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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Advances in Online Monitoring for Measurement within Nuclear Fuel Reprocessing Streams

Abstract – Significant effort has been focused on the back end of the nuclear fuel cycle including treatment and reuse of irradiated fuel. Solvent extraction reprocessing methods that have been developed often contain various steps, with each one tailored to the separation of specific radionuclides, which are highly dependent upon solution properties. The instrumentation used to monitor these processes must be robust, require little or no maintenance, and be able to withstand harsh environments such as high radiation fields and aggressive chemical matrices. Our group has been investigating the use of optical spectroscopy for the on-line monitoring of actinides, lanthanides, and acid strength within fuel reprocessing streams. This paper describes the application of the electronic and vibrational spectroscopic techniques for radiochemical process monitoring. In this context, our team experimentally assessed the potential of Raman and UV-vis spectroscopic techniques for online real-time monitoring of the U(VI)/nitrate ion/nitric acid and Pu(VI/IV)/Np(VI/V/IV)/Nd(III) in solutions relevant to used nuclear fuel reprocessing. These techniques demonstrated robust performance in the repetitive batch measurements of each analyte in a wide concentration range using simulant and commercial dissolved used fuel solutions. Currently this has been demonstrated on the macroscopic scale, using sample probes requiring large solution volumes. In an effort to minimize waste and reduce dose to personnel, we have modified this technique to allow measurement at the microfluidic scale using a Raman microprobe. Under the current sampling environment, Raman samples typically require upwards of 10 mL and larger. Using the new sampling system, we can sample volumes at 10 µL or less, which is a scale reduction of over 1,000 fold in sample size. This will significantly reduce the requirements for handling hazardous materials, and greatly reduce the costs associated with worker protection from exposure to radioactive and hazardous materials, and costs due to waste disposal.

process monitoring, U, nitric acid, microfluidic, ↗
DOE OSTI2021

Inception Based Deep Convolutional Neural Network for Remaining Useful Life Estimation of Turbofan Engines

Accurate estimation of the remaining useful life (RUL) is a key component of condition based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available and physical modeling is often not feasible. In this paper, a deep convolutional neural network (CNN) architecture is investigated for its ability to estimate the RUL of turbofan engines. The input to the model is a window of time series data collected from the engine under test. Inputting raw sensor data allows features to be learned instead of manually determined. To incorporate the ability to detect features of differing lengths, inception modules are used in the neural network architecture. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set and high prognosis accuracy was achieved. The developed model was used in the 2021 PHM Society Data Challenge and received second place, further validating its ability to accurately estimate RUL.

DeVol, Nathaniel↗
NASA NTRS1993

Analysis of the GOES 6.7 micrometer channel observations during FIRE 2

Clouds form in moist environments. FIRE Phase II Cirrus Implementation Plan (August, 1990) noted the need for mesoscale measurements of upper tropospheric water vapor content. These measurements are needed for initializing and verifying numerical weather prediction models and for describing the environment in which cirrus clouds develop and dissipate. Various instruments where deployed to measure the water vapor amounts of the upper troposphere during FIRE II (e.g. Raman lidar, CLASS sonds and new cryogenic frost hygrometer on-board aircraft). The formation, maintenance and dissipation of cirrus clouds involve the time variation of the water budget of the upper troposphere. The GOES 6.7 mu m radiance observations are sensitive to the upper tropospheric relative humidity, and therefore proved extremely valuable in planning aircraft missions during the field phase of FIRE II. Warm 6.7 mu m equivalent black body temperatures indicate a relatively dry upper troposphere and were associated with regions generally free of cirrus clouds. Regions that were colder, implying more moisture was available may or may not have had cirrus clouds present. Animation of a time sequence of 6.7 mu m images was particularly useful in planning various FIRE missions. The 6.7 mu m observations can also be very valuable in the verification of model simulations and describing the upper tropospheric synoptic conditions. A quantitative analysis of the 6.7 mu m measurement is required to successfully incorporate these satellite observations into describing the upper tropospheric water vapor budget. Recently, Soden and Bretherton (1993) have proposed a method of deriving an upper tropospheric humidity based on observations from the GOES 6.7 mu m observations. The method is summarized in the next section. In their paper they compare their retrieval method to radiance simulations. Observations were also compared to ECMWF model output to assess the model performance. The FIRE experiment provides a unique opportunity to further verify the GOES upper tropospheric relative humidity retrieval scheme by providing (1) aircraft observations to cross-validate the calibration of the GOES 6.7 mu m channel, (2) accurate upper tropospheric water vapor concentrations for verification, and (3) veritical variability of upper tropospheric water vapor.

