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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Thermal stability and magnetization switching of composite free layer with perpendicular magnetic anisotropy

We present a micromagnetic study of a ferromagnetically exchange-coupled composite free layer design for perpendicular magnetic tunnel junctions. The thermal stability factor and critical switching current density depend on the exchange coupling strength and effective anisotropy ratio between the sublayers of the composite free layer. The energy barrier increases with an increase of the exchange coupling strength until it reaches a plateau in the strong-coupling limit. An optimal exchange coupling strength exists that leads to the minimal critical switching current and a significant improvement of the figure of merit defined as the ratio between the energy barrier and critical switching current. This behavior is obtained for smaller device sizes with uniform reversal and larger sizes with domain wall reversal.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Tritium Breeding Ratio Evaluation of Solid Breeder Concepts for the FESS-FNSF

This paper presents a parametric study of the Fusion Energy System Studies-Fusion Nuclear Science Facility’s (FNSF’s) tritium breeding performance for several solid breeder concepts, neutron multiplying materials, and blanket materials, assuming volume fractions based on the most recent FNSF design as a realistically representative fusion facility. In this study, we initially surveyed the tritium breeding ratio (TBR) of several solid breeder concepts by employing a simplified but efficient one-dimensional (1-D) infinite cylinder reduced-order model (ROM). Parametric studies were performed with the ROMs for the full range of breeder-to-multiplier ratios to identify the optimum mixture compositions for each breeder type that would lead to a maximum TBR. These optimized breeder-multiplier combinations were then homogenized with FNSF blanket component materials to estimate their impacts on the TBR. Subsequently, as a validation step for the optimal designs, TBR calculations were performed using a more realistic modified 1-D ROM with inner and outer breeding regions, as well as with a fully detailed 22.5-deg three-dimensional (3-D) sector of the FNSF to assess the impact of geometry details on the TBR. The differences between the two 1-D models were negligible, while the ROMs were able to correctly predict trends and identify the maximum and minimum TBR cases, as well as show consistent biases relative to the results produced by the full 3-D, 22.5-deg sector for specific breeder/multiplier combinations. Solid breeder concepts such as Li 2 O, Li 4 SiO 4 , and Li 8 ZrO 6 outperformed all others in this study in terms of TBR performance when combined with all the neutron multiplier materials selected. Here, an underlying goal of this study was to develop and improve rapid and reliable ROMs to aid designers during parametric optimizations of highly complex and computationally expensive fusion models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Quantum simulation of second-quantized Hamiltonians in compact encoding

We describe methods for simulating general second-quantized Hamiltonians using the compact encoding, in which qubit states encode only the occupied modes in physical occupation number basis states. These methods apply to second-quantized Hamiltonians composed of a constant number of interactions, i.e., linear combinations of ladder operator monomials of fixed form. Compact encoding leads to qubit requirements that are optimal up to logarithmic factors. Here, we show how to use sparse Hamiltonian simulation methods for second-quantized Hamiltonians in compact encoding, give explicit implementations for the required oracles, and analyze the methods. We also describe several example applications including the free boson and fermion theories, the φ 4 -theory, and the massive Yukawa model, all in both equal-time and light-front quantization. Our methods provide a general-purpose tool for simulating second-quantized Hamiltonians, with optimal or near-optimal scaling with error and model parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Novel small molecule FGF 23 inhibitors increase serum phosphate and improve skeletal abnormalities in Hyp mice

We report that excess fibroblast growth factor 23 (FGF23) causes hereditary hypophosphatemic rickets, such as X-linked hypophosphatemia (XLH) and tumor induced osteomalacia (TIO). A small molecule that specifically binds to FGF23 to prevent activation of the FGFR/a-Klotho complex has potential advantages over the currently approved systemically administered FGF23 blocking antibody. Using structure-based drug design we previously identified ZINC13407541 (N-[[2-(2-phenylethenyl)cyclopenten-1-yl]methylidene]hydroxylamine) as a small molecule antagonist for FGF23. Additional structure-activity studies developed a series of ZINC13407541 analogues with enhanced drug-like properties. In this study, we tested in a pre-clinical Hyp mouse homologue of XLH a direct connect analogue (8n) [(E)-2-(4-(tert-butyl)phenyl)cyclopent-1-ene-1-carbaldehyde oxime] that exhibited the greatest stability in microsomal assays, and 13a [(E)-2-((E)-4-methylstyryl)benzaldehyde oxime] that exhibited increased in vitro potency. Using cryo-electron microscopy (Cryo-EM) structure and computational docking, we identified a key binding residue (Q156) of the FGF23 antagonists, ZINC13407541 and its analogues (8n and 13a) in the N-terminal domain of FGF23 protein. Site-directed mutagenesis and bimolecular fluorescence complementation (BiFC)-fluorescence resonance energy transfer (FRET) assay confirmed the binding site of these three antagonists. We found that pharmacological inhibition of FGF23 with either of these compounds blocked FGF23 signaling and increased serum phosphate and 1,25(OH)2D concentrations in Hyp mice. Long-term parenteral treatment with 8n or 13a also enhanced linear bone growth, increased mineralization of bone, and narrowed the growth plate in Hyp mice. The more potent 13a compound had greater therapeutic effects in Hyp mice. Further optimization of these FGF23 inhibitors may lead to versatile drugs to treat excess FGF23-mediated disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Smoothing Lexis diagrams using kernel functions: A contemporary approach

