SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This report describes the Gearbox Vibration Diagnostic Analyzer installed in the NASA Lewis Research Center's 500 HP Helicopter Transmission Test Stand to monitor gearbox testing. The vibration of the gearbox is analyzed using diagnostic algorithms to calculate a parameter indicating damaged components.
Soil health encompasses a range of biological, chemical, and physical soil properties that sustain the commercial and ecological value of agroecosystems. Monitoring soil health requires a comprehensive set of diagnostics that can be cost-prohibitive for routine analyses. The soil microbiome provides a rich source of information about soil properties, which can be assayed in a high-throughput, cost-effective way. We evaluated the accuracy of random forest (RF) and support vector machine (SVM) regression and classification models in predicting 12 measures of soil health, tillage status, and soil texture from 16S rRNA gene amplicon data with an operationally relevant sample set. We validated the efficacy of the best performing models against independent datasets and also tested best practices for processing microbiome data for use in machine learning. Soil health metrics could be predicted from microbiome data with the best models achieving a Kappa value of ~0.65, for categorical assessments, and a R2 value of ~0.8, for numerical scores. Biological health ratings were better predicted than chemical or physical ratings. Validation with independent datasets revealed that models had general predictive value for soil properties, including yield. The ecological profiles of several taxa important for model accuracy matched the observed relationships with soil health, including Pyrinomonadaceae, Nitrososphaeraceae, and Candidatus Udeaobacter. Models trained at the highest taxonomic resolution proved most accurate, with losses in accuracy resulting from rarefying, sparsity filtering, and aggregating at higher taxonomic ranks. Furthermore, our study provides the groundwork for developing scalable technology to use microbiome-based diagnostics for the assessment of soil health.
Building-integrated cooling, heating and power (CHP) systems are more efficient than conventional systems at providing local power and thermal energy, and favorable fuel prices are bound to spur their increased adoption. It is estimated that the total CHP technical potential in the commercial building sector is approximately 76 GW, but only a small fraction of it is actually deployed. Advanced control and monitoring systems that realize the full potential of these CHP systems can transform this capacity into an asset for both the owner and the grid and also accelerate the adoption of these systems in the building sector. However, to realize the full benefit of the CHP systems, we must ensure persistence of energy efficient operations. It has been demonstrated by several studies that as much as 30% of building energy use is wasted. Much of the waste results from the inability to anticipate load variations (design day vs. non-design day operations; diurnal variation of loads), identify improper operation, and efficiently operate, control and maintain building systems. Moreover, many buildings, even those with adequate control infrastructure, lack knowledgeable staff to operate and maintain energy systems. Operators of buildings with CHP systems will constantly need to make tradeoffs (economic dispatch) between onsite power generation and purchasing power, between onsite heat production from distributed generation versus heating/cooling from conventional systems, and different passive and active load management measures. Much of the inefficiency in the current building operations can be eliminated by use of automated performance monitoring (PM), real-time commissioning verification (CxV) and automated fault detection and diagnostic (AFDD) tools. Automation can help system operators make intelligent decisions. Remote and continuous monitoring of system conditions and performance will enable better management and integration of CHP with existing building systems. Continuous PM, real-time CxV and AFDD could alleviate burdens for operations staff, enhance operations and maintenance (O&M), and improve reliability of building and CHP systems. To address the O&M challenges and to provide a means to maximize the rate-of-return of building-integrated CHP systems, the Building Technologies Office (BTO) within the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) initiated a project to design, develop, and field test a VOLTTRON™-based supervisory controller and associated open-source algorithms. These algorithms will ensure real-time optimal operation of a building-integrated CHP system, support electric grid reliability, and lead to achieving the goal of a clean, efficient, reliable and affordable next-generation integrated energy systems. This report lists the components for which PM, real-time CxV and AFDD algorithms will be developed, how the algorithms will be tested, and metrics that will be used to validate the algorithms and easy of deployment. Deployment of these algorithms in the field will result in savings reduction in energy consumption of between 10% and 20%.