Soden, B. J.↗
DOE OSTI2022

Utilisation of flavonoids from Lycium barbarum L. leaves cross‐linked with whey protein for the stabilisation and delivery of β ‐carotene emulsions

Summary Although β ‐carotene is an essential nutrient for the maintenance of human health, it is insoluble in water and extremely unstable, which limits its application. Here, we prepared a stable emulsification system of whey protein (WP)‐ Lycium barbarum L. leaves flavonoids (LBLF) complex as a carrier to load β ‐carotene. The rheological properties, antioxidant properties and stability of emulsion were studied, and the effects of emulsion on the biological retention of β ‐carotene and stability were investigated. The results indicate that, compared with WP, the WP‐LBLF emulsion has smaller particles, a smaller creaming index, and significantly enhanced chemical stability under light and heat treatment conditions. The thiobarbituric acid value of the WP‐LBLF emulsion (0.031 ± 0.016 mg/kg) was dramatically lower than that of the WP emulsion (0.045 ± 0.012 mg/kg) ( P < 0.05). In the simulated gastrointestinal digestion experiment, the retention rate of β ‐carotene was higher in WP‐LBLF emulsion. Additionally, the WP‐LBLF emulsion loaded with β ‐carotene has good salt tolerance and heat stability. In our study, WP‐LBLF was used as an emulsifier to load β ‐carotene emulsion to improve its bioavailability and stability, which provides a new preparation and application method for a complex functional emulsifier.

Fan, Yanli↗
NASA NTRSDate not supplied

Global Reference Atmospheric Model (GRAM) Advancements and Additions

Introduction:The Global Reference Atmospheric Model (GRAM) is one of the most widely used engineering models of the atmosphere. GRAM development and maintenance has been led by NASA Marshall Space Flight Center (MSFC). The NASA Science Mission Directorate (SMD) has provided funding support to upgrade the GRAMs since Fiscal Year 2018. NASA Langley Research Center has been working with MSFC on the upgrades.This presentation will provide details regarding the upgrades that have been made to the existing GRAMs, the development of new GRAMs, as well as the ongoing objectives, tasks, and milestones re-lated to the GRAM upgrades funded by NASA SMD. GRAM: The GRAMs are engineering-oriented atmospheric models that estimate mean values and statistical variations of the atmospheric properties for numerous planetary destinations.They provide mean values and variability for any point in the atmosphere as well as seasonal, geographic, and altitude variations. GRAM outputs include atmospheric density, temperature, pressure, winds, and chemical composition along a user-defined path.Theyhave been widely used by the engineering community because of their ability to create realistic dispersions. GRAMs have been integrated into high fidelity flight dynamic simulations of launch, entry, descent and landing (EDL), aerobraking and aerocapture. MSFC has been developing and updating GRAMs since 1974; GRAMs are currently available for Earth, Mars, Venus, Neptune, and Titan. GRAM Upgrade Status: Code Moderization.The planetary GRAMs are being rearchitected from Fortran to a common object-ori-entedC++ framework called the GRAM Suite. This new architecture creates a common GRAM library of data models and utilities. The first C++ releases of the existing planetary GRAMs (Mars, Venus, Neptune, and Titan-GRAM) in the GRAM Suite are straight conversions from the latest Fortran version. Model Upgrades.The focus of the model upgrade task is to improve the atmosphere models in the existing GRAMs and to establish a foundation for developing GRAMs for additional destinations. The GRAM ephemerishas been upgraded to the NASA Navigation and Ancillary Information Facility (NAIF) SPICE toolkit (version N0066). The calculation of the speed of sound has also been improved in the GRAMs. The GRAM team has received updated Mars General Circulation Model (MGCM) datasets from NASA Ames Research Center. Mars Global Ionosphere-Thermosphere Model (M-GITM) data is being obtained toreplace the Mars Thermospheric General Circulation Model (MTGCM) data in legacy Mars-GRAM. M-GITM and updated MGCM data will be incorporated into a future GRAM Suite release.Twoprojects that will improve the atmospheric model data in the GRAMs have been funded by the GRAM team since Fiscal Year 2020. Sanjay Limaye and Patrick Fry at the University of Wisconsin are reanalyzing the Venus Express radio occultation observations and analyzing the Akatsukiradio occultation observations.This will lead tothe calculation of number density, temperature, and pressure profiles for the 40-90 km altitude range. Kunio Sayanagi, Justin Garland, and Ryan McCabeat Hampton University are developing empirical global models for Venus,Jupiter, Saturn, Uranus, Neptune, and Titan that incorporates the latest data available for each of these planetary destinations. Upgraded GRAM Releases. GRAM Suite Version 1.0 was released in May 2020 and contains the rearchitected Neptune-GRAM, including the common GRAM framework and planet–specific code. GRAM Suite Version 1.1 was released in September 2020 and addedthe rearchitected Titan-GRAM to the GRAM Suite. A User Guide and Programmer’s Manualarereleased with all GRAMs. The rearchitected Mars and Venus-GRAMs will be released in upcomingversions of the GRAM Suite. New GRAM Releases. New GRAMs have been de-veloped for Uranus and Jupiter. Uranus-GRAM is based on an individual profile generated by Gary Allen (ARC) from Voyager 2 occultation data and will be released in GRAM Suite Version 1.2. Jupiter-GRAM is based on individual profile produced from Al Seiff’s Ju-piter model[1].Jupiter-GRAM will be released in GRAM Suite Version 1.3. Saturn-GRAM is currently under development and will be released in a future version of the GRAM Suite. Conclusions: GRAMs are vital and frequently used toolsets. Releases of the GRAM Suite, upgrades of the existing planetary GRAMs, and development of new planetary GRAMs are ongoing. NASA SMD funding has been essential to addressing current limitations and accomplishing GRAM developmental goals. Continua-tion ofSMD fundingwill ensure the development, up-grades, and maintenance of the GRAMs. References: [1] Seiff, A., et al. (1998) JGR, 103, 22,857-22,889. Acknowledgments: The authors gratefully acknowledge support from the NASA SMD

