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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Protonic transistors from thin reflecting films

Ionic transistors from organic and biological materials hold great promise for bioelectronics applications. Thus, much research effort has focused on optimizing the performance of these devices. Herein, we experimentally validate a straightforward strategy for enhancing the high to low current ratios of protein-based protonic transistors. Upon reducing the thickness of the transistors’ active layers, we increase their high to low current ratios 2-fold while leaving the other figures of merit unchanged. The measured ratio of 3.3 is comparable to the best values found for analogous devices. These findings underscore the importance of the active layer geometry for optimum protonic transistor functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Prototypic Enhanced Risk Monitor Framework and Evaluation - Advanced Reactor Technology Milestone: M3AT-15PN2301054

This research report summaries the development and evaluation of a prototypic enhanced risk monitor (ERM) methodology (framework) that includes alternative risk metrics and uncertainty analysis. This updated ERM methodology accounts for uncertainty in the equipment condition assessment (ECA), the prognostic result, and the probabilistic risk assessment (PRA) model. It is anticipated that the ability to characterize uncertainty in the estimated risk and update the risk estimates in real time based on equipment condition assessment (ECA) will provide a mechanism for optimizing plant performance while staying within specified safety margins. These results (based on impacting active component O&M using real-time equipment condition information) are a step towards ERMs that, if integrated with AR supervisory plant control systems, can help control O&M costs and improve affordability of advanced reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

The formation mechanism for printed silver-contacts for silicon solar cells

Screen-printing provides an economically attractive means for making Ag electrical contacts to Si solar cells, but the use of Ag substantiates a significant manufacturing cost, and the glass frit used in the paste to enable contact formation contains Pb. To achieve optimal electrical performance and to develop pastes with alternative, abundant, and non-toxic materials requires understanding the contact formation process during firing. Here, we use in-situ X-ray diffraction during firing to reveal the reaction sequence. The findings suggest that between 500 degrees C and 650 degrees C PbO in the frit etches the SiNx antireflective-coating on the solar cell, exposing the Si surface. Then, above 650 degrees C, Ag+ dissolves into the molten glass frit -- key for enabling deposition of metallic Ag on the emitter surface and precipitation of Ag nanocrystals within the glass. Ultimately, this work clarifies contact formation mechanisms and suggests approaches for development of inexpensive, nontoxic solar cell contacting pastes.

14 SOLAR ENERGY↗
DOE OSTI2023

How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system

Both model predictive control (MPC) and deep reinforcement learning control (DRL) have been presented as a way to approximate the true optimality of a dynamic programming problem, and these two have shown significant operational cost saving potentials for building energy systems. Furthermore, there is still a lack of in-depth quantitative studies on their approximation levels to the true optimality, especially in the building energy domain. To fill in the gap, this paper provides a numerical framework that enables the evaluation of the optimality levels of different controllers for building energy systems. This framework is then used to comprehensively compare the optimal control performance of both MPC and DRL controllers with given computation budgets for a single zone fan coil unit system. Note the optimality is estimated based on a user-specific selection of trade-off weights among energy costs, thermal comfort and control slew rates. Compared with the best optimality we can find through expensive optimization simulations, the best DRL agent can maximally approximate the optimality by 96.54%, which outperforms the best MPC whose optimality level is 90.11%. However, due to the stochasticity, the DRL agent is only expected to approximate the optimality by 90.42%, which is almost equivalent to the best MPC. Except for Proximal Policy Optimization (PPO), all DRL agents can have a better approximation to the optimality than the best MPC, and are expected to have better approximation than the MPC with a prediction horizon of 32 steps (15 min per step). In terms of reducing energy cost and thermal discomfort, MPC can outperform the rule-based control (RBC) by 18.47%–25.44%. DRL can be expected to outperform RBC by 18.95%–25.65% ,and the best DRL control policy can outperform RBC by 20.29%–29.72%. Although the comparison of the optimality level is performed in a perfect setting, e.g., MPC assumes perfect models, and DRL assumes a perfect offline training process and online deployment process, this can shed insight on their capabilities of approximating to the original dynamic programming problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

The Role of SnO2 Processing on Ionic Distribution in Double-Cation-Double Halide Perovskites

