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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Evaluating the Effect of Vane Trailing Edge Flow on Turbine Rim Sealing

Abstract Modern gas turbine development continues to move toward increased overall efficiency, driven in part by higher firing temperatures that point to a need for more cooling air to prevent catastrophic component failure. However, using additional cooling flow bled from the upstream compressor causes a corresponding detriment to overall efficiency. A primary candidate for cooling flow optimization is purge flow, which contributes to sealing the stator–rotor cavity and prevents ingestion of hot main gas path (MGP) flow into the wheelspace. Previous research has identified that the external main gas path flow physics play a significant role in driving rim seal ingestion. However, the potential impact of other cooling flow features on ingestion behavior, such as vane trailing edge (VTE) flow, is absent in the open literature. This paper presents experimental measurements of rim cavity cooling effectiveness collected from a one-stage turbine operating at engine-representative Reynolds and Mach numbers. Carbon dioxide (CO2) was used as a tracer gas in both the purge flow and vane trailing edge flow to investigate flow migration into and out of the wheelspace. Results show that the vane trailing edge flow does in fact migrate into the rim seal and that there is a superposition relationship between individual cooling flow contributions. Computational fluid dynamics (CFD) simulations using unsteady Reynolds-averaged Navier–Stokes (URANS) were used to confirm VTE flow ingestion into the rim seal cavity. Radial and circumferential traverse surveys were performed to quantify cooling flow radial migration through the main gas path with and without vane trailing edge flow. The surveys confirmed that vane trailing edge flow is entrained into the wheelspace as purge flow is reduced. Local CO2 measurements also confirmed the presence of VTE flow deep in the wheelspace cavity.

Engineering↗
DOE OSTI2020

Local Communication in Populations of Thermostatically Controlled Loads

Thermostatically controlled loads (TCLs) have shown great potential for demand response (DR) in electric grid operations. However, it has been commonly seen that DR events using TCLs may cause load synchronization and unwanted oscillatory effects, especially in homogeneous populations. In an attempt to mitigate the negative impacts of DR events, a decentralized method is proposed that modifies each thermostat behavior based on the activity of a small number of nearby TCLs. This feedback introduces the possibility of instability in the aggregate behavior. A stability analysis is performed on a linearized model of the aggregate system and the results of that analysis compared to simulation results. The proposed modification of thermostat behavior results in fourfold reduction in the post-DR peak while suppressing ensuing oscillations at the expense of a modest increase in compressor cycling. Further, the linearized model also provides insight into the aggregate behavior of the population.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Analysis of Energy Savings and CO2 Emission Reduction Contribution for Industrial Facilities in USA

Abstract Energy audits can identify energy consumptions, energy costs of the facility and evolve to develop measures to maximize efficiency, optimize supply energy, and eliminate waste. This paper investigates the potential energy savings at 20 different industrial sectors with 152 assessments for various facilities in Wisconsin, USA. On average, eight energy recommendations were suggested and applied in each facility. This paper provides a detailed guideline for each industry in terms of eight different energy categories: heating, ventilation, and air conditioning (HVAC) systems, heat recovery systems, electrical demand management, and utility bills, motors, compressors, waste management, and productivity enhancement, lighting, besides building envelope. In total, the energy savings were as follows: 98 million kWh in the shape of electricity, 561 billion British thermal units (BTUs) gas savings, 44 million gallons water savings, and 2-million-pound solid waste savings. Based on these savings, a 100-thousand-ton reduction in carbon dioxide emissions was obtained.