atmospheric models↗
DOE OSTI2020

WEC fault modelling and condition monitoring: A graph-theoretic approach

The nature of wave resources usually requires wave energy converter (WEC) components to handle peak loads (i.e., torques, forces, and powers) that are many times greater than their average loads, accelerating equipment degradation. Moreover, due to their isolated nature and harsh operating environment, WEC systems are projected to possess high operations and maintenance (O&M) cost, i.e., around 27% of their leveled cost of energy. As such, developing techniques to mitigate these costs through the application of condition monitoring and fault tolerant control will significantly impact the economic feasibility of grid connected WEC power. Toward this goal, models of faulty components are developed in the open source modeling platform, WEC-Sim, to estimate the performance and measurable states of a WEC operating with likely device and sensor failures. Two types of faulty component models are then applied to a point absorber WEC model with basic controller damping and spring forces. Resulting changes in device behavior are recorded as a benchmark, and a graph-theoretic approach is proposed for fault detection and identification utilizing multivariate time series. Simulation results demonstrate that these faults can greatly affect the WEC performance, and that the proposed method can effectively detect and classify different types of faults.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Concrete Structure Health Monitoring Using Vibro-acoustic Testing and Machine Learning

Assessment and management of aging concrete structures in nuclear power plants require a more systematic approach than simple reliance on margins of safety in existing codes. Research is required to deploy online monitoring capability to assess the health of concrete structures in nuclear power plants and perform predictive analytics based on heterogeneous measurements, in order to reduce the operation and maintenance costs. The research effort described in this report focuses on the health monitoring of concrete slabs containing reactive aggregates using the vibro-acoustic modulation (VAM) technique and predictive modeling. The research is developing an approach to enable detection, localization, and estimation of the extent of degradation in concrete slabs due to alkali-silica reaction (ASR). The project outcomes are generic and extendable to other degradation modes in concrete. A concrete slab without reinforcements was cast at Vanderbilt University and cured in aggressive conditions to accelerate ASR degradation. In this report, we develop a methodology for the automation of VAM testing, and the construction of machine learning models to facilitate damage localization in concrete structures. We investigate how machine learning models based on physics simulation data can be effectively utilized for damage localization (damage location and depth) and estimation in a concrete slab specimen. Our study indicates that these models could potentially support damage localization and estimation for NPP structures.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Preparation of Simulated LBL Defects for Round Robin Experiment

A critical characteristic of the TRISO fuel design is its ability to retain fission products. During reactor operation, the TRISO layers act as barriers to release of fission products not stabilized in the kernel. Each component of the TRISO particle and compact construction plays a unique role in retaining select fission products, and layer performance is often interrelated. The IPyC, SiC, and OPyC layers are barriers to the release of fission product gases such as Kr and Xe. The SiC layer provides the primary barrier to release of metallic fission products not retained in the kernel, as transport across the SiC layer is rate limiting due to the greater permeability of the IPyC and OPyC layers to many metallic fission products. These attributes allow intact TRISO coatings to successfully retain most fission products released from the kernel, with the majority of released fission products during operation being due to defective, damaged, or failed coatings. This dominant release of fission products from compromised particles contributes to the overall source term in reactor; causing safety and maintenance concerns and limiting the lifetime of the fuel. Under these considerations, an understanding of the nature and frequency of compromised particles is an important part of predicting the expected fission product release and ensuring safe and efficient operation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.

