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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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The fundamental concepts of the strainrange partitioning approach to high temperature, low low-cycle fatigue are reviewed. Procedures are presented by which the partitioned strainrange versus life relationships for any material can be generated. Laboratory tests are suggested for further verifying the ability of the method of strainrange partitioning to predict life.
The Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) has been providing power to the Mars Science Laboratory (MSL) rover, Curiosity, for surface operations since 2012. The Jet Propulsion Laboratory (JPL) has employed the Life Performance Prediction Model (LPPM) to generate predictions and outputs based upon flight data from the rover and experimental data from the thermoelectric (TE) couples. In order to make these predictions, LPPM requires various spacecraft inputs relevant to RTG performance such as fin root temperature, load voltage, general purpose heat source (GPHS) inventory, etc., combined with couple data such as thermoelectric properties, interface degradation, and sublimation properties. With tens of thousands of hours of flight data to date, LPPM can not only track performance to date, but also make predictions on the power output of the MMRTG through the 17-year end-of-design-life (EODL). These predictions are useful for mission planners and surface operators who rely on the MMRTG for power in order to perform the critical science necessary during the mission. This paper will showcase the power data extracted from the flight electronics, comparing it to the predictions and outputs generated from LPPM.
Machining dynamics research lays a solid foundation for machining operations by providing stable combinations of spindle speed and depth of cut. Furthermore, machine learning has been applied to predict tool life as a function of cutting speed. However, the existing research does not consider the discrete-event dynamics in machine shop, i.e., the machine tool needs to process a series of parts in queue under various practical production requirements. This paper addresses the integration of discrete-event dynamics and machining dynamics to achieve cost savings in machining. A learning-based cost function is first proposed for the studied integrated optimization problem of machine tool. The proposed cost function utilizes the predicted tool life under different stable cutting speeds for further optimizing speed selection of machine tool to deal with the discrete-event dynamics in machine shop. Then, according to the practical production requirements, effective mathematical optimization models are developed for the related integrated optimization problems with the consideration of cost, makespan and due date, respectively. Numerical results show the effectiveness of our proposed methods and also the potential to be used in practice.
Tests were conducted to predict remaining electrical insulation life of a polyimide-epoxy insulated 60 kW form-wound homopolar inductor generator following completion of 23,130 hours of turbo-generator endurance tests. The generator stator was sectioned to provide five sections, designated statorettes, and to permit 33 coil dielectric measurements on each section. Thermal aging tests were conducted on each section at select elevated temperatures to establish an Arrhenius life-temperature plot for end-life prediction and product or application use. The statorette fabrication technique is considered novel and provides means to establish insulation life-temperature profiles on completed electromagnetic components of rotating machines.
The estimation of remaining useful life is significant in the context of prognostics and health monitoring, and the prediction of remaining useful life is essential for online operations and decision-making. However, it is challenging to accurately predict the remaining useful life in practical aerospace applications due to the presence of various uncertainties that affect prognostic calculations, and in turn, render the remaining useful life prediction uncertain. It is challenging to identify and characterize the various sources of uncertainty in prognosis, understand how each of these sources of uncertainty affect the uncertainty in the remaining useful life prediction, and thereby compute the overall uncertainty in the remaining useful life prediction. In order to achieve these goals, this paper proposes that the task of estimating the remaining useful life must be approached as an uncertainty propagation problem. In this context, uncertainty propagation methods which are available in the literature are reviewed, and their applicability to prognostics and health monitoring are discussed.
A generalized methodology to predict the fatigue life and reliability of a rotating disk such as used for aircraft engine turbines and compressors is advanced. The approach incorporates the computed life of elemental stress volumes to predict system life and reliability. Disk speed and thermal gradients as well as design variables such as disk diameter and thickness and bolt hole size, number and location are considered.
A generalized methodology to predict the fatigue life and reliability of a rotating disk such as used for aircraft engine turbines and compressors is advanced. The approach incorporates the computed life of elemental stress volumes to predict system life and reliability. Disk speed and thermal gradients as well as design varibles such as disk diameter and thickness and bolt hole size, number and location are considered.
Two types of cadmium selenide telluride (CdSeTe) photovoltaic devices have been exposed to high-energy (150–1500 keV) protons with fluences ranging from 1 × 10 11 to 9 × 10 13 cm −2 . Pre- and post-irradiation current density vs voltage characteristic data were collected and analyzed. Arsenic-doped CdSeTe devices retained 80% of the power conversion efficiency (PCE) relative to control devices after exposure to 10 12 cm −2 650 keV protons, while copper-doped CdSeTe devices retained about 95% of the control PCE under the same irradiation condition. Displacement damage dose analysis coupled with simulations for duration-dependent performance in a medium Earth orbit space mission revealed superior PCE remaining factors, indicating greater resilience to proton bombardment than state-of-the-art multijunction III-V based space photovoltaic technologies.
