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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Supercritical Water Oxidation (SCWO) Trade Study and 2021 Final Report

SCWO is a promising technology whose main benefits is that it is capable of completely mineralizing organic compounds in wastewater and separating inorganic salts. This means that SCWO can theoretically function as a single step water treatment system. It performs the functions of both the primary treatment system and the secondary brine drying function to achieve near 100% water recovery. It can also handle solids so no pretreatment of the feed is required. A preliminary trade study indicates that SCWO could be competitive with the ISS state of the art if its power consumption could be reduced. Thermal and mechanical energy recovery system exist that could reduce SCWO energy consumption to a level that is competitive with the state of the art. In addition, increasing the feed wastewaters organic content by including other wastes, such as feces, could produce a reactor that is thermally self-sustaining. There are 5 different types of SCWO reactors. They are; tubular, tank, impingement, transpiring wall reactors (TWR) and super critical water mixing (SCWM) reactors. Of these the best suited for NASA missions are the impingement and SCWM reactors. The impingement reactor is best suited for near term missions because it can treat the low organic content wastewaters such as are expected for initial Lunar and Mars missions and has the ability to remove precipitated inorganic solids. The SCWM reactor is best suited for longer duration CELSS type missions where high organic content wastewater is available and will resolve solids fouling and corrosion issues. In addition, these two reactors have a long NASA heritage and have been developed specifically for space flight application. Tubular reactors suffer from plugging issues which is a safety concern. Tank reactors will increase in size inversely with gravity, which will trade poorly in Lunar and Mars environments where gravity is reduced. TWR reactors require high organic content wastewater feed and the SCWM reactor is an optimized version of the TWR for NASA applications. It is recommended that in FY 2022 NASA continue to fully evaluate the ability of SCWO to produce potable water and remove inorganics as a solid byproduct. This should be done using the impingement reactor for near term missions and SCWM for longer duration missions. NASA should also develop optimized recuperative heat exchangers and pressure recovery devices to reduce power consumption and investigate inclusion of feces and other organic wastes into the wastewater model to increase its organic content and further reduce thermal energy requirements.

Supercritical Water Oxidation↗
NASA NTRSDate not supplied

Nodal Numerical Modeling of Submerged Helium Injection in a Cryogenic Propellant Tank

Subcooling of cryogenic propellant by helium injection is one of the most effective methods for suppressing bulk boiling and keeping subcooled propellant conditions for pre-launch, launch, and post-launch pressurization applications. For tank pressurization, submerged helium injection can substantially reduce helium consumption by infusing gaseous propellant into the tank ullage. This paper presents a mathematical model of the helium bubbling process in liquid oxygen to estimate the amount of oxygen vapor absorbed by the rising helium bubbles and the amount of subcooling of liquid oxygen due to evaporative heat and mass transfer. This mathematical model was incorporated in a simulation model of tank pressurization built with Generalized Fluid System Simulation Program (GFSSP), a general-purpose flow network code developed at NASA/Marshall Space Flight Center. The numerical predictions of subcooling have been compared with the experimental data of Cho et al. which investigated the propellant subcooling effect as a function of system pressure, helium injection temperature, and flowrate for a non-drained submerged injection system. The numerical predictions of helium consumption have been compared with the test data from a NASA Centaur test vehicle which included both direct and submerged injection with draining of propellants. The numerical model developed with GFSSP has been validated against two sets of experimental data and has been shown to predict both propellant subcooling and helium consumption to within 30% in most cases. The test data used for the model validation were taken in 1-g, but the mass diffusion model was developed to be applied in both 1-g and micro-g environments.

