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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

An Innovative High Throughput Genome Releaser for Rapid and Efficient PCR Screening

High-throughput PCR screening is vital in synthetic biology and metabolic engineering as it allows researchers to rapidly analyze and detect numerous targeted genetic mutation in the genome. Current challenges for high-throughput PCR screening in synthetic biology include efficiently preparing genomic DNA, optimizing protocols for diverse sample types, managing contamination risks, and effectively analyzing the large volumes of data generated while ensuring consistent and accurate results. In this study, we present the development of a High Throughput Genome Releaser (HTGR), an innovative device addressing common challenges in screening PCR. This genome DNA releaser is designed based on a squash method for rapid, cost-effective, and efficient DNA release, optimized for subsequent PCR reactions. After experimenting with various synthetic materials, we selected a plastic that closely replicates the smooth surface and compression properties of microscope slides, ensuring reliable performance. We engineered a device featuring a 96-Well Plate and a shear applicator, operable both manually and automatically, and compatible with standard liquid-handling robot platform. This compatibility enhances ease of use in high-throughput PCR workflows. Additionally, we developed software to support its automatic functions. Our results demonstrated that the specially engineered 96-Well Plate and HTGR can effectively squash fungal spores , which release enough genome DNA for PCR screening. The genome releaser facilitates the preparation of PCR-amplifiable genomic DNA substrate from 96 samples within minutes, eliminates the need for extraction buffers, and is adaptable to a wide range of microorganisms and cells, which could significantly advance biomanufacturing processes.

Yuan, Guoliang [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2016

System and method for design and optimization of grid connected photovoltaic power plant with multiple photovoltaic module technologies

A system and method of using one or more DC-DC/DC-AC converters and/or alternative devices allows strings of multiple module technologies to coexist within the same PV power plant. A computing (optimization) framework estimates the percentage allocation of PV power plant capacity to selected PV module technologies. The framework and its supporting components considers irradiation, temperature, spectral profiles, cost and other practical constraints to achieve the lowest levelized cost of electricity, maximum output and minimum system cost. The system and method can function using any device enabling distributed maximum power point tracking at the module, string or combiner level.

14 SOLAR ENERGY↗
DOE OSTI2020

Stochastic Analysis for Long Term Capital Structures, Systems, and Components Refurbishment and Replacement

As commercial Nuclear Power Plants (NPPs) pursue extended plant operation in the form of Second License Renewal (SLR), opportunities exist for these plants to provide capital investments to ensure long-term safe and economic performance. At the current time, several utilities have announced an intention to pursue extended operation for one or more of their NPPs via SLR . The goal of this research is to develop a risk-informed approach to evaluate and prioritize plant capital investments made in preparation for, and during the period of, extended plant operations to support decisions for NPP operations. Since the capital investments are influenced by various factors, such as markets, safety and regulatory, the decision-making process of NPP operations should take into account relevant factors for balancing risks, costs and profits. The traditional method of capital budgeting is based on the priority list of candidate projects using economic measures such as benefit-investment ratio, net present value (NPV) and internal rate of return. In the literatures, the problem of capital budgeting or the variant can be represented by an appropriate knapsack problem. The knapsack approach to capital budgeting takes as input as investment, along with the cost and profit of each project. The objective of capital budgeting is to find the combination of the binary decisions for every investment such that the overall profit is as large as possible. The output is a collection of projects to be carried out, and we refer this selected collection of projects as a project portfolio. One limitation of traditional optimization models for capital budgeting is that they do not account for risk/uncertainty in profit and cost streams associated with individual projects, they do not account for risk in resource availability in future years [1,2,3]. Projects can incur cost over-runs, especially when projects are large, performed infrequently, and when there is risk regarding technical viability, external contractors, and/or suppliers of requisite parts and materials. Occasionally, projects are performed ahead of schedule and with cost savings. Planned budgets for capital improvements can be cut and key personnel may be lost. Or, there may be surprise windfalls in budgets for maintenance activities due to decreased costs for “unplanned” maintenance. In these cases, how should we resolve capital budgeting when we have risk forecasts for costs, profits and budgets? One approach we proposed in this summary is to re-solve the optimization models based on assumed statistical distributions of given parameters. If these distributions were not available, a two-stage stochastic optimization approach can be used to provide priority lists to decision-makers to support better risk-informed decisions [4, 5]. In this summary, we will only focus on the first approach.

