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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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Poster describing the planned startup process of a solid oxide gas turbine hybrid power system to be constructed at a 20 kW scale. The work is part of the ARPA-E project award to NETL under the INTEGRATE program.
The goal of this project was to probe fundamental kinetics questions regarding the gas phase reactive behavior of oxygenates. In order to succeed, our program necessitated both the development of new experimental tools and the development of theory based kinetic models to explain the chemistry of oxygenate fuels. By using advanced hybrid additive manufacturing/ traditional manufacturing techniques to create specialized ceramic micro-reactors, the team was able to simulate extreme high-temperature environments with much greater precision and durability than was previously possible. These tiny, high-tech tubes allow researchers to capture and identify "fleeting" chemical species—molecules that exist for only a fraction of a millisecond—using sophisticated light sources and mass spectrometry. Additionally, the team supported the development of a tabletop VUV laser system for isomer detection without a synchrotron. Through this work, we have successfully mapped out the specific chemical pathways of various oxygen-rich fuels, solving long-standing mysteries about how these substances break down kinetically. Ultimately, this research provides the fundamental knowledge needed to design next-generation engines and fuels that are better for the environment.
Sugarcane is a major crop of unclear origins due to its complex polyploid interspecific genome. We analyzed genome ancestries using whole-genome sequence data from 390 representative accessions based on repeated k-mers and chloroplast phylogeny. The results provided evidence that Saccharum officinarum was domesticated in the New Guinea region from the S. robustum wild species and revealed that its genome is a mosaic involving different S. robustum subgroups. We discovered a wild Saccharum contributor to most modern cultivars, likely originating from East Melanesia. We highlighted two early centers of sugarcane diversification associated with human transport, one in continental Asia through hybridization with different S. spontaneum subgroups and one in the Melanesian and Polynesian islands via hybridization with the discovered ancestor and Miscanthus. Finally, we revealed the genome ancestry of modern cultivars, highlighting untapped wild Saccharum diversity as a source of alleles for breeding programs.
LCA-PyTorch is a code repository which contains PyTorch implementations of the Locally Competitive Algorithm (LCA), which is a biologically-plausible sparse coding model. LCA-PyTorch allows for the training, testing, and analysis of single layer LCA networks, multi-layer LCA networks, and hybrid LCA-based deep neural network models on a wide variety of applications and data types. LCA-PyTorch was developed in Python, a high-level programming language that takes advantage of the Python ecosystem of high-quality open-source packages for machine learning. LCA-PyTorch interfaces heavily with the open-source PyTorch Python package.
Brief Recap of Hybrid ceramic/CMC materials at RTRC (UTRC) Development of concept for turbine airfoils Exploration of materials systems for armor Phase I DoE Project Goals Progress within the Phase I program
High-performance computing systems are rapidly evolving into heterogeneous platforms that fuse quantum accelerators with traditional classical processing units (CPUs) and graphical processing units (GPUs). This convergence calls for runtimes capable of managing both classical and quantum workloads in a unified manner. We introduce an intelligent, task-based runtime that marries the Intelligent RuntIme System (IRIS) asynchronous scheduler with a quantum programming stack through the Quantum Intermediate Representation Execution Engine (QIR-EE). Our design allows programs written in the quantum intermediate representation (QIR) to be dispatched concurrently to a variety of back-ends, including multiple quantum simulators and nascent quantum processors, enabling genuine hybrid execution on a single node. To illustrate its practicality, we partition a 4-qubit and 20-qubit circuit into three sub-circuits using quantum circuit cutting via the QCut library. Each sub-circuit is simulated independently by the QIR-EE driver within IRIS, after which a classical post-processing step merges the simulation results to recover the outcome of the original full-circuit computation. This case study demonstrates how finer task granularity can enable the parallel execution and lower the simulation burden per quantum task while preserving overall accuracy, highlighting the feasibility of our hybrid approach.