Cryogenics↗
NASA NTRSDate not supplied

The Effects of Chronic Sleep Restriction on Calorie and Macronutrient Intake

Introduction: Chronic sleep restriction (CSR) has been associated with increased calorie intake and increased consumption of fats and carbohydrates, with inconsistent changes in protein. However, the majority of studies have either been observational field studies with no sleep intervention, or laboratory-based studies where food availability may not have reflected participants’ real-world choices. We hypothesized that calorie, fat, and carbohydrate intake would increase during a week of imposed CSR compared to a week of sleep satiation (SS) among individuals living in their home environment. Methods: Twelve healthy participants (6 females) kept a fixed sleep-wake schedule, with a constant waketime, at home for four weeks (actigraphy confirmed compliance). During weeks one and three, participants maintained 9 hours in bed. During weeks two and four, participants were randomly assigned to experimental weeks of 5 and 9 hours of time-in-bed in a crossover design. Participants documented their food consumption during both experimental weeks using a picture-based meal logging application (MealLogger). Intake of calories and macronutrients were classified by two blinded evaluators. Descriptive statistics were calculated in SAS (Cary, NC). Results: Participants averaged 4.43 ± 0.33 (SD) hours of sleep per night during CSR compared to 7.42 ± 0.42 hours during SS. Participants consumed a daily average of 1812 ± 672 kilocalories, 71 ± 31 grams of total fat, 217 ± 69 grams of carbohydrates, and 84 ± 40 grams of protein during CSR, compared to 1682 ± 514 kilocalories, 68 ± 23 grams of total fat, 198 ± 61 grams of carbohydrates, and 77 ± 32 grams of protein during SS. Conclusion: Preliminary descriptive findings suggest that, on average, participants consumed more calories, from an increase in consumption of each macronutrient group, during a week of sleep restriction compared to a week of sleep satiation. Further analysis is needed to determine whether these differences are statistically different and to identify when calories were consumed in each of the experimental conditions.

chronic sleep restriction↗
NASA NTRSDate not supplied

Food Acceptability, Menu Fatigue, and Aversion During the Sirius-21 Analog Mission

Spaceflight food systems are closed systems, meaning they are restricted to the variety and quantity of food launched within resource limitations of the mission. The acceptability of the food system has been linked to caloric intake and associated nutritional benefits. ISS crew members receive substantial personal food preferences (around 25% of their total intake) but have noted in debriefs that they would prefer more food variety and preference for the length of the missions and they tire of certain foods over six months. Limits on variety and personal preference are expected to increase as missions become further from Earth. It is possible that menu fatigue leads to decreases in acceptability and increased aversion to available foods that may contribute to the body mass loss often experienced by spaceflight crews. However, the impact of repeat food consumption on acceptability has not yet been systematically investigated across potential spaceflight food systems. Limited variety and crew preferences within food categories may have more severe physical and behavioral health and performance consequences as mission duration increases. Characterizing the relationship between food acceptability and mission duration will contribute to defining requirements for an acceptable food system that will support crew health and performance on long duration missions. This study evaluated acceptability of the food system used in the SIRIUS-21 8-month mission in Moscow, Russia. To capture in situ scoring of repeat consumption experiences, we administered a weekly food acceptability questionnaire to all 5 crewmembers throughout the mission. Crewmembers scored foods they consumed at 3 meals every 6 days on a 9-point hedonic scale and provided open-ended feedback regarding food context, attributes, and the meal. The sequence was intended to capture all days in the 7-day menu cycle. The repetition was intended to target repeat food ratings that may provide insight into onset of menu fatigue where they might stop eating some foods or limit their choices to their favorites. Every 30 days crewmembers completed two additional surveys to characterize the food system, any changes to the food system, and their experience with the food system. Data collection is complete. Data are currently being assessed by descriptive statistics and visualizations of the ratings over time. The primary goal is to look for trends in the trajectories of ratings of the food items over time – both within and between individuals – to determine if there are substantial decreases in acceptability across all foods, or within specific food categories (e.g., vegetables). The number of times a particular item is scored provides some information on the frequency of consumption (preference), and the overall number of unique items can give a measure of overall variety. We will assess whether individuals were more likely to have more observations on food items that are rated higher, and whether those who rated foods higher had more or less overall variety. Since the data are composed of longitudinal subjective ratings, repeated measures techniques will be used to quantify trends over time and how these may relate to preference and variety. We will assess how these trends relate to their characterization of and experience with the SIRIUS-21 food system. We will also provide content analysis of any additional feedback collected on the open-ended questions and debrief interviews.

