo methods, especially for higher-dimensional problems. Finally, we also demonstrate efficacy on a conjunction analysis problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

An étude on global vacuum energy sequester

Recently two of the authors proposed a mechanism of vacuum energy sequester as a means of protecting the observable cosmological constant from quantum radiative corrections. The original proposal was based on using global Lagrange multipliers, but later a local formulation was provided. Subsequently other interesting claims of a different non-local approach to the cosmological constant problem were made, based again on global Lagrange multipliers. We examine some of these proposals and find their mutual relationship. We explain that the proposals which do not treat the cosmological constant counterterm as a dynamical variable require fine tunings to have acceptable solutions. Furthermore, the counterterm often needs to be retuned at every order in the loop expansion to cancel the radiative corrections to the cosmological constant, just like in standard GR. These observations are an important reminder of just how the proposal of vacuum energy sequester avoids such problems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Hollow-grams: generalized entanglement wedges from the gravitational path integral

Recently, Bousso and Penington (BP) made a proposal for the entanglement wedge associated to a gravitating bulk region. In this paper, we derive this proposal in time-reflection symmetric settings using the gravitational path integral. To do this, we exploit the connection between random tensor networks (RTNs) and fixed-geometry states in gravity. We define the entropy of a bulk region in an RTN by removing tensors in that region and computing the entropy of the open legs thus generated in the “hollowed” RTN. We thus derive the BP proposal for RTNs and hence, also for fixed-geometry states in gravity. By then expressing a general holographic state as a superposition over fixed-geometry states and using a diagonal approximation, we provide a general gravitational path integral derivation of the BP proposal. We demonstrate that the saddles computing the Rényi entropy Sn depend on how the bulk region is gauge-invariantly specified. Nevertheless, we show that the BP proposal is universally reproduced in the n → 1 limit.

2D Gravity↗
DOE OSTI2022

aphBO-2GP-3B: a budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture

High-fidelity complex engineering simulations are often predictive, but also computationally expensive and often require substantial computational efforts. The mitigation of computational burden is usually enabled through parallelism in high-performance cluster (HPC) architecture. Optimization problems associated with these applications is a challenging problem due to the high computational cost of the high-fidelity simulations. In this paper, an asynchronous parallel constrained Bayesian optimization method is proposed to efficiently solve the computationally expensive simulation-based optimization problems on the HPC platform, with a budgeted computational resource, where the maximum number of simulations is a constant. The advantage of this method are three-fold. Firstly, the efficiency of the Bayesian optimization is improved, where multiple input locations are evaluated parallel in an asynchronous manner to accelerate the optimization convergence with respect to physical runtime. This efficiency feature is further improved so that when each of the inputs is finished, another input is queried without waiting for the whole batch to complete. Second, the proposed method can handle both known and unknown constraints. Third, the proposed method samples several acquisition functions based on their rewards using a modified GP-Hedge scheme. The proposed framework is termed aphBO-2GP-3B, which means asynchronous parallel hedge Bayesian optimization with two Gaussian processes and three batches. The numerical performance of the proposed framework aphBO-2GP-3B is comprehensively benchmarked using 16 numerical examples, compared against other 6 parallel Bayesian optimization variants and 1 parallel Monte Carlo as a baseline, and demonstrated using two real-world high-fidelity expensive industrial applications. The first engineering application is based on finite element analysis (FEA) and the second one is based on computational fluid dynamics (CFD) simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD

Age-related macular degeneration (ARMD) is one of the most common retinal syndromes that occurs in elderly people. Different eye testing techniques such as fundus photography and optical coherence tomography (OCT) are used to clinically examine the ARMD-affected patients. Many researchers have worked on detecting ARMD from fundus images, few of them also worked on detecting ARMD from OCT images. However, there are only few systems that establish the correspondence between fundus and OCT images to give an accurate prediction of ARMD pathology. In this paper, we present fully automated decision support system that can automatically detect ARMD by establishing correspondence between OCT and fundus imagery. The proposed system also distinguishes between early, suspect and confirmed ARMD by correlating OCT B-scans with respective region of the fundus image. In first phase, proposed system uses different B-scan based features along with support vector machine (SVM) to detect the presence of drusens and classify it as ARMD or normal case. In case input OCT scan is classified as ARMD, region of interest from corresponding fundus image is considered for further evaluation. The analysis of fundus image is performed using contrast enhancement and adaptive thresholding to detect possible drusens from fundus image and proposed system finally classified it as early stage ARMD or advance stage ARMD. The proposed system is tested on local data set of 100 patients with100 fundus images and 6800 OCT B-scans. Proposed system detects ARMD with the accuracy, sensitivity, and specificity ratings of 98.0, 100, and 97.14%, respectively.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Volumetric Density-Equalizing Reference Map with Applications

The density-equalizing map, a technique developed for cartogram creation, has been widely applied to data visualization but only for 2D applications. Here, we propose a novel method called the volumetric density-equalizing reference map (VDERM) for computing density-equalizing map for volumetric domains. Given a prescribed density distribution in a volumetric domain in $\mathbb{R}^3$, the proposed method continuously deforms the domain, with different volume elements enlarged or shrunk according to the density distribution. With the aid of the proposed method, medical and sociological data can be visualized via deformations of 3D objects. The method can also be applied to adaptive remeshing and shape modeling. Furthermore, by exploiting the time-dependent nature of the proposed method, applications to shape morphing can be easily achieved. Experimental results are presented to demonstrate the effectiveness of the proposed method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Refined Global Segmentation of X-Ray CT Images for Multi-phase Geomaterials

X-ray computed tomography images of three-phase silica sand and glass bead specimens are analyzed and used to evaluate the segmentation performances of Otsu-, and recursion-based multilevel algorithms. A global image segmentation technique that combines iterative and recursive algorithms, namely a refined statistics-based global segmentation is proposed for segmenting multi-phase granular geomaterials. The performance of the proposed algorithm is tested by segmenting partially saturated silica sand and glass bead specimens. For the tested silica sand specimen, the refined statistics method estimated void ratio and degree of saturation were 0.67 and 39.35%. The estimates for the glass bead specimen yielded 0.64 and 43.49%, respectively. The true void ratio (0.66) and degree of saturation (37.71%) were determined with a user-controlled Image processing software package—Image-Pro. It was found that the proposed method estimated the void ratio and the degree of saturation with 1.52 and 4.35 percent errors for the silica sand and with 15.63 and 0.34 percent errors for the glass bead, respectively. The computational time of the proposed method was found to be shorter than other methods considered. Overall, it is concluded that the proposed technique performed better in segmenting three-phase granular geomaterials.

42 ENGINEERING↗
DOE OSTI2019

Ultra-compact low loss electro-optical nanobeam cavity modulator embedded photonic crystal

In this paper, a hybrid electro-optical modulator based on a one-dimensional photonic crystal nanobeam cavity is proposed. The main component of the cavity builder is the indium–tin–oxide that is a transparent conductive oxide. The proposed structure is used to minimize optical losses and also to have a high-quality factor with a tunability from the distributed feedback system. The proposed modulator has a wide range of resonant tunable in a footprint of 1.892 μm{sup 2}. This structure has the ability to shift the wavelength of 1.55 μm using a very small voltage 0.1 V. Simulation results show that the proposed structure has an insertion loss of 0.027 dB, extension ratio 3.484 dB, 119.89 GHz modulation speed, and 0.59 aJ/bit modulation energy. By comparing the proposed method parameters and other modulators based on the one-dimensional photonic crystal nanobeam cavity, a significant improvement in modulation voltage, insertion losses, energy consumption, and footprint have been observed.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Evolutionary computation algorithms to coordinating order acceptance and batch delivery for an integrated supply chain scheduling