Prantikos, Konstantinos [Argonne National Laborato↗
DOE OSTI2025

Virtual Refrigerant Charge Sensing Method for Next-Generation Refrigerant in Residential Heat Pumps

The charge level of refrigerant in heat pump systems significantly affects their operational performance. Virtual refrigerant charge (VRC) sensing technology has been well-established for traditional refrigerants (HFCs and HCFCs) for its low cost compared to physical sensors. However, other than traditional refrigerants, HFOs are increasingly used in next-generation heat pumps; whether these conventional VRC sensing methods remain applicable for heat pump systems utilizing next-generation refrigerants requires further investigation. To address these issues, this study develops a low-cost VRC sensing method for next-generation refrigerant heat pumps used in residential buildings. The developed algorithm is evaluated by using simulation models to evaluate the accuracy, considering an R454B heat pump with a nominal heating capacity of 51K Btu/hr (14.95 kW) as an example, and compared with those of the two reference VRC sensing algorithms. Though the developed VRC sensing algorithm and the two reference methods can accurately predict the charge level for the R454B heat pump system (with mean absolute percentage error for various cooling and heating conditions less than 7%), the developed VRC sensing algorithm uses fewer sensors and improves the overall accuracy for heating conditions by 7.1%, and the accuracy for undercharge cooling conditions 14.2%, compared with a mainstream algorithm. This technology will complement physical leakage detectors, and promote the adoption of next-generation heat pump systems, along with reducing wasted energy and maintenance costs.

Liang, Chenjiyu↗
DOE OSTI2025

Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique

Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure. The captured edge profiles are transformed into essential boundary conditions. This allows the construction of a strongly formulated boundary value problem (BVP), classified as the Dirichlet problem. Capturing boundary conditions from the digital image is novel, although a similar approach was applied to the point cloud data. It was shown that the CPCM is more efficient in this hybrid simulation framework than the weak-form-based numerical schemes. Unlike the finite element method (FEM), it can avoid aligning boundary nodes with regression points. A three-point bending test of a rubber beam was simulated to validate the developed technique. The simulation results were benchmarked against numerical results by ANSYS and various relevant numerical schemes. The technique can effectively solve the Dirichlet-type BVP, yielding accurate deformation, stress, and strain values across the entire problem domain when employing a linear strain model and increasing the number of CPCM nodes. In addition, comparative analysis with conventional displacement tracking techniques verifies the developed technique’s robustness. The proposed technique effectively circumvents the inherent limitations of traditional monitoring methods resulting from the reliance on physical gauges or target markers so that a robust and non-contact solution for remote structural health monitoring in real-scale infrastructures can be provided, even in unfavorable experimental environments.

Chemistry↗
NASA NTRS1993

Cirrus cloud development in a mobile upper tropospheric trough: The November 26th FIRE cirrus case study

The period from 18 UTC 26 Nov. 1991 to roughly 23 UTC 26 Nov. 1991 is one of the study periods of the FIRE (First International Satellite Cloud Climatology Regional Experiment) 2 field campaign. The middle and upper tropospheric cloud data that was collected during this time allowed FIRE scientists to learn a great deal about the detailed structure, microphysics, and radiative characteristics of the mid latitude cirrus that occurred during that time. Modeling studies that range from the microphysical to the mesoscale are now underway attempting to piece the detailed knowledge of this cloud system into a coherent picture of the atmospheric processes important to cirrus cloud development and maintenance. An important component of the modeling work, either as an input parameter in the case of cloud-scale models, or as output in the case of meso and larger scale models, is the large scale forcing of the cloud system. By forcing we mean the synoptic scale vertical motions and moisture budget that initially send air parcels ascending and supply the water vapor to allow condensation during ascent. Defining this forcing from the synoptic scale to the cloud scale is one of the stated scientific objectives of the FIRE program. From the standpoint of model validation, it is also necessary that the vertical motions and large scale moisture budget of the case studies be derived from observations. It is considered important that the models used to simulate the observed cloud fields begin with the correct dynamics and that the dynamics be in the right place for the right reasons.