Grace L. Douglas↗
NASA NTRSDate not supplied

Pick-and-Eat Space Crop Production Flight Testing on the International Space Station

Growing fresh, nutritious, palatable produce for crew consumption during spaceflight may provide health-promoting, bioavailable nutrients and enhance the astronaut dietary experience as we move toward longer-duration missions. However, requirements to support consistent growth of a variety of high-quality crops under spaceflight environmental conditions remain unclear. This study explores the potential to grow crops for consumption on the International Space Station (ISS) using Veggie, NASA’s vegetable production chamber system. VEG-04A and VEG-04B were two flight tests with ground components conducted with the leafy green crop mizuna mustard. In each location, mizuna was grown in two Veggie units simultaneously, with the chambers set to different red-to-blue-to-green light formulations. Preflight verification testing with various lighting treatments was conducted to down-select two treatments that contributed to the best desirable growth and sensory qualities in mizuna mustard. For the flight tests, one Veggie was programmed as “red-rich” with an average of 270 μmol m-2 s-1 of 630 nm red light, 30 μmol m-2 s-1 of 455 nm blue light, and 30 μmol m-2 s-1 of 530 nm green light. The second Veggie was “blue-rich” with an average of 150 µmol m-2 s-1 of 630 nm red light, 150 µmol m-2 s-1 of 455 nm blue light, and 30 µmol m-2 s-1 of 530 nm green light. Light quality is known to impact plant growth, nutrition, microbiology, and sensory characteristics on Earth, and the Veggie flight tests examined how these impacts might differ in microgravity. VEG-04A, a 35-day growth test with a single harvest, was initiated in June and harvested in July 2019. VEG-04B, a 56-day test with three harvests from the same plants, assessed sustained productivity. Preflight testing for VEG-04B was conducted after the VEG-04A flight test to improve the operations, approaches, and watering requirements, which resulted in better crop establishment in the VEG-04B flight test. VEG-04B was initiated in October 2019 with harvests at four, six, and eight weeks after initiation. At all of the harvests, the astronauts froze half of the edible plant tissue to return to Earth and weighed the remaining half using the Mass Measurement Device (MMD). Weighed samples were then cleaned with produce-sanitizing wipes, and consenting crew members participated in sensory evaluations of the fresh produce. The remaining sanitized produce was available for crew consumption as desired. Frozen flight samples were returned to Earth for chemical and microbial analyses to assess nutritional quality and food safety. Flight-grown mizuna was generally more acceptable to the crew and had higher nutrient levels, although mizuna grown in the ground control performed better in terms of higher yield and lower microbial load. This presentation will focus on results from the nutritional and sensory analyses, including how nutrients identified as key for supplementing the crew diet varied across lighting treatments, harvest approaches, and spaceflight versus ground conditions. It is our hope that these tests on the ISS will help mitigate the risk of an inadequate food supply for long-duration missions by adding fresh vegetables to the crew diet. This study was supported by NASA’s Human Research and Space Biology Programs through the HERO NNJ13ZSA002N-ILSRA grant solicitation.

Jess Bunchek↗
NASA NTRSDate not supplied

Nodal Modeling of Tank Pressurization and Draining using a Multi-Node-Ullage Approach

