Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “flexible operation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,045 records · Page 58

Free-standing membrane incorporating single-atom catalysts for ultrafast electroreduction of low-concentration nitrate

The release of wastewaters containing relatively low levels of nitrate (NO 3 ⁻) results in sufficient contamination to induce harmful algal blooms and to elevate drinking water NO 3 ⁻ concentrations to potentially hazardous levels. In particular, the facile triggering of algal blooms by ultra-low concentrations of NO 3 ⁻ necessitates the development of efficient methods for NO 3 ⁻ destruction. However, promising electrochemical methods suffer from weak mass transport under low reactant concentrations, resulting in long treatment times (on the order of hours) for complete NO 3 ⁻ destruction. In this study, we present flow-through electrofiltration via an electrified membrane incorporating nonprecious metal single-atom catalysts for NO 3 ⁻ reduction activity enhancement and selectivity modification, achieving near-complete removal of ultra-low concentration NO 3 ⁻ (10 mg-N L -1 ) with a residence time of only a few seconds (10 s). By anchoring Cu single atoms supported on N-doped carbon in a carbon nanotube interwoven framework, we fabricate a free-standing carbonaceous membrane featuring high conductivity, permeability, and flexibility. The membrane achieves over 97% NO 3 ⁻ removal with high N 2 selectivity of 86% in a single-pass electrofiltration, which is a significant improvement over flow-by operation (30% NO 3 ⁻ removal with 7% N 2 selectivity). This high NO 3 ⁻ reduction performance is attributed to the greater adsorption and transport of nitric oxide under high molecular collision frequency coupled with a balanced supply of atomic hydrogen through H 2 dissociation during electrofiltration. Overall, our findings provide a paradigm of applying a flow-through electrified membrane incorporating single-atom catalysts to improve the rate and selectivity of NO 3 ⁻ reduction for efficient water purification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Bilinear dynamic mode decomposition for quantum control

Abstract Data-driven methods for establishing quantum optimal control (QOC) using time-dependent control pulses tailored to specific quantum dynamical systems and desired control objectives are critical for many emerging quantum technologies. We develop a data-driven regression procedure, bilinear dynamic mode decomposition (biDMD), that leverages time-series measurements to establish quantum system identification for QOC. The biDMD optimization framework is a physics-informed regression that makes use of the known underlying Hamiltonian structure. Further, the biDMD can be modified to model both fast and slow sampling of control signals, the latter by way of stroboscopic sampling strategies. The biDMD method provides a flexible, interpretable, and adaptive regression framework for real-time, online implementation in quantum systems. Further, the method has strong theoretical connections to Koopman theory, which approximates nonlinear dynamics with linear operators. In comparison with many machine learning paradigms minimal data is needed to construct a biDMD model, and the model is easily updated as new data is collected. We demonstrate the efficacy and performance of the approach on a number of representative quantum systems, showing that it also matches experimental results.

97 MATHEMATICS AND COMPUTING↗

Quantum Networks for Resilient Power Grids: Theory and Simulated Evaluation

Quantum networks are considered the future of secure communication in the coming quantum era. Yet there lack significant efforts on developing practical quantum networks for power grids. Here, in this paper, we establish a quantum network-based power grid (QNetGrid) framework and develops a real-time, reliable, flexible, programmable, and cost-effective QNetGrid software testbed containing repeater-based quantum communication, quantum routing, real software-defined networking (SDN) switches, and real-time networked microgrids (NMs) operations. It makes the following contributions: 1) a repeater and routing based quantum network simulator (QNSim) is developed, 2) repeaters with and without quantum memories are respectively simulated in QNSim, 3) different routing scenarios in QNetGrid are investigated, 4) a real-time QNetGrid software testbed is built in RTDS incorporating QNSim and real SDN switches, and 5) various test cases are designed, and experimental results produced with the QNetGrid testbed provide valuable insights for building quantum networks in power grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Initial Results on a New Light-Duty 2.7-L Opposed-Piston Gasoline Compression Ignition Multi-Cylinder Engine