Moving toward a future of efficient, accessible, and less carbon-reliant energy devices has been at the forefront of energy research innovations for the past 30 years. Metal-halide perovskite (MHP) thin films have gained significant attention due to their flexibility of device applications and tunable capabilities for improving power conversion efficiency. Serving as a gateway to optimize device performance, consideration must be given to chemical synthesis processing techniques. Therefore, how does common substrate processing techniques influence the behavior of MHP phenomena such as ion migration and strain? Here, we demonstrate how a hybrid approach of chemical bath deposition (CBD) and nanoparticle SnO2 substrate processing significantly improves the performance of (FAPbI3)0.97(MAPbBr3)0.03 by reducing micro-strain in the SnO2 lattice, allowing distribution of K+ from K-Cl treatment of substrates to passivate defects formed at the interface and produce higher current in light and dark environments. X-ray diffraction reveals differences in lattice strain behavior with respect to SnO2 substrate processing methods. Through use of conductive atomic force microscopy (c-AFM), conductivity is measured spatially with MHP morphology, showing higher generation of current in both light and dark conditions for films with hybrid processing. Additionally, time-of-flight secondary ionization mass spectrometry (ToF-SIMS) observed the distribution of K+ at the perovskite/SnO2 interface, indicating K+ passivation of defects to improve the power conversion efficiency (PCE) and device stability. We show how understanding the role of ion distribution at the SnO2 and perovskite interface can help reduce the creating of defects and promote a more efficient MHP device.

conductive atomic force microscopy↗
DOE OSTI2024

Shape Optimization of Header Pipes in Power Plants for Enhanced Efficiency and Environmental Sustainability

In a power plant, the header pipe plays a pivotal role in optimizing the performance of diverse systems by serving as a central conduit for the collection and distribution of steam within the plant. This paper investigates the significance of header pipes within power plant setups, highlighting their critical influence on reliability, efficiency, and the performance of the power plant as a whole. The concept of shape optimization emerges as a crucial factor in power plant design and operation, with the potential to maximize performance while minimizing the use of materials. Shape optimization not only enhances efficiency but also contributes to reducing the environmental footprint of power plant installations. In this paper, we initially developed a methodology designed for optimizing header shapes with the primary goal of reducing the usage of costly new alloy materials and lowering the overall maintenance operation expenses. Secondly, we conducted a case study based on an authentic header sourced from an operational power plant.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗
DOE OSTI2021

Towards an Agent-Based Blackboard System for Reactor Design Optimization

The field of reactor design is rich with opportunities for applications of computational optimization algorithms; these applications can range from preliminary core design to reactor shuffling patterns. Many of these schemes rely on sets of previously generated solutions (sometimes referred to as “generations”) to inform future decisions. While it is important to build upon prior knowledge, this process requires a full generation of solutions to be formed before future solutions can be examined. Rather than relying on a generational scheme to perform an optimization, we propose using an agent-based approach in conjunction with a blackboard framework for performing reactor design optimizations. Utilizing an agent-based approach allows agents to perform tasks independently, while retaining the ability to build off of previous solutions. We develop an agent-based blackboard system (ABBS) for determining the Pareto front (PF) in sodium fast reactor design optimization problems and compared this with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Our goal is to evaluate the viability of the ABBS in producing a PF that is comparable with the NSGA-II algorithm. The design space consists of the fuel height, fuel smear, and plutonium fraction in the core, and we seek to minimize the reactivity swing and plutonium mass, while maximizing the burnup. The diversity, coverage, and spread of the PFs generated by the two methods are examined, and the ABBS is able to converge to the same PF as the NSGA-II algorithm. These results show that the ABBS is able to find optimal designs that are similar to those found by the NSGA-II algorithm. We conclude our study by applying the ABBS to the design of a sodium-cooled fast reactor to dispose of weapons-grade plutonium. The ABBS finds a core design that can burn upwards of 17.5 kg of weapons-grade plutonium per year and degrade an additional 195 kg of weapons-grade plutonium per year into non-weapons-grade material.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Dark Energy Survey: Galaxy sample for the baryonic acoustic oscillation measurement from the final dataset

In this paper, we present and validate the galaxy sample used for the analysis of the baryon acoustic oscillation (BAO) signal in the Dark Energy Survey (DES) Y6 data. The definition is based on a color and redshift-dependent magnitude cut optimized to select galaxies at redshifts higher than 0.6, while ensuring a high-quality photo- z determination. The optimization is performed using a Fisher forecast algorithm, finding the optimal i -magnitude cut to be given by i < 19.64 + 2.894 z ph . For the optimal sample, we forecast an increase in precision in the BAO measurement of ∼ 25 % with respect to the Y3 analysis. Our BAO sample has a total of 15,937,556 galaxies in the redshift range 0.6 < z ph < 1.2 , and its angular mask covers 4 , 273.42 deg 2 to a depth of i = 22.5 . We validate its redshift distributions with three different methods: directional neighborhood fitting algorithm (DNF), which is our primary photo- z estimation; direct calibration with spectroscopic redshifts from VIPERS, which is a spectroscopic galaxy sample that overlaps with our BAO sample and is complete within our selection cuts; and clustering redshift using SDSS galaxies. The fiducial redshift distribution is a combination of these three techniques performed by modifying the mean and width of the DNF distributions to match those of VIPERS and clustering redshift. In this paper, we also describe the methodology used to mitigate the effect of observational systematics, which is analogous to the one used in the Y3 analysis. This paper is one of the two dedicated to the analysis of the BAO signal in DES Y6. In its companion paper, we present the angular diameter distance constraints obtained through the fitting to the BAO scale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Enhancing Indacenodithiophene Acceptor Crystallinity via Substituent Manipulation Increases Organic Solar Cell Efficiency

