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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Decentralized Low-Rank State Estimation for Power Distribution Systems

This article considers the low-observability state estimation problem in power distribution networks and develops a decentralized state estimation algorithm leveraging the matrix completion methodology. Matrix completion has been shown to be an effective technique in state estimation that exploits the low dimensionality of the power system measurements to recover missing information. This technique can utilize an approximate (linear) load flow model, or it can be used with no physical models in a network where no information about the topology or line admittance is available. The direct application of matrix completion algorithms requires solving a semi-definite programming (SDP) problem, which becomes computationally challenging for large networks. We therefore develop a decentralized algorithm that capitalizes on the popular proximal alternating direction method of multipliers (proximal ADMM). The method allows us to distribute the computation among different areas of the network, leading to a scalable algorithm. By doing all computations at individual control areas and only communicating with neighboring areas, the algorithm eliminates the need for data to be sent to a central processing unit and thus increases efficiency and contributes to the goal of autonomous control of distribution networks. We illustrate the advantages of the proposed algorithm numerically using standard IEEE test cases.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2024

A Measurement-Based Adaptive Voltage Regulation Method Considering Topology Changes

This paper proposes an online adaptive data-driven distributed energy resource (DER) dispatch optimization method for voltage control considering topology changes. By using a local sensitivity factor (LSF)-enabled voltage control, traditional DER control can be reformulated into a linear programming (LP) problem, leading to faster computation speeds. Power injection alteration and topology changes are two common operational changes in the distribution network that can affect the LSF and voltage control performance. To address this issue, a robust estimation method is developed to adjust the sensitivity matrix at each time step for the time-varying power injection changes. When topology changes occur, only the allocated predominant LSF submatrices are updated based on measurement data, allowing for a fast adaptation to the system reconfiguration. Results obtained from a real distribution feeder in Southern California demonstrate its robustness as compared to traditional volt-var control and constant LSF matrix dispatch control methods.

DERs↗
DOE OSTI2023

Complementarity and the unitarity of the black hole S -matrix

Recently, Akers et al. proposed a non-isometric holographic map from the interior of a black hole to its exterior. Within this model, we study properties of the black hole S-matrix, which are in principle accessible to observers who stay outside the black hole. Specifically, we investigate a scenario in which an infalling agent interacts with radiation both outside and inside the black hole. Because the holographic map involves postselection, the unitarity of the S-matrix is not guaranteed in this scenario, but we find that unitarity is satisfied to very high precision if suitable conditions are met. If the internal black hole dynamics is described by a pseudorandom unitary transformation, and if the operations performed by the infaller have computational complexity scaling polynomially with the black hole entropy, then the S-matrix is unitary up to corrections that are superpolynomially small in the black hole entropy. Furthermore, while in principle quantum computation assisted by postselection can be very powerful, we find under similar assumptions that the S-matrix of an evaporating black hole has polynomial computational complexity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Smart Matrix Development for Direct Carbonate Fuel Cell