Abstract Gasoline compression ignition (GCI) is a cost-effective approach to achieving diesel-like efficiencies with low emissions. The fundamental architecture of the two-stroke Achates Power Opposed-Piston (OP) Engine enables GCI by decoupling piston motion from cylinder scavenging, allowing for flexible and independent control of cylinder residual fraction and temperature leading to improved low-load combustion. In addition, the high peak cylinder pressure and noise challenges at high-load operation are mitigated by the lower brake mean effective pressure (BMEP) operation and faster heat release for the same pressure rise rate of the OP Engine. These advantages further solidify the performance benefits of the OP Engine and demonstrate the near-term feasibility of advanced combustion technologies, enabled by the opposed-piston architecture. This paper presents initial results from steady-state testing on a brand new 2.7-L OP GCI multi-cylinder engine (MCE) designed for light-duty truck applications. Successful GCI operation calls for a high compression ratio (CR), leading to higher combustion stability at low loads, higher efficiencies, and lower cycle HC + NOX emissions. Initial results show a cycle average brake thermal efficiency (BTE) of 31.7%, which is already greater than 11% conventional engines, after only ten weeks of testing. Emissions results suggest that Tier 3 Bin 160 levels can be achieved using a traditional diesel after-treatment system. Combustion noise was well controlled at or below the United States Council for Automotive Research limits. In addition, initial results on catalyst light-off mode with GCI are also presented.

Energy & Fuels↗

Characterization of Heat Transfer in a Trapped-Vortex Combustor Designed for High-Temperature Material Testing

Next generation gas turbines will reduce carbon emissions by improving their cycle efficiencies and implementing hydrogen as a low-carbon fuel. The combined-cycle efficiency can be improved by increasing the combustor outlet temperature and decreasing the cooling air required for hot-section parts. Ultra-high temperature ceramic matrix composites (CMC) can survive increasingly extreme environments due to their strong mechanical properties at expected conditions. The introduction of CMCs in high-hydrogen flame environments requires testing at realistic conditions that mimic the thermochemical and fluid mechanic states in gas-turbine combustors. In this work, we describe a new experiment that has been designed to test high-temperature materials in a combustor-relevant environment. Its trapped-vortex combustor chamber design allows for high levels of fuel flexibility and wide flame stability limits. Testing of monolithic silicon carbide (SiC) samples in this environment was done to characterize the heat transfer to material samples over a wide range of operating conditions, including variations in fuel composition and thermal power. Heat flux measurements were verified in non-reacting and reacting environments after which they were benchmarked with previous literature to verify the operation of the facility with heated air. Testing in combustor-relevant conditions shows high levels of heat flux to the material samples in the combustor, particularly downstream of the anchored flame.

Richins, Porter [Pennsylvania State University , ,↗

Design, Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) v0.1.0

The DISPATCHES software will enable the design and optimization of Integrated Energy Systems (IES). The platform will support hybridizing existing generation facilities (such as nuclear and coal plants) as well as greenfield hybrid designs that incorporate advanced technologies (such as Advanced Modular Reactors and Coal FIRST), and it will enable the consideration of multiple technology options with different time constants. The design of the IES will be optimized for operation within the bulk power system via energy market signals (day ahead, real-time, and ancillary services) obtained from a production cost model. The goal of DISPATCHES is to identify flexible, dynamic designs that are responsive to grid market signals resulting in highly efficient, cost effective tightly coupled hybrid energy systems that can act as a "black box" to the grid to provide increased flexibility and resiliency.

Gunter, Daniel↗

RxnRover/amlro

AMLRO (Active Machine Learning Reaction Optimizer) is an open-source framework designed to accelerate chemical reaction optimization using active learning with classical machine learning regression models. AMLRO integrates space-filling sampling strategies (e.g., Sobol and Latin Hypercube sampling) with iterative model training, prediction, and experiment selection to efficiently navigate complex reaction spaces. The platform supports multiple regression models, flexible multi-objective definitions, and user-defined parameter bounds, enabling data-efficient optimization from small initial datasets. AMLRO is designed for ease of use by experimentalists and can operate as a standalone decision-support tool or be integrated into closed-loop automated experimentation workflows.

