Thursday, March 21, 2013

. . .Kisah Liliyana Natsir Mengejar Impian. . .

Ibu pertiwi harus bangga memiliki seorang puteri yang telah beberapa kali mampu mengibarkan sang Merah Putih di puncak tertinggi dunia dan mengumandangkan Lagu Indonesia Raya di dunia Internasional. Lewat sektor olahraga kebanggan negeri ini, Bulutangkis, Liliyana Natsir sudah pernah membanggakan negeri ini dengan menujarai All England, menjadi Juara Dunia dan meraih medali perak Olimpiade. Dia mebuktikan bahwa untuk menjadi seorang juara diperlukan perjuangan tanpa henti dan pengorbanan. Kisah suskses terakhirnya adalah dengan menjadi Juara All England untuk kedua kalinya secara beruntun.

Liliyana Natsir bersama Tontowi Ahmad menjuarai All England untuk kedua kalinya.

Lagu Garuda di Dadaku membahana di National Indoor Arena, Birmingham, Inggris, Kamis (10/3/2013) lalu meski hanya sejumlah kecil suporter Indonesia di bangku penonton All England Superseries Premier yang melantunkannya. Para pendukung juga mengangkat bendera Merah Putih tinggi-tinggi setelah pasangan Tontowi Ahmad/Liliyana Natsir memastikan kemenangan mereka atas pasangan China, Zhang Nan/Zhao Yunlei, dua game langsung 21-13, 21-17 dalam kejuaraan klasik itu.

Tontowi langsung mengucap syukur sementara Liliyana melompat girang sambil mengepalkan tangan ke udara beberapa kali. Liliyana merasa tidak percaya dia dan Tontowi bisa kembali meraih gelar dalam turnamen itu.

"Masa sih dua kali beruntun. Padahal semuanya mengincar kami yang juara bertahan," tutur Liliyana menggambarkan perasaannya saat itu.

Tahun lalu, anak asuhan Richard Mainaky itu juga mencatat sejarah karena berhasil merebut kembali gelar ganda campuran bagi Indonesia di ajang All England, sejak pasangan Christian Hadinata/Imelda Wigoeno mendapatkannya pada tahun 1979.

Dengan mata berkaca-kaca Liliyana mempersembahkan kemenangannya untuk keluarga yang selalu mendukung dia, serta sang pelatih yang terus menggemblengnya sejak masih berpasangan dengan Novi Widianto.

"Untuk keluarga yang selalu support, untuk pelatih yang tidak bosan-bosan mengingatkan. Semoga dengan prestasi ini bisa lebih percaya diri," ujar Liliyana usai acara penyambutan kepulangannya dari Inggris di Terminal II Bandara Soekarno-Hatta, Rabu (20/3).

Anak Mama


Mata Liliyana, yang akrab disapa Butet, kembali berkaca-kaca saat sang ibunda, Jin Chen, menghampiri dia. "Ini mama saya," ujar Butet, yang wajahnya lantas berubah sumringah.

Butet kemudian memeluk erat mamanya, yang khusus datang dari Manado, Sulawesi Utara, untuk menyambut kepulangan putrinya.

Liliyana memang dikenal begitu lengket dengan mamanya. Bahkan, saat memutuskan merantau ke Jakarta pada usia 12 tahun untuk bergabung dengan klub PB Tangkas, Liliyana masih ditemani mamanya hingga tiga bulan.

"Saya sampai kos (indekos) dekat Tangkas," ujar Jin Chen.

Jin Chen tak pernah lepas memantau Adek, panggilan sayangnya untuk Liliyana. Dia selalu menemani Liliyana pada masa awal sang anak masuk asrama PB Tangkas.

"Tante selalu urus dia sendiri dari bayi sampai besar. Bahkan, dia dan kakaknya selalu tante suapin setiap makan, baju disiapkan, benar-benar tergantung sama tante," kenang Jin Chen.