FCE’s (FuelCell Energy, Inc.) SureSourceTM products based on high-temperature internal-reforming direct carbonate fuel cell (DFC®) technology are striving to meet growing worldwide demand for high-efficiency ultra-clean power for on-site electric and combined heat and power (CHP) generation. It promises ~20% higher energy conversion efficiency than existing technologies and its large-scale deployment would help to reduce GHG (Green House Gas) emission. In this program, FCE, teamed with University of Connecticut (UConn) and Illinois Institute of Technology (IIT), successfully developed an innovative electrolyte matrix (dubbed smart matrix since it promises higher power output, longer service life, and cost reduction gains concurrently) to enable the products to worldwide deployment. The electrolyte membrane (matrix) holds a very important key to fulfill these promises. The matrix, a porous microstructure consisting mainly of ultra-fine sub-micron α-LiAlO 2 powders sandwiched between two electrodes, immobilizes liquid electrolyte, isolates fuel from oxidant and facilitates ionic transport. However, faster coarsening of α-LiAlO 2 at the reducing anode side lowers capillary electrolyte-retention and eventually leads to matrix drying, limiting stack service life. This program, spanning over 3.5 years, successfully developed a high-yield cost-effective commercial-ready smart matrix with robust and stable fine-pore microstructure to enable the SureSource plants meeting DOE 2020 targets for CHP distributed generation fuel cell systems. Fundamental coarsening mechanism was established by investigating in detail the effects of electrolyte chemistry, powder morphology, powder purity, electrolyte chemistry, gas atmosphere and temperature. More stable powders with higher crystallinity, less surface disorder, less metastable impurities, less agglomeration and more uniform particle-size distribution have improved stability. FCE devised an innovative cost-effective additive approach to disrupt the fundamental coarsening process to enhance stability. The smart matrix design was validated in numerous long-term accelerated single cells and several 30kW technology stacks, many operated for >5,000 hours. Manufacturing process was successfully optimized to produce high-yield full-area (1 m 2 ) product-size smart matrices. Field deployment has been initiated. Successful development of this innovative smart matrix enables a quantum jump in useful life (100%) and power output (>20%). These improvements may project to >20% lower COE (Cost of Electricity) making the SureSource highly competitive with U.S. grid electricity cost leading to large-scale deployment, realizing the U.S. Government’s energy, environment, and economic agenda.

03 NATURAL GAS↗
DOE OSTI2025

Analytical Identification Method of Generalized Short‐Circuit Ratio Using Phasor Measurement Units

This paper introduces a novel analytical approach for the identification of the admittance matrix and the generalized short-circuit ratio (gSCR) in power systems integrated with renewable energy sources. The proposed method leverages voltage and current measurements from phasor measurement units (PMUs) to construct a least squares objective function, which is then solved using matrix calculus and partial derivatives. Unlike conventional optimization algorithms, this approach provides an analytical solution that substantially reduces data requirements, enabling the efficient and accurate identification of the gSCR with smaller datasets. Additionally, its fixed computational complexity allows for real-time updates as new data are collected, ensuring continuous refinement of the system of equations and enabling rapid, precise gSCR calculations. The method also exhibits strong robustness against measurement noise, making it well-suited for practical applications in dynamic power systems. The combination of reduced data requirements, real-time adaptability, noise robustness and fixed computational load establishes this method as a highly efficient and reliable tool for real-time power system stability analysis. Case studies on an EPRI 36-bus system demonstrate the method's effectiveness, highlighting its accuracy in closely matching true gSCR values, even under diverse disturbances and noisy conditions.

Han, Zelei [Hohai University, Nanjing (China)] (OR↗
NASA NTRS1998

Effects of Fiber/Matrix Interface and its Composition on Mechanical Properties of Hi Nicalon/Celsian Composites

Fiber-reinforced ceramic matrix composites (CMC) are prospective candidate materials for high temperature structural applications in aerospace, energy conservation, power generation, nuclear, petrochemical, and other industries. At NASA Lewis, we are investigating celsian matrix composites reinforced with various types of silicon carbide fibers. The objective of the present study was to investigate the effects of fiber/matrix interface and its composition on the mechanical properties of silicon carbide (Hi-Nicalon) fiber-reinforced celsian matrix composites.

Bansal, Narottam P.↗
DOE OSTI2022

An Ensemble Approach to Computationally Efficient Radiological Anomaly Detection and Isotope Identification

Radiological source search is a challenging task involving detection and identification of weak sources in a constantly changing radiological background. As of now, many radiological source detection algorithms have been proposed; however, their computational complexity, and hence reliance on power intensive processing units inhibit low-power applications of radiological source search systems. In this work, we introduce the anomaly filter (AF) algorithm; a computationally light, yet effective time-series source detection algorithm based on exponential weighted moving average (EWMA) and Poisson deviance statistics. Then, we demonstrate that the proposed algorithm can be used in ensemble with other more computationally intensive source detection and identification algorithms to achieve both increased detection performance and reduced power consumption. The proposed AF algorithm and the ensemble algorithms were thoroughly benchmarked against several existing source detection and identification algorithms. The results show that the AF algorithm outperforms existing conventional source detection algorithms, and the ensemble approach improves the overall performance of existing source detection and isotope identification algorithms. Furthermore, the AF algorithm and the Non-negative Matrix Factorization approach based source identification (NMF-ID) algorithm were combined and implemented on a singleboard microcontroller and the power consumption was measured. This ensemble algorithm reduced the power consumption of the NMF-ID algorithm almost by a factor of 100, while improving the detection performance of the overall system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1981