Energy & Fuels↗
DOE OSTI2022

Use of Multiple Tracer Gases to Quantify Vane Trailing Edge Flow Into Turbine Rim Seals

Abstract Overlapping features are commonly used as rim seals between stationary and rotating components in a turbine stage. These rim seals are used to prevent main gas path ingestion to the wheelspace cavity, which reduces the lifespan of critical engine components such as the turbine disk. In addition to the overlapping features, purge flow, diverted from the compressor, is injected into the rim cavity to act as an airflow sealing mechanism. Previous research identified that in addition to the purge flow in the rim cavity, cooling flow from the vane trailing edge (VTE) is ingested into the rim seal cavity carrying the potential to cool components in the wheelspace. These previous findings, however, were not able to distinctly separate purge from VTE cooling flows, which is the contribution of this paper based on uniquely using two different tracer gases. A one-stage test turbine operating at engine-relevant conditions and consisting of real engine hardware was used to validate and quantify the ingestion of the VTE flow by independently seeding the purge and VTE flows with two different tracer gases. Experimental results show the presence of VTE flow in the rim seal throughout all purge flowrates evaluated. Circumferential variation of VTE flow was also studied both experimentally and computationally using a computational fluid dynamics model. Results showed that ingested VTE flow can reduce the detrimental effect of hot gas ingestion particularly at higher purge flowrates.

Engineering↗
DOE OSTI2022

Attributes of Bi-Directional Turbomachinery for Pumped Thermal Energy Storage

Abstract In this paper, we (i) present a methodology for determining the aerodynamic performance of bi-directional turbomachines for pumped thermal energy storage, i.e., turbomachines designed to operate as a compressor in one direction, and then as a turbine in the opposite direction, (ii) carry out performance computations for such turbomachines, and (iii) propose principles for conceptual design of these devices. Focus is placed on using the energy storage cycle not only to identify the novel requirements placed on bi-directional turbomachines but also to estimate the effect of these requirements on the efficiency of the energy storage process. In particular, the difference between aerodynamic loading in forward and in backward operation causes the blading to work at incidences leading to the performance below maximum efficiency, resulting in a lower round-trip efficiency. The description of the design principles includes determination of the number of stages, definition of nondimensional parameters for blading selection, and optimization of two-dimensional blading for bi-directional operation. The assessment of stage count shows the relationship between relative Mach number, pressure ratio, and round-trip efficiency. The nondimensional parameters are assessed through a bi-directional analogue to existing “Smith charts,” for the efficiency of single-direction turbomachines, as a function of camber and stagger. The blade shape evaluation and optimization show how the blade profile can be modified to address the requirements of a bi-directional turbomachine, enabling an increase in round-trip efficiency of two percentage points compared to a baseline double circular arc configuration.

Engineering↗
DOE OSTI2022

LIQUID AIR COMBINED CYCLE

Hybrid integration of thermal energy storage with gas turbines can provide compact, cost-effective, long-duration energy storage while reducing the fuel consumption of dispatchable resources needed for the reliability of renewable dominant electric grids. The Liquid Air Combined Cycle™ (LACC) is a hybrid energy storage system using cryogenic liquid air as an energy storage medium and gas turbine exhaust heat to extract stored energy. The storage tank is charged using liquefaction processes employing electric motor-driven compressors to pressurize the air, heat exchangers to reject heat of compression, and expanders to reduce the temperature and liquefy the air. Proven cryogenic refrigeration processes can be selected based on capital cost (per kg/s of liquid air produced), efficiency (kJ per kg of air produced), and operating factors including startup speed and load following capability. This paper presents results of studies undertaken for the U.S. Department of Energy to evaluate cost and performance tradeoffs for charge and discharge cycle components, optimize charge and discharge cycles, and assess the techno-economic potential of LACC technology.

Conlon, William↗
DOE OSTI2026

Enhancing ZFP: A Statistical Approach to Understanding and Reducing Error Bias in a Lossy Floating-Point Compression Algorithm

The amount of data generated and gathered in scientific simulations and data collection applications is continuously growing, putting mounting pressure on storage and bandwidth concerns. A means of reducing such issues is data compression; but, lossless data compression is typically ineffective when applied to floating-point data. Thus, users tend to apply a lossy data compressor, which allows for small deviations from the original data. It is essential to understand how the error from lossy compression impacts the accuracy of the data analytics. Thus, we must analyze not only the compression properties but the error as well. In this paper, we provide a statistical analysis of the error caused by ZFP compression, a state-of-the-art, lossy compression algorithm explicitly designed for floating-point data. We show that the error is indeed biased and propose simple modifications to the algorithm to neutralize the bias and further reduce the resulting error.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Region-adaptive, Error-controlled Scientific Data Compression using Multilevel Decomposition