The purpose of the pressurization system in liquid rocket propulsion is to control the pressure in the gas space of the propellant tank (known as the ullage space) and the propellant mass flowrate to the engine. A mathematical model is required to predict the amount of pressurant necessary to ensure that pressure and temperature levels inside the tank remain within acceptable limits and that the propellant pressure leaving the tank satisfies the net positive suction pressure (NPSP) requirement of the pump feeding the engine. Nodal codes typically model tank pressurization and draining using a single node to represent the ullage and a single node to represent the propellant. As the tank drains, the ullage node grows and the propellant node shrinks. The heat transfer between ullage to wall and ullage to propellant is governed by natural convection. Designers of liquid propulsion systems often use empirical correlations to estimate the “Collapse Factor” which represents the ratio of pressurant required with heat transfer and the amount of pressurant required without heat transfer. A single node ullage model of tank pressurization was developed using GFSSP to compute the collapse factor reasonably well and later was used to model tank pressurization during test firing of the FASTRAC rocket engine. The predicted tank pressure compared well with the test data. In the early 1970’s, pressurization and drain tests with liquid methane were performed at NASA Lewis Research Center in a vacuum chamber. A 5 ft diameter spherical aluminum tank was tested to drain from 95% to 5% full using gaseous helium, hydrogen, nitrogen and methane as pressurant. Tests were conducted with different pressurant inlet temperatures and drain times. Measured data include pressurant requirement, amount of pressurant condensed, and ullage and wall temperatures at various heights in the ullage space at the end of draining. A single node GFSSP model was developed to simulate helium pressurization of the methane tank. Predicted helium consumption was 8-23% less than measured. The average error of the six test cases was 16%. A single ullage node with multiple solid node model was developed using Thermal Desktop. Predicted helium consumption compares with the test data within 2%. This paper describes the development of a GFSSP multi-node ullage model of the test configuration and compares the predicted pressurant consumption for both helium and autogenous pressurization using gaseous methane with experimental as well as TD predictions.

Nodal Model↗
NASA NTRSDate not supplied

Energy-Optimized Path Planning for Uas in Varying Winds Via Reinforcement Learning

In this paper we propose a reinforcement learning (RL) algorithm for path planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to UAV path planning problems are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs need to account for dynamically changing flying conditions such as weather, obstacle or no-fly zones, degraded vehicle health, and off-nominal battery power consumption. Machine learning methods such as reinforcement learning (RL) have the potential to revolutionize how vehicles navigate in such uncertain environments. In this study, we compute UAV trajectories from a pre-determined starting position to a target cell within a 7X7 grid environment by optimizing parameters for mission assurance and safety limits in addition to the energy consumption and operation time. The UAV navigates the grid by taking actions to move in any of the eight cardinal and inter-cardinal directions, under constant thrust profile. The resultant UAV state is sampled from a probability distribution which accounts for the UAV’s action, local wind velocity, and the presence of obstacles or boundaries. As the unmanned airspace gets more complex due to multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance need to be made. Our Markov Decision Process (MDP) environment model can capture any combination of these in the optimization objective, making it novel compared to other work in the field.

trajectory planning↗
DOE OSTI2012

Experimental Performance of R-1234yf and R-1234ze as Drop-in Replacements for R-134a in Domestic Refrigerators

Concerns about anthropogenic climate change have generated an interest in low global warming potential (GWP) refrigerants and have spawned policies and regulations that encourage the transition to low GWP refrigerants. Recent research has largely focused on hydrofluoroolefins (HFOs), including R-1234yf (GWP = 4) as a replacement for R-134a (GWP = 1430) in automotive air-conditioning applications. While R-1234yf and R-1234ze (GWP = 6) have been investigated theoretically as a replacements for R-134a in domestic refrigeration, there is a lack of experimental evidence. This paper gives experimental performance data for R-1234yf and R-1234ze as drop-in replacements for R134a in two household refrigerators one baseline and one advanced technology. An experiment was conducted to evaluate and compare the performance of R-134a to R-1234yf and R-1234ze, using AHAM standard HRF-1 to evaluate energy consumption. These refrigerants were tested as drop-in replacements, with no performance enhancing modifications to the refrigerators. In Refrigerator 1 and 2, R-1234yf had 2.7% and 1.3% higher energy consumption than R-134a, respectively. This indicates that R-1234yf is a suitable drop-in replacement for R-134a in domestic refrigeration applications. In Refrigerator 1 and 2, R-1234ze had 16% and 5.4% lower energy consumption than R-134a, respectively. In order to replace R-134a with R-1234ze in domestic refrigerators the lower capacity would need to be addressed, thus R-1234ze might not be suitable for drop-in replacement.