Ionic conductivity and thermoelectric power of pure and Al2O3-dispersed AgI

Ionic and electronic conductivities, and thermoelectric power have been measured for AgI and AgI containing a dispersion of submicron size Al2O3 particles. While the dispersion of Al2O3 enhances the ionic conductivity significantly, it does not affect the electronic properties of the matrix. The enhancement is a strong function of the size and concentration of the dispersoid. Various models have been tested to account for the enhanced conduction. However, the complex behavior of the present results points out the need for more sophisticated theoretical models. Ionic conduction and thermoelectric power data suggest that the dispersed Al2O3 generates an excess of cation vacancies and thereby enhances the conductivity and suppresses the thermoelectric power of the matrix. The individual heats of transport of cation interstitials and vacancies have been estimated and compared to their respective migration energies.

Shahi, K.↗
DOE OSTI2024

Learning model combining convolutional deep neural network with a self-attention mechanism for AC optimal power flow

Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. Furthermore, this supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.

42 ENGINEERING↗
DOE OSTI2025

Mitigation of DESI fiber assignment incompleteness effect on two-point clustering with small angular scale truncated estimators

We present a method to mitigate the effects of fiber assignment incompleteness in two-point power spectrum and correlation function measurements from galaxy spectroscopic surveys, by truncating small angular scales from estimators. We derive the corresponding modified correlation function and power spectrum windows to account for the small angular scale truncation in the theory prediction. We validate this approach on simulations reproducing the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) with and without fiber assignment. We show that we recover unbiased cosmological constraints using small angular scale truncated estimators from simulations with fiber assignment incompleteness, with respect to standard estimators from complete simulations. Additionally, we present an approach to remove the sensitivity of the fits to high k modes in the theoretical power spectrum, by applying a transformation to the data vector and window matrix. We find that our method efficiently mitigates the effect of fiber assignment incompleteness in two-point correlation function and power spectrum measurements, at low computational cost and with little statistical loss.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Soft-Switching Solid-State Transformer With Reduced Conduction Loss

Solid-state transformers (SSTs) are a promising solution photovoltaic (PV), wind, traction, data center, battery energy storage system (BESS), and fast charging electric vehicle (EV) applications. The traditional SSTs are typically three-stage, i.e., hard-switching cascaded multilevel rectifiers and inverters with dual active bridge (DAB) converters, which leads to bulky passives, low efficiency, and high electromagnetic interference (EMI). This article proposes a new soft-switching solid-state transformer (S4T). The S4T has full-range zero-voltage switching (ZVS), electrolytic capacitor-less dc link, and controlled dv/dt, which reduces EMI. The S4T comprises two reverse-blocking current-source inverter (CSI) bridges, auxiliary branches for ZVS, and transformer magnetizing inductor as a reduced dc link with 60% ripple. Compared with the prior S4T, an effective change on the leakage inductance diode is made to reduce the number of the devices on the main power path by 20% for significant conduction loss saving and retain the same functionality of damping the resonance between the leakage and resonant capacitors and recycling trapped leakage energy. The conduction loss saving is crucial, being the dominating loss mechanism in SSTs. Importantly, the proposed single-stage SST not only holds the potential for high power density and high efficiency but also has full functionality, e.g., multiport dc loads integration, voltage regulation, and reactive power compensation, unlike the traditional single-stage matrix SST. The S4T can achieve single-stage isolated bidirectional dc–dc, ac–dc, dc–ac, or ac–ac conversion. It can also be configured input-series output-parallel (ISOP) in a modular way for medium-voltage (MV) grids. Hence, the S4T is a promising candidate for the SST. The full functionality, e.g., voltage buck–boost, multiport, etc., and the universality of the S4T for the dc–dc, dc–ac, and ac–ac conversion are verified through the simulations and experiments of two-port and three-port MV prototypes based on 3.3 kV SiC MOSFETs in dc–dc, dc–ac, and ac–ac modes at 2 kV.

