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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Compton transmission polarimeter for DC and SRF electron photo-injectors

The production of high-current and intense spin polarized electron beams is of great importance in electron-based facilities. Tests are planned to produce such beams in 2023 using GaAs-based photocathodes installed in the Brookhaven National Lab RHIC Coherent electron Cooling superconducting radiofrequency (SRF) photogun [1]. A fast and efficient electron polarimeter operating in the MeV energy range is required to measure the beam spin polarization. While Mott polarimeters provide larger measured asymmetries, a Compton Transmission polarimeter is well suited in the few MeV energy range. In this work, we report on a relatively compact and cost-effective Compton transmission polarimeter which has been built and calibrated at Jefferson Lab (JLab). First, we present the design of the polarimeter radiator, polarized target analyzing magnet, BGO detector assembly and data acquisition system. Next, results of a two-week commissioning study performed at the JLab Upgraded Injector Test Facility will be described. Here, a well-known polarized electron beam produced from a bulk GaAs photocathode in a dc high-voltage photogun was first measured in a 180 keV Mott scattering polarimeter, then used to characterize and calibrate the Compton transmission polarimeter as a function of the polarized target magnetization and beam properties. Finally, we report an effective analyzing power of the Compton polarimeter and compare experimental results with those produced via Geant4 simulations.

Blume, G.↗
DOE OSTI2024

Domestication of Algae for Increasing Biomass Productivity

Microalgae cultivation processes have been developed for the production of a variety of bioproducts, however currently only a few species are used in commercial applications. Their domestication, that is strain improvements, is still in its infancy, with major advances required, specifically to maximize biomass productivity a limiting factor in microalgae production. This requires a deep understanding of algal biology, in particular to develop superior strains without the need of genetic technologies that would require lengthy regulatory permits, and often limit consumer acceptance. Adaptive Laboratory Evolution techniques, alone or in conjunction with sexual recombination, can allow for rapid develop of improved strains and their industrial production. Light harvesting antenna reduction has been a major approach to achieve increased photon utilization efficiency by cultures operating under full sunlight conditions due to higher light saturation levels, allowing for higher productivities under outdoor conditions. Decades of research yielded some promising results under controlled conditions with a few specific mutant strains. However, these failed to achieve the anticipated higher productivities in actual algal mass cultures, in part due to the inability of single mutations to overcome photoinhibition, reactive oxygen species, and other pleiotropic impacts on the complex metabolic processes of photosynthesis. Higher productivity strains will require multiple genetic improvements. We report on recent Adaptive Laboratory Evolution with the green alga Scenedesmus obliquus resulting in higher biomass productivity in open pond cultivation. Coupling our approach with sexual recombination and genome sequencing provides a path to algal domestication suitable for large-scale, low-cost biomass production.

09 BIOMASS FUELS↗
DOE OSTI2024

Enabling Ultralow Volume Analysis with a High-Resolution Ion Mobility Mass Spectrometry Platform

Of all the molecules thought to exist in the universe, it is estimated that researchers only know the chemical structures of 5% of them. Identifying the chemical structures of the remaining 95% has proven extremely challenging because many molecules exhibit low abundance, are contained in small volumes (e.g., <10 nL), do not readily ionize, exhibit similar structures to other molecules, etc. No single analytical technique exists to definitively identify the structure of an unknown molecule, and thus multiple different molecular measurements are typically made (i.e., multi-modal approach). Ion mobility (IMS) and mass spectrometry (MS) are two key tools that researchers use to determine the chemical structures of unknown molecules, and recently high-resolution and ultrahigh resolution IMS-MS instruments have provided greater confidence than ever before. However, HR-IMS-MS instruments typically exhibit low ion utilization efficiency, meaning they require large amounts of sample for an analysis (e.g., >10 µL). Unfortunately, this limitation prohibits the analysis of small volume samples where many unknown molecules exist. Described herein are the efforts made to enable the analysis of ultralow volumes with an HR-IMS-MS platform. A new scanning technique, termed a ‘stuttered traveling wave scan’, was developed as a replacement for the dual-gated scanning technique and works by halting the traveling waves after allowing ions to separate and then repeatedly restarting and stopping the traveling waves to incrementally move ions from the SLIM to the Orbitrap. Ions were stored inside the SLIM while the TWs were stopped, allowing the Orbitrap to perform high-resolution mass analysis. When the Orbitrap was ready, the TWs were restarted for short periods of time (<10 ms) to move ions from the SLIM to the Orbitrap. It was discovered that lower TW amplitudes and speeds than used during IMS separation were required to produce IMS peaks with the highest signal intensities and best resolving powers. The stuttered TW scan was found to produce similar resolutions and signal intensities compared to the dual-gated scanning technique. A new IMS design possessing an intersecting ‘tee’ with a reversible traveling wave was also developed to improve ion utilization efficiency during cyclic operation, which is necessary when only a single IMS spectrum can be acquired, such as when analyzing ultralow volume samples. The new capabilities described in this report lay the groundwork for acquiring HR-IMS-MS spectra of ultralow volume biological samples, such as single cells, where HR-IMS-MS can help elucidate the structures of unknown compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

Presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗
DOE OSTI2024

Domesticating the green alga Scenedesmus obliquus

Microalgae cultivation processes exist only for a few species used in commercial applications. Their domestication is still in its infancy, with major advances required, specifically to overcome limiting factors in microalgae production. The goal is to develop superior strains without the need of genetic technologies that would require lengthy regulatory permits, and often limit consumer acceptance. Adaptive Laboratory Evolution techniques, alone or in conjunction with sexual recombination, can allow for rapid develop of improved strains and their industrial production. Approaches include increased photon utilization efficiency by cultures operating under full sunlight conditions due to higher light saturation levels, allowing for higher productivities under outdoor conditions. Decades of research yielded some promising results under controlled conditions with a few specific mutant strains. However, these failed to achieve the anticipated higher productivities in actual algal mass cultures, in part due to the inability of mutations to overcome photoinhibition, reactive oxygen species, and other pleiotropic impacts on photosynthesis.

09 BIOMASS FUELS↗
DOE OSTI2025

Nine Canyon Long-Duration Energy Storage: A Feasibility Study

The Nine Canyon Long Duration Energy Storage (LDES) Feasibility Study explores the technical and economic viability of deploying advanced energy storage technologies at Energy Northwest's (EN) Nine Canyon (9C) Wind Project site in Benton County, Washington. Supported by the Washington State Department of Commerce and the U.S. Department of Energy’s Office of Electricity under its LDES Voucher Program, the study represents a collaborative effort between EN, Pacific Northwest National Laboratory (PNNL), and ARES North America. At the core of this effort is the development of a generalized techno-economic modeling framework and evaluation tool designed to assess the value proposition of LDES projects across a variety of contexts. The modeling tool is technology-agnostic and accommodates user-defined parameters such as rated power, energy duration, round-trip efficiency, capital and operational costs, and dispatch constraints. It also integrates economic inputs, including market prices, energy revenue structures, and financing parameters to evaluate performance through key metrics. The tool provides utilities with a transparent, adaptable platform to support decision-making, investment prioritization, and portfolio planning for various storage technologies. To guide scenario design and interpretation, the study first surveyed the LDES technology landscape, including lithium-ion batteries, flow batteries, non-hydro gravity storage, and thermo-mechanical systems, comparing cost trajectories, technical performance, safety and hazards, materials sourcing and recyclability, and spatial/siting considerations. This literature-grounded review highlights technology trade-offs and reinforces the need to align technology choice with site characteristics, use cases, and project objectives. A companion chapter examines ownership structures (EN ownership, third-party ownership, shared models) and offtake options (energy marketing, capacity/energy PPAs, time-of-use PPAs, block-delivery PPAs, and tolling), where PPAs (power purchase agreements) represent contractual arrangements for buying and selling electricity. The chapter also highlights implications for risk allocation, capital access, operational control, and revenue certainty. The study also evaluates supervisory control and data acquisition (SCADA) and transmission interconnection pathways, options include upgrading the existing SCADA or deploying a dedicated LDES controller, with attention to protection schemes, data telemetry, cybersecurity, and regulatory coordination with BPA. In addition, an ARES-specific geotechnical and hydrology assessment presented in the appendix screens multiple corridors for slope stability, bearing capacity, cut-and-fill magnitude, and stormwater behavior.