Solving problems related to planning and operations of large-scale power systems is challenging on classical computers due to their inherent nature as mixed-integer and nonlinear problems. Quantum computing provides new avenues to approach these problems. We develop a hybrid quantum-classical algorithm for the Unit Commitment (UC) problem in power systems which aims at minimizing the total cost while optimally allocating generating units to meet the hourly demand of the power loads. The hybrid algorithm combines a variational quantum algorithm (VQA) with a classical Benders-type heuristic. The resulting algorithm computes approximate solutions to UC in three stages: i) a collection of UC vectors capable meeting the power demand with lowest possible operating costs is generated based on VQA; ii) a classical sequential least squares programming (SLSQP) routine is leveraged to find the optimal power level corresponding to a predetermined number of candidate vectors; iii) in the last stage, the approximate solution of UC along with generating units power level combination is given. To demonstrate the effectiveness of the presented method, three different systems with 3 generating units, 10 generating units, and 26 generating units were tested for different time periods. In addition, convergence of the hybrid quantum-classical algorithm for select time periods is proven out on IonQ's Forte system.
Ambidirectionality, which is the ability of structural elements to move beyond a reference state in two opposite directions, is common in nature. However, conventional soft materials are typically limited to a single, unidirectional deformation unless complex hybrid constructs are used. We exploited the combination of mesogen self-assembly, polymer chain elasticity, and polymerization-induced stress to design liquid crystalline elastomers that exhibit two mesophases: chevron smectic C (cSmC) and smectic A (SmA). Inducing the cSmC-SmA–isotropic phase transition led to an unusual inversion of the strain field in the microstructure, resulting in opposite deformation modes (e.g., consecutive shrinkage or expansion and right-handed or left-handed twisting and tilting in opposite directions) and high-frequency nonmonotonic oscillations. Further, this ambidirectional movement is scalable and can be used to generate Gaussian transformations at the macroscale.
We posit that AI methods can be leveraged to significantly enhance the predictive skill of forward Earth system modeling (ESM) activities. A hybrid framework incorporating traditional ESM modeling, inference methods, and AI techniques could make better use of both measured and computed information as well as computational resources by targeting inference tasks at program priorities, such as the predictability of precipitation extremes. This runtime pathway to closing the simulation/analysis-data/model improvement loop will streamline the traditional offline pathway to model improvement, which is based on domain science expertise, while suggesting guidance for further observations and measurements.
Concentrating solar power (CSP) plants present a promising path towards utility-scale renewable energy. The power tower, or central receiver, configuration can achieve higher operating temperatures than other forms of CSP, and, like all forms of CSP, naturally pairs with comparatively inexpensive thermal energy storage, which allows CSP plants to dispatch electricity according to market price incentives and outside the hours of solar resource availability. Currently, CSP plants commonly include a steam Rankine power cycle and several heat exchange components to generate high-pressure steam using stored thermal energy. The efficiency of the steam Rankine cycle depends on the temperature of the plant's operating fluid, and so is a main concern of plant operators. However, the variable nature of the solar resource and the conservatism with which the receiver is operated prevent perfect control over the receiver outlet temperature. Therefore, during periods of solar variability, collection occurs at lower-than-design temperature. To support operator decisions in a real-time setting, we develop a revenue-maximizing non-convex mixed-integer, quadradically-constrained program which determines a dispatch schedule with sub-hourly time fidelity and considers temperature-dependent power cycle efficiency. The exact nonlinear formulation proves intractable for real-time decision support. Here we present exact and inexact techniques to improve problem tractability that include a hybrid nonlinear and linear formulation. Our approach admits solutions within approximately 3% of optimality, on average, within a five-minute time limit, demonstrating its usability for decision support in a real-time setting.
A discussion of many of the recently implemented features of GAMESS (General Atomic and Molecular Electronic Structure System) and LibCChem (the C++ CPU/GPU library associated with GAMESS) is presented. These features include fragmentation methods like the fragment molecular orbital, effective fragment potential and effective fragment molecular orbital methods, hybrid MPI/OpenMP approaches to Hartree-Fock and resolution of the identity second order perturbation theory. Many new coupled cluster theory methods have been implemented in GAMESS, as have multiple levels of density functional/tight binding theory. The role of accelerators, especially graphical processing units, is discussed in the context of the new features of LibCChem, as is the associated problem of power consumption as the power of computers increases dramatically. The process by which a complex program suite like GAMESS is maintained and developed is considered. Future developments are briefly summarized.