14 SOLAR ENERGY↗
DOE OSTI2025

Sizing and Location Selection of Medium‐Voltage Back‐to‐Back Converters for DER‐Dominated Distribution Systems

Medium‐voltage back‐to‐back (MVB2B) converters can connect two distribution systems and quantifiably transfer power between them. This function can enable the MVB2B converter to exchange distributed energy resource (DER)‐generated power between two systems and bring significant value to enhancing distribution system DER adoption. Our previous work analysed and demonstrated the value MVB2B converter can bring to DER integration. As continuous work, this paper presents a methodology that helps address the MVB2B converter sizing and location selection problem in distribution systems with high DER penetrations. The proposed methodology aims to address three critical problems for MVB2B converter implementation in the real world: (1) which distribution systems are better to be connected, (2) what converter size is appropriate for connecting the distribution systems, and (3) where the optimal connection points are in the systems for connecting the MVB2B converter. The proposed methodology has been demonstrated by case studies that include various scenarios involving distribution systems with different dominated load types and high photovoltaic penetrations. The results demonstrate that selecting the optimal converter size based on net revenue and time of return considerations leads to a balance between maximizing energy savings and minimizing financial payback periods. Furthermore, feeder pair selection based on load profile standard deviation effectively identifies systems that derive the greatest value from MVB2B integration. Finally, an optimized connection point selection approach using a voltage load sensitivity matrix ensures minimal system impact while facilitating efficient power exchange. These findings provide practical insights for the real‐world deployment of MVB2B converters to enhance DER hosting capacity and improve grid resilience.

14 SOLAR ENERGY↗
DOE OSTI2023

Advanced Measurements for Resilient Integration of Inverter-Based Resources: PROGRESS MATRIX Year-1 Report

As nearly every aspect of the electric power grid undergoes rapid change, measurement technologies that support grid operation and planning must evolve as well. Recent large-scale deployments of inverter-based resources (IBRs) have brought to the forefront the critical need for new measurement technologies. Though these IBRs are vital to achieving the nation’s clean energy goals, their rapid deployment has in some cases led to negative impacts on the reliability and security of the bulk power system (BPS). Advanced power system measurements, including synchronized phasor and waveform measurements, are key to making IBR integration secure and reliable. To this end, the Department of Energy (DOE) initiated a project in 2022 to develop advanced measurement capabilities and analytics that will accelerate adoption of IBRs while improving the reliability and resilience of the BPS. This report discusses a portion of the findings from the project’s first year, which focused on surveying existing measurement capabilities of partner utilities and comparing these capabilities with the requirements of applications that support IBR integration. This report also discusses how these findings will guide the development and demonstration of a set of applications in the project’s second year.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1989

Comparison of joint space versus task force load distribution optimization for a multiarm manipulator system

It is often proposed that the redundancy in choosing a force distribution for multiple arms grasping a single object should be handled by minimizing a quadratic performance index. The performance index may be formulated in terms of joint torques or in terms of the Cartesian space force/torque applied to the body by the grippers. The former seeks to minimize power consumption while the latter minimizes body stresses. Because the cost functions are related to each other by a joint angle dependent transformation on the weight matrix, it might be argued that either method tends to reduce power consumption, but clearly the joint space minimization is optimal. A comparison of these two options is presented with consideration given to computational cost and power consumption. Simulation results using a two arm robot system are presented to show the savings realized by employing the joint space optimization. These savings are offset by additional complexity, computation time and in some cases processor power consumption.