Building-integrated cooling, heating, and power (CHP) systems are more efficient than conventional systems at providing local power and thermal energy, and favorable fuel prices are bound to spur their increased adoption. However, to realize the full benefit of the CHP systems, we must ensure persistence of energy efficient operations. Much of the inefficiency in the current building operations can be eliminated by use of automated performance monitoring (PM), real-time commissioning verification (CxV) and automated fault detection and diagnostic (AFDD) tools. Automation can help system operators make intelligent decisions. Remote and continuous monitoring of system conditions and performance will enable better management and integration of CHP with existing building systems. Continuous PM, real-time CxV, and AFDD could alleviate burdens for operations staff, enhance operations and maintenance (O&M), and improve reliability of building and CHP systems. To address the O&M challenges and to provide a means to maximize the rate-of-return of building-integrated CHP systems, the Building Technologies Office (BTO) within the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) initiated a project to design, develop, and field test a VOLTTRON™-based supervisory controller and associated open-source algorithms. These algorithms will ensure real-time optimal operation of a building-integrated CHP system, support electric grid reliability, and lead to achieving the goal of clean, efficient, reliable, and affordable next-generation integrated energy system. Previous report listed the components for which PM, real-time CxV, and AFDD algorithms will be developed, how the algorithms will be tested, and the metrics that will be used to validate the algorithms and their ease of deployment. Deployment of these algorithms in the field will result in a reduction in energy consumption of between 10% and 20% (for both CHP and conventional building systems). This report builds upon the previous report by detailing the process by which PNNL will implement performance monitoring and real-time commissioning algorithms for CHP systems in conjunction with the use of the VOLTTRON CHP economic dispatch agent in host facilities.
Abstract. Numerical models used in weather and climate prediction take into account a comprehensive set of atmospheric processes (i.e., phenomena) such as the resolved and unresolved fluid dynamics, radiative transfer, cloud and aerosol life cycles, and mass or energy exchanges with the Earth's surface. In order to identify model deficiencies and improve predictive skills, it is important to obtain process-level understanding of the interactions between different processes. Conditional sampling and budget analysis are powerful tools for process-oriented model evaluation, but they often require tedious ad hoc coding and large amounts of instantaneous model output, resulting in inefficient use of human and computing resources. This paper presents an online diagnostic tool that addresses this challenge by monitoring model variables in a generic manner as they evolve within the time integration cycle. The tool is convenient to use. It allows users to select sampling conditions and specify monitored variables at run time. Both the evolving values of the model variables and their increments caused by different atmospheric processes can be monitored and archived. Online calculation of vertical integrals is also supported. Multiple sampling conditions can be monitored in a single simulation in combination with unconditional sampling. The paper explains in detail the design and implementation of the tool in the Energy Exascale Earth System Model (E3SM) version 1. The usage is demonstrated through three examples: a global budget analysis of dust aerosol mass concentration, a composite analysis of sea salt emission and its dependency on surface wind speed, and a conditionally sampled relative humidity budget. The tool is expected to be easily portable to closely related atmospheric models that use the same or similar data structures and time integration methods.
In this study, the capabilities of a physics-informed convolutional neural network (CNN) for reconstructing the temperature field from a limited set of measurements taken at the boundaries of internal flows are demonstrated. Such an approach enables the development of less invasive monitoring methods for real-time plant diagnostics. As a test case, a Molten Salt Fast Reactor (MSFR) design was selected. This circulating fuel reactor has received interest from both scientific and industrial communities due to its intrinsic safety and sustainability. Molten salt flows in such reactors, however, can present highly localized temperature peaks that can induce significant thermal stresses onto the vessel walls. At these local maxima, the salt temperature may exceed a thousand kelvins, which makes a direct measurement challenging or even unfeasible. The proposed CNN algorithm allows one to detect indirectly such discontinuities through an accurate, albeit indirect, temperature measurement method during reactor operation. The datasets employed to train and test the machine learning models in the present work were generated with Nek5000, a computational fluid dynamics (CFD) code developed at Argonne National Laboratory. The CNN algorithm is trained with CFD results that span a set of MSFR operational power and flow ranges. Here, to demonstrate the efficacy of the algorithm, predictions are made for test cases contained within the training range but for which the CFD data were not used when training. Results demonstrate that the proposed technique properly characterizes temperature peaks and distributions within the domain for a broad range of scenarios.
A research effort was conducted to explore the application of artificial intelligence technology to automation of fault monitoring and diagnosis as an aid to the flight crew. Human diagnostic reasoning was analyzed and actual accident and incident cases were reconstructed. Based on this analysis and reconstruction, diagnostic concepts were conceived and implemented for an aircraft's engine and hydraulic subsystems. These concepts are embedded within a multistage approach to diagnosis that reasons about time-based, causal, and qualitative information, and enables a certain amount of graceful degradation. The diagnostic concepts are implemented in a computer program called Faultfinder that serves as a research prototype.