Abstract Introduction We conducted a prospective observational study on 205 trauma patients at a level I trauma facility to test the hypothesis that a compensatory reserve measurement (CRM) would identify higher risk for progression to shock and/or need a life‐saving interventions (LSIs) earlier than systolic blood pressure (SBP) and blood lactate (LAC). Methods A composite outcome metric included blood transfusion, procedural LSI, and mortality. Discrete measures assessed as abnormal (ab) were SBP <90 mmHg, CRM <60%, and LAC >2.0. A graded categorization of shock was defined as: no shock (normal [n] SBP [n‐SBP], n‐CRM, n‐LAC); sub‐clinical shock (ab‐CRM, n‐SBP, n‐LAC); occult shock (n‐SBP, ab‐CRM, ab‐LAC); or overt shock (ab‐SBP, ab‐CRM, ab‐LAC). Results Three patients displayed overt shock, 53 displayed sub‐clinical shock, and 149 displayed no shock. After incorporating lactate into the analysis, 86 patients demonstrated no shock, 25 were classified as sub‐clinical shock, 91 were classified as occult shock, and 3 were characterized as overt shock. Each shock subcategory revealed a graded increase requiring LSI and transfusion. Initial CRM was associated with progression to shock (odds ratio = 0.97; p < .001) at an earlier time than SBP or LAC. Conclusions Initial CRM uncovers a clinically relevant subset of patients who are not detected by SBP and LAC. Our results suggest CRM could be used to more expeditiously identify injured patients likely to deteriorate to shock, with requirements for blood transfusion or procedural LSI.
The expansion of renewable technologies and electrification of the transportation sector is driving increased demand for next-generation battery materials that provide higher power and energy density with superior cycling and calendar life stability. Silicon (Si) has a theoretical capacity nearly 10x that of graphite, and is therefore a promising anode material candidate to meet these rigorous performance demands. While leading Si anode battery demonstrations are approaching target metrics for cycle life, a series of complex and interrelated modes of reactivity lead to reduced calendar life and therefore challenge practical adoption of these materials. Deconvoluting the degradation processes that impact Si calendar life is critical to informing the rational and accelerated design of improved Si materials. In the present work, we employ novel sampling techniques and GC-MS-FID characterization to measure gas-phase composition during initial Si cycling, which we tie to selective mechanisms of Si passivation. We utilize a tiered analysis approach to identify and quantify the gas-phase reaction products associated with three advanced Si material candidates under practical operating conditions. Ex situ analysis of Si powders (pure chemical reactivity) is coupled with nondestructive in situ sampling of Si electrodes in a practical pouch-cell format (coupled chemical and electrochemical reactivity). We link the observed gas-phase species evolution to electrochemical behavior and measured calendar life of the three Si materials. Further, we evaluate the voltage-resolved evolution of gas-phase species for one such Si nanomaterial, where nonmonotonic gas generation implies competition between passivating reaction pathways. The measured gas-phase compositional data serves as a critical input for our advanced electrochemical SEI models to identify favorable vs unfavorable reaction pathways to stabilize Si. In addition to bolstering a fundamental understanding of Si reactivity, the present approach informs specific and quantifiable gas-phase metrics tied to calendar life improvements in Si, which can streamline and accelerate the process of next-generation material development.
Fatigue life at elevated temperature can be predicted by introducing parametric values obtained from monotonic constitutive behavior into the Universal-Slopes Equation. For directionally solidified MAR-M200+HF at 975 C, these parameters are the maximum stress achievable under entirely plastic (time-independent) and purely creep (time-dependent) conditions and the corresponding inelastic strains, as well as the elastic modulus. For materials which exhibit plasticity/creep interaction, two more pairs of monotonic parameters must be evaluated for fatigue life prediction. This life-prediction method based on the Universal-Slopes Equation, resulted from a constitutive model characterizing monotonic and cyclic data as inelastic strainrate as a function of inelastic strain. Characterizing monotonic data is this way, permitted distinction between different material responses such as strain-hardening, strain-softening, and dynamic recovery effects. Understanding and defining the region of influence of each of these effects facilitated formulation of the constitutive model in relation to the mechanical and microstructural processes occurring in the material under cyclic loading.
The NASA Energy Efficient Engine (E(sup 3)-Engine) is used as the basis of a Weibull-based life and reliability analysis. Each component's life and thus the engine's life is defined by high-cycle fatigue (HCF) or low-cycle fatigue (LCF). Knowing the cumulative life distribution of each of the components making up the engine as represented by a Weibull slope is a prerequisite to predicting the life and reliability of the entire engine. As the engine Weibull slope increases, the predicted lives decrease. The predicted engine lives L(sub 5) (95 % probability of survival) of approximately 17,000 and 32,000 hr do correlate with current engine maintenance practices without and with refurbishment. respectively. The individual high pressure turbine (HPT) blade lives necessary to obtain a blade system life L(sub 0.1) (99.9 % probability of survival) of 9000 hr for Weibull slopes of 3, 6 and 9, are 47,391 and 20,652 and 15,658 hr, respectively. For a design life of the HPT disks having probable points of failure equal to or greater than 36,000 hr at a probability of survival of 99.9 %, the predicted disk system life L(sub 0.1) can vary from 9,408 to 24,911 hr.