42 ENGINEERING↗
DOE OSTI2022

Support Vector Machines for Classification of Direct Energy Deposition Standoff Distance for Improved Process Control

A critical factor in the implementation of direct energy deposition is the ability to maintain the standoff distance between the nozzle and the build surface, as this influences powder capture efficiency and overall part quality. Due to process-related variations, layer height may vary, causing unintended variation in standoff distance and poor build quality. While prior work has utilized contact probing to qualify standoff distance during processing, in situ methods for qualification of standoff distance are of major interest. The present work seeks to understand efficacy of image-based methods for classifying standoff distance variation in real-time using support vector machines (SVMs). It was hypothesized that the size of the melt pool and the amount of spatter will have significant correlations with deviations in the standoff distance; thus, SVMs were used on a dataset that is comprised of morphological features of melt pool size and image entropy. The SVM model was used to classify melt pool images into categories according to standoff distance variation from nominal. K-folds cross validation was used to find the optimal hyperparameters for the SVM model. To understand the impact of the selected features on the classification performance and inference speed, multiple models were trained with differing numbers of included features. Results for classification score, inference time, and image preprocessing/feature extraction from these data are reported. The present results show that the SVM model was able to predict the standoff distance classification with an accuracy of 97 percent and a speed of 0.122 s per image, making it a viable solution for real-time control of standoff distance.

Klesmith, Zoe↗
DOE OSTI2015

SU-E-T-213: Comparison of Treatment Efficiency of Gamma Knife SRS Plans for Brain Metastases with Different Planning Methods

Purpose: To improve Gamma Knife SRS treatment efficiency for brain metastases and compare the differences of treatment time and radiobiological effects between two different planning methods of automatic filling and manual placement of shots with inverse planning. Methods: T1-weighted MRI images with gadolinium contrast from five patients with a single brain metastatic-lesion were used in this retrospective study. Among them, two were from primary breast cancer, two from primary melanoma cancer and one from primary prostate cancer. For each patient, two plans were generated in Leksell GammaPlan10.1.1 for radiosurgical treatment with a Leksell GammaKnife Perfexion machine: one with automatic filling, automatic sector configuration and inverse optimization (Method1); and the other with manual placement of shots, manual setup of collimator sizes, manual setup of sector blocking and inverse optimization (Method2). Dosimetric quality of the plans was evaluated with parameters of Coverage, Selectivity, Gradient-Index and DVH. Beam-on Time, Number-of-Shots and Tumor Control Probability(TCP) were compared for the two plans while keeping their dosimetric quality very similar. Relative reduction of Beam-on Time and Number-of-Shots were calculated as the ratios among the two plans and used for quantitative analysis. Results: With very similar dosimetric and radiobiological plan quality, plans created with Method 2 had significantly reduced treatment time. Relative reduction of Beam-on Time ranged from 20% to 51 % (median:29%,p=0.001), and reduction of Number-of-Shots ranged from 5% to 67% (median:40%,p=0.0002), respectively. Time of plan creation for Method1 and Method2 was similar, approximately 20 minutes, excluding the time for tumor delineation. TCP calculated for the tumors from differential DVHs did not show significant difference between the two plans (p=0.35). Conclusion: The method of manual setup combined with inverse optimization in LGP for treatment of brain metastatic lesions with the Perfexion can achieve significantly higher time efficiency without degrading treatment quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Microbial production of high octane and high sensitivity olefinic ester biofuels

Abstract Background Advanced spark ignition engines require high performance fuels with improved resistance to autoignition. Biologically derived olefinic alcohols have arisen as promising blendstock candidates due to favorable octane numbers and synergistic blending characteristics. However, production and downstream separation of these alcohols are limited by their intrinsic toxicity and high aqueous solubility, respectively. Bioproduction of carboxylate esters of alcohols can improve partitioning and reduce toxicity, but in practice has been limited to saturated esters with characteristically low octane sensitivity. If olefinic esters retain the synergistic blending characteristics of their alcohol counterparts, they could improve the bioblendstock combustion performance while also retaining the production advantages of the ester moiety. Results Optimization of Escherichia coli isoprenoid pathways has led to high titers of isoprenol and prenol, which are not only excellent standalone biofuel and blend candidates, but also novel targets for esterification. Here, a selection of olefinic esters enhanced blendstock performance according to their degree of unsaturation and branching. E. coli strains harboring optimized mevalonate pathways, thioester pathways, and heterologous alcohol acyltransferases (ATF1, ATF2, and SAAT) were engineered for the bioproduction of four novel olefinic esters. Although prenyl and isoprenyl lactate titers were limited to 1.48 ± 0.41 mg/L and 5.57 ± 1.36 mg/L, strains engineered for prenyl and isoprenyl acetate attained titers of 176.3 ± 16.0 mg/L and 3.08 ± 0.27 g/L, respectively. Furthermore, prenyl acetate (20% bRON = 125.8) and isoprenyl acetate (20% bRON = 108.4) exhibited blend properties comparable to ethanol and significantly better than any saturated ester. By further scaling cultures to a 2-L bioreactor under fed-batch conditions, 15.0 ± 0.9 g/L isoprenyl acetate was achieved on minimal medium. Metabolic engineering of acetate pathway flux further improved titer to attain an unprecedented 28.0 ± 1.0 g/L isoprenyl acetate, accounting for 75.7% theoretical yield from glucose. Conclusion Our study demonstrated novel bioproduction of four isoprenoid oxygenates for fuel blending. Our optimized E. coli production strain generated an unprecedented titer of isoprenyl acetate and when paired with its favorable blend properties, may enable rapid scale-up of olefinic alcohol esters for use as a fuel blend additive or as a precursor for longer-chain biofuels and biochemicals.