Accurate assessment of skeletal muscle forces and net joint torque is essential for preventing fatigue-related injuries, optimizing physical training, and monitoring disease progression in neuromuscular conditions. However, existing joint torque evaluation techniques are hindered by limited portability and high operational costs, confining their use to controlled laboratory or clinical settings. Despite substantial advances in wearable joint torque estimation systems, ongoing challenges such as power constraints, bulky wired setups, and susceptibility to environmental or motion artifacts underscore the urgent need for truly batteryless, wireless solutions deployable in real-world settings. This paper proposes a novel surface acoustic wave (SAW)-based force myography (FMG) system for noninvasive measurement of joint torque, validated against a gold-standard electromechanical dynamometer. The approach uses a single SAW sensor embedded in an armband to detect volumetric biceps brachii changes, with a second-order polynomial mapping sensor output and elbow angle to torque. Seven participants were tested in both isometric (15°–90°) and isokinetic (10°/s and 20°/s) supinated elbow flexion tasks. Under isometric conditions, subject-specific calibration achieved a normalized root-mean-square error (NRMSE) of 13.6% ± 6.0% and R 2 = 0.834 ± 0.180, while a group-level model yielded 14.4% ± 6.8% and 0.808 ± 0.208, respectively. For isokinetic trials, the group model produced an NRMSE of 24.1% ± 6.6% at 10°/s and 24.9% ± 08.9% at 20°/s, highlighting the feasibility of using a single-sensor SAW-FMG setup across different speeds. Because SAW devices support wireless, battery-free operation, the proposed system offers a pathway to portable, real-time monitoring for sports medicine, rehabilitation, and clinical diagnostics.
The objective of this analysis is to estimate and assess the stresses in the passive plates caused by a limiting plasma disruption. The objective of this calculation is also to address observed damage to mounting hardware after completion of NSTX operation. These observations initiated diagnostic efforts during the Upgrade project to monitor the severity of the disruptions that were causing the damage, and planned instrumentation to trend the behavior of the mounting hardware. The Recovery Project mission for the passive plate hardware is to appropriately reinforce the passive plates with stainless steel ribs mounted on the back of the plate to reduce their flexure and stress and reinforcements of the “biscuit” components of the bracketry to transfer loads without damage to bolts, biscuits, and bracket plates and welds. Mid-plane disruptions and quenches were manageable with NSTX vintage hardware. The Vertical Displacement Event (VDE) disruptions position the plasma closer to the passive plates and are more severe than the centered plasma. Earlier work pointed to the P1-P5 VDE as limiting. This has been confirmed by A. Brooks work in support of the load generation effort in reference. During vertical displacement large counter currents are generated in the plate as the plasma approaches it. - as would be expected from passive plates. When the plasma disrupts the currents in the plates are reversed, which also reverses the loading on the plates. This report documents the methodology and results of transient structural analysis on the plasma, passive plates support brackets. The loads induced by eddy and halo currents have been considered, and were generated using a separate electromagnetic analysis (NSTXU_1_11_2_1_CALC_051). The structural model also incorporates the effects from preload. Calculation was performed with the assumption that existing biscuits are not participating in the load bearing except the biscuits connected to jumper bolts, which are fully engaged. Results show that with the planned reinforcements, Passive plates and mounting structures satisfy static and fatigue criteria in all areas except welds, where separate analysis was performed. Both symmetric and asymmetric (with locating block) designs were analyzed, and although stresses were higher without locating block they still satisfy NSTX design criteria.
Developments in NASA technology of possible interest to outside users are reported. Complied are descriptions of a number of instruments that have proven useful in monitoring and treating patients. Described are several diagnostic, prosthetic, and therapeutic devices as well as patent information on new innovations in aerospace medicine technology.
A system is presented which processes FORTRAN based software systems to surface potential problems before they become execution malfunctions. The system complements the diagnostic capabilities of compilers, loaders, and execution monitors rather than duplicating these functions. Also, it emphasizes frequent sources of FORTRAN problems which require inordinate manual effort to identify. The principle value of the system is extracting small sections of unusual code from the bulk of normal sequences. Code structures likely to cause immediate or future problems are brought to the user's attention. These messages stimulate timely corrective action of solid errors and promote identification of 'tricky' code. Corrective action may require recoding or simply extending software documentation to explain the unusual technique.