Karber, Kyle M↗
DOE OSTI2012

Assessing Energy Impact of Plug-In Hybrid Electric Vehicles: Significance of Daily Distance Variation over Time and Among Drivers

Accurate assessment of the impact of plug-in hybrid electric vehicles (PHEVs) on petroleum and electricity consumption is a necessary step toward effective policies. Variations in daily vehicle miles traveled (VMT) over time and among drivers affect PHEV energy impact, but the significance is not well understood. This paper uses a graphical illustration, a mathematical derivation, and an empirical study to examine the cause and significance of such an effect. The first two methods reveal that ignoring daily variation in VMT always causes underestimation of petroleum consumption and overestimation of electricity consumption by PHEVs; both biases increase as the assumed PHEV charge-depleting (CD) range moves closer to the average daily VMT. The empirical analysis based on national travel survey data shows that the assumption of uniform daily VMT over time and among drivers causes nearly 68% underestimation of expected petroleum use and nearly 48% overestimation of expected electricity use by PHEVs with a 40-mi CD range (PHEV40s). Also for PHEV40s, consideration of daily variation in VMT over time but not among drivers similar to the way the utility factor curve is derived in SAE Standard SAE J2841 causes underestimation of expected petroleum use by more than 24% and overestimation of expected electricity use by about 17%. Underestimation of petroleum use and overestimation of electricity use increase with larger-battery PHEVs.

02 PETROLEUM↗
DOE OSTI2013

Cross-Sector Impact Analysis of Industrial Efficiency Measures

The industrial or manufacturing sector is a foundational component to all economic activity. In addition to being a large direct consumer of energy, the manufacturing sector also produces materials, products, and technologies that influence the energy use of other economic sectors. For example, the manufacturing of a lighter-weight vehicle component affects the energy required to ship that component as well as the fuel efficiency of the assembled vehicle. Many energy efficiency opportunities exist to improve manufacturing energy consumption, however comparisons of manufacturing sector energy efficiency investment opportunities tend to exclude any impacts that occur once the product leaves the factory. Expanding the scope of analysis to include energy impacts across different stages of product life-cycle can highlight less obvious opportunities and inform actions that create the greatest economy-wide benefits. We present a methodology and associated analysis tool (LIGHTEnUP Lifecycle Industry GHgas, Technology and Energy through the Use Phase) that aims to capture both the manufacturing sector energy consumption and product life-cycle energy consumption implications of manufacturing innovation measures. The tool architecture incorporates U.S. national energy use data associated with manufacturing, building operations, and transportation. Inputs for technology assessment, both direct energy saving to the manufacturing sector, and indirect energy impacts to additional sectors are estimated through extensive literature review and engineering methods. The result is a transparent and uniform system of comparing manufacturing and use-phase impacts of technologies.

ACEEE↗
DOE OSTI2013

An International Survey of Electric Storage Tank Water Heater Efficiency and Standards

Water heating is a main consumer of energy in households, especially in temperate and cold climates. In South Africa, where hot water is typically provided by electric resistance storage tank water heaters (geysers), water heating energy consumption exceeds cooking, refrigeration, and lighting to be the most consumptive single electric appliance in the home. A recent analysis for the Department of Trade and Industry (DTI) performed by the authors estimated that standing losses from electric geysers contributed over 1,000 kWh to the annual electricity bill for South African households that used them. In order to reduce this burden, the South African government is currently pursuing a programme of Energy Efficiency Standards and Labelling (EES&L) for electric appliances, including geysers. In addition, Eskom has a history of promoting heat pump water heaters (HPWH) through incentive programs, which can further reduce energy consumption. This paper provides a survey of international electric storage water heater test procedures and efficiency metrics which can serve as a reference for comparison with proposed geyser standards and ratings in South Africa. Additionally it provides a sample of efficiency technologies employed to improve the efficiency of electric storage water heaters, and outlines programs to promote adoption of improved efficiency. Finally, it surveys current programs used to promote HPWH and considers the potential for this technology to address peak demand more effectively than reduction of standby losses alone