Lexis diagrams are rectangular arrays of event rates indexed by age and period. Analysis of Lexis diagrams is a cornerstone of cancer surveillance research. Typically, population-based descriptive studies analyze multiple Lexis diagrams defined by sex, tumor characteristics, race/ethnicity, geographic region, etc. Inevitably the amount of information per Lexis diminishes with increasing stratification. Several methods have been proposed to smooth observed Lexis diagrams up front to clarify salient patterns and improve summary estimates of averages, gradients, and trends. In this article, we develop a novel bivariate kernel-based smoother that incorporates two key innovations. First, for any given kernel, we calculate its singular values decomposition, and select an optimal truncation point—the number of leading singular vectors to retain—based on the bias-corrected Akaike information criterion. Second, we model-average over a panel of candidate kernels with diverse shapes and bandwidths. The truncated model averaging approach is fast, automatic, has excellent performance, and provides a variance-covariance matrix that takes model selection into account. We present an in-depth case study (invasive estrogen receptor-negative breast cancer incidence among non-Hispanic white women in the United States) and simulate operating characteristics for 20 representative cancers. The truncated model averaging approach consistently outperforms any fixed kernel. Our results support the routine use of the truncated model averaging approach in descriptive studies of cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

The past, present, and future of nuclear fuel

Abstract New reactor concepts have motivated study of a variety of nuclear fuel types. Most nuclear fuels have their origins dating back to the very beginnings of nuclear materials. We survey the most prevalent types of nuclear fuels and their properties and give some historical context as to their development. We end with our perspective on what the next 50 years of nuclear fuel research might lead to. In our opinion, while optimized microstructures and chemistries are certainly on the horizon, the biggest developments will be the continued integration of modeling and simulation with experiments to extract the greatest amount of energy possible from existing fuel candidates in a safe and economical way. Graphical abstract

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Cost-Optimized Cold Climate Heat Pump Development and Field Test

Cold climate heat pumps (CCHPs) expand the heat pump market to climates where heating demand is dominant. They can achieve more than 70% energy savings compared with electric resistance heating and operate at lower cost than using tank-stored propane to fuel a furnace. A high-efficiency heat pump with a heating seasonal performance factor (HSPF)—as defined by the Air-Conditioning, Heating, and Refrigeration Institute (AHRI 2008)—greater than 10.0 would be more efficient than gas heating in terms of source energy. However, developing a cost-effective CCHP involves some challenges. A typical single-speed, air-source heat pump (ASHP) with an HSPF of 7.7 Btu/Wh does not work well under cold outdoor temperature conditions typical of cold climate locations for four major reasons: 1. Discharge temperature is too high—The low suction pressure and high compression pressure ratio at low ambient temperatures causes high compressor discharge temperatures in excess of the maximum limit for many of the compressors on the market. Furthermore, system charge of a heat pump is usually optimized in the cooling mode, which leads to overcharge conditions in the heating mode, further increasing the discharge temperature. 2. Heating capacity is insufficient if sized to meet the building design cooling load— Heating capacity of a single-speed heat pump decreases with ambient temperature. The heating capacity at -13°F (-25°C) typically decreases to 20%–40% of the rated heating capacity at 47°F (8.3°C) (~equivalent to the rated cooling capacity at 95°F [35°C]). Therefore, a single-speed heat pump, typically sized to match the building design cooling load, cannot provide adequate heating capacity to match the building heating load at low ambient temperatures. The capacity deficit is filled by inefficient resistance heat, thus lowering the system efficiency and significantly increasing power demand. 3. Cyclic loss is significant if sized to meet the building design heating load—If a single-speed heat pump is sized to meet the heating load, it will be significantly oversized relative to the cooling load in many cold climates. This will cause excessive on/off cyclic loss during the cooling and heating operations at moderately low ambient temperatures. Thus, capacity modulation capability (e.g., using a variable-speed or multi-stage compressor) is necessary for a CCHP, which uses its full capacity to meet the peak heating load and partial capacity to meet the cooling and part-load heating loads. 4. Coefficient of performance (COP) is low—Heating COP degrades significantly at low ambient temperatures owing to the large temperature difference between the heat source and sink. A target CCHP should be sized to meet the building design heating load while minimizing the cyclic loss for the cooling and heating operations at moderate ambient temperatures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Development of Advanced Solid Sorbents for Direct Air Capture (Final Report)