The demand for more carbon efficient power sources and a decrease in natural gas prices has decreased the desire for coal power. This decrease in demand has led to massive job losses in coal mining regions over the past decade. The purpose of this project is to develop a hybrid energy system utilizing both a coal power plant and advanced reactor, which is competitive with natural gas by improving on profitability and decreasing carbon emissions. This report details the problem with a summary of the impact on the coal industry and the availability of renewable energy sources in the Appalachian region. Because of the geography of the region, variable renewable energy sources are not available without significant size and siting restrictions. However, biomass in the form of wood waste is abundant and can be used as a carbon neutral energy source. Combining biomass and coal processing, in addition to thermal power plants, can increase system profits and efficiency by providing peaking power and conversion opportunities for secondary markets. The electric load is based on publicly available demand data from Appalachian Power, which services the western Virginia and southern West Virginia in the Appalachian region. The demand information is combined by service, normalized, and scaled to an average demand of 1000 kW, which will be the basis for sizing the hybrid energy system. A traditional screening curve analysis for a coal plant and advanced reactor shows that the least cost design varies significantly based on the assumed discount rate and capital recovery period. An optimization program to size the design in TEAL based on the load curve gives 10 optimal designs, all with a negative resulting net present value (NPV) and a coal plant capacity of less than 15%. Including profits from selling captured carbon at a flat rate results in a positive NPV; however, the coal capacity factor only increases to about 40%. There are limitations with this optimization as well since the price of CO2 is likely to decrease as more is sold to the conversion market. The suggested design will combine coal power, an advanced reactor, and coal and biomass coprocessing to produce a variety of products that can be sold to the conversion market while increasing system efficiency. The analysis of conversion pathways for coal and biomass reveals that multiple options will need to be included in the analysis to produce the optimal system design. Three systems will be optimized and compared to determine the best design based on the figures of merit of total NPV and cost of carbon avoided. The first system will include a coal power plant and an advanced reactor that will sell electricity to the grid to meet demand and sell captured carbon to the conversion market. The second system adds a high-temperature steam electrolysis plant, which will utilize electricity during times of low demand to produce hydrogen and sell it to the conversion market. The third system adds biomass and coal processing with options for hydrocarbon oils, syngas to be produced for the conversion market, and electricity generation to power components within the system or provide peaking power. This analysis will be based on a new approach that combines traditional screening curve methods with a dispatch algorithm that optimizes the system based on the opportunity cost of different production options. The resulting optimization algorithm should provide results with less processing time than HERON’s decision tree method. The results from this analysis will determine an optimal design and reinforce the benefits of coal power when used in a hybrid energy system. The initial results show that the addition of a secondary market for carbon sales could result in a positive NPV and increases the capacity factor of the coal plant as compared to a design with only sales to the electricity market. The addition of more markets and additional coal consumption from biomass coprocessing could increase NPV further, replace carbon in other markets through the sale of biomass-derived hydrocarbons, and demonstrate the value of coal power technology.
In recent years, critical infrastructure and power grids have experienced a series of cyber-attacks, leading to temporary, widespread blackouts of considerable magnitude. Since most substations are unmanned and have limited physical security protection, cyber breaches into power grid substations present a risk. Nowadays, the susceptibility of SDN architecture to cyber-attacks has exhibited a notable increase in recent years, as indicated by research findings. This suggests a growing concern regarding the potential for cybersecurity breaches within the SDN framework. In this paper, we propose a hybrid intrusion detection system (IDS)-integrated SDN architecture for detecting and preventing the injection of malicious IEC 61850-based generic object-oriented system event (GOOSE) messages in a digital substation. Additionally, this program locates the fault’s location and, as a form of mitigation, disables a certain port. Furthermore, implementation examples are demonstrated and verified using a hardware-in-the-loop (HIL) testbed that mimics the functioning of a digital substation.