25 ENERGY STORAGE↗
DOE OSTI2024

Numerical Simulation of Biogenic Fluid Catalytic Cracking (BFCC) Regenerators at Different Scales with MFIX-Exa

Catalytic Fast Pyrolysis (CFP) is a process that converts biomass into liquid intermediates suitable for transportation fuels by rapidly heating it in the presence of a catalyst, aiming to produce stable oils with reduced oxygen content. During CFP, the catalyst can become deactivated by the accumulation of coke, a carbon-rich deposit formed from the decomposition of biomass components. Unlike in petroleum refining, regenerating coked catalysts from biomass pyrolysis requires specific approaches due to the different chemical nature of the coke formed. An experimental technique, Temperature Programmed Oxidation (TPO), was used to study the de-coking process by gradually increasing temperature while monitoring the production of CO and CO2, which provides data for kinetic modeling. Utilizing data from TPO experiments, coke combustion kinetic model was developed to describe the rate of coke removal at different temperatures, allowing for simulation of regeneration processes. Then kinetic model is integrated into MFIX-Exa for the simulation of Biogenic Fluid Catalytic Cracker (BFCC) regenerator at different scales, enabling analysis of catalyst flow, temperature distribution, and regeneration efficiency under various operating conditions.

biogenic fluid catalytic cracking↗
DOE OSTI2022

Making Wireless Power Matter (CRADA Final Report)

The Resonant Link project team investigated the integration of their novel, self-resonant magnetic structure for electromagnetic coils into inductive wireless power transfer systems. Relative to conventional wireless power coils, their multi-layer self-resonant structure (the “MSRS”) reduces losses by up to 6x and lowers to BOM cost by up to 10x. These power-dense, cost-effective coils are a promising path towards the first value-add wireless power systems in a variety of applications, from phones to medical devices to kitchen appliances to mobile robots to electric vehicles. The researchers explored development of the power electronics for wireless power transfer systems using the multi-layer self-resonant structure (MSRS) concept that efficiently transmits power over air gaps for in-home applications, including through-wall power transfer, appliances built around the Ki and Qi standards, and other potential use cases This project specifically focused on the power electronics design, development, and testing for these applications. Resonant Link’s proprietary wireless power technology holds the promise of matching the performance of wired chargers in size and efficiency, especially when operated from a DC distribution grid.

42 ENGINEERING↗
DOE OSTI2023

Storing Electricity with Salt and Iron (CRADA Final Report)

As part of the Cyclotron Road program, Inlyte Energy, Inc. investigated low-cost energy storage for the electricity grid via batteries manufactured from abundant materials. Low-cost energy storage can solve the problem of the intermittency of wind and solar power, thereby allowing these sources of energy to scale more rapidly. The project team explored development of a Na-FeCl 2 battery—which had as its starting redox-active materials the abundant materials table salt (NaCl) and iron—that can operate safely and efficiently over a long lifetime, and be produced at very low-cost. The Na-FeCl 2 battery is architecturally similar to the commercialized Zebra (Na-NiCl 2 ) batteries, which have demonstrated safety, efficiency, and long lifetime. This project explored and assessed key design changes, including the use of planar Na-β’’-alumina solid electrolyte and lower temperatures (~200 °C) with polymer seals aimed at decreasing costs.

25 ENERGY STORAGE↗
DOE OSTI2023

Potential Cooling Energy Savings of Economizer Control and Artificial-Neural-Network-Based Air-Handling Unit Discharge Air Temperature Control for Commercial Building

Heating, ventilation, and air-conditioning (HVAC) systems play a significant role in building energy consumption, accounting for around 50% of total energy usage. As a result, it is essential to explore ways to conserve energy and improve HVAC system efficiency. One such solution is the use of economizer controls, which can reduce cooling energy consumption by using the free-cooling effect. However, there are various types of economizer controls available, and their effectiveness may vary depending on the specific climate conditions. To investigate the cooling energy-saving potential of economizer controls, this study employs a dry-bulb temperature-based economizer control approach. The dry-bulb temperature-based control strategy uses the outdoor air temperature as an indicator of whether free cooling can be used instead of mechanical cooling. This study also introduces an artificial neural network (ANN) prediction model to optimize the control of the HVAC system, which can lead to additional cooling energy savings. To develop the ANN prediction model, the EnergyPlus program is used for simulation modeling, and the Python programming language is employed for model development. The results show that implementing a temperature-based economizer control strategy can lead to a reduction of 7.6% in annual cooling energy consumption. Moreover, by employing an ANN-based optimal control of discharge air temperature in air-handling units, an additional 22.1% of cooling energy savings can be achieved. In conclusion, the findings of this study demonstrate that the implementation of economizer controls, especially the dry-bulb temperature-based approach, can be an effective strategy for reducing cooling energy consumption in HVAC systems. Additionally, using ANN prediction models to optimize HVAC system controls can further increase energy savings, resulting in improved energy efficiency and reduced operating costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