RTI International is partnering with Creare, and Mohammed VI Polytechnic University (UM6P) successfully met all major technical objectives as outlined below: Identified one MOF adsorbent for DAC; Identified one amine-P-dendrimer adsorbent for DAC; Performed Computational fluid dynamics simulations of the MOF and amine-P-dendrimer adsorbents and validate them with experimental data; Selected one adsorbent for DAC; Demonstrated the scale-up of selected candidate and perform cost review; Performed a preliminary process design; Performed technology maturation plan and environmental health and safety risk assessment. After a thorough comparison between both adsorbents (MOFs and P-dendrimers) and based on their technical merits, the amine-based P-dendrimers sorbent was selected as the best performing adsorbent for DAC. In fact, the amine-based P-dendrimers have demonstrated superior performance over the MOF sorbents under relevant DAC conditions. The selected sorbent has been used in subsequent projects (DE-FE0032099 and DE-AR0001412) and has demonstrated similar superior performance with no loss of activity after long-term sorption-desorption testing (e.g., 100 cycles). This sorbent has demonstrated a very high and stable sorption performance, excellent kinetics, and fast regeneration at 80 °C, specifically at 75% relative humidity (RH). The incorporation of RTI’s high-performance, high-durability amine-based P-dendrimers sorbent and Creare’s hybrid additive manufacturing (H-AM) technology that will produce high performance, compact heat and mass exchange structures at low cost using methods, will lead to a contactor that is optimized for wind-driven operation. This contactor technology provides a technically viable pathway for reducing the cost of DAC to <$100/tonne of CO 2 .

36 MATERIALS SCIENCE↗
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 OSTI2023

Integrated Electrocoagulation, Ultrafiltration, Membrane Distillation, and Crystallization for Treating Produced Water

Produced water (PW) generated from hydraulic fracturing operations was treated using an integrated electrocoagulation, ultrafiltration, membrane distillation, and crystallization process (EC UF MDC). The aim was to determine the viability of this integrated process for maximizing water recovery. The results obtained here indicate that optimizing the various unit operations could lead to increased recovery of PW. Membrane fouling limits all membrane separation processes. A pretreatment step to suppress fouling is essential. Here, removal of total suspended solids (TSS) and total organic carbon (TOC) was achieved by electrocoagulation (EC) followed by ultrafiltration (UF). The hydrophobic membrane used in membrane distillation may be fouled by dissolved organic compounds. Reducing membrane fouling is essential to increase the long-term durability of the membrane distillation (MD) system. In addition, combining membrane distillation with crystallization (MDC) can help reduce scale formation. By inducing crystallization in the feed tank, scale formation on the MD membrane was suppressed. The integrated EC UF MDC process can impact Water Resources/Oil & Gas Companies. Conservation of surface and groundwater is possible by treating and reusing PW. Additionally, treating PW reduces the amount of PW disposed in Class II disposal wells and promotes more environmentally sustainable operations.

42 ENGINEERING↗
DOE OSTI2022

Commercial Building Sensors and Controls Systems: Barriers, Drivers, and Costs

Building sensors and controls systems, including building automation systems, consists of the sensor-based devices installed in buildings and the control and automation of those devices. Optimized sensors and controls systems could lead to 29% annual energy savings in commercial buildings and are integral to the growth of grid-interactive efficient buildings. Only 8% of small commercial buildings, however, have installed sensors and controls systems. This is largely due to cost barriers. This work seeks to increase the transparency of system costs and identify specific barriers and drivers for increased adoption. To gather industry insights, the team reached out to building owners, vendors, and contractors and conducted 20 interviews with the goal of collecting cost data and market characteristics regarding building sensors and controls. The qualitative interview data was analyzed using grounded theory to identify overarching concepts, such as barriers, drivers, and future directions on the field. From this analysis, primary barriers were found to be complexity, a lack of knowledge, and money. Primary drivers were found to be the knowledge of data, savings, and remote access. The qualitative cost data was collected in the form of invoices during the interviews. The cost values were used to develop a percentage-based cost stack which identifies the average fraction of the total cost attributed to each category (hardware, software, labor, fees, and taxes). This greater understanding of costs and barriers associated with commercial building sensors and controls systems lays the groundwork for future steps in increasing system adoption, reducing energy consumption, and market transformation.