Dia baru berpisah dengan putrinya saat Liliyana menyatakan ingin serius mendalami bulu tangkis. Jin Chen lantas kembali ke Manado untuk mengurus bisnis onderdil dan bengkel yang dijalankan bersama suaminya, Benno Natsir.

"Saat saya mau pulang dia nangis-nangis di asrama karena tidak mau pisah dengan tante. Tetapi tante ajak pulang pun dia tidak mau jo. Dia minta tante tinggal di Jakarta, biar kakaknya di Manado diurus papanya," ujar Jin Chen dengan logat Manado.

Sejak itulah Liliyana dipanggil Butet, si bungsu yang suka menangis dan tidak bisa lepas dari mamanya.

"Dahulu temannya orang Batak bilang kalau di kampungnya, seperti Liliyana ini dipanggilnya Butet. Akhirnya malah keterusan," kata Jin Chen, lalu tertawa.

Kini Jin Chen bisa tertawa mengenang pengorbanannya saat itu. Kadang dia masih tak percaya anaknya bisa menjadi atlet bulu tangkis berprestasi yang kerap mengharumkan nama bangsa di kancah Internasional.

"Semua pengorbanannya, jauh dari orangtua, nangis-nangis, sekarang terhapus. Saya bilang sama dia kalau saya senang Adek sukses di olahraga," ujarnya.

Bakat Olahraga

Liliyana tidak lahir dari keluarga atlet. Akan tetapi, sejak kecil dia punya bakat di bidang olahraga. Di sekolahnya, dahulu dia juga dikenal jago bermain basket.

Kegemaran keluarga bermain bulu tangkis membuat perempuan kelahiran 9 September 1985 itu tertarik dengan olahraga tersebut. Ayahnya, Benno Natsir, mengarahkan Liliyana sampai dia bergabung dengan klub bulu tangkis lokal, Pisok, pada usia sembilan tahun.

Setelah Liliyana menjadi atlet besar pun, Benno masih sering mengarahkan putri bungsunya bertanding.

"Papanya suka mengarahkan, kasih tahu kelemahan lawan-lawannya dan harus bagaimana," kata Jin Chen, yang fasih menyebut deretan lawan Liliyana dan istilah-istilah bulu tangkis.

"Sampai sekarang kami selalu pantau dan terus memotivasi dia. Dia itu maunya juara. Kalau kalah selalu bilang ke saya, ’saya mau balas dia, Ma’. Lalu saya memotivasinya kalau mau balas harus lebih giat berlatih," kata dia.

Kini segudang prestasi telah Liliyana raih. Sebelum berpasangan dengan Tontowi, Liliyana pernah menjadi pemain ganda campuran nomor satu Indonesia bersama Nova Widianto. Bersama Nova, dia meraih gelar juara dalam Kejuaraan Dunia BWF pada tahun 2005 dan 2007 serta berbagai prestasi bergengsi lainnya.

Ia juga pernah bermain di nomor ganda putri bersama rekannya Vita Marissa (2007-2008) dan meraih prestasi membanggakan seperti juara Indonesia Terbuka 2008 dan China Masters 2007. Selanjutnya, Liliyana ingin meraih kembali gelar Kejuaraan Dunia bersama Tontowi Ahmad. "Kalau sama dia kan belum dapat," katanya.

Sementara sang mama, yang berada tepat di sebelah Liliyana, cuma ingin putrinya segera memiliki kekasih.

"Lily cepat punya pacar," lontarnya dan tertawa.

"Itu pasti dipikirkan, tetapi berjalannya waktu saja karena peraturan PBSI ketat. Nanti inginnya setelah Olimpiade lagi," jawab Liliyana, yang disambut riuh oleh para wartawan.