09 BIOMASS FUELS↗
DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗
DOE OSTI2016

Evaluating structure selection in the hydrothermal growth of FeS 2 pyrite and marcasite

While the ab initio prediction of the properties of solids and their optimization towards new proposed materials is becoming established, little predictive theory exists as to which metastable materials can be made and how, impeding their experimental realization. Here we propose a quasi-thermodynamic framework for predicting the hydrothermal synthetic accessibility of metastable materials and apply this model to understanding the phase selection between the pyrite and marcasite polymorphs of FeS 2 . We demonstrate that phase selection in this system can be explained by the surface stability of the two phases as a function of ambient pH within nano-size regimes relevant to nucleation. This result suggests that a first-principles understanding of nano-size phase stability in realistic synthesis environments can serve to explain or predict the synthetic accessibility of structural polymorphs, providing a guideline to experimental synthesis via efficient computational materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2018

The Influence of Local Distortions on Proton Mobility in Acceptor Doped Perovskites

Optimizing proton conduction in solids remains the most promising solution for achieving intermediate temperature (~750–1000 K) solid oxide fuel cell devices, and enabling selective membranes for H 2 separation. Proton conduction, a thermally activated process, exhibits its highest rates in yttrium (Y) acceptor doped BaZrO 3 at an optimal doping level of 20% Y. The presence of extended defects such as grain boundaries has typically generated a wide variability in reported conductivity values. This has hindered a fundamental mechanistic understanding of how (acceptor) doping levels correlate with the activation energy of protons to produce an optimal doping level for fast proton transport. While isolated dopants have been suggested as the primary source of proton trapping, our results indicate that it is the local dopant-density that matters. Here in this work, we show that increasing the local dopant density promotes localized lattice distortions in the presence of point defects such as oxygen-vacancies or proton interstitials. An increasing distortion amplitude traps the point defects more strongly in the form of polarons, forming defect-clusters at higher concentrations. This leads to a monotonic increase in the activation energy (and hence a decrease in proton mobility) as observed in our measurements. The optimum doping level can now be explained as a competition between increasing proton concentration with doping levels and increasing activation energy due to defect-clusters formed by defect-polarons. Based on our findings, we demonstrate how to improve proton conductivity in doped BaZrO 3 , by inhibiting this dopant-lattice polaronic interaction. Finally, this approach should be generally applicable for ionic conduction in perovskite oxides such as oxygen-ion conduction in solid-oxide fuel cells and alkali-ion conduction in solid-state batteries where carriers might get trapped as defect-polarons.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-E-T-07: 4DCT Robust Optimization for Esophageal Cancer Using Intensity Modulated Proton Therapy

Purpose: To develop a 4DCT robust optimization method to reduce the dosimetric impact from respiratory motion in intensity modulated proton therapy (IMPT) for esophageal cancer. Methods: Four esophageal cancer patients were selected for this study. The different phases of CT from a set of 4DCT were incorporated into the worst-case dose distribution robust optimization algorithm. 4DCT robust treatment plans were designed and compared with the conventional non-robust plans. Result doses were calculated on the average and maximum inhale/exhale phases of 4DCT. Dose volume histogram (DVH) band graphic and ΔD95%, ΔD98%, ΔD5%, ΔD2% of CTV between different phases were used to evaluate the robustness of the plans. Results: Compare to the IMPT plans optimized using conventional methods, the 4DCT robust IMPT plans can achieve the same quality in nominal cases, while yield a better robustness to breathing motion. The mean ΔD95%, ΔD98%, ΔD5% and ΔD2% of CTV are 6%, 3.2%, 0.9% and 1% for the robustly optimized plans vs. 16.2%, 11.8%, 1.6% and 3.3% from the conventional non-robust plans. Conclusion: A 4DCT robust optimization method was proposed for esophageal cancer using IMPT. We demonstrate that the 4DCT robust optimization can mitigate the dose deviation caused by the diaphragm motion.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Advanced Control Architecture and Sensor Information Development for Process Automation, Optimization, and Imaging of Chemical Looping Systems