With the increasing digitalization of processes throughout the lifecycle of buildings, data exchanged between stakeholders and between building systems has grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent, hindering the development of applications that can be reused across buildings and limiting the scalability of innovative solutions. Semantics refers to the description of the meaning of the data in a way that can be consistently understood by applications. Recently, several competing initiatives have been developing metadata schemas and ontologies to express this semantic information for different applications in the building domain. This paper systematically reviews these schemas and conducts an analysis of five of them to evaluate their applicability to three high-value use cases for building operations: energy audits, automated fault detection and diagnostics and optimal control. The survey finds 40 schemas published in the last 10 years but but their actual use in industry is difficult to estimate. Among the five selected ontologies, several gaps are highlighted in relation to the three use cases. Recommendations for the future include better harmonization of these initiatives, more centralized repositories and search engines for these schemas as well as better industry engagement to facilitate their adoption.

Smart Building, Sematic, Metadata, Ontology, Data ↗
DOE OSTI2023

Early Fault Detection in Particle Accelerator Power Electronics Using Ensemble Learning

Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advanced experimental facility setup that mimics SNS operating conditions, the authors successfully conducted 21 early fault detection experiments, where fault precursors are introduced in the system to a degree enough to cause degradation in the waveform signals, but not enough to reach a real fault. Nine different machine learning techniques based on ensemble trees, convolutional neural networks, support vector machines, and hierarchical voting ensembles are proposed to detect the fault precursors. Although all 9 models have shown a perfect and identical performance during the training and testing phase, the performance of most models has decreased in the next test phase once they got exposed to realworld data from the 21 experiments. The hierarchical voting ensemble, which features multiple layers of diverse models, maintains a distinguished performance in early detection of the fault precursors with 95% success rate (20/21 tests), followed by adaboost and extremely randomized trees with 52% and 48% success rates, respectively. The support vector machine models were the worst with only 24% success rate (5/21 tests). The study concluded that a successful implementation of machine learning in the SNS or particle accelerator power systems would require a major upgrade in the controller and the data acquisition system to facilitate streaming and handling big data for the machine learning models. In addition, this study shows that the best performing models were diverse and based on the ensemble concept to reduce the bias and hyperparameter sensitivity of individual models.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Simulation-aided characterization of a versatile water-based condensation particle counter for atmospheric airborne research

Capturing the vertical profiles and horizontal variations of atmospheric aerosols often requires accurate airborne measurements. With the advantage of avoiding health and safety concerns related to the use of butanol or other chemicals, water-based condensation particle counters have emerged to provide measurements under various environments. However, airborne deployments are relatively rare due to the lack of instrument characterization under reduced pressure at flight altitudes. This study investigates the performance of a commercial “versatile” water-based condensation particle counter (vWCPC, model 3789, TSI, Shoreview, MN, USA) under various ambient pressure conditions (500–920 hPa) with a wide range of particle total number concentrations (1500–70 000 cm -3 ). The effect of conditioner temperature on vWCPC 3789 performance at low pressure is examined through numerical simulation and laboratory experiments. We show that the default instrument temperature setting of 30 °C for the conditioner is not suitable for airborne measurement and that the optimal conditioner temperature for low-pressure operation is 27°. Under the optimal conditioner temperature (27°), the 7 nm cut-off size is also maintained. Additionally, we show that insufficient droplet growth becomes more significant under the low-pressure operation. The counting efficiency of the vWCPC 3789 can vary up to 20 % for particles of different chemical compositions (e.g., ammonium sulfate and sucrose particles). However, such variation is independent of pressure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

BOPTEST as a Platform for Building Controls and Grid-Interactive Buildings Workforce Training