Mace, Gerald G.↗
NASA NTRS1994

A rose by any other name: Certification seen as process rather than content

Green (1990) believes that the two main factors safeguarding flying from human error are both related to certification and regulation. First is the increasingly proceduralized nature of flying whereby as much as possible is reduced to a rule-based activity. Second is the emphasis placed upon training and competency checking of aircrew in simulators and in the air, both generally and for all particular types of aircraft flown. This leaves, believes Green, other human factors that are relatively unaddressed as yet and which can give rise to human reliability problems. These include: hardware factors and especially pilot/co-pilot relationships; and system factors including fatigue and cost/safety trade-offs. He also, importantly, identifies problems with the integration of the 'electronic crew member' following increased automation. Human reliability failures with artificial intelligence and automation, due to over-reliance on the system fail-safe mechanisms, or to operator under- confidence in the integrity or self-regulating capacity of the system, or to out-of-loop effects, are widely accepted as being due to deficiencies in plant design, planning, management and maintenance more than to 'operator error' - Reason's (1990) latent error or organization pathogens argument. Reliability failures in complex systems are well enough documented to give cause for concern and at least promote a debate on the merits of a full certification program. The purpose of this short paper is to seek out and explore what is valuable in certification, at the least to show that the benefits outweigh the disadvantages and at best to identify positive outcomes perhaps not obtainable in other ways. On both sides of the debate on certification there is general agreement on the need for a better human factors perspective and effort in complex aviation systems design. What is at issue is how this is to be promoted. It is incumbent upon opponents of certification to say how else such promotion be enabled. This is an exploratory and philosophical review, not a focused and specific one, and it will draw upon much that is not firmly in the domain of complex aviation systems.

Wilson, John R.↗
NASA NTRS2010

A New Analysis Tool Assessment for Rotordynamic Modeling of Gas Foil Bearings

Gas foil bearings offer several advantages over traditional bearing types that make them attractive for use in high-speed turbomachinery. They can operate at very high temperatures, require no lubrication supply (oil pumps, seals, etc.), exhibit very long life with no maintenance, and once operating airborne, have very low power loss. The use of gas foil bearings in high-speed turbomachinery has been accelerating in recent years, although the pace has been slow. One of the contributing factors to the slow growth has been a lack of analysis tools, benchmarked to measurements, to predict gas foil bearing behavior in rotating machinery. To address this shortcoming, NASA Glenn Research Center (GRC) has supported the development of analytical tools to predict gas foil bearing performance. One of the codes has the capability to predict rotordynamic coefficients, power loss, film thickness, structural deformation, and more. The current paper presents an assessment of the predictive capability of the code, named XLGFBTH (Texas A&M University). A test rig at GRC is used as a simulated case study to compare rotordynamic analysis using output from the code to actual rotor response as measured in the test rig. The test rig rotor is supported on two gas foil journal bearings manufactured at GRC, with all pertinent geometry disclosed. The resulting comparison shows that the rotordynamic coefficients calculated using XLGFBTH represent the dynamics of the system reasonably well, especially as they pertain to predicting critical speeds.

Howard, Samuel A.↗
DOE OSTI2023

A Self-Sustained CPS Design for Reliable Wildfire Monitoring

Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. Here, the proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5× faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluation of the Energy, Hygrothermal, and Thermal Capacity Performance of Cross-Laminated Timber