The increase of computer processing speed is significantly outpacing improvements in network and storage bandwidth, leading to the big data challenge in modern science, where scientific applications can quickly generate much more data than that can be transferred and stored. As a result, big scientific data must be reduced by a few orders of magnitude while the accuracy of the reduced data needs to be guaranteed for further scientific explorations. Moreover, scientists are often interested in some specific spatial/temporal regions in their data, where higher accuracy is required. The locations of the regions requiring high accuracy can sometimes be prescribed based on application knowledge, while other times they must be estimated based on general spatial/temporal variation. In this paper, we develop a novel multilevel approach which allows users to impose region-wise compression error bounds. Our method utilizes the byproduct of a multilevel compressor to detect regions where details are rich and we provide the theoretical underpinning for region-wise error control. With spatially varying precision preservation, our approach can achieve significantly higher compression ratios than single-error bounded compression approaches and control errors in the regions of interest.We conduct the evaluations on two climate use cases – one targeting small-scale, node features and the other focusing on long, areal features. For both use cases, the locations of the features were unknown ahead of the compression. By selecting approximately 16% of the data based on multi-scale spatial variations and compressing those regions with smaller error tolerances than the rest, our approach improves the accuracy of post-analysis by approximately 2 × compared to single-error-bounded compression at the same compression ratio. Using the same error bound for the region of interest, our approach can achieve an increase of more than 50% in overall compression ratio.

Gong, Qian↗
DOE OSTI2025

STZ: A High Quality and High Speed Streaming Lossy Compression Framework for Scientific Data

Error-bounded lossy compression is one of the most efficient solutions to reduce the volume of scientific data. For lossy compression, progressive decompression and random-access decompression are critical features that enable on-demand data access and flexible analysis workflows. However, these features can severely degrade compression quality and speed. To address these limitations, we propose a novel streaming compression framework that supports both progressive decompression and random-access decompression while maintaining high compression quality and speed. Our contributions are three-fold: (1) we design the first compression framework that simultaneously enables both progressive decompression and random-access decompression; (2) we introduce a hierarchical partitioning strategy to enable both streaming features, along with a hierarchical prediction mechanism that mitigates the impact of partitioning and achieves high compression quality—even comparable to state-of-the-art (SOTA) non-streaming compressor SZ3; and (3) our framework delivers high compression and decompression speed, up to 6.7 × faster than SZ3.

Wang, Daoce [University of Nebraska, Omaha]↗
DOE OSTI2025

Stability-preserving Lossy Compression for Large-scale Partial Differential Equations

Checkpoint/Restart (C/R) strategies are vital for fault tolerance in PDE-based scientific simulations, yet traditional checkpointing incurs significant I/O overhead. Lossy compression offers a scalable solution by reducing checkpoint data size, but conventional methods often lack control over physical invariants (e.g., energy), leading to instability such as oscillations or divergence in Partial Differential Equations (PDE) systems. This paper introduces a stability-preserving compression approach tailored for PDE simulations by explicitly controlling kinetic and potential energy perturbations to ensure stable restarts. Extensive experiments conducted across diverse PDE configurations demonstrate that our method maintains numerical stability with minimal error magnification—even across multiple checkpoint-restart cycles—outperforming state-of-the-art lossy compressors. Parallel evaluations on the Frontier supercomputer show up to 8.4× improvement in checkpoint write performance and 6.3× in read performance, while maintaining relative L2 errors ∼ 2e-6 throughout continued simulation. These results provide practical guidance for balancing compression accuracy, stability, and computational efficiency in large-scale PDE applications.