This paper proposes a novel approach of coordinating decisions in an integrated supply chain (ISC): coordinating order acceptance (OA) and batch delivery (BD) due to round trip transportation (RTT) and using third-party logistics (3PL) vehicles. The paper aims at trading off among accepted orders revenue, delivery costs as well as any penalties incurred in the ISC to maximize the total benefit. A novel mixed-integer programming is proposed for the problem. In addition, the paper provides a heuristic to form batches and develops a hybrid evolutionary computation algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA) to solve the problem. An information sharing mechanism is improved and applied. To explore and to locate the proposed PSO in a better neighborhood, a local search is proposed. Taguchi experimental design is utilized to set the appropriate values of the algorithms’ parameters and random instances are generated to evaluate the performance of the algorithms. The paper investigates the profitability sensitivity of the problem to parameters and analyzes the effect of the changes in the parameters on the performance of our proposed algorithms. The attained results show the appropriate performance of our algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Capsule network-based semantic segmentation model for thermal anomaly identification on building envelopes

Thermography technology is widely used to inspect thermal anomalies in building façade systems. Computer vision-based techniques provide opportunities to autonomously detect such heat anomalies to significantly improve the efficiency of decision-making for building envelope retrofitting and maintenance. Here, in this work, we propose a novel Capsule Network-based deep learning model – CapsLab – that detects and identifies thermal anomalies by semantic segmentation. CapsLab is built based on our proposed prediction-tuning capsule (PT-Capsule) layer. Different from a traditional capsule layer, which consists of part-whole transformation and capsule-routing process, the proposed layer is composed of a prediction and tuning process, which helps decreasing the number of model parameters significantly. While the applicability of traditional Capsule Networks (CapsNets) has been limited to simpler tasks and smaller datasets due to their scalability issue, we can leverage the lightweight of the proposed PT-Capsule layer, and apply it to the semantic segmentation task. In this work, we also employ our previously presented performance metric, referred to as the Anomaly Identification Metric (AIM) (Kakillioglua et al. 2021), to evaluate the segmentation outputs. Traditional performance metrics do not accurately reflect the true performance of the segmentation models in thermal anomaly identification due to the high subjectivity in the annotation process and higher overlap ratio sensitivity of the standard metrics. AIM, on the other hand, is robust to these drawbacks. Experimental results show, both qualitatively and quantitatively, that our proposed segmentation method can effectively segment the thermal anomalies. Specifically, our model provides 9.38% and 13.53% improvements over the baseline model – DeepLabV3+ – based on traditional mIoU score and the AIM score, respectively, while requiring less model parameters and less computation at the same time. In addition, the scores that the AIM metric generates better align with the scores provided by building performance experts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Decentralized wind uncertainty management: Alternating direction method of multipliers based distributionally-robust chance constrained optimal power flow

How to manage wind power uncertainty in system operation is an urgent and important issue, especially under substantially increasing penetration levels of wind generation in electric power systems. With increasing numbers of wind power plants (WPPs) integrated into power systems, their variability and uncertain power outputs become a challenge to maintaining system reliability. Traditional stochastic optimization-based models face the curse of dimensionality when the number of WPPs increases. This paper proposes a novel decentralized wind power uncertainty management model. In this model, conventional generators optimize their power capacity output and their participation to mitigate wind power uncertainty locally with only limited information exchange with the system operators. First, a distributionally-robust chance-constrained optimal power flow (DRCC-OPF) model is deployed to schedule the generation and reserve considering the wind power forecast uncertainty. Then, the alternating direction method of multipliers (ADMM) is used to reformulate the DRCC-OPF model into the distributed optimization model for each conventional generator and WPP. The tests on a small PJM 5-bus system and the IEEE 118-bus system demonstrate that the proposed decentralized model can increase the operation reliability without sacrificing system operating cost, especially when the wind power penetration levels are high. Using the proposed model, the optimal solutions can be obtained within one minute, for all studied cases, which verifies the computation efficiency of the proposed decentralized model. Furthermore, considering a large number of WPPs integrated into the system, the proposed decentralized method obtains a lower operating cost within one minute which demonstrates its efficiency to deal with high dimension of uncertainties.

17 WIND ENERGY↗