advanced building controls↗
DOE OSTI2023

Commercial Building Sensors and Controls Systems: Barriers and Drivers: Preprint

Building sensors and controls systems, including building automation systems, comprise the sensor-based devices installed in buildings as well as the control and automation of those devices. Optimized sensors and controls systems could lead to 29% annual energy savings in commercial buildings and are integral to the growth of grid-interactive efficient buildings. Only 13% of small commercial buildings, however, have installed sensors and controls systems, largely because of cost barriers. To accelerate adoption, this work seeks to increase the transparency of system costs and identify specific barriers and drivers. To gather industry insights, the team reached out to building owners, vendors, and contractors and conducted 21 interviews with the goal of collecting cost data and market characteristics regarding building sensors and controls. We collected the cost data in the form of invoices and used it to develop a percentage-based cost category breakdown. The interview data were analyzed using grounded theory to identify overarching concepts such as barriers, drivers, and future directions. From this analysis, we found the primary barriers to be complex and confusing systems, lack of user skills, and financial concerns, and the primary drivers to be operational benefits, insight into operations, and remote access to data. The future directions analysis highlighted the potential technological solutions to address gaps and barriers, as well as predicted drivers to increase adoption. This greater understanding of the costs, barriers, and drivers associated with commercial building sensors and controls systems lays the groundwork for increasing system adoption, reducing energy consumption, and transforming the market.

building automation system↗
DOE OSTI2026

ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images

Multispectral imaging (MSI) plays a critical role in material classification, environmental monitoring, and remote sensing. However, MSI sensors typically have wavelength-dependent resolution, which limits downstream analysis. MSI super-resolution (MSI-SR) methods address this limitation by reconstructing all bands at a common high spatial resolution. Existing methods can achieve high reconstruction quality but often rely on spatially-coupled optimization or large learning-based models, leading to significant computational cost and limiting their use in large-scale or time-critical settings. In this paper, we introduce ResSR, a computationally efficient, model-based MSI-SR method that achieves high-quality reconstruction without supervised training or spatially-coupled optimization. Notably, ResSR decouples spectral and spatial processing into two sequential steps. ResSR first computes a spectrally-informed high-resolution estimate of the MSI using singular value decomposition together with a spatially-decoupled approximate forward model. It then applies a residual correction step to restore low-frequency spatial consistency while preserving high-frequency detail recovered by the spectral reconstruction. ResSR achieves comparable or improved reconstruction quality relative to existing MSI-SR methods while being

Sullivan, Haley [ORNL] (ORCID:0000000274069217)↗
DOE OSTI2020

Machine learning coupled multi-scale modeling for redox flow batteries

The reaction distribution in macro or device-scale has been studied for redox flow batteries. The reaction distribution on electrode pore-scale structure however is not well understood, lacking especially on how the reaction distribution on the pore-scale may impact the overall performance of a flow battery. This study introduces for the first time a framework of a multi-scale model that provides understanding of the relationship between the pore-scale electrode structure reaction and the device-scale electrochemical reaction uniformity within the flow battery. A reduced order model is constructed based on 128 pore-scale simulations, which provide a quantitative relationship between the battery operation conditions (inlet velocity, current density, inlet concentration) and the surface reaction uniformity for the pore-scale sample. The multi-scale framework upscales this pore-scale surface reaction uniformity to device-scale combined uniformity. Based on the multi-scale model, a time-varying optimization of the inlet velocity is established, leading to significant reduction on pump power consumption with targeted surface reaction uniformity. The multi-scale model establishes the critical link between the micro-structure of a flow battery component and its performance at the macro-scale, therefore providing rationale for further operational or material optimization.

