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NASA NTRS1995

Chromospheric Evaporation and Decimetric Radio Emission in Solar Flares

We have discovered decimetric signatures of the chromospheric evaporation process. Evidence for the radio detection of chromospheric evaporation is based on the radio-inferred values of (1) the electron density, (2) the propagation speed, and (3) the timing, which are found to be in good agreement with statistical values inferred from the blueshifted Ca xix soft X-ray line. The physical basis of our model is that free-free absorption of plasma emission is strongly modified by the steep density gradient and the large temperature increase in the upflowing flare plasma. The steplike density increase at the chromospheric evaporation front causes a local discontinuity in the plasma frequency, manifested as almost infinite drift rate in decimetric type III bursts. The large temperature increase of the upflowing plasma considerably reduces the local free-free opacity (due to the T-(exp -3/2) dependence) and thus enhances the brightness of radio bursts emitted at the local plasma frequency near the chromospheric evaporation front, while a high-frequency cutoff is expected in the high-density regions behind the front, which can be used to infer the velocity of the upflowing plasma. From model calculations we find strong evidence that decimetric bursts with a slowly drifting high-frequency cutoff are produced by fundamental plasma emission, contrary to the widespread belief that decimetric bursts are preferentially emitted at the harmonic plasma level. We analyzed 21 flare episodes from 1991-1993 for which broadband (100-3000 MHz) radio dynamic spectra from Phoenix, hard X-ray data from BATSE/CGRO, and soft X-ray data from GOES were available. We detected slowly drifting high-frequency cutoffs between 1.1 and 3.0 GHz, with drift rates of -41 +/- 32 MHz/s, extending over time intervals of 24 +/- 23 s. Developing a density model for type III-emitting flare loops based on the statistically observed drift rate of type III bursts by Alvarez & Haddock, we infer velocities of up to 360 km/s for the upflowing plasma, with an average of v(sub CE) = 236 +/- 130 km /s for episodes with 5-15 s duration. The mean electron density of the upflowing plasma is n(sub e) = 5.2(+/-3.1) x 10(exp 10) /cu cm when it is first detected in radio, at coronal altitudes of h(sub 0) = 9.2 +/- 2.3 Mm.

Aschwanden, Markus J.↗

NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

The field of prognostics and health management provides quantitative methods for monitoring and predicting the health of physical systems. Prognostics algorithms are useful in that they can be employed to assess the current state of a system, propagate the system state throughout time, and predict potential anomalies or failures that may occur. However, effective prognosis can be challenging to achieve in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for some prognostics applications. In this work, we implement three new algorithmic approaches to prediction (sampling methods, variable prediction time step, variable prediction sample size) with the goal of improving computational efficiency while minimizing decrease in model accuracy. To quantitatively analyze our results, we examine a use-case of degradation of a Lithium-ion battery. Notably, through this work it was found that none of the sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. However, our results show that prediction accuracy is highly dependent on the time step used, and that an appropriate time step can optimize both model accuracy and simulation efficiency. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment in an application-specific manner may prove useful in some applications. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency of prognosis.

Prognostics↗