Gong, Qian [ORNL] (ORCID:0000000235704142)↗
DOE OSTI2026

QProR: An Efficient Framework for Quantity-of-Interest Based Progressive Retrieval with Guaranteed Error Control

Scientific applications generate an unprecedented volume of data, overwhelming the network and file systems’ bandwidth and posing challenges for efficient and scalable data retrieval and analysis. Progressive data compression offers a promising solution by enabling on-demand retrieval at reduced size. However, existing progressive methods either fail to bound the errors in essential quantities of interest (QoIs) derived from raw data or suffer from suboptimal retrieval efficiency. In this work, we propose QProR, an efficient QoI-based progressive framework that optimizes progressive retrieval for target QoIs. Our key contributions include: (1) a systematic framework that integrates error-controlled lossy compressors with bitplane encoding while decoupling the two processes for high flexibility and adaptability; (2) a novel weighted bitplane encoding method which incorperates QoI knowledge into data refactoring to enhance retrieval efficiency; (3) an optimized retrieval strategy that accounts for the varying impacts of different variables on multivariate QoIs; (4) comprehensive evaluations using six real-world datasets from multiple scientific applications and thorough comparisons against state of the arts. Experimental results demonstrate that QProR achieves up to 80.38% reduction in the retrieval size under the same requested QoI error tolerance, when compared with the best-performing existing methods. When transferring 384 GB of scientific data to remote sites, QProR delivers up to 1.68 × speedup in the end-to-end data transfer performance.

Li, Wenbo [University of Kentucky]↗
DOE OSTI2021

Achieving 5,000-h and 8,000-h Low-PGM Electrode Durability on Automotive Drive Cycles

Whereas total Pt loading in anode and cathode catalysts below 0.125 mg cm −2 is required to meet the stringent cost target for automotive fuel cell systems (FCS) for light duty vehicles, low-loaded cathode catalysts are susceptible to unacceptable aging-related performance losses at high current densities. A framework model, validated by accelerated stress test data, has identified cell voltage, relative humidity (RH) and temperature as the key operating variables that affect degradation of a high-activity d-PtCo/C cathode catalyst with 0.1 mg cm −2 Pt loading. Drive cycle simulations indicate that these can be controlled by properly selecting the minimum FCS power, compressor-expander module (CEM) turndown, and stack coolant temperature. The optimum system parameters are 4-kW e minimum power for an 80-kW e FCS, CEM turndown of 12.5, and 66 °C average coolant exit temperature that combine to limit the maximum cell voltage to 850 mV and outlet RH to 90%–100%. Depending on Pt loading, the mismatch between actual and allowable degradation for 10% power loss over 5,000-h lifetime requires the stack to be oversized by 2.4%–5%, resulting in 8.4%–41% lower Pt utilization and 7.1%–20.5% penalty in stack cost. The corresponding results for 8,000-h lifetime are 10.3%-14% stack oversizing, 23%–51.8% lower Pt utilization, and 24.1%–35.4% stack cost penalty.

08 HYDROGEN↗
DOE OSTI2020

kokkosSZ (kSZ) A Portable Cellerator Implementation of SZ using Kokkos Programming Model

kSZ is a Kokkos-based implementation of the world-widely used SZ lossy compressor. We use Kokkos because it provides abstractions for both parallel execution of code and data management, which can be used to support portable implementation across different accelerator technologies. Kokkos can support OpenMP/OpenMPTarget, oneAPI, Pthreads, and CUDA as backend programming models.