Temperature effects, reconditioning, divergencies, capacity, charge rates, depth of discharge, and cell matching and their effects on battery life are discussed. The development of a practical strategy for predicting battery life by subjecting cells to simulated cyclic operation is also addressed.
Was the emergence of life a predictable outcome of chemical evolution on earth? Could evolution produce life very different from ours? These are one of the oldest questions in the field of the origin of life that not only have broad philosophical implications but also impact how we approach the problem from the methodological standpoint. Framing the issue in terms of the dichotomy between contingency and determinism is not a fortunate because these two terms in their conventional meaning are neither mutually exclusive nor jointly exhaustive. Determinism, represented in natural sciences by Newtonian physics, relies on the assumption that every event is causally determined by a chain of previous events. In the context of the origin of life it means that once the initial conditions on the early earth have been specified further evolution follows inevitably. Considering uncertainties about conditions on the prebiotic earth, many plausible sets of initial conditions can be defined, each followed by a separate deterministic trajectory. This conventional understanding of determinism does not admit contingency. Further, it has no implications for evaluating how many sets of initial conditions lead to the emergence of life. It appears that a better framing of the problem is as follows: given plausible sets of initial conditions on the early earth how probable and broadly spread are evolutionary trajectories that lead to life? Instead of undertaking an impossible task of specifying microscopic initial conditions for all components of the system one uses a reduced representation of this system and specify only a small set of essential macroscopic parameters, values (or ranges of values) of which can be identified, inferred or estimated from experiment, theory or historical record. The following evolutionary trajectories are still governed by laws of physics and chemistry but become probabilistic and "contingency" is admitted as variations in other variables in the system. A similar reasoning is common in other fields of science, for example in statistical mechanics. Some trajectories lead to life, perhaps in different forms, whereas others do not. Of our true interest is the ratio of these two outcomes. The issue of determinism does not directly enter the picture. The debate about the likelihood of the emergence of life is quite old. One view holds that the origin of life is an event governed by chance, and the result of so many random events (contingencies) is unpredictable. This view was eloquently expressed by Monod. In his book "Chance or Necessity" he argued that life was a product of "nature's roulette." In an alternative view, expressed in particular by deDuve and Morowitz, the origin of life is considered a highly probable or even inevitable event (although its details need not be determined in every respect). Only in this sense the origin of life can be considered a "deterministic event".
Prediction of behavior and support requirements during the entire life cycle of a system enables accurate cost estimates by using the Monte Carlo simulation by computer. The system reduces the ultimate cost to the procuring agency because it takes into consideration the costs of initial procurement, operation, and maintenance.
Analysis was made to determine correlation between number and size of carbide particles and rolling-element fatigue. Correlation was established, and carbide factor was derived that can be used to predict fatigue life more effectively than such variables as heat treatment, chemical composition, and hardening mechanism.
This study evaluates novel electrolytes tailored for Si-containing anodes to promote calendar-life. Drawing inspiration from advancements in electrolytes for Li-metal cells, the work investigates correlations between predicted electrolyte properties and measured electrochemical performance using several machine-learning models. By leveraging machine learning and advanced modeling techniques, this study aims to establish predictive frameworks that accelerate calendar-aging experiments and inform rational electrolyte design for Si-containing cells. In the present study, fifteen different electrolytes are evaluated in a Si-containing cell using an accelerated calendar-life protocol. For each electrolyte considered, 87 properties (features) from the Advanced Electrolyte Model were produced to identify key property/performance relationships. In this study, the best performing electrolytes were generally those formulations that included non-coordinating fluoroether solvents, and the most predictive features for long-term calendar-life were features related to salt concentration and electrolyte viscosity as well as early capacity, ionic conductivity, and Coulombic efficiency measurements. The framework developed in this study correlating electrolyte properties to measured electrochemical performance is expected to accelerate electrolyte design for Si-containing anodes and ultimately enable high-energy-density, long-life Li-ion batteries.
A model to calculate fatigue life is developed based on the assumption that fatigue life is entirely composed of crack growth from an initial microstructural inhomogeneity. Specifically, growth is considered to start from either an ellipsoidal void, a cracked particle, or a debonded particle. The capability of predicting fatigue life from material microstructure is based on linear elastic fracture mechanics principles, the sizes of the crack-initiating microstructural inhomogeneities, and an initiation parameter that is proportional to the cyclic plastic zone size. A key aspect of this modeling approach is that it is linked with a general purpose probability program to analyze the effect of the distribution of controlling microstructural features within the material. This enables prediction of fatigue stress versus life curves for various specimen geometries using distributional statistics obtained from characterizations of the microstructure. Results are compared to experimental fatigue data from an aluminum alloy.