The Syngas Chemical Looping (SCL) process developed at The Ohio State University (OSU) uses an iron-based oxygen carrier to convert syngas into high-purity hydrogen while simultaneously capturing carbon dioxide that is readily sequestrable. A 25 kWth sub-pilot unit was successfully demonstrated multiple times on the OSU Clean Coal Research Facility, and a 250 kWth – 3 MWth pilot demonstration unit has been designed and constructed at the National Carbon Capture Center (NCCC) in Wilsonville, Alabama. The focus of this project is to develop multiple technologies to automate the startup and shutdown of chemical looping system. Success of this project will propel the advanced carbon capture method toward commercialization, hence be disruptive to the fossil fuel and power industries. Advanced process automation control architecture and imaging and optimization sensor software were developed for OSU chemical looping process. A high-level controller (HLC) consisting of decision-making and controller-selection logic integrated with sliding mode controllers (SMCs) were developed to perform chemical looping process startup and shutdown. Babcock & Wilcox Power Generation Group’s FocalPoint Optimization System was implemented to optimize the process operating conditions. The process automation controller and optimization information were realized and tested on OSU’s existing sub-pilot chemical looping test unit under atmospheric condition. The implemented control architecture was able to perform the complete process procedure from startup, steady-state reaction operation to shutdown with minimal operator intervention. The controller was also implemented on the pressurized syngas chemical looping (SCL) pilot test unit constructed at the National Carbon Capture Center (NCCC), demonstrating its pressure control capability with a designed transient state trajectory. An electrical capacitance volume tomography (ECVT) sensor device and its software were developed to image the packed moving bed and slugging fluidized bed of oxygen carriers in chemical looping systems at elevated temperatures. The ECVT device constructed was capable of tracking the subtle voidage variance along the moving bed axis as a feature for directly measuring the oxygen carrier particles’ linear velocity. Also, the high temperature ECVT device was used to study the fluidization characteristics of Geldart Group D particles.

08 HYDROGEN↗
DOE OSTI2023

Optimal adjustment sets for causal query estimation in partially observed biomolecular networks

Abstract Causal query estimation in biomolecular networks commonly selects a ‘valid adjustment set’, i.e. a subset of network variables that eliminates the bias of the estimator. A same query may have multiple valid adjustment sets, each with a different variance. When networks are partially observed, current methods use graph-based criteria to find an adjustment set that minimizes asymptotic variance. Unfortunately, many models that share the same graph topology, and therefore same functional dependencies, may differ in the processes that generate the observational data. In these cases, the topology-based criteria fail to distinguish the variances of the adjustment sets. This deficiency can lead to sub-optimal adjustment sets, and to miss-characterization of the effect of the intervention. We propose an approach for deriving ‘optimal adjustment sets’ that takes into account the nature of the data, bias and finite-sample variance of the estimator, and cost. It empirically learns the data generating processes from historical experimental data, and characterizes the properties of the estimators by simulation. We demonstrate the utility of the proposed approach in four biomolecular Case studies with different topologies and different data generation processes. The implementation and reproducible Case studies are at https://github.com/srtaheri/OptimalAdjustmentSet.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations

Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Here, our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerates the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE, is applied to chaotic systems such as the Kuramoto-Sivashinsky equation, the two-dimensional Kolmogorov flow, and ERA5 reanalysis data for the atmosphere. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics.

Chaotic dynamical systems↗
DOE OSTI2024

Multivariate Bayesian Optimization of CoO Nanoparticles for CO 2 Hydrogenation Catalysis

The hydrogenation of CO 2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO 2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO 2 and assessed for thermocatalytic CO 2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO 2 and a conventionally prepared catalyst. The optimized CoO/SiO 2 catalyst consistently exhibited higher activity and CH 4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Cost-Optimal Pathways to 75% Fuel Reduction in Remote Alaskan Villages: Preprint