ECP↗
DOE OSTI2023

system2atoms

system2atoms is a suite of models that can simulate the performance of hydrogen infrastructure across multiple scales. Examples of the different scale levels include: 1. System Scale - techno-economic analysis for hydrogen applications, integration of hydrogen infrastructure with the electrical grid, and supply and demand constraints across applications 2. Component Scale - continuous-flow catalyst beds, electrochemical conversion units, gas-phase separators, pumps, compressors, storage tanks, evaporators, heaters, and coolers 3. Material Scale - surface reactions, degradation mechanisms, and catalyst poisoning

Mcnenly, MatthewJ↗
DOE OSTI2024

H2Integrate [SWR-23-31]

H2Integrate(H2I) is an open-source Python package for hybrid systems engineering design and technoeconomic analysis. It models and optimizes hybrid energy plants that produce electricity, hydrogen, ammonia, steel, and other products. H2Integrate is designed to be flexible and extensible, allowing users to create their own components and models for various energy systems. The tool currently includes distributed energy generation (wind, solar, wave, tidal), battery storage, hydrogen, ammonia, methanol, and steel technologies. Other elements such as desalination systems, pipelines, compressors, and storage systems can also be included as developed by users. Some modeling capabilities in H2Integrate are provided by integrating existing tools, such as HOPP, PySAM, ORBIT, and ProFAST. The H2Integrate tool is built on top of NASA's OpenMDAO framework, which provides a powerful and flexible environment for modeling and optimization.

King, Jennifer [National Renewable Energy Laborato↗
DOE OSTI2023

Enabling off-highway diesel engine downsizing and performance improvement using electrically assisted turbocharging

Internal combustion engine (ICE) downsizing through various turbocharging configurations is generally known by the powertrain design community as an effective means to reduce frictional losses, increase waste heat recovery, and improve fuel efficiency while increasing engine power density. However, often is the case that turbocharging strategies, including variable geometry turbochargers, and regulated two-stage turbochargers, incur the performance tradeoff between transient response and fuel economy (pumping losses) at high engine speeds. For off-highway vehicles having particularly transient and high-powered duty cycles, efforts to improve this tradeoff and increase operational flexibility have turned to evaluating various electrified air system architectures. In this study, a 4.5 L diesel-ICE configured with an electrically driven compressor (eBooster ® ) is placed in series with a conventional turbocharger and integrated into a 48 V mild-hybrid powertrain architecture. The objective of this powertrain configuration is to enable engine downsizing by 34%, replacing the current 6.8 L ICE platform with the hybridized 4.5 L ICE concept. The commercial 1-D simulation software GT-SUITE is used for powertrain system development and system optimization. Development and validation of the GT-SUITE model and air system controls is concurrently supported through experimental data collection. The simulation model development includes using machine learning methods for optimizing exhaust gas dilution, injection timing, and eBooster ® power to improve steady-state and transient brake-specific fuel consumption while minimizing criteria pollutant emissions. It was found that total specific fluid consumption over the standardized non-road transient engine duty cycle could be reduced by 18% over the current 6.8 L engine by using an optimized eBoosted 4.5 L engine. The hybridized 4.5 L engine concept concurrently showed sufficient transient response capability and nearly an order of magnitude reduction in duty cycle total soot production.

25 ENERGY STORAGE↗
DOE OSTI2023

Physically-based control-oriented modeling for turbocharged stoichiometric spark-ignited engine with cooled EGR and flexible VVT systems

Accurate estimation and prediction of engine gas exchange system and in-cylinder conditions are critical for spark-ignited engine control and diagnostic algorithm development. In this paper, a physically-based, control-oriented model for a 2.8 l turbocharged, variable valve timing (VVT) and low pressure (LP) exhaust gas recirculation (EGR)-utilizing SI engine was developed. The model includes the impact of modulation to any combination of 10 actuators, including the throttle valve, compressor bypass valve, fueling rate, waste-gate, LP EGR valve, number of deactivated cylinders, intake valve open (IVO) timing, intake valve close (IVC) timing, exhaust valve open (EVO) timing and exhaust valve close (EVC) timing. The accuracy of the model in capturing engine dynamics was demonstrated by validating it against high-fidelity engine GT-Power simulation results for various drive cycles, particularly emphasizing elevated loads. In comparison to the open literature, novel contributions of the effort described in this paper includes in-cylinder gas composition modeling and turbine-out pressure estimation.

Zhang, Xu↗