Kulathunga, Dulitha Prasanna [Iowa State Universit↗

Parallel Runtime Interface for Fortran (PRIF): A Multi-Image Solution for LLVM Flang

Fortran compilers that provide support for Fortran’s native parallel features often do so with a runtime library that depends on details of both the compiler implementation and the communication library, while others provide limited or no support at all. This paper introduces a new generalized interface that is both compiler- and runtime-library-agnostic, providing flexibility while fully supporting all of Fortran’s parallel features. The Parallel Runtime Interface for Fortran (PRIF) was developed to be portable across shared- and distributed-memory systems, with varying operating systems, toolchains and architectures. It achieves this by defining a set of Fortran procedures corresponding to each of the parallel features defined in the Fortran standard that may be invoked by a Fortran compiler and implemented by a runtime library. PRIF aims to be used as the solution for LLVM Flang to provide parallel Fortran support. This paper also briefly describes our PRIF prototype implementation: Caffeine.

Bonachea, Dan↗

Enhancing Lifetime and Reducing Costs for Fish Diversion Netting Structures (Abstract)

This effort will focus on technology transfer and commercialization of antifouling coatings with an enthusiastic and engaged industrial team. Environmental requirements and operational demands call for a nontoxic coating/paint to prevent fouling on fish passage guidance netting at hydropower facilities. For example, one netting customer estimated the capital cost for compliance at $\$12$ million to $\$15$ million. This project will build partnerships between PNNL and private companies to optimize, demonstrate, mature, and commercialize a novel PNNL-developed technology that addresses this critical coating need of the hydropower industry. This effort will support modification of existing coatings for application to flexible netting structures. Industrial partners include commercial coating development specialist (Lorama), hydrophobic material manufacturer and paint developer (Dry Surface Technologies), aquatic applications specialists (Prometheus Innovations and River Connectivity Systems), and hydropower netting producer (Pacific Netting Products). Engagement with the U.S. Army Corps of Engineers (USACE) and Bureau of Reclamation (BOR), two hydropower operators, throughout the project will provide expertise and field test sites that will provide crucial proof of real-world performance data (additional details provided in Teaming section). Taylor Shellfish Farms will provide organisms and fouling expertise as well as a perspective of potential broader impacts for the blue economy. Sample netting will demonstrate performance in a range of environments for key hydropower applications. PNNL will work with industrial partners to overcome commercialization barriers as well as resolving any manufacturing or regulatory issues. This Phase 1 effort is focused on technology optimization for application to fish passage guidance netting and technology validation as verified by independent testing (through USACE, BOR, Taylor Shellfish and Prometheus Innovations). Through this effort, SLIC will be demonstrated for netting applications at technology readiness level (TRL) 5. The field test data will allow optimization of SLIC formulation and performance which is key to enabling technology transfer of a mature proven technology to industry and production of a viable commercial product specifically focused for hydropower needs.

13 HYDRO ENERGY↗

Hybridization Assessment of Trybrid Pumped Storage Hydropower System—Part 1: A Case Study of Corral Summit

