The design and demonstration of a compact single-ended laser-absorption-spectroscopy sensor for measuring temperature and HO in high-temperature combustion gases is presented. The primary novelty of this work lies in the design, demonstration, and evaluation of a sensor architecture that uses a single lens to provide single-ended, alignment-free (after initial assembly) measurements of gas properties in a combustor without windows. We demonstrate that the sensor is capable of sustaining operation at temperatures up to at least 625 K and is capable of withstanding direct exposure to high-temperature (≈1000  K) flame gases for long durations (at least 30 min) without compromising measurement quality. The sensor employs a fiber bundle and a 6 mm diameter antireflection-coated lens mounted in a 1/8 NPT-threaded stainless-steel body to collect laser light that is backscattered off native surfaces. Distributed-feedback tunable diode lasers (TDLs) with a wavelength near 1392 nm and 1343 nm were used to interrogate well-characterized HO absorption transitions using wavelength-modulation-spectroscopy techniques. The sensor was demonstrated with measurements of gas temperature and HO mole fraction in a propane-air burner with a measurement bandwidth up to 25 kHz. In addition, this work presents an improved wavelength-modulation spectroscopy spectral-fitting technique that reduces computational time by a factor of 100 compared to previously developed techniques.

Fields in the social sciences, such as education research, have started to expand the use of computer-based research methods to supplement traditional research approaches. Natural language processing techniques, such as topic modeling, may support qualitative data analysis by providing early categories that researchers may interpret and refine. This study contributes to this body of research and answers the following research questions: (RQ1) What is the relative coverage of the latent Dirichlet allocation (LDA) topic model and human coding in terms of the breadth of the topics/themes extracted from the text collection? (RQ2) What is the relative depth or level of detail among identified topics using LDA topic models and human coding approaches? A dataset of student reflections was qualitatively analyzed using LDA topic modeling and human coding approaches, and the results were compared. The findings suggest that topic models can provide reliable coverage and depth of themes present in a textual collection comparable to human coding but require manual interpretation of topics. The breadth and depth of human coding output is heavily dependent on the expertise of coders and the size of the collection; these factors are better handled in the topic modeling approach.

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