Building automation and controls are becoming increasingly complex with the emergence of Grid Integrated Efficient Buildings (GEBs) as well as new highly efficient sequences of operation and data-driven control schemes. However, there remains a significant gap in hands-on training opportunities for building operators and technicians to gain practical experience with advanced control systems in a low-risk environment. This paper presents BOPTEST (Building Optimization Performance Test) as a suitable platform for workforce training in building controls and GEB technologies. BOPTEST provides a suite of standardized building simulation test cases with a REST API, real-time control interfaces through BACnet, semantic models connecting users to building data, and built-in calculation of control metrics and performance indicators. The platform enables trainees to interact with virtual buildings using industry-standard protocols while learning how to implement and innovate control strategies. The training platform is designed to offer a structured and interactive learning experience for building engineers, helping them effectively develop, learn, and retain skills in fault identification, troubleshooting, and correction. The workflow is divided into three main phases: 1) Setup, 2) Exercise, and 3) Review, each comprising specific activities performed by either the instructor or the student. Initial pilot training sessions have yielded positive feedback from instructors and participants and demonstrates that BOPTEST effectively fills an industry need for a low-risk training resource via simulation of real building control systems, allowing trainees to gain practical experience before working in the field. The platform's ability to provide immediate performance feedback while maintaining familiar industry interfaces makes it particularly suitable for workforce development programs. This work provides a replicable model for leveraging building simulation in control education and training.

Paul, Lazlo↗
DOE OSTI2020

Transactive Electric Water Heater Agent: Design and Performance Evaluation

Electric water heaters (EWHs) are usually equipped with inbuilt thermostats to measure water temperature near the installed positions. Most of the existing EWHs have only two thermostats and often do not have water flow sensor installed. Estimation of state of heat energy (SOHE) inside the hot water tank, which is crucial for efficient control and operation of the EWH, under such imperfect system conditions is very challenging. This paper, therefore, designs a transactive EWH agent (TEWHA) to better estimate SOHE in presence of measurement errors and imperfect system knowledge. The TEWHA, built-in Python, approximates the EWH physics using limited measurements obtained from the ground truth GridLABD model. The performance of the TEWHA is validated against ground truth system for three scenarios: a) ideal condition where TEWHA has complete knowledge of the system, b) imperfect condition where TEWHA has incomplete system information, and c) noised condition where TEWHA measurements contain random errors. Finally, an optimization problem is formulated and solved to demonstrate how TEWHA could use the estimated and measured parameters for a transactive control of EWH. Index Terms—Demand flexibility

demand flexibility, distribution system, electric ↗
DOE OSTI2020

Dynamic Modelling and Performance Evaluation of a Chemisorption Heat Pump for Cold Climate

Space heating is a significant contributor to the energy consumption in residential buildings, especially in cold climates where electric-driven heat pumps lose the advantage of high efficiency and conventional gas-combustion furnaces have limited efficiency. Chemical adsorption (chemisorption) heat pumps (CSHP) have the potential for producing heating with high energy efficiency under cold operating conditions. A CSHP operates intermittently between desorption and adsorption modes, and their performance is affected by system design and transient system control. This study developed a dynamic model and used it to evaluate the daily system performance under different designs and controls, particularly the buffer tank size and the controlled minimum heating power. The simulation results revealed the effects of these parameters on the system performance and helped identify the optimal design and control for the highest coefficient of performance and heating capacity.

Yang, Zhiyao↗
DOE OSTI2020

Analysis of RF high power sources for 1MW – range, 10 MeV CW industrial accelerator.

Cobalt-60 is the dominant source of ionizing radiation for sterilizing products in the $200B/year medical device industry. Security and accessibility concerns around this radioactive material - sourced from countries including Russia and China - present national security and economic risks. There is a continued national urgency to replace such high-risk sources with low-risk alternatives such as electron or x-ray beams driven by accelerators. While e-beam sources have been in use for many years, they still represent a small portion of the market. X-ray sources are becoming economical alternatives. Meeting the processing capacity required to replace Co-60 requires a step-change in the capability, efficiency, and overall operating cost of electron accelerators to make adoption feasible. The radiofrequency (RF) power that drives accelerating cavities represents significant capital and operating costs for these accelerators. For accelerator-based systems to be a viable alternative for these applications, RF systems must be economical to manufacture, deploy, and operate.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Systems and methods for tensor scheduling

A technique for efficient scheduling of operations in a program for parallelized execution thereof using a multi-processor runtime environment having two or more processors includes constraining the type or number of loop optimization transforms that may be explored such that memory and processing capacity available for the scheduling task are not exceeded, while facilitating a tradeoff between memory locality, parallelization, and/or data communication between memory modules of the multi-processor runtime environment.

Meister, Benoit J.↗