This report is a part of the deliverables for technical assistance provided to Cat Creek Energy for the Coral Summit Trybrid (Triple Hybrid-Pumped Storage Hydropower, Battery Energy Storage System, and photovoltaic solar energy) energy project. This report explores the operational benefits and challenges of hybridizing an open loop PSH (200MW) located at Mackay, Custer County, Idaho with solar PV (Ground mount 300MW and floating 40MW) and battery (720MWhr). This document reports two activities performed as a part of the hybridization assessment task 1) optimal resource allocation and energy management strategy, and 2) power quality and reliability assessment. From optimal resource allocation and energy management strategy (activity 1), the following key findings can be observed: • Conventional PSH (CPSH) with two reversible pump turbines and separate penstocks can provide required flexibility equivalent to that from two ternary PSH with separate penstock. With single unit CPSH, upper reservoir head cannot be maintained accurately, the variation of water level is rapid and pump mode flexibility is not available. These disadvantages can be overcome by single unit TPSH. However, using two CPSH units with separate penstocks also overcome these disadvantages with the formation of the hydraulic short circuit between two conventional units. • Flooding of the lower reservoir is a severe concern when considering continuous operation for black start. This limits the duration of continuous operation from PSH alone to around 50 hours. Due to the complementary PV and battery action, the duration of continuous operation and smooth power output can be extended. • An optimization problem is framed that maximizes the power output on an hourly basis while minimizing constraint violations and respecting seasonal variations of solar PV and load profiles . Two value streams, arbitrage and baseload generation are served by this profile. It was uncovered that for smooth power output during regular operation, PV curtailment will be required, or the battery capacity needs to be increased above 90MW to accommodate additional PV. From power quality and reliability assessment (activity 2) the following takeaway points can be observed: • The Trybrid, when integrated at the Lost River bus, and limited to 250MW in generation mode and -150MW in the pump mode, causes no violation of voltage or flow.

13 - HYDRO ENERGY↗

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Benefits of Dual Fuel Heat Pump Grid-responsive Control: A Model-based Control Optimization Approach Using Building and Equipment Co-simulation

Conventional dual fuel heat pumps lack the intelligent control mechanisms to efficiently manage the switch between heat pump and furnace, leading to sub-optimal energy usage and, in some cases, increased operating costs. To resolve this gap, this study applies optimized control on hybrid heat pumps. With a focus on equipment control strategies, we compare the performances of five spacing heating equipment, including a conventional heat pump (HP), a conventional furnace, a dual fuel heat pump (DFHP) with conventional control, a dual fuel heat pump with smart control, and a novel seamlessly fuel flexible heat pump (SFFHP). While DFHP runs on either gas or electricity at any given moment, SFFHP concurrently consumes gas and electricity by continuously optimizing the proportion of each. In this research, a co-simulation framework is developed by integrating a building envelope model with a physics-based heat pump simulation model to analyze the benefits of grid-responsive controls of DFHP and SFFHP. The model-based optimal controls adjust the operation of the heat pump and gas furnace based on utility price signals and marginal grid emission to minimize utility cost and CO 2 emissions for multiple climate zones, different utility tariffs, and marginal grid emission scenarios. Case studies in Chicago and Los Angeles demonstrate that SFFHP and DFHP, with model-based optimal control, can deliver significant reductions in peak demand, utility cost, and CO 2 emission. In Chicago, SFFHP and smart controlled DFHP yield up to 64.7% and 61.7% utility cost reduction and up to 15.7% and 8.5% CO 2 emission reduction compared to the gas furnace. In Los Angeles, SFFHP and smart controlled DFHP achieve up to 43.6% and 40.1% utility cost reduction and up to 13.8% and 14.1% CO2 emission reduction compared to conventional heat pumps. In conclusion, by leveraging the fuel flexibility nature of dual fuel heat pumps, the model-based control optimization approach makes dual fuel heat pump an attractive option for demand response programs.

Control↗

Benchmarking thermal energy storage cost for industrial process heat

Process heat accounts for roughly half of industrial energy demand, and currently 95% of process heat is derived from the combustion of natural gas, oil, and coal. Electrification of industrial heating could be an alternative, potentially expanding locations suitable for manufacturing; however, industrial facility owners may desire energy storage to stabilize energy costs. In this work, the economic benefits of pairing thermal storage with electrified process heat to reduce the average price paid for energy are analyzed. Cost savings focus on energy arbitrage, or leveraging flexible energy pricing schemes, alone. The cost of natural gas combustion across decades (2019-2060) is compared to the costs of electricity and thermal energy storage in four United States Independent System Operator (ISO) regions. Systems installed today may not yield positive net present value (NPV) compared to the use of natural gas. However, using estimated electricity prices, systems installed in 2030 using arbitrage alone could be profitable when compared to natural gas in some regions of the U.S. Furthermore, if capital expenditures could be reduced by 50% for sensible thermal storage systems by 2030, profitable systems are found across all regions. This implies that electrification of industrial process heat, when paired with inexpensive thermal energy storage systems, could be less expensive than brownfield natural gas systems, using arbitrage as the only source of revenue and without a dependency on any future policy drivers such as pricing externalities that could further incentivize the electrification of industrial process heat.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. Finally, this underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