The post-fullerene indacenodithiophene acceptor ITIC is a highly effective n-type component of high-performance bulk-heterojunction (BHJ) polymer solar cells (PSCs) for reasons that are not well-understood. Here, the impact of the ITIC alkyl substituent architecture on PSC active layer film morphology, charge transport, and photovoltaic (PV) performance is investigated with the donor polymers PBDB-T and PBDB-TF. On progressing from n-propyl to n-hexyl to n-nonyl ITIC substituents, PSC power conversion efficiency (PCE) increases from <0.1 % to 9.31% to 10.24%, respectively. BHJ blend morphology, carrier recombination dynamics, and PV performance with both donor polymers as probed by AFM, XRD, GIWAXS, and light intensity dependence correlate with marked differences in ITIC acceptor crystallinity. The DSC cold crystallization temperatures of the nhexyl and n-nonyl-functionalized acceptors are found to closely track the annealing temperatures for optimum PSC performance. These results identify a promising strategy for optimizing the performance of post-fullerene acceptor PSCs.

14 SOLAR ENERGY↗
DOE OSTI2014

Advances on multijunction solar cell characterization aimed at the optimization of real concentrator performance

Multijunction solar cells (MJSC) are usually developed to maximize efficiency under test conditions and not under real operation. This is the case of anti-reflective coatings (ARC), which are meant to minimize Fresnel reflection losses for a family of incident rays at room temperature. In order to understand and quantify the discrepancies between test and operation conditions, we have experimentally analyzed the spectral response of MJSC for a variety of incidence angles that are in practice received by a concentrator cell in high-concentration photovoltaic (HCPV) receiver designs. Moreover, we characterize this angular dependence as a function of temperature in order to reproduce real operation conditions. As the refractive index of the silicone is dependent on temperature, an optical mismatch is expected. Regarding other characterization techniques, a method called Relative EL Homogeneity Analysis (RELHA) is applied to processed wafers prior to dicing, allowing to diagnose the wafer crystalline homogeneity for each junction. Finally, current (I)-voltage (V) characterization under strongly unbalanced light spectra has also been carried out for a number of low-level irradiances, providing insight on each junction shunt resistance and corresponding radiative coupling.

14 SOLAR ENERGY↗
DOE OSTI2020

Laser-heated capillary discharge waveguides as tunable structures for laser-plasma acceleration

Laser-heated capillary discharge waveguides are novel, low plasma density guiding structures able to guide intense laser pulses over many diffraction lengths and have recently enabled the acceleration of electrons to 7.8 GeV by using a laser-plasma accelerator (LPA). These devices represent an improvement over conventional capillary discharge waveguides, as the channel matched spot size and plasma density can be tuned independently of the capillary radius. This has allowed the guiding of petawatt-scale pulses focused to small spot sizes within large diameter capillaries, preventing laser damage of the capillary structure. High performance channel-guided LPAs require control of matched spot size and density, which experiments and simulations reported here show can be tuned over a wide range via initial discharge and laser parameters. In this paper, measurements of the matched spot size and plasma density in laser-heated capillary discharges are presented, which are found to be in excellent agreement with simulations performed using the MHD code MARPLE. Strategies for optimizing accelerator performance are identified based on these results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

A High Performance Block Eigensolver for Nuclear Configuration Interaction Calculations

As on-node parallelism increases and the performance gap between the processor and the memory system widens, achieving high performance in large-scale scientific applications requires an architecture-aware design of algorithms and solvers. We focus on the eigenvalue problem arising in nuclear Configuration Interaction (CI) calculations, where a few extreme eigenpairs of a sparse symmetric matrix are needed. Here, we consider a block iterative eigensolver whose main computational kernels are the multiplication of a sparse matrix with multiple vectors (SpMM), and tall-skinny matrix operations. We then present techniques to significantly improve the SpMM and the transpose operation SpMM T by using the compressed sparse blocks (CSB) format. We achieve 3-4× speedup on the requisite operations over good implementations with the commonly used compressed sparse row (CSR) format. We develop a performance model that allows us to correctly estimate the performance of our SpMM kernel implementations, and we identify cache bandwidth as a potential performance bottleneck beyond DRAM. We also analyze and optimize the performance of LOBPCG kernels (inner product and linear combinations on multiple vectors) and show up to 15× speedup over using high performance BLAS libraries for these operations. The resulting high performance LOBPCG solver achieves 1.4× to 1.8× speedup over the existing Lanczos solver on a series of CI computations on high-end multicore architectures (Intel Xeons). We also analyze the performance of our techniques on an Intel Xeon Phi Knights Corner (KNC) processor.

97 MATHEMATICS AND COMPUTING↗