Organon Diagnostics has developed, under NASA sponsorship, a monitoring system to test the capability of a water recovery system to reject the passage of viruses into the recovered water. In this system, a non-pathogenic marker virus, bacteriophage F2, is fed into the process stream before the recovery unit and the reclaimed water is assayed for its presence. An engineering preliminary design has been performed as a parallel effort to the laboratory development of the marker virus test system. Engineering schematics and drawings present a preliminary instrument design of a fully functional laboratory prototype capable of zero-G operation.
Engine durability and performance retention concepts are discussed. Other topics include engine diagnostics for performance retention and engine condition monitoring systems.
NASA is conducting a program which will establish a technology base for the orderly evolution of reusable space propulsion systems. As part of that program, NASA initiated a Structural Integrity and Durability effort for advanced high-pressure oxygen-hydrogen rocket engine technology. That effort focuses on the development of: (1) accurate analytical models to describe flow fields; aerothermodynamic loads; structural responses; and fatigue/fracture, from which life prediction codes can be evolved; and (2) advanced instrumentation with capabilities to verify the codes in an SSME-like environment as well as the potential for future use as diagnostic sensors for real-time condition monitoring of critical engine components.
As part of its responsibility for stratospheric monitoring, the Climate Analysis Center derives time trends of various dynamic parameters from NMC stratospheric analyses. Selected figures from this stratospheric monitoring data base are published in Climate Diagnostics Bulletin in March and October, after each hemispheric winter. During the Northern Hemisphere winter of December 1983-February 1984 several warming events may be seen in the plot of 60 deg. N zonal mean temperatures for 10 mb. Minor warmings may be noted in early December, late December, mid January and early February. A major warming with the 60 deg. N zonal mean temperatures reaching -40C is observed in late February, associated with a circulation reversal. In all of the minor warming episodes, there is a polarward movement of the Aleutian anticyclone; however, at 10 mb the North Pole remains in the cyclonic circulation of the stratospheric vortex which is not displaced far from its usual position. In the case of the later February major warming, the 10 mb circulation pattern over the North Pole is anticyclonic, and the cyclonic circulation has moved to the south and east with a considerable elongation. Cross sections of heat transport and momentum transport are not dramatically different for the minor and major warming episodes.
NASA is conducting a program which will establish a technology base for the orderly evolution of reusable space propulsion systems. As part of that program, NASA initiated a Structural Integrity and Durability effort for advanced high-pressure oxygen-hydrogen rocket engine technology. That effort focuses on the development of: (1) accurate analytical models to describe flow fields; aerothermodynamic loads; structural responses; and fatigue/fracture, from which life prediction codes can be evolved; and (2) advanced instrumentation with capabilities to verify the codes in an SSME-like environment as well as the potential for future use as diagnostic sensors for real-time condition monitoring of critical engine components.
A 100 Mbit/s FDDI (fiber distributed data interface) network interface unit is described that supports real-time data, voice and video. Its high-speed interrupt-driven hardware architecture efficiently manages stream and packet data transfer to the FDDI network. Other enhancements include modular single-mode laser-diode fiber optic links to maximize node spacing, optic bypass switches for increased fault tolerance, and a hardware performance monitor to gather real-time network diagnostics.
The Diagnostics Testbed Facility (DTF) located at the Stennis Space Center (SSC) is used for obtaining extensive sets of H2O2 exhaust plume emission spectral data for the SSME critical components related elements and materials. The SSME related elements and materials are simulated by mixing appropriate amounts of compounds of their respective constituent elements in an aqueous solution which is injected into the combustion chamber of the DTFT. Five of the most critical components of the SSME which have experienced very severe wear and tear problems in the past are analyzed. These are high pressure turbopump (HPTP) turbine blades, HPTP turbine disks, HPTP bearing, main injector LOX posts, and the main combustion chamber structural shell. The alloys used in the manufacturing of these components are MAR-M 246 + Hf, Waspaloy X, AISI 440C, Haynes 188, and Inconel 718, respectively. The experimental setup and procedures at the DTF are described; stratospheric data for the five alloys are presented; and strategies for the material identification in the SSME exhaust plume are discussed.