flow batteries, machine learning, multi-scale mode↗
DOE OSTI2024

Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine

The advancements in next-generation sequencing have made it possible to effectively detect somatic mutations, which has led to the development of personalized neoantigen cancer vaccines that are tailored to the unique variants found in a patient’s cancer. These vaccines can provide significant clinical benefit by leveraging the patient’s immune response to eliminate malignant cells. However, determining the optimal vaccine dose for each patient is a challenge due to the heterogeneity of tumors. To address this challenge, we formulate a mathematical dose optimization problem based on a previous mathematical model that encompasses the immune response cascade produced by the vaccine in a patient. We propose an optimization approach to identify the optimal personalized vaccine doses, considering a fixed vaccination schedule, while simultaneously minimizing the overall number of tumor and activated T cells. To validate our approach, we perform in silico experiments on six real-world clinical trial patients with advanced melanoma. We compare the results of applying an optimal vaccine dose to those of a suboptimal dose (the dose used in the clinical trial and its deviations). Our simulations reveal that an optimal vaccine regimen of higher initial doses and lower final doses may lead to a reduction in tumor size for certain patients. Our mathematical dose optimization offers a promising approach to determining an optimal vaccine dose for each patient and improving clinical outcomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Materials Design using an Active Subspace-based Batch Bayesian Optimization Approach

Integrated computational materials engineering (ICME) calls for integrating simulation tools and/or experiments to develop new materials and materials systems. However, implementation of ICME approaches is challenging mainly due to the considerable computational expense of such frameworks and large dimensionality of the design space. Addressing these challenges is thus critical to the success of ICME initiatives. We present here a specific Bayesian optimization framework designed to address these two challenges. In particular, we propose an active subspace batch Bayesian optimization framework. The framework makes use of dimension reduction via the active subspace method and makes use of the ability to query in parallel via the batch Bayesian optimization approach. Here, the integration of these techniques leads to significant efficiency improvements while maintaining accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Optimization Modeling for Advanced Syngas to Olefin Reactive Systems

Reactor designs with mixed catalysts play an important role in transforming a multiple reactor system to single-shot reactors. In addition to savings in capital and ease of implementation, single-shot reactors are useful to break equilibrium limitations, thereby increasing the yield and selectivity of desired product as shown in previous studies. However, the nonlinear and highly exothermic nature of mixed-catalyst systems makes it difficult for commercial process simulation and optimization tools to optimize these systems. This study describes the development and application of optimization strategies for mixed-catalyst, single-shot reactors for syngas to olefin (STO) processes. Finding the optimal catalyst distribution is challenging and requires advanced solution strategies for singular optimal control problems, which are poorly conditioned and often lead to flat response surfaces. The graded bed and partial-moving finite-element approaches are used to find the optimal catalyst distribution that maximizes the olefins yield. A 1.3% increase in the yield is observed from one zone to three zones. The yield further improves from three zones to the exact infinite dimensional solution by 0.2%. This improvement can be realized in practice by changing only the catalyst distribution, without any extra investment. Lastly, the results suggest that a mixed-catalyst single shot reactor bed can be applied to other reaction mechanisms to increase reactor performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Analysis of Orthogonal Efflux and Permeation Properties of Compounds Leads to the Discovery of New Efflux Pump Inhibitors

Optimization of compound permeation into Gram-negative bacteria is one of the most challenging tasks in the development of antibacterial agents. Two permeability barriers–the passive diffusion barrier of the outer membrane (OM) and active drug efflux–act synergistically to protect cells from the antibacterial action of compounds. In Escherichia coli (E. coli) and relatives, these two barriers sieve compounds based on different physicochemical properties that are defined by their interactions with OM porins and efflux pumps, respectively. In this study, we critically tested the hypothesis that the best substrates and inhibitors of efflux pumps are compounds that can effectively permeate the OM and are available at relatively high concentrations in the periplasm. For this purpose, we filtered a large subset of the ZINC15 database of commercially available compounds for compounds containing a primary amine, a chemical feature known to facilitate the uptake through E. coli general porins. The assembled library was screened by ensemble docking to AcrA, the periplasmic component of the AcrAB-TolC efflux pump, followed by experimental testing of the top predicted binders for antibacterial activities, efflux recognition, and inhibition. In this work, we found that the filtered primary amine library is a rich source of compounds with efflux-inhibiting activities and identified efflux pump inhibitors with novel chemical scaffolds effective against E. coli AcrAB-TolC and efflux pumps of multidrug-resistant clinical isolates of Acinetobacter baumannii. However, primary amines are not required for the recognition of compounds by efflux pumps and their efflux-inhibitory activities.

60 APPLIED LIFE SCIENCES↗