In recent years, the CNNs have achieved great successes in the image processing tasks, e.g., image recognition and object detection. Unfortunately, traditional CNN's classication is found to be easily misled by increasingly complex image features due to the usage of pooling operations, hence unable to preserve accurate position and pose information of the objects. To address this challenge, a novel neural network structure called Capsule Network has been proposed, which introduces equivariance through capsules to signicantly enhance the learning ability for image segmentation and object detection. Due to its requirement of performing a high volume of matrix operations, CapsNets have been generally accelerated on modern GPU platforms that provide highly optimized software library for common deep learning tasks. However, based on our performance characterization on modern GPUs, CapsNets exhibit low effciency due to the special program and execution features of their routing procedure, including massive unshareable intermediate variables and intensive syn- chronizations, which are very dicult to optimize at software level. To address these challenges, we propose a hybrid computing architecture design named PIM-CapsNet. It preserves GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet, while pipelining with an off-chip in-memory acceleration solution that effectively tackles routing procedure's ineffciency by leveraging the processing-in-memory capability of today's 3D stacked memory. Using routing procedure's inherent parallellization feature, our design enables hierarchical improvements on CapsNet inference effciency through minimizing data movement and maximizing parallel processing in memory. Evaluation results demonstrate that our proposed design can achieve substantial improvement on both performance and energy savings for CapsNet inference, with almost zero accuracy loss. The results also suggest good performance scalability in optimizing the routing procedure with increasing network size.
The United States (U.S.) Department of Energy’s (DOE’s) Light Water Reactor Sustainability (LWRS) Program Flexible Plant Operations and Generation (FPOG) Pathway is developing options to help U.S. nuclear power plants (NPPs) better integrate with intermittent wind and solar capacity and the recent surge of natural gas power generation. Research is focusing on improving NPP flexibility through hybrid production of electricity and other products, such as hydrogen (H 2 ) and energy storage for the purpose of shifting power production to a later time. In the case of H 2 production, the clean electrical and thermal power from an NPP can be used to split water using electrolysis. This report outlines the opportunity for NPPs to participate in a first-of-akind (FOAK) commercial nuclear H 2 project intended to bring industry partners together to create regional clean H 2 hubs. The Bipartisan Infrastructure Law (BIL) will fund at least one hub up to $1.25 billion as federal cost share totally no less than 50% to execute a nuclear H 2 project. The report discusses the set of activities that are now underway or that are planned for completion by the FPOG Pathway to reduce the economic, technical, regulatory, and safety risks of these projects. DOE cross-program activities are being coordinated to ensure success in the timeframe allowed by the BIL. Figure ES-1 shows the approximate schedule of coordinated research and development (R&D) and pilot demonstration projects leading up to the first commercial nuclear H 2 production project. Execution of this plan requires DOE and industry collaboration. DOE research accomplishments are being provided to the electric utilities or industries looking to participate in the H 2 hub proposal and project execution process.
A common solution to deal with the stochasticity introduced by fast-ramping wind power integration is to equip wind farms (WFs) with energy storage systems (ESSs) to formulate hybrid WF-ESSs. In addition to leveling off wind power fluctuations during normal operations, a hybrid WF-ESS can be a flexible power source to accumulate the power system restoration. In this paper, we propose a rolling optimization model for transmission network recovery and load restoration considering the contributions of WF-ESSs. The proposed model is formulated as a mixed integer linear programming problem that simultaneously optimizes the amount and location of restorable load blocks as well as the restoration lines. The cold load pickup features of interrupted loads considering the outage duration are modeled in detail. A chance-constrained method is employed to deal with the uncertainty of wind power, and a rolling horizon-based framework is adopted to reduce the influence of forecast error. Case studies are conducted on both New England 39-bus system and part of a provincial power system in China. The results show that the load restoration process can be significantly accelerated by employing the proposed method and contributions of hybrid WF-ESSs to power system restoration are validated.
Monte Carlo N-Particle (MCNP)1 is a general-purpose Monte Carlo particle transport code developed by Los Alamos National Laboratory (LANL). To efficiently handle long simulations, MCNP version 6 (MCNP6) supports parallel execution using two primary programming models: • Shared-memory task-based threading using OpenMP (Open Multi-Processing), and • Distributed-memory calculations using MPI (Message Passing Interface). The OpenMP and MPI programming models enable MCNP6 to scale from desktop systems to high-performance computing (HPC) clusters, allowing users to run MCNP in one of three parallel modes: • OpenMP-only, • MPI-only, and • Hybrid (MPI + OpenMP). The choice of parallelization mode depends on the underlying computer architecture and the characteristics of the simulation problem.
Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.