There are thousands of isolated, diesel-powered microgrids that deliver energy to remote communities around the world at very high energy costs. The Remote Communities Renewable Energy program aims to help these communities reduce their fuel consumption and lower their energy costs through the use of high penetration renewable energy. As part of this program, the REopt modeling platform for energy system integration and optimization was used to analyze cost-optimal pathways toward achieving a combined 75% reduction in diesel fuel and fuel oil consumption in a select Alaskan village. In addition to the existing diesel generator and fuel oil heating technologies, the model was able to select from among wind, battery storage, and dispatchable electric heaters to meet the electrical and thermal loads. The model results indicate that while 75% fuel reduction appears to be technically feasible it may not be economically viable at this time. When the fuel reduction target was relaxed, the results indicate that by installing high-penetration renewable energy, the community could lower their energy costs by 21% while still reducing their fuel consumption by 54%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building

This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Low Global Warming Potential Refrigerants for Commercial Refrigeration Systems

Supermarket refrigeration systems account for approximately 50% of supermarket energy use, placing this class of equipment among the highest energy consumers in the commercial building domain. In addition, the commonly used refrigeration system in supermarket applications is the multiplex direct expansion (DX) system, which is prone to refrigerant leaks due to its long lengths of refrigerant piping. This leakage reduces the efficiency of the system and increases the impact of the system on the environment. The high Global Warming Potential (GWP) of the hydrofluorocarbon (HFC) refrigerants commonly used in these systems, coupled with the large refrigerant charge and the high refrigerant leakage rates leads to significant direct emissions of greenhouse gases into the atmosphere. Environmental concerns are driving regulations for the heating, ventilating, air-conditioning and refrigeration (HVAC&R) industry towards lower GWP alternatives to HFC refrigerants. Existing lower GWP refrigerant alternatives include hydrocarbons, such as propane (R-290) and isobutane (R-600a), as well as carbon dioxide (R-744), ammonia (R-717), and R-32. In addition, new lower GWP refrigerant alternatives are currently being developed by refrigerant manufacturers, including hydrofluoro-olefin (HFO) and unsaturated hydrochlorofluorocarbon (HCFO) refrigerants. The selection of an appropriate refrigerant for a given refrigeration application should be based on several factors, including the GWP of the refrigerant, the energy consumption of the refrigeration system over its operating lifetime, and leakage of refrigerant over the system lifetime. For example, focusing on energy efficiency alone may overlook the significant environmental impact of refrigerant leakage; while focusing on GWP alone might result in lower efficiency systems that result in higher indirect impact over the equipment lifetime. Thus, the objective of this Collaborative Research and Development Agreement (CRADA) between Honeywell and the Oak Ridge National Laboratory (ORNL) is to develop a Life Cycle Climate Performance (LCCP) modeling tool for optimally designing HVAC&R equipment with lower life cycle greenhouse gas emissions, and the selection of alternative working fluids that reduce the greenhouse gas emissions of HVAC&R equipment. In addition, an experimental evaluation program is used to measure the coefficient of performance (COP) and refrigerating capacity of various refrigerant candidates, which have differing GWP values, in commercial refrigeration equipment. Through a cooperative effort between industry and government, alternative working fluids will be chosen based on maximum reduction in greenhouse gases at minimal cost impact to the consumer. This project will ultimately result in advancing the goals of reducing greenhouse gas emissions through the use of low GWP working fluids and technologies for HVAC&R and appliance equipment, resulting in cost-competitive products and systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

An experimental investigation on the characteristics of heat pipes with annular type composite wick structure

Here, an experimental investigation has been conducted on the hydraulic characteristics of annular type wick structures for heat pipes. An experimental facility which can measure porosity, permeability, and effective pore radius of the wick structures in a vacuum condition is established. Nine different types of multi-layered (6 layers in total) composite screen meshes are characterized. Based on the measurement results, a wick structure composed of one layer of 100×100 mesh, three layers of 400×400 mesh, and two layers of 60×60 mesh is determined to have the highest permeability to effective pore radius ratio $(K/r_{eff})$ and was selected as a targeted sample. The wick-to-wall gap effect on the hydraulic characteristics of annular type wick structure is also investigated by measuring the permeability and effective pore radius of the sample wick structure with varying gap widths. The result shows that the permeability increases as the gap increases from 0 mm to 1.2 mm. After a peak at 1.2 mm, the permeability decreases as the gap increases and converges to the value of the case measured without a wall structure. The effective pore radius becomes smaller as the gap increases, making a peak at a gap distance of 1.2 mm. This result implies that there is an optimal point in gap distance which is determined to be 1.2 mm for the selected composite mesh structure. A capillary limitation correlation with a multiplying factor which explains the enhanced performance of the wick due to the gap is suggested. An annular wick type heat pipe is constructed and tested. The capillary limitation of the heat pipe showed good agreement with the suggested correlation.

42 ENGINEERING↗