54 ENVIRONMENTAL SCIENCES↗

Preliminary proof-of-concept of real-time divertor heat flux control from infrared cameras with nitrogen injection in the DIII-D tokamak

In future tokamak reactors like ITER and the Fusion Pilot Plant (FPP), real-time feedback control of heat flux to the plasma-facing components (PFC) will be critical for steady-state operation. This work presents the first experimental demonstration of real-time divertor heat flux estimation with infrared thermography and feedback control with impurity seeding on the DIII-D tokamak. The flexible infrastructure of the Plasma Control System (PCS) on DIII-D makes this new capability possible. The PCS software runs on a gateway computer system, and five real-time compute nodes. An array of low latency streaming digitizers from D-TACQ Solutions connects to these real-time computers to collect and process data, and send commands to actuators during plasma discharges. This system handles the signal IO from the tokamak and allows the PCS to utilize the diagnostic data necessary to perform control in real-time. Feedback control on heat flux was accomplished by feeding infrared camera data from the “Infrared TV” (IRTV) camera to a custom-developed User Datagram Protocol (UDP) server. This server transmits infrared data to a newly developed PCS algorithm that estimates the heat flux to PFC. Here, a proportional integral derivative (PID) controller minimizes the error between a heat flux reference and the real-time estimate by injecting nitrogen gas into the divertor.

Algorithm development↗

MiniMOD

SAND2025-03854O MiniMod is a user-friendly software tool designed to assess the performance of high-performance computing (HPC) systems. Researchers can use the program to test communication methods and computational tasks to understand how different setups can affect application efficiency. This software is particularly useful for optimizing network performance in scientific research, simulations, and data analysis. MiniMod‘s flexible design allows users to make informed decisions about their computing environments, which can enhance productivity and results in real-world applications. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Dosanjh, Matthew [Sandia National Lab. (SNL-CA), L↗

AGR-2 Loose Particle Heating Tests in the Furnace for Irradiated TRISO Testing

A furnace system was developed to thermally expose individual tristructural-isotropic (TRISO) coated particles to expand upon established postirradiation examination (PIE) safety testing that is used to explore particle failures and fission product release from compacts. Two systems were installed in the Irradiated Fuels Examination Facility (IFEL) at Oak Ridge National Laboratory (ORNL). The targeted design was intended to be simple and flexible to allow a range in conditions to be explored. Initial inert atmosphere testing indicated the presence of particle failures, so modifications to the testing approach were implemented to improve the furnace operation. Ultimately, burnback particles were analyzed to provide a direct measurement of fission product release, thus avoiding the complications from integral release that were observed during whole compact testing. Particles were exposed to temperatures ranging 1,150–1,300°C for up to 1,500 h and 1,600°C up to 500 h. No indication of uniform release of fission products such as 106 Ru, 125 Sb, or 154 Eu was observed, however, indication of particle dependent 154 Eu release was suggested for select particles. Exploration of 110m Ag release was limited to 1,150°C and 1,300°C up to 500 h. The analysis indicated that there was nonuniform release of 110m Ag and supports a possible bimodal temperature dependence on silver release.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Using MLIR Framework for Codesign of ML Architectures Algorithms and Simulation Tools

MLIR (Multi-Level Intermediate Representation), is an extensible compiler framework that supports high-level data structures and operation constructs. These higher-level code representations are particularly applicable to the artificial intelligence and machine learning (AI/ML) domain, allowing developers to more easily support upcoming heterogeneous AI/ML accelerators and develop flexible domain specific compilers/frameworks with higher-level intermediate representations (IRs) and advanced compiler optimizations. The result of using MLIR within the LLVM compiler framework is expected to yield significant improvement in the quality of generated machine code, which in turn will result in improved performance and hardware efficiency

97 MATHEMATICS AND COMPUTING↗