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

System and method of vehicle operating condition management

A vehicle operating condition profile can be determined over a given route while also considering imposed constraints such as deviation from time targets, deviation from maximum governed speed limits, etc. Given current vehicle speed, engine state and transmission state, the present disclosure optimally manages the engine map and transmission to provide a recommended vehicle operating condition that optimizes fuel consumption in transitioning from one vehicle state to a target state. Exemplary embodiments provide for offline and online optimizations relative to fuel consumption. The benefit is increased freight efficiency in transporting cargo from source to destination by minimizing fuel consumption and maintaining drivability.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

A New Distributed Optimization for Community Microgrids Scheduling

This paper proposes a distributed optimization model for community microgrids considering the building thermal dynamics and customer comfort preference. The microgrid central controller (MCC) minimizes the total cost of operating the community microgrid, including fuel cost, purchasing cost, battery degradation cost and voluntary load shedding cost based on the customers' consumption, while the building energy management systems (BEMS) minimize their electricity bills as well as the cost associated with customer discomfort due to room temperature deviation from the set point. The BEMSs and the MCC exchange information on energy consumption and prices. When the optimization converges, the distributed generation scheduling, energy storage charging/discharging and customers' consumption as well as the energy prices are determined. In particular, we integrate the detailed thermal dynamic characteristics of buildings into the proposed model. The heating, ventilation and air-conditioning (HVAC) systems can be scheduled intelligently to reduce the electricity cost while maintaining the indoor temperature in the comfort range set by customers. Numerical simulation results show the effectiveness of proposed model.

alternating direction method of multipliers (ADMM)↗
DOE OSTI2015

Prediction Models for Dynamic Demand Response

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D 2 R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D 2 R, which we address in this paper. Our first contribution is the formal definition of D 2 R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D 2 R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D 2 R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D 2 R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D 2 R. Also, prediction models require just few days’ worth of data indicating that small amounts of historical training data can be used to make reliable predictions, simplifying the complexity of big data challenge associated with D 2 R.

Aman, Saima↗
DOE OSTI2016

Load Disaggregation Technologies: Real World and Laboratory Performance

Low cost interval metering and communication technology improvements over the past ten years have enabled the maturity of load disaggregation (or non-intrusive load monitoring) technologies to better estimate and report energy consumption of individual end-use loads. With the appropriate performance characteristics, these technologies have the potential to enable many utility and customer facing applications such as billing transparency, itemized demand and energy consumption, appliance diagnostics, commissioning, energy efficiency savings verification, load shape research, and demand response measurement. However, there has been much skepticism concerning the ability of load disaggregation products to accurately identify and estimate energy consumption of end-uses; which has hindered wide-spread market adoption. A contributing factor is that common test methods and metrics are not available to evaluate performance without having to perform large scale field demonstrations and pilots, which can be costly when developing such products. Without common and cost-effective methods of evaluation, more developed disaggregation technologies will continue to be slow to market and potential users will remain uncertain about their capabilities. This paper reviews recent field studies and laboratory tests of disaggregation technologies. Several factors are identified that are important to consider in test protocols, so that the results reflect real world performance. Potential metrics are examined to highlight their effectiveness in quantifying disaggregation performance. This analysis is then used to suggest performance metrics that are meaningful and of value to potential users and that will enable researchers/developers to identify beneficial ways to improve their technologies.