Cross-laminated timber (CLT) construction is gaining momentum in the US because it offers multiple advantages over traditional construction methods. Benefits that have received the most attention focus on constructability, the environment, and protection (e.g., blast resistance), although CLT construction is likely to offer other benefits, as well. Still, these have not been studied at length because such evaluations are costly, requiring long-term assessments in an actual building and specialized technical knowledge. Among the possible benefits, CLT construction likely provides a higher-performing building envelope. Using CLT panels to enclose a building means fewer joints in the opaque envelope than what is required in traditional stick-framed construction. Fewer joints mean fewer locations where the air- and water-resistive barrier (WRB) could be compromised; thus, a CLT building enclosure may require less maintenance and have a longer lifespan than a traditionally built structure because of fewer air and water leaks. In addition, CLT’s thermal mass moderates indoor temperatures, allowing the heating, ventilation, and air conditioning (HVAC) system to operate more efficiently during peak hours, reducing operational energy consumption throughout the lifetime of the CLT building (Salonvaara et al., 2022). Furthermore, more stable indoor temperatures can increase occupant comfort. The CLT’s thermal mass can also reduce energy costs by adjusting to utility time-of-use pricing without affecting occupant comfort. The ability of CLT buildings to bridge periods without HVAC operation prepares them for future grid interaction and provides a certain level of resilience against power outages. Researchers have attempted to quantify these benefits; however, their work is based on simplified simulations with numerous assumptions. To correctly understand the benefits, an actual building must be monitored. Therefore, information needs to be gathered on indoor and outdoor temperatures, HVAC energy consumption, thermostat setpoints, temperatures, and thermal transport in CLT components to comprehend how these parameters are affected by the CLT’s thermal mass. These data are needed to reduce the number of assumptions and calibrate simulation models to optimize HVAC controls to minimize overall energy consumption, reduce energy use and higher fees during peak demand, and maintain occupant comfort. Additionally, the calibrated simulation model allows the optimization exercise to be repeated in various US climates. Potential benefits can be tailored to buildings in various locations, and decisions can be made on where CLT construction could be most advantageous. Furthermore, monitoring and simulation results are needed to evaluate the durability of the CLT structures in different climates. This project’s researchers gathered information to help understand and quantify the benefits of CLT buildings concerning operational energy, moderated indoor temperatures, and comfort; the dynamic operation to provide grid services; and resilience in times of power outage. Through the corroboration of simulation models with real-world measurements, this study paves the way for extrapolating findings to other climatic zones and building typologies, thereby broadening the understanding of CLT’s multifaceted benefits and reinforcing its position as a material of choice in sustainable construction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Materials-to-device design of hybrid metal-polymer heat exchanger tubes for low temperature waste heat recovery

Heat exchangers for low temperature (<150 °C) waste heat recovery (WHR) are challenging due to severe constraints on materials costs, manufacturing methods, and maintenance opportunities after installation. Here, the use of polymers instead of metals, offers an avenue for cost reduction in WHR applications, provided the typical low thermal conductivity of polymers (~0.2 W m –1 K –1 ) that results in poor overall heat transfer coefficient can be overcome. Previous approaches to enhance the thermal conductivity of polymers remain problematic in terms of cost, thermal, and thermomechanical considerations. In this work, we propose a novel hybrid metal-polymer composite assembled from strips of polymer and copper. Polymer strips with metal-clad edges are wound helically and the interfaces between the strips joined to obtain a tube for a cross-flow heat exchanger. Interfaces are designed to enhance effective heat conduction across the material and increase the overall heat transfer coefficient. Through numerical simulations, we obtain metal-polymer layouts that achieve wall effective thermal conductivity ~1 W m –1 K –1 at ~23% or ~35% volume fraction of copper or aluminum, respectively. The conductivity enhancement is sufficient for typical low temperature WHR, resulting in a ~20% enhancement in the overall heat transfer coefficient compared to a similar all-polymer heat exchanger. Thermomechanical simulations revealed that the composite pipe designs can withstand >1.4 MPa internal pressure, under the normal operating conditions of the heat exchanger. Our work provides guidelines for designing macroscopic metal-polymer composites that can be adapted for specific requirements such as reduced materials costs, ease of manufacturing, and heat transfer enhancement. In conclusion, the optimized design of hybrid metal-polymer heat exchanger tubes potentially provides a scalable and cost-effective route toward harvesting waste heat from low temperature sources.

42 ENGINEERING↗
NASA NTRS2017

Robonaut 2 and You: Specifying and Executing Complex Operations

Crew time is a precious resource due to the expense of trained human operators in space. Efficient caretaker robots could lessen the manual labor load required by frequent vehicular and life support maintenance tasks, freeing astronaut time for scientific mission objectives. Humanoid robots can fluidly exist alongside human counterparts due to their form, but they are complex and high-dimensional platforms. This paper describes a system that human operators can use to maneuver Robonaut 2 (R2), a dexterous humanoid robot developed by NASA to research co-robotic applications. The system includes a specification of constraints used to describe operations, and the supporting planning framework that solves constrained problems on R2 at interactive speeds. The paper is developed in reference to an illustrative, typical example of an operation R2 performs to highlight the challenges inherent to the problems R2 must face. Finally, the interface and planner is validated through a case-study using the guiding example on the physical robot in a simulated microgravity environment. This work reveals the complexity of employing humanoid caretaker robots and suggest solutions that are broadly applicable.

Baker, William↗