Sumber : olahraga.kompas.com

Sunday, March 17, 2013

Program Untuk Menghitung Bilangan Reynolds dari Aliran Fluida di dalam Pipa Menggunakan MATLAB

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

Saya mencoba untuk membuat program menghitung bilangan Reynolds untuk kondisi aliran fluida di dalam pipa sebagai berikut :



===========

function Re = Reynolds()
disp ('This program is for fluid flowing inside a pipe')
Rho = input (' Input Density of Working Fluid in kg/m3 : ');
V = input (' Input Velocity in m/s : ');
Lc = input (' Input Characteristic Length in m : ');
M = input (' Input Viscosity of Working Fluid in kg.s/m2 : ');

Re = (Rho*V*Lc)/M;
disp (' ')
disp (' ===> Value of Dimensionless Reynolds Number = ', num2str(Re))
disp (' ')

if Re < 2100
disp ('Laminar Flow')
elseif Re > 4000
disp ('Turbulen Flow')
else disp ('Transitional Flow')
end


===========


Segala bentuk masukan terkait program ini akan sangat membantu.
Semoga bermanfaat.

Wednesday, March 13, 2013

Balancing

Berikut ini adalah hal-hal sederhana yang sering dilupakan dalam proses balancing.

Kriteria untuk evaluasi unbalance

SEBELUM BALANCING :

1. Lihat data historis :
- Gambar mesin
- Data balancing sebelumnya
- Peralatan yg digunakan
- Informasi pada Name plate
- Data trend vibrasi (spectrum jika mungkin)

2. Pastikan Unbalance adalah penyebab dominan Vibrasi, bukan dari :
- bearing rusak
- misalignment
- soft foot
- seal rubs
- komponen kendor
- rotor/impeller kotor
- salah dari proses

KETIKA MELAKUKAN BALANCING :

1. Ikuti semua prosedur keselamatan
2. Gunakan hukum “Tiga kali”
3. Pastikan data dan catat semuanya
4. Kalkulasi 2 kali
5. Pastikan lokasi dan berat beban unbalance
6. Pasang beban trial pada radius yg sama untuk tiap kali uji
7. Lepaskan beban daripada menambahkan (jika praktis)
8. Pastikan kondisi proses dan kecepatan mesin adalah sama untuk tiap kali uji
9. Bandingkan vibrasi unbalance dengan vibrasi overall

KESALAHAN BALANCING :

1. Kesalahan Operator :
- Beban trial dipindahkan ke arah yg salah
- Beban trial terlalu berat atau terlalu ringan (salah hitung)
- Beban trial lepas waktu diputar
- Data dari lokasi yg salah
- Sangat tergantung pada program computer
- Melupakan pencatatan korreksi dan hasilnya

2. Kesalahan prosedur :
- Pemasangan beban pada rotor tidak sempurna
- Peralatan balancing yg belum dikalibrasi
- Peralatan balancing disetting tidak sempurna

3. Kesalahan perencanaan :
- Tidak melihat data historis
- Vibrasi bukan dari Unbalance
- Beban korreksi tidak ada atau tidak tepat beratnya
- Rotor kotor

Bila titik tengah gravitasi putaran shaft TIDAK sama dengan titik tengah geometris shaft maka disebut UNBALANCE. Besar UNBALANCE tergantung dari gaya sentrifugal yang terjadi pada saat operasi. Unbalance dapat dibayangkan sebagai berat yang dipasang secara eksentrik di badan yang berputar. Salah satu indikator dalam proses balancing adalah Perubahan Fasa. Perubahan fasa adalah fungsi dari perubahan sudut/lokasi beban trial, dan menjadi semakin sensitif jika mendekati “true balance”. Jadi perubahan sudut sedikit saja pada saat mendekati balance membuat perubahan yang cukup besar kepada fasa. Jika terjadi kesulitan sewaktu proses balancing, bisa saja kesulitan tersebut diakibatkan oleh berat impeller/fan yang bergerak sedikit saja secara angular atau radial. Untuk cek situasi ini, hilangkan semua beban trial dan cek fasa dan/atau amplitudo untuk perubahan yang besar sewaktu pengukuran.

 
Contoh unbalance dari rotor berbentuk piring

Sumber : Prüftechnik AG, Germany.