Soloway, Donald I.↗
DOE OSTI2021

Sparse Control Synthesis for Uncertain Responsive Loads With Stochastic Stability Guarantees

In this report, recent studies have demonstrated the potential of flexible loads in providing frequency response services, predominantly due to their availability and cost-effectiveness. However, uncertainty and variability in various weather-related and end-use behavioral factors often impact the reliability of demand-side control performance. This work addresses this problem with the design of a demand-side control to achieve frequency response under load uncertainties. Our approach involves modeling the load uncertainties via stochastic processes that appear as both multiplicative and additive in the power system dynamics. Recently developed mean square exponential stability (MSES) results for continuous-time linear stochastic systems are applied to pose the control synthesis problem which results in an LMI-based optimization problem. Additional costs and constraints are added to the LMI-based controller synthesis to ensure MSES, improve closed-loop transient performance, maximize tolerable uncertainties, and promote sparsity in the controller. Additionally, the fundamental limitations between the tolerable uncertainties and control efforts while ensuring MSES are discussed. Further, the control synthesis problem for the case of the full-state measurement is generalized to the case of partial-state measurements. The proposed control synthesis is illustrated on an IEEE 39 bus system with rigorous studies to demonstrate the role of sparsity, closed-loop transient performance, tolerable uncertainties, and control efforts while ensuring MSES and achieving frequency response.

42 ENGINEERING↗
DOE OSTI2018

Interaction between Al and atomic layer deposited (ALD) ZrN under high-energy heavy ion irradiation

Uranium-molybdenum (U-Mo) particles dispersed in an aluminum matrix is the most promising candidate fuel to convert high-power research and test reactors in Europe from using high-enriched to using low-enriched fuel. However, chemical interaction between the U-Mo and the Al matrix leads to undesirable fuel behavior. Zirconium nitride (ZrN) is used as a diffusion barrier between the U-Mo fuel particles and the Al matrix. To understand the potential microstructural evolution of ZrN during irradiation, a high-energy heavy ion (84 MeV Xe) irradiation experiment was performed on atomic layer deposited (ALD) nanocrystalline ZrN deposited on an Al plate. A fluence of 1.86 x 10 17 ions/cm 2 , or 90.3 dpa was reached during this experiment. Both analytic transmission electron microscopy (TEM) and synchrotron microbeam X-ray diffraction (ARD) techniques were utilized to investigate the kinetics of radiation-induced grain growth of ZrN at various radiation doses based on the Williamson-Hall analyses. The grain growth kinetics can be described by a power law expression, D n - D$n\atop{0}$ = K phi, with n = 5.1. The Al-ZrN interaction products (Al 3 Zr and AIN) created by radiation-induced ballistic mixing/radiation-enhanced diffusion and their corresponding formation mechanism were determined from electron diffraction and elemental composition analysis. These experimental results were confirmed by first principle thermodynamic density functional theory (DFT) calculations. The results from this ion irradiation study were also compared to in-pile irradiation data from physical vapor deposited (PVD) ZrN samples for a comprehensive evaluation of the interaction between Al and ZrN and its influence on diffusion barrier performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Assessing the Feasibility of Bordered Block Diagonal Reordering in Power System Matrices using Fully Convolutional Network

In electromagnetic transient (EMT) simulations for power systems and inverter-based resources (IBRs), the arrangement of states within the system's linear equations, represented by matrix A in Ax=b, is critical. The state ordering in matrix A can highlight distinct characteristics of the system's graph, and identifying an optimal state ordering is crucial for efficient computation. The choice of state ordering, however, is dependent on the solver used, as each solver may perform optimally with different matrix patterns. With a wide array of matrix reordering algorithms available, selecting the most suitable one becomes challenging without insights into the matrix's ideal configuration. To address this, the paper proposes a fully convolutional network (FCN) to evaluate the reordering potential of the A matrix into a bordered block diagonal (BBD) pattern, which is commonly observed in power system and IBR modeling. The FCN's assessment aims to streamline the solver's operation, which in turn could substantially reduce the computational time required to find a solution.

Xia, Qianxue↗