Mayhorn, Ebony T.↗
DOE OSTI2016

Development of High Performance Composite Foam Insulation with Vacuum Insulation Cores

Development of a high performance thermal insulation (thermal resistance or R-value per inch of R-12 hr-ft2- F/Btu-in or greater), with twice the thermal resistance of state-of-the-art commercial insulation materials ( R6/inch for foam insulation), promises a transformational impact in the area of building insulation. In 2010, in the US, the building envelope-related primary energy consumption was 15.6 quads, of which 5.75 quads were due to opaque wall and roof sections; the total US consumption (building, industrial and transportation) was 98 quads. In other words, the wall and roof contribution was almost 6% of the entire US primary energy consumption. Building energy modeling analyses have shown that adding insulation to increase the R-value of the external walls of residential buildings by R10-20 (hr-ft2- F/Btu) can yield savings of 38-50% in wall-generated heating and cooling loads. Adding R20 will require substantial thicknesses of current commercial insulation materials, often requiring significant (and sometimes cost-prohibitive) alterations to existing buildings. This article describes the development of a next-generation composite insulation with a target thermal resistance of R25 for a 2 inch thick board (R12/inch or higher). The composite insulation will contain vacuum insulation cores, which are nominally R35-40/inch, encapsulated in polyisocyanurate foam. A recently-developed variant of vacuum insulation, called modified atmosphere insulation (MAI), was used in this research. Some background information on the thermal performance and distinguishing features of MAI has been provided. Technical details of the composite insulation development and manufacturing as well as laboratory evaluation of prototype insulation boards are presented.

Biswas, Kaushik↗
DOE OSTI2017

Statistical Analysis of Solar PV Power Frequency Spectrum for Optimal Employment of Building Loads

In this paper, a statistical analysis of the frequency spectrum of solar photovoltaic (PV) power output is conducted. This analysis quantifies the frequency content that can be used for purposes such as developing optimal employment of building loads and distributed energy resources. One year of solar PV power output data was collected and analyzed using one-second resolution to find ideal bounds and levels for the different frequency components. The annual, seasonal, and monthly statistics of the PV frequency content are computed and illustrated in boxplot format. To examine the compatibility of building loads for PV consumption, a spectral analysis of building loads such as Heating, Ventilation and Air-Conditioning (HVAC) units and water heaters was performed. This defined the bandwidth over which these devices can operate. Results show that nearly all of the PV output (about 98%) is contained within frequencies lower than 1 mHz (equivalent to ~15 min), which is compatible for consumption with local building loads such as HVAC units and water heaters. Medium frequencies in the range of ~15 min to ~1 min are likely to be suitable for consumption by fan equipment of variable air volume HVAC systems that have time constants in the range of few seconds to few minutes. This study indicates that most of the PV generation can be consumed by building loads with the help of proper control strategies, thereby reducing impact on the grid and the size of storage systems.

boxplot↗
DOE OSTI2016

Enhanced separation of rare earth elements

Industrial rare earth separation processes utilize PC88A, a phosphonic acid ligand, for solvent extraction separations. The separation factors of the individual rare earths, the equipment requirements, and chemical usage for these flowsheets are well characterized. Alternative ligands such as Cyanex® 572 and the associated flowsheets are being investigated at the pilot scale level to determine if significant improvements to the current separation processes can be realized. These improvements are identified as higher separation factors, reduced stage requirements, or reduced chemical consumption. Any of these improvements can significantly affect the costs associated with these challenging separation proccesses. A mid/heavy rare earth element (REE) separations flowsheet was developed and tested for each ligand in a 30 stage mixer-settler circuit to compare the separation performance of PC88A and Cyanex® 572. The ligand-metal complex strength of Cyanex® 572 provides efficient extraction of REE while significantly reducing the strip acid requirements. Reductions in chemical consumption have a significant impact on process economics for REE separations. Partitioning results summarized Table 1 indicate that Cyanex® 572 offers the same separation performance as PC88A while reducing acid consumption by 30% in the strip section for the mid/heavy REE separation. Flowsheet Effluent Compositions PC88A Cyanex® 572 Raffinate Mid REE Heavy REE 99.40% 0.60% 99.40% 0.60% Rich Mid REE Heavy REE 2.20% 97.80% 0.80% 99.20% Liquor Strip Acid Required 3.4 M 2.3 M Table 1 – Flowsheet results comparing separation performance of PC88A and Cyanex® 572 for a mid/heavy REE separation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